User:Sakari Kuikka: Difference between revisions

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= The submitted proposal to STN call  =
{{#l:ILVES_application_final with man month table.pdf}} This is the version that was submitted on 29th. Needs a table of man months and publication plan list, these will be submitted on Monday
This is the pdf sent to Jyrki & FEM on Tuesday morning  {{#l:ILVES_hakemus_final2b_Sakke_comments.pdf}}
This is the figure to section 11.
{{#l:Fig on the responsibility section 11.pdf}}
The last modification file to Heimonen Aarnipaja ltd. Thanks Jyrki for excellent help !!
{{#l:ILVES_hakemus_back_from_jyrki tuesday morning sakke last comments back.pdf}}
= Background documents for public =
= Background documents for public =


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Approach of ILVES, explained by Sakke to help you improving the text:  
Approach of ILVES, explained by Sakke to help you improving the text:  


{{#l:Ilves all hand made pictures for proposal.pptx }}
* {{#l:Ilves all hand made pictures for proposal.pptx }}
 
{{#l:ILVES master version 4.pdf}}
Curret version of research plan:
 
* {{#l:ILVES master version 4.pdf}}
* {{#l:ILVES master version 4.pdf}}
* {{#l:A Singapore NTU March 2015 Kuikka.pdf}}
* {{#l:A Singapore NTU March 2015 Kuikka.pdf}}
* In Finnish, listen Radio Iltapäivä and seek for Sakari Kuikka öljyonnettomuus [http://areena.yle.fi/radio/2713848 Sakari Kuikka in radio this week (In Finnish)]
* In Finnish, listen Radio Iltapäivä and seek for Sakari Kuikka öljyonnettomuus [http://areena.yle.fi/radio/2713848 Sakari Kuikka in radio this week (In Finnish)]
*  
* [[/Users/skuikka/Desktop/AADATA/projects/ILVES consortium/slides and talks of ILVES workshop/a_Singapore NTU March 2015 Kuikka.mov|Talk on ILVES approach]]
[[/Users/skuikka/Desktop/AADATA/projects/ILVES consortium/slides and talks of ILVES workshop/a_Singapore NTU March 2015 Kuikka.mov|Talk on ILVES approach]]
 
* {{#l:ILVES master version 4.pdf}}
* {{#l:A Singapore NTU March 2015 Kuikka.pdf}}
* {{#l:A Singapore NTU March 2015 Kuikka.pdf}}
* {{#l:IPad image 2015-3-25-1429933525846 0.jpg}}
* {{#l:IPad image 2015-3-25-1429933525846 0.jpg}}
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* {{#l:Climate change impacts risk analysis.pdf}}
* {{#l:Climate change impacts risk analysis.pdf}}
* {{#l:Ilves discussions.pptx}}
* {{#l:Ilves discussions.pptx}}
* [http://www.nwfsc.noaa.gov/research/divisions/cb/documents/atlantis_ecosystem_model.pdf Atlantis Ecosystem model]


ILVES Writing units :
ILVES Writing units :
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* {{#l:ILVES Commitment FINNPILOT.docx}}
* {{#l:ILVES Commitment FINNPILOT.docx}}


= ILVES application =
== ILVES application ==


'''ILVES''' {{comment|# |(happy face of ilves animal here) the biggest feline predator in Europe, a high status species that is still killed in Finland, does not eat human like great white in Aussies,) logo here From Marja & Pihla Kuikka, Marja has seen a happy family on the yeard in 201x with Kaisa and Sakke)|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 04:21, 22 April 2015 (UTC)}}
===1 Principal investigator (PI) of the consortium, team leaders, sites of research, name of consortium, date of research plan===


'''Consortium 29.4.2015 Project full title: Ilmastomyönteiset ja vähäriskiset kuljetusvaihtoehdot  
Name of consortium: Ilmastomyönteiset ja vähäriskiset kuljetusvaihtoehdot – Developing low carbon and low risk transport systems (ILVES)


== Project applicants and responsible persons ==
Date of research plan: 29 April 2015
1) Inari: do a small figure of people under differnt skills:a network model of expertise and interaction.Coordinator is in the decision node and citizen activities in aims, between are key wp’s and their methods. This will repalce the text 
Consortium ILVES 29.1.1y 2015  
Project full title: Ilmastomyönteiset ja vähäriskiset kuljetusvaihtoehdot Developing low carbon and low risk transport systems
Project applicants and responsible persons
1) PI (responsible leader of consortium) Professor (fisheries management) Sakari Kuikka University of Helsinki, Department of Environmental Sciences, (UH/FEM) Finland, Professor Elja Arjas, Associate Professor Jani Luoto, Department of Political and Economic Studies.  Prof Petri Myllymäki,
2) Dr Juha Honkatukia VATT Institute for economic research VATT Finland
3) Dr Jukka-Pekka Jalkanen Finnish Meteorological Institute (FMI)
4) Dr, Adjunct Professor Henrik Ringblom Åbo Akademi, BALEX Finland
5) Research Manager, Master mariner Justiina Halonen, Kymenlaakson ammattikorkeakoulu, University of Applied Sciences Finland. 
6) Dr Jyri Vilko Lappeenranta University of Technology, School of Business and Management Finland, professor Pekka Sutela, professor Heikki Haario.
7) Dr Miina Karjalainen Kotka Maritime Research Association (KMRA)
8) Prof. Olli Varis Helsinki University of Technology Finland
9) Dr Rich Little, Dr Beth Fulton, Dr Lee, Dr XX  Commonwealth scientific organization, CSIRO, Australia.
10) Prof xx and dr YY,  University of Waikato, New Zealand:
11) Dr Jouni Tuomisto, NHA  human and seal components for inland water oil spills, experiences in risk communication, support of best practices in open science supporting risk communication.
12) Dr, CEO, Anders Madsen, Hugin. Further development of the world leading Bayesian network decision support software, application of Pearl’s see and do functions to observed data and future policy predictions, interactive learning of values and priors from experts, policy administration, scientists, end users,
A private company to take care of Elja’s salary?


A private company to take care of Elja’s salary?
Project applicants and responsible persons:
# UH/FEM: PI (responsible leader of consortium) Professor, fisheries management, Sakari Kuikka University of Helsinki: scientific leadership and motivation, Bayesian analysis, decision modeling, Department of Environmental Sciences, Finland, Professor emeritus Elja Arjas causal learning from non-experimental data, Associate Professor Jani Luoto, Department of Political and Economic Studies, Bayesian methods in economics and effective algorithms
# VATT: Dr Juha Honkatukia VATT Institute for Economic Research, Finland, expertise in spatial economy and economic policy in Finland
# FMI: Dr Jukka-Pekka Jalkanen Finnish Meteorological Institute (FMI), Finland, expertise in fleet CO 2 releases and fleet dynamics of large international fleets
# ÅBO: Dr, Adjunct Professor Henrik Ringbom Åbo Akademi, BALEX Finland, Finland, expertise both from operational oil combatting management (former head of environmental unit in EMSA) and in theory of power of marine legislation in risk governance
# KYAMK: Research Manager, Master mariner Justiina Halonen, Kymenlaakson ammattikorkeakoulu, Kymenlaakso University of Applied Sciences, Finland, expertise in education or maritime actors, simulation tools of fleet decision making
# LUT: Dr Jyri Vilko Lappeenranta University of Technology, School of Business and Management, Finland, expert in logistics and related risk and decision analysis, professor Pekka Sutela, world know expert in Russian political uncertainty, professor Heikki Haario, expertise in effective algorithms to find probabilistic dependencies in complex problems
# KMRA: Dr Miina Karjalainen, Kotka Maritime Research Association (KMRA), Finland, expertise in environmental settings of Gulf of Finland and in interdisciplinary analysis of maritime activities
# AALTO: Prof. Olli Varis, Aalto University, Finland, internationally known expert of political and environmental development in third world countries, especially in Middle East
# CSIRO: Dr Rich Little, expertise in societal design of insurance policies, Dr Beth Fulton, complex ecosystem – human interaction models and their operational use, Dr Ken Lee & Dr Simon Barry oil spill risk management in Australia, Commonwealth Scientific and Industrial Re-search Organization (CSIRO), Australia
# NHA: Dr Jouni Tuomisto, National Institute for Health and Welfare (NHA), Finland, expertise on impacts of environment on human health
# HUGIN: Dr, CEO, Anders Madsen, HUGIN Ex-pert A/S, Denmark
# DUKE: Kenneth Reckhow, Duke University, USA, expertise in Gdecision analysis and Gulf of Mexico oil spill
# FEM Risk governance and calculus ltd: To be established in summer 2015 by the help of University of Helsinki


===2 Rationale===


=== The overall approach of ILVES. ===
[[File:Ilves traffic options.png|thumb|400px|'''Fig. 1.''' The ILVES approach where different forms of traffic and possible development options are shown. Picture describes the selection of best (time, cost and low CO 2 ) traffic options between maritime traffic, inland water routes, land routes, Helsinki-Tallin tunnel and Kymi canal. Threatened nature values in specified areas are also shown. Picture includes CO 2 emissions of maritime traffic in the Baltic Sea(green=low intensity, yellow=moderate, red=high), locations of threatened species and habitats in the Archipelago Sea and the Gulf of Finland (yellow=moderate oil combating prioritization, orange=high, red =very high), average oiling probability in the Archipelago Sea and the Gulf of Finland based on accident hot spots (white lines describe oil drifting), railways (black dash line), highways with intensive truck traffic of over 2000 tons per year (thick blue line), Helsinki-Tallinn tunnel (black double dash line), inland rivers and lakes (light blue narrow lines and polygons), occurrence of Saimaa ringed seal (species picture), and Saimaa and Kymi canals (thick light blue lines)]]
[[File:Ilves biomass policy advice.png|thumb|400px|'''Fig. 2.''' Picture shows the impact of biomass point estimate on management decision and causing wrong policy advice.]]


We link risks to human life, we use information to inform people and companies on their action and their impacts, and creating interest by information.
The proposal develops risk management tools to support decision-making in the Finnish transportation sector. The main aim is to find means to decrease the greenhouse gas (GHG) emissions of the sector and thus, in consequence, to help to achieve the international and national GHG emission reduction targets. The scope of the proposal includes transportation of cargo and passengers on road and railways, and shipping in the Baltic Sea (Fig. 1). The considered time horizon expands to year 2050, which is the target year of the EU’s policy aiming at significant reductions of GHG emissions from transport sector. This proposal aims at developing solutions matching the goals defined in the EU White paper on transport (EC, 2011) from the point of view of Finnish society.


== 2) Rationale ==
As far as these aims are concerned, the environmental record of shipping can and must be improved by on-board technology, fuels, and operations. Overall, the EU CO<sub>2</sub> -emissions from maritime transport should, according to the White Paper, be cut by 40% (if feasible 50%) by 2050, compared to 2005 levels (EC, 2011). Also Finland has set specific objectives for the climate policy implementation. According to the maritime strategy of Finland for 2014–2022, Finland will be a forerunner in winter and environmental technology, and will export high competence in those fields. Furthermore, the deep water fairway in Saimaa Lake District is part of the core network corridors defined in the Connecting Europe Facility of the EU Infrastructure Package. Developing the inland waterway (IWW) system in Finland would support the White Paper (EC, 2011) targets. However, GHG emissions are not the only environmental concern that needs to be addressed. ILVES will study the effects of the policy options on several other socio-economic and environmental factors. For instance, a single oil spill in the Gulf of Finland can incur costs up to one billion euros. This cost would be shared between ship-owners through their insurance companies, international oil pollution compensation funds, and Baltic Sea countries. Finland is a member of the Supplementary Fund of the International Oil Pollution Compensation (IOPC) Funds, which has a compensation limit of SDR 750 million (€ 961 million).
'''Aims'''
'''Information needs in society''': The main aim of the proposal is to develop a risk management tools to support policy decisions in the Finnish transportation sector and, as a consequence, to support the overall governance to slow down the global climate change. The scope includes transportation of cargo and passengers on road, railways, and shipping in the Baltic Sea. The considered time horizon expands to 2050. This year was selected as a target for future EU transport policy, which aims at significant reduction of GHG emissions from traffic sector. This proposal aims at finding answers how these goals could be achieved from the point of view of Finnish society.


To find these answers we need sophisticated modelling tools. Therefore, from the methodological point of view, our main objective is to develop new techniques for policy design, which are based on extensive use of the  underlying scientific theory, existing data sets, publications, and expert knowledge (Haapasaari et al 2015 under minor revisions). We especially plan to combine the information from these sources in such a way that the awareness of environmental risk governance increases among citizens, policy makers, and scientists. This goal is accomplished by summarizing information using probabilistic forecasts, which are then made available to the public. To be more concrete, we forecast the future scenarios by the forward simulation version of the developed model fitted to relevant data, including economic and climate indicators. Indicators describe the state of interest variables by the noise included to likelihood function. We then forecast probabilistic outcomes of the key interest variables conditional on designed policy actions. In particular, we consider the forecast horizons of 10, 20, and 30 years. We use the probabilistic forecasts as an input in a decision model, where the optimal policy, given the underlying key uncertainties, can be evaluated. This can be done separately or jointly with the key societal aims to understand the role of precise aims in the policy support (Kuikka and Varis 19 xx, Varis and Kuikka 19 xxr).
However, the compensation is paid only if oil pollution result in an actual and quantifiable economic loss related to property damage, oil combating and clean-up costs, economic losses in fisheries, mariculture or tourism, and costs of reinstatement of the environment (IOPC Funds, 2013). Hence, losses to environmental values that are typically difficult to assess are excluded from the compensation scheme.


As far as these societal aims are concerned, the environmental record of shipping can and must be improved by both on-board technology and better fuels and operations. Overall, the EU CO2 emissions from maritime transport should be cut by 40% (if feasible 50%) by 2050, compared to 2005 levels (EC (2011)). This has been stated in the White Paper (EC (2011)), which can be considered to express the will inside the EU. Thereby, also Finland has its own aims to implement climate policy. Indeed, according to the maritime strategy of Finland for 2014–2022, Finland will be a forerunner in winter and environmental technology, and will export high competence in those fields. Furthermore, the deep water fairway in Saimaa Lake District is part of the core network corridors defined in the Connecting Europe Facility of the EU Infrastructure Package. Developing the inland waterway (IWW) system in Finland would support the White Paper (EC (2011)) targets. One of the main targets is to shift 30% of road freight transport, where the distance is above 300 km, from road to rail by 2030, and the more than 50% by 2050. Another strategic target calls for ensuring that all primary seaports are sufficiently connected to, where possible, inland waterway system by 2050. The IWW development also contributes to the aims of Finnish Maritime Strategy 2014-2022 in improving and maintaining marine and inland water ways, to promote novel technologies to support sustainable and competitive vessel traffic, also in wintry conditions, and to contribute to the growth of Russian transit traffic via Finland.
Furthermore, inland water area, especially the Lake Saimaa district has several protected areas, where the habitats of protected species are close to the Saimaa deep water route where the merchant vessels are sailing. Accident and near miss incidents recorded by maritime authorities reveal that the accidental risk in the Saimaa Lake is relatively high compared to the sea areas. Challenging navigating environment emphasize the importance of piloting or compensatory service for vessels.


However, building such an efficient transport network requires substantial resources. Science can contribute significantly to obtain optimal policy design, and due to the extreme costs, even small adjustments in infrastructure investments can pay back to national and private economy and society's welfare (for example, the cost of EU infrastructure development to match the demand for transport is estimated to be over € 1.15 trillion in 2010-2030, EC (2011)). We support directly the related decision-making and give strategic advice how to do new related research by carrying out the value of information analysis using the developed decision models (Mäntyniemi et al 20 xx ICES J), including oil spill risk management model and CO2 policy evaluation model. In particular, the project studies several policy options that have a potential to improve the efficiency of transportation, and thus decrease the CO2 emissions and decrease the investment costs. An important set of management options includes structural issues, such as the development of the Finnish dry port structure and railway network (Lättila et al., 2013), the reduction of the number of coastal harbors (Tapaninen, 2015), the promotion of inland water ways and harbors, the construction of the Kymijoki canal, and the proposed railway tunnel between Helsinki and Tallinn (???CAN ULLA PROVIDE REFERENCES?).
The project studies several policy options that have a potential to improve the efficiency of transportation and thus decrease the GHG emissions and investment costs on traffi c infrastructure. The set of management options includes structural issues, such as the development of the
Finnish dry port structure and railway network, reduction of the number of coastal harbors (Tapaninen, 2015), promotion of inland water ways and harbors, construction of the Kymijoki canal, and the proposed railway tunnel between Helsinki and Tallinn.


The CO2 and other greenhouse gas emissions are not the only environmental concerns that need to be addressed. A fact in the risk analysis is that a single oil spill in the Gulf of Finland can incur costs up to one billion euros. This future risk is related to the alternative traffic combinations. Furthermore, inland water area, especially the Lake Saimaa district has several protected areas, where the habitats of protected species are close to the Saimaa deep water route used by merchant vessels. Deep water route is proven to be difficult to navigate because of its narrowness and fast currents, which also create the fact that no oil combating vessel can decrease the risk of oily shorelines significantly. Accident and near miss incidents recorded by maritime authorities reveal that the accidental risk in the Saimaa Lake is relatively high compared to the sea areas (Finnpilot, 2014). Challenging navigating environment emphasize the importance of piloting or compensatory service for vessels. The project studies the effects of the described policy options on several socio-economic and environmental factors. In addition to CO2 and other greenhouse emissions, we concentrate on oil spill risks related to ecological and economic consequences of oil spills and the food security of Finland. The risk analysis framework includes risk definition (our WP 2), risk analysis (WP 3–WP 5), risk management (WP 6) and risk communication (all WP, plus WP 7 especially). The challenge to provide interdisciplinary risk information is a demanding one (Haapasaari et al., 2012). Therefore, we will take new steps in risk communication by including cognitive scientists ( ?? TAKE CONTACT; SAKKE) to get feedback from stakeholders on interdisciplinary risk estimates, and from artists to describe the well-known and poorly known unseen risks.
Sophisticated modelling tools are necessary to assess the combinations of actions which lead to GHG emission reductions from the transport sector. Therefore, from the methodological point of view, our main objective is to develop new techniques for policy design, which are based on extensive use of the underlying scientifi c theory, existing data sets, publications, and expert knowledge to provide risk related advice (Haapasaari et al., accepted). We will combine existing knowledge by summarizing information using probabilistic forecasts. To be more concrete, we forecast the future scenarios by the forward simulation version of the developed model fitted to relevant data, including economic and climate indicators. Indicators describe the state of interest variables by the noise included to likelihood function. We then forecast probabilistic outcomes of the key interest variables conditional on designed policy actions. We use these forecasts as an input in a decision model, where the optimal policy, given the underlying key uncertainties, can be evaluated. This can be done separately or jointly with the key societal aims to understand the role of precise aims in the policy support (Kuikka and Varis 1997, Varis and Kuikka 1997).


It is also important to note that the purely economic direct costs of oil spill that realize on markets would be shared between insurance companies, international oil pollution compensation funds, and Baltic Sea countries. Finland is a member of the Supplementary Fund of the International Oil Pollution Compensation (IOPC) Funds, which has a compensation limit of SDR 750 million (€ 961 million). However, the compensation is paid only if oil pollution result in an actual and quantifiable economic loss related to property damage, oil combating and clean-up costs, economic losses in fisheries, mariculture or tourism, and costs of reinstatement of the environment (IOPC Funds, 2013). Hence, losses to environmental values that are typically difficult to assess are left outside from the compensation scheme. Therefore, we plan to apply Bayesian techniques to assess the value of information and value of control in planning the policies (that is, we use value-of-control analysis). This is, to our knowledge, the first study to apply the Bayesian causal modelling techniques to the planning of future legislation options. We are especially looking forward to apply the Pearls algorithm (ANTTI: 1995 vai 2000 paperi) to the optimal policy design under the case where many variables of a noisy chain link the decisions to aims (Varis & Kuikka, climate change arcticle)  (the fig on policy evaluations by cutting the links here: ANTTI ET AL: FIG SOMEWEHERE HERE).
The rationale of probabilistic approach is described in Fig. 2 which shows how the use of a point estimate can provide wrong policy advice in risk averse decision making. For instance, if the criterion is to avoid the risk level of small biomass, a point estimate would suggest policy A that provides better expected value (Fig. 2). However, the uncertainty related to this option is estimated to be higher than that of policy option B.


The possible funding of ILVES consortium would be very important for the next steps in participating units. For FEM research group in UH, this would be an important next step from the papers made in the Gulf of Finland to the rest of Baltic Sea. It is of strategic importance for the Finnish and Baltic Sea wide oil disaster risk management that the methods are made more well-known in other area. We expect that our way to evaluate the impacts of various insurance practices will be highly relevant for international insurance practices. We also develop general policy evaluation tools for cases, where the amount of fata, models, papers, experts and other sources of information vary in terms of their quality. We do it in probabilistic ways, meaning that the quality of the information is in a key role in the analysis.
The proposal supports decision-making related to efficient allocation of resources. Science can help to find the optimal policy design, and due to the extreme costs, even small adjustments in infrastructure investments can pay back to national and private economy and society’s welfare (for instance, the cost of EU infrastructure development to match the demand for transport is estimated to be over 1.15 trillion € in 2010–2030 (EC, 2011)). Furthermore, we give strategic advice how to allocate research resources by carrying out the value of information analysis (Mäntyniemi et al., 2009). We apply Bayesian techniques to assess the value of information and value of control in planning the policies (that is, we use value-of-control analysis to say what should be managed).


As already discussed, instead of using point estimates, we base our analysis on probability distributions. Fig 1 describes why the use of a point estimate can provide wrong policy advice in risk averse decision making. If the criteria is to avoid the risk level of small biomass (limit in the Fig), a point estimate would suggest policy A that provides better expected value. However, the uncertainty related to this option is estimated to be higher than that of policy option B.
This is, to our knowledge, the first study to apply the Bayesian causal modelling techniques to the planning of future legislation options. We are especially looking forward to apply the Pearls algorithm (Pearl, 1995) to the optimal policy design under the case where many variables of a noisy chain link the decisions to aims (Fig. 3) (Varis and Kuikka, 1997).


For this proposal, we have improved the FEM team by recruiting more skills from UH and Finnish universities and international scientific bodies.
The proposal develops methods that are relevant from the national as well as international point of view. We develop general policy evaluation tools for cases, where the amount of fata, models, papers, experts and other sources of information vary in terms of their quality. We also expect that our approach to evaluate the impacts of various insurance practices will be highly relevant for international insurance practices. Furthermore, the methodology developed related oil spill risks can be seen important for the Finnish and Baltic Sea oil risk management.
Our approaches are built upon the following publications:


1) '''approach''' was published for the first time in Klemola et al 2009, but no citations have been obtained to this paper, in a maritime academic journal. First step in this
[[File:Ilves recognizing best practises.png|thumb|400px|'''Fig. 3''']]
2) '''methodological process''' was published in of  REF INKKU: MY PUBLICATION LIST IN WEB, REF TEPPO PAPER IN ICES ASC. This paper was not accepted in a journal of coastal processes, based mainly on oceanology where causalities are based on theory of physics.   Thereafter, we have systematically estimated and analyzed various components of the oil spill  risks:
3) '''impacts on nature,''' (Lecklin et al) shows how the ''behavior'' of  e.g. birds must be taken into account in conditional probabilities of  impacts (close to concept of likelihood in usual statistical models), i.e. do e.g. migrating birds in GoF actively look for oily places where the surface is deadly calm and can seal avoid the oily areas? 
4) using the concepts of '''value-of-control''' and status of the species to plan spatial decisions of locating oil booms by an risk indicator
5) implementing the previous '''knowledge to a practical software''' distributed to firemen who decide in practice (Kokkonen et al 2010)
6) Helle et al (xx) carried out an '''operational test by a local BBN decision model:'''how the logistics is going to impact the chance to act in different sizes of accidents,under the aim to safeguard the nature values of the hot spot areas around Tvärminne field station
7) evaluating the current '''effectiveness of oil combatting''' fleet in Finland under observed weather conditions (Lehikoinen et al 2013), being the technical basis of the Helle et al in revision   
8)  Jolma et al (201 x) demonstrated how to use '''spatial models and software’s to estimate key parameters''' to the BBn model
9) Helle et al (in minor revision, Journal of Environmental  Management) made a probabilistic cost effectiveness analysis of whether the last oil comabtting vessels investiment (45 million euros) was profitable anymore in Finland. Answer was no, which created discussions in Finland.
INKKU: keep these papers here to help readers to realize what we have done:


* Juntunen, T., T. Rosqvist, J. Rytkönen and S. Kuikka. 2005. How to Model the Oil Combatting Technologies and Their Impacts on Ecosystem: a Bayesian Networks. Page CM 2005/S:2002 in 2005 ICES Annual Science Conference Aberdeen, United Kingdom.
For this proposal, we have improved the FEM team by recruiting more skills from UH and Finnish universities and international scientific bodies. At the moment, our approaches are built upon the following publications:
* Klemola, E., Kuronen, J., Kalli, J., Arola, T., Hänninen, M., Lehikoinen, A., Kuikka, S., Kujala, P. and Tapaninen, U. 2009. A cross-disciplinary approach to minimising the risks of maritime transport in the Gulf of Finland. World Review of Intermodal Transportation Research 2(4): 343–363.  
* '''Klemola et al.'''' (2009) published the approach of Bayesian statistics in oil spill risk analysis, but no citations have been obtained to this paper, in a maritime academic journal. There seems to be no tradition for advanced risk analysis in the area.
* Kokkonen, T., Ihaksi, T., Jolma, A. and Kuikka, S. 2010. Dynamic mapping of nature values to support prioritization of coastal oil combating. Environmental Modelling & Software, 25 (2010) 248–257.  
* '''Juntunen et al.''' (2005). First step in the methodological process was published in this paper in ICES annual science congress. It was not accepted in a journal of coastal processes. Journal was focusing on oceanology where causalities are based on theory of physics.
* Helle, I., Lecklin, T., Jolma, A. & Kuikka S. 2011. Modeling the effectiveness of oil combating from an ecological perspective - A Bayesian network for the Gulf of Finland; the Baltic Sea. Journal of Hazardous Materials 185(1):182-192.
* '''Lecklin et al.''' (2011) showed how the behavior of e.g. birds must be taken into account in conditional probabilities of impacts (close to concept of likelihood in usual statistical models), i.e. do e.g. migrating birds in GoF actively look for oily places where the surface is deadly calm and can seal avoid the oily areas?
* Lecklin, T., Ryömä, R. and Kuikka, S. 2011. A Bayesian network for analyzing biological acute and long-term impacts of an oil spill in the Gulf of Finland.  Marine Pollution Bulletin 62 (2011) 2822-2835.
* '''Ihaksi et al.''' (2011) used the concepts of value-of-control, vulnerability of the population and conservation status of the species to plan spatial decisions of locating oil booms by an risk indicator.
* Ihaksi,T., Kokkonen, T., Helle, I., Jolma, A.,Lecklin, T. and Kuikka, S. 2011. Combining conservation value, vulnerability, and effectiveness of mitigation actions in spatial conservation decisions: an application to coastal oil spill combating. Environmental Management. 47: 802–813.  
* '''Kokkonen et al.''' (2010) implemented the spatial knowledge to practical software distributed to firemen who decide in practice, and they liked the product.
* Lehikoinen, A., Luoma, E., Mäntyniemi, S. and Kuikka, S. (2013) Optimizing the Recovery Efficiency of Finnish Oil Combating Vessels in the Gulf of Finland Using Bayesian Networks. Environmental Science and Technology, 47(4):1792-1799.[Link]
* '''Helle et al.''' (2011) carried out an operational test by a local BBN decision model: how e.g. the logistic limits is going to impact the chance to act in different sizes of accidents, under the aim to safeguard the nature values of the hot spot areas around Tvärminne field station. In a case of big spill, both booms and time run out.
* Jolma, A., Lehikoinen, A., Helle, I. and Venesjärvi, R. (2014). A software system for assessing the spatially distributed ecological risk posed by oil shipping. Environmental Modelling & Software, 61:1-11. [Link]
* '''Lehikoinen et al.''' (2013) evaluated the current effectiveness of oil combatting fleet in Finland under observed weather conditions
* Lehikoinen,A., Hänninen, M. Jenni Storgård, Emilia Luoma, Samu Mäntyniemi & Sakari Kuikka. (n print) A Bayesian network for assessing the collision induced risk of an oil accident in the Gulf of Finland. Environmental Science and Technology
* '''Jolma et al.''' (2014) demonstrated how to use spatial models and software’s to estimate key parameters to the BBN model.
* Helle, I., Ahtiainen, H., Luoma, E., Hänninen, M., Kuikka, S. Where should we invest in oil spill management? A probabilistic approach for a cost-benefit analysis under uncertainty. Accepted  with minor revisions.
* '''Helle et al.''' (accepted) made a probabilistic cost effectiveness analysis of whether the last oil combatting vessels investment (45 million euros) was profitable anymore in Finland. Answer was no, which created discussions in Finland.


PHOTO OF THE ICE AND BREAKER
===3 Societal signifi cance and impact===


Fig. X. The combination of unusual ice conditions for mariners and the existence of rocks in unpredictable areas make the Gulf  of Finland and Archipelago Sea as very difficult areas to navigation and ship operations. In the Gulf of Bothnia, the islands and rock are not numerous, but there are  moving ice filds.: XX VALTTERI: MODIFY THIS
Transport of goods and people is fundamental to any modern society. International and national trade support creation of jobs, improving employment and welfare. Finland is isolated by sea from the most of the import areas and export markets. The value of the exported goods and services was 77.6 billion € constituting 40% of the GDP in 2013. In terms of weight, 88% of the exported freight was transported by sea in 2012. Harbors link Finland to foreign markets. In 2012, 17 harbors processed over 1 million tons of freight each. The on-shore and off-shore operations, vessel construction and logistics of transport are optimized by business logic. Simultaneously, they have to acknowledge sustainable development and environmental constraints. The aspects of economic competitiveness and environmental risk can be evaluated in a decision support framework. They integrate knowledge related to future CO<sub>2</sub> -emission reduction abatement technologies and costs, logistic solutions and the assigned environmental risks with the utilities perceived by the policy makers and stakeholders. As a result, decision support will facilitate balanced argumentation and provide societal, commercial, and environmental benefits in Finland.


== 3 Societal significance and impact ==
The names of the EU maritime legislation packages (e.g. Erika I, Erika II) carry the names of accidents. This is an indication of inertia in science–policy interaction influencing the maritime risk governance in Europe: major accidents provide impulse to improvements in legislation. But, unfortunately, only after the hazard has come true with severe consequences. If same risk management approach would have been adapted in the arenas of nuclear safety and aviation, the earth would be an unpleasant place to live in. No doubt, there are many lessons to learn from those arenas, where it is everyone’s imperative interest that no accidents take place. In nuclear risk management, all actions improving safety are based on model outcomes, demonstrating the strong role of science.
Transport is fundamental to our economy and society. Finland is economically an island, and our export and import takes place by the shipping. Mobility is vital also for the internal market and for the quality of life of citizens as they enjoy their freedom to travel. Transport enables economic growth and job creation: however, it must be sustainable and acknowledge resource and environmental constraints.
An important science – policy interaction issue is in maritime risk governance, why the names of the EU legislation packages carry the names of accidents. If same risk management approach would have been adapted to nuclear risk management, the earth would be an unpleasant place to live in. There seem to be a lot of lessons to learn from flying business where it is everyone’s biggest interest that no accidents take place. In nuclear risk management, all actions are based on model outcomes, demonstrating that the role of science is very strong. We will adapt the most valuable lessons to learn from these fields and support their implementation in maritime risk management (Haapasaari et al accepted with minor revisions Marine Policy), where the academic scientific methodological background is weak compared to fisheries. In fisheries, an important field of applied “engineering” ecology, risk methodology is well advanced (Fletcher, 2015). In nuclear power management there is a strong trust to build the actions on complex risk models.  Same attitude would help maritime risk management to learn more effectively from all information sources.
The main societal impacts of ILVES approach are as follows, if the project findings are implemented successfully: 1) findings will supporting the policy to achieve the targets in CO2 emission reduction 2) investments based on suggested chain of creating new jobs along inland water ways 3) improved state of environment 4) improving the interest to apply best practices in companies that create main risks, leading to higher quality in all activities.
The strategic answers of ILVES to the 4 questions made by the call are as follows:
A) How can we improve resource efficiency and support the move towards a circular economy, which will serve to boost exports and competence-based growth in Finland
If the project findings will be implemented by the Finnish government, the need to use fossil energy in shipping and other traffic will decrease. The project findings will support the development of new shipping technology in Finland and therefore support exports. The new methods to develop legislation and other national policy options will support the development towards competence-based growth, as creation of such development needs a combination of national actions (taxes, subsidies, legislation, customer behavior). The new transportation options to inland water will boost local investments. As no development can take place without negative impacts, we look at the oil spill risk changes related to various transportation options.
B) What are the requirements for climate neutrality and resource efficiency in society?
We will study the requirements for climate neutral society, by calculating with a Bayesian decision model, what are the prerequisites of the national and international policies to achive the desired state of the climate aims in transportation policy. Bayesian network models can calculate the states of the system from causes to effects like any models, but they can also calculate backwards, .i. from desired aims back to required policies. This methodology will be important is overall support of climate policy by scientific tools. We will study how the oil companies, shipping companies and the users of these services increase their interest to avoid environmental disasters and customer responsible consumption to decrease the CO2 emissions. The policy options of society (taxes, laws) are compared to these ways to govern the environmental impacts in society.
C) In what ways can the public sector best support the overall transition so as to maintain a well-managed move towards a climate-neutral and resource-scarce society?
There is no clear answer to this question yet, but we will develop methods, legislation planning tools, practical policy actions and new governance solutions to reach these goals. The potential big impacts of resource-scarce international markets will be studied by the risk analysis of worldwide food production. New machine learning methods are applied to the worldwide food production data sets. Same methodology is used to learn traffic risks from large marine data sets.
D) How can we ensure that businesses, employees, the public sector and consumers possess the resources and skills that promote an ability to adapt to the changes and risks brought about by disruptive technologies?
We will analyse how new shipping technologies (like new fuel requirements, use of electric power in inland water to avoid oil spill risks) and shipping options can be used to support the climate policy in EU and Finland. We will look at the customer behaviour is selecting low carbon products from markets (SYKE; Jyri) and how the role of NGO’s should be revised in the support of creating interests for companies to apply best available techniques to their shipping practises.
How to link together the customer selection and the co1.1y  Risk analysis based on the knowledge provided by Professor Pekka Sutela: what is the probability that the agreement on which the use of Saimaa channel is based on , will continue to be in force in the future. This is a main political risk factor for the investments needed to develop inland waterway traffic.


== 4 Objectives ==
We will adapt the most valuable lessons from these disciplines and advocate their implementation in maritime risk management, where the scientific methodological background is weak compared to e.g. precautionary fisheries management. In fisheries, an important field of applied “engineering” ecology, i.e. risk methodology, is highly advanced. In nuclear power management there is a strong trust to build the actions on complex risk models. A comparable approach would assist maritime risk management to learn more effectively from all disciplines.
The project involves six topics. These sub-projects have close interconnections, and they support each other.


=== Topic 1: Analysis of Historical data and meta-analysis on publications  ===
The main societal impacts of ILVES approach are as follows, given the project findings are implemented successfully:
# findings will supporting the policy to achieve the targets in CO<sub>2</sub>-emission reduction
# investments based on suggested chain of creating new jobs along inland water ways
# improved state of environment
# improving the interest to apply best practices in companies that create main risks, leading to higher quality in all activities.


While the considered theoretical models may help to identify the effects of the policy actions, we also use the Pearl’s (20xx) approach to try to identify causalities from non-experimental data, including  data from policy actions over the years. We believe this is a valuable approach for all policy analysis in the society. Using the empirical and theory based knowledge, we then estimate the likely future impacts of policy actions on the key variables of interest by simulating the future probabilistic developments of these variables conditional on the policy actions. In particular, we consider the forecast horizons of 10, 20, and 30 years to provide clear yardsticks for policy impact analysis, and also to learn causalities effectively on the basis of the policy. We insert these predicted conditional probability distributions to a decision model, where the implementation uncertainty (i.e., how likely it is that a policy will be realized in the way proposed) will be evaluated by the experts in jurisdiction. In this decision model, we take into account the uncertainty coming from expert judgement by integrating the probabilistic judgements of several experts in the decision model (c.f., the Bayesian influence diagram model of Kuikka et al 1999). The decision model will also provide value-of-information estimates (Mäntyniemiet al 20 xx), which describe what variables should be known more precisely at the time when decisions are made. This information is used in the project to focus the data analysis and modelling to policy relevant variables. In the planning of potential new policies, we also use the value-of-control analysis (KTS SAKEN VÄIKKÄRI; OLIvier TMS), where a probabilistic variables is made at least partly controllable by adding a new decision variable to the model. This analysis will reflect back to the planning of new legislation. This modelling approach will provide estimates of the likelihood to achieve the given gas emissions for the Finnish fleets, and  the related economic, social, and environmental interests in probabilistic terms.
The strategic answers of ILVES to the 4 questions made by the call are as follows:
:'''A) How can we improve resource effi ciency and support the move towards a circular economy, which will serve to boost exports and competence-based growth in Finland?''' If the project findings will be implemented by the Finnish government, the need to use fossil energy in shipping and other traffic will decrease. The project findings will support the development of new shipping technology in Finland and therefore support exports. The new methods to develop legislation and other national policy options will support the development towards competence-based growth, as creation of such development needs a combination of national actions (taxes, subsidies, legislation, customer behavior). The new transportation options to inland water will boost local investments. As no development can take place without negative impacts, we look at the oil spill risk changes related to various transportation options.
:'''B) What are the requirements for climate neutrality and resource efficiency in society?''' We will study the requirements for climate neutral society, by calculating with a Bayesian decision model, what are the prerequisites of the national and international policies to achieve the desired state of the climate aims in transportation policy. Bayesian network models can calculate the states of the system from causes to effects like any models, but they can also calculate backwards, .i. from desired aims back to required policies. This methodology will be important is overall support of climate policy by scientific tools. We will study how the oil companies, shipping companies and the users of these services increase their interest to avoid environmental disasters to decrease the CO 2 emissions. Different policy options (such as taxes, fees, financial instruments and sanctions) are compared to these ways to govern the environmental impacts in society. The legal feasibility of the policy options, bearing in mind the international nature of maritime transport and consequential international law limitations will form part of this assessment.
:'''C) In what ways can the public sector best support the overall transition so as to maintain a well-managed move towards a climate-neutral and resource-scarce society?''' There is no clear answer to this question yet, but we will develop methods, legislation planning tools, practical policy actions and new governance solutions to reach these goals. The potential big impacts of resource-scarce international markets will be studied by the risk analysis of worldwide food production. New machine learning methods are applied to the worldwide food production data sets. Same methodology is used to learn traffic risks from large marine data sets.
:'''D) How can we ensure that businesses, employees, the public sector and consumers possess the resources and skills that promote an ability to adapt to the changes and risks brought about by disruptive technologies?''' We will analyze how new shipping technologies (such as new fuels, use of electric power in inland water to avoid oil spill risks) and shipping options can be used to support the climate policy in the EU and Finland. We will look at the customer behavior is selecting low carbon products from markets and how the role of NGO’s should be revised in the support of creating interests for companies to apply best available techniques to their shipping practices. In addition, we will analyze risks related to the Finnish-Russian agreement on which the use of Saimaa channel is based on. This is a main political risk factor for the investments needed to develop inland waterway traffic.


=== Topic 2: Structuring Decision Model ===
===4 Objectives, expected results===


As far as the structural analysis is concerned, we apply an emulator model (O'Hagan (2006)) to learn from the behaviour of the complex micro economic models (XX JUHA: insert here the references). We also try to identify causalities from the data, by combining the information of the micro economic model with the alternative views of the causal structure of the system. This will be done using the views of different experts and stakeholders on causalities. If successful, the learning from causalities by combining micro economic models and experts opinions will be a novelty in economic and environmental analysis. The methodology may have a major impact on the understanding of the impacts of policy actions (yearly interventions by total allowable catches) on stock dynamics, or in environmental management of water quality. It is obvious that proposed method can be used in any policy evaluation settings where similar data is available, like in the evaluation of economic policies.
[[Image:Ilves approach diagram.png|thumb|400px|Fig. 5. The linkages between topics in ILVES approach. This is basically a description of a learning chain and network in the ILVES.]]
[[File:Ilves risk areas maritime traffic.png|thumb|400px|'''Fig. 6.''' Definition of risk areas of maritime traffic in the Gulf of Finland based on traffic data (Lehikoinen et al. 2015)]]


=== Topic 3: Data Analysis and Algorithms ===
The project involves six topics (Fig. 5). These sub-projects have close interconnections, and they support each other.


In practice, risks and probabilities can not be directly measured. Therefore, we need sophisticated modelling tools to estimate them. Our goal is to develop new techniques for probabilistic forecasting, including descriptive univariate and multivariate time series methods (see, for example, Lanne et al. (2012), Lanne and Luoto (2013), Karlsson (2013), and Amisano and Geweke (2013)) and models that combine the  underlying scientific theory with data (see, for example, Adolfson et al. (2007), Del Negro and Schorfheide (2011), and Kuikka et al. (2014)). We especially plan to devise methods for combining the forecasts from the different models, to incorporate all available information into the probabilistic forecasts (see, for example, Geweke and Amisano (2011) and Billio et al. (2013) and the references therein, and a public use of probabilistic rather forecast model in FMI, WEBLINK HERE). We intend to build upon the work in Waggoner and Zha (2012) and Billio et al. (2013). These papers deal with situations where all the models can be misspecified and their relative importance may change over time. The solution of such forecasting problem involves time-varying forecasting combination weights (or model weights), and is therefore computationally very challenging. Due to these challenges, the authors are able to apply their methods only for small and medium scale forecasting models. Our goal in this project is the development of statistical tools and techniques for combining the predictive densities of large scale models under the described situation. This can be motivated by the complexity of the mechanisms behind the key variables of interest. In particular, their (true) joint distribution may be a function of tens of variables, and it does not have to be time invariant.
====Topic 1: Analysis of Historical data and meta-analysis on publications====


There is also an under evaluated link between the Bayesian parameter estimation and decision models. Many of the algorithms use extensive time in estimating the tail probabilities of the distributions. However, the desired accuracy of the approximation of the target posterior distribution is linked to the decisions. In particular, if the ranking of the decision models is no longer sensitive to the quality of the estimates of the posterior, it is likely that the algorithm can be stopped. Therefore, the use of decision models together with parameter estimation is essential for such online decisions or fast decision making, where there is no time to wait for better estimates.
While the considered theoretical models may help to identify the effects of the policy actions, we also use the Pearl’s (2000) approach to try to identify causalities of interest from non-experimental data, including data on policy actions in the past. We believe this would be a valuable approach for all policy analysis in the society. Using the empirical and theory based knowledge, we then estimate the likely future impacts of policy actions on the key variables of interest, by simulating the future developments of these variables conditionally on the policy actions. In particular, we consider the forecast horizons of 10, 20, and 30 years, which provide clear yardsticks for an analysis of policy impacts. We insert these predictions into a decision model, where the implementation uncertainty (i.e., how likely it is that a policy will be adopted in the way proposed) will be evaluated by the experts in maritime policy and law. In this decision model, we account for different types of uncertainty due to usin expert judgments, and integrate them into the considered decision model by applying suitable probabilistic weighting. The decision model will also provide value-of-information estimates (Mäntyniemi et al., 2009), which describe what variables should be known more precisely at the time when decisions are made. This information is used in the project to focus the data analysis and the modelling on policy relevant variables. In the planning of potential new policies, we also use the value-of-control analysis, where each probabilistic variable is made at least partly controllable by adding a new decision variable to the model. The analysis will reflect back to the planning of new legislation. This approach to modelling will provide estimates of the likelihood to achieve the given GHG emission reduction targets for the Finnish fl eets, and the related economic, social, and environmental interests in probabilistic terms.


=== Topic 4: Health and Well-being Impacts of Oil Spills In Saimaa ===
====Topic 2: Structuring Decision Model====


As already discussed, inland water area, especially the Lake Saimaa district has several protected areas, where the habitats of protected species are close to the Saimaa deep water route used by merchant vessels. Deep water route is proven to be difficult to navigate because of its narrowness and fast currents, which also create the fact that no oil combating vessel can decrease the risk of oily shorelines significantly. Accident and near miss incidents recorded by maritime authorities reveal that the accidental risk in the Saimaa Lake is relatively high compared to the sea areas (Finnpilot, 2014). Challenging navigating environment emphasize the importance of piloting or compensatory service for vessels. The project studies the effects of the described policy options on several socio-economic and environmental factors. In addition to CO2 and other greenhouse emissions, we concentrate on oil spill risks related to ecological and economic consequences of oil spills and the food security of Finland. The risk analysis framework includes risk definition (our WP 2), risk analysis (WP 3–WP 5), risk management (WP 6) and risk communication (all WP, plus WP 7 especially). The challenge to provide interdisciplinary risk information is a demanding one (Haapasaari et al., 2012). Therefore, we will take new steps in risk communication by including cognitive scientists ( ?? TAKE CONTACT; SAKKE) to get feedback from stakeholders on interdisciplinary risk estimates, and from artists to describe the well-known and poorly known unseen risks.
As far as the structural analysis is concerned, we apply an emulator model to learn from the behavior of the complex micro economic models. We also try to identify causalities from the data, by combining the information of the micro economic model with the alternative views of the causal structure of the system. This will be achieved by an effective utilization of the views of different experts and stakeholders on causalities. If successful, the learning from causalities by combining micro economic models and expert opinions will be a novelty in economic and environmental analysis. The methodology may have a major impact on the understanding of the impacts of policy actions (yearly interventions by total allowable catches) on stock dynamics, or in environmental management of water quality. The proposed method can be used in any policy evaluation setting, where similar data are available, as in the evaluation of economic policies.


=== Topic 5: Planning the Future Policy Options ===
====Topic 3: Data Analysis and Algorithms====


Alternative future scenarios for environmental policy changes in shipping, like the efficacy of Emission Control Areas, vessel speed limitations and use of LNG as fuel will be tested using actual traffic data from Automatic Identification System (AIS) (Jalkanen et al. (2009, 2012) and Johansson et al. (2013)) and chemical transport modeling (Jonson et al. (2015)). These facilitate the evaluation of environmental performance of maritime policy changes and have been already used as background scientific material at HELCOM and IMO (Backer et al. (2011), HELCOM (2014), PBL (2012), and Smith et al. (2014)). The use of actual ship traffic patterns and volumes aim at reducing the cumulative uncertainty of the cost/benefit analysis, thus improving the overall performance of the Bayesian approach (Helle et al, under minor revisions, JEM).  The shipping scenario work directly contributes to the revision of the national program of measures of the marine strategy, the first version of which already incorporates Bayesian modelling. Our proposal extends the work described in the national program of measures by offering a more complete view on different transport models and including several future scenarios up to year 2050.
In practice, risks and probabilities can not be directly measured. Therefore, we need sophisticated modelling tools to assess them. Our goal is to develop new techniques for probabilistic forecasting, including descriptive time series methods and models that combine the underlying scientific theory with data (e.g. Kuikka et al. (2014)). We especially plan to devise methods for combining the forecasts from the different models, to incorporate all available information into the probabilistic forecasts. Our goal in this project is the development of statistical tools and techniques for combining the predictive distributions obtained from large scale models in the described situation.


=== Topic 6: Software Devolopment ===
There is also an under evaluated link between the Bayesian parameter estimation and decision models. Many of the algorithms use extensive time in estimating the tail probabilities of the distributions. However, the desired accuracy of the approximation of the target posterior distribution is linked to the decisions. In particular, if the ranking of the decision options is no longer sensitive to the quality of the estimates of the posterior, the algorithm can be stopped from running. Therefore, the use of decision models together with parameter estimation is essential for such online decisions or fast decision making in industrial applications, where there is no time to wait for better estimates.
Tähän jotain


{{comment|# |Slide|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 04:21, 22 April 2015 (UTC)}}
====Topic 4: Health and Well-being Impacts of Oil Spills in Saimaa====


== 5 Research methods and material, support from research environment ==
We will a detailed look at the risks of toxicology of oil spills on inland water ways (Fig. 6). The Saimaa deep-water route is particularly difficult to navigate because of its narrowness and fast currents, with the consequence that no oil-combating vessel can significantly decrease the risk of oily shorelines. Accident and near miss incidents recorded by maritime authorities reveal that the accidental risk in the Saimaa Lake is high compared to the sea areas. The Finnish inland water area, and especially the Lake Saimaa district, has several protected areas, where the habitats of protected species are close to a deep water route used by merchant vessels (Fig. 7).
=== 5.1 Research Methods ===
In addition to existing standard research methods commonly used in empirical and theoretical analyses, one of the main objectives of the project is the introduction of new methods that will subsequently be subjected to the scrutiny of empirical applications. Moreover, the properties of the new methods and major modifications of existing methodology will be studied by means of simulation experiments. Because of the complexity of the models to be considered, computer intensive methods based on simulation will play a central role in the majority of the empirical and theoretical analyses.
{{comment|# ||--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 04:21, 22 April 2015 (UTC)}}


=== 5.2 Data management plan ===
Oil spill may also potentially have an impact on human health. The current exposure to PAHs through eating fish from Saimaa will be evaluated. Assessment is based on literature survey of PAH concentrations in different fish species and use of fish consumption data available from previous studies. Health impact assessment is done by utilizing the previously developed open access model, which have been successfully used for determining health impacts of herring consumption in Finland. The current exposure to PAHs through eating fish from Saimaa will be evaluated. Assesment is based on literature survey of PAH concentrations in different fish species and use of fish consumption data available from previous studies. Health impact assessment is done by utilizing the previously developed open access model, which have been successfully used for determining health impacts of herring consumption in Finland. The societal values of recreational use of Saimaa are first estimated based on a literature review of societal utility of inland water bodies and then the questionnaire is used to evaluate the effects of oils spills on wellbeing.
The data used in the empirical applications will be drawn from various sources, including commercial databases, such as the Datastream database as well as databases provided by central banks and other government agencies over the internet, and official statistics.


We utilize the existing large databases of:
====Topic 5: Planning the Future Policy Options====
# VATT and Bank of Finland for the economic data
# vessel databases of TRAFI
# traffic databases of TRAFI, already analysised in TUT modelling of CO2 emissions (REF)
# CO2 footrpint estimates of SYKE and University of Oulu (Jyri: insert here the name of the expert or databases)
# fish stock estimates of ICES (International Council For the Exploration of the Sea) for the impacts on stocks and fisheries
# threatened species database of SYKE, which is already linked (REF) to vessel accident estimates (REF), and to the spread of oil after an accident on a given area. spread of oil is based on the use of XX model where the observed weather data is used to estimate likely hit of oil to the threatened species
# bird databases of SYKE
# TRAFI databases from WGMABS report
# Ship emission, pollutant transport and numerical weather prediction datasets of the Finnish Meteorological Institute
# Bank Of Finland Databases on national economy and on the experts judgements based over the years to enable the comparison off an expert and models in future predictions (testing Pearl’s statement that
# The knowledge bases of CSIRO to apply best insurance practices in oil production idustry to vessel traffic, especially tankers.
Atlantis will be use to generate prior probabilities for the effect of human activities
such as oil and gas transportation on the ecosystem. It is computer model that integrates
physical, chemical, ecological, and fisheries dynamics in a three-dimensional, spatially explicit
domain. Sub-models represent simulate oceanographic processes, biogeochemical factors
driving primary production, food web relations among functional groups, habitat
interactions, and fishing. We will apply the Atlantis to the Baltic and simulate a range of
human activities and evaluate potential trade-offs between that management is confronted with.
We will consider the direct effects on harvested species of economic interest, and the indirect
effects on other components of the food web. The results can be interpreted on the level of
individual species abundance, or through the use of ecosystem indicators.
# MEERI 2012 Calculation system for Finnish waterborne traffic emissions, sub model of the calculation system LIPASTO 2012 '''??? ALREADY MENTIONED? ''' ??? '''The most recent version of MEERI is based on FMI ship emission model'''
# Underwater multi beam survey data, Meritaito Oy/Liikennevirasto '''availability not confirmed yeT'''


ALL PARTNERS: list here the databases you are going to use
Alternative future scenarios for environmental policy changes in shipping (efficacy of Emission Control Areas, vessel speed limitations and use of LNG as fuel) will be tested using actual traffic data from Automatic Identification System (AIS) (Jalkanen et al., 2012) and chemical transport modeling. These facilitate the evaluation of environmental performance of maritime policy changes and have been already used as background scientific material at HELCOM and IMO. The use of actual ship traffic patterns and volumes aim at reducing the cumulative uncertainty of the cost/benefit analysis, thus improving the overall performance of the Bayesian approach (Helle et al., Journal of Environmental Management). The shipping scenario work directly contributes to the revision of the national program of measures of the marine strategy, the first version of which already incorporates Bayesian modelling. Our proposal extends the work described in the national program of measures by offering a more complete view ondifferent transport models and including several future scenarios up to year 2050.
We will provide these databases for other users in the open database system of OPASNET. The data sets of this proposal are described on page .XX


However, as the data is only historical observations, the more usefull information for other scientists than those in ILVES consortium are the estimates of interest variables (like risks, xx,xx). We will provide probabilistic databases of the estimates to allow effective estimation of prior probabilities for future analysis. This will enable the more effective learning in sciences, where it is important from the point of view of end user of the information, that estimates include also other knowledge than just the data that happens to be observed in single studies.
====Topic 6: Software Development====


In expert elicitation, we use the following experts:
We will further develop software development and test with people out there: are people interested about our products given their values? We will use systematically Metropol feedback systems in the yearly ICES WGMABS open risk communication meetings (2016: St Petersburg to meet industry, 2017 to help international WWF to adapt those parts of ILVES and WGMABS approaches which are needed for successful risk governance.
# The economic expert panel ?? Of Finland, which has, by the help of model estimates and data, evaluated yearly the future economic growth of Finland. By vomparing this to the actual realised economic development, observed and estimated later on, we evaluate the probabilistic exactness (likelihood functions for the decision model) of the predictability of Finnish economy.


== 6 Ethical issues ==  
====Topic 8. Analysis of food security====


The key ethical issue is the controversy in scientist life: can my risk communication wait until my paper is published and available (see the problem of dioxin, GOHERR webpages). {{comment|# |In an email of xxth April 2015, ICES officer Maria dd wrote that  CCCCCCCCCCC, copy from email here|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 04:21, 22 April 2015 (UTC)}}
The food security projections will be drawn from global analyses of food consumption-production dynamics, taking into account risks related to hydrological and climatic factors, climate change (Kummu et al.,2014), supply-loss-demand chain uncertainties and risks, as well as trade factors (Porkka et al. 2015). These analyses provide results in several scales including by country, by food-production unit and partly also by grid cell. Results for Finland can thus be directly obtained, and the trade flows as well as forcing functions, e.g., from climatic risks can implicitly be calculated; the former ones even singled out by trading partner. The outcomes of global food security models will be used as inputs to Bayesian network models, and the vulnerability approach will be used to assess the societal and environmental risks, resilience and adaptation capacity of the food production-trade-consumption system.


Our proposal does not contain work with human embryo/foetus, humans or animals. This proposal does not have research components, which include genetic data, personal information (religion, sexual or political orientation) or tracking of people. Currently, XX senior researchers out of YY in the consortium are female. Recruitment as well as the advancement and salary of the employed researchers are based solely on personal achievements and not on gender.
====Topic 9: Dissemination====


== 7 Implementation: schedule, budget, distribution of work ==
In here, we will especially give talks and obtain feedback on spoken messages, by using the modern web based techniques (Fig. 9). Systematic use of Metropol feedback systems in the yearly ICES WGMABS open risk communication meetings (2016: St Petersburg to meet industry, 2017 to help international WWF to adapt those parts of ILVES and WGMABS approaches which are needed for successful risk governance) is likely a very good opportunity to start discussions before disasters take place.


=== WP1 ===
===5 Research methods and material, support from research environment===
WP1) Analysis of historical data and meta-analysis of publications: information to predict:


;Description of work and role of participants
====5.1 Research Methods====


In expert elicitation, we use the following experts:
In addition to existing standard research methods commonly used in empirical and theoretical analyses, one of the main objectives of the project is the introduction of new methods that will subsequently be subjected to the scrutiny of empirical applications. Moreover, the properties of the new methods and major modifications of existing methodology will be studied by means of simulation experiments. Because of the complexity of the models to be considered, computer intensive methods based on simulation will play a central role in the majority of the empirical and theoretical analyses.
The economic expert panel ?? Of Finland, which has, by the help of model estimates and data, evaluated yearly the future economic growth of Finland. By vomparing this to the actual realised economic development, observed and estimated later on, we evaluate the probabilistic exactness (likelihood functions for the decision model) of the predictability of Finnish economy
The political situation in Russia offers an intereresting and highly valid question to many areas in society to utilize the probabilistic prediction models we provide by using several experts (Uusitalo et al 200?)


Expert elicitation of future risks: Even though the Finnish fleet creates an important element of oil spill risks, and especially so if we consider the potential of the inland water ways and their management, the Russian export of oil through the Gulf of Finland is the main element in overall risk in the Gulf of Finland. Costs of a big spill, like spill of Pristine in 20 xx, can cause costs of one thousand million euros. Of these costs, insurances and international oil foundation cover costs up to xxx 000 000 euros, and the rest of the costs would come to Finnish taxpayers, if the GoF is cleaned as much as it should be from ecological point of view. The Russian export can be redirected to e.g. Markets in China, which would have an impact on risks in GoF. In order to estimate this probability, we use experts to eliviate the required future probabilities.  
The overall modeling approach including partners is as follows. The ILVES approach is strongly led by Bayesian inference and decision analysis tools. The experiences of FEM group are used in developing and leading this process. The economic estimation and simulation models of VATT, together with the large datasets, are used to estimate economic changes. The UH/ECON will provide skills in the Bayesian time series analysis and in testing the theories given the observations. HUT will provide the models related to world wide food security and likely future risks. FMI will provide state of the art models to describe the emissions of shipping fleets, their atmospheric dispersion and impacts to the human health and the environment, LTU will provide knowledge in Bayesian MCMC analysis of complex models, the models used to manage logistic chains, and the expert knowledge and international expert views of Russian development having a potential impact on planned logistic pathways through Saimaa lake area. Åbo Akademi will provide expert knowledge in evaluation of current and potentially future national and international maritime legislation and its probabilistic impact on risks. KMRA will add to the stakeholder contacts and dissemination. KYAMK will provide practical experience from maritime activities, and the databases of inland shipping routes and possibilities. University of Duke will provide Bayesian modelling skills in analysis of the Gulf of Mexico oil spill impact analysis economic analysis. CSIRO will provide Bayesian expertise in exploitation and risk analysis, analysis of insurances to increase the interest to avoid accidents, and expertise in oil spill risk analysis. HUGIN Expert A/S will look at the interactive web tools to analyze the utility functions of the decision making. Moreover, it will develop more expert judgment orientated interface for Hugin software. Cost benefit analysis of developing inland waterways: In the probabilistic cost benefit analysis of the more intensive use of inland water ways, KYAMK will compile the SYKE data of the location of threatened species in areas where an oil spill is possible. Moreover, we value the damages to the use of summer cabins in the lake area by implementing an interactive questionnaire in the web, aiming to estimate the willingness to pay of the Finnish cottages owners to prevent spills. The potential losses of nature values and recreational values are the key elements of risks related to new traffic options in inland water ways. Moreover, as a an alternative policy of safeguarding nature values only (to see whether the aims lead to different policies) we will use the techniques of Ihaksi et al. (2011) to estimate the impacts of possible oil accident on threatened species. Among these, the Saimaa purpose seal is the most threatened and charismatic species, where Finland has a responsibility in safeguarding the population.


D 1.1y 1,D 1.1y 1.1y  D 1.1y 5
In the modeling of oil spill impacts in an ecosystem, whole ILVES team will invest on the combined knowledge of theory and data sets. UH/FEM and CSIRO will use Atlantis ecosystem model (Fulton et al., 2011) as a platform, it includes both theoretical thinking and parameterizations in various parts of worlds oceans. UH (8 pm for Bayesian part of oil risk) and CSIRO (8 pm for making other parameter settings available and modeling them) lead the process and Duke provides the Gulf of Mexico data sets and causal learning in non-experimental data with human induced impacts.  
In this task,


=== WP2 ===
Sub-models of Atlantis simulate oceanographic processes, estuarine and atmospheric inputs, nutrient cycles and biogeochemical factors driving primary production, predator-prey relationships among functional groups, habitat interactions, species movements and invasive species and human uses of marine ecosystems, such as fishing, aquaculture, energy and transport. Saying in other words from causal inference, Atlantis is a set of human induced impacts and ecosystem causal knowledge and model can be used as ”reality with likelihood based provision of simulated noisy data sets” when testing whether causal algorithms find a correct model structure from these simulated data sets.


WP 2 Structuring Decision model , inserting policy causal knowledge and value-of-information analysis of prior knowledge
We create a learning component to Atlantis by using Bayesian inference, where some of the parameter values are obtained from other areas where Atlantis has been parameterized, some are coming from publications related to previous spills and some are expert judgment’s. Especially, we look at the Gulf of Mexico oil spill databases to learn oil impacts (Duke). We will apply the Atlantis to the Baltic and link the spatial risk estimate values (Lehikoinen et al., 2015) to the ecosystem. This is a combination of theory and data in historical data analysis and in future simulations.


The WP 2 leader is Dr Honkatukia who is an expert on statistical and dynamic models, and knows well the information needs in several policy areas in Finland, based on VATT model XX JUHA: UPDATE THIS
For the risk analysis models of the worldwide food security and its impacts on the food security in Finland, Aalto will use the expert judgments on the chances that the fleets do no operate like assumed, due to for example harbor strikes. The food security models on the expert judgement models, where the aim of the Bayesian analysis is to model the uncertainties in causalities. These models are based on link matrixes, and they currently include expert understanding without extensive data analysis, and therefore they can be used as priors for more data based analysis. We use the machine learning algorithms of FEM Consultations (to be developed in this project) to run these additional analysis, using the extensive data sets of FAO. The sensitivities and risks of the system will thereafter be analyzed by sensitivity analysis that focus especially on uncertainties in causal relationships, like carried out for climate change models in Varis and Kuikka (1997).
Task 1.1 1 Data and model outcome compilation: historical observations and VATT model estimates to be used in causal learning and parameter estimation of decision models Dr Juha Honkatukia and Dr Miina Karjalainen D1.1y 3-1.1y 5
- KYAMK,
Task 1.1y 3 Expert knowledge elicitation: Leader Ulla Tapaninen : implementation UH
Expert elicitation of future risks: The probabilistic model of traffic chains: This needs to be computationally fast algorithm, but still being able to provide scientifically justified estimates of uncertainty, The parameter estimation by historical data and future simulation of traffic chains will be modelled by linking traffic equations to Pearl’s See and Do functions (Pearl 20 xx) by using the simple parameter estimation approach developed by Varis and Kuikka (19 xx ) approaches


In the analysis of inland water ways, LUT will also look also at the Kymijoki option which is estimated economically several times and the estimates can be used to look at the optional costs of such traffic option, where we invest on the inland harbor chains, by the Russian uncertainty of keeping the Saimaa channel open must be taken into account. We use expert knowledge (Professor Pekka Sutela and e.g. Russian transport specialist Professor Evgeny Korovyakovskiy) to look at the conditional probabilities needed to define impacting processes and potential futures. VATT will analyze an economic risk analysis by recreational values, where willingness to pay estimates to avoid spills are exploited (Helle et al., accepted) and by making a new questionnaire in the web to cottage owners.


In building the option of investing on new channel option for river Kymijoki, UH/FEM will analyze the sediment contamination is a risk related to the building of channel, where the toxic elements in sediments are then released and a human health risk activated This needs to be compared to the risks caused to e.g. Saimaa ringed seal, which is a small population where the safeguarding responsibility of Finland is of very high status. We apply the risk methodology developed for GoF to the inland waterways to be able to compare the environmental risks caused by traffic options. This knowledge is made available to customers by using the new tools in web to show.


Objectives
In order to support methods for vessel safety evaluations, KYAMK will focus their work on the operational safety risk in inland waterborne traffic is estimated with expert in the field; pilots and captains of the vessels (Fig. 8). Reconstructing the operating environment of Saimaa deep-water route and the connected fairways to the navigation simulator enables focused ship maneuver training in identified high-risk areas. Shipping route specific simulation allows also to test the performance and applicability of different size and types of vessels.
1) Law impact in p


Description of work and role of participants
====5.2 Data management plan====
The WP 4 leader is Dr xx who is an expert on x statistical and dynamic


Task 1.1 Development of fast algorithms for decision support: professor Haario
The data used in the empirical applications will be drawn from various sources, including commercial databases, such as the Datastream database as well as databases provided by central banks and other government agencies over the internet, and official statistics. In addition to data, we apply modern methods of biometrics to learn conditional probabilities of causalities from published papers.
Anders, Jani, Olli,  


Task 1.3 Analysis or world main forces development: the migrations in Baltic Sea area and its link to the rest of the world: Professor Olli Varis
We utilize the existing large databases of:
 
* VATT and Bank of Finland for the economic data
In the risk analysis models of the worldwide food security and its impacts on the food security in Finland, we use the expert judgments on the chances that the fleets do no operate like assumed, due to for example harbor strikes. We build the food security models on the expert judgement models of Varis et al ( xcxx), where the aim of the Bayesian analysis is to model the uncertainties in causalities. These models are based on link matrixes of Pearl (20 xx), and they currently include expert understanding without extensive data analysis, and therefore they can be used as priors for more data based analysis. We use the machine learning algorithms of WEKA software to run these additional analysis, usin g the extensive data sets of FAO (REF Olli). The sensitivities and risks of the system will thereafter be analyzed by sensitivity analysis that focus especially on uncertainties in causal relationships, like carried out for climate change models in Varis and Kuikka (20 xx' Climatic Change).
* National and international vessel databases of Trafi
 
* Lloyds register database for vessels around the world (to be paid by the project)
In the analysis of inland water ways, we look at the Kymijoki option which is estimated economically several times and the estimates can be used to look at the optional costs of such traffic option, where we invest on the inland harbor chains, by the Russian uncertainty of keeping the Saimaa channel open must be taken into account. We use expert knowledge (Professor Pekka Sutela and e.g. Russian transport specialist Professor Evgeny Korovyakovskiy) to look at the likelihood
* Traffic databases of Trafi, already analyzed in TUT modelling of CO 2 emissions
 
* CO<sub>2</sub> footprint estimates of SYKE and University of Oulu
 
* Fish stock estimates of ICES (International Council For the Exploration of the Sea) for the impacts on stocks and fisheries
future trends  D 1.1y 6-1.1y 8
* Threatened species database of SYKE, which is already linked (Jolma et al., 2015) to vessel accident estimates, and to the spread of oil after an accident on a given area spread of oil is based on the use of model where the observed weather data is used to estimate likely hit of oil to the threatened species
Task 1.1y 3 Providing probabilistic
* Tiira bird databases
D 1.1y 1,D 1.1y 1.1y  D 1.1y 5
* Ship emission, pollutant transport and numerical weather prediction datasets of the Finnish Meteorological Institute
In this task,
* Bank Of Finland Databases on national economy and on the experts judgments based over the years to enable the comparison off an expert and models in future predictions
Task 1.1y 4 Detailed analysis D 1.1y 9-D 1.1y 11
* The knowledge bases of CSIRO to apply best insurance practices in oil production industry to vessel traffic, especially tankers.
This is our detailed test of.
D 1.1y 2 UH(10) SMHI(7) SLU(2). Journal MS:. Month xx.y
D 1.1y 3 SLU(12) EMI(2) UH(in kind 2). 18 Journal MS: Month xx.y
D 1.1y 4 SLU(9) FGFRI(2) EMI(2) UH(in kind 4). Journal MS:. Month xx.
D 1.1y 5 SLU(12) SMHI(4) FGFRI(1) UH(in kind 2). Month xx.y
D 1.1y 6 SLU(9). Journal MS: Month xx.y
D 1.1y 7 SLU(10) SMHI(7) UH(2). Journal MS: Month xx.
D  1.1y 8 SLU(10) SMHI(7 UH(2).  Journal MS: Month xx.y
D1.1y 9 EMI(12) FGFRI(3) UH(in kind 3). Journal MS: Month xx.y
D 1.1xx EMI(12) UH(4+in kind 4) FGFRI(2). Journal MS: Month xx.
D1.1y 11 EMI(10) UH (in kind 4) FGFRI(2) Journal MS: Month xx.
 
=== WP3 ===
Answering to the probabilistic questions of decision models: data analysis and algorithms
 
 
WP 3 Answering  to the probabilistic questions of decision models: data analysis add algorithm development :Dr Olli Varis, Dr Sakari Kuikka
1) To apply appropriate estimation method to each subnet of the decision model (MCMC, machine learning,  expert judgement,
 
2) 2) To develop such MCMC algorithm that stays in those areas of probability distributions, where the decisions are most sensitive on estimates and the aims defined.
 
 
=== WP4 Health and wellbeing impacts of oil spills in Saimaa ===
 
;Description of work and role of participants
Polyaromatic hydrocarbons (PAHs) are the key environmental pollutants released by oil spills. Some of the PAH compounds, especially benzo[a]pyrene is highly carcinogenic and it is listed as a Group 1 carcinogen by the International Agency for Research on Cancer (IARC). PAHs are persistent pollutants, which bind to the water sediments from where they end-up to food chain. Humans are exposed to PAHs through fish and meat.
 
Saimaa environment has a high societal value for recreational use as being one of the areas with high number of holiday cottages in Finland. Oil spills caused by waterway transports can have major impact on recreational use of the Saimaa area causing ban of fishing and swimming in the lake and decreasing societal wellbeing with increased pollution.
Objective of this work package is to assess the health and wellbeing impacts of oil spills in Saimaa and provide input on policy evaluation of alternative transport modes, defined in WP5.
 
;Task 4.1 Current PAH in Saimaa fish and health
Current exposure to PAHs through eating fish from Saimaa will be evaluated. Assessment is based on literature survey of PAH concentrations in different fish species and use of fish consumption data available from previous studies (REFS). Health impact assessment is done by utilizing the previously developed open access model, which have been successfully used for determining health impacts of herring consumption in Finland (REF).
 
;Task 4.2 Effects of oil spills on health and wellbeing in Saimaa region
Change in PAH exposure caused by oil spills in Saimaa is evaluated based on data collected from literature. It’s anticipated that oil spills have more important effects on wellbeing than on health. Wellbeing effects are studied by using a questionnaire targeted to the population of Saimaa area. The societal values of recreational use of Saimaa are first estimated based on a literature review of societal utility of inland water bodies and then the questionnaire is used to evaluate the effects of oils spills on wellbeing.
 
The WP 4 leader is Dr Jouni Tuomisto who is an expert on environmental health and impact assessment
 
Task 1.1y 1 Spatial modelling of fish productivity D1.1y 3-1.1y 5
future trends  D 1.1y 6-1.1y 8
Task 1.1y 3 Providing probabilistic
D 1.1y 1,D 1.1y 1.1y  D 1.1y 5
In this task,
Task 1.1y 4 Detailed analysis D 1.1y 9-D 1.1y 11
This is our detailed test of.
{{attack|# |What is this text?|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 14:22, 27 April 2015 (UTC)}}
 
=== WP5 ===
Planning the future policy options: value of control analysis and the use of new causal interventions to reach future aims
Description of work and role of participants
Cost benefit analysis of developing inland waterways: In the probabilistic cost benefit analysis of the more intensive use of inland wayer ways, we will use the SYKE data of the location of threatened species in areas where an oil spill is possible. Moreover, we value the damages to the use of summer cabines in the lake area by implementing an interactive questionnaire in the web, aiming to estimate the willingness to pay of the Finnish cottages owners to prevent spills (see Helle et al for a probabilistic cost benefit analysis of using the resources on preventive actions of accidents, compared to the use of resources on new oil combatting vessels). The potential losses of nature values and recreational values are the key elements of risks related to new traffic options in inland water ways. Moreover, as a an alternative policy of safeguarding nature values only (to see whether the aims lead to different policies) we will use the techniques of Ihaksi et al (20xx) to estimate the impacts of possible oil accident on threatened species. Among these, the Saimaa purpose seal is the most threatened and charismatic species, where Finland has a responsibility in safeguarding the population.
 
In the analysis of inland water ways, the economic risk for recreational values is estimated by willingness to pay estimates to avoid spills (Ahtiainen, 20 xx, Helle et al accepted with minor revisions) and by making a new questionnaire in the web to cottage owners.
In building the option of investing on new channel option, the sediment contamination is a riks related to the building of channel (ref 1999), where the toxic elements in edimenta are then released and a human health risk activated This needs to be compared to the risks caused to e.g. Saimaa ringed seal, which is a small population where the safeguarding responsibility of Finland is of very high status. We apply the risk methodology developed for GoF to the inland water ways to be able to compare the environmental risks caused by traffic options. This knowledge is made available to customers by using the new tools in web to show
In order to improve vessel safety, the operational safety risk in inland waterborne traffic is estimated with expert in the field; pilots and captains of the vessels. Reconstructing the operating environment of Saimaa deep water route and the connected fairways to the navigation simulator, enables focused shipmanoeuvre training in identified high-risk areas. Shipping route specific simulation allows also to test the performance and applicability of different size and types of vessels. Simulation tests are conducted with competent crews, ie. pilots, in order to debar skill-related interference. In addition, simulated operating environment can act also as a platform to demonstrate the oil spill response capability; response times, accessibility of incident locations and optimal disposition of oil spill response vessels.
 
The WP 4 leader is Dr xx who is an expert on x statistical and dynamic
Task 1.1y 1 Spatial modelling of fish productivity D1.1y 3-1.1y 5
future trends  D 1.1y 6-1.1y 8
Task 1.1y 3 Providing probabilistic
D 1.1y 1,D 1.1y 1.1y  D 1.1y 5
In this task,
Task 1.1y 4 Detailed analysis D 1.1y 9-D 1.1y 11
This is our detailed test of.
 
=== WP6 ===
 
WP 6 Software development and test with people out there: are people interested about our products given their values?
 
3) Learning Hugin
 
Systematic use of Metropol  feedback systems in the yearly ICES WGMABS open risk communication meetings (2016: St Petersburg to meet industry, 2017 to help international WWF to adapt those parts of ILVES and WGMABS approaches which are needed for successful risk governance
 
WP 7 Dissemination: talking and savig the talks and feedback
Objectives
4) Learning  and using Hugin with people.
5) Learning what is seen histrorically and
Systematic use of Metropol feedback systems in the yearly ICES WGMABS open risk communication meetings (2016: St Petersburg to meet industry, 2017 to help international WWF to adapt those parts of ILVES and WGMABS approaches which are needed for successful risk governance
 
=== WP 7) Dissemination ===
WP7. Dissemination: messages to stakeholders (Partners involved: '''UHel''', all)
 
Task 1.1y 4 Graphical analysis together with citizens: Leader Waikato University, New Zealand 
D 1.1y 9-D 1.1y 11
This is our detailed test of how people understand the graphics and how they can insert their causal knowledge and own data to learning systems, including values ( a link to WP xx, Hugin webbased system). We test different options from the cognition point of view, ie.how understandable the information contents of the data sets and/ or simulation results are,
Task 1.1y 4 Optimal data treatment of all data: how to discretize for various purposes.
The final use of the data or simulation results in decision making has an impact on how to discretize the data sets to get a fully interactive model including the most essential information content of data, or future simulations, like is the interest.  In here, we will look how this data teartment should be done to effectively support decision making
WP 2: 1) Data compilation and expert knowledge elicitation: making knowledge ready. Coordinator is FMI. This WP will look after the data sets and their management.
 
: messages to stakeholders
: {{comment|# |Don’t study if you cannot implement: possible legislation and the probability distributions from their behavior if you implement a certain policy (xx ICES paper|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 04:21, 22 April 2015 (UTC)}}
:Expert elicitation of future risks:
 
End-users and stakeholders will be informed about the project results, in order to develop understanding and agree to solutions on complex issues, or issues of concern. ??REFERENCE TO THE ILVES Stakeholder Support Group (ISSG)??
 
The best practices and lessons learned will also be taken into account in the training and education by KUAS and KMRA (<-CAN BE MODIFIED WHEN MORE TEXT IS ADDED TO WP6).
 
 
{{attack|# |The text below seems to belong to some workpackages. Where?|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 12:35, 24 April 2015 (UTC)}}
 
Cost benefit analysis of developing inland waterways: In the probabilistic cost benefit analysis of the more intensive use of inland wayer ways, we will use the SYKE data of the location of threatened species in areas where an oil spill is possible. Moreover, we value the damages to the use of summer cabines in the lake area by implementing an interactive questionnaire in the web, aiming to estimate the willingness to pay of the Finnish cottages owners to prevent spills (see Helle et al for a probabilistic cost benefit analysis of using the resources on preventive actions of accidents, compared to the use of resources on new oil combatting vessels). The potential losses of nature values and recreational values are the key elements of risks related to new traffic options in inland water ways. Moreover, as a an alternative policy of safeguarding nature values only (to see whether the aims lead to different policies)  we will use the techniques of Ihaksi et al (20xx) to estimate the impacts of possible oil accident on threatened species. Among these, the Saimaa purpose seal is the most threatened and charismatic species, where Finland has a responsibility in safeguarding the population. 
 
In the risk analysis models of the worldwide food security and its impacts on the food security in Finland, we use the expert judgments on the chances that the fleets do no operate like assumed, due to for example harbor strikes. We build the food security models on the expert judgement models of Varis et al ( xcxx), where the aim of the Bayesian analysis is to model the uncertainties in causalities. These models are based on link matrixes of Pearl (20 xx), and they currently include expert understanding without extensive data analysis, and therefore they can be used as priors for more data based analysis. We use the machine learning algorithms of WEKA software to run these additional analysis, usin g the extensive data sets of FAO (REF Olli). The sensitivities and risks of the system will thereafter be analyzed by sensitivity analysis that focus especially on uncertainties in causal relationships, like carried out for climate change models in Varis and Kuikka (20 xx' Climatic Change).
 
In the analysis of inland water ways, we look at the Kymijoki option which is estimated economically several times and the estimates can be used to look at the optional costs of such traffic option, where we invest on the inland harbor chains, by the Russian uncertainty of keeping the Saimaa channel open must be taken into account. We use expert knowledge (Professor Pekka Sutela and e.g. Russian transport specialist Professor Evgeny Korovyakovskiy) to look at the likelihood
 
We use the social network analysis to look how the information flows between stakeholders to understand and learn from available information and to adapt this to the development of traffic chains, underlining the importance of scientific information and learning. This ill show us how to allocate the production of scientific knowledge to end users.  The combination of academic, administrative and industry partners in using the information provided bu ILVES is tudied by this approach and the dissemination plan is updated based on these findings. We construct a scientifically based approach to plan our dissemination (carried out by Merikotka research association). 
 
In the analysis of inland water ways, the economic risk for recreational values is estimated by willingness to pay estimates to avoid spills (Ahtiainen, 20 xx, Helle et al accepted with minor revisions) and by making a new questionnaire in the web to cottage owners.
 
In building the option of investing on new channel option, the sediment contamination is a riks related to the building of channel (ref 1999), where the toxic elements in edimenta are then released and a human health risk activated  This needs to be compared to the risks caused to e.g. Saimaa ringed seal, which is a small population where the safeguarding responsibility of Finland is of very high status. We apply the risk methodology developed for GoF to the inland water ways to be able to compare the environmental risks caused by traffic options. This knowledge is made available to customers by using the  new tools in web to show 
 
In order to improve vessel safety, the operational safety risk in inland waterborne traffic is estimated with expert in the field; pilots and captains of the vessels. Reconstructing the operating environment of Saimaa deep water route and the connected fairways to the navigation simulator, enables focused shipmanoeuvre training in identified high-risk areas. Shipping route specific simulation allows also to test the performance and applicability of different size and types of vessels. Simulation tests are conducted with competent crews, ie. pilots, in order to debar skill-related  interference. In addition, simulated operating environment can act also as a platform to demonstrate the oil spill response capability; response times, accessibility of incident locations and optimal disposition of oil spill response vessels.
 
'''???OBS.''' In order to take full advantage of the navigation simulator (located in Kotka, KUAS), there is a need to modify its database, ie update the digitalized nautical charts of inland waterways and visualization of the fairways concerned. Utilizing underwater infrastructure data obtained with multi beam survey would make the risk modeling more precise.
IF FITS TO THE BUGDET ALLOCATED TO US: total 80 000€ incl. 40 000e/FAIRWAY MODELLING AND 35 000€ VISUALIZATION, plus 1000 € per vessel type wanted. '''Current data also needed. SYKE?'''
 
Atlantis: The oil spill element could be applied to Australian north coast oil production environment. I know there is biodiversity data and threatened species {{comment|# |(see Annukka's paper this week in Environmental Science and Technology for Gulf of Finland) This could be started when I visit CSIRO, plan is one year starting next autumn.  See also ICES newsletter in February for a paper that describes the work in WGMABS working group.|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 04:21, 22 April 2015 (UTC)}}
 
{{comment|# |Jyri: FMEA: causal learning in risk management (link to Duke, have text from there)|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 04:21, 22 April 2015 (UTC)}}
 
Jyri: FMEA: causal learning in risk management (link to Duke, have text from there) The FMEA (Failure Mode and Effect Analysis) framework can be used to investigate the potential failure modes and their causes and effects in the supply chain processes. FMEA allows identifying and analyzing potential failure modes in a system, and identifying actions that could eliminate or reduce the likelihood of potential failure (Chuang 2002).
 
Currently, the increasing demand for efficiency and sustainability is one of the driving forces that both public and private organizations are facing. Considering the usage of the inland water ways and other potential transportation channels and pro-environmental aspects, it can be argued that the utilization of the transportation system is unbalanced. In order to investigate the potential of the whole logistics system a more holistic perspective is needed. To fill this gap in the current body of knowledge we aim to study the transportation system from the end user perspective. The most essential elements of the study will include management of the supply chain risks as well as the infrastructural and operational antecedents enabling the utilization of more environmental friendly transportation as well as information exchange from the perspective of current systems applied. The elements mentioned will allow to form a broader perspective of the Finnish transportation system and its potential in facilitating more environmental friendly and low-risk operations.
 
One of the under-researched aspects of the logistics systems is the inland water ways. Inland water logistics answers the program question: Inland water logistics reduces emissions and concurrently utilizes existing resources i.e. inland water transportation routes and replaces trucking and railway transports. However this requires the development of information systems of inland water logistics:
* Existing information systems for import and export have interfaces to systems of maritime transports, trucking and railway transports, and, for example, customs, insurance companies, forwarding agencies, harbours and companies. Sometimes these existing information systems may have an interface also to inland water logistics. Existing information systems are developed mainly to serve import and export. Our proposal is that based on these existing information systems, we develop the information system of inland water logistics. This new system would serve:
 
:* as a part of the existing information systems of import and export like the current trucking and railway transports;
:* as independent logistics system when other interfaces are not needed, like the inland water transport between domestic cities;
:* as independent import and export logistics system, for example, in logistics through channel of Saimaa; interfaces are required to customs, insurance companies, forwarding agencies, harbours and companies;
:* in emerging new requirements.
 
* Logistics risks can be reduced using inland water logistics because inland water logistics offers a new transport option in addition to truck and railway transports. Inland water logistics uses less imported fuel compared to transported tons. New risks include environment risks and winter time risks. Risk identification, assessment and mitigation of consequences are considered also in the planning the information system. This work includes a clarification how these risks are managed or solved in, for example, Sweden, Russia, etc.
 
WE WOULD need also insurance specialisgs here: one option is Rich Little, CSIRO, when evaluating Koe teksti how combinations of knowledge and actions impact the uncertainty of the resources (see paper in Ecological Letters). This may be a good suggestion for one part of their co-operation.
 
Inland water system development requires the development of insurance options.
 
== 8 Research teams, collaboration ==
 
The consortium consists of the following research teams:
 
=== 1) University of Helsinki, Fisheries and Environmental management group (FEM) ===
 
The group leader and the PI of ILVES proposal is professor Sakari Kuikka, who is specialiced to multidisciplinary decision analysis by Bayesian decision models. This group consists of biologists, social scientists, economists (in plural if you join, Jani  ), statisticians, mathematicians and engineering scientists. The interdisciplinary research group (link to group webpages here) applies Bayesian statistics and decision theory to managemnt of natural resources and environmental values. Group was, together with professor Corander's group in statistics, where FEM has close co-operation, ranked as third in the series of "Societal impact" in the evaluation of research groups in the University of Helsinki evaluations, in 201 x). The quality indexes of the publications were 9th  and 10th best ampng the 156 evaluated groups (insert here the link to overall report and to Bayes group) Group aims to futher improve the interdisciplinary risk analysis and effective learning in science. Kuikka has been coordinator in 4 FP or Horizon 2020 projects of EU: 1) PRONE, which was about developing risk methodology for fisheries, 2) ECOKNOWS, which was about developing Bayesian models and learning databases in fisheries science, 3) IBAM, which was about use of Bayesian integrative methods in environmental management and 3) current project GOHERR, which is about developing governance for human and ecosystem health management of Baltic Sea. Kuikka is also the chair of ICES working group for Working Group on Risks of Maritime Activities in the Baltic Sea (WGMABS), which aims to develop a new oil risk management and advisory system for Baltic Sea, being an important route to implement the project findings in active policy. Professor Samu Mäntyniemi is specialised in Bayesian risk analysis.
 
Hyttinen, Antti (tutkijatohtori) 02941 51164, 040-7525515 Tietojenkäsittelytieteen laitos
 
1 henkilön tiedot tulostettiin.
http://jmlr.org/papers/v14/hyttinen13a.html
 
{{comment|# |
* ALL PARTNERS: mention the key advisory roles you have in society
* FMI involvement in Baltic Sea NOx Emission Control Area application
* FMI involvement in North Sea NOx Emission Control Area background studies (economic and human helath impacts assessments)
* FMI involvement in the 3rd IMO GHG study
* FMI reports annual ship emissions in the Baltic Sea area
- and then same amount of text from everyone. FEM text needs to be a bit longer, but something like 12 lines from everyone, please.
|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 04:21, 22 April 2015 (UTC)}}
 
=== 2) VATT Juha: ===
 
=== 3) Finnish Meteorological Institute ===
 
FMI is a leading expert in meteorology, air quality, climate change, earth observation, marine and arctic research areas. The main objective of FMI is to improve the safety and the quality of living of Finnish citizens. In order to do this the FMI observes the physical state of the atmosphere, its chemical composition and electromagnetic phenomena. The Institute has several laboratories which analyze the most important air pollutants, develop new measurement techniques and test the reliability of the measurements. Dr. Jukka-Pekka Jalkanen is a senior researcher in the dispersion modelling group of the FMI. He is the head developer of STEAM emission model for maritime traffic. He has written 32 peer-reviewed papers of which 17 most recent ones concern ship emissions. Dr Jalkanen has acted as WP leader in several shipping related projects, (SAMBA which was a feasibility study of the use of satellite AIS in ship emission modeling for the European Space agency, SNOOP was about environmental impacts of shipping, BSR Innoship was about human health impact of shipping) and is currently involved in two projects (KAMON, SHEBA) concentrating on wintertime navigation and sustainable shipping scenario studies in the Baltic Sea (SHEBA). FMI will benefit, including salary strategy of meteorologists and comparisons to competing weather forecasts producers, from the methodology to rank models and experts in the comparison of realized weather and predicted weather. This offers a business idea to some private company. 
 
=== 5)  LUT: ===
pinser professors to concince
And this is what I hope from LUT   :
 
6) Adjunct professor Jyri Vilko professor in logistics, has applied simulayion studies to evaluate alternative logistics (refs) and is a leading Finnish expert in his field.  Professor Haario is an expert in Bayesian parameter estimation of complex models and has developed probabilistic Bayesian version of the FMI weather forecast model (HEIKKI: Ref + mahdolinen weblinkki). Professor  Pekka Sutela is a world known expert in national economics, expertising in political stability in Russia. , and providing valuable expert knowledge in addition to model simulations for the evaluation of policy success and economic development. There is a connection to Thailand’s best business school logistics scientists to support the distribution of methods to third countries (Jyri inser here the details)
 
=== 7) Duke University, USA ===
Assistant professor Fan Li’s main research interest in statistical methodology lies in causal inference, that is, designs and methods of analyses to evaluate treatments, interventions or actions in randomized experiments or observational studies, and their applications to social sciences, economics, health policy, psychology and epidemiology. Dr Li also hase a strong interest in statistical methods for big and complex data, especially neuroimaging data, with an emphasis on developing advanced Bayesian inferential and computational methods. Li works also on missing data, variable selection and small area estimation which means Fan: what ??  and is important for our project in the application of xx to the xx data set and policy evaluation.
 
=== 8) Kotka Maritime Research Association (KMRA) ===
 
KMRA operates in close collaboration with the maritime industry, universities, research organisations, institutes and authorities both nationally and internationally. The aim of the KMRA is to improve the interaction between science and society to make the most of the results, by conveying theory into practice. The practical solutions based on scientific research can improve the profitability of maritime industries and decrease the environmental impacts of maritime transportation. KMRA has coordinated interdisciplinary projects where practical tools have been developed to support decision making. Most significant projects in theis field have been 1) SAFGOF (ERDF funding) and 2) MIMIC (Central Baltic Interreg) where traffic growth scenarios, accident probabilities and biological information about consequences of oil accidents were combined to produce probabilistic risk maps; 3) OILRISK (Central Baltic Interreg) where the impacts of oil on coastal and marine species and habitats were evaluated and estimates how well certain nature values can be safeguarded with booms or protective sheets were given, and 4) TOPCONS (ENPI CBC) where a tool to support maritime spatial planning was developed. KMRA has been the responsible partner for coordination, internal and external communication and information/publicity activities in these projects. In ILVES consortium the role of KMRA is to communicate and disseminate the essential findings of the project to the target groups and end users within the maritime sector.
 
=== 9) National Institute for Health and Welfare ===
 
'''National Institute for Health and Welfare (THL)''' is a government research institute. It has wide expertise in public health, environmental health and health impact assessment. THL is the main developed and user of open policy practice and Opasnet worldwide. THL has gained good practical experience about what practices work and what don't, and where the problems lie in science-policy interface when opening data and models. The Unicorn research group in THL is led by Adjunct professor, '''MD Jouni Tuomisto''' (JT; THL PI). Tasks: health modelling, decision analysis, Opasnet web-workspace. ''Merits'': More than 20 years of expertise in environmental health issues, risk assessment, decision analysis, modelling, and decision support. Developed the method open policy practice and the web-workspace Opasnet. 98 peer-reviewed scientific articles. ''Career'': Researcher in KTL and later THL since 1992; post-doc fellow in Harvard School of Public Health 2000-2001; academy researcher 2005-2010; senior researcher and chief researcher since 2010; head of the Assessment and Modelling Unit 2010-2014.
 
=== 10)  KUAS, Kymenlaakso University of Applied Sciences, Seafaring and logistics ===
 
KUAS Seafaring research and development activities focus on maritime safety management i.e. preventive and response measures to marine pollution, as well as on maritime training. KUAS has proven competence in areas of marine technology and sustainable energy solutions related to port operations and sea transport, and has experienced in developing energy technologies and methods, such as the measurement and analysis of the ship generated emissions.
 
Master Mariner Justiina Halonen is an expert in ship-source oil spill response. Halonen educates oils spill response tactics for Finnish authorities and has conducted regional spill response contingency planning over ten years. Halonen's reasearch interests include maritime safety management, safety performance indicators in inland waterborne traffic and ship manoeuvring simulation.
 
=== The consortium as a whole ===


The modelling approach is strongly lead by Bayesian inference and decision analysis tools. The experiences of FEM group are used in developing and leading this process. The economic estimation and simulation models of VATT, together with the large datasets, are used to estimate economic changes. The UH/ECON will provide skills in the Bayesian time series analysis and in testing the theories given the observations. HUT will provide the models related to world wide food security and likely future risks. FMI will provide state of the art models to describe the GHG emissions of shipping fleets, their atmospheric dispersion and impacts to the human health and the environment, LTU will provide knowledge in Bayesian mcmc analysis of complex models, the models used to manage logistic chains, and the expert knowledge and international expert views of Russian development having a potential impact on planned logistic pathways through Saimaa lake area.  Åbo Akademi will provide expert knowledge in evaluation of current and potentially future national and international maritime legislation and its probabilistic impact on risks. KMRA will add to the stakeholder contacts and dissemination. City of Helsinki will provide expert knowledge in modelling logistics and an information end user aspect to the analysis. KYAMK will provide practical experience from maritime activities, and the databases of inland shipping routes and possibilities. University of Duke will provide Bayesian modelling skills in economic analysis and will contribute to oil spill management. CSIRO will provide Bayesian expertise in exploitation and risk analysis, analysis of insurances to increase the interest to avoid accidents, and expertise in oil spill risk analysis. FAN AND RICH: do we need Waikatu? Coul Duke provide the same services, or do your models require such expert skills that they can not be easily applied by other scientists than yourself only, i.e. how easily applicable they are to learn new methods  ? Reason why I think Waikatu is that I would like to link practical and graphically educative (=supporting understanding the elements of future policy evaluations ?:  University of Waikatu will provide expertise in the use of artifical intelligence methods to enable learning databases. Their WEKA software (LINK) is a leading software in AI field and provides effective data handling and use of Bayesian network models. The consortium offers an excellent combination of skills which are needed to support environmental and economic policy with modern calculus systems. Understanding correctly the real causal relationships in a system where society makes a new intervention must be based on as good causal understanding as possible. The priors of the models must use the existing published papers as effectively as possible. Here the modelling skills and expert understanding of ILVES consortium meet in a unique way to solve practical problems.
As a novelty, we will provide, in addition to raw data sets, the databases of probabilistic model estimates. These will be established to the web pages governed by VATT. This will enable the estimation of probabilistic dependencies for any combination of input – output data sets. However, as the data is only historical observations, the more useful information for other scientists than those in ILVES consortium are the estimates of interest variables. We will provide probabilistic databases of the estimates to allow effective estimation of prior probabilities for future analysis. This will enable the more effective learning in sciences, where it is important from the point of view of end user of the information, that estimates include also other knowledge than just the data that happens to be observed in single studies.


== 9 Mobility plan ==
In expert elicitation, we use the best experts available in Finland and thus follow the approach taken by Kuikka and Varis (1997) in the modeling of climate change impacts.


{{comment|# |Everyone: what trips you do and longer visits between partners in order to strategically support the learning and use of research findings in your home institutes. This needs STRATEGIC thinking, as we have partners from outside Finland
===6 Ethical issues===
|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 04:21, 22 April 2015 (UTC)}}


UH/FEM Sakari Kuikka will visit CSIRO in winter 2015 – summer 2016. During this trip, the oil spill risk methods of Finland will be reviewed and the CSIRO modelling skills will be used to plan the insurance schemes as tools to manage the oil disaster risks. Also the adding of oil spill element to Atlantis will be developed during this visit.
Our proposal does not contain work with human embryo/fetus, humans or animals. This proposal does not have research components, which include genetic data, personal information (religion, sexual or political orientation) or tracking of people. Recruitment as well as the advancement and salary of the employed researchers are based solely on personal achievements and not on gender.


LUT School of Business and Management (LBM): Jyri Vilko will visit Thammasat University in the winter 2015-2016. During this researcher exchange he will collaborate with the local researchers in researching the inland water ways usage potential in South East Asian and Finnish perspectives. In 2017 Professor Vilko will visit the Massey University, in New Zealand. The aim of the visit is to collaborate in studying supply chain relationships and responsibilities in multi modal logistics.
===7 Implementation: schedule, budget, distribution of work===


== 10 Key literature ==
Tables 1 and 2 give the overall schedule of the project, and the distribution between partners. The total budget is given in the tables of the proposal in Finish Academy system. The current plant of the distribution of work is like here, but it is likely that there are revision needs that will be handled in the Consortium meetings that will be arranged every 6 months.
PBL (2012). Netherlands Environmental Assessment Agency, Assessment of the environmental impacts and health benefits of a nitrogen emission control area in the North SeaThe Hague/Bilthoven. ISBN: 978-90-78645-99-3.


Backer H., Durkin M., Haldin J., Karhu J., Korpinen S., Laamanen M., Laurila J., Meski L., Pyhälä M. & Stankiewicz M. 2011. HELCOM Activities 2011: Overview. Baltic Sea Environment Proceedings No. 132, Helsinki Commission, Helsinki, 50 p.
===8 Research teams, collaboration===


Damgaard, C. and Weiner, J. (2000). Describing inequality in plant size or fecundity. Ecology 81: 1139-1142.
The consortium consists of the following research teams:


European Commission (EC) 2011. White Paper. Roadmap to a Single European Transport Area – Towards a competitive and resource efficient transport system. COM(2011)144 final, Brussels, 28.3.2011.
'''1) University of Helsinki, Fisheries and Environmental management group (FEM), Finland


Fletcher, W. J. (2015). Review and refinement of an existing qualitative risk assessment method for application within an ecosystem-based management framework. ICES Journal of Marine Science 72(3): 1043-1056.
The group leader and the PI of ILVES proposal is professor Sakari Kuikka, who is specialized to multidisciplinary decision analysis by Bayesian decision models. This group consists of biologists, social scientists, economists, statisticians, mathematicians and engineering scientists. The interdisciplinary research group (link to group webpages here) applies Bayesian statistics and decision theory to management of natural resources and environmental values. Group was, together with professor Corander’s group in statistics, where FEM has close co-operation, ranked as third in the series of “Societal impact” in the evaluation of research groups in the University of Helsinki evaluations, in 2012. Kuikka has been coordinator in 4 FP or Horizon 2020 projects of EU: 1) PRONE, which was about developing risk methodology for fisheries, 2) ECOKNOWS, which was about developing Bayesian models and learning databases in fisheries science, 3) IBAM, which was about use of Bayesian integrative methods in environmental management and 3) current project GOHERR, which is about developing governance for human and ecosystem health management of Baltic Sea. Kuikka is also the chair of ICES working group for Working Group on Risks of Maritime Activities in the Baltic Sea (WGMABS), which aims to develop a new oil risk management and advisory system for Baltic Sea, being an important route to implement the project findings in active policy (Fig. 9). Professor Samu Mäntyniemi is specialized in Bayesian risk analysis.


Haapasaari, P., Kulmala, S. and Kuikka, S. (2012). Growing into interdisciplinarity: how to converge biology, economics and social science in fisheries research? Ecology and Society 17(1): 6.
'''2) VATT Institute for Economic Research, Policy Analysis and Modelling Unit, Finland


Helsinki Commission (HELCOM). 2014. Emissions from Baltic Sea shipping in 2013. Environment Fact Sheet series. http://www.helcom.fi/baltic-sea-trends/environment-fact-sheets/hazardous-substances/emissions-from-baltic-sea-shipping/ 
VATT is a public body under the Finnish Ministry of Finance. The Institute has complete independence in conducting its research, which meets high standards of scientific quality. The purpose of VATT research is to support informed decision-making and its focus is on policy-relevant topics. VATT research covers the analysis of public finances and evaluation of economic reforms, the labour market, environmental policies, and the long-term prospects of the economy including energy and climate policy considerations. VATT participates in EU projects such as APRAISE to design effective, efficient and efficacious policy mixes to achieve environmental objectives under different circumstances which are socially acceptable; and Low Carbon Finland 2050 project, which aims at identifying robust roadmaps for competitive low carbon society and sustainable green growth strategies for Finland.


Helle, I., Lecklin, T., Jolma, A. and Kuikka S. (2011). Modeling the effectiveness of oil combating from an ecological perspective - A Bayesian network for the Gulf of Finland; the Baltic Sea. Journal of Hazardous Materials 185(1):182-192.
'''3) Finnish Meteorological Institute, Finland


Helle, I., Ahtiainen, H., Luoma, E., Hänninen, M. and Kuikka, S. Where should we invest in oil spill management? A probabilistic approach for a cost-benefit analysis under uncertainty. Submitted to Journal of Environmental Management.
FMI is a leading expert in meteorology, air quality, climate change, earth observation, marine and arctic research areas. The main objective of FMI is to improve the safety and the quality of living of Finnish citizens. In order to do this the FMI observes the physical state of the atmosphere, its chemical composition and electromagnetic phenomena. The Institute has several laboratories which analyze the most important air pollutants, develop new measurement techniques and test the reliability of the measurements. Dr. Jukka-Pekka Jalkanen is a senior researcher in the dispersion modelling group of the FMI. He is the head developer of STEAM emission model for maritime traffic. He has written 32 peer-reviewed papers of which 17 most recent ones concern ship emissions. Dr Jalkanen has acted as WP leader in several shipping related projects, and is currently involved in two projects concentrating on wintertime navigation (KAMON) and sustainable shipping scenario studies in the Baltic Sea (SHEBA).


Ihaksi, T., Kokkonen, T., Helle, I., Jolma, A.,Lecklin, T. and Kuikka, S. (2011). Combining conservation value, vulnerability, and effectiveness of mitigation actions in spatial conservation decisions: an application to coastal oil spill combating. Environmental Management 47: 802–813.
'''4) Åbo Akademi, Finland
IOPC Funds (2013). Claims Manual. October 2013 Edition.


Jalkanen, J.-P., Brink, A., Kalli, J., Pettersson, H., Kukkonen, J. and Stipa, T. (2009). A modelling system for the exhaust emissions of marine traffic and its application in the Baltic Sea area. Atmospheric Chemistry and Physics 9: 9209-9223.
The role of Åbo Akademi (Department of Law) is to assess the legal framework for the different policy options discussed in the ILVES proposal, based on existing national, international and EU legislation in the area and on the constraints imposed by international and EU law. Adjunct Professor Henrik Ringbom has performed similar work both at the Scandinavian Institute of Maritime Law, where he is currently a part-time Professor, and as a Head of the Environment Unit at the European Maritime Safety Agency (EMSA). At Åbo Akademi he is currently, in close cooperation with the Faculty of Law at the University of Turku, in charge of setting up BALEX (Baltic Area Legal Studies); a research and teaching center that specifically focuses on legal issues of relevance for the Baltic Sea region.
Jalkanen, J.-P., Johansson, L., Brink, A., Kalli, J., Kukkonen, J. and Stipa, T. (2012). Extension of an assessment model of ship traffic exhaust emissions for particulate matter and carbon monoxide, Atmospheric Chemistry and Physics 12: 2641-2659.


Jalkanen, J.-P. and Johansson, L. (2014). Emissions form Baltic Sea shipping in 2013. HELCOM Baltic Sea Environment Fact Sheets. Online, viewed 15.4.2015, http://www.helcom.fi/baltic-sea-trends/environment-fact-sheets/hazardous-substances/emissions-from-baltic-sea-shipping/
{| {{prettytable}}
|+'''Table 1. Person months
Johansson, L., Jalkanen, J.-P., Kalli, J. and Kukkonen, J. (2013). The evolution of shipping emissions and the costs of recent and forthcoming emission regulations in the northern European emission control area. Atmospheric Chemistry and Physics 13: 11375-11389.
|---
| 1. UH || 190.4
Jolma, A., Lehikoinen, A., Helle, I. and Venesjärvi, R. (2014). A software system for assessing the spatially distributed ecological risk posed by oil shipping. Environmental Modelling & Software 61: 1-11.
|---
| 2. VATT || 47,6
|---
| 3. FMI || 68
|---
| 4. ÅBO || 47,6
|---
| 5. KYAM || 47,6
|---
| 6. LTU || 68
|---
| 7. KMRC || 47,6
|---
| 8. Aalto University || 47,6
|---
| 9. CSIRO || (220 000 e)
|---
| 10. DUKE || (220 000 e)
|---
| 11. NHA || 47,6
|---
| 12. HUGIN || 47,6
|---
| 13. FEM Consultations || 47,6
|}


Jonson, J. E., Jalkanen, J.-P., Johansson, L., Gauss, M. and Denier van der Gon, H. A. C. (2015). Model calculations of the effects of present and future emissions of air pollutants from shipping in the Baltic Sea and the North Sea. Atmospheric Chemistry and Physics 15: 783-798.
[[File:Ilves topics and tasks.png|600px]]


Kleiber, W., Raftery, A. E. and & Gneiting, T. (2011). Geostatistical Model Averaging for Locally Calibrated Probabilistic Quantitative Precipitation Forecasting. Journal of American Statistical Association 106(496): 1291–1303.
'''5) KYAMK, Kymenlaakso University of Applied Sciences, Seafaring and logistics, Finland


Klemola, E., Kuronen, J., Kalli, J., Arola, T., Hänninen, M., Lehikoinen, A., Kuikka, S., Kujala, P. and Tapaninen, U. (2009). A cross-disciplinary approach to minimising the risks of maritime transport in the Gulf of Finland. World Review of Intermodal Transportation Research 2(4): 343–363.  
KYAMK Seafaring research and development activities focus on maritime safety management i.e. preventive and response measures to marine pollution, as well as on maritime training. KYAMK has proven competence in areas of marine technology and sustainable energy solutions related to port operations and sea transport, and has experienced in developing energy technologies and methods. Master Mariner Justiina Halonen is an expert in ship-source oil spill response. Halonen educates oils spill response tactics for Finnish authorities and has conducted regional spill response contingency planning over ten years. Halonen’s reasearch interests include maritime safety management, safety performance indicators in inland waterborne traffic and ship maneuvering simulation.


Kokkonen, T., Ihaksi, T., Jolma, A. and Kuikka, S. (2010). Dynamic mapping of nature values to support prioritization of coastal oil combating. Environmental Modelling & Software 25: 248–257.
'''6) Lappeenranta University of Technology, School of Business and Management, Finland
Kuikka, S., Vanhatalo, J., Pulkkinen, H., Mäntyniemi, S. and Corander J. (2014). Experiences in Bayesian Inference in Baltic Salmon Management. Statistical Science 29(1): 42-49.


Lappalainen J., Kunnaala V., Nygren P. & Tapaninen U. 2011. Luotsauksen vaikuttavuus. Publications from the Centre for Maritime Studies, University of Turku, B 184, 67 p. [In Finnish]
Adjunct professor Jyri Vilko (professor in logistics) has applied simulation studies to evaluate alternative logistics and is the leading Finnish expert in his field. Professor Haario is an expert in Bayesian parameter estimation of complex models and has developed probabilistic Bayesian version of the FMI weather forecast model. Professor Pekka Sutela is a world known expert in national economics and in political stability in Russia. He provides valuable expert knowledge to simulations of policy success and economic development. There is a connection to Thailand’s best business school logistics scientists to support the distribution of methods to third countries. Associate professor Ossi Taipale is specialist in software development with long experience in implementations of sales and transportation networks.


Lecklin, T., Ryömä, R. and Kuikka, S. (2011). A Bayesian network for analyzing biological acute and long-term impacts of an oil spill in the Gulf of Finland. Marine Pollution Bulletin 62: 2822-2835.
'''7) Kotka Maritime Research Association (KMRA), Finland


Lehikoinen, A., Luoma, E., Mäntyniemi, S. and Kuikka, S. (2013). Optimizing the Recovery Efficiency of Finnish Oil Combating Vessels in the Gulf of Finland Using Bayesian Networks. Environmental Science and Technology 47(4): 1792-1799.
KMRA operates in close collaboration with the maritime industry, universities, research organizations, institutes and authorities both nationally and internationally. The aim of the KMRA is to improve the interaction between science and society to make the most of the results, by conveying theory into practice (Fig. 10). The practical solutions based on scientific research can improve the profitability of maritime industries and decrease the environmental impacts of maritime transportation. KMRA has coordinated interdisciplinary projects where practical tools have been developed to support decision making. KMRA has been the responsible partner in many projects for coordination, internal and external communication and information/publicity activities in these projects. In ILVES consortium the role of KMRA is to communicate and disseminate the essential findings of the project to the target groups and end users within the maritime sector.


Lehikoinen, A., Hänninen, M., Storgård, J., Luoma, E., Mäntyniemi, S. and Kuikka, S. (in print). A Bayesian Network for Assessing the Collision Induced Risk of an Oil Accident in the Gulf of Finland.
'''8) Aalto University, Water & Development Research Group, Finland
Environmental Science and Technology. DOI: 10.1021/es501777g.


Liimatainen, H. (2010). Shippers’ Views on Environmental Reporting of Logistics and Implications for
The Water & Development Research Group is a cross-disciplinary research group operating at the Aalto University. The group has a long research tradition in water and development issues as well as in integrated management of water resources. The group leader, prof. Olli Varis has a strong expertise in water resources management, development and food security research, environmental and social impact assessment, computational modeling, multidisciplinary natural resources and development studies. He is also well-known for his methodological publications on computational modeling and data analysis, especially from the field of Bayesian networks and related decision analysis. He has coordinated and participated in many national and international research projects.
Logistics Service Providers. Logistics Research Network Conference 2010 Proceedings, September
8-10, Harrogate, United Kingdom.


Liimatainen, H. (2011). Utilization of fuel consumption data in an ecodriving incentive system for
'''9) Commonwealth Scientific and Industrial Research Organization (CSIRO), Australia
heavy-duty vehicle drivers. IEEE Transactions on Intelligent Transport Systems 12(4): 1087-1095.


Liimatainen, H., Kallionpää, E. and Pöllänen, M. (2012). Building a national action plan for improving the
CSIRO is Australia’s national science organization and a leading multidisciplinary research organization. In ILVES, CSIRO will contribute to oil spill risk analysis, studying financial risk management and insurance as a policy tool, and the development of oil spill modules of varying complexity for the Atlantis ecosystem model. Dr. Rich Little (team leader) is a Senior Research Scientist at CSIRO Marine and Atmospheric Research. His research specializes in modelling population dynamics, economics, and management decision-making in natural resource and marine environmental science.
energy efficiency and reducing the CO2 emission of road freight transport. Proceedings of the 17th
International Symposium on Logistics (ISL2012), July 8-11, Cape Town, South Africa.


Liimatainen, H., Kallionpää, E., Pöllänen, M., Stenholm, P., Tapio, P. and McKinnon, A. (2014).
'''10) National Institute for Health and Welfare (NHA), Finland
Decarbonising road freight in the future – Detailed scenarios of the carbon emissions of Finnish
road freight transport in 2030 using a Delphi method approach. Technological Forecasting and
Social Change 81: 177–191.


Liimatainen, H. and Nykänen, K. (2011). Carbon footprinting road freight operations - is it really that
National Institute for Health and Welfare (THL) is a government research institute. It has wide expertise in public health, environmental health and health impact assessment. THL is the main developed and user of open policy practice and Opasnet worldwide. THL has gained good practical experience about what practices work and what don’t, and where the problems lie in science-policy interface when opening data and models. The Unicorn research group in THL is led by Adjunct professor, MD Jouni Tuomisto (JT;THL PI). Tasks: health modelling, decision analysis, Opasnet web-workspace. Merits: More than 20 years of expertise in environmental health issues, risk assessment, decision analysis, modelling, and decision support. Developed the method open policy practice and the web-workspace Opasnet. 98 peer-reviewed scientific articles.
difficult? Logistics Research Network Conference 2011 Proceedings, September 7-9, Southampton,
United Kingdom.


Liimatainen, H. and Pöllänen, M. 2010. Trends of energy efficiency in Finnish road freight transport
'''11) HUGIN Expert A/S, Denmark
1995-2009 and forecast to 2016. Energy Policy 38(12): 7676-7686.


Lättilä, L., Henttu, V. and Hilmola, O.-P. (2013). Hinterland operations of sea ports do matter: Dry port usage effects on transportation costs and CO2 emissions. Transportation Research Part E 55: 23–42.
HUGIN EXPERT A/S is a Danish SME established in 1989. The company is a leading provider of tools and services for advanced decision support based on complex statistical models known as probabilistic graphical models (i.e., Bayesian Belief networks and influence diagrams). Since 1989 the company has collaborated with some of the World’s largest IT companies and its technology is being utilized in a wide variety of IT systems. HUGIN EXPERT has a long history of active participation in RTD projects, for instance, supported by the European Commission or companies. HUGIN EXPERT will play an important role in ILVES in relation to the use of Bayesian network technology. This includes both the use of the HUGIN software tool for development and deployment of Bayesian network models as well as the development of Bayesian network software to support the needs and requirements of ILVES. The Chief Executive Officer of HUGIN EXPERT is Anders L Madsen. He has a PhD in Decision Support Systems (1999). Since 2011 he has been an adjunct Professor of Computer Science, Aalborg University, Denmark.


Min, S.-K., Simonis, D. and Hense, A. (2007). Probabilistic climate change predictions applying Bayesian model averaging. Philosophical Transactions of the Royal Society A 365: 2103-2116.
'''12) Duke University, USA


Mäntyniemi, S., Uusitalo, L., Peltonen, H., Haapasaari, P. and Kuikka, S. 2013. Integrated age-structured length-based stock assessment model with uncertain process variances, structural uncertainty and environmental covariates: case of Central Baltic herring. Canadian Journal of Fisheries and Aquatic Sciences 70(9): 1317-1326.
Duke University is one of the premier research universities in the US; it has excellent research programs in environmental sciences and Bayesian statistics. The Duke team will be led by Professor Kenneth H. Reckhow, who has focused much of his 38 year research career on Bayesian water quality modeling. In addition, Dr. Reckhow has served as Chair of the US National Academy of Sciences Panel on the USEPA Total Maximum Daily Load Program (2001), as a member of the US National Academy of Sciences Panel on the USGS National Water Quality Assessment (2000-01), as a member of the US National Academy of Sciences Panel on Restoration of the Everglades Ecosystem (2003-05), as Chair of the US National Academy of Sciences Panel on Chesapeake Bay Restoration, and is currently Chair of the USEPA Board of Scientific Counsellors overseeing EPA’s internal and externally-funded water research. He has published two books and over 100 papers, principally on water quality modeling, monitoring, and pollutant loading analysis, with a focus on uncertainty, risk, and decision analysis, often involving Bayesian analysis.


Mäntyniemi, S., Kuikka, S., Rahikainen, M., Kell, L.T. and Kaitala, V. 2009. The value of Information in fisheries management: Nort Sea herring as an example. ICES Journal of Marine Science 66: 2278-2283.
The consortium as a whole offers an excellent combination of skills which are needed to support environmental and economic policy with modern calculus systems. Understanding correctly the real causal relationships in a system where society makes a new intervention must be based on as good causal understanding as possible. The priors of the models must use the existing published papers as effectively as possible. Here the modeling skills and expert understanding of ILVES consortium meet in a unique way to solve practical problems.


O'Neill, B.C., Kriegler, E., Riahi, K., Ebi, K.L., Hallegatte, S., Carter, T.R., Mathur, R. and van Vuuren, D.P. 2014. A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Climatic Change 122(3): 387-400.
'''13) FEM Risk governance and calculus ltd.


Räisänen, J., Ruokolainen, L. and Ylhaisi, J. 2010. Weighting of model results for improving best estimates of climate change. Climate Dynamics 35(2-3): 407-422.  
A company that will be established to pay costs and salary of prof emeritus Elja Arjas. Aims e.g. to help implementing environmentally based vetting criteria and insurance fees in maritime activities. Owns copyright of new causal discover algorithms.


Smith, T. W. P., Jalkanen, J. P., Anderson, B. A., Corbett, J. J., Faber, J., Hanayama, S., O'Keeffe, E., Parker, S., Johansson, L., Aldous, L., Raucci, C., Traut, M., Ettinger, S., Nelissen, D., Lee, D. S., Ng, S., Agrawal, A., Winebrake, J. J., Hoen, M., Chesworth, S., and Pandey, A. 2014. Third IMO GHG Study; International Maritime Organization (IMO) London, UK, June 2014.
===9 Mobility plan===


Tapaninen, U. 2015. Suomen satamaverkko murroksessa – analyysi satamien erikoistumisesta ja lukumäärästä (The changing sea port network in Finland - an analysis of specialization and number of Finnish ports). Terra 127: 1, xx–xx.
UH/FEM Sakari Kuikka will visit CSIRO in winter 2015–summer 2016. During this trip, the oil spill risk methods of Finland will be reviewed and the CSIRO modelling skills will be used to plan the insurance schemes as tools to manage the oil disaster risks. Also the adding of oil spill element to Atlantis will be developed during this visit. Kuikka will also visit Waikato University in New Zealand, who are developing Weka software for data analysis with Bayesian nets, as well as graphical interfaces to evaluate large data or simulation datasets. Kuikka will also visit Duke University for approximately two months in years 2015–2018. 30 000 € is included in the budget.


Vanhatalo, J., Veneranta, L. and Hudd, R. (2012). Species distribution modeling with Gaussian processes: A case study with the youngest stages of sea spawning whitefish (Coregonus lavaretus L. s.l.) larvae. Ecological Modelling 228: 49-58.
LUT School of Business and Management (LBM): Jyri Vilko will visit Thammasat University in the winter 2015-2016. During this researcher exchange he will collaborate with the local researchers in researching the inland water ways usage potential in South East Asian and Finnish perspectives. In 2017 Professor Vilko will visit the Massey University, in New Zealand. The aim of the visit is to collaborate in studying supply chain relationships and responsibilities in multi modal logistics. 30 000 € is included in the budget.


Vanhatalo, J., Tuomi, L., Inkala, A., Helle, I. and Pitkänen, H. (2013). Probabilistic Ecosystem Model for Predicting the Nutrient Concentrations in the Gulf of Finland under Diverse Management Actions. Environmental Science & Technology 47(1): 334-341.
Dr Inari Helle will visit CSIRO in 2016 for a year to provide knowledge from the oil spill risk analysis carried out in FEM group Finland. Moreover, she will adapt the best practices rules applied in Australian oil industry and offer the essential parts of these to the recommendations made for international companies dealing wit oil carrying.


Dr Riikka Venesjärvi will visit Duke University in the end of 2016 – beginning 2017 in order to work with the Gulf of Mexico data sets and their provision to meta-analysis in order to establish a learning system in the model framework built to Atlantic model, CSIRO. The related Bayesian algorithms will be developed together with LTU, Duke, UH and CSIRO.


{{comment|# | Ulla: TERRAssa tulee juttu Suomen satamista: 12 – 16 satamaa, riippuu tavaramääristä, eli kuinka paljon erilaisia konttisatamia tms (oli satamateoria), menisi sitten rautatielle, jos olisi satama niin menisi rautateille
Rich Little will visit Finland for a half year to tech the tactical level of simulations of resources dyamics and related level of governance by using the insurace systems as srtategic and direcive risk govermamce tools.
|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 04:21, 22 April 2015 (UTC)}}


THE LAST 4 PAGES:
===10 Key literature===


== 11 Interaction plan ==
* European Commission (EC) (2011). White Paper. Roadmap to a Single European Transport Area – Towards a competitive and resource efficient transport system. COM(2011)144 final, Brussels, 28.3.2011.
* Fulton, E.A., Link, J., Kaplan, I.C., Johnson, P., Savina-Rolland, M., Ainsworth, C., Horne, P., Gorton, R., Gamble, R.J., Smith, T. and Smith, D. (2011). Lessons in modelling and management of marine ecosystems: The Atlantis experience. Fish and Fisheries, 12:171–188.
* Haapasaari P., Helle I., Lehikoinen A., Lappalainen J., and Kuikka S. A proactive approach to maritime safety policy making for the Gulf of Finland: seeking best practices. Accepted to Marine Policy.
* Helle, I., Lecklin, T., Jolma, A. and Kuikka S. (2011). Modeling the effectiveness of oil combating from an ecological perspective - A Bayesian network for the Gulf of Finland; the Baltic Sea. Journal of Hazardous Materials 185(1):182–192.
* Helle, I., Ahtiainen, H., Luoma, E., Hänninen, M. and Kuikka, S. Where should we invest in oil spill management? A probabilistic approach for a cost-benefit analysis under uncertainty. Accepted to the Journal of Environmental Management.
* Ihaksi,T., Kokkonen, T., Helle, I., Jolma, A., Lecklin, T. and Kuikka, S. (2011). Combining conservation value, vulnerability, and effectiveness of mitigation actions in spatial conservation decisions: an application to coastal oil spill combating. Environmental Management. 47: 802–813. IOPC Funds (2013). Claims Manual. October 2013 Edition.
* Jalkanen, J.-P., Johansson, L., Brink, A., Kalli, J., Kukkonen, J. and Stipa, T. (2012). Extension of an assessment model of ship traffic exhaust emissions for particulate matter and carbon monoxide, Atmospheric Chemistry and Physics 12: 2641-2659.
* Jolma, A., Lehikoinen, A., Helle, I. and Venesjärvi, R. (2014). A software system for assessing the spatially distributed ecological risk posed by oil shipping. Environmental Modelling & Software 61: 1–11.
* Juntunen, T., Rosqvist, T., Rytkönen, J. and Kuikka, S. (2005). How to Model the Oil Combatting Technologies and Their Impacts on Ecosystem: a Bayesian Networks. Page CM 2005/S:2002 in 2005 ICES Annual Science Conference Aberdeen, United Kingdom.
* Klemola, E., Kuronen, J., Kalli, J., Arola, T., Hänninen, M., Lehikoinen, A., Kuikka, S., Kujala, P. and Tapaninen, U. (2009). A cross-disciplinary approach to minimising the risks of maritime transport in the Gulf of Finland. World Review of Intermodal Transportation Research 2(4): 343–363.
* Kokkonen, T., Ihaksi, T., Jolma, A. and Kuikka, S. (2010). Dynamic mapping of nature values to support prioritization of coastal oil combating. Environmental Modelling & Software, 25 (2010) 248–257.
* Kuikka, S. and Varis, O. (1997). Uncertainties of climatic change impacts in Finnish watersheds: a Bayesian network analysis of expert knowledge. Boreal Environment Research 2: 109–128.
* Kuikka, S., Vanhatalo, J., Pulkkinen, H., Mäntyniemi, S. and Corander J. (2014). Experiences in Bayesian Inference in Baltic Salmon Management. Statistical Science 29(1): 42–49.
* Kummu, M., Gerten, D., Heinke, J., Konzmann, M. & Varis, O. (2014). Climate-driven interannual variability of water scarcity in food production potential: A global analysis. Hydrology and Earth System Sciences 18: 447–461.
* Lecklin, T., Ryömä, R. and Kuikka, S. (2011). A Bayesian network for analyzing biological acute and long-term impacts of an oil spill in the Gulf of Finland. Marine Pollution Bulletin 62: 2822–2835.
* Lehikoinen, A., Luoma, E., Mäntyniemi, S. and Kuikka, S. (2013) Optimizing the Recovery Efficiency of Finnish Oil Combating Vessels in the Gulf of Finland Using Bayesian Networks. Environmental Science and Technology, 47(4):1792-1799. DOI: 10.1021/es303634f
* Lehikoinen, A., Hänninen, M., Storgård, J., Luoma, E., Mäntyniemi, S. and Kuikka, S. (in print). A Bayesian Network for Assessing the Collision Induced Risk of an Oil Accident in the Gulf of Finland. Environmental Science and Technology. DOI: 10.1021/es501777g.
* Mäntyniemi, S., Kuikka, S., Rahikainen, M., Kell, L.T. and Kaitala, V. (2009). The value of Information in fisheries management: North Sea herring as an example. ICES Journal of Marine Science 66: 2278–2283.
* Pearl, J. 1995. Causal diagrams for empirical research. Biometrika 82: 669-688.
* Pearl, J. 2000. Causality: Models, Reasoning, and Inference. Cambridge University Press, Cambridge.
* Porkka, M., Kummu, M., Siebert, S. and Varis, O. (2013). From food insufficiency towards trade dependency: A historical analysis of global food availability. PLoS One DOI:10.1371/journal.pone.0082714.
* Tapaninen, U. (2015). Suomen satamaverkko murroksessa – analyysi satamien erikoistumisesta ja lukumäärästä (The changing sea port network in Finland – an analysis of specialization and number of Finnish ports). Terra 127: 1.
* Varis, O. & Kuikka, S. (1997). BENE-EIA: A Bayesian Approach to Expert Judgement Elicitation With Case Studies On Climatic Change Impacts on Surface Waters. Climatic Change 37: 539–563.


=== 11.1 Objectives of interaction ===
===11 Interaction plan===


The very basic philosophy of risk communication in ILVES approach differs from that usual in science:  we aim to have an impact, not in first hand the number of
====11.1 Objectives of interaction====


Transport security is high on the EU’s agenda. The EU’s comprehensive approach of policy, legislation and monitoring of air and maritime transport safety  should be further consolidated and strengthened through cooperation with major international partners.A risk based approach to the security of cargo need to be modelled by the extensive data sets and high number of existing probabilistic models describing the collision and grounding risks of the vessels, requiring an interdisciplinary cost benefit models to evaluate the policy options.  
Since the objective of ILVES is to produce new techniques for policy design which relies on extensive use of scientific theory, datasets, literature and expert knowledge, the information is summarized within a probabilistic framework. The very basic philosophy of risk communication in the ILVES approach extends the conventional methods in science: our primary aim is to have an impact on policy. However, our work needs to be of a high scientific quality in order to justify the policy advice.


Our aims of interaction include several levels, and the objectives are internal and external. Because these two are tightly coupled, same means of interaction can be applied to both objectives.


Tulevina vuosikymmeninä arktisten investointien arvioidaan yltävän 100 miljardiin euroon. Näin todettiin pääministerin ilmoituksessa arktisesta strategiasta lokakuussa 2013. Samassa yhteydessä myönnettiin nöyrästi, että voimien yhdistäminen ei aina ole ollut suomalainen vahvuus, joten siihen on kiinnitettävä erityistä huomiota. Tässä onkin pysähtymisen paikka. Yritysten yhteistyö on välttämätöntä liiketoiminnan syntymiseksi.
The main objective of internal interaction is to ensure functioning communication between the consortium research teams. Successful working requires continuous flow of information between several partners, but in addition to this “traditional” interaction related to research work, the integration of knowledge in ILVES approach involves great deal of learning at many levels. Hence, we do not only aim at information flows between research teams but a more profound approach of mutual learning. Although this is a challenging task and may call for a new mindset, we trust this aim is achievable within the consortium.


Liikenne- ja viestintäministeriön Fintrip-ohjelmassa on kehitetty määrätietoisesti verkostomaista toimintamallia oman hallinnonalan tutkimus- ja kehittämistoiminnan koordinoimiseksi ja alan toimijoiden verkostojen törmäyttämiseksi. Yhtenä tuloksena on verkosto, joka suunnittelee Arktisen merellisen osaamiskeskuksen perustamista. Tämä hanke yhdistää kaupunkeja, yrityksiä ja ministeriöitä, jokaista omassa roolissaan. Yhteistyötä tarvitaan koulutuksessa, tutkimuksessa, tuotekehityksessä, markkinoinnissa ja viennissä. Potentiaalisia asiakkaita löytyy kansainvälisistä öljy-yhtiöistä, vakuutusyhtiöistä ja laivanvarustamoista. Osaamiskeskuksen myötä Suomi voi profiloitua arktisen operoinnin osaajaksi, ympäristön puolustajaksi ja cleantechin huippumaaksi
The objectives of external interactions are manifold. As we aim at finding solutions that have international significance related to maritime safety and oil spill risk assessment, our objective is to develop new scientific methodologies to support policies enhancing reduction of greenhouse gases and increasing transport safety. At a national level, the specific features of Finnish transportation will be taken into account and the stakeholders operating in the field of transportation in Finland will be the key end-users of knowledge produced by ILVES.


Press releases: We will follow the path taken by UH/FEM in oil spill risk management press relations on the weeks 16 and 17 of 2015. @Sakari_Kuikka tweeted, as chair of ICES WGMABS, close to xx times. These thweets were retiited xx times and they spread to dd endusers. This took place in only zz days. The press release made by FEM on the 20th April 2015 lead, for example, to an radio interview on Radio Suomi on the 21st. This happening was made known to about  zz endusers on the 20th April 2015, which lead to feedback, by email, from only  qq information endusers. This type of reaction chains are used both in studies of risk communication and cognition, and in the active
====11.2 Target group/stakeholders/partners====


Events, seminars, public talks: The first seminar of ILVES was arranged on the 24th April, 2015, in Viikki, lecture room bb. This was made known on the 21st April, and there were xx endusers, zz scientists and dd industry participants in the seminar. A yearly workshop will be related to ICES WGMABS, where Kuikka is chair (links to
The partnership of the project include experts of emission modeling, security supply modeling and logistic solutions modeling, interdisciplinary risk analysis, policy design, economic research, insurance policy, legislation and risk communication (see consortium). The integration of knowledge in ILVES approach involves a great deal of interaction among all these disciplines, and to this end the information will be collected into one web based platform. We have a high number of letters of commitment from outsiders: City of Helsinki, Ministry of Traffic and Communication, Environment Ministry, The Waterway Association of Finland, the Finnish Border Guard, CISRO Australia.


Workshops: We will conduct the Metropol platform (xx link) and the ILVES risk communication platform (made in BONUS GOHERR on the 21st Aprill 2015, see LINK )
The three main target groups will be:


Science trucks: are these eatables or what ?
'''1) POLICY-MAKERS:'''' the theme related decision-makers (politicians, authorities), which are operating in international, national and regional level. The proposal will improve significantly the possibilities to change maritime policy as a more science based policy. Therefor such a policy dialogue between policy-makers and scientist is essential. This group includes also operational authorities, e.g. oil rescue services.


Theatre, drama: Pihla’s supervisor in France, Pihla’s school
'''2) MARITIME INDUSTRY:''' all representatives related to  maritime transport; shipping companies, port operators, operators and transporters (road, railway), representatives from shipping technology, investors.


Other art channels:     Tuula, Seppo, Pihla’s group  
'''3) CITIZENS:''' NGO’s, product consumers, citizens. In the Fintrip program of the Ministry of Traffic and Communication, a network of research and innovation activities has been planned. ILVES consortium will contribute heavily to this activity by offering best available scientific tools for interdisciplinary risk analysis. The program defines that co-operation is needed in education, research, product development and export. Potential customers include international oil industry, insurance companies and shipping companies. ILVES will create scientific risk analysis products for these actors. Also, ILVES approach has potential to contribute to the long term programme of measures when national plans are revised. The time period covered by the national programmes is usually short, the next 5 years, whereas ILVES looks at 30 years to the future. Also at an international level the results of the research have importance for several actors. The review by Haapasaari et al. (accepted) on the best practices of risk governance in nuclear risk analysis framework will be used as an example to adapt the new approaches. When suggesting the risk governance for international maritime activities (called Blue Belt in EU’s White Paper ), we also use the good experiences obtained from EU Common Fisheries Policy, where the involvement of stakeholders to yearly policy decisions is well organized. The new ICES working group [http://www.ices.dk/community/groups/Pages/WGMABS.aspx  WGMABS], chaired by Sakari Kuikka, will be used as one way to disseminate the findings to society. HELCOM (Helsinki Commission) is an active customer for such advice. In addition, the assessment of oil spill risks related to inland waterways will offer the regional rescue services a database of ecological values that can be used in contingency planning and, in case of an oil spill, to allocate oil combating resources.


How we learn scienfically and practically from these risk communication steps; UH cognition and communication sciences (Prof ff
Stakeholders according to targeted societal impacts will be:
# Findings supporting the policy to achieve CO 2 emissions
#* Policy-makers international, national level
# Investments based suggested chain of creating new jobs along inland water ways
#* Regional representatives from the region of South-Karelia and other regions linked to the Lake Saimaa area
#* Regional authorities, policy-makers, maritime industry operating in inland waters, key representatives from industry using the transportation, citizens
# Improved state of environment
#* Policy makers, NGO’s, regional groups (e.g. environmental policy council of the region of Kymenlaakso), citizens
# Improving the interest to apply best practices in companies that create main risks
#* Industry representatives, Finnish Chambers of Commerce


=== 11.2 Target group/stakeholders/partners ===
The social network analysis is used to look how the information flows between stakeholders. The aim is to construct a scientifically based approach to plan effective dissemination.


The links of the ILVES consortium to the rest of the society are arranged in several ways.  Even though the administration of risk lies in e.g. Trafi (traffic planning), Ministry of Environment ( SYKE (oil,            .
====11.3 Means of interaction====


We have commitments of interests from xx (link, dd, ee, cc, ff, etc.
The main platform for interaction will be an interactive website. It will serve as the platform for internal communication and mutual learning. In the website, the end-users can test the policy options by using a decision support tool. In the interactive tool the objective settings are inquired from the users in such a way, that the decision model can rank the decision alternatives. This will create a learning database from the value weights of the stakeholders (task of Hugin company, Denmark) and citizens (separately for different groups). The interactive web pages will be set up at the onset of the project, and maintained also after the project closure.


ILVES Scientific advisory board (ISAB): Elja
The Enduser Advisory Board (EAB) will be established for the project. The EAB will be chaired by Dr Anita Mäkinen (Trafi), responsible for maritime and air traffic gas emissions. We use the risk governance lessons learned in aviation to help identifying risk governance options for maritime activities. This will be based on the active role of the Trafi agency, which is responsible for the traffic policy design in Finland. The EAB will be responsible for providing the formulation of relevant policy options and the probabilities for the likely implementation success of policies. The other invited EAB members include:


ILVES Stakeholder Support  Group (ISSG) defined
There will be tailored events for each target group: conferences and workshops to disseminate the scientific results and policy options compiled by the project, but also to raise awareness of the technical platform intended to facilitate the information exchange. In addition, engaging the stakeholders via EAB very early on ensures that the end-users and beneficiaries will have access to the latest progress of the project online.


Recently, a Bayesian approach was used in the planning of the Finnish programme of measures for achieving a good environmental status in marine waters (Ministry of Environment, 2015). The ILVES approach has a possibility to contribute to the long term programme of measures when national plans are revised. The time period covered by the the national programme is short, the next 5 years, whereas ILVES looks at 30 years to the future.
Timing of the events will be planned to strengthen the existing events. For example, ILVES will arrange, together with ICES (International Council for the Exploration of the Sea) a yearly workshop for relevant stakeholder groups. The workshops will be related to the work of ICES WGMABS (ICES Working Group on Risks of Maritime Activities in the Baltic Sea), which is chaired by prof. Sakari Kuikka. The meetings will include participants from industry, NGO’s, policy makers and scientists. This activity is the basis of risk communication in ILVES. It is alreadyagreed that WGMABS and ILVES consortium will arrange together the next ICES WGMABS workshop in 2016. Media relations will be established based on the existing strong networks of partner consortium. Instead of one-way communication, an interactive approach will be created: this will include the web platform for information sharing, but also engaging the stakeholders (and also the general public) via social media.
Ulla: you may also want a small salary budget, and then you could be the one who coordinates a subgroup of Coordination Committee, where Anita is chair. This could consist of local end users who provide expert knowledge on the policies?  This subgroup could be named as: Expert knowledge elicitation group


The chair of the advisory committee, Dr Anita Mäkinen, is responsible about maritime and air traffic gas emissions. We use the risk governance lessons learned in aviation to help identifying risk governance options for maritime activities. This will be based on the active role of the TRAFI agency, which is responsible for the traffic policy design in Finland. Moreover, the representative  of the Liikenneministeriö will be in advisory commitee, making the interactions with decision makers to be as close as possible.  
We will also build on the art in risk communication. Cartoon artist and humorist Seppo Leinonen (sepponet.fi) will provide material how the humor may open ways to communicate risks. It is especially important for human cognition, that we understand the causalities correctly, as otherwise we cannot link the observations to hypotheses. We need communication that creates an interest to understand causalities, and supports the hidden intuitive understanding of causalities in our imagination.


The review by Haapasaari et al on the best practices of risk governance in nuclear risk analysis framework will be used as an example to adapt the new approaches. When suggesting the risk governance for international maritime activities (called Blue Belt in EU whitebook), we also use the good experiences obtained from EU Common fisheries policy, where the involvement of stakeholders to yearly policy decisions is well organised and studied in REF. The new ICES working group WGMABS (insert the link here), chair Sakari Kuikka, will be used as one way to disseminate the findings to society. HELCOM is an active customer for such advice.  
It is often said that there is no way to impact human values, which are said to come from home’s values and atmospheres. Here, we will use the beauty of Baltic Sea as a potentially effective, and likely the only that matters, way to have any imact on people’s values. Do I love the ocean or just the city life with movies representing Artificial Reality (AR)? It is also said that humor can break barriers that would otherwise be difficult to break. Tht may be the case e.g. between NGO and industry frank communication of risks. Here, we will apply, as a small test, the humor by clownery. The skill to be funny is based on the fact that a human will find her funny features by interactive processes. This is based on a summer school in France, where a student in theater articulacy, is taking part.
* Neste Oil
* Shell/Jorma Ollila?


* UPM_n puunhankinnan puolelta (Esa Korhonen, Heli Rantala Stora Ensosta) voisi olla kiinnostusta, uuden sellutehtaan sijoittuminen. läsisatamien kehittäminen olisi tällöin mahdollinen
====11.4 Responsibilities and implementation====
* huoltovarmuuskeskus: Hilmolan on tehnyt Tallinnan tunnelista huoltovarmuusanalysin. Raija Viljanen voisi kirjoittaa suosituksen
* * 2008 HELCOM resolutions: need for revision to fit with current risks and national and EU legislation
Saimaan Kanavan Neuvottelukunta and Suomen Vesitieyhdistys ry. /Finnish Waterway Association, (toiminnanjohtaja Heli Koukkula-Teixeira, hallituksen pj. Kyösti Vesterinen) are asked to join the societal advisory board of the consortium.


Russian inland water way strategy/Professor, Dr. Science (Econ.) Tatjana A. Pantina, Vice-Rector for Research, the Admiral Makarov State University for Maritime and Inland Shipping, St. Petersburg
Coordinator and KMRA are responsible for implementing the stakeholder communication plan. KMRA will be in charge of organizing the meetings and communication with end-users (Fig. 11). KMRA has operated in close collaboration with the maritime industry, universities, research organizations, institutes and authorities both nationally and internationally. KMRA has coordinated interdisciplinary projects where practical tools have been developed to support decision making. Most significant projects in the field have been 1) SAFGOF (ERDF funding) and 2) MIMIC (Central Baltic Interreg) where traffic growth scenarios, accident probabilities and biological information about consequences of oil accidents were combined to produce probabilistic risk maps; 3) OILRISK (Central Baltic Interreg) where the impacts of oil on coastal and marine species and habitats were evaluated and estimates how well certain nature values can be safeguarded with booms or protective sheets were given, and 4) TOPCONS (ENPI CBC) where a tool to support maritime spatial planning was developed. KMRA has been the responsible partner for coordination, internal and external communication and information/publicity activities in these projects.


Inland water way system, benchmarking: Sweden: Johan Lantz, Senior Advisor, Swedish Maritime Administration. In EU-level: European Federation of Inland Ports, Kathrin Obst, EFIP Director Kathrin.Obst@inlandports.be
In ILVES consortium the role of KMRA is to communicate and disseminate the essential findings of the project to the target groups and end users within the maritime sector. KMRA will organize all meetings and communicate with end-users, and also save the communication records from meetings related to relevance and understand ability of Bayesian inference.
Neste Oil ja Fortum, bioöljyt?
Ensuring the communication with other inland water area related project groups, such as ”WATER: connecting people” by Metsähallitus/ Jari Ilmonen/Luontopalvelut + Sanna-Kaisa Juvonen ja Mikko Tiira,  Vesienhoidon, luonnonsuojelun sekä elinkeinojen ja väestön tarpeiden yhteensovittaminen


=== 11.3 Means of interaction ===
====11.5 Schedule====


We will conduct interactive webpages, where endusers can test the policy options by using the decision model. This will include such sensitivity analysis, where the objective settings are asked from users so precisely, that the decision model can rank the decision alternatives. This will create a learning database from the value weights of the stakeholders and citizens (separately for different groups). The decision model will enable the decisions really implemented in practise, and will estimate the weights of aims, as the knowledge base is known and the decisons are known. Then the only missing thing from equation is the objective function.  
ILVES consortium will make a detailed plan of the interaction activities. The interaction will start by creating the web-based platform for communication and data exchange. This platform will be further developed during the project and serve as the platform for end-users to test the models and to collect information from them.


* remember Jarnos methods and publications to estimate the areal alcohol consumption: could that be used to help in spatial analysis of impacts ?
The first version of the model will be designed by the end of the first year of the project. To this first version a conceptual framework will created which will be elaborated in further communication and feedback with the stakeholders and experts.
* Anita Mäkinen will be the chair of the Advisory board which is responsible to provide the formulation of relevant policy options and the probabilities for the likely implementation success of policies.
* Coordination committee: Chair Dr, docent Anita Mäkinen @trafi.fi, Lassi Hilska Liikenneneuvos, johtava asiantuntija Liikenneverkot Liikennepolitiikan osasto  @lvm.fi,  Merivakuutus, Helsingin, Turun, Oulun ja Kotkan satamat, Swedish inland
* Kari Kosonen on FINPILTilta olisi kiinnostunut kehittämään sisävesiliikennettä, he eivät laskuta Saimaalta täyttä kustannusta. KYAMKilla aineistoja.  
* Liikennevirastosta joku ohjausryhmään, sieltä raideliikenteen ihminen voisi olla VR:n sijasta mukana
* Lolan Eriksson: LVM: Anita, Ulla ehdotti
* Jorma Härkönen logistiikkakeskus, voi auttaa löytämään sopivat partnerit,
* sisävesiliikenneyhidtyksen johtaja (SEIRA).
* Anita veti meriliikenneryhmää jossa mietittiin uusia toimenpiteitä,
Henrik Ringblom: EMSA as end user
* coordination committee Expert knowledge elicitation group
* UPM


=== 11.4 Responsibilities and implementation ===
The second, updated version will be available by the end of the third year. This version will be used for policy analysis and evaluation during the fourth year. The evaluations will be carried out in co-operation with the end-users in order to elaborate the dependencies and causalities of the model.


{{attack|# |This place is for responsibilities about the INTERACTION with stakeholders. WP tasks were moved to under the WPs.|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 12:47, 24 April 2015 (UTC)}}
This tested version will be updated during the last two years and to decrease the scientific uncertainties to a minimum. The main forum for raising awareness among policy-makers and authorities will be the Enduser Advisory Board (EAB). During the project, raising the awareness of  the project among stakeholders, end-users and also among general public will be carried out by organizing tailored events (conferences, workshops, public events) and informing them over the internet, including social media.


=== 11.5 Schedule ===
Solution and know-how sharing will be taken on a practical level by applying the latest information collected by the project on training and information activities targeted towards the maritime sector.


saimaan pränddi arv
After the project, the internet platform will remain open and the lessons learned will be available to all interested parties.

Latest revision as of 09:12, 11 May 2015

The submitted proposal to STN call

ILVES_application_final with man month table This is the version that was submitted on 29th. Needs a table of man months and publication plan list, these will be submitted on Monday

This is the pdf sent to Jyrki & FEM on Tuesday morning ILVES_hakemus_final2b_Sakke_comments


This is the figure to section 11.

Fig on the responsibility section 11

The last modification file to Heimonen Aarnipaja ltd. Thanks Jyrki for excellent help !!

ILVES_hakemus_back_from_jyrki tuesday morning sakke last comments back

Background documents for public

Approach of ILVES, explained by Sakke to help you improving the text:

ILVES Writing units :

A) Helsinki Viikinkaari 2 FEM group, Department of environmental sciences FEM interdisciplinary risk and decision analysis group by Bayesian approaches

The expert pane is chaired by Dr Anita Mäkinen

B) Särkisalo, Adress: Säckvikintit,famous place for big pikes ! :) Sakke's biggest is 10,5 kg. See Haukikirja: Pekka Hannula and Sakari Kuikka ;)

C) FMI headquarters Helsinki

File:Cabine
think, focus, write, call

Basis for the letter of commitment,to be signed by your company and organisation,to be send to the coordinator sakari.kuikka@helsinki.fi:

ILVES application

1 Principal investigator (PI) of the consortium, team leaders, sites of research, name of consortium, date of research plan

Name of consortium: Ilmastomyönteiset ja vähäriskiset kuljetusvaihtoehdot – Developing low carbon and low risk transport systems (ILVES)

Date of research plan: 29 April 2015

Project applicants and responsible persons:

  1. UH/FEM: PI (responsible leader of consortium) Professor, fisheries management, Sakari Kuikka University of Helsinki: scientific leadership and motivation, Bayesian analysis, decision modeling, Department of Environmental Sciences, Finland, Professor emeritus Elja Arjas causal learning from non-experimental data, Associate Professor Jani Luoto, Department of Political and Economic Studies, Bayesian methods in economics and effective algorithms
  2. VATT: Dr Juha Honkatukia VATT Institute for Economic Research, Finland, expertise in spatial economy and economic policy in Finland
  3. FMI: Dr Jukka-Pekka Jalkanen Finnish Meteorological Institute (FMI), Finland, expertise in fleet CO 2 releases and fleet dynamics of large international fleets
  4. ÅBO: Dr, Adjunct Professor Henrik Ringbom Åbo Akademi, BALEX Finland, Finland, expertise both from operational oil combatting management (former head of environmental unit in EMSA) and in theory of power of marine legislation in risk governance
  5. KYAMK: Research Manager, Master mariner Justiina Halonen, Kymenlaakson ammattikorkeakoulu, Kymenlaakso University of Applied Sciences, Finland, expertise in education or maritime actors, simulation tools of fleet decision making
  6. LUT: Dr Jyri Vilko Lappeenranta University of Technology, School of Business and Management, Finland, expert in logistics and related risk and decision analysis, professor Pekka Sutela, world know expert in Russian political uncertainty, professor Heikki Haario, expertise in effective algorithms to find probabilistic dependencies in complex problems
  7. KMRA: Dr Miina Karjalainen, Kotka Maritime Research Association (KMRA), Finland, expertise in environmental settings of Gulf of Finland and in interdisciplinary analysis of maritime activities
  8. AALTO: Prof. Olli Varis, Aalto University, Finland, internationally known expert of political and environmental development in third world countries, especially in Middle East
  9. CSIRO: Dr Rich Little, expertise in societal design of insurance policies, Dr Beth Fulton, complex ecosystem – human interaction models and their operational use, Dr Ken Lee & Dr Simon Barry oil spill risk management in Australia, Commonwealth Scientific and Industrial Re-search Organization (CSIRO), Australia
  10. NHA: Dr Jouni Tuomisto, National Institute for Health and Welfare (NHA), Finland, expertise on impacts of environment on human health
  11. HUGIN: Dr, CEO, Anders Madsen, HUGIN Ex-pert A/S, Denmark
  12. DUKE: Kenneth Reckhow, Duke University, USA, expertise in Gdecision analysis and Gulf of Mexico oil spill
  13. FEM Risk governance and calculus ltd: To be established in summer 2015 by the help of University of Helsinki

2 Rationale

Fig. 1. The ILVES approach where different forms of traffic and possible development options are shown. Picture describes the selection of best (time, cost and low CO 2 ) traffic options between maritime traffic, inland water routes, land routes, Helsinki-Tallin tunnel and Kymi canal. Threatened nature values in specified areas are also shown. Picture includes CO 2 emissions of maritime traffic in the Baltic Sea(green=low intensity, yellow=moderate, red=high), locations of threatened species and habitats in the Archipelago Sea and the Gulf of Finland (yellow=moderate oil combating prioritization, orange=high, red =very high), average oiling probability in the Archipelago Sea and the Gulf of Finland based on accident hot spots (white lines describe oil drifting), railways (black dash line), highways with intensive truck traffic of over 2000 tons per year (thick blue line), Helsinki-Tallinn tunnel (black double dash line), inland rivers and lakes (light blue narrow lines and polygons), occurrence of Saimaa ringed seal (species picture), and Saimaa and Kymi canals (thick light blue lines)
Fig. 2. Picture shows the impact of biomass point estimate on management decision and causing wrong policy advice.

The proposal develops risk management tools to support decision-making in the Finnish transportation sector. The main aim is to find means to decrease the greenhouse gas (GHG) emissions of the sector and thus, in consequence, to help to achieve the international and national GHG emission reduction targets. The scope of the proposal includes transportation of cargo and passengers on road and railways, and shipping in the Baltic Sea (Fig. 1). The considered time horizon expands to year 2050, which is the target year of the EU’s policy aiming at significant reductions of GHG emissions from transport sector. This proposal aims at developing solutions matching the goals defined in the EU White paper on transport (EC, 2011) from the point of view of Finnish society.

As far as these aims are concerned, the environmental record of shipping can and must be improved by on-board technology, fuels, and operations. Overall, the EU CO2 -emissions from maritime transport should, according to the White Paper, be cut by 40% (if feasible 50%) by 2050, compared to 2005 levels (EC, 2011). Also Finland has set specific objectives for the climate policy implementation. According to the maritime strategy of Finland for 2014–2022, Finland will be a forerunner in winter and environmental technology, and will export high competence in those fields. Furthermore, the deep water fairway in Saimaa Lake District is part of the core network corridors defined in the Connecting Europe Facility of the EU Infrastructure Package. Developing the inland waterway (IWW) system in Finland would support the White Paper (EC, 2011) targets. However, GHG emissions are not the only environmental concern that needs to be addressed. ILVES will study the effects of the policy options on several other socio-economic and environmental factors. For instance, a single oil spill in the Gulf of Finland can incur costs up to one billion euros. This cost would be shared between ship-owners through their insurance companies, international oil pollution compensation funds, and Baltic Sea countries. Finland is a member of the Supplementary Fund of the International Oil Pollution Compensation (IOPC) Funds, which has a compensation limit of SDR 750 million (€ 961 million).

However, the compensation is paid only if oil pollution result in an actual and quantifiable economic loss related to property damage, oil combating and clean-up costs, economic losses in fisheries, mariculture or tourism, and costs of reinstatement of the environment (IOPC Funds, 2013). Hence, losses to environmental values that are typically difficult to assess are excluded from the compensation scheme.

Furthermore, inland water area, especially the Lake Saimaa district has several protected areas, where the habitats of protected species are close to the Saimaa deep water route where the merchant vessels are sailing. Accident and near miss incidents recorded by maritime authorities reveal that the accidental risk in the Saimaa Lake is relatively high compared to the sea areas. Challenging navigating environment emphasize the importance of piloting or compensatory service for vessels.

The project studies several policy options that have a potential to improve the efficiency of transportation and thus decrease the GHG emissions and investment costs on traffi c infrastructure. The set of management options includes structural issues, such as the development of the Finnish dry port structure and railway network, reduction of the number of coastal harbors (Tapaninen, 2015), promotion of inland water ways and harbors, construction of the Kymijoki canal, and the proposed railway tunnel between Helsinki and Tallinn.

Sophisticated modelling tools are necessary to assess the combinations of actions which lead to GHG emission reductions from the transport sector. Therefore, from the methodological point of view, our main objective is to develop new techniques for policy design, which are based on extensive use of the underlying scientifi c theory, existing data sets, publications, and expert knowledge to provide risk related advice (Haapasaari et al., accepted). We will combine existing knowledge by summarizing information using probabilistic forecasts. To be more concrete, we forecast the future scenarios by the forward simulation version of the developed model fitted to relevant data, including economic and climate indicators. Indicators describe the state of interest variables by the noise included to likelihood function. We then forecast probabilistic outcomes of the key interest variables conditional on designed policy actions. We use these forecasts as an input in a decision model, where the optimal policy, given the underlying key uncertainties, can be evaluated. This can be done separately or jointly with the key societal aims to understand the role of precise aims in the policy support (Kuikka and Varis 1997, Varis and Kuikka 1997).

The rationale of probabilistic approach is described in Fig. 2 which shows how the use of a point estimate can provide wrong policy advice in risk averse decision making. For instance, if the criterion is to avoid the risk level of small biomass, a point estimate would suggest policy A that provides better expected value (Fig. 2). However, the uncertainty related to this option is estimated to be higher than that of policy option B.

The proposal supports decision-making related to efficient allocation of resources. Science can help to find the optimal policy design, and due to the extreme costs, even small adjustments in infrastructure investments can pay back to national and private economy and society’s welfare (for instance, the cost of EU infrastructure development to match the demand for transport is estimated to be over 1.15 trillion € in 2010–2030 (EC, 2011)). Furthermore, we give strategic advice how to allocate research resources by carrying out the value of information analysis (Mäntyniemi et al., 2009). We apply Bayesian techniques to assess the value of information and value of control in planning the policies (that is, we use value-of-control analysis to say what should be managed).

This is, to our knowledge, the first study to apply the Bayesian causal modelling techniques to the planning of future legislation options. We are especially looking forward to apply the Pearls algorithm (Pearl, 1995) to the optimal policy design under the case where many variables of a noisy chain link the decisions to aims (Fig. 3) (Varis and Kuikka, 1997).

The proposal develops methods that are relevant from the national as well as international point of view. We develop general policy evaluation tools for cases, where the amount of fata, models, papers, experts and other sources of information vary in terms of their quality. We also expect that our approach to evaluate the impacts of various insurance practices will be highly relevant for international insurance practices. Furthermore, the methodology developed related oil spill risks can be seen important for the Finnish and Baltic Sea oil risk management.

Fig. 3

For this proposal, we have improved the FEM team by recruiting more skills from UH and Finnish universities and international scientific bodies. At the moment, our approaches are built upon the following publications:

  • Klemola et al.' (2009) published the approach of Bayesian statistics in oil spill risk analysis, but no citations have been obtained to this paper, in a maritime academic journal. There seems to be no tradition for advanced risk analysis in the area.
  • Juntunen et al. (2005). First step in the methodological process was published in this paper in ICES annual science congress. It was not accepted in a journal of coastal processes. Journal was focusing on oceanology where causalities are based on theory of physics.
  • Lecklin et al. (2011) showed how the behavior of e.g. birds must be taken into account in conditional probabilities of impacts (close to concept of likelihood in usual statistical models), i.e. do e.g. migrating birds in GoF actively look for oily places where the surface is deadly calm and can seal avoid the oily areas?
  • Ihaksi et al. (2011) used the concepts of value-of-control, vulnerability of the population and conservation status of the species to plan spatial decisions of locating oil booms by an risk indicator.
  • Kokkonen et al. (2010) implemented the spatial knowledge to practical software distributed to firemen who decide in practice, and they liked the product.
  • Helle et al. (2011) carried out an operational test by a local BBN decision model: how e.g. the logistic limits is going to impact the chance to act in different sizes of accidents, under the aim to safeguard the nature values of the hot spot areas around Tvärminne field station. In a case of big spill, both booms and time run out.
  • Lehikoinen et al. (2013) evaluated the current effectiveness of oil combatting fleet in Finland under observed weather conditions
  • Jolma et al. (2014) demonstrated how to use spatial models and software’s to estimate key parameters to the BBN model.
  • Helle et al. (accepted) made a probabilistic cost effectiveness analysis of whether the last oil combatting vessels investment (45 million euros) was profitable anymore in Finland. Answer was no, which created discussions in Finland.

3 Societal signifi cance and impact

Transport of goods and people is fundamental to any modern society. International and national trade support creation of jobs, improving employment and welfare. Finland is isolated by sea from the most of the import areas and export markets. The value of the exported goods and services was 77.6 billion € constituting 40% of the GDP in 2013. In terms of weight, 88% of the exported freight was transported by sea in 2012. Harbors link Finland to foreign markets. In 2012, 17 harbors processed over 1 million tons of freight each. The on-shore and off-shore operations, vessel construction and logistics of transport are optimized by business logic. Simultaneously, they have to acknowledge sustainable development and environmental constraints. The aspects of economic competitiveness and environmental risk can be evaluated in a decision support framework. They integrate knowledge related to future CO2 -emission reduction abatement technologies and costs, logistic solutions and the assigned environmental risks with the utilities perceived by the policy makers and stakeholders. As a result, decision support will facilitate balanced argumentation and provide societal, commercial, and environmental benefits in Finland.

The names of the EU maritime legislation packages (e.g. Erika I, Erika II) carry the names of accidents. This is an indication of inertia in science–policy interaction influencing the maritime risk governance in Europe: major accidents provide impulse to improvements in legislation. But, unfortunately, only after the hazard has come true with severe consequences. If same risk management approach would have been adapted in the arenas of nuclear safety and aviation, the earth would be an unpleasant place to live in. No doubt, there are many lessons to learn from those arenas, where it is everyone’s imperative interest that no accidents take place. In nuclear risk management, all actions improving safety are based on model outcomes, demonstrating the strong role of science.

We will adapt the most valuable lessons from these disciplines and advocate their implementation in maritime risk management, where the scientific methodological background is weak compared to e.g. precautionary fisheries management. In fisheries, an important field of applied “engineering” ecology, i.e. risk methodology, is highly advanced. In nuclear power management there is a strong trust to build the actions on complex risk models. A comparable approach would assist maritime risk management to learn more effectively from all disciplines.

The main societal impacts of ILVES approach are as follows, given the project findings are implemented successfully:

  1. findings will supporting the policy to achieve the targets in CO2-emission reduction
  2. investments based on suggested chain of creating new jobs along inland water ways
  3. improved state of environment
  4. improving the interest to apply best practices in companies that create main risks, leading to higher quality in all activities.

The strategic answers of ILVES to the 4 questions made by the call are as follows:

A) How can we improve resource effi ciency and support the move towards a circular economy, which will serve to boost exports and competence-based growth in Finland? If the project findings will be implemented by the Finnish government, the need to use fossil energy in shipping and other traffic will decrease. The project findings will support the development of new shipping technology in Finland and therefore support exports. The new methods to develop legislation and other national policy options will support the development towards competence-based growth, as creation of such development needs a combination of national actions (taxes, subsidies, legislation, customer behavior). The new transportation options to inland water will boost local investments. As no development can take place without negative impacts, we look at the oil spill risk changes related to various transportation options.
B) What are the requirements for climate neutrality and resource efficiency in society? We will study the requirements for climate neutral society, by calculating with a Bayesian decision model, what are the prerequisites of the national and international policies to achieve the desired state of the climate aims in transportation policy. Bayesian network models can calculate the states of the system from causes to effects like any models, but they can also calculate backwards, .i. from desired aims back to required policies. This methodology will be important is overall support of climate policy by scientific tools. We will study how the oil companies, shipping companies and the users of these services increase their interest to avoid environmental disasters to decrease the CO 2 emissions. Different policy options (such as taxes, fees, financial instruments and sanctions) are compared to these ways to govern the environmental impacts in society. The legal feasibility of the policy options, bearing in mind the international nature of maritime transport and consequential international law limitations will form part of this assessment.
C) In what ways can the public sector best support the overall transition so as to maintain a well-managed move towards a climate-neutral and resource-scarce society? There is no clear answer to this question yet, but we will develop methods, legislation planning tools, practical policy actions and new governance solutions to reach these goals. The potential big impacts of resource-scarce international markets will be studied by the risk analysis of worldwide food production. New machine learning methods are applied to the worldwide food production data sets. Same methodology is used to learn traffic risks from large marine data sets.
D) How can we ensure that businesses, employees, the public sector and consumers possess the resources and skills that promote an ability to adapt to the changes and risks brought about by disruptive technologies? We will analyze how new shipping technologies (such as new fuels, use of electric power in inland water to avoid oil spill risks) and shipping options can be used to support the climate policy in the EU and Finland. We will look at the customer behavior is selecting low carbon products from markets and how the role of NGO’s should be revised in the support of creating interests for companies to apply best available techniques to their shipping practices. In addition, we will analyze risks related to the Finnish-Russian agreement on which the use of Saimaa channel is based on. This is a main political risk factor for the investments needed to develop inland waterway traffic.

4 Objectives, expected results

Fig. 5. The linkages between topics in ILVES approach. This is basically a description of a learning chain and network in the ILVES.
Fig. 6. Definition of risk areas of maritime traffic in the Gulf of Finland based on traffic data (Lehikoinen et al. 2015)

The project involves six topics (Fig. 5). These sub-projects have close interconnections, and they support each other.

Topic 1: Analysis of Historical data and meta-analysis on publications

While the considered theoretical models may help to identify the effects of the policy actions, we also use the Pearl’s (2000) approach to try to identify causalities of interest from non-experimental data, including data on policy actions in the past. We believe this would be a valuable approach for all policy analysis in the society. Using the empirical and theory based knowledge, we then estimate the likely future impacts of policy actions on the key variables of interest, by simulating the future developments of these variables conditionally on the policy actions. In particular, we consider the forecast horizons of 10, 20, and 30 years, which provide clear yardsticks for an analysis of policy impacts. We insert these predictions into a decision model, where the implementation uncertainty (i.e., how likely it is that a policy will be adopted in the way proposed) will be evaluated by the experts in maritime policy and law. In this decision model, we account for different types of uncertainty due to usin expert judgments, and integrate them into the considered decision model by applying suitable probabilistic weighting. The decision model will also provide value-of-information estimates (Mäntyniemi et al., 2009), which describe what variables should be known more precisely at the time when decisions are made. This information is used in the project to focus the data analysis and the modelling on policy relevant variables. In the planning of potential new policies, we also use the value-of-control analysis, where each probabilistic variable is made at least partly controllable by adding a new decision variable to the model. The analysis will reflect back to the planning of new legislation. This approach to modelling will provide estimates of the likelihood to achieve the given GHG emission reduction targets for the Finnish fl eets, and the related economic, social, and environmental interests in probabilistic terms.

Topic 2: Structuring Decision Model

As far as the structural analysis is concerned, we apply an emulator model to learn from the behavior of the complex micro economic models. We also try to identify causalities from the data, by combining the information of the micro economic model with the alternative views of the causal structure of the system. This will be achieved by an effective utilization of the views of different experts and stakeholders on causalities. If successful, the learning from causalities by combining micro economic models and expert opinions will be a novelty in economic and environmental analysis. The methodology may have a major impact on the understanding of the impacts of policy actions (yearly interventions by total allowable catches) on stock dynamics, or in environmental management of water quality. The proposed method can be used in any policy evaluation setting, where similar data are available, as in the evaluation of economic policies.

Topic 3: Data Analysis and Algorithms

In practice, risks and probabilities can not be directly measured. Therefore, we need sophisticated modelling tools to assess them. Our goal is to develop new techniques for probabilistic forecasting, including descriptive time series methods and models that combine the underlying scientific theory with data (e.g. Kuikka et al. (2014)). We especially plan to devise methods for combining the forecasts from the different models, to incorporate all available information into the probabilistic forecasts. Our goal in this project is the development of statistical tools and techniques for combining the predictive distributions obtained from large scale models in the described situation.

There is also an under evaluated link between the Bayesian parameter estimation and decision models. Many of the algorithms use extensive time in estimating the tail probabilities of the distributions. However, the desired accuracy of the approximation of the target posterior distribution is linked to the decisions. In particular, if the ranking of the decision options is no longer sensitive to the quality of the estimates of the posterior, the algorithm can be stopped from running. Therefore, the use of decision models together with parameter estimation is essential for such online decisions or fast decision making in industrial applications, where there is no time to wait for better estimates.

Topic 4: Health and Well-being Impacts of Oil Spills in Saimaa

We will a detailed look at the risks of toxicology of oil spills on inland water ways (Fig. 6). The Saimaa deep-water route is particularly difficult to navigate because of its narrowness and fast currents, with the consequence that no oil-combating vessel can significantly decrease the risk of oily shorelines. Accident and near miss incidents recorded by maritime authorities reveal that the accidental risk in the Saimaa Lake is high compared to the sea areas. The Finnish inland water area, and especially the Lake Saimaa district, has several protected areas, where the habitats of protected species are close to a deep water route used by merchant vessels (Fig. 7).

Oil spill may also potentially have an impact on human health. The current exposure to PAHs through eating fish from Saimaa will be evaluated. Assessment is based on literature survey of PAH concentrations in different fish species and use of fish consumption data available from previous studies. Health impact assessment is done by utilizing the previously developed open access model, which have been successfully used for determining health impacts of herring consumption in Finland. The current exposure to PAHs through eating fish from Saimaa will be evaluated. Assesment is based on literature survey of PAH concentrations in different fish species and use of fish consumption data available from previous studies. Health impact assessment is done by utilizing the previously developed open access model, which have been successfully used for determining health impacts of herring consumption in Finland. The societal values of recreational use of Saimaa are first estimated based on a literature review of societal utility of inland water bodies and then the questionnaire is used to evaluate the effects of oils spills on wellbeing.

Topic 5: Planning the Future Policy Options

Alternative future scenarios for environmental policy changes in shipping (efficacy of Emission Control Areas, vessel speed limitations and use of LNG as fuel) will be tested using actual traffic data from Automatic Identification System (AIS) (Jalkanen et al., 2012) and chemical transport modeling. These facilitate the evaluation of environmental performance of maritime policy changes and have been already used as background scientific material at HELCOM and IMO. The use of actual ship traffic patterns and volumes aim at reducing the cumulative uncertainty of the cost/benefit analysis, thus improving the overall performance of the Bayesian approach (Helle et al., Journal of Environmental Management). The shipping scenario work directly contributes to the revision of the national program of measures of the marine strategy, the first version of which already incorporates Bayesian modelling. Our proposal extends the work described in the national program of measures by offering a more complete view ondifferent transport models and including several future scenarios up to year 2050.

Topic 6: Software Development

We will further develop software development and test with people out there: are people interested about our products given their values? We will use systematically Metropol feedback systems in the yearly ICES WGMABS open risk communication meetings (2016: St Petersburg to meet industry, 2017 to help international WWF to adapt those parts of ILVES and WGMABS approaches which are needed for successful risk governance.

Topic 8. Analysis of food security

The food security projections will be drawn from global analyses of food consumption-production dynamics, taking into account risks related to hydrological and climatic factors, climate change (Kummu et al.,2014), supply-loss-demand chain uncertainties and risks, as well as trade factors (Porkka et al. 2015). These analyses provide results in several scales including by country, by food-production unit and partly also by grid cell. Results for Finland can thus be directly obtained, and the trade flows as well as forcing functions, e.g., from climatic risks can implicitly be calculated; the former ones even singled out by trading partner. The outcomes of global food security models will be used as inputs to Bayesian network models, and the vulnerability approach will be used to assess the societal and environmental risks, resilience and adaptation capacity of the food production-trade-consumption system.

Topic 9: Dissemination

In here, we will especially give talks and obtain feedback on spoken messages, by using the modern web based techniques (Fig. 9). Systematic use of Metropol feedback systems in the yearly ICES WGMABS open risk communication meetings (2016: St Petersburg to meet industry, 2017 to help international WWF to adapt those parts of ILVES and WGMABS approaches which are needed for successful risk governance) is likely a very good opportunity to start discussions before disasters take place.

5 Research methods and material, support from research environment

5.1 Research Methods

In addition to existing standard research methods commonly used in empirical and theoretical analyses, one of the main objectives of the project is the introduction of new methods that will subsequently be subjected to the scrutiny of empirical applications. Moreover, the properties of the new methods and major modifications of existing methodology will be studied by means of simulation experiments. Because of the complexity of the models to be considered, computer intensive methods based on simulation will play a central role in the majority of the empirical and theoretical analyses.

The overall modeling approach including partners is as follows. The ILVES approach is strongly led by Bayesian inference and decision analysis tools. The experiences of FEM group are used in developing and leading this process. The economic estimation and simulation models of VATT, together with the large datasets, are used to estimate economic changes. The UH/ECON will provide skills in the Bayesian time series analysis and in testing the theories given the observations. HUT will provide the models related to world wide food security and likely future risks. FMI will provide state of the art models to describe the emissions of shipping fleets, their atmospheric dispersion and impacts to the human health and the environment, LTU will provide knowledge in Bayesian MCMC analysis of complex models, the models used to manage logistic chains, and the expert knowledge and international expert views of Russian development having a potential impact on planned logistic pathways through Saimaa lake area. Åbo Akademi will provide expert knowledge in evaluation of current and potentially future national and international maritime legislation and its probabilistic impact on risks. KMRA will add to the stakeholder contacts and dissemination. KYAMK will provide practical experience from maritime activities, and the databases of inland shipping routes and possibilities. University of Duke will provide Bayesian modelling skills in analysis of the Gulf of Mexico oil spill impact analysis economic analysis. CSIRO will provide Bayesian expertise in exploitation and risk analysis, analysis of insurances to increase the interest to avoid accidents, and expertise in oil spill risk analysis. HUGIN Expert A/S will look at the interactive web tools to analyze the utility functions of the decision making. Moreover, it will develop more expert judgment orientated interface for Hugin software. Cost benefit analysis of developing inland waterways: In the probabilistic cost benefit analysis of the more intensive use of inland water ways, KYAMK will compile the SYKE data of the location of threatened species in areas where an oil spill is possible. Moreover, we value the damages to the use of summer cabins in the lake area by implementing an interactive questionnaire in the web, aiming to estimate the willingness to pay of the Finnish cottages owners to prevent spills. The potential losses of nature values and recreational values are the key elements of risks related to new traffic options in inland water ways. Moreover, as a an alternative policy of safeguarding nature values only (to see whether the aims lead to different policies) we will use the techniques of Ihaksi et al. (2011) to estimate the impacts of possible oil accident on threatened species. Among these, the Saimaa purpose seal is the most threatened and charismatic species, where Finland has a responsibility in safeguarding the population.

In the modeling of oil spill impacts in an ecosystem, whole ILVES team will invest on the combined knowledge of theory and data sets. UH/FEM and CSIRO will use Atlantis ecosystem model (Fulton et al., 2011) as a platform, it includes both theoretical thinking and parameterizations in various parts of worlds oceans. UH (8 pm for Bayesian part of oil risk) and CSIRO (8 pm for making other parameter settings available and modeling them) lead the process and Duke provides the Gulf of Mexico data sets and causal learning in non-experimental data with human induced impacts.

Sub-models of Atlantis simulate oceanographic processes, estuarine and atmospheric inputs, nutrient cycles and biogeochemical factors driving primary production, predator-prey relationships among functional groups, habitat interactions, species movements and invasive species and human uses of marine ecosystems, such as fishing, aquaculture, energy and transport. Saying in other words from causal inference, Atlantis is a set of human induced impacts and ecosystem causal knowledge and model can be used as ”reality with likelihood based provision of simulated noisy data sets” when testing whether causal algorithms find a correct model structure from these simulated data sets.

We create a learning component to Atlantis by using Bayesian inference, where some of the parameter values are obtained from other areas where Atlantis has been parameterized, some are coming from publications related to previous spills and some are expert judgment’s. Especially, we look at the Gulf of Mexico oil spill databases to learn oil impacts (Duke). We will apply the Atlantis to the Baltic and link the spatial risk estimate values (Lehikoinen et al., 2015) to the ecosystem. This is a combination of theory and data in historical data analysis and in future simulations.

For the risk analysis models of the worldwide food security and its impacts on the food security in Finland, Aalto will use the expert judgments on the chances that the fleets do no operate like assumed, due to for example harbor strikes. The food security models on the expert judgement models, where the aim of the Bayesian analysis is to model the uncertainties in causalities. These models are based on link matrixes, and they currently include expert understanding without extensive data analysis, and therefore they can be used as priors for more data based analysis. We use the machine learning algorithms of FEM Consultations (to be developed in this project) to run these additional analysis, using the extensive data sets of FAO. The sensitivities and risks of the system will thereafter be analyzed by sensitivity analysis that focus especially on uncertainties in causal relationships, like carried out for climate change models in Varis and Kuikka (1997).

In the analysis of inland water ways, LUT will also look also at the Kymijoki option which is estimated economically several times and the estimates can be used to look at the optional costs of such traffic option, where we invest on the inland harbor chains, by the Russian uncertainty of keeping the Saimaa channel open must be taken into account. We use expert knowledge (Professor Pekka Sutela and e.g. Russian transport specialist Professor Evgeny Korovyakovskiy) to look at the conditional probabilities needed to define impacting processes and potential futures. VATT will analyze an economic risk analysis by recreational values, where willingness to pay estimates to avoid spills are exploited (Helle et al., accepted) and by making a new questionnaire in the web to cottage owners.

In building the option of investing on new channel option for river Kymijoki, UH/FEM will analyze the sediment contamination is a risk related to the building of channel, where the toxic elements in sediments are then released and a human health risk activated This needs to be compared to the risks caused to e.g. Saimaa ringed seal, which is a small population where the safeguarding responsibility of Finland is of very high status. We apply the risk methodology developed for GoF to the inland waterways to be able to compare the environmental risks caused by traffic options. This knowledge is made available to customers by using the new tools in web to show.

In order to support methods for vessel safety evaluations, KYAMK will focus their work on the operational safety risk in inland waterborne traffic is estimated with expert in the field; pilots and captains of the vessels (Fig. 8). Reconstructing the operating environment of Saimaa deep-water route and the connected fairways to the navigation simulator enables focused ship maneuver training in identified high-risk areas. Shipping route specific simulation allows also to test the performance and applicability of different size and types of vessels.

5.2 Data management plan

The data used in the empirical applications will be drawn from various sources, including commercial databases, such as the Datastream database as well as databases provided by central banks and other government agencies over the internet, and official statistics. In addition to data, we apply modern methods of biometrics to learn conditional probabilities of causalities from published papers.

We utilize the existing large databases of:

  • VATT and Bank of Finland for the economic data
  • National and international vessel databases of Trafi
  • Lloyds register database for vessels around the world (to be paid by the project)
  • Traffic databases of Trafi, already analyzed in TUT modelling of CO 2 emissions
  • CO2 footprint estimates of SYKE and University of Oulu
  • Fish stock estimates of ICES (International Council For the Exploration of the Sea) for the impacts on stocks and fisheries
  • Threatened species database of SYKE, which is already linked (Jolma et al., 2015) to vessel accident estimates, and to the spread of oil after an accident on a given area spread of oil is based on the use of model where the observed weather data is used to estimate likely hit of oil to the threatened species
  • Tiira bird databases
  • Ship emission, pollutant transport and numerical weather prediction datasets of the Finnish Meteorological Institute
  • Bank Of Finland Databases on national economy and on the experts judgments based over the years to enable the comparison off an expert and models in future predictions
  • The knowledge bases of CSIRO to apply best insurance practices in oil production industry to vessel traffic, especially tankers.

As a novelty, we will provide, in addition to raw data sets, the databases of probabilistic model estimates. These will be established to the web pages governed by VATT. This will enable the estimation of probabilistic dependencies for any combination of input – output data sets. However, as the data is only historical observations, the more useful information for other scientists than those in ILVES consortium are the estimates of interest variables. We will provide probabilistic databases of the estimates to allow effective estimation of prior probabilities for future analysis. This will enable the more effective learning in sciences, where it is important from the point of view of end user of the information, that estimates include also other knowledge than just the data that happens to be observed in single studies.

In expert elicitation, we use the best experts available in Finland and thus follow the approach taken by Kuikka and Varis (1997) in the modeling of climate change impacts.

6 Ethical issues

Our proposal does not contain work with human embryo/fetus, humans or animals. This proposal does not have research components, which include genetic data, personal information (religion, sexual or political orientation) or tracking of people. Recruitment as well as the advancement and salary of the employed researchers are based solely on personal achievements and not on gender.

7 Implementation: schedule, budget, distribution of work

Tables 1 and 2 give the overall schedule of the project, and the distribution between partners. The total budget is given in the tables of the proposal in Finish Academy system. The current plant of the distribution of work is like here, but it is likely that there are revision needs that will be handled in the Consortium meetings that will be arranged every 6 months.

8 Research teams, collaboration

The consortium consists of the following research teams:

1) University of Helsinki, Fisheries and Environmental management group (FEM), Finland

The group leader and the PI of ILVES proposal is professor Sakari Kuikka, who is specialized to multidisciplinary decision analysis by Bayesian decision models. This group consists of biologists, social scientists, economists, statisticians, mathematicians and engineering scientists. The interdisciplinary research group (link to group webpages here) applies Bayesian statistics and decision theory to management of natural resources and environmental values. Group was, together with professor Corander’s group in statistics, where FEM has close co-operation, ranked as third in the series of “Societal impact” in the evaluation of research groups in the University of Helsinki evaluations, in 2012. Kuikka has been coordinator in 4 FP or Horizon 2020 projects of EU: 1) PRONE, which was about developing risk methodology for fisheries, 2) ECOKNOWS, which was about developing Bayesian models and learning databases in fisheries science, 3) IBAM, which was about use of Bayesian integrative methods in environmental management and 3) current project GOHERR, which is about developing governance for human and ecosystem health management of Baltic Sea. Kuikka is also the chair of ICES working group for Working Group on Risks of Maritime Activities in the Baltic Sea (WGMABS), which aims to develop a new oil risk management and advisory system for Baltic Sea, being an important route to implement the project findings in active policy (Fig. 9). Professor Samu Mäntyniemi is specialized in Bayesian risk analysis.

2) VATT Institute for Economic Research, Policy Analysis and Modelling Unit, Finland

VATT is a public body under the Finnish Ministry of Finance. The Institute has complete independence in conducting its research, which meets high standards of scientific quality. The purpose of VATT research is to support informed decision-making and its focus is on policy-relevant topics. VATT research covers the analysis of public finances and evaluation of economic reforms, the labour market, environmental policies, and the long-term prospects of the economy including energy and climate policy considerations. VATT participates in EU projects such as APRAISE to design effective, efficient and efficacious policy mixes to achieve environmental objectives under different circumstances which are socially acceptable; and Low Carbon Finland 2050 project, which aims at identifying robust roadmaps for competitive low carbon society and sustainable green growth strategies for Finland.

3) Finnish Meteorological Institute, Finland

FMI is a leading expert in meteorology, air quality, climate change, earth observation, marine and arctic research areas. The main objective of FMI is to improve the safety and the quality of living of Finnish citizens. In order to do this the FMI observes the physical state of the atmosphere, its chemical composition and electromagnetic phenomena. The Institute has several laboratories which analyze the most important air pollutants, develop new measurement techniques and test the reliability of the measurements. Dr. Jukka-Pekka Jalkanen is a senior researcher in the dispersion modelling group of the FMI. He is the head developer of STEAM emission model for maritime traffic. He has written 32 peer-reviewed papers of which 17 most recent ones concern ship emissions. Dr Jalkanen has acted as WP leader in several shipping related projects, and is currently involved in two projects concentrating on wintertime navigation (KAMON) and sustainable shipping scenario studies in the Baltic Sea (SHEBA).

4) Åbo Akademi, Finland

The role of Åbo Akademi (Department of Law) is to assess the legal framework for the different policy options discussed in the ILVES proposal, based on existing national, international and EU legislation in the area and on the constraints imposed by international and EU law. Adjunct Professor Henrik Ringbom has performed similar work both at the Scandinavian Institute of Maritime Law, where he is currently a part-time Professor, and as a Head of the Environment Unit at the European Maritime Safety Agency (EMSA). At Åbo Akademi he is currently, in close cooperation with the Faculty of Law at the University of Turku, in charge of setting up BALEX (Baltic Area Legal Studies); a research and teaching center that specifically focuses on legal issues of relevance for the Baltic Sea region.

Table 1. Person months
1. UH 190.4
2. VATT 47,6
3. FMI 68
4. ÅBO 47,6
5. KYAM 47,6
6. LTU 68
7. KMRC 47,6
8. Aalto University 47,6
9. CSIRO (220 000 e)
10. DUKE (220 000 e)
11. NHA 47,6
12. HUGIN 47,6
13. FEM Consultations 47,6

5) KYAMK, Kymenlaakso University of Applied Sciences, Seafaring and logistics, Finland

KYAMK Seafaring research and development activities focus on maritime safety management i.e. preventive and response measures to marine pollution, as well as on maritime training. KYAMK has proven competence in areas of marine technology and sustainable energy solutions related to port operations and sea transport, and has experienced in developing energy technologies and methods. Master Mariner Justiina Halonen is an expert in ship-source oil spill response. Halonen educates oils spill response tactics for Finnish authorities and has conducted regional spill response contingency planning over ten years. Halonen’s reasearch interests include maritime safety management, safety performance indicators in inland waterborne traffic and ship maneuvering simulation.

6) Lappeenranta University of Technology, School of Business and Management, Finland

Adjunct professor Jyri Vilko (professor in logistics) has applied simulation studies to evaluate alternative logistics and is the leading Finnish expert in his field. Professor Haario is an expert in Bayesian parameter estimation of complex models and has developed probabilistic Bayesian version of the FMI weather forecast model. Professor Pekka Sutela is a world known expert in national economics and in political stability in Russia. He provides valuable expert knowledge to simulations of policy success and economic development. There is a connection to Thailand’s best business school logistics scientists to support the distribution of methods to third countries. Associate professor Ossi Taipale is specialist in software development with long experience in implementations of sales and transportation networks.

7) Kotka Maritime Research Association (KMRA), Finland

KMRA operates in close collaboration with the maritime industry, universities, research organizations, institutes and authorities both nationally and internationally. The aim of the KMRA is to improve the interaction between science and society to make the most of the results, by conveying theory into practice (Fig. 10). The practical solutions based on scientific research can improve the profitability of maritime industries and decrease the environmental impacts of maritime transportation. KMRA has coordinated interdisciplinary projects where practical tools have been developed to support decision making. KMRA has been the responsible partner in many projects for coordination, internal and external communication and information/publicity activities in these projects. In ILVES consortium the role of KMRA is to communicate and disseminate the essential findings of the project to the target groups and end users within the maritime sector.

8) Aalto University, Water & Development Research Group, Finland

The Water & Development Research Group is a cross-disciplinary research group operating at the Aalto University. The group has a long research tradition in water and development issues as well as in integrated management of water resources. The group leader, prof. Olli Varis has a strong expertise in water resources management, development and food security research, environmental and social impact assessment, computational modeling, multidisciplinary natural resources and development studies. He is also well-known for his methodological publications on computational modeling and data analysis, especially from the field of Bayesian networks and related decision analysis. He has coordinated and participated in many national and international research projects.

9) Commonwealth Scientific and Industrial Research Organization (CSIRO), Australia

CSIRO is Australia’s national science organization and a leading multidisciplinary research organization. In ILVES, CSIRO will contribute to oil spill risk analysis, studying financial risk management and insurance as a policy tool, and the development of oil spill modules of varying complexity for the Atlantis ecosystem model. Dr. Rich Little (team leader) is a Senior Research Scientist at CSIRO Marine and Atmospheric Research. His research specializes in modelling population dynamics, economics, and management decision-making in natural resource and marine environmental science.

10) National Institute for Health and Welfare (NHA), Finland

National Institute for Health and Welfare (THL) is a government research institute. It has wide expertise in public health, environmental health and health impact assessment. THL is the main developed and user of open policy practice and Opasnet worldwide. THL has gained good practical experience about what practices work and what don’t, and where the problems lie in science-policy interface when opening data and models. The Unicorn research group in THL is led by Adjunct professor, MD Jouni Tuomisto (JT;THL PI). Tasks: health modelling, decision analysis, Opasnet web-workspace. Merits: More than 20 years of expertise in environmental health issues, risk assessment, decision analysis, modelling, and decision support. Developed the method open policy practice and the web-workspace Opasnet. 98 peer-reviewed scientific articles.

11) HUGIN Expert A/S, Denmark

HUGIN EXPERT A/S is a Danish SME established in 1989. The company is a leading provider of tools and services for advanced decision support based on complex statistical models known as probabilistic graphical models (i.e., Bayesian Belief networks and influence diagrams). Since 1989 the company has collaborated with some of the World’s largest IT companies and its technology is being utilized in a wide variety of IT systems. HUGIN EXPERT has a long history of active participation in RTD projects, for instance, supported by the European Commission or companies. HUGIN EXPERT will play an important role in ILVES in relation to the use of Bayesian network technology. This includes both the use of the HUGIN software tool for development and deployment of Bayesian network models as well as the development of Bayesian network software to support the needs and requirements of ILVES. The Chief Executive Officer of HUGIN EXPERT is Anders L Madsen. He has a PhD in Decision Support Systems (1999). Since 2011 he has been an adjunct Professor of Computer Science, Aalborg University, Denmark.

12) Duke University, USA

Duke University is one of the premier research universities in the US; it has excellent research programs in environmental sciences and Bayesian statistics. The Duke team will be led by Professor Kenneth H. Reckhow, who has focused much of his 38 year research career on Bayesian water quality modeling. In addition, Dr. Reckhow has served as Chair of the US National Academy of Sciences Panel on the USEPA Total Maximum Daily Load Program (2001), as a member of the US National Academy of Sciences Panel on the USGS National Water Quality Assessment (2000-01), as a member of the US National Academy of Sciences Panel on Restoration of the Everglades Ecosystem (2003-05), as Chair of the US National Academy of Sciences Panel on Chesapeake Bay Restoration, and is currently Chair of the USEPA Board of Scientific Counsellors overseeing EPA’s internal and externally-funded water research. He has published two books and over 100 papers, principally on water quality modeling, monitoring, and pollutant loading analysis, with a focus on uncertainty, risk, and decision analysis, often involving Bayesian analysis.

The consortium as a whole offers an excellent combination of skills which are needed to support environmental and economic policy with modern calculus systems. Understanding correctly the real causal relationships in a system where society makes a new intervention must be based on as good causal understanding as possible. The priors of the models must use the existing published papers as effectively as possible. Here the modeling skills and expert understanding of ILVES consortium meet in a unique way to solve practical problems.

13) FEM Risk governance and calculus ltd.

A company that will be established to pay costs and salary of prof emeritus Elja Arjas. Aims e.g. to help implementing environmentally based vetting criteria and insurance fees in maritime activities. Owns copyright of new causal discover algorithms.

9 Mobility plan

UH/FEM Sakari Kuikka will visit CSIRO in winter 2015–summer 2016. During this trip, the oil spill risk methods of Finland will be reviewed and the CSIRO modelling skills will be used to plan the insurance schemes as tools to manage the oil disaster risks. Also the adding of oil spill element to Atlantis will be developed during this visit. Kuikka will also visit Waikato University in New Zealand, who are developing Weka software for data analysis with Bayesian nets, as well as graphical interfaces to evaluate large data or simulation datasets. Kuikka will also visit Duke University for approximately two months in years 2015–2018. 30 000 € is included in the budget.

LUT School of Business and Management (LBM): Jyri Vilko will visit Thammasat University in the winter 2015-2016. During this researcher exchange he will collaborate with the local researchers in researching the inland water ways usage potential in South East Asian and Finnish perspectives. In 2017 Professor Vilko will visit the Massey University, in New Zealand. The aim of the visit is to collaborate in studying supply chain relationships and responsibilities in multi modal logistics. 30 000 € is included in the budget.

Dr Inari Helle will visit CSIRO in 2016 for a year to provide knowledge from the oil spill risk analysis carried out in FEM group Finland. Moreover, she will adapt the best practices rules applied in Australian oil industry and offer the essential parts of these to the recommendations made for international companies dealing wit oil carrying.

Dr Riikka Venesjärvi will visit Duke University in the end of 2016 – beginning 2017 in order to work with the Gulf of Mexico data sets and their provision to meta-analysis in order to establish a learning system in the model framework built to Atlantic model, CSIRO. The related Bayesian algorithms will be developed together with LTU, Duke, UH and CSIRO.

Rich Little will visit Finland for a half year to tech the tactical level of simulations of resources dyamics and related level of governance by using the insurace systems as srtategic and direcive risk govermamce tools.

10 Key literature

  • European Commission (EC) (2011). White Paper. Roadmap to a Single European Transport Area – Towards a competitive and resource efficient transport system. COM(2011)144 final, Brussels, 28.3.2011.
  • Fulton, E.A., Link, J., Kaplan, I.C., Johnson, P., Savina-Rolland, M., Ainsworth, C., Horne, P., Gorton, R., Gamble, R.J., Smith, T. and Smith, D. (2011). Lessons in modelling and management of marine ecosystems: The Atlantis experience. Fish and Fisheries, 12:171–188.
  • Haapasaari P., Helle I., Lehikoinen A., Lappalainen J., and Kuikka S. A proactive approach to maritime safety policy making for the Gulf of Finland: seeking best practices. Accepted to Marine Policy.
  • Helle, I., Lecklin, T., Jolma, A. and Kuikka S. (2011). Modeling the effectiveness of oil combating from an ecological perspective - A Bayesian network for the Gulf of Finland; the Baltic Sea. Journal of Hazardous Materials 185(1):182–192.
  • Helle, I., Ahtiainen, H., Luoma, E., Hänninen, M. and Kuikka, S. Where should we invest in oil spill management? A probabilistic approach for a cost-benefit analysis under uncertainty. Accepted to the Journal of Environmental Management.
  • Ihaksi,T., Kokkonen, T., Helle, I., Jolma, A., Lecklin, T. and Kuikka, S. (2011). Combining conservation value, vulnerability, and effectiveness of mitigation actions in spatial conservation decisions: an application to coastal oil spill combating. Environmental Management. 47: 802–813. IOPC Funds (2013). Claims Manual. October 2013 Edition.
  • Jalkanen, J.-P., Johansson, L., Brink, A., Kalli, J., Kukkonen, J. and Stipa, T. (2012). Extension of an assessment model of ship traffic exhaust emissions for particulate matter and carbon monoxide, Atmospheric Chemistry and Physics 12: 2641-2659.
  • Jolma, A., Lehikoinen, A., Helle, I. and Venesjärvi, R. (2014). A software system for assessing the spatially distributed ecological risk posed by oil shipping. Environmental Modelling & Software 61: 1–11.
  • Juntunen, T., Rosqvist, T., Rytkönen, J. and Kuikka, S. (2005). How to Model the Oil Combatting Technologies and Their Impacts on Ecosystem: a Bayesian Networks. Page CM 2005/S:2002 in 2005 ICES Annual Science Conference Aberdeen, United Kingdom.
  • Klemola, E., Kuronen, J., Kalli, J., Arola, T., Hänninen, M., Lehikoinen, A., Kuikka, S., Kujala, P. and Tapaninen, U. (2009). A cross-disciplinary approach to minimising the risks of maritime transport in the Gulf of Finland. World Review of Intermodal Transportation Research 2(4): 343–363.
  • Kokkonen, T., Ihaksi, T., Jolma, A. and Kuikka, S. (2010). Dynamic mapping of nature values to support prioritization of coastal oil combating. Environmental Modelling & Software, 25 (2010) 248–257.
  • Kuikka, S. and Varis, O. (1997). Uncertainties of climatic change impacts in Finnish watersheds: a Bayesian network analysis of expert knowledge. Boreal Environment Research 2: 109–128.
  • Kuikka, S., Vanhatalo, J., Pulkkinen, H., Mäntyniemi, S. and Corander J. (2014). Experiences in Bayesian Inference in Baltic Salmon Management. Statistical Science 29(1): 42–49.
  • Kummu, M., Gerten, D., Heinke, J., Konzmann, M. & Varis, O. (2014). Climate-driven interannual variability of water scarcity in food production potential: A global analysis. Hydrology and Earth System Sciences 18: 447–461.
  • Lecklin, T., Ryömä, R. and Kuikka, S. (2011). A Bayesian network for analyzing biological acute and long-term impacts of an oil spill in the Gulf of Finland. Marine Pollution Bulletin 62: 2822–2835.
  • Lehikoinen, A., Luoma, E., Mäntyniemi, S. and Kuikka, S. (2013) Optimizing the Recovery Efficiency of Finnish Oil Combating Vessels in the Gulf of Finland Using Bayesian Networks. Environmental Science and Technology, 47(4):1792-1799. DOI: 10.1021/es303634f
  • Lehikoinen, A., Hänninen, M., Storgård, J., Luoma, E., Mäntyniemi, S. and Kuikka, S. (in print). A Bayesian Network for Assessing the Collision Induced Risk of an Oil Accident in the Gulf of Finland. Environmental Science and Technology. DOI: 10.1021/es501777g.
  • Mäntyniemi, S., Kuikka, S., Rahikainen, M., Kell, L.T. and Kaitala, V. (2009). The value of Information in fisheries management: North Sea herring as an example. ICES Journal of Marine Science 66: 2278–2283.
  • Pearl, J. 1995. Causal diagrams for empirical research. Biometrika 82: 669-688.
  • Pearl, J. 2000. Causality: Models, Reasoning, and Inference. Cambridge University Press, Cambridge.
  • Porkka, M., Kummu, M., Siebert, S. and Varis, O. (2013). From food insufficiency towards trade dependency: A historical analysis of global food availability. PLoS One DOI:10.1371/journal.pone.0082714.
  • Tapaninen, U. (2015). Suomen satamaverkko murroksessa – analyysi satamien erikoistumisesta ja lukumäärästä (The changing sea port network in Finland – an analysis of specialization and number of Finnish ports). Terra 127: 1.
  • Varis, O. & Kuikka, S. (1997). BENE-EIA: A Bayesian Approach to Expert Judgement Elicitation With Case Studies On Climatic Change Impacts on Surface Waters. Climatic Change 37: 539–563.

11 Interaction plan

11.1 Objectives of interaction

Since the objective of ILVES is to produce new techniques for policy design which relies on extensive use of scientific theory, datasets, literature and expert knowledge, the information is summarized within a probabilistic framework. The very basic philosophy of risk communication in the ILVES approach extends the conventional methods in science: our primary aim is to have an impact on policy. However, our work needs to be of a high scientific quality in order to justify the policy advice.

Our aims of interaction include several levels, and the objectives are internal and external. Because these two are tightly coupled, same means of interaction can be applied to both objectives.

The main objective of internal interaction is to ensure functioning communication between the consortium research teams. Successful working requires continuous flow of information between several partners, but in addition to this “traditional” interaction related to research work, the integration of knowledge in ILVES approach involves great deal of learning at many levels. Hence, we do not only aim at information flows between research teams but a more profound approach of mutual learning. Although this is a challenging task and may call for a new mindset, we trust this aim is achievable within the consortium.

The objectives of external interactions are manifold. As we aim at finding solutions that have international significance related to maritime safety and oil spill risk assessment, our objective is to develop new scientific methodologies to support policies enhancing reduction of greenhouse gases and increasing transport safety. At a national level, the specific features of Finnish transportation will be taken into account and the stakeholders operating in the field of transportation in Finland will be the key end-users of knowledge produced by ILVES.

11.2 Target group/stakeholders/partners

The partnership of the project include experts of emission modeling, security supply modeling and logistic solutions modeling, interdisciplinary risk analysis, policy design, economic research, insurance policy, legislation and risk communication (see consortium). The integration of knowledge in ILVES approach involves a great deal of interaction among all these disciplines, and to this end the information will be collected into one web based platform. We have a high number of letters of commitment from outsiders: City of Helsinki, Ministry of Traffic and Communication, Environment Ministry, The Waterway Association of Finland, the Finnish Border Guard, CISRO Australia.

The three main target groups will be:

1) POLICY-MAKERS:' the theme related decision-makers (politicians, authorities), which are operating in international, national and regional level. The proposal will improve significantly the possibilities to change maritime policy as a more science based policy. Therefor such a policy dialogue between policy-makers and scientist is essential. This group includes also operational authorities, e.g. oil rescue services.

2) MARITIME INDUSTRY: all representatives related to maritime transport; shipping companies, port operators, operators and transporters (road, railway), representatives from shipping technology, investors.

3) CITIZENS: NGO’s, product consumers, citizens. In the Fintrip program of the Ministry of Traffic and Communication, a network of research and innovation activities has been planned. ILVES consortium will contribute heavily to this activity by offering best available scientific tools for interdisciplinary risk analysis. The program defines that co-operation is needed in education, research, product development and export. Potential customers include international oil industry, insurance companies and shipping companies. ILVES will create scientific risk analysis products for these actors. Also, ILVES approach has potential to contribute to the long term programme of measures when national plans are revised. The time period covered by the national programmes is usually short, the next 5 years, whereas ILVES looks at 30 years to the future. Also at an international level the results of the research have importance for several actors. The review by Haapasaari et al. (accepted) on the best practices of risk governance in nuclear risk analysis framework will be used as an example to adapt the new approaches. When suggesting the risk governance for international maritime activities (called Blue Belt in EU’s White Paper ), we also use the good experiences obtained from EU Common Fisheries Policy, where the involvement of stakeholders to yearly policy decisions is well organized. The new ICES working group WGMABS, chaired by Sakari Kuikka, will be used as one way to disseminate the findings to society. HELCOM (Helsinki Commission) is an active customer for such advice. In addition, the assessment of oil spill risks related to inland waterways will offer the regional rescue services a database of ecological values that can be used in contingency planning and, in case of an oil spill, to allocate oil combating resources.

Stakeholders according to targeted societal impacts will be:

  1. Findings supporting the policy to achieve CO 2 emissions
    • Policy-makers international, national level
  2. Investments based suggested chain of creating new jobs along inland water ways
    • Regional representatives from the region of South-Karelia and other regions linked to the Lake Saimaa area
    • Regional authorities, policy-makers, maritime industry operating in inland waters, key representatives from industry using the transportation, citizens
  3. Improved state of environment
    • Policy makers, NGO’s, regional groups (e.g. environmental policy council of the region of Kymenlaakso), citizens
  4. Improving the interest to apply best practices in companies that create main risks
    • Industry representatives, Finnish Chambers of Commerce

The social network analysis is used to look how the information flows between stakeholders. The aim is to construct a scientifically based approach to plan effective dissemination.

11.3 Means of interaction

The main platform for interaction will be an interactive website. It will serve as the platform for internal communication and mutual learning. In the website, the end-users can test the policy options by using a decision support tool. In the interactive tool the objective settings are inquired from the users in such a way, that the decision model can rank the decision alternatives. This will create a learning database from the value weights of the stakeholders (task of Hugin company, Denmark) and citizens (separately for different groups). The interactive web pages will be set up at the onset of the project, and maintained also after the project closure.

The Enduser Advisory Board (EAB) will be established for the project. The EAB will be chaired by Dr Anita Mäkinen (Trafi), responsible for maritime and air traffic gas emissions. We use the risk governance lessons learned in aviation to help identifying risk governance options for maritime activities. This will be based on the active role of the Trafi agency, which is responsible for the traffic policy design in Finland. The EAB will be responsible for providing the formulation of relevant policy options and the probabilities for the likely implementation success of policies. The other invited EAB members include:

There will be tailored events for each target group: conferences and workshops to disseminate the scientific results and policy options compiled by the project, but also to raise awareness of the technical platform intended to facilitate the information exchange. In addition, engaging the stakeholders via EAB very early on ensures that the end-users and beneficiaries will have access to the latest progress of the project online.

Timing of the events will be planned to strengthen the existing events. For example, ILVES will arrange, together with ICES (International Council for the Exploration of the Sea) a yearly workshop for relevant stakeholder groups. The workshops will be related to the work of ICES WGMABS (ICES Working Group on Risks of Maritime Activities in the Baltic Sea), which is chaired by prof. Sakari Kuikka. The meetings will include participants from industry, NGO’s, policy makers and scientists. This activity is the basis of risk communication in ILVES. It is alreadyagreed that WGMABS and ILVES consortium will arrange together the next ICES WGMABS workshop in 2016. Media relations will be established based on the existing strong networks of partner consortium. Instead of one-way communication, an interactive approach will be created: this will include the web platform for information sharing, but also engaging the stakeholders (and also the general public) via social media.

We will also build on the art in risk communication. Cartoon artist and humorist Seppo Leinonen (sepponet.fi) will provide material how the humor may open ways to communicate risks. It is especially important for human cognition, that we understand the causalities correctly, as otherwise we cannot link the observations to hypotheses. We need communication that creates an interest to understand causalities, and supports the hidden intuitive understanding of causalities in our imagination.

It is often said that there is no way to impact human values, which are said to come from home’s values and atmospheres. Here, we will use the beauty of Baltic Sea as a potentially effective, and likely the only that matters, way to have any imact on people’s values. Do I love the ocean or just the city life with movies representing Artificial Reality (AR)? It is also said that humor can break barriers that would otherwise be difficult to break. Tht may be the case e.g. between NGO and industry frank communication of risks. Here, we will apply, as a small test, the humor by clownery. The skill to be funny is based on the fact that a human will find her funny features by interactive processes. This is based on a summer school in France, where a student in theater articulacy, is taking part.

11.4 Responsibilities and implementation

Coordinator and KMRA are responsible for implementing the stakeholder communication plan. KMRA will be in charge of organizing the meetings and communication with end-users (Fig. 11). KMRA has operated in close collaboration with the maritime industry, universities, research organizations, institutes and authorities both nationally and internationally. KMRA has coordinated interdisciplinary projects where practical tools have been developed to support decision making. Most significant projects in the field have been 1) SAFGOF (ERDF funding) and 2) MIMIC (Central Baltic Interreg) where traffic growth scenarios, accident probabilities and biological information about consequences of oil accidents were combined to produce probabilistic risk maps; 3) OILRISK (Central Baltic Interreg) where the impacts of oil on coastal and marine species and habitats were evaluated and estimates how well certain nature values can be safeguarded with booms or protective sheets were given, and 4) TOPCONS (ENPI CBC) where a tool to support maritime spatial planning was developed. KMRA has been the responsible partner for coordination, internal and external communication and information/publicity activities in these projects.

In ILVES consortium the role of KMRA is to communicate and disseminate the essential findings of the project to the target groups and end users within the maritime sector. KMRA will organize all meetings and communicate with end-users, and also save the communication records from meetings related to relevance and understand ability of Bayesian inference.

11.5 Schedule

ILVES consortium will make a detailed plan of the interaction activities. The interaction will start by creating the web-based platform for communication and data exchange. This platform will be further developed during the project and serve as the platform for end-users to test the models and to collect information from them.

The first version of the model will be designed by the end of the first year of the project. To this first version a conceptual framework will created which will be elaborated in further communication and feedback with the stakeholders and experts.

The second, updated version will be available by the end of the third year. This version will be used for policy analysis and evaluation during the fourth year. The evaluations will be carried out in co-operation with the end-users in order to elaborate the dependencies and causalities of the model.

This tested version will be updated during the last two years and to decrease the scientific uncertainties to a minimum. The main forum for raising awareness among policy-makers and authorities will be the Enduser Advisory Board (EAB). During the project, raising the awareness of the project among stakeholders, end-users and also among general public will be carried out by organizing tailored events (conferences, workshops, public events) and informing them over the internet, including social media.

Solution and know-how sharing will be taken on a practical level by applying the latest information collected by the project on training and information activities targeted towards the maritime sector.

After the project, the internet platform will remain open and the lessons learned will be available to all interested parties.