Difference between revisions of "Climate change policies and health in Kuopio"

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==Answer==
 
==Answer==
  
−
*[http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=a4sBYqRnk4MXNj0M Results from an assessment model run]  
+
*[http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=xUA9zDXiadLXxi60 Results from an assessment model run] 10.6.2015.
−
*{{#l:Climate change policies and health in Kuopio_model results.pdf}}
+
*{{#l:Urgenche building model run for Kuopio.pdf}}
  
 
===Conclusions===
 
===Conclusions===
Line 63: Line 63:
  
 
===Results===
 
===Results===
 +
 +
==== 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
 +
 +
objects.latest("Op_en5488", code_name = "energyUseAnnual") # [[Energy use of buildings]] energyUse
 +
objects.latest("Op_en5488", code_name = "efficiencyShares") # [[Energy use of buildings]]
 +
objects.latest("Op_en2791", code_name = "emissionstest") # [[Emission factors for burning processes]]
 +
objects.latest("Op_en2791", code_name = "emissionFactors") # [[Emission factors for burning processes]]
 +
objects.latest("Op_en7328", code_name = "emissionLocations") # [[Kuopio energy production]]
 +
objects.latest("Op_en7328", code_name = "fuelShares") # [[Kuopio energy production]]
 +
objects.latest("Op_en5141", code_name = "fuelUse") # [[Energy balance]]
 +
 +
## Exposure
 +
 +
objects.latest("Op_en5813", code_name = "exposure") # [[Intake fractions of PM]] uses Humbert iF as default.
 +
 +
###################### Health assessment
 +
 +
objects.latest('Op_en2261', code_name = 'totcases') # [[Health impact assessment]] totcases and dependencies.
 +
objects.latest('Op_en5461', code_name = 'DALYs') # [[Climate change policies and health in Kuopio]] DALYs, DW, L
 +
 +
frexposed <- 1 # fraction of population that is exposed
 +
bgexposure <- 0 # Background exposure to an agent (a level below which you cannot get in practice)
 +
BW <- 70 # Body weight (is needed for RR calculations although it is irrelevant for PM2.5)
 +
 +
##################### CALCULATIONS
 +
 +
renovationRate <- EvalOutput(renovationRate) * 10 # Rates for 10-year periods
 +
renovationRate@marginal[colnames(renovationRate@output) == "Age"] <- TRUE
 +
renovationShares <- EvalOutput(renovationShares)
 +
colnames(renovationShares@output)[colnames(renovationShares@output) == "Startyear"] <- "Obsyear"
 +
stockBuildings <- EvalOutput(stockBuildings)
 +
stockBuildings <- oapply(stockBuildings, cols = c("City_area"), FUN = sum)
 +
changeBuildings <- EvalOutput(changeBuildings)
 +
changeBuildings <- oapply(changeBuildings, cols = c("City_area"), FUN = sum)
 +
 +
buildings <- EvalOutput(buildings)
 +
 +
buildings@output$RenovationPolicy <- factor(
 +
buildings@output$RenovationPolicy,
 +
levels = c("BAU", "Active renovation", "Effective renovation"),
 +
ordered = TRUE
 +
)
 +
 +
buildings@output$EfficiencyPolicy <- factor(
 +
buildings@output$EfficiencyPolicy,
 +
levels = c("BAU", "Active efficiency"),
 +
ordered = TRUE
 +
)
 +
 +
energyUse <- EvalOutput(energyUse)
 +
fuelUse <- EvalOutput(fuelUse)
 +
fuelUse <- fuelUse * 1E-3 *3600 # kWh -> MJ
 +
 +
emissions <- EvalOutput(emissions)
 +
 +
population <- 1E+5 # stockBuildings is using another population to divide floor area into City areas.
 +
 +
exposure <- EvalOutput(exposure)
 +
exposure@output <- exposure@output[exposure@output$Area == "Average" , ] # Kuopio is an average area,
 +
# rather than rural or urban.
 +
 +
totcases <- EvalOutput(totcases)
 +
totcases <- oapply(totcases, cols = c("Age", "Sex"), FUN = sum)
 +
 +
DALYs <- EvalOutput(DALYs)
 +
 +
###################### GRAPHS AND OUTPUTS
 +
 +
bui <- suomenna(oapply(buildings * 1E-6, cols = c("City_area", "buildingsSource"), FUN = sum))
 +
 +
ggplot(subset(bui, RenovationPolicy == "BAU" & EfficiencyPolicy == "BAU"), aes(x = Time, weight = buildingsResult, fill = Heating)) + geom_bar(binwidth = 5) +
 +
theme_gray(base_size = BS) +
 +
labs(
 +
title = "Building stock in Kuopio",
 +
x = "Time",
 +
y = "Floor area (M m2)"
 +
)
 +
 +
if(figstofile) ggsave("Figure3.eps", width = 8, height = 7)
 +
 +
ggplot(bui, aes(x = Time, weight = buildingsResult, fill = Building))+geom_bar()+facet_grid(Efficiency~Heating)
 +
 +
ggplot(subset(bui, EfficiencyPolicy == "BAU"), aes(x = Time, weight = buildingsResult, fill = Renovation)) +
 +
geom_bar(binwidth = 5) +
 +
facet_grid(. ~ RenovationPolicy) + theme_gray(base_size = BS) +
 +
labs(
 +
title = "Building stock in Kuopio by renovation policy",
 +
x = "Time",
 +
y = "Floor area (M m2)"
 +
)
 +
 +
ggplot(subset(bui, RenovationPolicy == "BAU"), aes(x = Time, weight = buildingsResult, fill = Efficiency)) + geom_bar(binwidth = 5) +
 +
facet_grid(. ~ EfficiencyPolicy) + theme_gray(base_size = BS) +
 +
labs(
 +
title = "Building stock in Kuopio by efficiency policy",
 +
x = "Time",
 +
y = "Floor area (M m2)"
 +
)
 +
 +
ggplot(subset(bui, RenovationPolicy == "BAU" & EfficiencyPolicy == "BAU"), aes(x = Time, weight = buildingsResult, fill = Building)) + geom_bar(binwidth = 5) +
 +
theme_gray(base_size = BS) +
 +
labs(
 +
title = "Building stock in Kuopio",
 +
x = "Time",
 +
y = "Floor area (M m2)"
 +
)
 +
 +
 +
ggplot(subset(suomenna(energyUse), EfficiencyPolicy == "BAU"), aes(x = Time, weight = energyUseResult * 1E-6, fill = Heating)) + geom_bar(binwidth = 5) +
 +
facet_wrap( ~ RenovationPolicy) + theme_gray(base_size = BS) +
 +
labs(
 +
title = "Energy used in heating in Kuopio",
 +
x = "Time",
 +
y = "Heating energy (GWh /a)"
 +
)
 +
 +
if(figstofile) ggsave("Figure4.eps", width = 11, height = 7)
 +
 +
ggplot(suomenna(energyUse), aes(x = Time, weight = energyUseResult * 1E-6, fill = Heating)) + geom_bar(binwidth = 5) +
 +
facet_grid(EfficiencyPolicy ~ RenovationPolicy) + theme_gray(base_size = BS) +
 +
labs(
 +
title = "Energy used in heating in Kuopio",
 +
x = "Time",
 +
y = "Heating energy (GWh /a)"
 +
)
 +
 +
emis <- suomenna(truncateIndex(emissions, cols = "Fuel", bins = 5))
 +
 +
ggplot(subset(emis, EfficiencyPolicy == "BAU" & RenovationPolicy == "BAU" & Pollutant != "CO2eq"), aes(x = Time, weight = emissionsResult, fill = Fuel)) + geom_bar(binwidth = 5) +
 +
facet_grid(Pollutant ~ FuelPolicy, scale = "free_y") + theme_gray(base_size = BS) +
 +
labs(
 +
title = "Emissions from heating in Kuopio",
 +
x = "Time",
 +
y = "Emissions (ton /a)"
 +
)
 +
 +
if(figstofile) ggsave("Figure5.eps", width = 8, height = 7)
 +
 +
ggplot(subset(emis, EfficiencyPolicy == "BAU" & RenovationPolicy == "BAU"), aes(x = Time, weight = emissionsResult, fill = Fuel)) + geom_bar(binwidth = 5) +
 +
facet_grid(Pollutant ~ ., scale = "free_y") + theme_gray(base_size = BS) +#FuelPolicy
 +
labs(
 +
title = "Emissions from heating in Kuopio",
 +
x = "Time",
 +
y = "Emissions (ton /a)"
 +
)
 +
 +
ggplot(subset(emis, EfficiencyPolicy == "BAU" & FuelPolicy == "BAU"), aes(x = Time, weight = emissionsResult, fill = Emission_site)) + geom_bar(binwidth = 5) +
 +
facet_grid(Pollutant ~ RenovationPolicy, scale = "free_y") + theme_gray(base_size = BS) +
 +
labs(
 +
title = "Emissions from heating in Kuopio",
 +
x = "Time",
 +
y = "Emissions (ton /a)"
 +
)
 +
 +
ggplot(subset(emis, EfficiencyPolicy == "BAU" & FuelPolicy == "BAU"), aes(x = Time, weight = emissionsResult, fill = Fuel)) + geom_bar(binwidth = 5) +
 +
facet_grid(Pollutant ~ RenovationPolicy, scale = "free_y") + theme_gray(base_size = BS) +
 +
labs(
 +
title = "Emissions from heating in Kuopio",
 +
x = "Time",
 +
y = "Emissions (ton /a)"
 +
)
 +
 +
ggplot(subset(suomenna(exposure), RenovationPolicy == "BAU" & EfficiencyPolicy == "BAU" & FuelPolicy == "BAU"), aes(x = Time, weight = exposureResult, fill = Heating)) +
 +
geom_bar(binwidth = 5) + facet_grid(Area ~ Emission_height) + theme_gray(base_size = BS) +
 +
labs(
 +
title = "Exposure to PM2.5 from heating in Kuopio",
 +
x = "Time",
 +
y = "Average PM2.5 (µg/m3)"
 +
)
 +
 +
ggplot(subset(suomenna(exposure), EfficiencyPolicy == "BAU"), aes(x = Time, weight = exposureResult, fill = Heating)) + geom_bar(binwidth = 5) + facet_grid(FuelPolicy ~ RenovationPolicy) + theme_gray(base_size = BS) +
 +
labs(
 +
title = "Exposure to PM2.5 from heating in Kuopio",
 +
x = "Time",
 +
y = "Average PM2.5 (µg/m3)"
 +
)
 +
 +
ggplot(subset(suomenna(totcases), EfficiencyPolicy == "BAU" & FuelPolicy == "BAU"), aes(x = Time, weight = totcasesResult, fill = Heating))+geom_bar(binwidth = 5) +
 +
facet_grid(Response ~ RenovationPolicy) +
 +
theme_gray(base_size = BS) +
 +
labs(
 +
title = "Health effects of PM2.5 from heating in Kuopio",
 +
x = "Time",
 +
y = "Health effects (deaths /a)"
 +
)
 +
 +
cat("Total DALYs/a by different combinations of policy options.\n")
 +
 +
dal <- subset(suomenna(DALYs), Response == "Total mortality")
 +
oprint(aggregate(dal["DALYsResult"], by = dal[c("Time", "EfficiencyPolicy", "RenovationPolicy", "FuelPolicy")], FUN = sum))
 +
 +
ggplot(subset(dal, FuelPolicy == "BAU"), aes(x = Time, weight = DALYsResult, fill = Heating))+geom_bar(binwidth = 5) +
 +
facet_grid(EfficiencyPolicy ~ RenovationPolicy) +
 +
theme_gray(base_size = BS) +
 +
labs(
 +
title = "Health effects in DALYs of PM2.5 from heating in Kuopio",
 +
x = "Time",
 +
y = "Health effects (DALY /a)"
 +
)
 +
 +
ggplot(subset(dal, Time == 2030), aes(x = RenovationPolicy, weight = DALYsResult, fill = Heating))+geom_bar() +
 +
facet_grid(EfficiencyPolicy ~ FuelPolicy) +
 +
theme_gray(base_size = BS) +
 +
labs(
 +
title = "Health effects in DALYs of PM2.5 from heating in Kuopio 2030",
 +
x = "Biofuel policy in district heating",
 +
y = "Health effects (DALY /a)"
 +
)
 +
 +
######## Buildings in Kuopio on map
 +
if(FALSE){
 +
# Calculate locations for Kuopio districts
 +
 +
temp <- buildings
 +
temp@output <- subset(temp@output,
 +
Time == 2030 & EfficiencyPolicy == "BAU" & RenovationPolicy == "BAU"
 +
)
 +
temp <- unkeep(temp, sources = TRUE, prevresults = TRUE)
 +
temp <- oapply(temp, cols = c("Building", "Heating", "Efficiency", "Renovation"), FUN = sum)
 +
 +
####!------------------------------------------------
 +
districts <- tidy(opbase.data("Op_en5932.kuopio_city_districts"), widecol = "Location") # [[Building stock in Kuopio]]
 +
####i------------------------------------------------
 +
 +
colnames(districts) <- gsub("[ \\.]", "_", colnames(districts))
 +
districts <- Ovariable("districts", data = data.frame(districts, Result = 1))
 +
 +
temp <- temp * districts
 +
 +
MyRmap(
 +
ova2spat(
 +
temp,
 +
coord = c("E", "N"),
 +
proj4string = "+init=epsg:3067"
 +
), # National Land Survey uses EPSG:3067 (ETRS-TM35FIN)
 +
plotvar = "Result",
 +
legend_title = "Floor area",
 +
numbins = 8,
 +
pch = 19,
 +
cex = 2
 +
)
 +
}
 +
 +
if(saveobjects) {
 +
objects.put(list = ls())
 +
cat(c("All objects archived. Write down the key of the run to retrieve them with objects.get. Objects: ",
 +
ls(), "\n"))
 +
}
 +
 +
</rcode>
 +
 +
==== Sensitivity analysis ====
 +
 +
* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=1zu5BF0w5a3miRtv Sensitivity analysis 26.7.2015] with 750 iterations
 +
 +
<rcode label="Run sensitivity analysis" graphics=1 store=0 variables="
 +
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$Area == "Average" , ] # Kuopio is an average area,
 +
# rather than rural or urban.
 +
exposure <- oapply(exposure, cols = c(
 +
    "Emission_height",
 +
"Area"
 +
), FUN = sum)
 +
 +
totcases <- EvalOutput(totcases)
 +
totcases <- oapply(totcases, cols = c("Age", "Sex"), FUN = sum)
 +
 +
DALYs <- EvalOutput(DALYs)
 +
 +
cost <- Ovariable("cost",
 +
dependencies = data.frame(Name = c("DALYs", "emissions")),
 +
formula = function(...) {
 +
dals <- DALYs
 +
dals@output <- dals@output[dals@output$Time == "2030" , ]
 +
dals <- oapply(DALYs, INDEX = c("EfficiencyPolicy", "RenovationPolicy", "FuelPolicy", "Iter"), FUN = sum)
 +
emi <- emissions
 +
emi@output <- emi@output[emi@output$Pollutant == "CO2direct" & emi@output$Time == "2030" , ]
 +
emi <- oapply(emissions, INDEX = c("EfficiencyPolicy", "RenovationPolicy", "FuelPolicy", "Iter"), FUN = sum)
 +
cost <- dals * 50000 + emi * 15
 +
bau <- cost
 +
bau@output <- subset(bau@output, FuelPolicy == "BAU" & RenovationPolicy == "BAU" & EfficiencyPolicy == "BAU")
 +
bau <- unkeep(bau, cols = c( "EfficiencyPolicy", "RenovationPolicy", "FuelPolicy"), prevresults = TRUE)
 +
bau <- bau * Ovariable(
 +
output = data.frame(Objective = c("Direct", "BAU comparison"), Result = c(0, 1)),
 +
marginal = c(TRUE, FALSE)
 +
)
 +
cost <- cost - bau
 +
return(cost)
 +
}
 +
)
 +
 +
t1 <- subset(construction@output, Building == "Apartment houses")
 +
t2 <- subset(efficiencyRatio@output, Efficiency == "New")
 +
t3 <- subset(efficiencyShares@output, Efficiency == "New" & Time == "2030" & EfficiencyPolicy == "BAU")
 +
t4 <- subset(emissionFactors@output, Fuel == "Peat" & Pollutant == "PM2.5")
 +
t5 <- subset(emissionFactors@output, Fuel == "Peat" & Pollutant == "CO2direct")
 +
t6 <- subset(energyFactor@output, Building == "Apartment houses" & Heating == "District")
 +
t7 <- subset(ERF@output, Exposure_agent == "PM2.5" & Response == "Total mortality")
 +
t8 <- subset(heatingShares@output, Heating == "District" & Building == "Apartment houses" & Time == "2030")
 +
t9 <- subset(renovationShares@output, RenovationPolicy == "Active renovation" & Renovation == "Sheath reform" & Obsyear == "2030")
 +
 +
testvariable <- Ovariable("testvariable", data = data.frame(
 +
Iter = c(
 +
t1$Iter,
 +
t2$Iter,
 +
t3$Iter,
 +
t4$Iter,
 +
t5$Iter,
 +
t6$Iter,
 +
t7$Iter,
 +
t8$Iter,
 +
t9$Iter
 +
),
 +
Variable = c(
 +
rep("Construction of apartment houses", openv$N),
 +
rep("Efficiency ratio", openv$N),
 +
rep("Efficiency shares", openv$N),
 +
rep("PM2.5 emission factor", openv$N),
 +
rep("CO2 emission factor", openv$N),
 +
rep("Energy factor of apartment houses", openv$N),
 +
rep("Exposure-response funtion of PM2.5", openv$N),
 +
rep("Future heating shares", openv$N),
 +
rep("Shares of renovation types", openv$N)
 +
),
 +
Result = c(
 +
t1$constructionResult,
 +
t2$efficiencyRatioResult,
 +
t3$efficiencySharesResult,
 +
t4$emissionFactorsResult,
 +
t5$emissionFactorsResult,
 +
t6$energyFactorResult,
 +
t7$ERFResult,
 +
t8$heatingSharesResult,
 +
t9$renovationSharesResult
 +
)
 +
))
 +
 +
tornado <- Ovariable("tornado",
 +
dependencies = data.frame(Name = c("cost", "testvariable")),
 +
formula = function(...) {
 +
test <- cost * testvariable
 +
indices <- unique(test@output[test@marginal & ! colnames(test@output) %in% "Iter"])
 +
out <- data.frame()
 +
for(i in 1:nrow(indices)) {
 +
temp <- merge(test, indices[i,])@output
 +
temp <- cor(
 +
temp[[paste(cost@name, "Result", sep = "")]],
 +
temp[[paste(testvariable@name, "Result", sep = "")]],
 +
method = "spearman"
 +
)
 +
out <- rbind(out, data.frame(indices[i,], Result = temp))
 +
}
 +
return(out)
 +
}
 +
)
 +
 +
tornado <- EvalOutput(tornado)
 +
 +
ggplot(tornado@output, aes(x = Variable, y = tornadoResult, colour = Objective)) +
 +
geom_point(position = "jitter", size = 2)+coord_flip() + theme_gray(base_size = 24) +
 +
labs(
 +
title = "Importance diagram with direct or incremental cost",
 +
y = "Spearman correlation vs. cost",
 +
x = "Uncertain input variable to correlate"
 +
)
 +
 +
cortable <- tornado@output
 +
# Remove those that actually are not probabilistic
 +
cortable <- cortable[!cortable$Variable %in% c("CO2 emission factor", "Energy factor of apartment houses") , ]
 +
cortable <- reshape(
 +
cortable,
 +
v.names = "tornadoResult",
 +
timevar = "Objective",
 +
idvar = c("FuelPolicy", "RenovationPolicy", "EfficiencyPolicy", "Variable"),
 +
drop = c("costSource", "testvariableSource", "tornadoSource"),
 +
direction = "wide"
 +
)
 +
 +
cat("Spearman correlations between the outcome (cost) and probabilistic input variables. Cost is either A) direct cost or B) incremental compared with BAU.\n")
 +
 +
oprint(cortable)
 +
 +
if(saveobjects) {
 +
objects.put(list = ls())
 +
cat(c("All objects archived. Write down the key of the run to retrieve them with objects.get. Objects: ",
 +
ls(), "\n"))
 +
}
 +
 +
</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
 
; Calculate building stock into the future
Line 69: Line 662:
 
* Full data are stored in the ovariables. Before evaluating, extra columns and rows are removed. The first part of the code is about this.
 
* Full 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=Bs2tNHNhku5qoes0 Full model run with corrected table 10th March 2015]
+
* [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=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)
 
* [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)
Line 239: Line 832:
 
################ Transport and fate
 
################ Transport and fate
  
−
####!------------------------------------------------
+
objects.latest("Op_en5813", code_name = "initiate") # [[Intake fractions of PM]], iF
−
iF <- Ovariable("iF", ddata = "Op_en3435", subset = "Intake fractions of PM")
 
−
# [[Exposure to PM2.5 in Finland]] Humbert et al 2011 data
 
−
emissionLocations <- Ovariable("emissionLocations", ddata = "Op_en3435", subset = "Emission locations")
 
−
####i------------------------------------------------
 
−
 
 
−
colnames(iF@data) <- gsub("[ \\.]", "_", colnames(iF@data))
 
−
iF@data$iFResult <- iF@data$iFResult * 1E-6
 
−
colnames(emissionLocations@data) <- gsub("[ \\.]", "_", colnames(emissionLocations@data))
 
−
emissionLocations@data$emissionLocationsResult <- 1
 
  
 
emissions <- EvalOutput(emissions)
 
emissions <- EvalOutput(emissions)
Line 314: Line 898:
 
objects.latest('Op_en2261', code_name = 'initiate') # [[Health impact assessment]] dose, RR, totcases.  
 
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_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
 
 
directs <- tidy(opbase.data("Op_en5461", subset = "Direct inputs"), direction = "wide") # [[Climate change policies and health in Kuopio]]
 
directs <- tidy(opbase.data("Op_en5461", subset = "Direct inputs"), direction = "wide") # [[Climate change policies and health in Kuopio]]
 
####i------------------------------------------------
 
####i------------------------------------------------
Line 407: Line 989:
  
 
####!------------------------------------------------
 
####!------------------------------------------------
−
districts <- tidy(opbase.data("Op_en3435.kuopio_city_districts"), widecol = "Location") # [[Exposure to PM2.5 in Finland]]  
+
districts <- tidy(opbase.data("Op_en5932.kuopio_city_districts"), widecol = "Location") # [[Building stock in Kuopio]]  
 
####i------------------------------------------------
 
####i------------------------------------------------
  
Line 427: Line 1,009:
 
cex = 2
 
cex = 2
 
)
 
)
−
 
−
if(exists("server")) # The following code only works from Opasnet server.
 
−
{
 
−
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 = "Age of building",
 
−
numbins = 8,
 
−
pch = 19,
 
−
cex = 0.3
 
−
) # Draw the map.
 
−
}
 
  
 
</rcode>
 
</rcode>
Line 455: Line 1,019:
  
 
* [[Building stock in Kuopio]]
 
* [[Building stock in Kuopio]]
−
* [[Exposure to PM2.5 in Finland]]
+
* [[Intake fractions of PM]]
 
* [[OpasnetUtils/Drafts]]
 
* [[OpasnetUtils/Drafts]]
 
* [[Energy use of buildings]]
 
* [[Energy use of buildings]]
 +
* [[Kuopio energy production]]
 
* [[Emission factors for burning processes]]
 
* [[Emission factors for burning processes]]
 
* [[Population of Kuopio]]
 
* [[Population of Kuopio]]
Line 466: Line 1,031:
 
* [[Burden of disease in Finland]]
 
* [[Burden of disease in Finland]]
 
* [[Climate change policies and health in Kuopio]] DALY weights etc  
 
* [[Climate change policies and health in Kuopio]] DALY weights etc  
−
* [http://www.doria.fi/bitstream/handle/10024/94404/isbn9789522655578.pdf?sequence=2 Sanni Väisänen]: Greenhouse gas emissions from peat and biomass-derived fuels, electricity and heat — Estimation of various production chains by using LCA methodology {{comment|# |This work should be carefully read. It may affect conclusions.|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 13:27, 20 March 2014 (EET)}}
 
  
 
===Decisions===
 
===Decisions===
  
 +
* Efficiency policy (index EfficiencyPolicy): Relates to the shares of efficiency types when new buildings are built (ovariable efficiencyShares).
 +
** BAU: The shares are like in [[Energy use of buildings#Energy efficiency in heating]]
 +
** Active efficiency: Passive buildings increase the market share by 25 and 10 %-units at the expense of low-energy buildings since 2020 and 2040, respectively.
 +
* Biofuel policy (index FuelPolicy): Increase the share of biofuels in the Haapaniemi power plant (ovariable fuelShares).
 +
** BAU: The shares are like in [[Emission factors for burning processes#Emission factors for heating]] (Fuel use in different heating types): Peat 84 %, wood 4 %, heavy oil 12 %.
 +
** Biofuel increase: Peat 49.5 %, wood 49.5 %, heavy oil 1 %.
 +
* Renovation policy (index RenovationPolicy): Existing buildings are renovated (typically after 25 years of age) for better energy efficiency. Different renovations produce different results (ovariables renovationRate, renovationShares).
 +
** BAU: Default renovation rate is 3 % /a if the age of the building is >= 25 a. For renovation shares, see [[Building stock in Kuopio#Renovations]].
 +
** Active renovation: The renovation rate of all renovation types is 4.5 % /a.
 +
** Effective renovation: The renovation rate is 3 % /a as in BAU, but all renovations are the most effective. i.e. sheath reforms.
 +
 +
{{hidden|
 
<t2b name='Decisions' index='Decision maker,Decision,Option,Variable,Cell,Change,Unit' obs='Amount' desc='Description' unit='-'>
 
<t2b name='Decisions' index='Decision maker,Decision,Option,Variable,Cell,Change,Unit' obs='Amount' desc='Description' unit='-'>
 
Builders|EfficiencyPolicy|BAU|efficiencyShares||Add||0|
 
Builders|EfficiencyPolicy|BAU|efficiencyShares||Add||0|
Line 490: Line 1,066:
 
Building owner|RenovationPolicy|Active renovation|renovationShares||Add|fraction|0|
 
Building owner|RenovationPolicy|Active renovation|renovationShares||Add|fraction|0|
 
</t2b>
 
</t2b>
 +
}}
  
 
=== Direct inputs ===
 
=== Direct inputs ===
  
−
<t2b name='Direct inputs' index='Exposure agent,Trait,Observation' locations='Cases,DW,L' desc='Description' unit='-'>
+
<t2b name='Direct inputs' index='Exposure agent,Response,Observation' locations='Cases,DW,L' desc='Description' unit='-'>
 
PM2.5|Total mortality|877|1|11|Actually "Mortality (all cause)". In 2009 for Pohjois-Savo area 1090 / 100 000 from death cause registry.
 
PM2.5|Total mortality|877|1|11|Actually "Mortality (all cause)". In 2009 for Pohjois-Savo area 1090 / 100 000 from death cause registry.
 
PM2.5|Work loss days (WLDs)|323135|0.02|0.003|
 
PM2.5|Work loss days (WLDs)|323135|0.02|0.003|
Line 511: Line 1,088:
 
|Lung cancer||1|11|Guesswork. The same as total mortality.
 
|Lung cancer||1|11|Guesswork. The same as total mortality.
 
</t2b>
 
</t2b>
 +
 +
<rcode name="DALYs" label="Initiate DALYs (developers only)" embed=1>
 +
# Code Climate change policies and health in Kuopio/DW
 +
library(OpasnetUtils)
 +
 +
directs <- tidy(opbase.data("Op_en5461", subset = "Direct inputs"), direction = "wide") # [[Climate change policies and health in Kuopio]]
 +
colnames(directs) <- gsub(" ", "_", colnames(directs))
 +
 +
DW <- Ovariable("DW", data = data.frame(directs["Response"], Result = directs$DW))
 +
L <- Ovariable("L", data = data.frame(directs["Response"], Result = directs$L))
 +
 +
DALYs <- Ovariable("DALYs",
 +
dependencies = data.frame(Name = c("DW", "L")),
 +
formula = function(...) {
 +
out <- totcases * DW * L
 +
return(out)
 +
}
 +
)
 +
 +
objects.store(DALYs, DW, L)
 +
cat("Objects DALYs, DW, L stored.\n")
 +
 +
</rcode>
  
 
===Specific actions - real and potential===
 
===Specific actions - real and potential===
Line 537: Line 1,137:
 
* Well-being...
 
* 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. [[:en:Weighted product model|Weighted product model]]).
+
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.
  
−
<t2b name="Endpoints" index="Stakeholder,Variable,Cell,Observation" locations="Model,Result,Description" unit="-">
+
* Stakeholders: City of Kuopio, Citizens, Budget office of Kuopio
−
City of Kuopio|Greenhouse gas emissions in Kuopio|Sector: Total|Weighted sum|1|
 
−
Citizens|Greenhouse gas emissions in Kuopio|Sector: Total|Weighted product|0.5|
 
−
Citizens|Health impacts of climate policies in Kuopio|Sector: Total|Weighted product|0.5|
 
−
Budget office of Kuopio|City budget total||Weighted sum|1|
 
−
</t2b>
 
  
 
===Assessment-specific data===
 
===Assessment-specific data===

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

Error creating thumbnail: Unable to save thumbnail to destination
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

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

Error creating thumbnail: Unable to save thumbnail to destination
The plume of Haapaniemi power plant in January, 2014.
Error creating thumbnail: Unable to save thumbnail to destination
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

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