Climate change policies and health in Kuopio

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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.

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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.

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

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.

Example model run (running the model takes more than 6 min, so use this ready-made result)

+ Show code - Hide code

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

language <- "EN"
openv.setN(0) # use medians instead of whole sampled distributions
#openv.setN(1000)

objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]] findrest
objects.latest("Op_en6289", code_name = "initiate") # [[Building model]] buildings, heatingEnergy. # Generic building model.
objects.latest("Op_en5417", code_name = "initiate") # [[Population of Kuopio]] population 
objects.latest("Op_en5932", code_name = "initiate") # [[Building stock in Kuopio]] Building ovariables: buildingStock,  renovation,
	#renovationShares, construction, constructionAreas, buildingTypes, heatingShares, heatingSharesNew, eventyear, efficiencies
objects.latest("Op_en3435", code_name = "disperse") # [[Exposure to PM2.5 in Finland]] iF, emissionLocations
#Summarised Piltti matrix, another copy of the code on a more reasonable page 
# Default run: en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=aXDIVDboftr1bTEd
objects.latest("Op_en2791", code_name = "initiate") # [[Emission factors for burning processes]] emissionFactors, fuelTypes
objects.latest("Op_en5488", code_name = "initiate") # [[Energy use of buildings]] energyUse, efficienciesNew, savingPotential, 
objects.latest('Op_en2261', code_name = 'initiate') # [[Health impact assessment]] dose, RR, totcases. 
objects.latest('Op_en5917', code_name = 'initiate') # [[Disease risk]] disincidence
objects.latest('Op_en5827', code_name = 'initiate') # [[ERFs of environmental pollutants]] ERF, threshold
#objects.latest('Op_en5453', code_name = 'initiate') # [[Burden of disease in Finland]] BoD

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)

directs <- tidy(opbase.data("Op_en5461", subset = "Direct inputs"), direction = "wide")
colnames(directs) <- gsub(" ", "_", colnames(directs))

colnames(iF@output)[colnames(iF@output) == "City.area"] <- "Emission_site"
colnames(iF@output)[colnames(iF@output) == "Emissionheight"] <- "Emission_height"
colnames(emissionLocations@data) <- gsub(" ", "_", colnames(emissionLocations@data))

# Calculate intake fractions for Kuopio districts
districts <- tidy(opbase.data("Op_en3435.kuopio_city_districts"), widecol = "Location") # [[Exposure to PM2.5 in Finland]] 
colnames(districts) <- gsub("\\.", "_", colnames(districts))
districts <- Ovariable("districts", data = data.frame(districts, Result = 1))

if(language == "EN") deci <- "Decisions" else deci <- "Päätökset"

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

DecisionTableParser(decisions)

# Remove previous decisions, if any. 
rm(
	"buildings",
	"buildingStock",
	"buildingTypes",
	"construction",
	"efficiencies",
	"efficienciesNew",
	"energyUse",
	"heatingShares",
	"heatingSharesNew",
	"renovation",
	"renovationShares",
	"fuelTypes",
	"year",
	envir = openv
)

fuelTypes <- CheckDecisions(EvalOutput(fuelTypes, verbose = TRUE))

#################### Manage the data before calculating

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

if(!exists("language")) language <- "FI"

obsyear <- (192:205) * 10

####### Remove columns and rows that are not needed from data. These must be done before EvalOutput.

# This is the population of Kuopio (i.e. population living in the building stock)

population <- EvalOutput(population, verbose = TRUE)

pop <- oapply(population, cols = "City_area", FUN = "sum") # Sum across city areas.
#pop <- unkeep(pop * 1, prevresults = TRUE) # Remove populationResult column because it would cause trouble in merge.
pop <- population / (1*pop)

buildingStock@data <- buildingStock@data[
	buildingStock@data$Observation == "AreaHR" , 
	colnames(buildingStock@data) != "Observation"
]

buildingStock <- buildingStock * pop

construction@data <- construction@data[
	construction@data$Observation == "Area" , 
	colnames(construction@data) != "Observation"
]
construction <- construction * constructionAreas / 100 / 3 # Statistics are for three years (2010-2012)

energyUse@data <- energyUse@data[
	energyUse@data[["Energy use"]] == "Heat" , 
	colnames(energyUse@data) != "Energy use"
]

savingPotential@data <- savingPotential@data[
	savingPotential@data$Observation == "Relative" , 
	colnames(savingPotential@data) != "Observation"
]
savingPotential <- 1 - savingPotential / 100 
# Note: If you add decisions to savingPotential, this formula must be updated.

# The data are not yet specific to construction year, so remove index:
heatingSharesNew@data <- heatingSharesNew@data[colnames(heatingSharesNew@data) != "Constructed"]

# Fill in Heating types and convert from % to fraction.
heatingSharesNew <- findrest(heatingSharesNew, cols = "Heating", total = 100)
result(heatingSharesNew) <- result(heatingSharesNew) / 100 

heatingShares <- EvalOutput(heatingShares, verbose = TRUE)
result(heatingShares) <- result(heatingShares) / 100 # From % to fraction

# Fill in the rest of the data (the last emission class was omitted because it is determined by the total).
efficienciesNew <- findrest(efficienciesNew, cols = "Efficiency", total = 100) 
result(efficienciesNew) <- result(efficienciesNew) / 100

# When renovations are done, which type are they?
renovationShares <- findrest(renovationShares, cols = "Renovation", total = 100)
result(renovationShares) <- result(renovationShares) / 100

# What fraction of buildings is renovated per year?
renovation <- EvalOutput(renovation, verbose = TRUE)
result(renovation) <- result(renovation) / 100 # From % to fraction

disincidence <- disincidence / 100000 # Change units from 1/100000py to 1/py.

###################### Actual model

buildings <- EvalOutput(buildings, verbose = TRUE)

####### Sort factors

buildings@output <- buildings@output[
	buildings@output$EfficiencyPolicy == "BAU" , 
	colnames(buildings@output) != "EfficiencyPolicy"
]

buildings@output$Renovation <- factor(
	buildings@output$Renovation, 
	levels = c("None", "General", "Windows", "Technical systems", "Sheath reform"),
	ordered = TRUE
)

buildings@output$Efficiency <- factor(
	buildings@output$Efficiency,
	levels = c("Old", "New", "Low-energy", "Passive"),
	ordered = TRUE
)

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

heatingEnergy <- EvalOutput(heatingEnergy, verbose = TRUE)
heatingEnergy <- unkeep(heatingEnergy, cols = "Building2", sources = TRUE, prevresults = TRUE)

if(exists("server")) # The following code only works from Opasnet server.
{
	temp <- buildings * districts
	temp@output <- temp@output[temp@output$Year == 2040 , ]
	temp <- unkeep(temp, sources = TRUE, prevresults = TRUE)
	temp@output <- dropall(temp@output)
	temp <- oapply(temp, cols = c("Building", "Heating", "Efficiency", "Renovation"), FUN = "sum", na.rm = TRUE)

	if(language == "EN") lege <- "Floor area" else lege <- "Rakennuspinta-ala"

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

####### Calculate emissions

emis <- heatingEnergy
emis@output <- emis@output[emis@output$Year >= 1980 , ]
emis <- oapply(emis, cols = c("Efficiency", "Renovation", "Building"), FUN = "sum")
emis@output$Year <- as.numeric(as.character(emis@output$Year))

emis <- emis * fuelTypes * emissionFactors * 3.6  * 1E-9 # convert from kWh /a to MJ /a and mg to ton

emis <- unkeep(emis, sources = TRUE, prevresults = TRUE)

emis <- emis * emissionLocations
emis@output$Emission_site <- ifelse(
	emis@output$Emission_site == "At site of consumption",
	as.character(emis@output$City_area),
	as.character(emis@output$Emission_site)
)

emis2 <- oapply(
	emis,
	cols = c("Heating", "Burner", "Fuel", "City_area", "Emission_site", "Emission_height", "emissionLocationsSource"),
	FUN = sum
)

ggplot(emis@output, aes(x = Year, weight = Result, fill = Emission_site)) + geom_bar() + facet_grid( Pollutant ~ ., scales = "free_y")

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

pop <- 5E+5

iF <- 1E-7

exposure <- Ovariable("exposure", 
	# emis is in ton /a
	# iF = conc (g /m3) * pop (#) * BR (m3 /s) / emis (g /s) <=> conc = emis * iF / BR / pop # conc is the exposure concentration
	dependencies = data.frame(Name = c("emis2", "iF", "pop")),
	formula = function(...) {
		BR <- 20 # Nominal breathing rate (m^3 /d)
		BR <- BR / 24 / 3600 # m^3 /s
		out <- 1E+12 / 365 / 24 / 3600 # Emission scaling from ton /a to ug /s.
		out <- (emis2 * out) * iF / BR / pop # the actual equation
		out <- unkeep(out, prevresults = TRUE, sources = TRUE)
		return(out)
	}
)
exposure <- EvalOutput(exposure, verbose = TRUE)

colnames(exposure@output)[colnames(exposure@output) == "Pollutant"] <- "Exposure_agent"
ggplot(exposure@output, aes(x = Year, weight = exposureResult)) + geom_bar() + facet_grid(Exposure_agent ~ FuelPolicy, scales = "free_y")

totcases <- EvalOutput(totcases)
#totcases@output <- totcases@output[!is.na(result(totcases)) , ] # Not needed with new oapply

#cases <- totcases * Ovariable("incre", data = data.frame(Exposcen = c("BAU", "No exposure"), Result = c(1, -1)))
cases <- oapply(totcases, cols = c("Exposcen", "Age", "Sex", "City_area"), FUN = sum)

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

#ggplot(cases@output[cases@output$Causes == "PM2.5+" , ], aes(x = Year, weight = Result/10, fill = Disease)) + geom_bar() + facet_grid(FuelPolicy ~ RenovationPolicy, scale = "free_y")

DALYs <- cases * DW * L
#DALYs <- oapply(DALYs, cols = c("Trait"), FUN = sum)
DALYs@output <- DALYs@output[DALYs@output$Trait != "Lung cancer" , ] # Has to be removed to avoid double counting.

#ggplot(DALYs@output[DALYs@output$Causes == "PM2.5+" , ], aes(x = Year, weight = Result/10, fill = Response.metric)) + geom_bar() + facet_grid(FuelPolicy ~ RenovationPolicy, scale = "free_y")
ggplot(DALYs@output, aes(x = Year, weight = Result, fill = Trait)) + geom_bar() + facet_grid(RenovationPolicy ~ FuelPolicy)


############################# Plot graphs

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

if(exists("server")) # The following code only works from Opasnet server.
{
	if(language == "EN") lege2 <- "Age of building" else lege2 <- "Rakennuksen ikä"

	shp	<- readOGR('PG:host=localhost user=postgres dbname=spatial_db','kuopio_house')
	proj4string(shp) <- ("+init=epsg:3067") # The map projection of NLS Finland.

	epsg4326String <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
	shp2	<- spTransform(shp,epsg4326String) # Convert to longitude-latitude projection.

	MyRmap(shp2, plotvar = "ika", legend_title = lege2, numbins = 8, pch = 19, cex = 0.3) # Draw the map.
}

if(exists("server")) BS <- 24 else BS <- 12

### Impact graphs in ENGLISH

if(language == "EN") {

	plo <- ggplot(buildings@output) + geom_bar() + facet_grid(. ~ RenovationPolicy) + theme_gray(base_size = BS) +
	aes(x = Year, weight = buildingsResult/1000000, fill = Renovation) + labs(y = "Floor area (M m2)", 
	title = "Building impacts of renovation policy")

	print(plo)

	plo <- ggplot(heatingEnergy@output) + geom_bar() + facet_grid(. ~ RenovationPolicy) + theme_gray(base_size = BS) + 
	labs(title = "Energy impacts of renovation policy", y = "Heating energy need (GWh /a)") +
	aes(x = Year, weight = heatingEnergyResult/1E+6, fill = Heating)

	print(plo)

	emis@output <- emis@output[emis@output$Renovation == "BAU" , ]

	plo <- ggplot(emis@output) + geom_bar() + facet_grid(Pollutant ~ FuelPolicy, scales = "free_y") + theme_gray(base_size = BS) + 
	labs(title = "Emission impacts of biofuel policy", y = "Emissions to air (ton /a)") +
	aes(x = Year, weight = Result, fill = Fuel)

	print(plo)

#	plo <- ggplot(health@output) + geom_bar() + facet_grid(FuelPolicy ~ RenovationPolicy) + theme_gray(base_size = BS) + 
#	labs(title = "Health impacts of fuel and renovation policy", y = "Premature deaths (# /a)") +
#	aes(x = Year, weight = Result, fill = Heating)
#
#	print(plo)
}

### Impact graphs in FINNISH

if(language == "FI") {

	plo <- ggplot(buildings@output) + geom_bar() + facet_grid(. ~ Remonttipolitiikka) + theme_gray(base_size = BS) +
	aes(x = Year, weight = buildingsResult/1000000, fill = Renovation) + labs(y = "Rakennuspinta-ala (M m2)", 
	title = "Remonttipolitiikan vaikutus rakennuskantaan")

	print(plo)

	plo <- ggplot(heatingEnergy@output) + geom_bar() + facet_grid(. ~ Remonttipolitiikka) + theme_gray(base_size = BS) + 
	labs(title = "Remonttipolitiikan vaikutus energiankulutukseen", y = "Lämmitysenergian tarve (GWh /a)") +
	aes(x = Year, weight = heatingEnergyResult/1E+6, fill = Heating)

	print(plo)

	emis@output <- emis@output[emis@output$Remonttipolitiikka == "BAU" , ]

	plo <- ggplot(emis@output) + geom_bar() + facet_grid(Pollutant ~ Bioenergiapolitiikka, scales = "free_y") + 
	theme_gray(base_size = BS) + labs(title = "Bioenergiapolitiikan päästövaikutukset", y = "Päästöt ilmaan (ton /a)") +
	aes(x = Year, weight = Result, fill = Fuel)

	print(plo)

#	plo <- ggplot(health@output) + geom_bar() + facet_grid(Bioenergiapolitiikka ~ Remonttipolitiikka) + theme_gray(base_size = BS) +
#	labs(title = "Remontti- ja bioenergiapolitiikan terveysvaikutukset", y = "Ennenaikaisia kuolemia (# /a)") +
#	aes(x = Year, weight = Result, fill = Heating)
#
#	print(plo)
}

ggplot(dose@output, aes(x = Year, y = doseResult, colour = RenovationPolicy)) + geom_point() + facet_grid(. ~ FuelPolicy)

ggplot(totcases@output, aes(x = Year, y = totcasesResult, colour = Trait)) + geom_point() + facet_grid(Trait ~ FuelPolicy)

ggplot(RR@output, aes(x = RRResult, colour = Trait)) + geom_density()

#ggplot(cases@output, aes(x = Year, weight = Result, fill = Trait)) + geom_bar() + facet_grid(RenovationPolicy ~ #FuelPolicy)

############ Health impact graph corrected. This should be done in a previous stage.

ter <- health
ter2 <- ter
ter2@output <- ter@output[
	ter@output$Bioenergiapolitiikka == "BAU" & 
	ter@output$Remonttipolitiikka == "BAU" ,
]
ter2 <- unkeep(ter2, cols = c("Bioenergiapolitiikka", "Remonttipolitiikka"))

bau <- Ovariable(data = data.frame(
	Bioenergiapolitiikka = rep(c("BAU", "Bioenergian lisäys"), each = 2),
	Remonttipolitiikka = rep(c("Aktiivinen remontointi", "BAU"), times = 2),
	Result = 1
))

bau <- bau * Ovariable(data = data.frame(
	Year = c(1980, 1990, 2000, 2010, 2020, 2030, 2040, 2050),
	Result = c(1,1,1,1,0,0,0,0)
))

ter <- ter * (1 - bau) + ter2 * bau
levels(ter@output$Heating) <- c("Kaukolämpö", "Sähkö", "Maalämpö", "Öljy", "Puu")
colnames(ter@output)[colnames(ter@output) == "Heating"] <- "Lämmitys"

ggplot(ter@output[ter@output$Tehokkuuspolitiikka == "BAU" , ], aes(x = Year, weight = Result/get("N", envir=openv),
	fill = Lämmitys)) + geom_bar() + facet_grid(Remonttipolitiikka ~ Bioenergiapolitiikka) + 
	labs(y = "Pienhiukkasten terveyshaitta")

ggplot(cases@output, aes(x = Year, weight = totcasesResult, fill = Trait)) + geom_bar() + facet_grid(RenovationPolicy ~ FuelPolicy)

ggplot(DALYs@output, aes(x = Year, weight = Result, fill = Trait)) + geom_bar() + facet_grid(RenovationPolicy ~ FuelPolicy)

Rationale

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Causal diagram of the building model.

Dependencies

Building stock in Kuopio
Exposure to PM2.5 in Finland
OpasnetUtils/Drafts
Energy use of buildings
Emission factors for burning processes
Population of Kuopio
Sanni Väisänen: Greenhouse gas emissions from peat and biomass-derived fuels, electricity and heat — Estimation of various production chains by using LCA methodology --# : This work should be carefully read. It may affect conclusions. --Jouni (talk) 13:27, 20 March 2014 (EET)

Decisions

In English

Decisions(-)
Obs	Decision maker	Decision	Option	Variable	Cell	Change	Unit	Amount	Description
1	Builders	EfficiencyPolicy	BAU	efficienciesNew	Efficiency:Passive;Constructed:2020-2029	Add		0
2	Builders	EfficiencyPolicy	Active efficiency	efficienciesNew	Efficiency:Passive;Constructed:2020-2029	Add		0.3	Given as fraction because that's how it is calculated in the model
3	Builders	EfficiencyPolicy	Active efficiency	efficienciesNew	Efficiency:Low-energy;Constructed:2020-2029	Add		-0.3	Given as fraction because that's how it is calculated in the model
4	Kuopion Energia	FuelPolicy	BAU	fuelTypes		Add		0
5	Kuopion Energia	FuelPolicy	Biofuel increase	fuelTypes	Burner:Large fluidized bed;Fuel:Wood	Replace	fraction	0.495
6	Kuopion Energia	FuelPolicy	Biofuel increase	fuelTypes	Burner:Large fluidized bed;Fuel:Peat	Replace	fraction	0.495
7	Kuopion Energia	FuelPolicy	Biofuel increase	fuelTypes	Burner:Large fluidized bed;Fuel:Heavy oil	Replace	fraction	0.01
8	Building owner	RenovationPolicy	BAU	renovation		Multiply		1
9	Building owner	RenovationPolicy	Active renovation	renovation		Multiply		1.5
10	Building owner	RenovationPolicy	Effective renovation	renovation		Multiply		1
11	Building owner	RenovationPolicy	Effective renovation	renovationShares	Renovation:Windows	Replace	%	0
12	Building owner	RenovationPolicy	Effective renovation	renovationShares	Renovation:Technical systems	Replace	%	0
13	Building owner	RenovationPolicy	Effective renovation	renovationShares	Renovation:Sheath reform	Replace	%	1
14	Building owner	RenovationPolicy	Effective renovation	renovationShares	Renovation:General	Replace	%	0
15	Building owner	RenovationPolicy	BAU	renovationShares		Add	%	0
16	Building owner	RenovationPolicy	Active renovation	renovationShares		Add	%	0

In Finnish: This table contains the same decisions, but when using this, the output graphs have Finnish titles.

Päätökset(-)
Obs	Decision maker	Decision	Option	Variable	Cell	Change	Unit	Amount	Description
1	City	Remonttipolitiikka	Aktiivinen remontointi	renovation		Multiply		1.5
2	City	Remonttipolitiikka	BAU	renovation		Multiply		1
3	Builders	Tehokkuuspolitiikka	BAU	efficienciesNew	Efficiency:Passive;Constructed:2020-2029	Add		0
4	Builders	Tehokkuuspolitiikka	Aktiivinen tehostaminen	efficienciesNew	Efficiency:Passive;Constructed:2020-2029	Add		0.3	Given as fraction because that's how it is calculated in the model
5	Builders	Tehokkuuspolitiikka	Aktiivinen tehostaminen	efficienciesNew	Efficiency:Low-energy;Constructed:2020-2029	Add		-0.3	Given as fraction because that's how it is calculated in the model
6	Kuopion Energia	Bioenergiapolitiikka	BAU	fuelTypes		Add		0
7	Kuopion Energia	Bioenergiapolitiikka	Bioenergian lisäys	fuelTypes	Burner:Large fluidized bed;Fuel:Wood	Replace	fraction	0.495
8	Kuopion Energia	Bioenergiapolitiikka	Bioenergian lisäys	fuelTypes	Burner:Large fluidized bed;Fuel:Peat	Replace	fraction	0.495
9	Kuopion Energia	Bioenergiapolitiikka	Bioenergian lisäys	fuelTypes	Burner:Large fluidized bed;Fuel:Heavy oil	Replace	fraction	0.01

Direct inputs

Direct inputs(-)
Obs	Exposure agent	Trait	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.

Specific actions - real and potential

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The plume of Haapaniemi power plant in January, 2014.

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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...

Who are the stakeholders in the assessment Climate change policies and health in Kuopio and what are their values (i.e., what variables do they want to optimize)? Result is a weight factor for a multi-criteria decision analysis (see e.g. Weighted product model).

Endpoints(-)
Obs	Stakeholder	Variable	Cell	Model	Result
1	City of Kuopio	Greenhouse gas emissions in Kuopio	Sector: Total	Weighted sum	1
2	Citizens	Greenhouse gas emissions in Kuopio	Sector: Total	Weighted product	0.5
3	Citizens	Health impacts of climate policies in Kuopio	Sector: Total	Weighted product	0.5
4	Budget office of Kuopio	City budget total		Weighted sum	1

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

Rationale

Dependencies

Decisions

Direct inputs

Specific actions - real and potential

Indicators

Assessment-specific data

See also

References

Keywords

Related files

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