README.md

Methods on prevention of double-counting and constructing characterization factors were taken from:

Cabernard, L., Pfister, S., & Hellweg, S. (2019). A new method for analyzing sustainability performance of global supply chains and its application to material resources. Science of The Total Environment, 684, 164–177. https://doi.org/10.1016/j.scitotenv.2019.04.434

Matlab Tool:

https://data.mendeley.com/datasets/nddmgkm3cc/1

Work with MRIO data

This R-package provides various methods for environmentally-extended multi-regional input–output (EE-MRIO) analysis. It offers different methods for calculating EEMRIO matrices and includes a set of diverse characterization factors.

First Steps

1 Install MRIO-Package

install.packages("remotes")
remotes::install_github("dvp17/mrio")

2 Calculate Matrix

Before you begin, set your working directory to the path where you store your Eora and Exiobase files:

e.g. setwd("C:/Data/Exiobase") or setwd("C:/Data/Eora")

setwd("C:/Data/Exiobase")
exio_cc <- mrio::exioloop(1995:2000, "cc", "pd")
utils::View(exio_cc[1:100,])

Please be aware that the process of calculating the matrices will take some time.

3 Create Dyads

setwd("C:/Data/Exiobase")
exio_cc_dyad <- mrio::dyads(1995:1996, exio_cc)
utils::View(exio_cc_dyad[1:100,])

Attention: The construction of dyads can be time-consuming. Please notice that no progress bar or estimated time for process completion is given before.

4 Look Into Characterization Factors

For additional information on which extensions are included for which environmental impact, have a look on the characterization factors listed in the package. Notice that calculation of water stress and biodiversity loss is a little more complex, which makes it difficult to provide a simple list on those CFs.

utils::View(mrio::cf_eora)
utils::View(mrio::cf_eora[mrio::cf_eora$cf_cc > 0,])

Recent Advancements

Plan



DvP17/mrio documentation built on May 6, 2020, 5 a.m.