Difference between revisions of "Climate change policies in Helsinki"

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This assessment was used for training in Decision analysis and risk management 2015 course. To see student contributions, see a previous version.

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: 2010-2060
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.

See also decisions in Climate change policies and health in Kuopio.

Show details

Decisions(-)
Obs	Decision maker	Decision	Option	Variable	Cell	Change	Unit	Amount	Description
1	Builders	EnergySavingPolicy	BAU	efficiencyShares		Add		0
2	Builders	EnergySavingPolicy	Energy saving moderate	efficiencyShares		Add		0
3	Builders	EnergySavingPolicy	Energy saving total	efficiencyShares	Efficiency:Passive;Time:2020,2030	Add	fraction	0.25	All input must be given in units that are used in respective ovariables.
4	Builders	EnergySavingPolicy	Energy saving total	efficiencyShares	Efficiency:Passive;Time:2040,2050,2060	Add	fraction	0.1
5	Builders	EnergySavingPolicy	Energy saving total	efficiencyShares	Efficiency:Low-energy;Time:2020,2030	Add	fraction	-0.25
6	Builders	EnergySavingPolicy	Energy saving total	efficiencyShares	Efficiency:Low-energy;Time:2040,2050,2060	Add	fraction	-0.1
7	Helen	FuelPolicy	BAU	fuelShares		Add		0
8	Helen	FuelPolicy	40 bio	fuelShares	Burner:Large fluidized bed;Fuel:Wood;Time:2020,2030,2040,2050,2060	Add	fraction	0.24
9	Helen	FuelPolicy	40 bio	fuelShares	Burner:Large fluidized bed;Fuel:Coal;Time:2020,2030,2040,2050,2060	Add	fraction	-0.24
10	Building owner	EnergySavingPolicy	BAU	renovationRate		Multiply	1 /a	1	Assumes BAU renovation rate = 1%/a for buildings >30 a old
11	Building owner	EnergySavingPolicy	Energy saving moderate	renovationRate		Multiply	1 /a	2
12	Building owner	EnergySavingPolicy	Energy saving total	renovationRate		Multiply	1 /a	3	3%/a is 100 % in 20 a

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

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Causal diagram of climate change policies in Helsinki. This assessment only relates to the building stock part of the diagram.

Stakeholders

City of Helsinki

Dependencies

Data files

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.

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 14.6.2015

+ 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((198:206) * 10)) # Observation years must be defined for an assessment.

BS <- 24 # base_size = font size in graphs
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)?
figstofile <- FALSE

## Additional index needed in followup of ovariables efficiencyShares and stockBuildings

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

##########################!------------------------------------------------
## Decisions
decisions <- opbase.data("Op_en7063", subset = "Decisions") # [[Climate change policies in Helsinki]]

## Building stock
objects.latest("Op_en7115", code_name = "stockBuildings") # [[Building stock in Helsinki]]
objects.latest("Op_en7115", code_name = "changeBuildings") # [[Building stock in Helsinki]]
objects.latest("Op_en7115", code_name = "renovationRate") # [[Building stock in Helsinki]]
objects.latest("Op_en7115", code_name = "renovationShares") # [[Building stock in Helsinki]]

objects.latest("Op_en6289", code_name = "initiate") # [[Building model]] # Generic building model.

## Energy use (needed for buildings submodel)
objects.latest("Op_en5488", code_name = "initiate") # [[Energy use of buildings]] 
objects.latest("Op_en5488", code_name = "efficiencyShares") # [[Energy use of buildings]] 

## Energy and emissions
objects.latest("Op_en7311", code_name = "emissionLocations") # [[Helsinki energy production]] also heatingShares
objects.latest("Op_en2791", code_name = "initiate") # [[Emission factors for burning processes]] 
objects.latest("Op_en7311", code_name = "fuelShares") # [[Helsinki energy production]]

## Exposure

## Health assessment

objects.latest('Op_en2261', code_name = 'initiate') # [[Health impact assessment]] dose, RR, totcases. 
objects.latest('Op_en5461', code_name = 'DALYs') # [[Climate change policies and health in Kuopio]] DALYs, DW, L

population <- 623732 # Contains only the Helsinki city, i.e. assumes no exposure outside city. (Wikipedia)
# Note: the population size does NOT affect the health impact if the exposure-response function in linear.
# However, it DOES affect exposure estimates.

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


################# Actual model and some intermediate processing.

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()

renovationRate <- EvalOutput(renovationRate) * 10 # Rates for 10-year periods

buildings <- EvalOutput(buildings)

buildings@output$EnergySavingPolicy <- factor(
		buildings@output$EnergySavingPolicy,
		levels = c("BAU", "Energy saving moderate", "Energy saving total"),
		ordered = TRUE
)

exposure <- EvalOutput(exposure)

exposure@output <- exposure@output[exposure@output$Area == "Average" , ] # Helsinki is an average area,
# rather than rural or urban.

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)

DALYs <- EvalOutput(DALYs)

####################### Post-processing and graphs

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, EnergySavingPolicy == "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(renovationRate@output, aes(x = Time, weight = renovationRateResult)) + geom_bar(binwidth = 5) + 
#		facet_grid(. ~ EnergySavingPolicy) + theme_gray(base_size = BS) +
#		labs(
#				title = "Renovation rate by energy saving policy",
#				x = "Time",
#				y = "Floor area (M m2)"
#		)

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

ggplot(bui, aes(x = Time, weight = buildingsResult, fill = Renovation)) + geom_bar(binwidth = 5) + 
		theme_gray(base_size = BS) + facet_grid(. ~ EnergySavingPolicy) +
		labs(
				title = "Building stock in Helsinki",
				x = "Time",
				y = "Floor area (M m2)"
		)

ggplot(subset(bui, EnergySavingPolicy == "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)"
		)

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

ggplot(hea, aes(x = Time, weight = heatingEnergyResult, fill = Heating)) + geom_bar(binwidth = 5) + # Tuplamuunnos *1e-6 pois
		facet_wrap( ~ EnergySavingPolicy) + 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, FuelPolicy == "BAU"), aes(x = Time, weight = emissionsResult, fill = Emission_site)) + geom_bar(binwidth = 5) +
			facet_grid(Pollutant ~ EnergySavingPolicy, scale = "free_y") + theme_gray(base_size = BS) +
			labs(
					title = "Emissions from heating in Helsinki",
					x = "Time",
					y = "Emissions (ton /a)"
			)

ggplot(subset(emissions@output, EnergySavingPolicy == "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(emissions@output, EnergySavingPolicy == "BAU" & FuelPolicy == "BAU"), aes(x = Time, weight = emissionsResult, fill = Fuel)) + geom_bar(binwidth = 5) +
#		facet_grid(Pollutant ~ . , scale = "free_y") + theme_gray(base_size = BS) +
#		labs(
#				title = "Emissions from heating in Helsinki",
#				x = "Time",
#				y = "Emissions (ton /a)"
#		)

ggplot(exposure@output, aes(x = Time, weight = exposureResult, fill = Heating)) + geom_bar(binwidth = 5) + 	
	facet_grid(EnergySavingPolicy ~ FuelPolicy) + theme_gray(base_size = BS) +
		labs(
				title = "Exposure to PM2.5 from heating in Helsinki",
				x = "Time",
				y = "Average PM2.5 (µg/m3)"
		)

ggplot(exposure@output, aes(x = Time, weight = exposureResult, fill = Fuel)) + geom_bar(binwidth = 5) + 	
	facet_grid(EnergySavingPolicy ~ FuelPolicy) + theme_gray(base_size = BS) +
		labs(
				title = "Exposure to PM2.5 from heating in Helsinki",
				x = "Time",
				y = "Average PM2.5 (µg/m3)"
		)

ggplot(subset(totcases@output, FuelPolicy == "BAU"), aes(x = Time, weight = totcasesResult, fill = Heating))+geom_bar(binwidth = 5) + 
		facet_grid(Response ~ EnergySavingPolicy) +
		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)

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") & Response == "Total mortality"
)

oprint(
		oapply(temp, INDEX = c("Time", "EnergySavingPolicy", "FuelPolicy"), FUN = sum),
		caption = "Table 1: Total DALYs/a by different combinations of policy options.",
		caption.placement = "top",
		include.rownames = FALSE
)

ggplot(subset(DALYs@output, Response == "Total mortality"), aes(x = Time, weight = DALYsResult, fill = Heating))+geom_bar(binwidth = 5) + 
		facet_grid(EnergySavingPolicy ~ FuelPolicy) +
		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), aes(x = FuelPolicy, weight = DALYsResult, fill = Heating))+geom_bar() + 
		facet_grid(EnergySavingPolicy ~ Response) +
		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 <- opbase.data("Op_en7115", subset = "Locations of city areas")
koord <- tidy(koord, widecol = "Location")
colnames(koord)[colnames(koord) == "City_area"] <- "Emission_site"
#colnames(koord)[colnames(koord) == "N"] <- "X"
#colnames(koord)[colnames(koord) == "E"] <- "Y"
koord$Result <- 1
koord <- Ovariable("koord", output = koord, marginal = colnames(koord) != "Result")

emis <- emissions * koord

emis@output <- subset(emis@output, EnergySavingPolicy == "BAU" & 
	FuelPolicy == "BAU" & Pollutant == "PM2.5")
emis <- oapply(emis, INDEX = c("Emission_site", "N", "E"), FUN = sum)

MyRmap(
	ova2spat(
		emis, 
		coord = c("E", "N"), 
		proj4string = "+init=epsg:3067"
	), # National Land Survey Finland uses ETRS89 / ETRS-TM35FIN
	#http://spatialreference.org/ref/epsg/etrs89-etrs-tm35fin/
	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.

⇤# : In the results the graph "Energy used in heating in Helsinki" shows a twice or thrice bigger energy use for Oil and Other than the table "Total energy consumption in Helsinki in 2013 (GWh)" on Helsinki energy consumption would suggest. --Heta (talk) 08:18, 11 June 2015 (UTC)

⇤# : Effective floor area of buildings by building type -table on page Building stock in Helsinki has the current building stock at 7 million m2 more than the graphs in the result, and the future estimates are even more higher than the code result estimates --Heta (talk) 10:49, 11 June 2015 (UTC)

Keywords

Helsinki, energy, building stock, heating, renovation.

References

Related files

@@ Line 51: / Line 51: @@
 Building owner|EnergySavingPolicy|BAU|renovationRate||Multiply|1 /a|1|Assumes BAU renovation rate = 1%/a for buildings >30 a old
 Building owner|EnergySavingPolicy|Energy saving moderate|renovationRate||Multiply|1 /a|2|
-Building owner|EnergySavingPolicy|Energy saving total|renovationRate||Multiply|1 /a|5|5%/a is 100 % in 20 a
+Building owner|EnergySavingPolicy|Energy saving total|renovationRate||Multiply|1 /a|3|3%/a is 100 % in 20 a
 </t2b>
 }}
@@ Line 143: / Line 143: @@
 obstime <- data.frame(Startyear = c((198:206) * 10)) # Observation years must be defined for an assessment.
-BS <- 24
+BS <- 24 # base_size = font size in graphs
 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
@@ Line 179: / Line 178: @@
 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_en5461', code_name = 'DALYs') # [[Climate change policies and health in Kuopio]] DALYs, DW, L
-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)
-# Note: the population size does NOT affect the health impact as it cancels out. However, it DOES affect
+# Note: the population size does NOT affect the health impact if the exposure-response function in linear.
-#	exposure estimates.
+# However, it DOES affect exposure estimates.
 #########################i--------------------------------------------------
@@ Line 211: / Line 205: @@
 buildings <- EvalOutput(buildings)
-#buildings@output$RenovationPolicy <- factor(
-#		buildings@output$RenovationPolicy,
-#		levels = c("BAU", "Active renovation", "Total renovation"),
-#		ordered = TRUE
-#)
 buildings@output$EnergySavingPolicy <- factor(
@@ Line 228: / Line 216: @@
 exposure@output <- exposure@output[exposure@output$Area == "Average" , ] # Helsinki is an average area,
 # rather than rural or urban.
-cat("Don't believe the health estimates. There is a bug in the model.\n")
 totcases <- EvalOutput(totcases)
@@ Line 236: / Line 222: @@
 totcases <- truncateIndex(totcases, cols = "Heating", bins = 5)
-#levels(totcases@output$Heating)[levels(totcases@output$Heating) == "Long-distance heating"] <- "District heating"
 DALYs <- EvalOutput(DALYs)
@@ Line 356: / Line 341: @@
 if(figstofile) ggsave("Figure8.eps", width = 11, height = 7)
-#cat("Total DALYs/a by different combinations of policy options.\n")
+cat("Total DALYs/a by different combinations of policy options.\n")
 temp <- DALYs

In English
Assessment	Main page \| Helsinki energy decision options 2015
Helsinki data	Building stock in Helsinki \| Helsinki energy production \| Helsinki energy consumption \| Energy use of buildings \| Emission factors for burning processes \| Prices of fuels in heat production \| External cost
Models	Building model \| Energy balance \| Health impact assessment \| Economic impacts
Related assessments	Climate change policies in Helsinki \| Climate change policies and health in Kuopio \| Climate change policies in Basel
In Finnish
Yhteenveto	Helsingin energiapäätös 2015 \| Helsingin energiapäätöksen vaihtoehdot 2015 \| Helsingin energiapäätökseen liittyviä arvoja \| Helsingin energiapäätös 2015.pptx

Difference between revisions of "Climate change policies in Helsinki"

Revision as of 09:51, 14 June 2015

Contents

Scope

Question

Intended use and users

Participants

Boundaries

Decisions and scenarios

Timing

Answer

Results

Conclusions

Rationale

Stakeholders

Dependencies

Discussions

Analyses

Indices

Calculations

See also

Other variables and pages to look at

Keywords

References

Related files

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