Damage vector for life-cycle analysis

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Question

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

Answer

There are two parts to this: damage vector and damage factor. For details, see Show results .

For an example of an actual LCA, see LCA of a coffee cup.

+ Show code- Hide code library(OpasnetUtils) # [[Damage vector for life-cycle analysis]]. We need impactsPerDollar, damagesPerImpact, and damages. objects.latest("Op_en5902", code_name = "initiate") impactsPerDollar <- EvalOutput(impactsPerDollar) damagesPerImpact <- EvalOutput(damagesPerImpact) cat("Impacts per dollar\n") oprint(head(impactsPerDollar@output)) cat("Damages per impact\n") oprint(head(damagesPerImpact@output))

Rationale

Damages are calculated using this formula:

$damages_{s,c,d} = \frac{ activity_s * impactsPD_{s,c} * damagesPI_{c,d} * 365}{normalisation_d},$

where

• damages are the damages caused by the activity in meaningful units such as DALYs,
• activity are the direct inputs of an activity (in Euro), such as in LCA of a coffee cup, table Direct inputs of a coffee cup,
• impactsPD or impactsPerDollar are data from the damage vector (size 430*17) in Opasnet Base of this page,
• damagesPI or damagesPerImpact are data from the Damage factors table below,
• normalisation are impacts turned into meaningful units such as in Normalisation data for life cycle assessments#Data
• the outcome is scaled by 365 to reflect yearly impacts ⇤--#: . I'm not sure why, if the activity is in functional units and it is not clear that the activity happens once per day. --Jouni (talk) 17:30, 29 January 2014 (EET) (type: truth; paradigms: science: attack)
• s is purchasing sector,
• c is unique category (this is often summed up so that it does not show in the damage variable),
• d is damage index.

Data

Damage vector

The damage vector contains 430 purchasing sectors and 17 unique categories of impact. See Show results .

Damage factors

Damage factors are described in the table below.

⇤--#: . The units should be explained! --Jouni (talk) 07:34, 28 December 2013 (EET) (type: truth; paradigms: science: attack)

Damage factors(-)

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

Calculations

+ Show code- Hide code library(OpasnetUtils) # Create the damage factor table based on data from [[Damage vector for life-cycle analysis]] damagesPerImpact <- opbase.data("Op_en5902.damage_factors") 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")) damages <- Ovariable("damages", dependencies = data.frame(Name = c( "damagesPerImpact", "impactsPerDollar", "activity", "normalisation" )), formula = function(...) { out <- activity * impactsPerDollar * damagesPerImpact # Actual equation. # out <- CollapseMarginal( # out, # cols = "Unique_categories", # fun = "sum" # ) # out <- out / normalisation * 365 # Normalise and scale to daily values. return(out) } ) objects.store(damagesPerImpact, impactsPerDollar, damages) cat("Ovariables damagesPerImpact, impactsPerDollar, damages saved.\n")

See also

• LCA of a coffee cup
• Normalisation data for life cycle assessments