Damage vector for life-cycle analysis

This page is a knowledge crystal of subtype variable. The page identifier is Op_en5902
Moderator:Nobody (see all) Click here to sign up.
This page is a stub. You may improve it into a full page.
Upload data {{#opasnet_base_link:Op_en5902}}

Question

What are the damages per unit purchased commodity using a life-cycle assessment?

Rationale

Data

damagefactors(-)

Obs	Unique_categories	Human health	Ecosystem quality	Climate change	Resources	Water consumption
1	Carcinogens	0.0000028	0	0	0	0
2	Non-carcinogens	0.0000028	0	0	0	0
3	Respiratory inorganics	0.0007	0	0	0	0
4	Ionizing radiation	0.00000000021	0	0	0	0
5	Ozone layer depletion	0.00105	0	0	0	0
6	Respiratory organics	0.00000213	0	0	0	0
7	Aquatic ecotoxicity	0	0.0000502	0	0	0
8	Terrestrial ecotoxicity	0	0.00791	0	0	0
9	Terrestrial acidification/nutrification	0	1.04	0	0	0
10	Land occupation	0	1.09	0	0	0
11	Aquatic acidification	0	0	0	0	0
12	Aquatic eutrophication	0	0	0	0	0
13	Global warming	0	0	1	0	0
14	Non-renewable energy	0	0	0	1	0
15	Mineral extraction	0	0	0	1	0
16	Water withdrawal	0	0	0	0	0
17	Water consumption	0	0	0	0	1

Example of coffee cup

Coffee cup inputs(Euro)

Obs	Purchasing_sector	Result
1	31131A - Sugar cane mills and refining	0.1
2	112120 - Dairy cattle and milk production	0.2
3	311820 - Cookie, cracker, and pasta manufacturing	0.5
4	311920 - Coffee and tea manufacturing	0.2
5	221100 - Electric power generation, transmission, and distribution	0.1
6	322299 - All other converted paper product manufacturing	0.04
7	335210 - Small electrical appliance manufacturing	0
8	335221 - Household cooking appliance manufacturing	0.01

Formula

+ Show code- Hide code library(OpasnetUtils) library(ggplot2) library(reshape2) objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]]. We need ovaMatrixProduct. show_bins <- 10 # How many different direct inputs to show? # Create the damage factor table based on data from [[Damage vector for life-cycle analysis]] damagesPerImpact <- opbase.data("Op_en5902.damagefactors") damagesPerImpact$Obs <- NULL damagesPerImpact <- Ovariable("damagesPerImpact", damagesPerImpact) # Take the impact factor table from the database. Do the same procedures as with damagesPerImpact. impactsPerDollar <- Ovariable("impactsPerDollar", data = opbase.data("Op_en5902")) # Take the direct requirements of an activity (in this case, producing a cup of coffee). activity <- opbase.data("Op_en5902.coffee_cup_inputs") activity$Obs <- NULL # Remove the redundant Obs column. cat("Primary prosesses related to a cup of coffee (in Euro)\n") oprint(activity) # Combine the direct requirements of a coffee cup with a full vector of requirements and fill empty cells with 0. activity <- merge( unique(EvalOutput(impactsPerDollar)@output["Purchasing_sector"]), activity, all.x = TRUE ) activity$Result[is.na(activity$Result)] <- 0 activity <- Ovariable("activity", data = activity) # Get the normalisation data for different damages and make an ovariable out of it. normalisation <- opbase.data("Op_en5904") # [[Normalisation data for life cycle assessments]] normalisation$Obs <- NULL oprint(head(normalisation)) normalisation <- Ovariable("normalisation", data = normalisation) ##########################333 damagesPerImpact <- EvalOutput(damagesPerImpact, N = 1) impactsPerDollar <- EvalOutput(impactsPerDollar, N = 1) activity <- EvalOutput(activity, N = 1) normalisation <- EvalOutput(normalisation, N = 1) #############################33 impactsPerDollar2 <- impactsPerDollar * activity # Multiply data matrix with activities. oprint(head(impactsPerDollar@output)) oprint(head(damagesPerImpact@output)) out <- ovaMatrixProduct( impactsPerDollar2, damagesPerImpact, c("Purchasing_sector", "Unique_categories"), c("Unique_categories", "Damage_categories") ) # Do a matrix multiplication out <- out / normalisation * 365 # Normalise and scale to daily values. # Merge all but show_bins largest bins to 'Other'. sums <- as.data.frame(as.table(tapply(out@output$Result, out@output["Purchasing_sector"], sum))) limit <- sort(sums$Freq, decreasing = TRUE)[show_bins] keeps <- sums[sums$Freq > limit , "Purchasing_sector"] keeps <- levels(keeps)[keeps] levels(out@output$Purchasing_sector) <- ifelse( levels(out@output$Purchasing_sector) %in% keeps, levels(out@output$Purchasing_sector), "Other" ) # Plot results on a bar graph. cat("Effects smaller than or equal to ", limit / sum(sums$Freq) * 100, " % of the total effect are not shown.\n") ggplot(out@output, aes(x = Damage, weight = Result, fill = Purchasing_sector)) + geom_bar() + theme_grey(base_size = 18) + theme(axis.text.x = element_text(angle = 45)) + labs( title = "Life cycle impacts of a cup of coffee", x = "Damage", y = "Amount" )

Related files

<mfanonymousfilelist></mfanonymousfilelist>