Climate change policies and health in Kuopio

This page is a knowledge crystal of subtype assessment. The page identifier is Op_en5461
Moderator:Jouni (see all)

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Main message:

Question:

What are the most beneficial ways from public health point of view to reduce GHG emissions in Kuopio?

Answer:

The target of 40 % GHG reduction seems realistic due to reforms in Haapaniemi power plant, assuming that GHG emissions for wood-based fuel is 0. Life-cycle impacts of the wood-based fuel have not yet been estimated.

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.

Details of scoping
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 See climate scenarios from Climate change policies in Kuopio. 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, Arja Asikainen THL: Marjo Niittynen, Jouni Tuomisto, Matti Jantunen. Other participants: University of Exeter Universität Stuttgart Other Urgenche research groups Other Urgenche cities

Answer

Conclusions

The target of 40 % GHG reduction seems realistic due to reforms in Haapaniemi power plant, assuming that GHG emissions for wood-based fuel is 0. Life-cycle impacts of the wood-based fuel have not yet been estimated.

Results

Model version 2

This model version was used to produce the corrected manuscript in July 2015.

Model run 21.7.2015 runs to the end but emissions are too large exp for wood after 1980.
Model run 22.7.2015 Bugs with fuelShares fixed. Now results are similar to the ones in the manuscript. Except that health impacts are 2-3 times higher, only partly due to higher wood burning in the 2000's.
Model run 23.7.2015 archived version. Also renovationShares and changeBuildings data corrected.
Model run 24.7.2015 archived version. This was used for the manuscript.

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### THIS CODE IS FROM PAGE [[Climate change policies and health in Kuopio]] (Op_en5461, code_name = "")
library(OpasnetUtils)
library(ggplot2)

### Technical parameters

openv.setN(0) # use medians instead of whole sampled distributions
objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]] findrest
BS <- 24 # base_size = font sixe in graphs
figstofile <- FALSE
saveobjects <- FALSE
finnish <- FALSE
suomenna <- function(ova) {
	if(class(ova) == "ovariable") out <- ova@output else out <- ova
	if("Heating" %in% colnames(out)) {
		out$Heating <- as.factor(out$Heating)
		levels(out$Heating)[levels(out$Heating) == "District heating"] <- "District"
	}
	if("Response" %in% colnames(out)) {
		out$Response <- as.factor(out$Response)
		levels(out$Response)[levels(out$Response) == "Cardiopulmonary mortality"] <- "Cardiopulmonary"
	}
	if("Pollutant" %in% colnames(out)) {
		out$Pollutant <- as.factor(out$Pollutant)
		levels(out$Pollutant)[levels(out$Pollutant) == "CO2trade"] <- "CO2official"
	}
	out$Time <- as.numeric(as.character(out$Time))
	return(out)
}

obstime <- Ovariable("obstime", data = data.frame(Obsyear = factor(seq(1920, 2050, 10), ordered = TRUE), Result = 1))

## Additional index needed in followup of ovariables efficiencyShares and stockBuildings

year <- Ovariable("year", data = data.frame(
	Constructed = factor(
		c("1799-1899", "1900-1909", "1910-1919", "1920-1929", "1930-1939", "1940-1949",
			"1950-1959", "1960-1969", "1970-1979", "1980-1989", "1990-1999",
			"2000-2010", "2011-2019", "2020-2029", "2030-2039", "2040-2049"
		), 
		ordered = TRUE
		), 
	Time = c(1880, 1910 + 0:14 * 10),
	Result = 1
))

###################### Decisions

decisions <- opbase.data('Op_en5461', subset = "Decisions") # [[Climate change policies and health in Kuopio]]

DecisionTableParser(decisions)

# Remove previous decisions, if any. 

forgetDecisions <- function() {
	for(i in ls(envir = openv)) {
		if("dec_check" %in% names(openv[[i]])) openv[[i]]$dec_check <- FALSE
	}
	return(cat("Decisions were forgotten.\n"))
}

forgetDecisions()

############################ IMPORT DATA AND MODELS

objects.latest("Op_en5417", code_name = "initiate") # [[Population of Kuopio]] 
# population: City_area
objects.latest("Op_en5932", code_name = "initiatetest") # [[Building stock in Kuopio]] Building ovariables: 
# buildingStock: Building, Constructed, City_area
# rateBuildings: Age, (RenovationPolicy)
# renovationShares: Renovation
# construction: Building
# constructionAreas: City_area
# buildingTypes: Building, Building2 
# heatingShares: Building, Heating, Eventyear
# heatingSharesNew: Building2, Heating
# eventyear: Constructed, Eventyear

###################### Actual building model
# The building stock is measured as m^2 floor area.

objects.latest("Op_en6289", code_name = "buildingstest") # [[Building model]] # Generic building model.

###################### Energy and emissions

objects.latest("Op_en5488", code_name = "energyUseAnnual") # [[Energy use of buildings]] energyUse
objects.latest("Op_en5488", code_name = "efficiencyShares") # [[Energy use of buildings]] 
objects.latest("Op_en2791", code_name = "emissionstest") # [[Emission factors for burning processes]] 
objects.latest("Op_en2791", code_name = "emissionFactors") # [[Emission factors for burning processes]] 
objects.latest("Op_en7328", code_name = "emissionLocations") # [[Kuopio energy production]]
objects.latest("Op_en7328", code_name = "fuelShares") # [[Kuopio energy production]]
objects.latest("Op_en5141", code_name = "fuelUse") # [[Energy balance]]

## Exposure

objects.latest("Op_en5813", code_name = "exposure") # [[Intake fractions of PM]] uses Humbert iF as default.

###################### Health assessment

objects.latest('Op_en2261', code_name = 'totcases') # [[Health impact assessment]] totcases and dependencies. 
objects.latest('Op_en5461', code_name = 'DALYs') # [[Climate change policies and health in Kuopio]] DALYs, DW, L

frexposed <- 1 # fraction of population that is exposed
bgexposure <- 0 # Background exposure to an agent (a level below which you cannot get in practice)
BW <- 70 # Body weight (is needed for RR calculations although it is irrelevant for PM2.5)

##################### CALCULATIONS

renovationRate <- EvalOutput(renovationRate) * 10 # Rates for 10-year periods
renovationRate@marginal[colnames(renovationRate@output) == "Age"] <- TRUE
renovationShares <- EvalOutput(renovationShares)
colnames(renovationShares@output)[colnames(renovationShares@output) == "Startyear"] <- "Obsyear"
stockBuildings <- EvalOutput(stockBuildings)
stockBuildings <- oapply(stockBuildings, cols = c("City_area"), FUN = sum)
changeBuildings <- EvalOutput(changeBuildings)
changeBuildings <- oapply(changeBuildings, cols = c("City_area"), FUN = sum)

buildings <- EvalOutput(buildings)

buildings@output$RenovationPolicy <- factor(
	buildings@output$RenovationPolicy,
	levels = c("BAU", "Active renovation", "Effective renovation"),
	ordered = TRUE
)

buildings@output$EfficiencyPolicy <- factor(
	buildings@output$EfficiencyPolicy,
	levels = c("BAU", "Active efficiency"),
	ordered = TRUE
)

energyUse <- EvalOutput(energyUse)
fuelUse <- EvalOutput(fuelUse)
fuelUse <- fuelUse * 1E-3 *3600 # kWh -> MJ

emissions <- EvalOutput(emissions)

population <- 1E+5 # stockBuildings is using another population to divide floor area into City areas.

exposure <- EvalOutput(exposure)
exposure@output <- exposure@output[exposure@output$Area == "Average" , ] # Kuopio is an average area,
	# rather than rural or urban.

totcases <- EvalOutput(totcases)
totcases <- oapply(totcases, cols = c("Age", "Sex"), FUN = sum)

DALYs <- EvalOutput(DALYs)

###################### GRAPHS AND OUTPUTS

bui <- suomenna(oapply(buildings * 1E-6, cols = c("City_area", "buildingsSource"), FUN = sum))

ggplot(subset(bui, RenovationPolicy == "BAU" & EfficiencyPolicy == "BAU"), aes(x = Time, weight = buildingsResult, fill = Heating)) + geom_bar(binwidth = 5) + 
	theme_gray(base_size = BS) +
	labs(
		title = "Building stock in Kuopio",
		x = "Time",
		y = "Floor area (M m2)"
	)

if(figstofile) ggsave("Figure3.eps", width = 8, height = 7)

ggplot(bui, aes(x = Time, weight = buildingsResult, fill = Building))+geom_bar()+facet_grid(Efficiency~Heating)

ggplot(subset(bui, EfficiencyPolicy == "BAU"), aes(x = Time, weight = buildingsResult, fill = Renovation)) + 
geom_bar(binwidth = 5) + 
	facet_grid(. ~ RenovationPolicy) + theme_gray(base_size = BS) +
	labs(
		title = "Building stock in Kuopio by renovation policy",
		x = "Time",
		y = "Floor area (M m2)"
	)

ggplot(subset(bui, RenovationPolicy == "BAU"), aes(x = Time, weight = buildingsResult, fill = Efficiency)) + geom_bar(binwidth = 5) + 
	facet_grid(. ~ EfficiencyPolicy) + theme_gray(base_size = BS) +
	labs(
		title = "Building stock in Kuopio by efficiency policy",
		x = "Time",
		y = "Floor area (M m2)"
	)

ggplot(subset(bui, RenovationPolicy == "BAU" & EfficiencyPolicy == "BAU"), aes(x = Time, weight = buildingsResult, fill = Building)) + geom_bar(binwidth = 5) + 
	theme_gray(base_size = BS) +
	labs(
		title = "Building stock in Kuopio",
		x = "Time",
		y = "Floor area (M m2)"
	)


ggplot(subset(suomenna(energyUse), EfficiencyPolicy == "BAU"), aes(x = Time, weight = energyUseResult * 1E-6, fill = Heating)) + geom_bar(binwidth = 5) +
	facet_wrap( ~ RenovationPolicy) + theme_gray(base_size = BS) +
	labs(
		title = "Energy used in heating in Kuopio",
		x = "Time",
		y = "Heating energy (GWh /a)"
	)

if(figstofile) ggsave("Figure4.eps", width = 11, height = 7)

ggplot(suomenna(energyUse), aes(x = Time, weight = energyUseResult * 1E-6, fill = Heating)) + geom_bar(binwidth = 5) +
	facet_grid(EfficiencyPolicy ~ RenovationPolicy) + theme_gray(base_size = BS) +
	labs(
		title = "Energy used in heating in Kuopio",
		x = "Time",
		y = "Heating energy (GWh /a)"
	)

emis <- suomenna(truncateIndex(emissions, cols = "Fuel", bins = 5))

ggplot(subset(emis, EfficiencyPolicy == "BAU" & RenovationPolicy == "BAU" & Pollutant != "CO2eq"), aes(x = Time, weight = emissionsResult, fill = Fuel)) + geom_bar(binwidth = 5) +
	facet_grid(Pollutant ~ FuelPolicy, scale = "free_y") + theme_gray(base_size = BS) +
	labs(
		title = "Emissions from heating in Kuopio",
		x = "Time",
		y = "Emissions (ton /a)"
	)

if(figstofile) ggsave("Figure5.eps", width = 8, height = 7)

ggplot(subset(emis, EfficiencyPolicy == "BAU" & RenovationPolicy == "BAU"), aes(x = Time, weight = emissionsResult, fill = Fuel)) + geom_bar(binwidth = 5) +
	facet_grid(Pollutant ~ ., scale = "free_y") + theme_gray(base_size = BS) +#FuelPolicy
	labs(
		title = "Emissions from heating in Kuopio",
		x = "Time",
		y = "Emissions (ton /a)"
	)

ggplot(subset(emis, EfficiencyPolicy == "BAU" & FuelPolicy == "BAU"), aes(x = Time, weight = emissionsResult, fill = Emission_site)) + geom_bar(binwidth = 5) +
	facet_grid(Pollutant ~ RenovationPolicy, scale = "free_y") + theme_gray(base_size = BS) +
	labs(
		title = "Emissions from heating in Kuopio",
		x = "Time",
		y = "Emissions (ton /a)"
	)

ggplot(subset(emis, EfficiencyPolicy == "BAU" & FuelPolicy == "BAU"), aes(x = Time, weight = emissionsResult, fill = Fuel)) + geom_bar(binwidth = 5) +
	facet_grid(Pollutant ~ RenovationPolicy, scale = "free_y") + theme_gray(base_size = BS) +
	labs(
		title = "Emissions from heating in Kuopio",
		x = "Time",
		y = "Emissions (ton /a)"
	)

ggplot(subset(suomenna(exposure), RenovationPolicy == "BAU" & EfficiencyPolicy == "BAU" & FuelPolicy == "BAU"), aes(x = Time, weight = exposureResult, fill = Heating)) + 
	geom_bar(binwidth = 5) + facet_grid(Area ~ Emission_height) + theme_gray(base_size = BS) +
	labs(
		title = "Exposure to PM2.5 from heating in Kuopio",
		x = "Time",
		y = "Average PM2.5 (µg/m3)"
	)

ggplot(subset(suomenna(exposure), EfficiencyPolicy == "BAU"), aes(x = Time, weight = exposureResult, fill = Heating)) + geom_bar(binwidth = 5) + 	facet_grid(FuelPolicy ~ RenovationPolicy) + theme_gray(base_size = BS) +
	labs(
		title = "Exposure to PM2.5 from heating in Kuopio",
		x = "Time",
		y = "Average PM2.5 (µg/m3)"
	)

ggplot(subset(suomenna(totcases), EfficiencyPolicy == "BAU" & FuelPolicy == "BAU"), aes(x = Time, weight = totcasesResult, fill = Heating))+geom_bar(binwidth = 5) + 
	facet_grid(Response ~ RenovationPolicy) +
	theme_gray(base_size = BS) +
	labs(
		title = "Health effects of PM2.5 from heating in Kuopio",
		x = "Time",
		y = "Health effects (deaths /a)"
	)

cat("Total DALYs/a by different combinations of policy options.\n")

dal <- subset(suomenna(DALYs), Response == "Total mortality")
oprint(aggregate(dal["DALYsResult"], by = dal[c("Time", "EfficiencyPolicy", "RenovationPolicy", "FuelPolicy")], FUN = sum))

ggplot(subset(dal, FuelPolicy == "BAU"), aes(x = Time, weight = DALYsResult, fill = Heating))+geom_bar(binwidth = 5) + 
	facet_grid(EfficiencyPolicy ~ RenovationPolicy) +
	theme_gray(base_size = BS) +
	labs(
		title = "Health effects in DALYs of PM2.5 from heating in Kuopio",
		x = "Time",
		y = "Health effects (DALY /a)"
	)

ggplot(subset(dal, Time == 2030), aes(x = RenovationPolicy, weight = DALYsResult, fill = Heating))+geom_bar() + 
	facet_grid(EfficiencyPolicy ~ FuelPolicy) +
	theme_gray(base_size = BS) +
	labs(
		title = "Health effects in DALYs of PM2.5 from heating in Kuopio 2030",
		x = "Biofuel policy in district heating",
		y = "Health effects (DALY /a)"
	)

######## Buildings in Kuopio on map
if(FALSE){
# Calculate locations for Kuopio districts

temp <- buildings
temp@output <- subset(temp@output,
	Time == 2030 & EfficiencyPolicy == "BAU" & RenovationPolicy == "BAU"
)
temp <- unkeep(temp, sources = TRUE, prevresults = TRUE)
temp <- oapply(temp, cols = c("Building", "Heating", "Efficiency", "Renovation"), FUN = sum)

####!------------------------------------------------
districts <- tidy(opbase.data("Op_en5932.kuopio_city_districts"), widecol = "Location") # [[Building stock in Kuopio]] 
####i------------------------------------------------

colnames(districts) <- gsub("[ \\.]", "_", colnames(districts))
districts <- Ovariable("districts", data = data.frame(districts, Result = 1))

temp <- temp * districts

MyRmap(
	ova2spat(
		temp, 
		coord = c("E", "N"), 
		proj4string = "+init=epsg:3067"
	), # National Land Survey uses EPSG:3067 (ETRS-TM35FIN)
	plotvar = "Result", 
	legend_title = "Floor area", 
	numbins = 8, 
	pch = 19, 
	cex = 2
)
}

if(saveobjects) {
	objects.put(list = ls())
	cat(c("All objects archived. Write down the key of the run to retrieve them with objects.get. Objects: ", 
ls(), "\n"))
}

Sensitivity analysis

Sensitivity analysis 26.7.2015 with 750 iterations

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### THIS CODE IS FROM PAGE [[Climate change policies and health in Kuopio]] (Op_en5461, code_name = "")
library(OpasnetUtils)
library(ggplot2)

### Technical parameters
openv.setN(num)
#rm(list = ls()) # Remove existing objects (necessary on your own computer)
saveobjects <- FALSE
objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]] findrest

obstime <- Ovariable("obstime", data = data.frame(Obsyear = factor(seq(2010, 2030, 10), ordered = TRUE), Result = 1))

## Additional index needed in followup of ovariables efficiencyShares and stockBuildings

year <- Ovariable("year", data = data.frame(
	Constructed = factor(
		c("1799-1899", "1900-1909", "1910-1919", "1920-1929", "1930-1939", "1940-1949",
			"1950-1959", "1960-1969", "1970-1979", "1980-1989", "1990-1999",
			"2000-2010", "2011-2019", "2020-2029", "2030-2039", "2040-2049"
		), 
		ordered = TRUE
		), 
	Time = c(1880, 1910 + 0:14 * 10),
	Result = 1
))

###################### Decisions

decisions <- opbase.data('Op_en5461', subset = "Decisions") # [[Climate change policies and health in Kuopio]]

DecisionTableParser(decisions)

# Remove previous decisions, if any. 

forgetDecisions <- function() {
	for(i in ls(envir = openv)) {
		if("dec_check" %in% names(openv[[i]])) openv[[i]]$dec_check <- FALSE
	}
	return(cat("Decisions were forgotten.\n"))
}

forgetDecisions()

############################ IMPORT DATA AND MODELS

objects.latest("Op_en5417", code_name = "initiate") # [[Population of Kuopio]] 
objects.latest("Op_en5932", code_name = "initiatetest") # [[Building stock in Kuopio]] Building ovariables: 
objects.latest("Op_en6289", code_name = "buildingstest") # [[Building model]] # Generic building model.

###################### Energy and emissions

objects.latest("Op_en5488", code_name = "energyUseAnnual") # [[Energy use of buildings]] energyUse
objects.latest("Op_en5488", code_name = "efficiencyShares") # [[Energy use of buildings]] 
objects.latest("Op_en2791", code_name = "emissionstest") # [[Emission factors for burning processes]] 
objects.latest("Op_en2791", code_name = "emissionFactors") # [[Emission factors for burning processes]] 
objects.latest("Op_en7328", code_name = "emissionLocations") # [[Kuopio energy production]]
objects.latest("Op_en7328", code_name = "fuelShares") # [[Kuopio energy production]]
objects.latest("Op_en5141", code_name = "fuelUse") # [[Energy balance]]

## Exposure and health assessment

objects.latest("Op_en5813", code_name = "exposure") # [[Intake fractions of PM]] uses Humbert iF as default.
objects.latest('Op_en2261', code_name = 'totcases') # [[Health impact assessment]] totcases and dependencies. 
objects.latest('Op_en5461', code_name = 'DALYs') # [[Climate change policies and health in Kuopio]] DALYs, DW, L

##################### CALCULATIONS

constructionAreas <- EvalOutput(constructionAreas)
constructionAreas@output$City_area <- "City centre"# We are not interested in locations in this analysis.
constructionAreas <- oapply(constructionAreas, cols = "", FUN = sum)
renovationRate <- EvalOutput(renovationRate) * 10 # Rates for 10-year periods
renovationShares <- EvalOutput(renovationShares)
stockBuildings <- EvalOutput(stockBuildings)
stockBuildings@output$City_area <- "City centre"
stockBuildings@output$Building <- "Apartment houses"
stockBuildings <- oapply(stockBuildings, cols = c(""), FUN = sum)
changeBuildings <- EvalOutput(changeBuildings)
changeBuildings@output$City_area <- "City centre"
changeBuildings@output$Building <- "Apartment houses"
changeBuildings@output <- changeBuildings@output[changeBuildings@output$EfficiencyPolicy == "BAU" , ]
changeBuildings <- oapply(changeBuildings, cols = c(""), FUN = sum)

buildings <- EvalOutput(buildings)
buildings@output <- buildings@output[buildings@output$Time == "2030" , ]
energyUse <- EvalOutput(energyUse)
energyUse <- oapply(energyUse, cols = c(
	"Efficiency",
	"Renovation"
), FUN = sum)
fuelUse <- EvalOutput(fuelUse)
fuelUse <- fuelUse * 1E-3 *3600 # kWh -> MJ
fuelUse <- oapply(fuelUse, cols = c(
	"Time"
), FUN = sum)
emissions <- EvalOutput(emissions)
emissions <- oapply(emissions, cols = c(
	"Fuel",
	"City_area",
	"Emission_site",
	"Heating"
), FUN = sum)

population <- 1E+5 # stockBuildings is using another population to divide floor area into City areas.

exposure <- EvalOutput(exposure)
exposure@output <- exposure@output[exposure@output$Area == "Average" , ] # Kuopio is an average area,
	# rather than rural or urban.
exposure <- oapply(exposure, cols = c(
    "Emission_height",
	"Area"
), FUN = sum)

totcases <- EvalOutput(totcases)
totcases <- oapply(totcases, cols = c("Age", "Sex"), FUN = sum)

DALYs <- EvalOutput(DALYs)

cost <- Ovariable("cost", 
	dependencies = data.frame(Name = c("DALYs", "emissions")),
	formula = function(...) {
		dals <- DALYs
		dals@output <- dals@output[dals@output$Time == "2030" , ]
		dals <- oapply(DALYs, INDEX = c("EfficiencyPolicy", "RenovationPolicy", "FuelPolicy", "Iter"), FUN = sum)
		emi <- emissions
		emi@output <- emi@output[emi@output$Pollutant == "CO2direct" & emi@output$Time == "2030" , ]
		emi <- oapply(emissions, INDEX = c("EfficiencyPolicy", "RenovationPolicy", "FuelPolicy", "Iter"), FUN = sum)
		cost <- dals * 50000 + emi * 15
		bau <- cost
		bau@output <- subset(bau@output, FuelPolicy == "BAU" & RenovationPolicy == "BAU" & EfficiencyPolicy == "BAU")
		bau <- unkeep(bau, cols = c( "EfficiencyPolicy", "RenovationPolicy", "FuelPolicy"), prevresults = TRUE)
		bau <- bau * Ovariable(
			output = data.frame(Objective = c("Direct", "BAU comparison"), Result = c(0, 1)),
			marginal = c(TRUE, FALSE)
		)
		cost <- cost - bau
		return(cost)
	}
)

t1 <- subset(construction@output, Building == "Apartment houses")
t2 <- subset(efficiencyRatio@output, Efficiency == "New")
t3 <- subset(efficiencyShares@output, Efficiency == "New" & Time == "2030" & EfficiencyPolicy == "BAU")
t4 <- subset(emissionFactors@output, Fuel == "Peat" & Pollutant == "PM2.5")
t5 <- subset(emissionFactors@output, Fuel == "Peat" & Pollutant == "CO2direct")
t6 <- subset(energyFactor@output, Building == "Apartment houses" & Heating == "District")
t7 <- subset(ERF@output, Exposure_agent == "PM2.5" & Response == "Total mortality")
t8 <- subset(heatingShares@output, Heating == "District" & Building == "Apartment houses" & Time == "2030")
t9 <- subset(renovationShares@output, RenovationPolicy == "Active renovation" & Renovation == "Sheath reform" & Obsyear == "2030")

testvariable <- Ovariable("testvariable", data = data.frame(
	Iter = c(
		t1$Iter,
		t2$Iter,
		t3$Iter,
		t4$Iter,
		t5$Iter,
		t6$Iter,
		t7$Iter,
		t8$Iter,
		t9$Iter
	),
	Variable = c(
		rep("Construction of apartment houses", openv$N),
		rep("Efficiency ratio", openv$N),
		rep("Efficiency shares", openv$N),
		rep("PM2.5 emission factor", openv$N),
		rep("CO2 emission factor", openv$N),
		rep("Energy factor of apartment houses", openv$N),
		rep("Exposure-response funtion of PM2.5", openv$N),
		rep("Future heating shares", openv$N),
		rep("Shares of renovation types", openv$N)
	),
	Result = c(
		t1$constructionResult,
		t2$efficiencyRatioResult,
		t3$efficiencySharesResult,
		t4$emissionFactorsResult,
		t5$emissionFactorsResult,
		t6$energyFactorResult,
		t7$ERFResult,
		t8$heatingSharesResult,
		t9$renovationSharesResult
	)
))

tornado <- Ovariable("tornado", 
	dependencies = data.frame(Name = c("cost", "testvariable")),
	formula = function(...) {
		test <- cost * testvariable
		indices <- unique(test@output[test@marginal & ! colnames(test@output) %in% "Iter"])
		out <- data.frame()
		for(i in 1:nrow(indices)) {
			temp <- merge(test, indices[i,])@output
			temp <- cor(
				temp[[paste(cost@name, "Result", sep = "")]], 
				temp[[paste(testvariable@name, "Result", sep = "")]],
				method = "spearman"
			)
			out <- rbind(out, data.frame(indices[i,], Result = temp))
		}
		return(out)
	}
)

tornado <- EvalOutput(tornado)

ggplot(tornado@output, aes(x = Variable, y = tornadoResult, colour = Objective)) + 
	geom_point(position = "jitter", size = 2)+coord_flip() + theme_gray(base_size = 24) +
	labs(
		title = "Importance diagram with direct or incremental cost",
		y = "Spearman correlation vs. cost",
		x = "Uncertain input variable to correlate"
	)

cortable <- tornado@output
# Remove those that actually are not probabilistic
cortable <- cortable[!cortable$Variable %in% c("CO2 emission factor", "Energy factor of apartment houses") , ]
cortable <- reshape(
	cortable, 
	v.names = "tornadoResult", 
	timevar = "Objective", 
	idvar = c("FuelPolicy", "RenovationPolicy", "EfficiencyPolicy", "Variable"),
	drop = c("costSource", "testvariableSource", "tornadoSource"), 
	direction = "wide"
)

cat("Spearman correlations between the outcome (cost) and probabilistic input variables. Cost is either A) direct cost or B) incremental compared with BAU.\n")

oprint(cortable)

if(saveobjects) {
	objects.put(list = ls())
	cat(c("All objects archived. Write down the key of the run to retrieve them with objects.get. Objects: ", 
ls(), "\n"))
}

Model version 1

This model version was used to produce the submitted manuscript in spring 2015.

Calculate building stock into the future

The dynamics is calculated by adding building floor area at time points greater than construction year, and by subtracting when time point is greater than demolition year. This is done by building category, not individually.
Also the renovation dynamics is built using event years: at an event, a certain amount of floor area is moved from one energy efficiency category to another.
Full data are stored in the ovariables. Before evaluating, extra columns and rows are removed. The first part of the code is about this.

Full model run with corrected table 13th March 2015
Full model run 23 Feb 2015
Old example model run (running the model takes more than 6 min, so use this ready-made result)

+ Show code - Hide code


### THIS CODE IS FROM PAGE [[Climate change policies and health in Kuopio]] (Op_en5461, code_name = "")

# Siirrä Kuopion-datat kässäristä linkin taakse Opasnettiin
# KÄssäriin vain yhteenvetotaulukko joka kaupungista.
# Mieti mitä sanotaan sisäilmasta. Perusmalli toimii ilmankin, ja Matin nostama miljoonan sisäilman hankaluus pitäisi lähinnä keskustella. Käytetäänkä ylileveitä jakaumia?
# Onko järkeä yhdistää kaupungit? Silloin tulisi NA:ta eri päätösten kohdalle, ja tämä pitäisi huomioida kuvissa (muutenkin kannattaisi slaissata data ennen kuvien piirtämistä).
# Tarkista iF jota käytetään: Mikä on iF-summary?


library(OpasnetUtils)
library(ggplot2)
library(rgdal)
library(maptools)
library(RColorBrewer)
library(classInt)
#library(OpasnetUtilsExt)
library(RgoogleMaps)

openv.setN(0) # use medians instead of whole sampled distributions

objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]] findrest

obstime <- data.frame(Startyear = (192:205) * 10) # Observation years must be defined for an assessment.

## Additional index needed in followup of ovariables efficiencyShares and stockBuildings

year <- Ovariable("year", data = data.frame(
	Constructed = factor(
		c("1799-1899", "1900-1909", "1910-1919", "1920-1929", "1930-1939", "1940-1949",
			"1950-1959", "1960-1969", "1970-1979", "1980-1989", "1990-1999",
			"2000-2010", "2011-2019", "2020-2029", "2030-2039", "2040-2049"
		), 
		ordered = TRUE
		), 
	Time = c(1880, 1905 + 0:14 * 10),
	Result = 1
))

BS <- 24
heating_before <- FALSE
efficiency_before <- TRUE
figstofile <- FALSE

###################### Decisions

decisions <- opbase.data('Op_en5461', subset = "Decisions") # [[Climate change policies and health in Kuopio]]

DecisionTableParser(decisions)

# Remove previous decisions, if any. 
rm(
	"buildings",
	"stockBuildings",
	"changeBuildings",
	"efficiencyShares",
	"energyUse",
	"heatingShares",
	"renovationShares",
	"renovationRate",
	"fuelShares",
	"year",
	envir = openv
)

############################ City-specific data

####!------------------------------------------------
objects.latest("Op_en5417", code_name = "initiate") # [[Population of Kuopio]] 
# population: City_area
objects.latest("Op_en5932", code_name = "initiate") # [[Building stock in Kuopio]] Building ovariables: 
# buildingStock: Building, Constructed, City_area
# rateBuildings: Age, (RenovationPolicy)
# renovationShares: Renovation
# construction: Building
# constructionAreas: City_area
# buildingTypes: Building, Building2 
# heatingShares: Building, Heating, Eventyear
# heatingSharesNew: Building2, Heating
# eventyear: Constructed, Eventyear
# efficiencyShares: Time, Efficiency

renovationRate <- EvalOutput(renovationRate) * 10 # Rates for 10-year periods

#################### Energy use (needed for buildings submodel)

####!------------------------------------------------
objects.latest("Op_en5488", code_name = "initiate") # [[Energy use of buildings]] 
# energyUse: Building, Heating
# efficiencyShares: Efficiency, Constructed
# renovationRatio: Efficiency, Building2, Renovation
####i------------------------------------------------

###################### Actual building model
# The building stock is measured as m^2 floor area.

####!------------------------------------------------
objects.latest("Op_en6289", code_name = "initiate") # [[Building model]] # Generic building model.
# buildings: formula-based
# heatingEnergy: formula-based
####i------------------------------------------------

buildings <- EvalOutput(buildings)

buildings@output$RenovationPolicy <- factor(
	buildings@output$RenovationPolicy,
	levels = c("BAU", "Active renovation", "Effective renovation"),
	ordered = TRUE
)

buildings@output$EfficiencyPolicy <- factor(
	buildings@output$EfficiencyPolicy,
	levels = c("BAU", "Active efficiency"),
	ordered = TRUE
)

bui <- oapply(buildings * 1E-6, cols = c("City_area", "buildingsSource"), FUN = sum)@output

ggplot(subset(bui, RenovationPolicy == "BAU" & EfficiencyPolicy == "BAU"), aes(x = Time, weight = buildingsResult, fill = Heating)) + geom_bar(binwidth = 5) + 
	theme_gray(base_size = BS) +
	labs(
		title = "Building stock in Kuopio",
		x = "Time",
		y = "Floor area (M m2)"
	)

if(figstofile) ggsave("Figure3.eps", width = 8, height = 7)

ggplot(subset(bui, EfficiencyPolicy == "BAU"), aes(x = Time, weight = buildingsResult, fill = Renovation)) + 
geom_bar(binwidth = 5) + 
	facet_grid(. ~ RenovationPolicy) + theme_gray(base_size = BS) +
	labs(
		title = "Building stock in Kuopio by renovation policy",
		x = "Time",
		y = "Floor area (M m2)"
	)

ggplot(subset(bui, RenovationPolicy == "BAU"), aes(x = Time, weight = buildingsResult, fill = Efficiency)) + geom_bar(binwidth = 5) + 
	facet_grid(. ~ EfficiencyPolicy) + theme_gray(base_size = BS) +
	labs(
		title = "Building stock in Kuopio by efficiency policy",
		x = "Time",
		y = "Floor area (M m2)"
	)

ggplot(subset(bui, RenovationPolicy == "BAU" & EfficiencyPolicy == "BAU"), aes(x = Time, weight = buildingsResult, fill = Building)) + geom_bar(binwidth = 5) + 
	theme_gray(base_size = BS) +
	labs(
		title = "Building stock in Kuopio",
		x = "Time",
		y = "Floor area (M m2)"
	)

###################### Energy and emissions

####!------------------------------------------------
objects.latest("Op_en2791", code_name = "initiate") # [[Emission factors for burning processes]] 
# emissionFactors: Burner, Fuel, Pollutant
# fuelShares: Heating, Burner, Fuel
####i------------------------------------------------

heatingEnergy <- EvalOutput(heatingEnergy)

################ Transport and fate

objects.latest("Op_en5813", code_name = "initiate") # [[Intake fractions of PM]], iF

emissions <- EvalOutput(emissions)
emissions@output$Time <- as.numeric(as.character(emissions@output$Time))

# Plot energy need and emissions

ggplot(subset(heatingEnergy@output, EfficiencyPolicy == "BAU"), aes(x = Time, weight = heatingEnergyResult * 1E-6, fill = Heating)) + geom_bar(binwidth = 5) +
	facet_wrap( ~ RenovationPolicy) + theme_gray(base_size = BS) +
	labs(
		title = "Energy used in heating in Kuopio",
		x = "Time",
		y = "Heating energy (GWh /a)"
	)

if(figstofile) ggsave("Figure4.eps", width = 11, height = 7)

emis <- truncateIndex(emissions, cols = "Emission_site", bins = 5)@output

ggplot(subset(emis, EfficiencyPolicy == "BAU" & RenovationPolicy == "BAU"), aes(x = Time, weight = emissionsResult, fill = Fuel)) + geom_bar(binwidth = 5) +
	facet_grid(Pollutant ~ FuelPolicy, scale = "free_y") + theme_gray(base_size = BS) +
	labs(
		title = "Emissions from heating in Kuopio",
		x = "Time",
		y = "Emissions (ton /a)"
	)

if(figstofile) ggsave("Figure5.eps", width = 8, height = 7)

ggplot(heatingEnergy@output, aes(x = Time, weight = heatingEnergyResult * 1E-6, fill = Heating)) + geom_bar(binwidth = 5) +
	facet_grid(EfficiencyPolicy ~ RenovationPolicy) + theme_gray(base_size = BS) +
	labs(
		title = "Energy used in heating in Kuopio",
		x = "Time",
		y = "Heating energy (GWh /a)"
	)

ggplot(subset(emis, EfficiencyPolicy == "BAU" & RenovationPolicy == "BAU"), aes(x = Time, weight = emissionsResult, fill = Fuel)) + geom_bar(binwidth = 5) +
	facet_grid(Pollutant ~ FuelPolicy, scale = "free_y") + theme_gray(base_size = BS) +
	labs(
		title = "Emissions from heating in Kuopio",
		x = "Time",
		y = "Emissions (ton /a)"
	)

ggplot(subset(emis, EfficiencyPolicy == "BAU" & FuelPolicy == "BAU"), aes(x = Time, weight = emissionsResult, fill = Emission_site)) + geom_bar(binwidth = 5) +
	facet_grid(Pollutant ~ RenovationPolicy, scale = "free_y") + theme_gray(base_size = BS) +
	labs(
		title = "Emissions from heating in Kuopio",
		x = "Time",
		y = "Emissions (ton /a)"
	)

ggplot(subset(emis, EfficiencyPolicy == "BAU" & FuelPolicy == "BAU"), aes(x = Time, weight = emissionsResult, fill = Fuel)) + geom_bar(binwidth = 5) +
	facet_grid(Pollutant ~ RenovationPolicy, scale = "free_y") + theme_gray(base_size = BS) +
	labs(
		title = "Emissions from heating in Kuopio",
		x = "Time",
		y = "Emissions (ton /a)"
	)

###################### Health assessment

####!------------------------------------------------
objects.latest('Op_en2261', code_name = 'initiate') # [[Health impact assessment]] dose, RR, totcases. 
objects.latest('Op_en5917', code_name = 'initiate') # [[Disease risk]] disincidence
directs <- tidy(opbase.data("Op_en5461", subset = "Direct inputs"), direction = "wide") # [[Climate change policies and health in Kuopio]]
####i------------------------------------------------

colnames(directs) <- gsub(" ", "_", colnames(directs))

### Use these population and iF values in health impact assessment. Why?

frexposed <- 1 # fraction of population that is exposed
bgexposure <- 0 # Background exposure to an agent (a level below which you cannot get in practice)
BW <- 70 # Body weight (is needed for RR calculations although it is irrelevant for PM2.5)

population <- 1E+5

exposure <- EvalOutput(exposure, verbose = TRUE)

ggplot(subset(exposure@output, RenovationPolicy == "BAU" & EfficiencyPolicy == "BAU" & FuelPolicy == "BAU"), aes(x = Time, weight = exposureResult, fill = Heating)) + 
	geom_bar(binwidth = 5) + facet_grid(Area ~ Emission_height) + theme_gray(base_size = BS) +
	labs(
		title = "Exposure to PM2.5 from heating in Kuopio",
		x = "Time",
		y = "Average PM2.5 (µg/m3)"
	)

exposure@output <- exposure@output[exposure@output$Area == "Average" , ] # Kuopio is an average area,
	# rather than rural or urban.

ggplot(subset(exposure@output, EfficiencyPolicy == "BAU"), aes(x = Time, weight = exposureResult, fill = Heating)) + geom_bar(binwidth = 5) + 	facet_grid(FuelPolicy ~ RenovationPolicy) + theme_gray(base_size = BS) +
	labs(
		title = "Exposure to PM2.5 from heating in Kuopio",
		x = "Time",
		y = "Average PM2.5 (µg/m3)"
	)

totcases <- EvalOutput(totcases)
totcases@output$Time <- as.numeric(as.character(totcases@output$Time))
totcases <- oapply(totcases, cols = c("Age", "Sex"), FUN = sum)

ggplot(subset(totcases@output, EfficiencyPolicy == "BAU" & FuelPolicy == "BAU"), aes(x = Time, weight = totcasesResult, fill = Heating))+geom_bar(binwidth = 5) + 
	facet_grid(Trait ~ RenovationPolicy) +
	theme_gray(base_size = BS) +
	labs(
		title = "Health effects of PM2.5 from heating in Kuopio",
		x = "Time",
		y = "Health effects (deaths /a)"
	)

DW <- Ovariable("DW", data = data.frame(directs["Trait"], Result = directs$DW))
L <- Ovariable("L", data = data.frame(directs["Trait"], Result = directs$L))

DALYs <- totcases * DW * L

cat("Total DALYs/a by different combinations of policy options.\n")

temp <- DALYs
temp@output <- subset(
	temp@output, 
	as.character(Time) %in% c("2010", "2030") & Trait == "Total mortality"
)

oprint(oapply(temp, INDEX = c("Time", "EfficiencyPolicy", "RenovationPolicy", "FuelPolicy"), FUN = sum))

ggplot(subset(DALYs@output, FuelPolicy == "BAU" & Trait == "Total mortality"), aes(x = Time, weight = Result, fill = Heating))+geom_bar(binwidth = 5) + 
	facet_grid(EfficiencyPolicy ~ RenovationPolicy) +
	theme_gray(base_size = BS) +
	labs(
		title = "Health effects in DALYs of PM2.5 from heating in Kuopio",
		x = "Time",
		y = "Health effects (DALY /a)"
	)

ggplot(subset(DALYs@output, Time == 2030 & Trait == "Total mortality"), aes(x = FuelPolicy, weight = Result, fill = Heating))+geom_bar() + 
	facet_grid(EfficiencyPolicy ~ RenovationPolicy) +
	theme_gray(base_size = BS) +
	labs(
		title = "Health effects in DALYs of PM2.5 from heating in Kuopio",
		x = "Biofuel policy in district heating",
		y = "Health effects (DALY /a)"
	)

######## Buildings in Kuopio on map

# Calculate locations for Kuopio districts

temp <- buildings
temp@output <- subset(temp@output,
	Time == 2030 & EfficiencyPolicy == "BAU" & RenovationPolicy == "BAU"
)
temp <- unkeep(temp, sources = TRUE, prevresults = TRUE)
temp <- oapply(temp, cols = c("Building", "Heating", "Efficiency", "Renovation"), FUN = sum)

####!------------------------------------------------
districts <- tidy(opbase.data("Op_en5932.kuopio_city_districts"), widecol = "Location") # [[Building stock in Kuopio]] 
####i------------------------------------------------

colnames(districts) <- gsub("[ \\.]", "_", colnames(districts))
districts <- Ovariable("districts", data = data.frame(districts, Result = 1))

temp <- temp * districts

MyRmap(
	ova2spat(
		temp, 
		coord = c("E", "N"), 
		proj4string = "+init=epsg:3067"
	), # National Land Survey uses EPSG:3067 (ETRS-TM35FIN)
	plotvar = "Result", 
	legend_title = "Floor area", 
	numbins = 8, 
	pch = 19, 
	cex = 2
)

Rationale

Causal diagram of the building model.

Dependencies

Decisions

Efficiency policy (index EfficiencyPolicy): Relates to the shares of efficiency types when new buildings are built (ovariable efficiencyShares).
- BAU: The shares are like in Energy use of buildings#Energy efficiency in heating
- Active efficiency: Passive buildings increase the market share by 25 and 10 %-units at the expense of low-energy buildings since 2020 and 2040, respectively.
Biofuel policy (index FuelPolicy): Increase the share of biofuels in the Haapaniemi power plant (ovariable fuelShares).
- BAU: The shares are like in Emission factors for burning processes#Emission factors for heating (Fuel use in different heating types): Peat 84 %, wood 4 %, heavy oil 12 %.
- Biofuel increase: Peat 49.5 %, wood 49.5 %, heavy oil 1 %.
Renovation policy (index RenovationPolicy): Existing buildings are renovated (typically after 25 years of age) for better energy efficiency. Different renovations produce different results (ovariables renovationRate, renovationShares).
- BAU: Default renovation rate is 3 % /a if the age of the building is >= 25 a. For renovation shares, see Building stock in Kuopio#Renovations.
- Active renovation: The renovation rate of all renovation types is 4.5 % /a.
- Effective renovation: The renovation rate is 3 % /a as in BAU, but all renovations are the most effective. i.e. sheath reforms.

Show details

Decisions(-)
Obs	Decision maker	Decision	Option	Variable	Cell	Change	Unit	Amount	Description
1	Builders	EfficiencyPolicy	BAU	efficiencyShares		Add		0
2	Builders	EfficiencyPolicy	Active efficiency	efficiencyShares	Efficiency:Passive;Time:2020,2025,2030,2035	Add	fraction	0.25	All input must be given in units that are used in respective ovariables.
3	Builders	EfficiencyPolicy	Active efficiency	efficiencyShares	Efficiency:Passive;Time:2040,2045,2050	Add	fraction	0.1
4	Builders	EfficiencyPolicy	Active efficiency	efficiencyShares	Efficiency:Low-energy;Time:2020,2025,2030,2035	Add	fraction	-0.25
5	Builders	EfficiencyPolicy	Active efficiency	efficiencyShares	Efficiency:Low-energy;Time:2040,2024,2050	Add	fraction	-0.1
6	Kuopion Energia	FuelPolicy	BAU	fuelShares		Add		0
7	Kuopion Energia	FuelPolicy	Biofuel increase	fuelShares	Burner:Large fluidized bed;Fuel:Wood;Time:2015,2020,2025,2030,2035,2040,2045,2050	Replace	fraction	0.495
8	Kuopion Energia	FuelPolicy	Biofuel increase	fuelShares	Burner:Large fluidized bed;Fuel:Peat;Time:2015,2020,2025,2030,2035,2040,2045,2050	Replace	fraction	0.495
9	Kuopion Energia	FuelPolicy	Biofuel increase	fuelShares	Burner:Large fluidized bed;Fuel:Heavy oil;Time:2015,2020,2025,2030,2035,2040,2045,2050	Replace	fraction	0.01
10	Building owner	RenovationPolicy	BAU	renovationRate		Multiply	1 /a	1
11	Building owner	RenovationPolicy	Active renovation	renovationRate		Multiply	1 /a	1.5
12	Building owner	RenovationPolicy	Effective renovation	renovationRate		Multiply	1 /a	1
13	Building owner	RenovationPolicy	Effective renovation	renovationShares	Renovation:Windows	Replace	fraction	0
14	Building owner	RenovationPolicy	Effective renovation	renovationShares	Renovation:Technical systems	Replace	fraction	0
15	Building owner	RenovationPolicy	Effective renovation	renovationShares	Renovation:Sheath reform	Replace	fraction	1
16	Building owner	RenovationPolicy	Effective renovation	renovationShares	Renovation:General	Replace	fraction	0
17	Building owner	RenovationPolicy	BAU	renovationShares		Add	fraction	0
18	Building owner	RenovationPolicy	Active renovation	renovationShares		Add	fraction	0

Direct inputs

Direct inputs(-)
Obs	Exposure agent	Response	Cases	DW	L	Description
1	PM2.5	Total mortality	877	1	11	Actually "Mortality (all cause)". In 2009 for Pohjois-Savo area 1090 / 100 000 from death cause registry.
2	PM2.5	Work loss days (WLDs)	323135	0.02	0.003
3	PM2.5	Restricted activity days (RADs)	31867	0.07	0.003	2.1 million in whole Finland
4	PM2.5	Infant mortality	3	1	81	<1 year old 2009 data for Pohjois-Savo area 244 / 100 000 from death registry. In 2009 in Kuopio 1110 <1 year olds.
5	PM2.5	COPD	339	0.099	15	Actually "Chronic bronchitis (>15 year olds)". Kelasto, includes astma cases too
6	PM2.5	Cardiovascular hospital admissions (number)	2109	0.253	0.017	21424 in year 2010 in Kuopio hospital. Hospital serves area with 817166 inhabitats.
7	PM2.5	Respiratory hospital admissions	1150	0.043	0.02	In 2007 1429.55 hospital discharges for respiratory disease / 100 000 in whole Finland. http://data.euro.who.int/hfadb/
8	PM2.5	Asthma medication use (children aged 5-14)	62	0.043	15	Kelasto
9	Mold/dampness	Asthma development (>15 year olds)	252	0.043	15	Kelasto-database
10	Mold/dampness	Asthma development (5-14 year olds)	62	0.043	15	Kelasto-database
11	Noise	Highly annoyed		0.02	1
12	Noise	Sleep disturbance		0.07	1
13	Noise	Myocardial infarction	1289	0.439	0.019663	13101 cases in Kuopio university Hospital in year 2010. Hospital serves area with 817166 inhabitats.
14	EC	Cardiovascular mortality	366	0.043	0.02	In 2009 for Pohjois-Savo area 455 / 100 000 from death cause registry.
15		Cardiopulmonary		1	11	Guesswork. The same as total mortality
16		Lung cancer		1	11	Guesswork. The same as total mortality.

+ Show code - Hide code

# Code Climate change policies and health in Kuopio/DW
library(OpasnetUtils)

directs <- tidy(opbase.data("Op_en5461", subset = "Direct inputs"), direction = "wide") # [[Climate change policies and health in Kuopio]]
colnames(directs) <- gsub(" ", "_", colnames(directs))

DW <- Ovariable("DW", data = data.frame(directs["Response"], Result = directs$DW))
L <- Ovariable("L", data = data.frame(directs["Response"], Result = directs$L))

DALYs <- Ovariable("DALYs", 
	dependencies = data.frame(Name = c("DW", "L")),
	formula = function(...) {
		out <- totcases * DW * L
		return(out)
	}
)

objects.store(DALYs, DW, L)
cat("Objects DALYs, DW, L stored.\n")

Specific actions - real and potential

The plume of Haapaniemi power plant in January, 2014.

The Iloharju heat plant is only used when the heat demand is high, i.e. at temperatures below ca. -15 °C. January, 2014.

Energy production
- New power plant unit in Haapaniemi: ability to use significantly more biomass in the production of district heat (2014)
- Enhancement of dispersed energy production with biofuels
- Wide scale transition to renewable energy sources in heating
Building stock
- Energy efficiency of buildings is increased: new stricter building regulations in Finland (2/2013)
- Education to building owners and managers: semblance of best practicies in heating and other use of energy. Possible reduction in energy use of building stock is about 10%, and mere beneficial health effects are expected.
Land use and transport
- If possible, PM emissions and noise are calculated based on updated version of Kuopio´s traffic network
- Alternatively, the effect of increased use of biofuels on GHG and CO2 emissions is evaluated.
- Possibilities of rail traffic in Kuopio
Other...

Indicators

Cardiovascular mortality
Pulmonar mortality
Well-being...

An archived version was planning to use Weighted product model to summarise results, but the idea was dropped.

Stakeholders: City of Kuopio, Citizens, Budget office of Kuopio

Assessment-specific data

Received

Building stock data
- Building registry
- Use of electricity by building type or type of activity
- Use of district heat by contract
- Amount of building stock renovated per year
- Amount of new building stock per year during 2010-2012
- Energy consumption in some of city´s own buildings before and after renovation
Energy production
- Fuels and emissions of Haapaniemi CHP plant
Traffic
- Regional plan on public transport

To be gathered

Updated traffic network model?
Estimates of the amount, area, volume and energy class of new buildings during next years (about 2014-2020)

References

Keywords

Climate Change, Kuopio, Green house gas emissions, Health, Energy

Related files

Climate change policies and health in Kuopio

Contents

Scope

Question

Boundaries

Scenarios

Intended users

Participants

Answer

Conclusions

Results

Model version 2

Sensitivity analysis

Model version 1

Rationale

Dependencies

Decisions

Direct inputs

Specific actions - real and potential

Indicators

Assessment-specific data

See also

References

Keywords

Related files

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