Climate change policies and health in Kuopio: Difference between revisions

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[[op_fi:Kuopion ilmastopolitiikka ja terveys]]
[[Category:Kuopio]]
[[Category:Climate change]]
[[Category:Urgenche]]
[[Category:Urgenche]]
{{assessment|moderator=Jouni|stub=Yes}}
{{assessment|moderator=Jouni}}
 
{{summary box
|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.}}
 
<nowiki>
{{#display_map:
62.900223, 27.637482, Kuopio
| zoom = 11
}}
</nowiki>


==Scope==
==Scope==
Line 8: Line 22:
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.
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.


{| class="wikitable collapsible collapsed"
!Details of scoping
|----
|
===Boundaries===
===Boundaries===


* Time: Year 2010 - 2030
* 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.
* 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===
===Scenarios===


* Two scenarios for each climate policy (which have not been defined yet): policy is A) implemented, B) not implemented.
* See climate scenarios from [[Climate change policies in Kuopio]].


===Intended users===
===Intended users===
Line 26: Line 44:


* Main participants:
* Main participants:
** City of Kuopio: Erkki Pärjälä, Mikko Savastola
** City of Kuopio: Erkki Pärjälä, Arja Asikainen
** [[THL]]: Marjo Niittynen, Jouni Tuomisto, Matti Jantunen.
** [[THL]]: Marjo Niittynen, Jouni Tuomisto, Matti Jantunen.
* Other participants:
* Other participants:
Line 33: Line 51:
** Other Urgenche research groups
** Other Urgenche research groups
** Other Urgenche cities
** Other Urgenche cities
|}


==Answer==
==Answer==
*[http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=xUA9zDXiadLXxi60 Results from an assessment model run] 10.6.2015.
*{{#l:Urgenche building model run for Kuopio.pdf}}
===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===
===Results===


Not yet available.
==== Model version 2 ====
 
:''This model version was used to produce the corrected manuscript in July 2015.
* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=5YrOTNdv6hO2Gg92 Model run 21.7.2015] runs to the end but emissions are too large exp for wood after 1980.
* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=CKE08J9mOmLlbtoi 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.
* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=iWTbYNM9MZOeQ0P7 Model run 23.7.2015] archived version. Also renovationShares and changeBuildings data corrected.
* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=PWq7mHEWjyFReXDV Model run 24.7.2015] archived version. This was used for the manuscript.
 
<rcode graphics=1 store0 variables="name:server|type:hidden|default:TRUE">
### 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


===Conclusions===
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)"
)


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


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


[[image:Causal diagram.PNG|thumb|This diagram is just a placeholder. A new file should be uploaded, because this is about another assessment.]]
dal <- subset(suomenna(DALYs), Response == "Total mortality")
oprint(aggregate(dal["DALYsResult"], by = dal[c("Time", "EfficiencyPolicy", "RenovationPolicy", "FuelPolicy")], FUN = sum))


<rcode name="answer" include="page:OpasnetBaseUtils|name:generic|page:Object-oriented_programming_in_Opasnet|name:answer">
ggplot(subset(dal, FuelPolicy == "BAU"), aes(x = Time, weight = DALYsResult, fill = Heating))+geom_bar(binwidth = 5) +
library(OpasnetBaseUtils)
facet_grid(EfficiencyPolicy ~ RenovationPolicy) +
library(xtable)
theme_gray(base_size = BS) +
n <- 5
labs(
title = "Health effects in DALYs of PM2.5 from heating in Kuopio",
x = "Time",
y = "Health effects (DALY /a)"
)


#################### Defines the S4 class "oassessment" which is the object type for open assessments.
ggplot(subset(dal, Time == 2030), aes(x = RenovationPolicy, weight = DALYsResult, fill = Heating))+geom_bar() +
setClass(
facet_grid(EfficiencyPolicy ~ FuelPolicy) +
"oassessment",
theme_gray(base_size = BS) +
representation(
labs(
names = "data.frame",  
title = "Health effects in DALYs of PM2.5 from heating in Kuopio 2030",
decisionmakers = "data.frame"
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"))
}


setMethod(
</rcode>
f = "print",
 
signature = signature("oassessment"),
==== Sensitivity analysis ====
definition = function(x) {
 
cat("Names\n")
* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=1zu5BF0w5a3miRtv Sensitivity analysis 26.7.2015] with 750 iterations
print(xtable(x@names), type = 'html')
 
cat("Decisionmakers\n")
<rcode label="Run sensitivity analysis" graphics=1 store=0 variables="
print(xtable(x@decisionmakers), type = 'html')
name:num|description:How many iterations? (For more, run on your own computer)|type:slider|options:1;100;1|default:10
">
### 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)


setMethod(
cost <- Ovariable("cost",  
f = "print",
dependencies = data.frame(Name = c("DALYs", "emissions")),
signature = signature("ovariable"),
formula = function(...) {
definition = function(x) {
dals <- DALYs
cat("Sample\n")
dals@output <- dals@output[dals@output$Time == "2030" , ]
print(xtable(x@sample[x@sample$Iter %in% 1:4, ]), type = 'html')
dals <- oapply(DALYs, INDEX = c("EfficiencyPolicy", "RenovationPolicy", "FuelPolicy", "Iter"), FUN = sum)
cat("Formula\n")
emi <- emissions
print(x@formula)
emi@output <- emi@output[emi@output$Pollutant == "CO2direct" & emi@output$Time == "2030" , ]
cat("Dependencies\n")
emi <- oapply(emissions, INDEX = c("EfficiencyPolicy", "RenovationPolicy", "FuelPolicy", "Iter"), FUN = sum)
print(x@dependencies)
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)
}
}
)
)


names.Kuopio <- data.frame(
t1 <- subset(construction@output, Building == "Apartment houses")
Name = c(
t2 <- subset(efficiencyRatio@output, Efficiency == "New")
"Climate change policies in Kuopio",  
t3 <- subset(efficiencyShares@output, Efficiency == "New" & Time == "2030" & EfficiencyPolicy == "BAU")
"Climate education in Kuopio",  
t4 <- subset(emissionFactors@output, Fuel == "Peat" & Pollutant == "PM2.5")
"Market allocation factor",  
t5 <- subset(emissionFactors@output, Fuel == "Peat" & Pollutant == "CO2direct")
"Cost curves for energy",  
t6 <- subset(energyFactor@output, Building == "Apartment houses" & Heating == "District")
"Energy transformations",  
t7 <- subset(ERF@output, Exposure_agent == "PM2.5" & Response == "Total mortality")
"Energy balance in Kuopio",  
t8 <- subset(heatingShares@output, Heating == "District" & Building == "Apartment houses" & Time == "2030")
"Greenhouse gas emissions in Kuopio"),  
t9 <- subset(renovationShares@output, RenovationPolicy == "Active renovation" & Renovation == "Sheath reform" & Obsyear == "2030")
Identifier = c("Op_en5466", "Op_en5582", "Op_en5535", "Op_en5478", "Op_en5472", "Op_en5469", "Op_en5483"),
 
Alias = c("decision", "education", "MAF", "cost.curve", "transformation", "energy.balance", "GHG.emission")
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)
}
)
)


names.Kuopio <- tidy(ob_baseGetData("opasnet_base", "Op_en5461"))
tornado <- EvalOutput(tornado)


ccph.Kuopio <- new("oassessment",  
ggplot(tornado@output, aes(x = Variable, y = tornadoResult, colour = Objective)) +
names = names.Kuopio,  
geom_point(position = "jitter", size = 2)+coord_flip() + theme_gray(base_size = 24) +
decisionmakers = data.frame(
labs(
Decisionmaker = rep("City of Kuopio", 6),
title = "Importance diagram with direct or incremental cost",
Decision = c(
y = "Spearman correlation vs. cost",
"Emission policy",
x = "Uncertain input variable to correlate"
"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."
)
)
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"
)
)


for(x in 1:nrow(ccph.Kuopio@names)) { # Objects with names as aliases are created and filled with data from Opasnet Base.
cat("Spearman correlations between the outcome (cost) and probabilistic input variables. Cost is either A) direct cost or B) incremental compared with BAU.\n")
assign(as.character(ccph.Kuopio@names$Alias[x]), make.ovariable(tidy(op_baseGetData("opasnet_base", ccph.Kuopio@names$Identifier[x]))))
 
# ccph.Kuopio@objects[[x]] <- get(as.character(ccph.Kuopio@names$Alias[x]))
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"))
}
}
print(decision)
education
MAF
cost.curve
transformation
energy.balance
GHG.emission


print(ccph.Kuopio)
</rcode>
 
==== Model version 1 ====
 
:''This model version was used to produce the submitted manuscript in spring 2015.
 
[[heande:Kuopio housing]]
 
; 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.
 
* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=s3zYXviOuNZtzMZz Full model run with corrected table 13th March 2015]
* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=Tm0DmRTpr2qTAaVW Full model run 23 Feb 2015]
* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=CcSsz4RJAZFaAAmE Old example model run] (running the model takes more than 6 min, so use this ready-made result)
 
<rcode graphics=1 store=0 variables="name:server|type:hidden|default:TRUE">
 
### 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------------------------------------------------


</rcode>
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


{{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. |--[[User:Jouni|Jouni]] 11:04, 1 May 2012 (EEST)}}
cat("Total DALYs/a by different combinations of policy options.\n")


{{attack|# |Slot "objects" is not needed, as variables are managed with their aliases as separate objects (ovariables). |--[[User:Jouni|Jouni]] 11:04, 1 May 2012 (EEST)}}
temp <- DALYs
temp@output <- subset(
temp@output,  
as.character(Time) %in% c("2010", "2030") & Trait == "Total mortality"
)


{{comment|# |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?|--[[User:Jouni|Jouni]] 08:55, 1 May 2012 (EEST)}}
oprint(oapply(temp, INDEX = c("Time", "EfficiencyPolicy", "RenovationPolicy", "FuelPolicy"), FUN = sum))


{{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.|--[[User:Jouni|Jouni]] 08:55, 1 May 2012 (EEST)}}
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)"
)


===Decision variables===
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)"
)


* Climate policy 1
######## Buildings in Kuopio on map
* Climate policy 2...
===Indicators===


* Cardiovascular mortality
# Calculate locations for Kuopio districts
* Pulmonar mortality
* Well-being...


===Other variables===
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)


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


<t2b index="Name,Identifier" obs="Alias" unit="-">
colnames(districts) <- gsub("[ \\.]", "_", colnames(districts))
Climate change policies in Kuopio|Op_en5466|decision
districts <- Ovariable("districts", data = data.frame(districts, Result = 1))
Climate education in Kuopio|Op_en5582|education
Market allocation factor|Op_en5535|MAF
Cost curves for energy|Op_en5478|cost.curve
Energy transformations|Op_en5472|transformation
Energy balance in Kuopio|Op_en5469|energy.balance
Greenhouse gas emissions in Kuopio|Op_en5483|GHG.emission
</t2b>


===Formula===
temp <- temp * districts


<rcode include="page:OpasnetBaseUtils|name:generic" graphics="Yes" variables="
MyRmap(
name:biofuels|description:Addition of biofuels in transportation (ktoe/a)|default:5|
ova2spat(
name:heatsave|description:How much energy is saved from the heating of buildings, compared with 2010? (%)|default:10">
temp,
library(OpasnetBaseUtils)
coord = c("E", "N"),
library(ggplot2)
proj4string = "+init=epsg:3067"
library(xtable)
), # National Land Survey uses EPSG:3067 (ETRS-TM35FIN)
plotvar = "Result",  
legend_title = "Floor area",
numbins = 8,
pch = 19,
cex = 2
)


cat("Loading functions and data.\n")
</rcode>


city    <- "Kuopio"
==Rationale==
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")
[[image:Building model causal diagram.png|thumb|400px|Causal diagram of the [[building model]].]]


energy <- energy[energy$Place == city, ]
===Dependencies===
energy$Amount <- as.numeric(energy$Amount)


# The policy changes are implemented.
* [[Building stock in Kuopio]]
* [[Intake fractions of PM]]
* [[OpasnetUtils/Drafts]]
* [[Energy use of buildings]]
* [[Kuopio energy production]]
* [[Emission factors for burning processes]]
* [[Population of Kuopio]]
* [[Building model]]
* [[Health impact assessment]]
* [[Disease risk]]
* [[ERFs of environmental pollutants]]
* [[Burden of disease in Finland]]
* [[Climate change policies and health in Kuopio]] DALY weights etc


energy.stra <- energy
===Decisions===
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.
* 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.


tran <- reshape(tran, idvar = c("Process", "Transformation", "Use", "Fuel"), timevar = "Observation", direction = "wide")
{{hidden|
colnames(tran) <- gsub("Result.", "", colnames(tran))
<t2b name='Decisions' index='Decision maker,Decision,Option,Variable,Cell,Change,Unit' obs='Amount' desc='Description' unit='-'>
tran$Amount <- as.numeric(tran$Amount)
Builders|EfficiencyPolicy|BAU|efficiencyShares||Add||0|
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.
Builders|EfficiencyPolicy|Active efficiency|efficiencyShares|Efficiency:Passive;Time:2040,2045,2050|Add|fraction|0.1|
Builders|EfficiencyPolicy|Active efficiency|efficiencyShares|Efficiency:Low-energy;Time:2020,2025,2030,2035|Add|fraction|-0.25|
Builders|EfficiencyPolicy|Active efficiency|efficiencyShares|Efficiency:Low-energy;Time:2040,2024,2050|Add|fraction|-0.1|
Kuopion Energia|FuelPolicy|BAU|fuelShares||Add||0|
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|
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|
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|
Building owner|RenovationPolicy|BAU|renovationRate||Multiply|1 /a|1|
Building owner|RenovationPolicy|Active renovation|renovationRate||Multiply|1 /a|1.5|
Building owner|RenovationPolicy|Effective renovation|renovationRate||Multiply|1 /a|1|
Building owner|RenovationPolicy|Effective renovation|renovationShares|Renovation:Windows|Replace|fraction|0|
Building owner|RenovationPolicy|Effective renovation|renovationShares|Renovation:Technical systems|Replace|fraction|0|
Building owner|RenovationPolicy|Effective renovation|renovationShares|Renovation:Sheath reform|Replace|fraction|1|
Building owner|RenovationPolicy|Effective renovation|renovationShares|Renovation:General|Replace|fraction|0|
Building owner|RenovationPolicy|BAU|renovationShares||Add|fraction|0|
Building owner|RenovationPolicy|Active renovation|renovationShares||Add|fraction|0|
</t2b>
}}


# Calculate conversion factors.
=== Direct inputs ===
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.
<t2b name='Direct inputs' index='Exposure agent,Response,Observation' locations='Cases,DW,L' desc='Description' unit='-'>
energy <- merge(tran, factors, by = c("Process", "Transformation"), all.x = TRUE)
PM2.5|Total mortality|877|1|11|Actually "Mortality (all cause)". In 2009 for Pohjois-Savo area 1090 / 100 000 from death cause registry.
energy$Amount <- energy$Amount * energy$Amount.x
PM2.5|Work loss days (WLDs)|323135|0.02|0.003|
energy <- energy[!colnames(energy) %in% c("Use.y", "Fuel.y", "Unit.x", "Amount.x", "Unit.y", "Amount.y")]
PM2.5|Restricted activity days (RADs)|31867|0.07|0.003|2.1 million in whole Finland
colnames(energy) <- gsub(".x", "", colnames(energy))
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.
PM2.5|COPD|339|0.099|15|Actually "Chronic bronchitis (>15 year olds)". Kelasto, includes astma cases too
PM2.5|Cardiovascular hospital admissions (number)|2109|0.253|0.017|21424  in year 2010 in Kuopio hospital. Hospital serves area with 817166 inhabitats.
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/
PM2.5|Asthma medication use (children aged 5-14)|62|0.043|15|Kelasto
Mold/dampness|Asthma development (>15 year olds)|252|0.043|15|Kelasto-database
Mold/dampness|Asthma development (5-14 year olds)|62|0.043|15|Kelasto-database
Noise|Highly annoyed||0.02|1|
Noise|Sleep disturbance||0.07|1|
Noise|Myocardial infarction|1289|0.439|0.019663|13101 cases in Kuopio university Hospital in year 2010. Hospital serves area with 817166 inhabitats.
EC|Cardiovascular mortality|366|0.043|0.02|In 2009 for Pohjois-Savo area 455 / 100 000 from death cause registry.
|Cardiopulmonary||1|11|Guesswork. The same as total mortality
|Lung cancer||1|11|Guesswork. The same as total mortality.
</t2b>


# Convert energy values to ktoe.
<rcode name="DALYs" label="Initiate DALYs (developers only)" embed=1>
energy$Amount <- ifelse(energy$Unit == "MWh", energy$Amount * mwh2ktoe, energy$Amount)
# Code Climate change policies and health in Kuopio/DW
energy[energy$Unit == "MWh", "Unit"] <- "ktoe"
library(OpasnetUtils)


fuels <- c("Coal and peat", "Crude oil", "Petrochemical products", "Gas", "Nuclear", "Hydro", "Geothermal solar wind", "Renewables and waste", "Electricity", "Heat")
directs <- tidy(opbase.data("Op_en5461", subset = "Direct inputs"), direction = "wide") # [[Climate change policies and health in Kuopio]]
colnames(directs) <- gsub(" ", "_", colnames(directs))


# Categorise energy to standard energy processes and calculate sums for each process.
DW <- Ovariable("DW", data = data.frame(directs["Response"], Result = directs$DW))
out <- merge(energy, classes, by.x = "Process", by.y = "Result")
L <- Ovariable("L", data = data.frame(directs["Response"], Result = directs$L))
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?
DALYs <- Ovariable("DALYs",
dependencies = data.frame(Name = c("DW", "L")),
formula = function(...) {
out <- totcases * DW * L
return(out)
}
)


ggplot(energy[energy$Fuel == "CO2e", ],  aes(x = Action, weight = Amount, fill = Process)) +geom_bar(position = "Stack")
objects.store(DALYs, DW, L)
ggplot(energy[energy$Fuel == "PM2.5", ], aes(x = Action, weight = Amount, fill = Process)) +geom_bar(position = "Stack")
cat("Objects DALYs, DW, L stored.\n")
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")


</rcode>
</rcode>


{{comment|# |This is a city-specific copy of the original code: [[Energy balance]].|--[[User:Jouni|Jouni]] 06:52, 27 January 2012 (EET)}}
===Specific actions - real and potential===
 
[[File:Haapaniemi at winter.JPG|thumb|300px|The plume of Haapaniemi power plant in January, 2014.]]
 
[[File:Iloharju at winter.JPG|thumb|300px|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 [http://en.opasnet.org/en-opwiki/index.php?title=Climate_change_policies_and_health_in_Kuopio&oldid=34738 archived version] was planning to use [[:en:Weighted product model|Weighted product model]] to summarise results, but the idea was dropped.


===Value variables===
* Stakeholders: City of Kuopio, Citizens, Budget office of Kuopio


===Analyses===
===Assessment-specific data===


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


==See also==
==See also==
Line 246: Line 1,163:
{{Urgenche}}
{{Urgenche}}


* [http://kuopio02.hosting.documenta.fi/kokous/2012209757-4.HTM Kuopion kaupunginhallitus 8.10.2012: Kuopion ja Siilinjärven joukkoliikennesuunnitelma] (Kuopio public transport plan)
* [http://www.ymparisto.fi/default.asp?contentid=423217&lan=FI Laskureita hiilijalanjäljen arviointiin ja seurantaan] ([http://www.ymparisto.fi/default.asp?contentid=423217&lan=EN Carbon footprint calculators]): SYNERGIA, JUHILAS, Ilmastodieetti, KASVENER, KUHILAS, Y-HIILARI
* [https://www.otakantaa.fi/fi-FI/Hankkeet/Millaisen_tiekartan_avulla_hiilineutraaliin_Suomeen_2050 Millaisen tiekarten avulla hiilineutraaliin Suomeen 2050]
* [http://www.kuopio.fi/c/document_library/get_file?uuid=ab67c50a-9558-423c-951e-fd96cf1aaabf&groupId=12141 Climate policy of Kuopio 2009 - 2020]
* [http://debattibaari.fi/yhteenveto/energialoppu/ DebattiBaari: energiakeskustelu]


==References==


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)<ref>Kuopio University 2005 (in Finnish)[http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=4&sqi=2&ved=0CEEQFjAD&url=http%3A%2F%2Fwww.bioenergiatieto.fi%2Fdefault%2F%3F__EVIA_WYSIWYG_FILE%3D6783%26name%3Dfile&ei=xZkVT-GaM8TE4gSs64zmAw&usg=AFQjCNG0Toaxlzzbr2fzU9g6cHPBXoKQdw&sig2=LhyRk6phRxeCf2VpZb0HzA]</ref>
<references/>
{| {{prettytable}}
|colspan="2" rowspan="2"| Main heating system
|colspan="4"| Type of estate
|----
| Detached house
| Farm
| Free-time place
| Total
|----
|colspan="2"| Stove heating
| 7.1
| 10
| 2
| 4.4
|----
|rowspan="3"| Central heating
| Wood
| 13.7
| 25.6
| ..
| 18.4
|----
| Oil
| 1.8
| 8.2
| ..
| 2.3
|----
| Electricity
| 2.5
| 7.8
| ..
| 2.8
|----
|colspan="2"| Straight electric heating
| 2.9
| 6.5
| 1.6
| 2.7
|----
|colspan="2"| District heating
| 1.1
| ..
| ..
| 1.2
|----
|colspan="2"| All
| 3.8
| 14.4
| 1.8
| 4.4
|----
|}


==References==
==Keywords==


<references/>
Climate Change, Kuopio, Green house gas emissions, Health, Energy


==Related files==
==Related files==


{{mfiles}}
{{mfiles}}

Latest revision as of 16:52, 11 January 2016


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.


{{#display_map: 62.900223, 27.637482, Kuopio | zoom = 11 }}

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.

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.

+ Show code

Sensitivity analysis

How many iterations? (For more, run on your own computer):

+ Show code

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.

+ Show code

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).
  • Biofuel policy (index FuelPolicy): Increase the share of biofuels in the Haapaniemi power plant (ovariable fuelShares).
  • 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.



Direct inputs

Data updated successfully!

Direct inputs(-)
ObsExposure agentResponseCasesDWLDescription
1PM2.5Total mortality877111Actually "Mortality (all cause)". In 2009 for Pohjois-Savo area 1090 / 100 000 from death cause registry.
2PM2.5Work loss days (WLDs)3231350.020.003
3PM2.5Restricted activity days (RADs)318670.070.0032.1 million in whole Finland
4PM2.5Infant mortality3181<1 year old 2009 data for Pohjois-Savo area 244 / 100 000 from death registry. In 2009 in Kuopio 1110 <1 year olds.
5PM2.5COPD3390.09915Actually "Chronic bronchitis (>15 year olds)". Kelasto, includes astma cases too
6PM2.5Cardiovascular hospital admissions (number)21090.2530.01721424 in year 2010 in Kuopio hospital. Hospital serves area with 817166 inhabitats.
7PM2.5Respiratory hospital admissions11500.0430.02In 2007 1429.55 hospital discharges for respiratory disease / 100 000 in whole Finland. http://data.euro.who.int/hfadb/
8PM2.5Asthma medication use (children aged 5-14)620.04315Kelasto
9Mold/dampnessAsthma development (>15 year olds)2520.04315Kelasto-database
10Mold/dampnessAsthma development (5-14 year olds)620.04315Kelasto-database
11NoiseHighly annoyed0.021
12NoiseSleep disturbance0.071
13NoiseMyocardial infarction12890.4390.01966313101 cases in Kuopio university Hospital in year 2010. Hospital serves area with 817166 inhabitats.
14ECCardiovascular mortality3660.0430.02In 2009 for Pohjois-Savo area 455 / 100 000 from death cause registry.
15Cardiopulmonary111Guesswork. The same as total mortality
16Lung cancer111Guesswork. The same as total mortality.

+ Show code

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)

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


References


Keywords

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

Related files

<mfanonymousfilelist></mfanonymousfilelist>

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