Climate change policies and health in Kuopio

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Main message:
Question:

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

Answer:

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


{{#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.

<rcode graphics=1 store0 variables="name:server|type:hidden|default:TRUE">

      1. THIS CODE IS FROM PAGE Climate change policies and health in Kuopio (Op_en5461, code_name = "")

library(OpasnetUtils) library(ggplot2)

      1. 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))

    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 ))

                                            1. Decisions

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

DecisionTableParser(decisions)

  1. Remove previous decisions, if any.

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

forgetDecisions()

                                                        1. IMPORT DATA AND MODELS

objects.latest("Op_en5417", code_name = "initiate") # Population of Kuopio

  1. population: City_area

objects.latest("Op_en5932", code_name = "initiatetest") # Building stock in Kuopio Building ovariables:

  1. buildingStock: Building, Constructed, City_area
  2. rateBuildings: Age, (RenovationPolicy)
  3. renovationShares: Renovation
  4. construction: Building
  5. constructionAreas: City_area
  6. buildingTypes: Building, Building2
  7. heatingShares: Building, Heating, Eventyear
  8. heatingSharesNew: Building2, Heating
  9. eventyear: Constructed, Eventyear
                                            1. Actual building model
  1. The building stock is measured as m^2 floor area.

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

                                            1. 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

    1. Exposure

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

                                            1. 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)

                                          1. 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)

                                            1. 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)" )

                1. Buildings in Kuopio on map

if(FALSE){

  1. 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)

        1. !------------------------------------------------

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

        1. 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

<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 ">

      1. THIS CODE IS FROM PAGE Climate change policies and health in Kuopio (Op_en5461, code_name = "")

library(OpasnetUtils) library(ggplot2)

      1. Technical parameters

openv.setN(num)

  1. 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))

    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 ))

                                            1. Decisions

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

DecisionTableParser(decisions)

  1. Remove previous decisions, if any.

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

forgetDecisions()

                                                        1. 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.

                                            1. 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

    1. 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

                                          1. 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( temppaste(cost@name, "Result", sep = ""), temppaste(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

  1. 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.
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.

<rcode graphics=1 store=0 variables="name:server|type:hidden|default:TRUE">

      1. THIS CODE IS FROM PAGE Climate change policies and health in Kuopio (Op_en5461, code_name = "")
  1. Siirrä Kuopion-datat kässäristä linkin taakse Opasnettiin
  2. KÄssäriin vain yhteenvetotaulukko joka kaupungista.
  3. Mieti mitä sanotaan sisäilmasta. Perusmalli toimii ilmankin, ja Matin nostama miljoonan sisäilman hankaluus pitäisi lähinnä keskustella. Käytetäänkä ylileveitä jakaumia?
  4. Onko järkeä yhdistää kaupungit? Silloin tulisi NA:ta eri päätösten kohdalle, ja tämä pitäisi huomioida kuvissa (muutenkin kannattaisi slaissata data ennen kuvien piirtämistä).
  5. Tarkista iF jota käytetään: Mikä on iF-summary?


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

  1. 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.

    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, 1905 + 0:14 * 10), Result = 1 ))

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

                                            1. Decisions

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

DecisionTableParser(decisions)

  1. Remove previous decisions, if any.

rm( "buildings", "stockBuildings", "changeBuildings", "efficiencyShares", "energyUse", "heatingShares", "renovationShares", "renovationRate", "fuelShares", "year", envir = openv )

                                                        1. City-specific data
        1. !------------------------------------------------

objects.latest("Op_en5417", code_name = "initiate") # Population of Kuopio

  1. population: City_area

objects.latest("Op_en5932", code_name = "initiate") # Building stock in Kuopio Building ovariables:

  1. buildingStock: Building, Constructed, City_area
  2. rateBuildings: Age, (RenovationPolicy)
  3. renovationShares: Renovation
  4. construction: Building
  5. constructionAreas: City_area
  6. buildingTypes: Building, Building2
  7. heatingShares: Building, Heating, Eventyear
  8. heatingSharesNew: Building2, Heating
  9. eventyear: Constructed, Eventyear
  10. efficiencyShares: Time, Efficiency

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

                                        1. Energy use (needed for buildings submodel)
        1. !------------------------------------------------

objects.latest("Op_en5488", code_name = "initiate") # Energy use of buildings

  1. energyUse: Building, Heating
  2. efficiencyShares: Efficiency, Constructed
  3. renovationRatio: Efficiency, Building2, Renovation
        1. i------------------------------------------------
                                            1. Actual building model
  1. The building stock is measured as m^2 floor area.
        1. !------------------------------------------------

objects.latest("Op_en6289", code_name = "initiate") # Building model # Generic building model.

  1. buildings: formula-based
  2. heatingEnergy: formula-based
        1. i------------------------------------------------

buildings <- EvalOutput(buildings)

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

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

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

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

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

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

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

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

                                            1. Energy and emissions
        1. !------------------------------------------------

objects.latest("Op_en2791", code_name = "initiate") # Emission factors for burning processes

  1. emissionFactors: Burner, Fuel, Pollutant
  2. fuelShares: Heating, Burner, Fuel
        1. i------------------------------------------------

heatingEnergy <- EvalOutput(heatingEnergy)

                                1. 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))

  1. 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)" )

                                            1. Health assessment
        1. !------------------------------------------------

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

        1. i------------------------------------------------

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

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

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

population <- 1E+5

exposure <- EvalOutput(exposure, verbose = TRUE)

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

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

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

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

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

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

DALYs <- totcases * DW * L

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

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

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

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

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

                1. Buildings in Kuopio on map
  1. 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)

        1. !------------------------------------------------

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

        1. 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 )

</rcode>

Rationale

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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

<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|Work loss days (WLDs)|323135|0.02|0.003| PM2.5|Restricted activity days (RADs)|31867|0.07|0.003|2.1 million in whole Finland 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>

<rcode name="DALYs" label="Initiate DALYs (developers only)" embed=1>

  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

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The plume of Haapaniemi power plant in January, 2014.
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The Iloharju heat plant is only used when the heat demand is high, i.e. at temperatures below ca. -15 °C. January, 2014.
  • Energy production
    • New power plant unit in Haapaniemi: ability to use significantly more biomass in the production of district heat (2014)
    • Enhancement of dispersed energy production with biofuels
    • Wide scale transition to renewable energy sources in heating
  • Building stock
    • Energy efficiency of buildings is increased: new stricter building regulations in Finland (2/2013)
    • Education to building owners and managers: semblance of best practicies in heating and other use of energy. Possible reduction in energy use of building stock is about 10%, and mere beneficial health effects are expected.
  • Land use and transport
    • If possible, PM emissions and noise are calculated based on updated version of Kuopio´s traffic network
    • Alternatively, the effect of increased use of biofuels on GHG and CO2 emissions is evaluated.
    • Possibilities of rail traffic in Kuopio
  • Other...

Indicators

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

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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