Climate change policies in Helsinki

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This assessment was used for training in Decision analysis and risk management 2015 course. To see student contributions, see a previous version.

Scope

Question

What is the energy need of buildings in Helsinki and the related greenhouse gas emissions and health impacts? How can these be affected by renovation of buildings and fuel changes in district heating?

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.

Participants

• Jouni Tuomisto (THL), the coordinator of the assessment
• Tha perticipants of the Decision analysis and risk management 2015 course.
• Helsinki Environment Centre
• Siemens and the City Performance Tool experts

Boundaries

• Time: 2010-2060
• Spatial: the city of Helainki

Decisions and scenarios

• Renovate 1 or 2 % of buildings per year.
• Change the district heating fuel from 100 % fossil to 50 % fossil, 50 % wood-based fuel.

See also decisions in Climate change policies and health in Kuopio.

Show details

Decisions(-) Obs Decision maker Decision Option Variable Cell Change Unit Amount Description 1 Builders EnergySavingPolicy BAU efficiencyShares Add 0 2 Builders EnergySavingPolicy Energy saving moderate efficiencyShares Add 0 3 Builders EnergySavingPolicy Energy saving total efficiencyShares Efficiency:Passive;Time:2020,2030 Add fraction 0.25 All input must be given in units that are used in respective ovariables. 4 Builders EnergySavingPolicy Energy saving total efficiencyShares Efficiency:Passive;Time:2040,2050,2060 Add fraction 0.1 5 Builders EnergySavingPolicy Energy saving total efficiencyShares Efficiency:Low-energy;Time:2020,2030 Add fraction -0.25 6 Builders EnergySavingPolicy Energy saving total efficiencyShares Efficiency:Low-energy;Time:2040,2050,2060 Add fraction -0.1 7 Helen FuelPolicy BAU fuelShares Add 0 8 Helen FuelPolicy 40 bio fuelShares Burner:Large fluidized bed;Fuel:Wood;Time:2020,2030,2040,2050,2060 Add fraction 0.24 9 Helen FuelPolicy 40 bio fuelShares Burner:Large fluidized bed;Fuel:Coal;Time:2020,2030,2040,2050,2060 Add fraction -0.24 10 Building owner EnergySavingPolicy BAU renovationRate Multiply 1 /a 1 Assumes BAU renovation rate = 1%/a for buildings >30 a old 11 Building owner EnergySavingPolicy Energy saving moderate renovationRate Multiply 1 /a 2 12 Building owner EnergySavingPolicy Energy saving total renovationRate Multiply 1 /a 5 5%/a is 100 % in 20 a

Timing

The data will be collected before the end of the Decision analysis and risk management 2015 course (12th May, 2015). The model runs will be finalised soon after that. Results will be available before September 2015.

Rationale

Causal diagram of climate change policies in Helsinki. This assessment only relates to the building stock part of the diagram.

Stakeholders

• City of Helsinki

Dependencies

• Building stock in Helsinki
• Energy use of buildings
• Emission factors for burning processes
• Climate change policies and health in Kuopio#Direct inputs
• District heating production units in Helsinki metropolitan area
• Exposure to PM2.5 in Finland
• OpasnetUtils/Drafts
• Health impact assessment
• Disease risk
• Exposure-response function
• Burden of disease in Finland
• Outdoor air temperature in Finland
• Unit price of carbon emission permits

Data files

• 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.

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 12.6.2015

+ 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 = c((198:206) * 10)) # 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 ############################ City-specific data ##########################!------------------------------------------------ ## Decisions decisions <- opbase.data("Op_en7063", subset = "Decisions") # [[Climate change policies in Helsinki]] ## Building stock objects.latest("Op_en7115", code_name = "stockBuildings") # [[Building stock in Helsinki]] objects.latest("Op_en7115", code_name = "changeBuildings") # [[Building stock in Helsinki]] objects.latest("Op_en7115", code_name = "renovationRate") # [[Building stock in Helsinki]] objects.latest("Op_en7115", code_name = "renovationShares") # [[Building stock in Helsinki]] objects.latest("Op_en6289", code_name = "initiate") # [[Building model]] # Generic building model. ## Energy use (needed for buildings submodel) objects.latest("Op_en5488", code_name = "initiate") # [[Energy use of buildings]] ## Energy and emissions objects.latest("Op_en7311", code_name = "emissionLocations") # [[Helsinki energy production]] also heatingShares objects.latest("Op_en2791", code_name = "initiate") # [[Emission factors for burning processes]] objects.latest("Op_en7311", code_name = "fuelShares") # [[Helsinki energy production]] ## Exposure ## 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_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) population <- 623732 # Contains only the Helsinki city, i.e. assumes no exposure outside city. (Wikipedia) # Note: the population size does NOT affect the health impact as it cancels out. However, it DOES affect # exposure estimates. #########################i-------------------------------------------------- ################# Actual model and some intermediate processing. DecisionTableParser(decisions) # Remove previous decisions, if any. forgetDecisions <- function() { for(i in ls(envir = openv)) { if("dec_check" %in% names(openv[[i]])) openv[[i]]$dec_check <- FALSE } return(cat("Decisions were forgotten.\n")) } forgetDecisions() renovationRate <- EvalOutput(renovationRate) * 10 # Rates for 10-year periods buildings <- EvalOutput(buildings) #buildings@output$RenovationPolicy <- factor( # buildings@output$RenovationPolicy, # levels = c("BAU", "Active renovation", "Total renovation"), # ordered = TRUE #) buildings@output$EnergySavingPolicy <- factor( buildings@output$EnergySavingPolicy, levels = c("BAU", "Energy saving moderate", "Energy saving total"), ordered = TRUE ) exposure <- EvalOutput(exposure) exposure@output <- exposure@output[exposure@output$Area == "Average" , ] # Helsinki is an average area, # rather than rural or urban. 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" DALYs <- EvalOutput(DALYs) ####################### Post-processing and graphs 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, EnergySavingPolicy == "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(renovationRate@output, aes(x = Time, weight = renovationRateResult)) + geom_bar(binwidth = 5) + # facet_grid(. ~ EnergySavingPolicy) + theme_gray(base_size = BS) + # labs( # title = "Renovation rate by energy saving policy", # x = "Time", # y = "Floor area (M m2)" # ) ggplot(bui, aes(x = Time, weight = buildingsResult, fill = Efficiency)) + geom_bar(binwidth = 5) + facet_grid(. ~ EnergySavingPolicy) + theme_gray(base_size = BS) + labs( title = "Building stock in Helsinki by efficiency policy", x = "Time", y = "Floor area (M m2)" ) ggplot(subset(bui, EnergySavingPolicy == "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, EnergySavingPolicy == "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)" ) 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 ggplot(hea, aes(x = Time, weight = heatingEnergyResult, fill = Heating)) + geom_bar(binwidth = 5) + # Tuplamuunnos *1e-6 pois facet_wrap( ~ EnergySavingPolicy) + theme_gray(base_size = BS) + labs( title = "Energy used in heating in Helsinki", x = "Time", y = "Heating energy (GWh /a)" ) if("Emission_site" %in% colnames(emissions@output)) { emis <- truncateIndex(emissions, cols = "Emission_site", bins = 5)@output ggplot(subset(emis, EnergySavingPolicy == "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(emissions@output, EnergySavingPolicy == "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(emissions@output, EnergySavingPolicy == "BAU" & FuelPolicy == "BAU"), aes(x = Time, weight = emissionsResult, fill = Fuel)) + geom_bar(binwidth = 5) + facet_grid(Pollutant ~ . , scale = "free_y") + theme_gray(base_size = BS) + labs( title = "Emissions from heating in Helsinki", x = "Time", y = "Emissions (ton /a)" ) #exposure <- EvalOutput(exposure) ggplot(subset(exposure@output, EnergySavingPolicy == "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)" ) ggplot(subset(exposure@output, EnergySavingPolicy == "BAU"), aes(x = Time, weight = exposureResult, fill = Heating)) + geom_bar(binwidth = 5) + facet_grid(FuelPolicy ~ .) + theme_gray(base_size = BS) + labs( title = "Exposure to PM2.5 from heating in Helsinki", x = "Time", y = "Average PM2.5 (µg/m3)" ) ggplot(subset(totcases@output, EnergySavingPolicy == "BAU" & FuelPolicy == "BAU"), aes(x = Time, weight = totcasesResult, fill = Heating))+geom_bar(binwidth = 5) + facet_grid(Response ~ .) + 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) #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") & Response == "Total mortality" ) oprint( oapply(temp, INDEX = c("Time", "EnergySavingPolicy", "FuelPolicy"), FUN = sum), caption = "Table 1: Total DALYs/a by different combinations of policy options.", caption.placement = "top", include.rownames = FALSE ) ggplot(subset(DALYs@output, Response == "Total mortality"), aes(x = Time, weight = DALYsResult, fill = Heating))+geom_bar(binwidth = 5) + facet_grid(EnergySavingPolicy ~ FuelPolicy) + 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), aes(x = FuelPolicy, weight = DALYsResult, fill = Heating))+geom_bar() + facet_grid(EnergySavingPolicy ~ Response) + 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 <- opbase.data("Op_en7115", subset = "Locations of city areas") koord <- tidy(koord, widecol = "Location") colnames(koord)[colnames(koord) == "City_area"] <- "Emission_site" #colnames(koord)[colnames(koord) == "N"] <- "X" #colnames(koord)[colnames(koord) == "E"] <- "Y" koord$Result <- 1 koord <- Ovariable("koord", output = koord, marginal = colnames(koord) != "Result") if("Emission_site" %in% colnames(emissions@output)) { emis <- emissions * koord emis@output <- subset(emis@output, RenovationPolicy == "BAU" & EnergySavingPolicy == "BAU" & FuelPolicy == "BAU" & Pollutant == "PM2.5") emis <- oapply(emis, INDEX = c("Emission_site", "N", "E"), FUN = sum) MyRmap( ova2spat( emis, coord = c("E", "N"), proj4string = "+init=epsg:3067" ), # National Land Survey Finland uses ETRS89 / ETRS-TM35FIN #http://spatialreference.org/ref/epsg/etrs89-etrs-tm35fin/ 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.

⇤--#: . In the results the graph "Energy used in heating in Helsinki" shows a twice or thrice bigger energy use for Oil and Other than the table "Total energy consumption in Helsinki in 2013 (GWh)" on Helsinki energy consumption would suggest. --Heta (talk) 08:18, 11 June 2015 (UTC) (type: truth; paradigms: science: attack)

⇤--#: . Effective floor area of buildings by building type -table on page Building stock in Helsinki has the current building stock at 7 million m2 more than the graphs in the result, and the future estimates are even more higher than the code result estimates --Heta (talk) 10:49, 11 June 2015 (UTC) (type: truth; paradigms: science: attack)

See also

Other related assessments

• 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

Keywords

Helsinki, energy, building stock, heating, renovation.

Related files

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