Climate change policies in Basel: Difference between revisions

Revision as of 14:20, 22 July 2015

[show] This page is a knowledge crystal of subtype assessment. The page identifier is Op_en5480
Moderator:heta (see all)

Upload data {{#opasnet_base_link:Op_en5480}}

Question

What are plausible policies to the city of Basel to mitigate and adapt to climate change? What are their climate and health impacts?

Answer

Full model run 23 Feb 2015

Rationale

Dependencies

Decisions

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

Climate change policies in Basel: Difference between revisions(-)
Obs	Decision maker	Decision	Option	Variable	Cell	Change	Unit	Amount	Description
1	Builders	EfficiencyPolicy	BAU	efficiencyShares		Add		0
2	Builders	EfficiencyPolicy	Active efficiency	efficiencyShares	Efficiency:Passive;Time:2020,2030	Add	fraction	0.25	All input must be given in units that are used in respective ovariables.
3	Builders	EfficiencyPolicy	Active efficiency	efficiencyShares	Efficiency:Passive;Time:2040,2050	Add	fraction	0.1
4	Builders	EfficiencyPolicy	Active efficiency	efficiencyShares	Efficiency:Low-energy;Time:2020,2030	Add	fraction	-0.25
5	Builders	EfficiencyPolicy	Active efficiency	efficiencyShares	Efficiency:Low-energy;Time:2040,2050	Add	fraction	-0.1
6	Basel district heating	FuelPolicy	BAU	fuelShares		Add		0
7	Basel district heating	FuelPolicy	Biofuel increase	fuelShares	Burner:Large fluidized bed;Fuel:Wood;Time:2020,2030,2040,2050	Add	fraction	0.2
8	Basel district heating	FuelPolicy	Biofuel increase	fuelShares	Burner:Large fluidized bed;Fuel:Gas;Time:2020,2030,2040,2050	Add	fraction	-0.2
9	Building owner	RenovationPolicy	BAU	renovationRate		Multiply	1 /a	1	Assumes BAU renovation rate = 1%/a for buildings >30 a old
10	Building owner	RenovationPolicy	Active renovation	renovationRate		Multiply	1 /a	2
11	Building owner	RenovationPolicy	Total renovation	renovationRate		Multiply	1 /a	5	5%/a is 100 % in 20 a

A previous version of a decision table is here:

Policy	Sector	Target	Action	Description
Renewable energy	Waterworks	0.5	Increase in use of renewable energy sources	50% of the energy requirements for hot water must come from renewable sources in case of new buildings or renovations of heating systems.
Renewable energy	Waterworks	0	BAU	The use of renewable energy sources doesn't change.
Energy efficiency	Building		New buildings are made energy efficient	Insulations of new buildings follow the requirements of whole Switzerland and new buildings must have a sunblind and thermal insulation for summer season.
Energy efficiency	Building		BAU	The energy efficiency of new buildings is not improved.
Incentive levy		0.05	5% levy on electricity bills.	5% Levy on each electricity bill for support (royalty) of modernising insulation of buildings (energy efficiency), renewable energy and pilot & demonstration buildings.
Incentive levy		0	BAU	There is no extra money for developing energy production and need.
Steering tax	Taxation		BAU	Mid 1990s the steering tax was introduced to the same amount as energy-prices reduction to avoid people wasting electricity.
Solar power advancement	Energy production		The increase of solar power in energy poduction.	Requires the regional energy supplier to accept an additional 2000 kWp of solar electricity annually at cost-covering prices and to feed this into its grid.
Solar power advancement	Energy production		BAU	No more solar power is used in energy production.

The strategy of Basel’s climate policy for GHG-reduction is predominantly an energy policy:

1.Use energy efficiently.

2.Advocate renewable energy.

3.Save energy. Sufficiency.

Model

Model version 2

This model version was developed for the updated manuscript in July 2015.

+ Show code - Hide code

library(OpasnetUtils)
library(ggplot2)
library(maptools)
library(RColorBrewer)
library(classInt)
#library(OpasnetUtilsExt)
library(RgoogleMaps)

### THIS CODE IS FROM PAGE [[Climate change policies in Basel]] (Op_en5480, code_name = "")

########### TECHNICAL PARAMETERS

openv.setN(0) # use medians instead of whole sampled distributions
objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]] findrest
obstime <- Ovariable("obstime", data = data.frame(Obsyear = factor(seq(2010, 2050, 10), ordered = TRUE), Result = 1))
BS <- 24
dummy <- 1
figstofile <- FALSE

###################### 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()

############################ Submodels and city-specific data

objects.latest("Op_en7044", code_name = "initiate") # [[Buildings in Basel]]
# Contains stockBuildings, changeBuildings, emissionLocations, heatingShares, renovationRate, renovationShares

#objects.latest("Op_en5488", code_name = "initiate") # [[Energy use of buildings]] 
objects.latest("Op_en6289", code_name = "buildingstest") # [[Building model]] # Generic building model.

### Energy and emissions
objects.latest("Op_en5488", code_name = "energyUseAnnual") # [[Energy use of buildings]] energyUse
objects.latest("Op_en5488", code_name = "efficiencyShares") # [[Energy use of buildings]]
objects.latest("Op_en2791", code_name = "emissionstest") # [[Emission factors for burning processes]] 
objects.latest("Op_en2791", code_name = "emissionFactors") # [[Emission factors for burning processes]] 
objects.latest("Op_en7044", code_name = "fuelShares") # [[Buildings in Basel]]
objects.latest("Op_en5141", code_name = "fuelUse") # [[Energy balance]]

## Exposure
objects.latest("Op_en5813", code_name = "exposure") # [[Intake fractions of PM]] uses Humbert iF as default.

###################### Health assessment

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

#objects.latest("Op_en2791", code_name = "initiate") # [[Emission factors for burning processes]] 
#iF <- Ovariable("iF", ddata = "Op_en3435", subset = "Intake fractions of PM") 
# [[Exposure to PM2.5 in Finland]] Humbert et al 2011 data
#colnames(iF@data) <- gsub("[ \\.]", "_", colnames(iF@data))
#iF@data$iFResult <- iF@data$iFResult * 1E-6

### 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]]

#colnames(directs) <- gsub(" ", "_", colnames(directs))

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 <- 192000 # Contains only the Basel city, i.e. assumes no exposure outside city.
# population <- 700000 # Contains the Basel 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.

######################## MODEL CALCULATIONS

renovationRate <- EvalOutput(renovationRate) * 10 # Rates for 10-year periods
renovationRate@marginal[colnames(renovationRate@output) == "Age"] <- TRUE
renovationShares <- EvalOutput(renovationShares)
colnames(renovationShares@output)[colnames(renovationShares@output) == "Startyear"] <- "Obsyear"
stockBuildings <- EvalOutput(stockBuildings)
#stockBuildings <- oapply(stockBuildings, cols = "City_area", FUN = sum)
changeBuildings <- EvalOutput(changeBuildings)
#changeBuildings <- oapply(changeBuildings, cols = "City_area", FUN = sum)

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
)

#heatingEnergy <- EvalOutput(heatingEnergy)

energyUse <- EvalOutput(energyUse)
fuelUse <- EvalOutput(fuelUse)
fuelUse <- fuelUse * 1E-3 *3600 # kWh -> MJ
emissions <- EvalOutput(emissions)
emissions@output$Time <- as.numeric(as.character(emissions@output$Time))

exposure <- EvalOutput(exposure)

totcases <- EvalOutput(totcases)
totcases@output$Time <- as.numeric(as.character(totcases@output$Time))
totcases <- oapply(totcases, cols = c("Age", "Sex"), FUN = sum)

#DW <- Ovariable("DW", data = data.frame(directs["Trait"], Result = directs$DW))
#L <- Ovariable("L", data = data.frame(directs["Trait"], Result = directs$L))

#DALYs <- totcases * DW * L
DALYs <- EvalOutput(DALYs)

############## OUTPUT GRAPHS AND TABLES

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 Basel 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 Basel 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 Basel 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 Basel",
		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 Basel",
		x = "Time",
		y = "Floor area (M m2)"
	)

# Plot energy need and emissions

hea <- oapply(energyUse * 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 = energyUseResult * 1E-6, fill = Heating)) + geom_bar(binwidth = 5) +
	facet_wrap( ~ RenovationPolicy) + theme_gray(base_size = BS) +
	labs(
		title = "Energy used in heating in Basel",
		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 Basel",
		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 Basel",
		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 Basel",
		x = "Time",
		y = "Emissions (ton /a)"
	)

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

exposure@output <- exposure@output[exposure@output$Area == "Urban" , ] # Basel 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 Basel",
		x = "Time",
		y = "Average PM2.5 (µg/m3)"
	)

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 Basel",
		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") & 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 Basel",
		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 Basel 2030",
		x = "Biofuel policy in district heating",
		y = "Health effects (DALY /a)"
	)
if(FALSE} {
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.
}

Model version 1

This model version was used for the original manuscript in spring 2015.

Corrected run 10th March 2015

+ Show code - Hide code

library(OpasnetUtils)
library(ggplot2)
library(maptools)
library(RColorBrewer)
library(classInt)
#library(OpasnetUtilsExt)
library(RgoogleMaps)

### THIS CODE IS FROM PAGE [[Climate change policies in Basel]] (Op_en5480, code_name = "")

openv.setN(0) # use medians instead of whole sampled distributions

#####!---------------------------------------------
objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]] findrest
#####i---------------------------------------------

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

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

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

DecisionTableParser(decisions)

# Remove previous decisions, if any. 
rm(
	"buildings",
	"stockBuildings",
	"changeBuildings",
	"efficiencyShares",
	"energyUse",
	"heatingShares",
	"renovationShares",
	"renovationRate",
	"fuelShares",
	envir = openv
)

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

####!------------------------------------------------
objects.latest("Op_en7044", code_name = "initiate") # [[Buildings in Basel]]
####i------------------------------------------------

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 Basel 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 Basel 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 Basel 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 Basel",
		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 Basel",
		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 Basel",
		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 Basel",
		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 Basel",
		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 Basel",
		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 <- 192000 # Contains only the Basel city, i.e. assumes no exposure outside city.
# population <- 700000 # Contains the Basel 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 Basel",
		x = "Time",
		y = "Average PM2.5 (µg/m3)"
	)

exposure@output <- exposure@output[exposure@output$Area == "Urban" , ] # Basel 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 Basel",
		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 Basel",
		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 Basel",
		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 Basel 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.

5 pillars of Basel-City’s energy policy

1. Conventional law

Buildings:
- Most stringent requirements in whole Switzerland regarding the insulation of new building (wall, roof, windows).
- 50% of the energy requirements for hot water must come from renewable sources in case of new buildings or renovations of heating systems
- New buildings must have a sunblind and thermal insulation for summer season
Industry:
- Large-scale electricity consumer in industry can be ask to do a safe-energy-analyse and do some ameliorations.
- etc.

2. An incentive levy

5% Levy on each electricity bill for support (royalty) of:
- Modernising insulation of buildings (energy efficiency)
- Renewable energy
- Pilot & Demonstration Buildings

3. A steering tax on electricity

Mid 1990s the steering tax was introduced to the same amount as energy-prices reduction to avoid people wasting electricity
The revenue is paid back; household-bonus & company-bonus

4.Advancement in solar power

Requires the regional energy supplier to accept an additional 2000 kWp of solar electricity annually at cost-covering prices and to feed this into its grid.

5. 2000-Watt-Society and Partnerships

A Project by the Canton of Basel-City and Novatlantis
Main focus of the 2000-watt-society:
- buildings
- mobility
  - “Near Zero Emission Vehicle"
  - “Clean Engine Vehicle“
  - Hydrogen-Driven Municipal Vehicle (hy.muve); a oxygen road sweeping vehicle
  - 100 ecological biogas taxis
  - e-mobility
    - Testing e-mobility
    - event packet for companies
    - carefree packet for companies and administration
    - e-sharing for private persons
    - Development strategy for e-bikes
    - Energy-tours www.energy-tours.bs.ch
- urban development
- carbon neutral administration
  - Effect as a prototype (paradigm)
  - Minergy P-Standard (shell and technical facilities) for new construction-buildings
  - Minergy Standard (shell) for renovation-buildings

Other policies

Air pollution control with influence on GHG-reduction

Most important political instrument:

Clean air regulation of Switzerland
Clean air plan of Basel-City and Country

Clean air plan of Basel-city and country

Expamples with influence on GHG-reduction in target traffic:

Ecological motor vehicle tax
Tightening commercial control over parking space
Mobility management and agglomeration program (urban planning)
Public intensive infrastructures are obligated to decrease their traffic intensity
Regulations and limitations in prestressed air polluted city quarters

Infrastructure enhancing climate policy of Basel

Waste incinerator
- Integrated into the district heating. Function as a thermic power plant: 200,000 tons of waste supply our city with the power the equivalent of about 50,000 tons of oil.
- Equipped with the most effective smoke exhaust filter systems
Wood-fire power station
- Integrated into the district heating. Function as a thermic power plant.
- Substitutes about 11‘000 tons of oil
Aquifere of the community Riehen*
- A geothermal power station with deepgroundwater of 66 °C connected together with the block heat and power plant into the community heating of Riehen.
Deep heat mining project
- This project had to be stopped unfortunately because the drillings have caused an earthquake on 2006.
Electricity by the IWB
- Electricity in Basel is 100% renewable.
- 99 % is hydraulic energy.
- The rest is solar energy or wind power.
- Basel is not allowed to consume nuclear power because due to law

Most important shifts of climate change which are expected in Switzerland till 2050

In winter, fall and springtime temperature will increase around 2°C in summer around 3°C
Decrease of Rainfall in summer of 20% and increase of 10% in winter
Generally more heatwaves, dry periods, strong rainfalls and floods

The Canton Basel-Stadt is most affected by:

Heat wave hot-spot-effects in the City (“heat-island”)
Rising ground-water temperatures, which decreases the possibility to use ground-water only for cooling-process.
Rising temperatures of the river Rhein during heat-waves, which results that industry is not possible to cool their production-process with Rhein water (fish kill).

Key words

The climate change, Politics, Basel, Urgenche

References