Damage vector for life-cycle analysis

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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

coffeecupinputs(Euro)

Obs	directRequirements	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

What is the smallest fraction of the total effect you want to see?: + Show code- Hide code library(OpasnetUtils) library(xtable) library(ggplot2) library(reshape) # Take the damage factor table from this page. damagesPerImpact <- opbase.data("Op_en5902.damagefactors") # Download the data from Opasnet Base. damagesPerImpact <- reshape( # Reshape it into the wide format. damagesPerImpact[ , colnames(damagesPerImpact) != "Obs"], times = "Result", timevar = "Unique_categories", idvar = "Damage_categories", direction = "wide" ) colnames(damagesPerImpact) <- gsub("Result.", "", colnames(damagesPerImpact)) # Remove extra "Result." from colnames. rownames(damagesPerImpact) <- damagesPerImpact[[1]] # Make the first column the rownames. damagesPerImpact <- damagesPerImpact[ , 2:ncol(damagesPerImpact)] # Remove the first column. damagesPerImpact <- t(as.matrix(damagesPerImpact)) # Turn the data.frame into a matrix and transpose it. # Take the impact factor table from the database. Do the same procedures as with damagesPerImpact. impactsPerDollar <- opbase.data("Op_en5902") impactsPerDollar <- reshape( impactsPerDollar, times = "Result", timevar = "Unique_categories", idvar = "Purchasing_sector", direction = "wide" ) colnames(impactsPerDollar) <- gsub("Result.", "", colnames(impactsPerDollar)) rownames(impactsPerDollar) <- impactsPerDollar[[1]] impactsPerDollar <- impactsPerDollar[ , 2:ncol(impactsPerDollar)] impactsPerDollar <- as.matrix(impactsPerDollar) # Take the coffee cup direct requirements. coffee <- opbase.data("Op_en5902.coffeecupinputs") # Download the data from Opasnet Base. coffee <- coffee[ , colnames(coffee) != "Obs"] cat("Primary prosesses related to a cup of coffee (in Euro)\n") print(xtable(coffee), type = 'html') coffee <- merge(data.frame(DirectRequirements = rownames(impactsPerDollar)), coffee, all.x = TRUE) coffee$Result <- ifelse(is.na(coffee$Result), 0, coffee$Result) normalisation <- EvalOutput(new("ovariable", name = "normalisation", data = tidy(opbase.data("Op_en5904"), objname="normalisation") )) normalisation@output impactsPerDollar <- impactsPerDollar * coffee$Result # Multiply data matrix with activities. out <- impactsPerDollar %*% damagesPerImpact # After matrix operations, turn the output into a data.frame for graphics. out <- as.data.frame(out) out$directRequirements <- rownames(out) out <- melt(out, idvars = "directRequirements", variable_name = "damages") out <- out[out$value >= sum(out$value) * limit , ] out <- dropall(out) # Rename the columns to reflect actual things. colnames(out)[colnames(out) == "value"] <- "Result" damages <- EvalOutput(new("ovariable", name = "Damage", data = out)) head(damages@output) damageFractions <- damages / normalisation * 365 head(damageFractions@output) cat("Effects smaller than", limit*100, "% of the total effect are not shown.\n") ggplot(out, aes(x = Damage, weight = Result, fill = directRequirements)) + geom_bar() + theme_grey(base_size = 24) + theme(axis.text.x = element_text(angle = 45)) + labs( title = "Life cycle impacts of a cup of coffee", x = "Damage", y = "Amount" )

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