Climate change policies and health in Kuopio

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Scope

Question

What are potential climate policies that reach the greenhouse emission targets in the city of Kuopio for years 2010-2030? What are their effects on health and well-being, and what recommendations can be given based on this? The national greenhouse emission target is to reduce greenhouse gas emissions by 20 % between 1990 and 2020; the city of Kuopio has its own, more ambitious target of 40 % for the same time period.

Boundaries

• Time: Year 2010 - 2030
• Spatial: Activities in Kuopio, Finland. Health and well-being effects due to policies anywhere, e.g. fine particle emissions in Kuopio increase cardiovascular mortality all over Finland.

Scenarios

• Two scenarios for each climate policy (which have not been defined yet): policy is A) implemented, B) not implemented.

Intended users

• The city of Kuopio.
• Other cities in Urgenche.
• Urgenche researchers are users from the methodological point of view.

Participants

• Main participants:
  • City of Kuopio: Erkki Pärjälä, Mikko Savastola
  • THL: Marjo Niittynen, Jouni Tuomisto, Matti Jantunen.
• Other participants:
  • University of Exeter
  • Universität Stuttgart
  • Other Urgenche research groups
  • Other Urgenche cities

Answer

Results

Not yet available.

Conclusions

The target of 40 % GHG reduction seems to be unrealistically ambitious.

Rationale

This diagram is just a placeholder. A new file should be uploaded, because this is about another assessment.

+ Show code- Hide code library(OpasnetBaseUtils) library(xtable) n <- 5 #################### Defines the S4 class "oassessment" which is the object type for open assessments. setClass( "oassessment", representation( names = "data.frame", decisionmakers = "data.frame" ) ) setMethod( f = "print", signature = signature("oassessment"), definition = function(x) { cat("Names\n") print(xtable(x@names), type = 'html') cat("Decisionmakers\n") print(xtable(x@decisionmakers), type = 'html') } ) setMethod( f = "print", signature = signature("ovariable"), definition = function(x) { cat("Sample\n") print(xtable(x@sample[x@sample$Iter %in% 1:4, ]), type = 'html') cat("Formula\n") print(x@formula) cat("Dependencies\n") print(x@dependencies) } ) names.Kuopio <- tidy(op_baseGetData("opasnet_base", "Op_en5461")) # Download the object names and identifiers. ccph.Kuopio <- new("oassessment", names = names.Kuopio, decisionmakers = data.frame( Decisionmaker = rep("City of Kuopio", 6), Decision = c( "Emission policy", "City energy efficiency plan", "Active transport plan", "Renewable energy plan", "Awareness in city", "General awareness" ), Outcome = rep("Greenhouse gas emissions in Kuopio", 6), Value = "A placeholder until we know how to use it." ) ) for(x in 1:nrow(ccph.Kuopio@names)) { # Objects with names as aliases are created and filled with data from Opasnet Base. assign(as.character(ccph.Kuopio@names$Result[x]), make.ovariable(tidy(op_baseGetData("opasnet_base", ccph.Kuopio@names$Identifier[x])))) } print(decision) #education #MAF #cost.curve #transformation print(energy.balance) #GHG.emission print(ccph.Kuopio)

----#: . Function tidy should reshape data.frame so that Unit and Amount would make two columns instead of being in the long format. --Jouni 11:23, 1 May 2012 (EEST) (type: truth; paradigms: science: comment)

----#: . We need several pages for an assessment: the main page contains the variable list; /Probabilities contains the P(B) values; /Values contains the assessment-specific and decision-specific value judgements (whatever they are); /Decisionmakers contains the list of decisionmakers, decisions and outcomes. (Could /Values and /Decisionmakers be merged?) --Jouni 11:23, 1 May 2012 (EEST) (type: truth; paradigms: science: comment)

----#: . For each ovariable, we must include the code "formula" from the variable page. Formula includes both formula and dependencies for the variable. Then, these are used when using make.ovariable. The names should be like formula.Op_en2345 and dependencies.Op_en2345. --Jouni 11:04, 1 May 2012 (EEST) (type: truth; paradigms: science: comment)

⇤--#: . Slot "objects" is not needed, as variables are managed with their aliases as separate objects (ovariables). --Jouni 11:04, 1 May 2012 (EEST) (type: truth; paradigms: science: attack)

----#: . oassessment@decisionmakers$Value is needed but it is not quite clear how it should be used. It is some kind of scenario tool for Outcome, as different decisionmakers may have different values. Should it be an index in a value variable? --Jouni 08:55, 1 May 2012 (EEST) (type: truth; paradigms: science: comment)

----#: . Päätöksenteon sokea piste: se mitä ihmiset eivät näe mutta eivät myöskään huomaa etteivät näe. Kuitenkin tutkimalla sitä mitä mitä ihmiset eivät näe saadaan selville asioita sokeasta pisteesta. Ymmärtämällä sokeaa pistettä voidaan keksiä asioita jotka järjestelmällisesti jäävät huomaamatta ja asioita, joilla voidaan korjata järjestelmällisiä puutteita. Avoin arviointi on tämmöinen päätöksenteon järjestelmällisten puutteiden korjausmekanismi. --Jouni 08:55, 1 May 2012 (EEST) (type: truth; paradigms: science: comment)

Decision variables

• Climate policy 1
• Climate policy 2...

Indicators

• Cardiovascular mortality
• Pulmonar mortality
• Well-being...

Assessment-specific data

Climate change policies and health in Kuopio(-)

Obs	Name	Identifier	Alias
1	Climate change policies in Kuopio	Op_en5466	decision
2	Climate education in Kuopio	Op_en5582	education
3	Market allocation factor	Op_en5535	MAF
4	Cost curves for energy	Op_en5478	cost.curve
5	Energy transformations	Op_en5472	transformation
6	Energy balance in Kuopio	Op_en5469	energy.balance
7	Greenhouse gas emissions in Kuopio	Op_en5483	GHG.emission

Formula

Addition of biofuels in transportation (ktoe/a): How much energy is saved from the heating of buildings, compared with 2010? (%): + Show code- Hide code library(OpasnetBaseUtils) library(ggplot2) library(xtable) cat("Loading functions and data.\n") city <- "Kuopio" energy <- summary.bring("Op_en5473") # Category:Energy balance tran <- tidy(op_baseGetData("opasnet_base", "Op_en5472")) # Energy transformations classes <- tidy(op_baseGetData("opasnet_base", "Op_en5476")) # Energy consumption classes cat("Running model.\n") energy <- energy[energy$Place == city, ] energy$Amount <- as.numeric(energy$Amount) # The policy changes are implemented. energy.stra <- energy energy.stra$Amount <- ifelse(energy.stra$Transformation == "Traffic biofuel production" & energy.stra$Fuel == "Petrochemical products", energy.stra$Amount + biofuels, energy.stra$Amount) energy.stra$Amount <- ifelse(energy.stra$Process == "Heating" & energy.stra$Use == "Output", energy$Amount * (1 - heatsave / 100), energy.stra$Amount) energy <- rbind(data.frame(Action = "BAU", energy), data.frame(Action = "Policy", energy.stra)) #### The transformation processes are included. tran <- reshape(tran, idvar = c("Process", "Transformation", "Use", "Fuel"), timevar = "Observation", direction = "wide") colnames(tran) <- gsub("Result.", "", colnames(tran)) tran$Amount <- as.numeric(tran$Amount) # Calculate conversion factors. factors <- merge(tran, energy, by = c("Process", "Transformation", "Use", "Fuel")) mwh2ktoe <- 3600 / (35 * 1000) # 1 MWh = 3600 MWs / (35 MJ / kgoe * 1000 kgoe/toe) factors$Amount.x <- factors$Amount.y / (factors$Amount.x * mwh2ktoe) # Add conversion factors to the energy table. energy <- merge(tran, factors, by = c("Process", "Transformation"), all.x = TRUE) energy$Amount <- energy$Amount * energy$Amount.x energy <- energy[!colnames(energy) %in% c("Use.y", "Fuel.y", "Unit.x", "Amount.x", "Unit.y", "Amount.y")] colnames(energy) <- gsub(".x", "", colnames(energy)) # Convert energy values to ktoe. energy$Amount <- ifelse(energy$Unit == "MWh", energy$Amount * mwh2ktoe, energy$Amount) energy[energy$Unit == "MWh", "Unit"] <- "ktoe" fuels <- c("Coal and peat", "Crude oil", "Petrochemical products", "Gas", "Nuclear", "Hydro", "Geothermal solar wind", "Renewables and waste", "Electricity", "Heat") # Categorise energy to standard energy processes and calculate sums for each process. out <- merge(energy, classes, by.x = "Process", by.y = "Result") out <- as.data.frame(as.table(tapply(out$Amount, out[c("Class", "Fuel", "Use", "Action")], sum))) out <- out[!is.na(out$Freq) & out$Fuel %in% fuels, ] # & out$Use == "Input", ] print(xtable(out), type = 'html') # NOW how do we tell the energy need in the balance sheet? ggplot(energy[energy$Fuel == "CO2e", ], aes(x = Action, weight = Amount, fill = Process)) +geom_bar(position = "Stack") ggplot(energy[energy$Fuel == "PM2.5", ], aes(x = Action, weight = Amount, fill = Process)) +geom_bar(position = "Stack") ggplot(energy[energy$Fuel == "Ash", ], aes(x = Action, weight = Amount, fill = Process)) +geom_bar(position = "Stack") ggplot(energy[energy$Fuel == "Heat", ], aes(x = Action, weight = Amount, fill = Process)) +geom_bar(position = "Stack")

----#: . This is a city-specific copy of the original code: Energy balance. --Jouni 06:52, 27 January 2012 (EET) (type: truth; paradigms: science: comment)

Analyses

• Decision analysis on each policy: Which option minimises the health risks?
• Value-of-information analysis for each policy about the major variables in the model and the total VOI.

See also

Urgenche research project 2011 - 2014: city-level climate change mitigation

Urgenche pages Urgenche main page · Category:Urgenche · Urgenche project page (password-protected) Relevant data Building stock data in Urgenche‎ · Building regulations in Finland · Concentration-response to PM2.5 · Emission factors for burning processes · ERF of indoor dampness on respiratory health effects · ERF of several environmental pollutions · General criteria for land use · Indoor environment quality (IEQ) factors · Intake fractions of PM · Land use in Urgenche · Land use and boundary in Urgenche · Energy use of buildings Relevant methods Building model · Energy balance · Health impact assessment · Opasnet map · Help:Drawing graphs · OpasnetUtils‎ · Recommended R functions‎ · Using summary tables‎ City Kuopio Climate change policies and health in Kuopio (assessment) · Climate change policies in Kuopio (plausible city-level climate policies) · Health impacts of energy consumption in Kuopio · Building stock in Kuopio · Cost curves for energy (prioritization of options) · Energy balance in Kuopio (energy data) · Energy consumption and GHG emissions in Kuopio by sector · Energy consumption classes (categorisation) · Energy consumption of heating of buildings in Kuopio · Energy transformations (energy production and use processes) · Fuels used by Haapaniemi energy plant · Greenhouse gas emissions in Kuopio · Haapaniemi energy plant in Kuopio · Land use in Kuopio · Building data availability in Kuopio · Password-protected pages: File:Heat use in Kuopio.csv · Kuopio housing City Basel Buildings in Basel (password-protected) Energy balances Energy balance in Basel · Energy balance in Kuopio · Energy balance in Stuttgart · Energy balance in Suzhou

The average usage of firewood in certain types of houses according to the wain heating system in 2000-2001 in Finland (m^3/household/year)

Main heating system		Type of estate
		Detached house	Farm	Free-time place	Total
Stove heating		7.1	10	2	4.4
Central heating	Wood	13.7	25.6	..	18.4
	Oil	1.8	8.2	..	2.3
	Electricity	2.5	7.8	..	2.8
Straight electric heating		2.9	6.5	1.6	2.7
District heating		1.1	..	..	1.2
All		3.8	14.4	1.8	4.4

Related files

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