Climate change policies and health in Kuopio: Difference between revisions
(→Decisions: year-specific decisions) |
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[http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=CcSsz4RJAZFaAAmE Example model run] (running the model takes more than 6 min, so use this ready-made result) | [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=CcSsz4RJAZFaAAmE Example model run] (running the model takes more than 6 min, so use this ready-made result) | ||
<rcode graphics="1" variables=" | <rcode graphics="1" variables="name:server|type:hidden|default:TRUE"> | ||
# SIirrä Kuopion-datat kässäristä linkin taakse Opasnettiin | |||
# KÄssäriin vain yhteenvetotaulukko joka kaupungista. | |||
"> | # Mieti mitä sanotaan sisäilmasta. Perusmalli toimii ilmankin, ja Matin nostama miljoonan sisäilman hankaluus pitäisi lähinnä keskustella. Käytetäänkä ylileveitä jakaumia? | ||
# Onko järkeä yhdistää kaupungit? Silloin tulisi NA:ta eri päätösten kohdalle, ja tämä pitäisi huomioida kuvissa (muutenkin kannattaisi slaissata data ennen kuvien piirtämistä). | |||
# Tarkista iF jota käytetään: Mikä on iF-summary? | |||
library(OpasnetUtils) | library(OpasnetUtils) | ||
library(ggplot2) | library(ggplot2) | ||
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library(RgoogleMaps) | library(RgoogleMaps) | ||
language <- "EN" | suomenna <- function(ova) { | ||
d <- ova@output | |||
if("Trait" %in% colnames(d)) { | |||
levels(d$Trait)[levels(d$Trait) == "Cancer"] <- "Syöpä" | |||
levels(d$Trait)[levels(d$Trait) == "CHD2"] <- "Sydänkuolema" | |||
levels(d$Trait)[levels(d$Trait) == "Stroke"] <- "Aivohalvaus" | |||
levels(d$Trait)[levels(d$Trait) == "Child's IQ"] <- "Lapsen ÄO" | |||
levels(d$Trait)[levels(d$Trait) == "Tooth defect"] <- "Hammasvaurio" | |||
levels(d$Trait)[levels(d$Trait) == "Dental defect"] <- "Hammasvaurio" | |||
levels(d$Trait)[levels(d$Trait) == "Vitamin D recommendation"] <- "D-vitamiinin saantisuositus" | |||
levels(d$Trait)[levels(d$Trait) == "Dioxin recommendation"] <- "Dioksiinin saantisuositus" | |||
} | |||
if("Exposure_agent" %in% colnames(ova@output)) { | |||
levels(d$Exposure_agent)[levels(d$Exposure_agent) == "Vitamin_D"] <- "D-vitamiini" | |||
levels(d$Exposure_agent)[levels(d$Exposure_agent) == "EPA"] <- "EPA" | |||
levels(d$Exposure_agent)[levels(d$Exposure_agent) == "DHA"] <- "DHA" | |||
levels(d$Exposure_agent)[levels(d$Exposure_agent) == "Omega3"] <- "Omega3" | |||
levels(d$Exposure_agent)[levels(d$Exposure_agent) == "PCDDF"] <- "Dioksiini" | |||
levels(d$Exposure_agent)[levels(d$Exposure_agent) == "PCB"] <- "PCB" | |||
levels(d$Exposure_agent)[levels(d$Exposure_agent) == "TEQ"] <- "TEQ" | |||
levels(d$Exposure_agent)[levels(d$Exposure_agent) == "MeHg"] <- "Metyylielohopea" | |||
} | |||
if("Hedelm" %in% colnames(d)) { | |||
levels(d$Hedelm)[levels(d$Hedelm) == FALSE] <- "Ei" | |||
levels(d$Hedelm)[levels(d$Hedelm) == TRUE] <- "Kyllä" | |||
} | |||
colnames(d)[colnames(d) == "Age"] <- "Ikäryhmä" | |||
colnames(d)[colnames(d) == "Exposure_agent"] <- "Altiste" | |||
colnames(d)[colnames(d) == "Trait"] <- "Vaste" | |||
colnames(d)[colnames(d) == "Ikä"] <- "Ikä" | |||
colnames(d)[colnames(d) == "Silakkamäärä"] <- "Silakkamäärä" | |||
return(d) | |||
} | |||
#language <- "EN" | |||
openv.setN(0) # use medians instead of whole sampled distributions | openv.setN(0) # use medians instead of whole sampled distributions | ||
#openv.setN(1000) | #openv.setN(1000) | ||
BS <- 18 | |||
objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]] findrest | objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]] findrest | ||
obsyear <- (192:205) * 10 # Observation years must be defined for an assessment. | |||
###################### Decisions | |||
decisions <- opbase.data('Op_en5461', subset = "Decisions") # [[Climate change policies and health in Kuopio]] | |||
decisions <- opbase.data('Op_en5461', subset = | |||
DecisionTableParser(decisions) | DecisionTableParser(decisions) | ||
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) | ) | ||
############################ City-specific data | |||
################# | ####!------------------------------------------------ | ||
objects.latest("Op_en5417", code_name = "initiate") # [[Population of Kuopio]] | |||
# population: City_area | |||
objects.latest("Op_en5932", code_name = "initiate") # [[Building stock in Kuopio]] Building ovariables: | |||
# buildingStock: Building, Constructed, City_area | |||
# renovation: Age, (RenovationPolicy) | |||
# renovationShares: Renovation | |||
# construction: Building | |||
# constructionAreas: City_area | |||
# buildingTypes: Building, Building2 | |||
# heatingShares: Building, Heating, Eventyear | |||
# heatingSharesNew: Building2, Heating | |||
# eventyear: Constructed, Eventyear | |||
# efficiencies: Constructed, Efficiency | |||
# | # Calculate locations for Kuopio districts | ||
districts <- tidy(opbase.data("Op_en3435.kuopio_city_districts"), widecol = "Location") # [[Exposure to PM2.5 in Finland]] | |||
####i------------------------------------------------ | |||
colnames(districts) <- gsub("\\.", "_", colnames(districts)) | |||
districts <- Ovariable("districts", data = data.frame(districts, Result = 1)) | |||
####### | ###################### Energy and emissions | ||
# | ####!------------------------------------------------ | ||
objects.latest("Op_en2791", code_name = "initiate") # [[Emission factors for burning processes]] | |||
# emissionFactors: Burner, Fuel, Pollutant | |||
# fuelTypes: Heating, Burner, Fuel | |||
objects.latest("Op_en5488", code_name = "initiate") # [[Energy use of buildings]] | |||
# energyUse: Building, Heating | |||
# efficienciesNew: Efficiency, Constructed | |||
# savingPotential: Efficiency, Building2, Renovation | |||
####i------------------------------------------------ | |||
fuelTypes@data <- merge(fuelTypes@data, data.frame(Year = 1900 + 0:16 * 5)) | |||
###### THESE SHOULD HAPPEN WHEN THE OVARIABLES ARE DEFINED | |||
colnames(iF@output)[colnames(iF@output) == "City.area"] <- "Emission_site" | |||
colnames(iF@output)[colnames(iF@output) == "Emissionheight"] <- "Emission_height" | |||
iF@output$Iter <- NULL | |||
colnames(emissionLocations@data) <- gsub(" ", "_", colnames(emissionLocations@data)) | |||
# Fill in Heating types and convert from % to fraction. | |||
heatingSharesNew <- findrest(heatingSharesNew, cols = "Heating", total = 100) / 100 | |||
efficienciesNew <- findrest(efficienciesNew, cols = "Efficiency", total = 100) / 100 | |||
renovationShares <- findrest(renovationShares, cols = "Renovation", total = 100) / 100 | |||
################ Transport and fate | |||
####!------------------------------------------------ | |||
objects.latest("Op_en3435", code_name = "disperse") # [[Exposure to PM2.5 in Finland]] | |||
# iF: Iter, Emissionheight, City.area ## THESE SHOULD BE UPDATED! (precalculated with N = 1) | |||
# emissionLocations: Heating, Emission site, Emission height | |||
#Summarised Piltti matrix, another copy of the code on a more reasonable page | |||
# Default run: en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=aXDIVDboftr1bTEd | |||
####i------------------------------------------------ | |||
fuelTypes <- CheckDecisions(EvalOutput(fuelTypes, verbose = TRUE)) | |||
###################### 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------------------------------------------------ | |||
# | 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) | |||
colnames(directs) <- gsub(" ", "_", colnames(directs)) | |||
# | #################### Manage the data before calculating | ||
# | # This is the population of Kuopio (i.e. population living in the building stock) | ||
population <- EvalOutput(population, verbose = TRUE) | |||
areaweight <- oapply(population, cols = "City_area", FUN = "sum") # Sum across city areas. | |||
areaweight <- population / (1 * areaweight) | |||
buildingStock <- buildingStock * areaweight | |||
construction <- construction * constructionAreas #/ 3 # Statistics are for three years (2010-2012) BUT PER YEAR?! | |||
####### | ###################### 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. | |||
# buildings: formula-based | |||
# heatingEnergy: formula-based | |||
####i------------------------------------------------ | |||
buildings | buildings <- EvalOutput(buildings, verbose = TRUE) | ||
) | |||
buildings@output$RenovationPolicy <- factor( | buildings@output$RenovationPolicy <- factor( | ||
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temp@output <- dropall(temp@output) | temp@output <- dropall(temp@output) | ||
temp <- oapply(temp, cols = c("Building", "Heating", "Efficiency", "Renovation"), FUN = "sum", na.rm = TRUE) | temp <- oapply(temp, cols = c("Building", "Heating", "Efficiency", "Renovation"), FUN = "sum", na.rm = TRUE) | ||
MyRmap( | MyRmap( | ||
ova2spat(temp, coord = c("E", "N"), proj4string = "+init=epsg:3067"), # National Land Survey uses EPSG:3067 (ETRS-TM35FIN) | ova2spat(temp, coord = c("E", "N"), proj4string = "+init=epsg:3067"), # National Land Survey uses EPSG:3067 (ETRS-TM35FIN) | ||
plotvar = "Result", legend_title = | plotvar = "Result", legend_title = "Floor area", numbins = 8, pch = 19, cex = 2 | ||
) | ) | ||
} | } | ||
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####### Calculate emissions | ####### Calculate emissions | ||
emis <- heatingEnergy | #emis <- heatingEnergy | ||
emis@output <- emis@output[emis@output$Year >= 1980 , ] | #emis@output <- emis@output[emis@output$Year >= 1980 , ] | ||
emis <- oapply(emis, cols = c("Efficiency", "Renovation", "Building"), FUN = "sum") | #emis <- oapply(emis, cols = c("Efficiency", "Renovation", "Building"), FUN = "sum") | ||
emis@output$Year <- as.numeric(as.character(emis@output$Year)) | #emis@output$Year <- as.numeric(as.character(emis@output$Year)) | ||
emissions <- Ovariable("emissions", | |||
dependencies = data.frame( | |||
Name = c( | |||
"heatingEnergy", | |||
"fuelTypes", | |||
"emissionFactors" | |||
) | |||
), | |||
formula = function(...) { | |||
out <- oapply(heatingEnergy, cols = c("Building", "Efficiency"), FUN = sum) | |||
out <- out * fuelTypes * emissionFactors * 3.6 * 1E-9 # convert from kWh /a to MJ /a and mg to ton | |||
out <- unkeep(out * emissionLocations, sources = TRUE, prevresults = TRUE) | |||
out@output$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("Heating", "Burner", "Fuel", "City_area"), | |||
FUN = sum | |||
) | |||
} | |||
) | ) | ||
#"Emission_site", "Emission_height", | |||
emissions <- EvalOutput(emissions) | |||
############################################# | |||
ggplot(emissions@output, aes(x = Year, weight = Result, fill = Emission_site)) + geom_bar() + facet_grid( Pollutant ~ ., scales = "free_y") | |||
ggplot( | #ggplot(health@output) + geom_bar() + theme_gray(base_size = BS) + labs(title = "Health impacts of fuel and renovation policy") + | ||
#aes(x = Year, weight = Result, fill = Heating) + labs(y = "Premature deaths (# /a)") # + facet_grid(FuelPolicy ~ RenovationPolicy) | |||
##############------------------------------ | |||
### Use these population and iF values in health impact assessment. Why? | ### Use these population and iF values in health impact assessment. Why? | ||
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### Impact graphs in ENGLISH | ### Impact graphs in ENGLISH | ||
ggplot(buildings@output) + geom_bar() + theme_gray(base_size = BS) + | |||
aes(x = Building, weight = buildingsResult/1000000, fill = Heating) + labs(y = "Floor area (M m2)", title = "Building impacts of renovation policy") + coord_flip() # + facet_grid(. ~ RenovationPolicy) | |||
plo <- ggplot(buildings@output) + geom_bar() + facet_grid(. ~ RenovationPolicy) + theme_gray(base_size = BS) + | plo <- ggplot(buildings@output) + geom_bar() + facet_grid(. ~ RenovationPolicy) + theme_gray(base_size = BS) + | ||
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print(plo) | print(plo) | ||
ggplot(heatingEnergy@output) + geom_bar() + theme_gray(base_size = BS) + labs(title = "Energy impacts of renovation policy") + | |||
aes(x = Building, weight = heatingEnergyResult/1E+6, fill = Heating) + labs(y = "Heating energy need (GWh /a)") + coord_flip() # + facet_grid(. ~ RenovationPolicy) | |||
plo <- ggplot(heatingEnergy@output) + geom_bar() + facet_grid(. ~ RenovationPolicy) + theme_gray(base_size = BS) + | plo <- ggplot(heatingEnergy@output) + geom_bar() + facet_grid(. ~ RenovationPolicy) + theme_gray(base_size = BS) + | ||
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emis@output <- emis@output[emis@output$Renovation == "BAU" , ] | emis@output <- emis@output[emis@output$Renovation == "BAU" , ] | ||
ggplot(emis@output) + geom_bar() + facet_grid(Pollutant ~ . , scales = "free_y") + theme_gray(base_size = BS) + labs(title = "Emission impacts of biofuel policy") + | |||
aes(x = Heating, weight = Result, fill = Fuel) + labs(y = "Emissions to air (ton /a)") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) | |||
plo <- ggplot(emis@output) + geom_bar() + facet_grid(Pollutant ~ FuelPolicy, scales = "free_y") + theme_gray(base_size = BS) + | plo <- ggplot(emis@output) + geom_bar() + facet_grid(Pollutant ~ FuelPolicy, scales = "free_y") + theme_gray(base_size = BS) + | ||
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# | # | ||
# print(plo) | # print(plo) | ||
### Impact graphs in FINNISH | ### Impact graphs in FINNISH | ||
if( | if(FALSE) { | ||
plo <- ggplot(buildings@output) + geom_bar() + facet_grid(. ~ Remonttipolitiikka) + theme_gray(base_size = BS) + | plo <- ggplot(buildings@output) + geom_bar() + facet_grid(. ~ Remonttipolitiikka) + theme_gray(base_size = BS) + | ||
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############ Health impact graph corrected. This should be done in a previous stage. | ############ Health impact graph corrected. This should be done in a previous stage. | ||
ter <- | ter <- DALYs | ||
ter2 <- ter | ter2 <- ter | ||
ter2@output <- ter@output[ | ter2@output <- ter@output[ | ||
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ggplot(DALYs@output, aes(x = Year, weight = Result, fill = Trait)) + geom_bar() + facet_grid(RenovationPolicy ~ FuelPolicy) | ggplot(DALYs@output, aes(x = Year, weight = Result, fill = Trait)) + geom_bar() + facet_grid(RenovationPolicy ~ FuelPolicy) | ||
############# Building stock in Kuopio | |||
################ EI TARVITTANE? | |||
energyEfficiency@data$Efficiency <- factor( | |||
energyEfficiency@data$Efficiency, | |||
levels = c("Old", "New", "Low-energy", "Passive"), | |||
ordered = TRUE | |||
) | |||
</rcode> | </rcode> |
Revision as of 20:40, 13 February 2015
Moderator:Jouni (see all) |
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Main message: |
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Question:
What are the most beneficial ways from public health point of view to reduce GHG emissions in Kuopio? The target of 40 % GHG reduction seems realistic due to reforms in Haapaniemi power plant, assuming that GHG emissions for wood-based fuel is 0. Life-cycle impacts of the wood-based fuel have not yet been estimated. |
{{#display_map:
62.900223, 27.637482, Kuopio
| zoom = 11
}}
Scope
Question
What are potential climate policies that reach the greenhouse emission targets in the city of Kuopio for years 2010-2030? What are their effects on health and well-being, and what recommendations can be given based on this? The national greenhouse emission target is to reduce greenhouse gas emissions by 20 % between 1990 and 2020; the city of Kuopio has its own, more ambitious target of 40 % for the same time period.
Details of scoping |
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Boundaries
Scenarios
Intended users
Participants
|
Answer
Conclusions
The target of 40 % GHG reduction seems realistic due to reforms in Haapaniemi power plant, assuming that GHG emissions for wood-based fuel is 0. Life-cycle impacts of the wood-based fuel have not yet been estimated.
Results
- Calculate building stock into the future
- The dynamics is calculated by adding building floor area at time points greater than construction year, and by subtracting when time point is greater than demolition year. This is done by building category, not individually.
- Also the renovation dynamics is built using event years: at an event, a certain amount of floor area is moved from one energy efficiency category to another.
- Full data are stored in the ovariables. Before evaluating, extra columns and rows are removed. The first part of the code is about this.
Example model run (running the model takes more than 6 min, so use this ready-made result)
Rationale
Dependencies
- Building stock in Kuopio
- Exposure to PM2.5 in Finland
- OpasnetUtils/Drafts
- Energy use of buildings
- Emission factors for burning processes
- Population of Kuopio
- Sanni Väisänen: Greenhouse gas emissions from peat and biomass-derived fuels, electricity and heat — Estimation of various production chains by using LCA methodology ----#: . This work should be carefully read. It may affect conclusions. --Jouni (talk) 13:27, 20 March 2014 (EET) (type: truth; paradigms: science: comment)
Decisions
Obs | Decision maker | Decision | Option | Variable | Cell | Change | Unit | Amount | Description |
---|---|---|---|---|---|---|---|---|---|
1 | Builders | EfficiencyPolicy | BAU | efficienciesNew | Efficiency:Passive;Constructed:2020-2029 | Add | 0 | ||
2 | Builders | EfficiencyPolicy | Active efficiency | efficienciesNew | Efficiency:Passive;Constructed:2020-2029 | Add | 0.3 | Given as fraction because that's how it is calculated in the model | |
3 | Builders | EfficiencyPolicy | Active efficiency | efficienciesNew | Efficiency:Low-energy;Constructed:2020-2029 | Add | -0.3 | Given as fraction because that's how it is calculated in the model | |
4 | Kuopion Energia | FuelPolicy | BAU | fuelTypes | Add | 0 | |||
5 | Kuopion Energia | FuelPolicy | Biofuel increase | fuelTypes | Burner:Large fluidized bed;Fuel:Wood;Year:2015,2020,2025,2030,2035,2040,2045,2050 | Replace | fraction | 0.495 | |
6 | Kuopion Energia | FuelPolicy | Biofuel increase | fuelTypes | Burner:Large fluidized bed;Fuel:Peat;Year:2015,2020,2025,2030,2035,2040,2045,2050 | Replace | fraction | 0.495 | |
7 | Kuopion Energia | FuelPolicy | Biofuel increase | fuelTypes | Burner:Large fluidized bed;Fuel:Heavy oil;Year:2015,2020,2025,2030,2035,2040,2045,2050 | Replace | fraction | 0.01 | |
8 | Building owner | RenovationPolicy | BAU | renovation | Multiply | 1 | |||
9 | Building owner | RenovationPolicy | Active renovation | renovation | Multiply | 1.5 | |||
10 | Building owner | RenovationPolicy | Effective renovation | renovation | Multiply | 1 | |||
11 | Building owner | RenovationPolicy | Effective renovation | renovationShares | Renovation:Windows | Replace | % | 0 | |
12 | Building owner | RenovationPolicy | Effective renovation | renovationShares | Renovation:Technical systems | Replace | % | 0 | |
13 | Building owner | RenovationPolicy | Effective renovation | renovationShares | Renovation:Sheath reform | Replace | % | 1 | |
14 | Building owner | RenovationPolicy | Effective renovation | renovationShares | Renovation:General | Replace | % | 0 | |
15 | Building owner | RenovationPolicy | BAU | renovationShares | Add | % | 0 | ||
16 | Building owner | RenovationPolicy | Active renovation | renovationShares | Add | % | 0 |
Direct inputs
Obs | Exposure agent | Trait | Cases | DW | L | Description |
---|---|---|---|---|---|---|
1 | PM2.5 | Total mortality | 877 | 1 | 11 | Actually "Mortality (all cause)". In 2009 for Pohjois-Savo area 1090 / 100 000 from death cause registry. |
2 | PM2.5 | Work loss days (WLDs) | 323135 | 0.02 | 0.003 | |
3 | PM2.5 | Restricted activity days (RADs) | 31867 | 0.07 | 0.003 | 2.1 million in whole Finland |
4 | PM2.5 | Infant mortality | 3 | 1 | 81 | <1 year old 2009 data for Pohjois-Savo area 244 / 100 000 from death registry. In 2009 in Kuopio 1110 <1 year olds. |
5 | PM2.5 | COPD | 339 | 0.099 | 15 | Actually "Chronic bronchitis (>15 year olds)". Kelasto, includes astma cases too |
6 | PM2.5 | Cardiovascular hospital admissions (number) | 2109 | 0.253 | 0.017 | 21424 in year 2010 in Kuopio hospital. Hospital serves area with 817166 inhabitats. |
7 | PM2.5 | Respiratory hospital admissions | 1150 | 0.043 | 0.02 | In 2007 1429.55 hospital discharges for respiratory disease / 100 000 in whole Finland. http://data.euro.who.int/hfadb/ |
8 | PM2.5 | Asthma medication use (children aged 5-14) | 62 | 0.043 | 15 | Kelasto |
9 | Mold/dampness | Asthma development (>15 year olds) | 252 | 0.043 | 15 | Kelasto-database |
10 | Mold/dampness | Asthma development (5-14 year olds) | 62 | 0.043 | 15 | Kelasto-database |
11 | Noise | Highly annoyed | 0.02 | 1 | ||
12 | Noise | Sleep disturbance | 0.07 | 1 | ||
13 | Noise | Myocardial infarction | 1289 | 0.439 | 0.019663 | 13101 cases in Kuopio university Hospital in year 2010. Hospital serves area with 817166 inhabitats. |
14 | EC | Cardiovascular mortality | 366 | 0.043 | 0.02 | In 2009 for Pohjois-Savo area 455 / 100 000 from death cause registry. |
15 | Cardiopulmonary | 1 | 11 | Guesswork. The same as total mortality | ||
16 | Lung cancer | 1 | 11 | Guesswork. The same as total mortality. |
Specific actions - real and potential
- Energy production
- New power plant unit in Haapaniemi: ability to use significantly more biomass in the production of district heat (2014)
- Enhancement of dispersed energy production with biofuels
- Wide scale transition to renewable energy sources in heating
- Building stock
- Energy efficiency of buildings is increased: new stricter building regulations in Finland (2/2013)
- Education to building owners and managers: semblance of best practicies in heating and other use of energy. Possible reduction in energy use of building stock is about 10%, and mere beneficial health effects are expected.
- Land use and transport
- If possible, PM emissions and noise are calculated based on updated version of Kuopio´s traffic network
- Alternatively, the effect of increased use of biofuels on GHG and CO2 emissions is evaluated.
- Possibilities of rail traffic in Kuopio
- Other...
Indicators
- Cardiovascular mortality
- Pulmonar mortality
- Well-being...
Who are the stakeholders in the assessment Climate change policies and health in Kuopio and what are their values (i.e., what variables do they want to optimize)? Result is a weight factor for a multi-criteria decision analysis (see e.g. Weighted product model).
Obs | Stakeholder | Variable | Cell | Model | Result | Description |
---|---|---|---|---|---|---|
1 | City of Kuopio | Greenhouse gas emissions in Kuopio | Sector: Total | Weighted sum | 1 | |
2 | Citizens | Greenhouse gas emissions in Kuopio | Sector: Total | Weighted product | 0.5 | |
3 | Citizens | Health impacts of climate policies in Kuopio | Sector: Total | Weighted product | 0.5 | |
4 | Budget office of Kuopio | City budget total | Weighted sum | 1 |
Assessment-specific data
Received
- Building stock data
- Building registry
- Use of electricity by building type or type of activity
- Use of district heat by contract
- Amount of building stock renovated per year
- Amount of new building stock per year during 2010-2012
- Energy consumption in some of city´s own buildings before and after renovation
- Energy production
- Fuels and emissions of Haapaniemi CHP plant
- Traffic
- Regional plan on public transport
To be gathered
- Updated traffic network model?
- Estimates of the amount, area, volume and energy class of new buildings during next years (about 2014-2020)
See also
- Kuopion kaupunginhallitus 8.10.2012: Kuopion ja Siilinjärven joukkoliikennesuunnitelma (Kuopio public transport plan)
- Laskureita hiilijalanjäljen arviointiin ja seurantaan (Carbon footprint calculators): SYNERGIA, JUHILAS, Ilmastodieetti, KASVENER, KUHILAS, Y-HIILARI
- Millaisen tiekarten avulla hiilineutraaliin Suomeen 2050
- Climate policy of Kuopio 2009 - 2020
- DebattiBaari: energiakeskustelu
References
Keywords
Climate Change, Kuopio, Green house gas emissions, Health, Energy
Related files
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