Buildings in Basel

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Question

What is the building stock in Basel?

Rationale

Detailed data can be found from heande:Buildings in Basel. This data is aggregated to the postal code level. The table uses Swiss Land Survey coordinates CH1903. [1] [2] Use this code in the ova2spat function:

proj4string = "+init=epsg:21781"

Locations of postal codes(epsg:21781)

Obs	Postal code	X	Y
1	4001	611296.55	267316.04
2	4051	611004.37	267137.18
3	4052	612735.06	266596.60
4	4053	611695.00	265635.41
5	4054	609694.31	266598.91
6	4055	609798.12	267812.13
7	4056	610271.78	268500.37
8	4057	611580.35	268976.81
9	4058	612781.70	268225.32
10	4059	611615.85	264689.04
11	4125	615721.84	269737.40
12	4126	617160.01	268852.20

Calculations

Initiation

This code creates ovariables that are needed to run the Building model and its ovariables buildings and heatingEnergy.

+ Show code- Hide code library(OpasnetUtils) # [[Buildings in Basel]], building stock, locations by postal codes (in A Swiss coordinate system) stockBuildings <- Ovariable("stockBuildings", ddata = "Op_en7044.postal_code_areas") colnames(stockBuildings@data)[colnames(stockBuildings@data) == "Eventyear"] <- "Time" # Geolocations of the buildings for emission calculations. emissionLocations <- Ovariable("emissionLocations", ddata = "Op_en7044.locations_of_postal_codes") colnames(emissionLocations@data)[colnames(emissionLocations@data) == "emissionLocationsResult"] <- "Y" emissionLocations@data$emissionLocationsResult <- 1 ####### DEFINE DUMMIES FOR MODEL PROPERTIES THAT ARE NOT NEEDED FOR BASEL construction <- Ovariable("construction", data = data.frame( Postal.code = 4051, Eventyear = 1871, Building = "Residential buildings with subsidiary use", Heating = "Electricity", Result = 0 )) changeBuildings <- 0 obstime <- data.frame(Startyear = 2010) heating_before <- TRUE # Should heatingShares be calculated before renovate and timepoints (or after)? efficiency_before <- TRUE # Should efficiencyShares be calculated before renovate and timepoints (or after)? efficiencyShares <- Ovariable("efficiencies", data = data.frame(Efficiency = "New", Result = 1)) # Current efficiencies heatingShares <- 1 # Heating types of current buildings. Exists as part of buildingStock heatingSharesNew <- 0 # Heating types of the buildings in the future renovationRate <- 0 # Percentage of renovations per year renovationShares <- 0 # Fraction of renovation type when renovation is done. Renovations not considered. # Ovariables energyUse and savingPotential must be taken from [[Energy use of buildings]]. # obsyear, # Years for which observations are calculated. Must be given in an assessment. openv.setN(0) if(FALSE) { objects.store( buildingStock, # Current building stock buildingTypes, # A dummy variable to combine two different indices: Building and Building2 construction, # Construction rate in the future efficiencies, # Energy efficiencies of current buildings efficienciesNew, # Energy efficiencies in the future emissionLocations, # Locations of buildings eventyear, # A dummy variable to combine time periods to numerical time axis. heatingShares, # Heating types of current buildings heatingSharesNew, # Heating types of the buildings in the future renovation, # Percentage of renovations per year renovationShares # Fraction of renovation type when renovation is done. ) cat("Objects buildingStock, # Current building stock buildingTypes, # A dummy variable to combine two different indices: Building and Building2 construction, # Construction rate in the future efficiencies, # Energy efficiencies of current buildings efficienciesNew, # Energy efficiencies in the future emissionLocations, # Locations of buildings eventyear, # A dummy variable to combine time periods to numerical time axis. heatingShares, # Heating types of current buildings heatingSharesNew, # Heating types of the buildings in the future renovation, # Percentage of renovations per year renovationShares # Fraction of renovation type when renovation is done. stored.\n") }

Model

+ Show code- Hide code library(OpasnetUtils) openv.setN(0) # use medians instead of whole sampled distributions objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]] findrest obstime <- data.frame(Startyear = 2012) # Observation years must be defined for an assessment. ## Additional index needed in followup of ovariables efficiencyShares and stockBuildings #year <- Ovariable("year", data = data.frame( # Constructed = factor( # c("1799-1899", "1900-1909", "1910-1919", "1920-1929", "1930-1939", "1940-1949", # "1950-1959", "1960-1969", "1970-1979", "1980-1989", "1990-1999", # "2000-2010", "2011-2019", "2020-2029", "2030-2039", "2040-2049" # ), # ordered = TRUE # ), # Time = c(1880, 1905 + 0:14 * 10), # Result = 1 #)) BS <- 24 heating_before <- TRUE efficiency_before <- TRUE ###################### Decisions decisions <- opbase.data('Op_en5461', subset = "Decisions") # [[Climate change policies and health in Kuopio]] DecisionTableParser(decisions) # Remove previous decisions, if any. rm( "buildings", "stockBuildings", "changeBuildings", "efficiencyShares", "energyUse", "heatingShares", "renovationShares", "renovationRate", "fuelShares", "year", envir = openv ) ############################ City-specific data ####!------------------------------------------------ objects.latest("Op_en5417", code_name = "initiate") # [[Population of Kuopio]] # population: City_area # objects.latest("Op_en5932", code_name = "initiate") # [[Building stock in Kuopio]] Building ovariables: objects.latest("Op_en7044", code_name = "initiate") # [[Buildings in Basel]] # buildingStock: Building, Constructed, City_area # rateBuildings: Age, (RenovationPolicy) # renovationShares: Renovation # construction: Building # constructionAreas: City_area # buildingTypes: Building, Building2 # heatingShares: Building, Heating, Eventyear # heatingSharesNew: Building2, Heating # eventyear: Constructed, Eventyear # efficiencyShares: Time, Efficiency head(renovationRate@output) head(renovationRate@data) renovationRate <- EvalOutput(renovationRate) * 5 # Rates for 5-year periods renovationRate@name <- "renovationRate" # This is needed for CheckDecisions renovationRate@output$renovationRateResult <- renovationRate@output$Result renovationRate@output$Result <- NULL #################### Energy use (needed for buildings submodel) ####!------------------------------------------------ objects.latest("Op_en5488", code_name = "initiate") # [[Energy use of buildings]] # energyUse: Building, Heating # efficiencyShares: Efficiency, Constructed # renovationRatio: Efficiency, Building2, Renovation ####i------------------------------------------------ ###################### Actual building model # The building stock is measured as m^2 floor area. ####!------------------------------------------------ objects.latest("Op_en6289", code_name = "initiate") # [[Building model]] # Generic building model. # buildings: formula-based # heatingEnergy: formula-based ####i------------------------------------------------ buildings <- EvalOutput(buildings, verbose = TRUE) buildings@output$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 colnames(bui) ggplot(subset(bui, EfficiencyPolicy == "BAU"), aes(x = Time, weight = Result, 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 = Result, 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 = Result, fill = Heating)) + geom_bar(binwidth = 5) + theme_gray(base_size = BS) + labs( title = "Building stock in Kuopio", x = "Time", y = "Floor area (M m2)" ) ggplot(subset(bui, RenovationPolicy == "BAU" & EfficiencyPolicy == "BAU"), aes(x = Time, weight = Result, fill = Building)) + geom_bar(binwidth = 5) + theme_gray(base_size = BS) + labs( title = "Building stock in Kuopio", x = "Time", y = "Floor area (M m2)" ) ###################### Energy and emissions ####!------------------------------------------------ objects.latest("Op_en2791", code_name = "initiate") # [[Emission factors for burning processes]] # emissionFactors: Burner, Fuel, Pollutant # fuelShares: Heating, Burner, Fuel ####i------------------------------------------------ #fuelShares <- CheckDecisions(EvalOutput(fuelShares, verbose = TRUE)) heatingEnergy <- EvalOutput(heatingEnergy, verbose = TRUE) ################ 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 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 # Old data: # objects.latest("Op_en3435", code_name = "disperse") # [[Exposure to PM2.5 in Finland]] # iF: Iter, Emissionheight, City.area ## THESE SHOULD BE UPDATED! (precalculated with N = 1) # emissionLocations: Heating, Emission site, Emission height # Summarised Piltti matrix, another copy of the code on a more reasonable page # Default run: en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=aXDIVDboftr1bTEd emissions <- EvalOutput(emissions) class(emissions@output$Time) emissions@output$Time <- as.numeric(as.character(emissions@output$Time)) # Plot energy need and emissions ggplot(heatingEnergy@output, aes(x = Time, weight = heatingEnergyResult * 1E-6, fill = Heating)) + geom_bar(binwidth = 5) + facet_grid(EfficiencyPolicy ~ RenovationPolicy, scale = "free_y") + theme_gray(base_size = BS) + labs( title = "Energy used in heating in Kuopio", 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 Kuopio", 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 Kuopio", x = "Time", y = "Emissions (ton /a)" ) ggplot(subset(emis, EfficiencyPolicy == "BAU" & FuelPolicy == "BAU"), aes(x = Time, weight = emissionsResult, fill = Fuel)) + geom_bar(binwidth = 5) + facet_grid(Pollutant ~ RenovationPolicy, scale = "free_y") + theme_gray(base_size = BS) + labs( title = "Emissions from heating in Kuopio", x = "Time", y = "Emissions (ton /a)" ) ###################### Health assessment ####!------------------------------------------------ objects.latest('Op_en2261', code_name = 'initiate') # [[Health impact assessment]] dose, RR, totcases. objects.latest('Op_en5917', code_name = 'initiate') # [[Disease risk]] disincidence 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 <- 5E+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 #DALYs@output <- DALYs@output[DALYs@output$Trait != "Lung cancer" , ] # Has to be removed to avoid double counting. 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 == 2020 & 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)" )

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