Buildings in Basel: Difference between revisions

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(→‎Initiation: emission locations added)
(initiation code rewritten. Should work)
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<rcode name="initiate" label="Initiate objects (developers only)" embed=1>
<rcode name="initiate" label="Initiate objects (developers only)" embed=1>
library(OpasnetUtils)
library(OpasnetUtils)
### First, take the [[Building stock in Kuopio]] for renovationRate and renovationShares and then replace
### other objects coming from that code.
objects.latest("Op_en5932", code_name = "initiate") #  [[Building stock in Kuopio]]


# [[Buildings in Basel]], building stock, locations by postal codes (in A Swiss coordinate system)
# [[Buildings in Basel]], building stock, locations by postal codes (in A Swiss coordinate system)
stockBuildings <- Ovariable("stockBuildings", ddata = "Op_en7044.postal_code_areas")
stockBuildings <- Ovariable("stockBuildings", ddata = "Op_en7044.postal_code_areas")
colnames(stockBuildings@data)[colnames(stockBuildings@data) == "Eventyear"] <- "Time"
colnames(stockBuildings@data)[colnames(stockBuildings@data) == "Eventyear"] <- "Time"
stockBuildings@data$Time <- 1980 # This makes the buildings old enough for renovation
stockBuildings@data$Renovation <- "None"
stockBuildings@data$Renovation <- "None"
stockBuildings@data$Efficiency <- "New"
stockBuildings@data$Efficiency <- "New"
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changeBuildings@data <- merge(changeBuildings@data, data.frame(Time = 2015 + 0:7 * 5))
changeBuildings@data <- merge(changeBuildings@data, data.frame(Time = 2015 + 0:7 * 5))


# Geolocations of the buildings for emission calculations.
# Geolocations of the buildings for emission calculations. OLD VERSION
emissionLocations <- Ovariable("emissionLocations", ddata = "Op_en7044.locations_of_postal_codes")
# emissionLocations <- Ovariable("emissionLocations", ddata = "Op_en7044.locations_of_postal_codes")
colnames(emissionLocations@data)[colnames(emissionLocations@data) == "emissionLocationsResult"] <- "Y"
# colnames(emissionLocations@data)[colnames(emissionLocations@data) == "emissionLocationsResult"] <- "Y"
emissionLocations@data$emissionLocationsResult <- 1
# emissionLocations@data$emissionLocationsResult <- 1
 
emissionLocations <- Ovariable("emissionLocations", ddata = "Op_en3435", subset = "Emission locations")
 
####### DEFINE DUMMIES FOR MODEL PROPERTIES THAT ARE NOT NEEDED FOR BASEL
 
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)


emissionLocations <- Ovariable("emissionLocations", ddata = "Op_en7044", subset = "Emission locations")


heatingShares <- 1 # This is already in the Basel data.


if(FALSE) {
objects.store(
objects.store(
buildingStock, # Current building stock
stockBuildings, # Current building stock
buildingTypes, # A dummy variable to combine two different indices: Building and Building2
changeBuildings, # Building stock change per year
construction, # Construction rate in the future
emissionLocations, # Locations of buildings and emissions
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
heatingShares, # Heating types of current buildings
heatingSharesNew, # Heating types of the buildings in the future
renovationRate, # Percentage of renovations per year
renovation, # Percentage of renovations per year
renovationShares # Fraction of renovation type when renovation is done.
renovationShares # Fraction of renovation type when renovation is done.
)
)


cat("Objects
cat("Objects
buildingStock, # Current building stock
stockBuildings,  
buildingTypes, # A dummy variable to combine two different indices: Building and Building2
changeBuildings,
construction, # Construction rate in the future
emissionLocations,
efficiencies, # Energy efficiencies of current buildings
heatingShares,
efficienciesNew, # Energy efficiencies in the future
renovationRate,
emissionLocations, # Locations of buildings
renovationShares
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")
stored.\n")
}
 
</rcode>
</rcode>


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Other sources|At site of consumption|Ground|
Other sources|At site of consumption|Ground|
</t2b>
</t2b>
==== Model ====
<rcode>
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)"
)
</rcode>


==See also==
==See also==

Revision as of 11:20, 20 February 2015



Question

What is the building stock in Basel?

Answer

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)
ObsPostal codeXY
14001611296.55267316.04
24051611004.37267137.18
34052612735.06266596.60
44053611695.00265635.41
54054609694.31266598.91
64055609798.12267812.13
74056610271.78268500.37
84057611580.35268976.81
94058612781.70268225.32
104059611615.85264689.04
114125615721.84269737.40
124126617160.01268852.20

Calculations

Initiation

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

+ Show code

Emission locations

Where and how do the emissions of heating take place?

Emission locations(-)
ObsHeatingEmission siteEmission heightDummy
1District4056High4056 is the postal code of the heat and power plant IWB, Hagenaustrasse 40/70 4056 Basel.
2Long-distance heating4056High
3Electricity4056High
4Geothermal4056High
5Centrifuge, hydro-extractor4056High
6Heating oilAt site of consumptionGround
7WoodAt site of consumptionGround
8GasAt site of consumptionGround
9CoalAt site of consumptionGround
10Solar heater/ collectorAt site of consumptionGround
11No energy sourceAt site of consumptionGround
12Other sourcesAt site of consumptionGround

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


Related files

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