LCA of a coffee cup: Difference between revisions

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(now it works)
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cat("Primary prosesses related to a cup of coffee (in Euro)\n")
cat("Primary prosesses related to a cup of coffee (in Euro)\n")
oprint(activity)
oprint(activity)
# This is not needed because zero rows are just redundant and ovariable product is more flexible than matrix product.
# 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)
activity <- Ovariable("activity", data = activity)
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damages <- EvalOutput(damages)
damages <- EvalOutput(damages)


damages <- truncateIndex(damages, c("Purchasing_sector", "Damage_categories")) # Truncates # of bins to 10.
damages2 <- truncateIndex(damages, c("Unique_categories", "Damage_categories")) # Truncates # of bins to 10.


# Plot results on a bar graph.
# 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. Numbers are NOT normalised, because there seems to be something strage in normalisaton.\n")
cat("At most ten largest damage categories or purchasing sectors are shown.\n")


ggplot(damages@output, aes(x = Damage_categories, weight = damagesResult, fill = Purchasing_sector)) + geom_bar() +
ggplot(damages2@output, aes(x = Damage_categories, weight = damagesResult, fill = Purchasing_sector)) + geom_bar() +
theme_grey(base_size = 18) +
theme_grey(base_size = 18) +
theme(axis.text.x = element_text(angle = 45)) +
theme(axis.text.x = element_text(angle = 45)) +
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)
)


ggplot(damages@output, aes(x = Damage_categories, weight = damagesResult, fill = Damage_categories)) + geom_bar() +
ggplot(damages2@output, aes(x = Damage_categories, weight = damagesResult, fill = Unique_categories)) + geom_bar() +
theme_grey(base_size = 18) +
theme_grey(base_size = 18) +
theme(axis.text.x = element_text(angle = 45)) +
theme(axis.text.x = element_text(angle = 45)) +
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y = "Amount"
y = "Amount"
)
)
damages2 <- truncateIndex(damages, c("Unique_categories", "Damage_categories", "Purchasing_sector"), bins = 4) # Truncates # of bins to 4.
oprint(summary(damages2))


</rcode>
</rcode>
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==See also==
==See also==


* [[Damage vector for life cycle analysis]]
* [[Damage vector for life-cycle analysis]]
* [[Normalisation data for life-cycle assessments]]
* [[Normalisation data for life cycle assessments]]


==Keywords==
==Keywords==

Revision as of 07:00, 28 December 2013



Question

What are the life cycle impacts of a coffee cup?

Answer

+ Show code

Rationale

Data

The data are based on discussions at CII, January 2013.[1]

Direct inputs of a coffee cup(Euro)
ObsPurchasing_sectorResultDescription
131131A - Sugar cane mills and refining0.1
2112120 - Dairy cattle and milk production0.2
3311820 - Cookie, cracker, and pasta manufacturing0.5
4311920 - Coffee and tea manufacturing0.2
5221100 - Electric power generation, transmission, and distribution0.1
6322299 - All other converted paper product manufacturing0.04
7335210 - Small electrical appliance manufacturing0
8335221 - Household cooking appliance manufacturing0.01

See also

Keywords

References

  1. ↑ Gregory Norris, personal communication.

Related files

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