Climate change policies in Helsinki: Difference between revisions

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Scope

----#: . 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. --Jouni (talk) 21:04, 25 March 2015 (UTC) (type: truth; paradigms: science: comment)

Question

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

Boundaries

Decisions and scenarios

Timing

Answer

Results

Conclusions

Rationale

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

Causal diagram of climate change policies in Helsinki.

Stakeholders

Dependencies

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.

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

+ 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 = (192:205) * 10) # 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
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

#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
#
#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_en7044", code_name = "initiate") # [[Buildings in Basel]]

# [[Building stock in Helsinki]]. Somehow only works if done in parts.
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 <- 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")
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"

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

####!------------------------------------------------
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

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
#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_en7044", 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

References

Related files