Difference between revisions of "Building stock in Kuopio"

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(code and decisions moved to Climate change policies and health in Kuopio)
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== Answer ==
 
== Answer ==
  
−
<t2b name='Decisions' index='Decision maker,Decision,Option,Variable,Cell,Change,Unit' obs='Amount' desc='Description' unit='-'>
+
For building stock results, see [[Climate change policies and health in Kuopio]].
−
City|RenovationPolicy|Active renovation|renovation||Multiply||1.5|
 
−
City|RenovationPolicy|BAU|renovation||Multiply||1|
 
−
Builders|EfficiencyPolicy|BAU|efficienciesNew|Efficiency:Passive;Constructed:2020-2029|Add||0|
 
−
Builders|EfficiencyPolicy|Active efficiency|efficienciesNew|Efficiency:Passive;Constructed:2020-2029|Add||0.3|Given as fraction because that's how it is calculated in the model
 
−
Builders|EfficiencyPolicy|Active efficiency|efficienciesNew|Efficiency:Low-energy;Constructed:2020-2029|Add||-0.3|Given as fraction because that's how it is calculated in the model
 
−
Kuopion Energia|FuelPolicy|BAU|fuelTypes||Add||0|
 
−
Kuopion Energia|FuelPolicy|Buofuel increase|fuelTypes|Burner:Large fluidized bed;Fuel:Wood|Add||0.6|
 
−
Kuopion Energia|FuelPolicy|Buofuel increase|fuelTypes|Burner:Large fluidized bed;Fuel:Peat|Add||-0.6|
 
−
</t2b>
 
−
 
 
−
; Calculate building stock into the future
 
−
* The dynamics is calculated by adding building floor area at time points greater than construction year, and by subtracting when time point is greater than demolition year. This is done by building category, not individually.
 
−
* Also the renovation dynamics is built using event years: at an event, a certain amount of floor area is moved from one energy efficiency category to another.
 
−
* Full data are stored in the ovariables. Before evaluating, extra columns and rows are removed. The first part of the code is about this.
 
−
 
 
−
[http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=8yJfK6fziHcFWNrS Example model run]
 
−
 
 
−
<rcode graphics="1">
 
−
library(OpasnetUtils)
 
−
library(ggplot2)
 
−
library(rgdal)
 
−
library(maptools)
 
−
library(RColorBrewer)
 
−
library(classInt)
 
−
library(OpasnetUtilsExt)
 
−
library(RgoogleMaps)
 
−
 
 
−
objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]] findrest
 
−
objects.latest("Op_en6289", code_name = "initiate") # [[Building model]] Generic building model.
 
−
objects.latest("Op_en5417", code_name = "initiate") # [[Population of Kuopio]]
 
−
objects.latest("Op_en5932", code_name = "initiate") # [[Building stock in Kuopio]] Building ovariables
 
−
#objects.latest("Op_en5932", code_name = "disperse") # [[Building stock in Kuopio]] Summarised Piltti matrix
 
−
# Default run: http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=aO1R3Xdcg2rASbKH
 
−
objects.latest("Op_en3435", code_name = "disperse") # [[Exposure to PM2.5 in Finland]] 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
 
−
objects.latest("Op_en2791", code_name = "initiate") # [[Emission factors for burning processes]]
 
−
objects.latest("Op_en5488", code_name = "initiate") # [[Energy use of buildings]]
 
−
 
 
−
colnames(iF@output)[colnames(iF@output) == "City.area"] <- "Emission.site"
 
−
colnames(iF@output)[colnames(iF@output) == "Emissionheight"] <- "Emission.height"
 
−
 
 
−
districts <- tidy(opbase.data("Op_en3435.kuopio_city_districts"), widecol = "Location") # [[Exposure to PM2.5 in Finland]]
 
−
districts <- Ovariable("districts", data = data.frame(districts, Result = 1))
 
−
 
 
−
decisions <- opbase.data('Op_en5932', subset = 'Decisions') # [[Building stock in Kuopio]]
 
−
DecisionTableParser(decisions)
 
−
 
 
−
# Remove previous decisions, if any.
 
−
rm(
 
−
"buildings",
 
−
"buildingStock",
 
−
"buildingTypes",
 
−
"construction",
 
−
"efficiencies",
 
−
"efficienciesNew",
 
−
"energyUse",
 
−
"heatingShares",
 
−
"heatingSharesNew",
 
−
"renovation",
 
−
"renovationShares",
 
−
"fuelTypes",
 
−
"year",
 
−
envir = openv
 
−
)
 
−
 
 
−
fuelTypes <- CheckDecisions(EvalOutput(fuelTypes))
 
−
 
 
−
#################### Manage the data before calculating
 
−
 
 
−
# The building stock is measured as m^2 floor area.
 
−
 
 
−
openv.setN(5) # Set the number of iterations to 1.
 
−
 
 
−
obsyear <- (192:205) * 10
 
−
 
 
−
####### Remove columns and rows that are not needed from data. These must be done before EvalOutput.
 
−
 
 
−
population <- EvalOutput(population)
 
−
temp <- oapply(population, cols = "City area", FUN = "sum") # Sum across city areas.
 
−
temp <- unkeep(temp * 1, prevresults = TRUE) # Remove populationResult column because it would cause trouble in merge.
 
−
population <- population / temp
 
−
 
 
−
buildingStock@data <- buildingStock@data[
 
−
buildingStock@data$Observation == "AreaHR" ,
 
−
colnames(buildingStock@data) != "Observation"
 
−
]
 
−
buildingStock <- buildingStock * population
 
−
 
 
−
construction@data <- construction@data[
 
−
construction@data$Observation == "Area" ,
 
−
colnames(construction@data) != "Observation"
 
−
]
 
−
construction <- construction * constructionAreas / 100 / 3 # Statistics are for three years (2010-2012)
 
−
 
 
−
energyUse@data <- energyUse@data[
 
−
energyUse@data[["Energy use"]] == "Heat" ,
 
−
colnames(energyUse@data) != "Energy use"
 
−
]
 
−
 
 
−
savingPotential@data <- savingPotential@data[
 
−
savingPotential@data$Observation == "Relative" ,
 
−
colnames(savingPotential@data) != "Observation"
 
−
]
 
−
savingPotential <- 1 - savingPotential / 100
 
−
# Note: If you add decisions to savingPotential, this formula must be updated.
 
−
 
 
−
# The data are not yet specific to construction year, so remove index:
 
−
heatingSharesNew@data <- heatingSharesNew@data[colnames(heatingSharesNew@data) != "Constructed"]
 
−
 
 
−
# Fill in Heating types and convert from % to fraction.
 
−
heatingSharesNew <- findrest(heatingSharesNew, cols = "Heating", total = 100)
 
−
result(heatingSharesNew) <- result(heatingSharesNew) / 100
 
−
 
 
−
heatingShares <- EvalOutput(heatingShares)
 
−
result(heatingShares) <- result(heatingShares) / 100 # From % to fraction
 
−
 
 
−
# Fill in the rest of the data (the last emission class was omitted because it is determined by the total).
 
−
efficienciesNew <- findrest(efficienciesNew, cols = "Efficiency", total = 100)
 
−
result(efficienciesNew) <- result(efficienciesNew) / 100
 
−
 
 
−
# When renovations are done, which type are they?
 
−
renovationShares <- findrest(renovationShares, cols = "Renovation", total = 100)
 
−
result(renovationShares) <- result(renovationShares) / 100
 
−
 
 
−
# What fraction of buildings is renovated per year?
 
−
renovation <- EvalOutput(renovation)
 
−
result(renovation) <- result(renovation) / 100 # From % to fraction
 
−
 
 
−
colnames(iF@output)[colnames(iF@output) == "City.area"] <- "Emission.site"
 
−
colnames(iF@output)[colnames(iF@output) == "Emissionheight"] <- "Emission.height"
 
−
 
 
−
###################### Actual model
 
−
 
 
−
buildings <- EvalOutput(buildings)
 
−
 
 
−
####### Sort factors
 
−
 
 
−
buildings@output <- buildings@output[
 
−
buildings@output$EfficiencyPolicy == "BAU" ,
 
−
colnames(buildings@output) != "EfficiencyPolicy"
 
−
]
 
−
 
 
−
buildings@output$Renovation <- factor(
 
−
buildings@output$Renovation,
 
−
levels = c("None", "General", "Windows", "Technical systems", "Sheath reform"),
 
−
ordered = TRUE
 
−
)
 
−
 
 
−
buildings@output$Efficiency <- factor(
 
−
buildings@output$Efficiency,
 
−
levels = c("Old", "New", "Low-energy", "Passive"),
 
−
ordered = TRUE
 
−
)
 
−
 
 
−
buildings@output$RenovationPolicy <- factor(
 
−
buildings@output$RenovationPolicy,
 
−
levels = c("BAU", "Active renovation"),
 
−
ordered = TRUE
 
−
)
 
−
 
 
−
heatingEnergy <- EvalOutput(heatingEnergy)
 
−
heatingEnergy <- unkeep(heatingEnergy, cols = "Building2", sources = TRUE, prevresults = TRUE)
 
−
 
 
−
temp <- buildings * districts
 
−
temp@output <- temp@output[temp@output$Year == 2040 , ]
 
−
temp <- unkeep(temp, sources = TRUE, prevresults = TRUE)
 
−
temp@output <- dropall(temp@output)
 
−
temp <- oapply(temp, cols = c("Building", "Heating", "Efficiency", "Renovation"), FUN = "sum", na.rm = TRUE)
 
−
 
 
−
MyRmap(
 
−
ova2spat(temp, coord = c("E", "N"), proj4string = "+init=epsg:3067"), # National Land Survey uses EPSG:3067 (ETRS-TM35FIN)
 
−
plotvar = "Result", legend_title = "Floor area", numbins = 8, pch = 19, cex = 2
 
−
)
 
−
 
 
−
####### Calculate emissions
 
−
 
 
−
emis <- heatingEnergy
 
−
emis@output <- emis@output[emis@output$Year >= 1980 , ]
 
−
emis <- oapply(emis, cols = c("Efficiency", "Renovation", "Building"), FUN = "sum")
 
−
emis@output$Year <- as.numeric(levels(emis@output$Year)[emis@output$Year])
 
−
 
 
−
emis <- emis * fuelTypes * emissionFactors * 3.6  * 1E-9 # convert from kWh /a to MJ /a and mg to ton
 
−
 
 
−
emis <- unkeep(emis, sources = TRUE, prevresults = TRUE)
 
−
 
 
−
emis <- emis * emissionLocations
 
−
colnames(emis@output)[colnames(emis@output) == "Emission site"] <- "Emission.site"
 
−
colnames(emis@output)[colnames(emis@output) == "Emission height"] <- "Emission.height"
 
−
emis@output$Emission.site <- ifelse(
 
−
emis@output$Emission.site == "At site of consumption",
 
−
levels(emis@output$City.area)[emis@output$City.area],
 
−
levels(emis@output$Emission.site)[emis@output$Emission.site]
 
−
)
 
−
 
 
−
###### Calculate health impacts
 
−
 
 
−
bg.mort <- 45182 / 5203826 # same values as used in PILTTI unit: deaths per person-year
 
−
 
 
−
erf <- Ovariable("erf", data = data.frame(Pollutant = "PM2.5", Result = 0.0097))
 
−
# J. T. Tuomisto, A. Wilson, et al. Uncertainty in mortality response to airborne fine particulate matter... 2008
 
−
# unit: m^3 /ug
 
−
 
 
−
health <- (emis * 1E+6 /365 / 24 / 3600) * iF / (20 / 24 / 3600) * (erf * 1E+6) * bg.mort
 
−
 
 
−
# emis is in ton /a
 
−
# iF = conc (g /m3) * pop (#) * BR (m3 /s) / emis (g /s) => conc = iF * emis / pop / BR
 
−
# HIA = conc * erf * pop * backgroundrisk = emis (g /s) * iF / BR (m3 /s) * erf (m3 /g) * backgroundrisk
 
−
 
 
−
############################# Plot graphs
 
−
 
 
−
######## Buildings in Kuopio on map
 
−
server <- TRUE
 
−
 
 
−
if(server) # The following code only works from Opasnet server.
 
−
{
 
−
shp <- readOGR('PG:host=localhost user=postgres dbname=spatial_db','kuopio_house')
 
−
proj4string(shp) <- ("+init=epsg:3067") # The map projection of NLS Finland.
 
−
 
 
−
epsg4326String <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
 
−
shp2 <- spTransform(shp,epsg4326String) # Convert to longitude-latitude projection.
 
−
 
 
−
MyRmap(shp2, plotvar = "ika", legend_title = "Age of building", numbins = 8, pch = 19, cex = 0.3) # Draw the map.
 
−
}
 
−
 
 
−
### Impact graphs
 
−
 
 
−
BS <- 24
 
−
 
 
−
ggplot(buildings@output) + geom_bar() + facet_grid(. ~ RenovationPolicy) + theme_gray(base_size = BS) +
 
−
aes(x = Year, weight = buildingsResult/1000000, fill = Renovation) + labs(y = "Floor area (M m2)", title = "Building impacts of renovation policy")
 
−
 
 
−
ggplot(heatingEnergy@output) + geom_bar() + facet_grid(. ~ RenovationPolicy) + theme_gray(base_size = BS) + labs(title = "Energy impacts of renovation policy") +
 
−
aes(x = Year, weight = heatingEnergyResult/1E+6, fill = Heating) + labs(y = "Heating energy need (GWh /a)")
 
−
 
 
−
emis@output <- emis@output[emis@output$Renovation == "BAU" , ]
 
−
 
 
−
ggplot(emis@output) + geom_bar() + facet_grid(Pollutant ~ FuelPolicy, scales = "free_y") + theme_gray(base_size = BS) + labs(title = "Emission impacts of biofuel policy") +
 
−
aes(x = Year, weight = Result, fill = Fuel) + labs(y = "Emissions to air (ton /a)")
 
−
 
 
−
ggplot(health@output) + geom_bar() + facet_grid(FuelPolicy ~ RenovationPolicy) + theme_gray(base_size = BS) + labs(title = "Health impacts of fuel and renovation policy") +
 
−
aes(x = Year, weight = Result, fill = Heating) + labs(y = "Premature deaths (# /a)")
 
−
 
 
−
</rcode>
 
  
 
== Rationale ==
 
== Rationale ==

Revision as of 21:58, 5 March 2014



Question

What is the building stock volume in Kuopio (previous, now and in the future)? What are emissions, exposures, and health impacts of heating of buildings in Kuopio? What are the impacts of two key policies:

  1. Changing fuel from peat to wood in the major CHP power pland Haapaniemi.
  2. Changing renovation of buildings to either more energy efficient (for each renovated building) or more active (more buildings renovated).

Answer

For building stock results, see Climate change policies and health in Kuopio.

Rationale

Building stock

Building registry data(#,m2,m3,#)
ObsBuildingConstructedNumberAreaBRVolumeBRAreaHRVolumeHRPopulationDescription
1Detached houses2000-20107563860811633314101479.9324433.4From building registry, except AreaHR and VolumeHR from heat registry
2Detached houses1990-19992916923824416139061.7124881.1
3Detached houses1980-198913324364661578407178797.9571620.7
4Detached houses1970-197911535339142135897154770.3494803.8
5Detached houses1960-196971423947882591095842.12306409.3
6Detached houses1950-1959769129148400137103224.9330012.2
7Detached houses1940-19494445942019980659599.3190540.2
8Detached houses1930-19392312949211781731007.75991332.4
9Detached houses1920-19291825236220914224430.3478104.3
10Detached houses1910-191983171416328711141.3135619.0
11Detached houses1900-190936485832669748860.69156208.7
12Detached houses1799-189980214169007810738.6134331.57
13Row houses2000-20103548851477614073.8944638.6
14Row houses1990-19991314467315529152676.57167076
15Row houses1980-19892155323816737186453.91274208.7
16Row houses1970-197924811250438869899723.58316296.5
17Row houses1960-19691695935421850067956.79215540.8
18Row houses1950-19591495199218103959914.57190033
19Row houses1940-1949150242667965560316.88191308.4
20Row houses1930-193918210967697238.00222957.0
21Row houses1920-19295066941944720105.5663769.5
22Row houses1910-1919105649310954021.11212753.9
23Row houses1900-1909601617477024126.6776523.4
24Row houses1799-189963075129322412.6677652.3
25Apartment houses2000-20101011949464499167322.2585906.9
26Apartment houses1990-1999260119428487611430730.51508275
27Apartment houses1980-1989393136886479881651065.82279816
28Apartment houses1970-1979149127426429284246841.7864357.7
29Apartment houses1960-1969122114214393011202112707729.1
30Apartment houses1950-195915555041236701256781.7899164
31Apartment houses1940-1949731752152956120935.9423477.3
32Apartment houses1930-193946121224778876206.17266848.7
33Apartment houses1920-19295164681882984489.45295854
34Apartment houses1910-19191753611658028163.1598618
35Apartment houses1900-190977360018943127562.5446681.5
36Apartment houses1799-18991591903724524849.8487015.87
37Leisure houses2000-20101292762210603427622106034
38Leisure houses1990-1999628253150959798253150959798
39Leisure houses1980-1989312106254328436106254328436
40Leisure houses1970-19791517313616701731361670
41Leisure houses1960-1969234180014275641800142756
42Leisure houses1950-195919772724658772724658
43Leisure houses1940-194932658119417658119417
44Leisure houses1930-1939111671402816714028
45Leisure houses1920-1929349616724961672
46Leisure houses1910-19197834530030834530030
47Leisure houses1900-190925817731580817731580
48Leisure houses1799-189911518721517518721517
49Offices2000-201011834251324571.1108975.1
50Offices1990-19993054751651267012.09297204.7
51Offices1980-198915158566116833506.05148602.4
52Offices1970-1979581823514611168.6849534.1
53Offices1960-196914128964226731272.31138695.5
54Offices1950-19591141751489124571.1108975.1
55Offices1940-19492658131900258077.15257577.4
56Offices1930-1939672032563413402.4259441.0
57Offices1920-19296184477348413402.4259441.0
58Offices1910-191937007283936701.20929720.5
59Offices1900-1909111405665824571.1108975.1
60Offices1799-18991088852876622337.3699068.2
61Commercial2000-20103160897506014.09833975.3
62Commercial1990-1999902741281016180422.91019260
63Commercial1980-19895940501135660118277.3668181.6
64Commercial1970-19799179906624018042.29101926
65Commercial1960-1969676132544012028.267950.7
66Commercial1950-1959939921738718042.29101926
67Commercial1940-19494058241690180187.97453004.5
68Commercial1930-19391029621279520046.99113251.1
69Commercial1920-192948512277778018.79745300.5
70Commercial1910-19191704002004.69911325.1
71Commercial1900-1909372511941674173.87419029.2
72Commercial1799-1899852212777816037.5990600.9
73Health and social sector2000-2010130534225.816458.0
74Health and social sector1990-1999162652917406767612.8263327.8
75Health and social sector1980-19892193542718288741.7345617.7
76Health and social sector1970-19795976274321129.082289.9
77Health and social sector1960-1969323794312677.449374.0
78Health and social sector1950-19597793267729580.6115205.9
79Health and social sector1940-19495661169521129.082289.9
80Health and social sector1930-19392661478451.632916.0
81Health and social sector1920-192900000
82Health and social sector1910-191900000
83Health and social sector1900-190921494878451.632916.0
84Health and social sector1799-1899230708451.632916.0
85Public2000-201000000
86Public1990-199917100653086322048113251.8
87Public1980-19898948357110375.553295.0
88Public1970-197900000
89Public1960-19699161535808511672.559956.9
90Public1950-1959121716443215563.379942.5
91Public1940-1949111774484114266.473280.6
92Public1930-1939275213282593.913323.8
93Public1920-192965129136397781.739971.2
94Public1910-1919131781296.96661.9
95Public1900-1909537511036484.733309.4
96Public1799-189900000
97Sports2000-20103160148925231.234573.8
98Sports1990-199991330377715693.5103721.2
99Sports1980-19892339931172140105.7265065.4
100Sports1970-19791101029801743.711524.6
101Sports1960-196943269706974.946098.3
102Sports1950-1959556114218718.657622.9
103Sports1940-1949560624678718.657622.9
104Sports1930-19394922146974.946098.3
105Sports1920-192911283681743.711524.6
106Sports1910-191900000
107Sports1900-190900000
108Sports1799-189900000
109Educational2000-201010965329128041.9118506.4
110Educational1990-199935126906409298146.7414772.4
111Educational1980-1989502824283710140209.5592532
112Educational1970-197924244147793967300.6284415.3
113Educational1960-196922406475608.423701.28
114Educational1950-1959439921160211216.847402.6
115Educational1940-194912556202804.211850.6
116Educational1930-19395173051901402159253.2
117Educational1920-192911514002804.211850.6
118Educational1910-191900000
119Educational1900-190911829338430846.1130357
120Educational1799-189942129706311216.847402.6
121Industrial2000-2010121403498312303.767793.0
122Industrial1990-1999735982825925974847.7412407.7
123Industrial1980-198910061742213089102531.1564942
124Industrial1970-1979305353324586030759.3169482.6
125Industrial1960-196925217747859925632.8141235.5
126Industrial1950-1959335451317191633835.3186430.9
127Industrial1940-1949165991184321640590390.7
128Industrial1930-193953774169435126.628247.1
129Industrial1920-19294101224834101.222597.7
130Industrial1910-1919173638001025.35649.4
131Industrial1900-19098782108202.545195.4
132Industrial1799-18995169265305126.628247.1
133Other2000-20101757839469355419385457.8395141.1
134Other1990-1999848368865135308541245.4190711.2
135Other1980-1989867617201241334642169.5194984.3
136Other1970-1979395666755284744119212.288833.7
137Other1960-1969266408885157495912937.859822.2
138Other1950-1959310474438165693415077.969717.6
139Other1940-194924410229835255311867.854874.5
140Other1930-193975507591879193647.916867.2
141Other1920-192993721012789594523.420915.3
142Other1910-191933419161503941605.17421.5
143Other1900-1909116340611206955642.126087.9
144Other1799-189941336801378781994.29220.7


New buildings per year

Floor area of new houses and additional construction per year(#,m2,m3)
ObsBuildingNumberAreaVolumeYearDescription
1Detached houses244-27135137-40041120728-1411082010-2012From city supervision of buildings
2Row houses26-3913120-1840844141-627212010-2012From city supervision of buildings
3Apartment houses21-3134815-55460128154-2093402010-2012From city supervision of buildings
4Commercial9-149742-8732349576-6512392010-2012From city supervision of buildings
5Offices3-6235-23891993-1065902010-2012From city supervision of buildings
6Industrial14-232948-1163813555-789062010-2012From city supervision of buildings
7Public2-5313-2819905-174702010-2012From city supervision of buildings
8Educational4-6220-147301745-747182010-2012From city supervision of buildings
9Health and social sector2-817-28843280-1750642010-2012From city supervision of buildings
10Sports02010-2012From city supervision of buildings? Empty row
11Leisure houses47-692859-36609909 126932010-2012From city supervision of buildings
12Other317-42119849-3619480607-1230132010-2012From city supervision of buildings

Construction areas

Where are new buildings built in Kuopio in the future? The numbers are fractions (in %) of total floor area built in Kuopio.

Construction areas(%)
ObsCity areaFraction of floor areaDescription
1City center5All numbers are guesstimates and not based on data.
2Petonen10
3Puijonlaakso2
4Saaristokaupunki17
5Neulamäki1
6Itkonniemi-Männistö-Linnanpelto1
7Haapaniemi1
8Särkiniemi-Särkilahti2
9Niirala1
10Saarijärvi1
11Jynkkä2
12Julkula2
13Inkilänmäki-Peipposenrinne1
14Päiväranta1
15Länsi-Puijo1
16Kelloniemi1
17Levänen2
18Kettulanlahti1
19Rahusenkangas-Kuivinniemi1
20Pitkälahti47

Removed buildings per year

Number of removed buildings has been 15-25 per year during 2009-2012 according dismantling permissions of the city. The actual number may be somewhat larger.

Renovations

Fraction of houses renovated per year(%)
ObsAgeResultDescription
100Assumption Pöyry 2011 s.27 says that on average, 3 % of buildings are renovated.
2201.5Assumption Result applies to buildings older than the value in the Age column.
3304Assumption
4501.5Assumption
51001Assumption
610001Assumption
Popularity of renovation types(%)
ObsRenovationFractionDescription
1Windows10-20
2Technical systems20-25
3Sheath reform15-20
4GeneralThe rest of renovations

Heating types of buildings

Fractions of houses according heating type(%)
ObsBuildingHeatingFractionOld fractionDescription
1Detached housesDistrict68.3682.5City of Kuopio
2Detached housesElectricity16.098.93
3Detached housesOil8.504.66
4Detached housesWood5.242.86
5Detached housesGeothermal1.811.04
6Row housesDistrict83.1100See table below
7Row housesElectricity16.9100Subtraction
8Apartment housesDistrict91.3100See table below
9Apartment housesOil8.7100Subtraction
10Leisure housesElectricity100100Assumption
11OfficesDistrict89.3100See table below
12OfficesOil10.7100Subtraction
13CommercialDistrict100100Assumption
14Health and social sectorDistrict100100Assumption
15PublicDistrict100100Assumption
16SportsDistrict100100Assumption
17EducationalDistrict85100See table below
18EducationalOil15100Subtraction
19IndustrialDistrict57100See table below
20IndustrialOil43100Subtraction
21OtherDistrict100100Assumption

Explanations:

  • "Old fraction" is based on data according to which the number of detached houses residing in the central city area but NOT district-heated is 700 (Excel-file from city of Kuopio). However, more accurate number is assumed to be around 3000 (email from Arja A. 8.5.2013), which is reflected in the current "Fraction" column.


Proportion of buildings in district heat in main urban area (Pitkälahti-Sorsalo) area of Kuopio. Data from N:\YMAL\Projects\Urgenche\WP10 Kuopio\Scenarios_data_ver6.xlsx

District heat fraction(#,m2)
ObsBuildingContractsFraction of contractsFloor areaFraction of floor areaDescription
1Apartment houses9580.64624170610.913
2Detached houses32410.525871910.476
3Row houses3930.3134990200.831
4Industry and warehouses1290.2863546660.57
5Office buildings900.6253305930.893
6Schools and research buildings610.5924122160.85
7Public services16705455070
8Other buildings3001234910
9Total50690.45452697450.706
10Total residential45920.51235032720.782
Future heating types(%)
ObsBuilding2HeatingConstructedFractionDescription
1ResidentialDistrictRest of heating
2ResidentialGeothermal5-10
3ResidentialElectricity10-15
4Non-residentialDistrict100


Combining different building type categorisations.

Building type comparisons(-)
ObsBuildingBuilding2Dummy
1Detached housesResidential
2Row housesResidential
3Apartment housesResidential
4CommercialNon-residential
5OfficesNon-residential
6IndustrialNon-residential
7PublicNon-residential
8EducationalNon-residential
9Health and social sectorNon-residential
10SportsNon-residential
11Leisure housesNon-residential
12OtherNon-residential

Calculations for ovariables

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Dependencies

Other preliminary calculations

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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


Keywords

References


Related files

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