Buildings in Basel: Difference between revisions

From Opasnet
Jump to navigation Jump to search
(→‎Calculations: started the Basel case)
 
(11 intermediate revisions by the same user not shown)
Line 11: Line 11:


== Rationale ==
== Rationale ==
=== Data ===


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. [http://spatialreference.org/ref/epsg/21782/] [http://en.wikipedia.org/wiki/Swiss_coordinate_system] Use this code in the ova2spat function:
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. [http://spatialreference.org/ref/epsg/21782/] [http://en.wikipedia.org/wiki/Swiss_coordinate_system] Use this code in the ova2spat function:
  proj4string = "+init=epsg:21781"
  proj4string = "+init=epsg:21781"
==== Renovations ====
Estimates from Laura Perez and Stephan Trüeb, unibas.ch N:\YMAL\Projects\Urgenche\WP9 Basel\Energy_scenarios_Basel_update.docx
<t2b name='Fraction of houses renovated per year' index="Age" obs="Result" desc="Description" unit= "%">
0|0|Estimates from Laura Perez and Stephan Trüeb
20|0|Assumption Result applies to buildings older than the value in the Age column.
25|1|
30|1|
50|1|
100|1|
1000|1|
</t2b>
<t2b name='Popularity of renovation types' index='Renovation' obs='Fraction' desc='Description' unit='%'>
None|0|
Windows|65|
Technical systems|30|
Sheath reform|5|
General|0|
</t2b>
==== Emission locations ====
Where and how do the emissions of heating take place?
<t2b name='Emission locations' index='Heating,Emission site,Emission height' obs='Dummy' unit='-'>
District|4056|High|4056 is the postal code of the heat and power plant IWB, Hagenaustrasse 40/70 4056 Basel.
Long-distance heating|4056|High|
Electricity|4056|High|
Geothermal|4056|High|
Centrifuge, hydro-extractor|4056|High|
Heating oil|At site of consumption|Ground|
Wood|At site of consumption|Ground|
Gas|At site of consumption|Ground|
Coal|At site of consumption|Ground|
Solar heater/ collector|At site of consumption|Ground|
No energy source|At site of consumption|Ground|
Other sources|At site of consumption|Ground|
</t2b>


<t2b name="Locations of postal codes" index="Postal code,X" obs="Y" unit="epsg:21781">
<t2b name="Locations of postal codes" index="Postal code,X" obs="Y" unit="epsg:21781">
Line 30: Line 73:
</t2b>
</t2b>


=== Calculations ===
==== Fuel use by heating type ====


==== Initiation ====
Basel-specific data about connections between Heating and fuel usage. Generic data should be taken from [[Energy balance]].


This code creates ovariables that are needed to run the [[Building model]] and its ovariables buildings and heatingEnergy.
<t2b name='Fuel use by heating type' index='Heating,Burner,Fuel' obs='Fraction' desc='Description' unit='-'>
Long-distance heating|Large fluidized bed|Waste|0.5|The values are from Wikipedia for Basel. See below.
<rcode name="initiate" label="Initiate objects (developers only)" embed=1>
Long-distance heating|Large fluidized bed|Wood|0.1|
library(OpasnetUtils)
Long-distance heating|Large fluidized bed|Gas|0.4|
Long-distance heating|Grid|Electricity|0|This is geothermally-generated district heating. Note: the fraction you need electricity is ca. 0.3*the amount of geoheat produced.
Coal|Large fluidized bed|Coal|1|
</t2b>


# [[Buildings in Basel]], building stock, locations by postal codes (in A Swiss coordinate system)
For guesstimates about Long-distance heating (which is used in Basel), see [[Climate change policies in Basel]] and [[:de:IWB]].
stockBuildings <- Ovariable("stockBuildings", ddata = "Op_en7044.postal_code_areas")
colnames(stockBuildings@data)[colnames(stockBuildings@data) == "Eventyear"] <- "Time"


# Geolocations of the buildings for emission calculations.
<rcode name="fuelShares" label="Initiate fuelShares (developers only)" embed=1>
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
## This code is Op_en7044/fuelShares on page [[Buildings in Basel]]


construction <- Ovariable("construction", data = data.frame(
library(OpasnetUtils)
Postal.code = 4051,
Eventyear = 1871,
Building = "Residential buildings with subsidiary use",
Heating = "Electricity",
Result = 0
))


changeBuildings <- 0
fuelShares <- Ovariable("fuelShares",
obstime <- data.frame(Startyear = 2010)
dependencies = data.frame(
heating_before <- TRUE # Should heatingShares be calculated before renovate and timepoints (or after)?
Name = "fuelSharesgeneric",
efficiency_before <- TRUE # Should efficiencyShares be calculated before renovate and timepoints (or after)?
Ident = "Op_en5141/fuelSharesgeneric" # [[Energy balance]]
efficiencyShares <- Ovariable("efficiencies", data = data.frame(Efficiency = "New", Result = 1)) # Current efficiencies
),
heatingShares <- 1 # Heating types of current buildings. Exists as part of buildingStock
formula = function(...) {
heatingSharesNew <- 0 # Heating types of the buildings in the future
renovationRate <- 0 # Percentage of renovations per year
dat <- EvalOutput(Ovariable("dat", ddata = "Op_en7044", subset = "Fuel use by heating type")) # [[Buildings in Basel]]
renovationShares <- 0 # Fraction of renovation type when renovation is done. Renovations not considered.
colnames(dat@output) <- gsub("[ \\.]", "_", colnames(dat@output))


# Ovariables energyUse and savingPotential must be taken from [[Energy use of buildings]].
out <- combine(fuelSharesgeneric, dat, name = "fuelShares")
# obsyear, # Years for which observations are calculated. Must be given in an assessment.
out <- out * Ovariable(output = data.frame(Time = 1900:2080, Result = 1), marginal = c(TRUE, FALSE))
 
out <- unkeep(out, prevresults = TRUE, sources = TRUE)
openv.setN(0)


return(out)
}
)


objects.store(fuelShares)
cat("Object fuelShares stored.\n")


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")
}
</rcode>
</rcode>


==== Model ====
=== Calculations ===


<rcode>
This code creates ovariables that are needed to run the [[Building model]] and its ovariables buildings and heatingEnergy.
<rcode name="initiate" label="Initiate objects (developers only)" embed=1>
library(OpasnetUtils)
library(OpasnetUtils)


openv.setN(0) # use medians instead of whole sampled distributions
# [[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) == "Built"] <- "Time"
colnames(stockBuildings@data)[colnames(stockBuildings@data) == "Postal code"] <- "City_area"


objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]] findrest
# Construction rate is assumed to be 2 % /a from the year 2010 building stock.


obstime <- data.frame(Startyear = 2012) # Observation years must be defined for an assessment.
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))


## Additional index needed in followup of ovariables efficiencyShares and stockBuildings
# Geolocations of the buildings for emission calculations. OLD VERSION
# emissionLocations <- Ovariable("emissionLocations", ddata = "Op_en7044.locations_of_postal_codes")
# colnames(emissionLocations@data)[colnames(emissionLocations@data) == "emissionLocationsResult"] <- "Y"
# emissionLocations@data$emissionLocationsResult <- 1


#year <- Ovariable("year", data = data.frame(
emissionLocations <- Ovariable("emissionLocations", ddata = "Op_en7044", subset = "Emission locations")
# Constructed = factor(
colnames(emissionLocations@data) <- gsub("[ \\.]", "_", colnames(emissionLocations@data))
# c("1799-1899", "1900-1909", "1910-1919", "1920-1929", "1930-1939", "1940-1949",
emissionLocations@data$emissionLocationsResult <- 1
# "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
heatingShares <- 1 # This is already in the Basel data.
heating_before <- TRUE
efficiency_before <- TRUE


###################### Decisions
renovationRate <- Ovariable('renovationRate',
 
dependencies = data.frame(Name = "dummy"),
decisions <- opbase.data('Op_en5461', subset = "Decisions") # [[Climate change policies and health in Kuopio]]
formula = function(...) {
 
temp <- tidy(opbase.data('Op_en7044', subset = 'Fraction of houses renovated per year'))
DecisionTableParser(decisions)
temp$Age <- round(as.numeric(as.character(temp$Age)))
 
out <- as.data.frame(approx(
# Remove previous decisions, if any.  
temp$Age,  
rm(
temp$Result,  
"buildings",
n = (max(temp$Age) - min(temp$Age) + 1),
"stockBuildings",
method = "constant"
"changeBuildings",
))
"efficiencyShares",
colnames(out) <- c("Age", "renovationRateResult")
"energyUse",
out$renovationRateResult <- out$renovationRateResult / 100
"heatingShares",
out <- Ovariable("renovationRate", output = out, marginal = c(TRUE, FALSE))
"renovationShares",
return(out)
"renovationRate",
}
"fuelShares",
"year",
envir = openv
)
)


############################ City-specific data
renovationShares <- Ovariable("renovationShares",
dependencies = data.frame(Name = "dummy"),
formula = function(...) {
out <- Ovariable("raw", ddata = 'Op_en7044', subset = 'Popularity of renovation types')
out <- findrest((out), cols = "Renovation", total = 100) / 100


####!------------------------------------------------
renovationyear <- Ovariable("renovationyear", data = data.frame(
objects.latest("Op_en5417", code_name = "initiate") # [[Population of Kuopio]]
Obsyear = factor(c(2020, 2030, 2040, 2050)),
# population: City_area
Result = 1
# 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)
out <- out * renovationyear # renovation shares repeated for every potential renovation year.


####!------------------------------------------------
out@output$Renovation <- factor(out@output$Renovation, levels = c(
objects.latest("Op_en5488", code_name = "initiate") # [[Energy use of buildings]]
"None",  
# energyUse: Building, Heating
"General",
# efficiencyShares: Efficiency, Constructed
"Windows",  
# renovationRatio: Efficiency, Building2, Renovation
"Technical systems",  
####i------------------------------------------------
"Sheath reform"
), ordered = TRUE)


###################### Actual building model
return(out)
# The building stock is measured as m^2 floor area.
}
)


####!------------------------------------------------
dummy <- 1
objects.latest("Op_en6289", code_name = "initiate") # [[Building model]] # Generic building model.
# buildings: formula-based
# heatingEnergy: formula-based
####i------------------------------------------------


buildings <- EvalOutput(buildings, verbose = TRUE)
objects.store(
 
stockBuildings, # Current building stock
buildings@output$RenovationPolicy <- factor(
changeBuildings, # Building stock change per year
buildings@output$RenovationPolicy,
emissionLocations, # Locations of buildings and emissions
levels = c("BAU", "Active renovation", "Effective renovation"),
heatingShares, # Heating types of current buildings
ordered = TRUE
renovationRate, # Percentage of renovations per year
renovationShares, # Fraction of renovation type when renovation is done. From [[Building stock in Kuopio]]
dummy # Needed by renovationShares
)
)


buildings@output$EfficiencyPolicy <- factor(
cat("Objects
buildings@output$EfficiencyPolicy,
stockBuildings,  
levels = c("BAU", "Active efficiency"),
changeBuildings,
ordered = TRUE
emissionLocations,
)
heatingShares,
 
renovationRate,
bui <- oapply(buildings * 1E-6, cols = c("City_area", "buildingsSource"), FUN = sum)@output
renovationShares
colnames(bui)
stored.\n")
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>
</rcode>
Line 407: Line 222:
<references/>
<references/>


==Related files==
==Related files==<!-- __OBI_TS:1424513031 -->

Latest revision as of 07:58, 23 July 2015



Question

What is the building stock in Basel?

Answer

Rationale

Data

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"

Renovations

Estimates from Laura Perez and Stephan Trüeb, unibas.ch N:\YMAL\Projects\Urgenche\WP9 Basel\Energy_scenarios_Basel_update.docx

Fraction of houses renovated per year(%)
ObsAgeResultDescription
100Estimates from Laura Perez and Stephan Trüeb
2200Assumption Result applies to buildings older than the value in the Age column.
3251
4301
5501
61001
710001
Popularity of renovation types(%)
ObsRenovationFractionDescription
1None0
2Windows65
3Technical systems30
4Sheath reform5
5General0

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
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

Fuel use by heating type

Basel-specific data about connections between Heating and fuel usage. Generic data should be taken from Energy balance.

Fuel use by heating type(-)
ObsHeatingBurnerFuelFractionDescription
1Long-distance heatingLarge fluidized bedWaste0.5The values are from Wikipedia for Basel. See below.
2Long-distance heatingLarge fluidized bedWood0.1
3Long-distance heatingLarge fluidized bedGas0.4
4Long-distance heatingGridElectricity0This is geothermally-generated district heating. Note: the fraction you need electricity is ca. 0.3*the amount of geoheat produced.
5CoalLarge fluidized bedCoal1

For guesstimates about Long-distance heating (which is used in Basel), see Climate change policies in Basel and de:IWB.

+ Show code

Calculations

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

+ Show code

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

Retrieved from "https://en.opasnet.org/index.php?title=Buildings_in_Basel&oldid=37866"