Difference between revisions of "Climate change policies in Helsinki"

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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: 2010-2040
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.

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.

Answer

Results

Conclusions

Rationale

File:Causal diagram of climate change policies in Helsinki.PNG

Causal diagram of climate change policies in Helsinki.

Stakeholders

City of Helsinki

Dependencies

Tähän muuttujien hierarkkinen kuvaus buildingsista taaksepäin:

buildings Building model löytyy koodi, jonka lopputulos on Saved ovariables buildings, heatingEnergy, emissions, exposure. mainitaan t2b- tai muu data, josta muuttuja lasketaan. [linkki Opasnet Baseen]
- stockBuildings: data = temp
- heatingShares <- 1 sivun Building stock in Helsinki koodissa
- efficiencyShares: Energy use of buildings Taulukko: Energy efficiency of the new buildings in the future. OpasnetBase
- changeBuildings = stock Buildings
- renovationShares: Building stock in Helsinki -sivun koodi ottaa datan taulukostaan Popularity of renovation types. OpasnetBase
- renovationRate: Building stock in Helsinki -sivun koodi ottaa datan taulukostaan Fraction of houses renovated per year. OpasnetBase
- obstime: koodissa tällä sivulla
- heating_before: FALSE
- efficiency_before: TRUE

Tasks given 15.4.2015 about Building stock in Helsinki. Please put the name of file, reference, or other source of your information to the column Description in each table, or under the table if the description is long.

Tables 1 and 2: Paula and Mohammad ALSO UPLOAD Helsingin_rakennuskanta_Facta_ajo_huhtikuu_2015 using Opasnet Base
Table 3: Sonja and Panayoitis and Anna
Tables 4 and 5: Anni, Mari, and Michael
Table 6: Aishat, Evans and Badejo
Tables 7-8: not done

The main data source are these two files. I recommend that all groups go through the whole material although not everything is relevant for all groups.

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.

TASKS FOR THE DARM COURSE PARTICIPANTS:

ALL GROUPS: Study Building model as it is closely connected to everything we do.
GROUP1 (Mari, Anni and Michael): Look at Building stock in Helsinki which is the format we are likely to get the data about Helsinki. Compare that with previous assessments Buildings in Basel and Building stock in Kuopio. Make a suggestion how to model buildings in Helsinki.
GROUP2 (Mohammad and Paula): Study Energy use of buildings and come up with a suggestion whether and how that variable can be used for the Helsinki case.
GROUP3 (Badejo, Evans and Aishat): Study Emission factors for burning processes and come up with a suggestion whether and how that variable can be used for the Helsinki case.
GROUP4 (Panayiotis): Study Climate change policies and health in Kuopio and expecially the table Direct inputs with DALY weights etc. Come up with a suggestion whether and how that variable can be used for the Helsinki case.

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↷

Analyses

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 3.6.2015

+ 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(2010, 2020, 2030, 2040, 2050, 2060)) # 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

###################### Decisions
#
#decisions <- opbase.data('Op_en5461', subset = "Decisions") # [[Climate change policies and health in Kuopio]]

#DecisionTableParser(decisions)

############################ City-specific data

#### First download Basel data and use that as a default. Then, replace Helsinki-specific parts.

####!------------------------------------------------
objects.latest("Op_en7115", code_name = "initiate") # [[Building stock in Helsinki]]

# [[Building stock in Helsinki]]
dat <- opbase.data("Op_en7115.stock_details")[ , c(
#	"Rakennus ID",
	"Sijainti",
	"Valmistumisaika",
#	"Julkisivumateriaali",
	"Käyttötarkoitus",
#	"Lämmitystapa",
	"Polttoaine",
#	"Rakennusaine",
#	"Varusteena koneellinen ilmanvaihto",
#	"Perusparannus",
#	"Kunta rakennuttajana",
#	"Energiatehokkuusluokka",
#	"Varusteena aurinkopaneeli",
#	"Tilavuus",
	"Result" # Rakennusala m2
)]

colnames(dat) <- c("City_area", "Time", "Building types in Facta", "Heating types in Facta", "stockBuildingsResult")
dat$Time <- as.numeric(substring(dat$Time, nchar(as.character(dat$Time)) - 3))
dat$Time <- as.numeric(as.character((cut(dat$Time, breaks = c(0, 1885 + 0:26*5), labels = as.character(1885 + 0:26*5)))))
dat$stockBuildingsResult <- as.numeric(as.character(dat$stockBuildingsResult))

build <- tidy(opbase.data("Op_en7115.building_types"))
colnames(build)[colnames(build) == "Result"] <- "Building"

heat <- tidy(opbase.data("Op_en7115.heating_types"))
colnames(heat)[colnames(heat) == "Result"] <- "Heating"

######################
# Korjaus
########################
temp <- as.character(heat$Heating)
temp[temp == "District heating"]  <- "District"
temp[temp == "Light oil"]  <- "Oil"
temp[temp == "Fuel oil"]  <- "Oil"

heat$Heating <- temp
########################################

dat <- merge(merge(dat, build), heat)[c("City_area", "Time", "Building", "Heating", "stockBuildingsResult")]

###################### Decisions

decisions <- opbase.data('Op_en5480') # [[Climate change policies in Basel]]

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()

temp <- aggregate(dat["stockBuildingsResult"], by = dat[c("Time", "Building", "Heating")], FUN =sum)
temp <- temp[!is.na(temp$stockBuildingsResult) , ]

stockBuildings <- Ovariable("stockBuildings", data = temp) # Replace Basel building data with Helsinki data

# Construction rate is assumed to be 2 % /a from the year 2010 building stock.

# changeBuildings is defined as in Basel but only created now to match Helsinki data.

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))

renovationRate <- EvalOutput(renovationRate) * 20 # Rates for 20-year periods

#################### Energy use (needed for buildings submodel)

####!------------------------------------------------
objects.latest("Op_en5488", code_name = "initiate") # [[Energy use of buildings]] 
####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.
####i------------------------------------------------

buildings <- EvalOutput(buildings)

buildings@output$RenovationPolicy <- factor(
		buildings@output$RenovationPolicy,
		levels = c("BAU", "Active renovation", "Total 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)
bui <- truncateIndex(bui, cols = "Heating", bins = 4)@output
#levels(bui$Heating)[levels(bui$Heating) == "Long-distance heating"] <- "District heating"

ggplot(subset(bui, EfficiencyPolicy == "BAU" & RenovationPolicy == "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(subset(bui, EfficiencyPolicy == "BAU"), aes(x = Time, weight = buildingsResult, fill = Renovation)) + geom_bar(binwidth = 5) + 
		facet_grid(. ~ RenovationPolicy) + theme_gray(base_size = BS) +
		labs(
				title = "Building stock in Helsinki by renovation policy",
				x = "Time",
				y = "Floor area (M m2)"
		)

ggplot(subset(bui, RenovationPolicy == "BAU"), aes(x = Time, weight = buildingsResult, fill = Efficiency)) + geom_bar(binwidth = 5) + 
		facet_grid(. ~ EfficiencyPolicy) + theme_gray(base_size = BS) +
		labs(
				title = "Building stock in Helsinki by efficiency policy",
				x = "Time",
				y = "Floor area (M m2)"
		)

ggplot(subset(bui, RenovationPolicy == "BAU" & EfficiencyPolicy == "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, RenovationPolicy == "BAU" & EfficiencyPolicy == "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)"
		)

###################### Energy and emissions

#############################
# Toinen korjaus
#############################
energyUse <- EvalOutput(energyUse)
levels(energyUse@output$Building)[levels(energyUse@output$Building) == "Detached houses"] <- "Residential"
temp <- energyUse@output[energyUse@output$Heating == "Oil",]
temp$Heating <- "Other"
energyUse@output <- rbind(energyUse@output, temp)
#############################

####!------------------------------------------------
objects.latest("Op_en2791", code_name = "initiate") # [[Emission factors for burning processes]] 
####i------------------------------------------------

heatingEnergy <- EvalOutput(heatingEnergy)

################ 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
####i------------------------------------------------

colnames(iF@data) <- gsub("[ \\.]", "_", colnames(iF@data))
iF@data$iFResult <- iF@data$iFResult * 1E-6

#################################
# Kolmas korjaus
# fuelShares:sta puuttuu Heating-Other lokaatio
# emissionLocations:sta puuttuu Heating-Oil,Other lokaatiot (myös "District heating")
# heatingEnergy:n Heating-District vastaa fuelShares:ssa jotain turvepohjaista tuotantoa
# emissions@formula:ssa yksi rivi bugaa
#################################
temp <- as.character(heatingEnergy@output$Heating)
temp[temp == "District"]  <- "District heating"
heatingEnergy@output$Heating <- temp

emissionLocations <- EvalOutput(emissionLocations)
#levels(emissionLocations@output$Building)[levels(emissionLocations@output$Building) == "Detached houses"] <- "Residential"
temp <- emissionLocations@output[emissionLocations@output$Heating == "District",]
temp$Heating <- "District heating"
emissionLocations@output <- rbind(emissionLocations@output, temp)
temp <- emissionLocations@output[emissionLocations@output$Heating == "Heating oil",]
temp$Heating <- "Oil"
emissionLocations@output <- rbind(emissionLocations@output, temp)
temp <- emissionLocations@output[emissionLocations@output$Heating == "Other sources",]
temp$Heating <- "Other"
emissionLocations@output <- rbind(emissionLocations@output, temp)

temp <- fuelShares@data[1,]
temp$Heating <- "Other"
temp$Burner <- "*"
temp$Fuel <- "*"
temp$Time <- "*"
temp$fuelSharesResult <- 0
fuelShares@data <- rbind(fuelShares@data, temp)

temp <- function(...){
out <- oapply(heatingEnergy, cols = c("Building", "Efficiency"), FUN = sum)
out <- out * fuelShares * emissionFactors * 3.6 * 1e-09
out <- unkeep(out * emissionLocations, sources = TRUE, prevresults = TRUE)
out@output$Emission_site <- as.factor(
	ifelse(
		out@output$Emission_site == "At site of consumption", 
		as.character(out@output$City_area), 
		as.character(out@output$Emission_site)
	)
)
out <- oapply(out, cols = c("Burner", "City_area"), FUN = sum)
return(out)
}
#################################

emissions <- EvalOutput(emissions)
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
#levels(hea$Heating)[levels(hea$Heating) == "Long-distance heating"] <- "District heating"

ggplot(hea, aes(x = Time, weight = heatingEnergyResult * 1E-6, fill = Heating)) + geom_bar(binwidth = 5) +
		facet_wrap( ~ RenovationPolicy) + theme_gray(base_size = BS) +
		labs(
				title = "Energy used in heating in Helsinki",
				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 Helsinki",
				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 Helsinki",
				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 Helsinki",
				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 # ERF is picked automatically
#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 <- 623732 # Contains only the Helsinki city, i.e. assumes no exposure outside city. (Wikipedia)
# population <- 700000 # Contains the Helsinki metropolitan area, as that is exposed to PM2.5.
# Note: the population size does NOT affect the health impact as it cancels out. However, it DOES affect 
#	exposure estimates.

exposure <- EvalOutput(exposure)

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 Helsinki",
				x = "Time",
				y = "Average PM2.5 (µg/m3)"
		)

exposure@output <- exposure@output[exposure@output$Area == "Urban" , ] # Helsinki is an urban 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 Helsinki",
				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)

totcases <- truncateIndex(totcases, cols = "Heating", bins = 5)
levels(totcases@output$Heating)[levels(totcases@output$Heating) == "Long-distance heating"] <- "District heating"

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 Helsinki",
				x = "Time",
				y = "Health effects (deaths /a)"
		)

if(figstofile) ggsave("Figure8.eps", width = 11, height = 7)

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

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") & Trait == "Total mortality"
)

oprint(oapply(temp, INDEX = c("Time", "EfficiencyPolicy", "RenovationPolicy", "FuelPolicy"), FUN = sum))

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 Helsinki",
				x = "Time",
				y = "Health effects (DALY /a)"
		)

ggplot(subset(DALYs@output, Time == 2030 & 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 Helsinki 2030",
				x = "Biofuel policy in district heating",
				y = "Health effects (DALY /a)"
		)

koord <- tidy(opbase.data("Op_en7115", subset = "Locations of postal codes"))
colnames(koord) <- c("Emission_site", "X", "Y")
koord$Result <- 1
koord <- Ovariable("koord", output = koord, marginal = c(TRUE, TRUE, TRUE, FALSE))

emis <- emissions * koord

emis@output <- subset(emis@output, RenovationPolicy == "BAU" & EfficiencyPolicy == "BAU" & FuelPolicy == "BAU" & Pollutant == "PM2.5")
emis <- oapply(emis, INDEX = c("Emission_site", "X", "Y"), FUN = sum)

MyRmap(
		ova2spat(
				emis, 
				coord = c("X", "Y"), 
				proj4string = "+init=epsg:21781"
		), # Swiss Land Survey uses  CH1903 
		# http://spatialreference.org/ref/epsg/21782/
		# http://en.wikipedia.org/wiki/Swiss_coordinate_system
		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.

Keywords

Helsinki, energy, building stock, heating, renovation.

References

Related files

@@ Line 9: / Line 9: @@
 == Scope ==
-{{comment|# |All: Suggest good scoping for the assessment. You may also consult people in Helsinki. You may also borrow ideas you have used in Homework4. This will be decided soon after Easter.|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 21:04, 25 March 2015 (UTC)}}
+=== Question ===
-=== 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: 2010-2040
+* 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.
 === 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.
 == Answer ==
@@ Line 35: / Line 49: @@
 === Stakeholders ===
+* City of Helsinki
 === Dependencies ===
@@ Line 83: / Line 99: @@
 * [[Health impact assessment]]
 * [[Disease risk]]
-* [[ERFs of environmental pollutants]]
+* [[Exposure-response function]]
 * [[Burden of disease in Finland]]
@@ Line 110: / Line 126: @@
 === Calculations ===
-[http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=U5Y9O1IV6TWfhSs2 Model run 3,6,2015]
+[http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=U5Y9O1IV6TWfhSs2 Model run 3.6.2015]
 <rcode graphics=1>
@@ Line 127: / Line 143: @@
 objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]] findrest
-#obstime <- data.frame(Startyear = (192:205) * 10) # Observation years must be defined for an assessment.
+obstime <- data.frame(Startyear = c(2010, 2020, 2030, 2040, 2050, 2060)) # Observation years must be defined for an assessment.
-obstime <- data.frame(Startyear = c(2010, 2030)) # Observation years must be defined for an assessment.
 BS <- 24
@@ Line 138: / Line 152: @@
 ## Additional index needed in followup of ovariables efficiencyShares and stockBuildings
-#year <- Ovariable("year", data = data.frame(
-#	Constructed = factor(
-#		c("1759-1899", "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
-#))
 ###################### Decisions
@@ Line 164: / Line 166: @@
 objects.latest("Op_en7115", code_name = "initiate") # [[Building stock in Helsinki]]
-# [[Building stock in Helsinki]]. Somehow only works if done in parts.
+# [[Building stock in Helsinki]]
-#datb <- opbase.data("Op_en7115.stock_details", include = list(Julkisivumateriaali = "")) # 18747 rows
-#datc <- opbase.data("Op_en7115.stock_details", exclude = list(Julkisivumateriaali = "")) # 30038 rows
 dat <- opbase.data("Op_en7115.stock_details")[ , c(
 #	"Rakennus ID",
@@ Line 182: / Line 182: @@
 #	"Varusteena aurinkopaneeli",
 #	"Tilavuus",
-	"Result"
+	"Result" # Rakennusala m2
 )]
-#dat <- rbind(datb, datc)[ , c(
-#	"Rakennus ID",
-#	"Sijainti",
-#	"Valmistumisaika",
-#	"Julkisivumateriaali",
-#	"Käyttötarkoitus",
-#	"Lämmitystapa",
-#	"Polttoaine",
-#	"Rakennusaine",
-#	"Varusteena koneellinen ilmanvaihto",
-#	"Perusparannus",
-#	"Kunta rakennuttajana",
-#	"Energiatehokkuusluokka",
-#	"Varusteena aurinkopaneeli",
-#	"Tilavuus",
-#	"Result"
-#)]
 colnames(dat) <- c("City_area", "Time", "Building types in Facta", "Heating types in Facta", "stockBuildingsResult")
@@ Line 574: / Line 557: @@
 == See also ==
+;Other related assessments
+* [[Helsinki energy decision 2015]]
 * [[Climate change policies and health in Kuopio]]
 * [[Climate change policies in Basel]]
@@ Line 595: / Line 580: @@
 * [[Gabi 4.3 - life cycle assessment software]]
-Possibly useful variables
+;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]]
@@ Line 639: / Line 624: @@
 * [[Heating systems in buildings in Finland]] SHOULD BE UPDATED FROM STAT FINLAND IF NEEDED
-Almost empty pages that should be removed
+;Almost empty pages that should be removed
 * [[Unit price of carbon emission permits]]
 * [[Outdoor air temperature in Finland]]
@@ Line 647: / Line 632: @@
 == Keywords ==
+Helsinki, energy, building stock, heating, renovation.
 == References ==

Difference between revisions of "Climate change policies in Helsinki"

Revision as of 15:31, 7 June 2015

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