Climate change policies in Helsinki

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Scope

----#: . All: Suggest good scoping for the assessment. You may also consult people in Helsinki. You may also borrow ideas you have used in Homework4. This will be decided soon after Easter. --Jouni (talk) 21:04, 25 March 2015 (UTC) (type: truth; paradigms: science: comment)

Intended use and users

A problem in the climate policy practices in the City of Helsinki is that there is not enough information about different costs and impacts of different climate change mitigation measures, especially in the long term. This is slowing down the decision-making process. The results of this course will be used at the City of Helsinki Environment Centre to assess the outcomes of different ways to reduce GHG emissions. The results will help in identifying the most favourable ways to cut GHG emissions.

Rationale

File:Causal diagram of climate change policies in Helsinki.PNG

Causal diagram of climate change policies in Helsinki.

Dependencies

Tähän muuttujien hierarkkinen kuvaus buildingsista taaksepäin:

• buildings Building model löytyy koodi, jonka lopputulos on Saved ovariables buildings, heatingEnergy, emissions, exposure. mainitaan t2b- tai muu data, josta muuttuja lasketaan. [linkki Opasnet Baseen]
  • stockBuildings: data = temp
  • heatingShares: ei löydy
  • efficiencyShares: Energy use in buildings Energy efficiency of the new buildings in the future
  • changeBuildings = stock Buildings
  • renovationShares: Buildings in Basel -sivun koodi ottaa datan taulukostaan Popularity of renovation types
  • renovationRate: Buildings in Basel -sivun koodi ottaa datan taulukostaan Rfaction of houses renovated per year
  • obstime: koodissa tällä sivulla
  • heating_before: FALSE
  • efficiency_before: TRUE

Tasks given 15.4.2015 about Building stock in Helsinki. Please put the name of file, reference, or other source of your information to the column Description in each table, or under the table if the description is long.

• Tables 1 and 2: Paula and Mohammad ALSO UPLOAD Helsingin_rakennuskanta_Facta_ajo_huhtikuu_2015 using Opasnet Base
• Table 3: Sonja and Panayoitis and Anna
• Tables 4 and 5: Anni, Mari, and Michael
• Table 6: Aishat, Evans and Badejo
• Tables 7-8: not done

The main data source are these two files. I recommend that all groups go through the whole material although not everything is relevant for all groups.

• Climate policies Helsinki data
• Climate policies Helsinki additional data
• Helsingin_rakennuskanta_Facta_ajo_huhtikuu_2015
• Additional sources of information (may be useful)
  • Motiva, Sami Seuna [1]
  • Tilastokeskus Jonna Hakala 09 1734 3419, energia@tilastokeskus.fi [2]
  • VTT: Energy efficiency scenarios of buildings (2014) [3]
  • VTT energy efficiency expert Jyri Nieminen
  • Tampere University of Technology: EPAT: Energy saving potential in Finnish housing stock by 2050. [4] See especially section 4.2.4.

TASKS FOR THE DARM COURSE PARTICIPANTS:

• ALL GROUPS: Study Building model as it is closely connected to everything we do.
• GROUP1 (Mari, Anni and Michael): Look at Building stock in Helsinki which is the format we are likely to get the data about Helsinki. Compare that with previous assessments Buildings in Basel and Building stock in Kuopio. Make a suggestion how to model buildings in Helsinki.
• GROUP2 (Mohammad and Paula): Study Energy use of buildings and come up with a suggestion whether and how that variable can be used for the Helsinki case.
• GROUP3 (Badejo, Evans and Aishat): Study Emission factors for burning processes and come up with a suggestion whether and how that variable can be used for the Helsinki case.
• GROUP4 (Panayiotis): Study Climate change policies and health in Kuopio and expecially the table Direct inputs with DALY weights etc. Come up with a suggestion whether and how that variable can be used for the Helsinki case.

• District heating production units in Helsinki metropolitan area
• Exposure to PM2.5 in Finland
• OpasnetUtils/Drafts
• Health impact assessment
• Disease risk
• ERFs of environmental pollutants
• Burden of disease in Finland

Discussions

These are some resolutions of discussions within the assessment.

• City level climate change mitigation is not useless although international treaties are important for success.D↷
• Climate change adaptation is not more important than mitigation on city level.R↻
• Citizens may have a key role in implementing city climate policies.D↷
• Food issues are underrepresented in climate discussions although food is a major emission source.R↻
• The role of district heating by nuclear energy in Helsinki is unclear.D↷
• There may be large uncertainty in CO2 emission factors of biofuels.D↷

Indices

The data will be classified according to these indices:

• Building: Residential, Public, Industrial, Other. For separating different use purposes of buildings.
• Constructed: Years of construction of the buildings in the format 1990-1999, 2000-2009, 2010-2013.
• Heating: District, Electricity, Geothermal, Oil, Wood,

The results of each ovariable will be measuring these things:

• buildings: total floor area in m².

Calculations

Model run 3,6,2015

+ Show code- Hide code ### THIS CODE IS FROM PAGE [[Climate change policies in Helsinki]] (Op_en7063, code_name = "") library(OpasnetUtils) library(ggplot2) library(rgdal) library(maptools) library(RColorBrewer) library(classInt) 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. obstime <- data.frame(Startyear = c(2010, 2030)) # Observation years must be defined for an assessment. BS <- 24 heating_before <- FALSE # Should heatingShares be calculated before renovate and timepoints (or after)? efficiency_before <- TRUE # Should efficiencyShares be calculated before renovate and timepoints (or after)? dummy <- 1 figstofile <- FALSE ## Additional index needed in followup of ovariables efficiencyShares and stockBuildings #year <- Ovariable("year", data = data.frame( # Constructed = factor( # c("1759-1899", "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 #)) ###################### Decisions # #decisions <- opbase.data('Op_en5461', subset = "Decisions") # [[Climate change policies and health in Kuopio]] #DecisionTableParser(decisions) ############################ City-specific data #### First download Basel data and use that as a default. Then, replace Helsinki-specific parts. ####!------------------------------------------------ objects.latest("Op_en7044", code_name = "initiate") # [[Buildings in Basel]] # [[Building stock in Helsinki]]. Somehow only works if done in parts. #datb <- opbase.data("Op_en7115.stock_details", include = list(Julkisivumateriaali = "")) # 18747 rows #datc <- opbase.data("Op_en7115.stock_details", exclude = list(Julkisivumateriaali = "")) # 30038 rows dat <- opbase.data("Op_en7115.stock_details")[ , c( # "Rakennus ID", "Sijainti", "Valmistumisaika", # "Julkisivumateriaali", "Käyttötarkoitus", # "Lämmitystapa", "Polttoaine", # "Rakennusaine", # "Varusteena koneellinen ilmanvaihto", # "Perusparannus", # "Kunta rakennuttajana", # "Energiatehokkuusluokka", # "Varusteena aurinkopaneeli", # "Tilavuus", "Result" )] #dat <- rbind(datb, datc)[ , c( # "Rakennus ID", # "Sijainti", # "Valmistumisaika", # "Julkisivumateriaali", # "Käyttötarkoitus", # "Lämmitystapa", # "Polttoaine", # "Rakennusaine", # "Varusteena koneellinen ilmanvaihto", # "Perusparannus", # "Kunta rakennuttajana", # "Energiatehokkuusluokka", # "Varusteena aurinkopaneeli", # "Tilavuus", # "Result" #)] colnames(dat) <- c("City_area", "Time", "Building types in Facta", "Heating types in Facta", "stockBuildingsResult") dat$Time <- as.numeric(substring(dat$Time, nchar(as.character(dat$Time)) - 3)) dat$Time <- as.numeric(as.character((cut(dat$Time, breaks = c(0, 1885 + 0:26*5), labels = as.character(1885 + 0:26*5))))) dat$stockBuildingsResult <- as.numeric(as.character(dat$stockBuildingsResult)) build <- tidy(opbase.data("Op_en7115.building_types")) colnames(build)[colnames(build) == "Result"] <- "Building" heat <- tidy(opbase.data("Op_en7115.heating_types")) colnames(heat)[colnames(heat) == "Result"] <- "Heating" ###################### # Korjaus ######################## temp <- as.character(heat$Heating) temp[temp == "District heating"] <- "District" temp[temp == "Light oil"] <- "Oil" temp[temp == "Fuel oil"] <- "Oil" heat$Heating <- temp ######################################## dat <- merge(merge(dat, build), heat)[c("City_area", "Time", "Building", "Heating", "stockBuildingsResult")] ###################### Decisions decisions <- opbase.data('Op_en5480') # [[Climate change policies in Basel]] DecisionTableParser(decisions) # Remove previous decisions, if any. forgetDecisions <- function() { for(i in ls(envir = openv)) { if("dec_check" %in% names(openv[[i]])) openv[[i]]$dec_check <- FALSE } return(cat("Decisions were forgotten.\n")) } forgetDecisions() temp <- aggregate(dat["stockBuildingsResult"], by = dat[c("Time", "Building", "Heating")], FUN =sum) temp <- temp[!is.na(temp$stockBuildingsResult) , ] stockBuildings <- Ovariable("stockBuildings", data = temp) # Replace Basel building data with Helsinki data # Construction rate is assumed to be 2 % /a from the year 2010 building stock. # changeBuildings is defined as in Basel but only created now to match Helsinki data. changeBuildings <- stockBuildings changeBuildings@name <- "changeBuildings" colnames(changeBuildings@data)[colnames(changeBuildings@data) == "stockBuildingsResult"] <- "changeBuildingsResult" changeBuildings@data$changeBuildingsResult <- changeBuildings@data$changeBuildingsResult * 0.02 changeBuildings@data$Time <- NULL changeBuildings@data <- merge(changeBuildings@data, data.frame(Time = 2015 + 0:7 * 5)) renovationRate <- EvalOutput(renovationRate) * 20 # Rates for 20-year periods #################### Energy use (needed for buildings submodel) ####!------------------------------------------------ objects.latest("Op_en5488", code_name = "initiate") # [[Energy use of buildings]] ####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. ####i------------------------------------------------ buildings <- EvalOutput(buildings) buildings@output$RenovationPolicy <- factor( buildings@output$RenovationPolicy, levels = c("BAU", "Active renovation", "Total 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) bui <- truncateIndex(bui, cols = "Heating", bins = 4)@output #levels(bui$Heating)[levels(bui$Heating) == "Long-distance heating"] <- "District heating" ggplot(subset(bui, EfficiencyPolicy == "BAU" & RenovationPolicy == "BAU"), aes(x = Time, weight = buildingsResult, fill = Heating)) + geom_bar(binwidth = 5) + theme_gray(base_size = BS) + labs( title = "Building stock in Helsinki by heating", x = "Time", y = "Floor area (M m2)" ) if(figstofile) ggsave("Figure6.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 Helsinki 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 Helsinki by efficiency policy", x = "Time", y = "Floor area (M m2)" ) 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 Helsinki", 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 Helsinki", x = "Time", y = "Floor area (M m2)" ) ###################### Energy and emissions ############################# # Toinen korjaus ############################# energyUse <- EvalOutput(energyUse) levels(energyUse@output$Building)[levels(energyUse@output$Building) == "Detached houses"] <- "Residential" temp <- energyUse@output[energyUse@output$Heating == "Oil",] temp$Heating <- "Other" energyUse@output <- rbind(energyUse@output, temp) ############################# ####!------------------------------------------------ objects.latest("Op_en2791", code_name = "initiate") # [[Emission factors for burning processes]] ####i------------------------------------------------ heatingEnergy <- EvalOutput(heatingEnergy) ################ Transport and fate ####!------------------------------------------------ iF <- Ovariable("iF", ddata = "Op_en3435", subset = "Intake fractions of PM") # [[Exposure to PM2.5 in Finland]] Humbert et al 2011 data ####i------------------------------------------------ colnames(iF@data) <- gsub("[ \\.]", "_", colnames(iF@data)) iF@data$iFResult <- iF@data$iFResult * 1E-6 ################################# # Kolmas korjaus # fuelShares:sta puuttuu Heating-Other lokaatio # emissionLocations:sta puuttuu Heating-Oil,Other lokaatiot (myös "District heating") # heatingEnergy:n Heating-District vastaa fuelShares:ssa jotain turvepohjaista tuotantoa # emissions@formula:ssa yksi rivi bugaa ################################# temp <- as.character(heatingEnergy@output$Heating) temp[temp == "District"] <- "District heating" heatingEnergy@output$Heating <- temp emissionLocations <- EvalOutput(emissionLocations) #levels(emissionLocations@output$Building)[levels(emissionLocations@output$Building) == "Detached houses"] <- "Residential" temp <- emissionLocations@output[emissionLocations@output$Heating == "District",] temp$Heating <- "District heating" emissionLocations@output <- rbind(emissionLocations@output, temp) temp <- emissionLocations@output[emissionLocations@output$Heating == "Heating oil",] temp$Heating <- "Oil" emissionLocations@output <- rbind(emissionLocations@output, temp) temp <- emissionLocations@output[emissionLocations@output$Heating == "Other sources",] temp$Heating <- "Other" emissionLocations@output <- rbind(emissionLocations@output, temp) temp <- fuelShares@data[1,] temp$Heating <- "Other" temp$Burner <- "*" temp$Fuel <- "*" temp$Time <- "*" temp$fuelSharesResult <- 0 fuelShares@data <- rbind(fuelShares@data, temp) temp <- function(...){ out <- oapply(heatingEnergy, cols = c("Building", "Efficiency"), FUN = sum) out <- out * fuelShares * emissionFactors * 3.6 * 1e-09 out <- unkeep(out * emissionLocations, sources = TRUE, prevresults = TRUE) out@output$Emission_site <- as.factor( ifelse( out@output$Emission_site == "At site of consumption", as.character(out@output$City_area), as.character(out@output$Emission_site) ) ) out <- oapply(out, cols = c("Burner", "City_area"), FUN = sum) return(out) } ################################# emissions <- EvalOutput(emissions) emissions@output$Time <- as.numeric(as.character(emissions@output$Time)) # Plot energy need and emissions hea <- oapply(heatingEnergy * 1E-6, cols = c("City_area", "buildingsSource"), FUN = sum) hea <- truncateIndex(hea, cols = "Heating", bins = 4)@output #levels(hea$Heating)[levels(hea$Heating) == "Long-distance heating"] <- "District heating" ggplot(hea, 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 Helsinki", x = "Time", y = "Heating energy (GWh /a)" ) emis <- truncateIndex(emissions, cols = "Emission_site", bins = 5)@output 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 Helsinki", x = "Time", y = "Emissions (ton /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 Helsinki", 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 Helsinki", 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 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]] ####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 <- 623732 # Contains only the Helsinki city, i.e. assumes no exposure outside city. (Wikipedia) # population <- 700000 # Contains the Helsinki metropolitan area, as that is exposed to PM2.5. # Note: the population size does NOT affect the health impact as it cancels out. However, it DOES affect # exposure estimates. exposure <- EvalOutput(exposure) 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 Helsinki", x = "Time", y = "Average PM2.5 (µg/m3)" ) exposure@output <- exposure@output[exposure@output$Area == "Urban" , ] # Helsinki is an urban 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 Helsinki", 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) totcases <- truncateIndex(totcases, cols = "Heating", bins = 5) levels(totcases@output$Heating)[levels(totcases@output$Heating) == "Long-distance heating"] <- "District heating" 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 Helsinki", x = "Time", y = "Health effects (deaths /a)" ) if(figstofile) ggsave("Figure8.eps", width = 11, height = 7) 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 Helsinki", 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 Helsinki 2030", x = "Biofuel policy in district heating", y = "Health effects (DALY /a)" ) koord <- tidy(opbase.data("Op_en7044", subset = "Locations of postal codes")) colnames(koord) <- c("Emission_site", "X", "Y") koord$Result <- 1 koord <- Ovariable("koord", output = koord, marginal = c(TRUE, TRUE, TRUE, FALSE)) emis <- emissions * koord emis@output <- subset(emis@output, RenovationPolicy == "BAU" & EfficiencyPolicy == "BAU" & FuelPolicy == "BAU" & Pollutant == "PM2.5") emis <- oapply(emis, INDEX = c("Emission_site", "X", "Y"), FUN = sum) MyRmap( ova2spat( emis, coord = c("X", "Y"), proj4string = "+init=epsg:21781" ), # Swiss Land Survey uses CH1903 # http://spatialreference.org/ref/epsg/21782/ # http://en.wikipedia.org/wiki/Swiss_coordinate_system plotvar = "Result", legend_title = "PM2.5 emissions (ton/a)", numbins = 4, pch = 19, cex = sqrt(result(emis)) * 3 ) # Map saved manually to .eps with width = 1280, height = 960 px.

See also

• Climate change policies and health in Kuopio
• Climate change policies in Basel
• Climate change policies in Kuopio
• Helsinki Region Infoshare

Other variables and pages to look at

• Thermal energy need in Helsinki Metropolitan Area
• Heating consumption of buildings
• Emission assessment of small-scale energy production in the Helsinki metropolitan area
• CLAIH assessment
• BIOHER assessment
• Unit heat consumption of buildings in Finland
• Assessment of small-scale energy production in Helsinki Metropolitan area
• Bioher model
• Claih model
• Housing stock in Helsinki metropolitan area
• Parameters, gabi-model

• Gabi 4.3 - life cycle assessment software

Possibly useful variables

• Assessment on impacts of emission trading on city-level (ET-CL)
• Exposure of Finnish subpopulations to fine particles due domestic wood combustion
• Prices of fuels in heat production
• Heating consumption of buildings
• Value of a life year (VOLY)
• Unit price of energy in residential heating in Finland
• Unit value of restricted activity days (RADs)
• Unit value of hospital admissions
• Value of a statistical life (VSL)
• Duration of morbidity
• Disability weights
• Morbidity in Finland LOOK AT THE ANALYTICA FILE
• Mortality in Finland
• Population of Finland
• Asthma prevalence
• Breathing rate
• Mortality in Finland
• Electricity production in Finland
• District heat production in Finland
• heande:HI:Policy for PM2.5 Exposure (ASK LAMA IF THIS CAN BE RELEASED!
• ERF for cold exposure and mortality
• ERF for heat exposure and morbidity
• ERF of ambient temperature on mortality
• ERF of indoor dampness on respiratory health effects
• Exposure to dampness and mold contamination in homes in Finland
• ERF for short term PM10 exposure and lower respiratory symptoms (LRS)
• ERF for short term PM10 exposure and medication usage by people with asthma
• ERF for short term PM2.5 exposure and minor restricted activity days (MRADs)
• ERF for short term PM2.5 exposure and work loss days (WLDs)
• ERF for short term PM2.5 exposure and restricted activity days (RADs)
• ERF for short term PM10 exposure and respiratory hospital admissions
• ERF for short-term PM10 exposure and cardiovascular hospital admissions
• Concentration-response to PM2.5 THIS IS THE NEWEST but many pages should be merged with this. RENAME?
• Exposure to PM2.5 in Finland
• PM 2.5 concentration indoors from indoor sources in Finland THIS IS A METHOD
• PM infiltration from outdoor air to indoor air THIS IS A METHOD
• N2O emissions from house stock heating in Finland
• CH4 emissions from house stock heating in Finland
• CO2 emissions from house stock heating in Finland
• Unit heat consumption of buildings in Finland
• Energy efficiency of buildings in Finland THIS IS ACTUALLY BUILDING STOCK OF FINLAND
• Heating systems in buildings in Finland SHOULD BE UPDATED FROM STAT FINLAND IF NEEDED

Almost empty pages that should be removed

• Unit price of carbon emission permits
• Outdoor air temperature in Finland
• PM2.5 concentration in Finland
• PM2.5 emissions from house stock heating in Finland
• New house stock production in Finland

Related files

• Model run for Kuopio
• Model run for Basel
• Tuomisto_Building_model_F1000Research