User:Sakari Kuikka

From Opasnet
Jump to navigation Jump to search

Background documents for public

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

Ilves all hand made pictures for proposal

ILVES master version 4 Curret version of research plan:

Talk on ILVES approach

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

ILVES ----#: . (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) --Jouni (talk) 04:21, 22 April 2015 (UTC) (type: truth; paradigms: science: comment)

Consortium 29.4.2015 Project full title: Ilmastomyönteiset ja vähäriskiset kuljetusvaihtoehdot

Project applicants and responsible persons

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 Barry Commonwealth Scientific and Industrial Research 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?


The overall approach of ILVES.

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.

2) Rationale

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 (?? RL: I don't think this proposal is going to slow down climate change - is it? you might have to be more explicit if it is). 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 the goals defined in the EU White paper on transport (EC, 2011) could be achieved from the point of view of Finnish society.

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.

To assess the combination of actions which lead to GHG emission reductions from transport sector 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, 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 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.

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 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.

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.

For this proposal, we have improved the FEM team by recruiting more skills from UH and Finnish universities and international scientific bodies. 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 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.
  • 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.
  • 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, 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,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.
  • 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]
  • 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,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
  • 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.
PHOTO OF THE ICE AND BREAKER

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

3 Societal significance and impact

Transport is fundamental to our economy and society. From a logistics point of view, Finland is an island, and our export and import takes place by 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 be learned from aviation 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 GHG emission reduction 2) investments based on suggested chain of creating new jobs along inland water ways ??? JPJ: DO WE MEAN CREATING JOBS ON GEOGRAPHICAL LOCATIONS NEAR THE INLAND WATERWAYS OR ARE WE SPEAKING ON A MORE GENERAL LEVEL "CREATING NEW JOBS UTILIZING THE INLAND WATERWAYS" 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 in 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. ??? JPJ: I DO NOT GET THE CONNECTION BETWEEN FOOD PRODUCTION AND GHG REDUCTIONS FROM TRANSPORT SECTOR 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

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

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 GHG emission reduction targets for the Finnish fleets, 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 (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.

Topic 3: Data Analysis and Algorithms

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.

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.

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

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. ??? JPJ: AN IDEA THAT CAME IN TO MY MIND. IS IT POSSIBLE TO USE LNG TO PROPEL THE INLAND WATERWAY TRAFFIC. THIS WOULD REDUCE THE RISK OF SPILLS UNLESS THE CARGO ITSELF IS OIL. 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.

Topic 5: Planning the Future Policy Options

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) and Ship Traffic Emission Assessment Model (Jalkanen et al. (2009, 2012) and Johansson et al. (2013)). The impacts of ship emissions will be evaluated using chemical transport modeling (Jonson et al. (2015), Sofiev (2006), Soares et al (2014)). 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.

Topic 6: Software Devolopment

Tähän jotain


----#: . Slide --Jouni (talk) 04:21, 22 April 2015 (UTC) (type: truth; paradigms: science: comment)

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.

----#: . --Jouni (talk) 04:21, 22 April 2015 (UTC) (type: truth; paradigms: science: comment)

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.

We utilize the existing large databases of:

  1. VATT and Bank of Finland for the economic data
  2. vessel databases of TRAFI
  3. traffic databases of TRAFI, already analysised in TUT modelling of CO2 emissions (REF)
  4. CO2 footrpint estimates of SYKE and University of Oulu (Jyri: insert here the name of the expert or databases)
  5. fish stock estimates of ICES (International Council For the Exploration of the Sea) for the impacts on stocks and fisheries
  6. 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
  7. bird databases of SYKE
  8. TRAFI databases from WGMABS report
  9. Ship emission, pollutant transport and numerical weather prediction datasets of the Finnish Meteorological Institute
  10. 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
  11. 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.

  1. 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
  2. Underwater multi beam survey data, Meritaito Oy/Liikennevirasto availability not confirmed yeT

ALL PARTNERS: list here the databases you are going to use 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.

In expert elicitation, we use the following experts:

  1. 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

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). ----#: . In an email of xxth April 2015, ICES officer Maria dd wrote that CCCCCCCCCCC, copy from email here --Jouni (talk) 04:21, 22 April 2015 (UTC) (type: truth; paradigms: science: comment)

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.

7 Implementation: schedule, budget, distribution of work

WP1

WP1) Analysis of historical data and meta-analysis of publications: information to predict:

Description of work and role of participants

In expert elicitation, we use the following experts: 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.

D 1.1y 1,D 1.1y 1.1y D 1.1y 5 In this task,

WP2

WP 2 Structuring Decision model , inserting policy causal knowledge and value-of-information analysis of prior knowledge

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 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


Objectives 1) Law impact in p

Description of work and role of participants 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 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

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


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. 

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

Beth Fulton is the principal developer and leader of the ecosystem model Atlantis, one of the best tools available for strategic evaluation of marine fisheries management issues. Atlantis is a whole-of-ecosystem modelling framework, one of the first to give equal weight to the biophysical and human components of the system. The Australian Fisheries Management Authority, together with fisheries managers in 19 international marine ecosystems, use Atlantis for fisheries management. The UN Food and Agriculture Organisation rated Atlantis the best in the world for the strategic evaluation of marine management. An Atlantis model for the Baltic, which is currently being developed will be used to capture the extent of human use in the Baltic, including oil and gas, transport, tourism, and commercial and recreational fishing, and the potential effects of cumulative and catastrophic effects on the marine ecosystem. Led by Beth, her team of modelers and marine ecosystem specialists will explore the impacts and effectiveness of management on the marine and coastal environment.

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. Atlantis will capture the effects of PAHs under an oil-spill event to produce informed priors of the effect on the marine ecosystem.

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. ⇤--#: . What is this text? --Jouni (talk) 14:22, 27 April 2015 (UTC) (type: truth; paradigms: science: attack)

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
----#: . 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 --Jouni (talk) 04:21, 22 April 2015 (UTC) (type: truth; paradigms: science: comment)
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).


⇤--#: . The text below seems to belong to some workpackages. Where? --Jouni (talk) 12:35, 24 April 2015 (UTC) (type: truth; paradigms: science: attack)

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 ----#: . (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. --Jouni (talk) 04:21, 22 April 2015 (UTC) (type: truth; paradigms: science: comment)

----#: . Jyri: FMEA: causal learning in risk management (link to Duke, have text from there) --Jouni (talk) 04:21, 22 April 2015 (UTC) (type: truth; paradigms: science: comment)

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

----#: . * 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. --Jouni (talk) 04:21, 22 April 2015 (UTC) (type: truth; paradigms: science: comment)

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.

9 Mobility plan

----#: . 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 --Jouni (talk) 04:21, 22 April 2015 (UTC) (type: truth; paradigms: science: comment)

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.

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.

10 Key literature

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.

Damgaard, C. and Weiner, J. (2000). Describing inequality in plant size or fecundity. Ecology 81: 1139-1142.

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.

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.

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.

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/

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. Submitted to 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., 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.

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/

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.

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.

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.

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.

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: 248–257.

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]

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.

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.

Liimatainen, H. (2010). Shippers’ Views on Environmental Reporting of Logistics and Implications for 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 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 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). 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 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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.


----#: . 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 --Jouni (talk) 04:21, 22 April 2015 (UTC) (type: truth; paradigms: science: comment)

THE LAST 4 PAGES:

11 Interaction plan

11.1 Objectives of interaction

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

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.


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.

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

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

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

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 )

Science trucks: are these eatables or what ?

Theatre, drama: Pihla’s supervisor in France, Pihla’s school

Other art channels: Tuula, Seppo, Pihla’s group

How we learn scienfically and practically from these risk communication steps; UH cognition and communication sciences (Prof ff

11.2 Target group/stakeholders/partners

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, .

We have commitments of interests from xx (link, dd, ee, cc, ff, etc.

ILVES Scientific advisory board (ISAB): Elja

ILVES Stakeholder Support Group (ISSG) defined

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. 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.

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.

  • 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
  • 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

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 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

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.

  • remember Jarnos methods and publications to estimate the areal alcohol consumption: could that be used to help in spatial analysis of impacts ?
  • 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

⇤--#: . This place is for responsibilities about the INTERACTION with stakeholders. WP tasks were moved to under the WPs. --Jouni (talk) 12:47, 24 April 2015 (UTC) (type: truth; paradigms: science: attack)

11.5 Schedule

saimaan pränddi arv

Retrieved from "https://en.opasnet.org/index.php?title=User:Sakari_Kuikka&oldid=36077"