Environmental impacts module

The Environmental Impacts module aims at enabling IAM groups to analyse wider lifecycle impacts of power production beyond greenhouse gases and air pollutants and including indirect effects not considered in the models.

Cite: Arvesen et al. for the LCA coefficients and Pehl et al. or Luderer et al. for MAgPIE LULUC coefficients as well as methodology on joining LCA and IAM data.

Files required to use the module

All files required to use the module are available here

Module Elements

Input Data

• lifecycle energy use and impact coefficients provided by NTNU NTNU_LCA_coefficients.xlsx
• land use and land-use change emissions coefficients from MAgPIE MAgPIE_LUC_GHG_coefficients.csv
• example IAM results

The data set of lifecycle impacts is differentiated by scenario (baseline or climate policy), region (nine IEA regions), power-sector technology, technology variant (different types of technologies employing the same energy conversion, see first worksheet of NTNU_LCA_coefficients.xlsx), lifecycle phase (construction, operation, end-of-life) and period (2010, 2030, 2050). Impacts include:

• use of bulk materials (cement, iron/steel, aluminium, copper)
• transportation services
• indirect energy use
• land occupation
• freshwater and marine eutrophication
• human, freshwater and terrestrial toxicity
• ionising radiation
• land use and land use change emissions (CO₂, CH₄, N₂O)

Source Code

• R script combining LCA coefficients with IAM results ADVANCE_Toolbox_EI.R
• several R scripts with utility functions called internally in scripts

The R script provided with the module combines IAM results with these coefficients to produce impacts differentiated by technology and lifecycle phase for different models, scenarios, regions and periods.

How to Use the Module

For a quick overview, you can read the standalone tutorial of the toolbox.

As the module is written in the R language, it is necessary to have a working R installation to run it. See the R website for details.

To use the module, edit the configuration block in ADVANCE_Toolbox_EI.R and

• point IAM_results_file to a file with IAM results in a format like the snapshots from the IIASA data base
• give a name for the output file to be generated with output_file
• adjust the mappings of scenarios, regions and technologies, if necessary
  • IAM scenarios are mapped either to the Baseline scenario for LCA coefficients (for no climate policy) or to the BLUE_Map scenario, which assumes efficiency improvements due to climate policy
  • IAM regions are mapped to the region from the THEMIS most closely resembling it. See worksheet "regionClassifications" in NTNU_LCA_coefficients.xlsx
  • IAM technologies are mapped to THEMIS technologies

Then run the script using Rscript ADVANCE_Toolbox_EI.R or any method provided by your R environment.

Module Output

The module will generate three files in the ./output/ subdirectory. The base file contains all impacts for the mix of technology variants (see below) in a format like that of the IIASA data base (i.e. with columns for model, scenario, region, variable, unit and the different time steps). The two files with _energy and _environment appended to the file name contain the data for embodied energy use and environmental impacts in a similar format, but with more columns for different dimensions, and also include the full range of technology variants.

Technical Details

IAM Input Data

The module requires input data on capacity, capacity additions and electricity production of power sector technologies. See the file Variable_Definitions.csv for a detailed listing and descriptions.

Lifecycle Coefficient Scenarios

The two scenarios for lifecycle impact coefficients differ in assumptions for future improvements in performance parameters for selected industrial process (Arvesen et al., submitted). They are based on the Baseline and BLUE Map scenarios of IEA (2010). The use of the BLUE Map scenario is suggested for all IAM scenarios with climate mitigation policy, as the assumed technology and efficiency improvements are generally in line with concerted climate mitigation efforts. The Baseline coefficients may be used for uncertainty analysis in addition to the assessment of baseline scenarios. For the time being, scenarios based on nationally determined contributions (NDC) should be based on the Baseline scenario, as efficiency improvements are likely to be lower than under stringent climate change mitigation policies.

Technology Variants

The LCA data set contains coefficients for different variations of the same technology (see first worksheet of NTNU_LCA_coefficients.xlsx). The "mix" variant holds the data for the share of technology variants assumed for a given period in Arvesen et al. (submitted), or for the "default" variant in the case of bioenergy (see below).

MAgPIE LULUC Coefficients

Coefficients for emissions from (induced) land use and land-use change were calculated using results from the MAgPIE land use model. They spanned eighteen scenarios along the three axes

• CO₂ tax level applied within the agricultural sector: 0, 5, or 30 $/tCO₂ in 2020, increasing by 5% p.a. (TAX0, TAX5 and TAX30)
• biomass feedstock and irrigation scheme:
  • rain-fed traditional biomass only (betr_rf)
  • rain-fed traditional and purpose-grown biomass (begr_brtr_rf)
  • irrigated traditional and purpose-grown biomass (begr_betr_ir)
• amount of biomass production in 2050: either 0 or 100 EJ/a

To compute effects due to the use of bioenergy, emissions from the scenarios with no biomass production were subtracted from those with biomass production (resulting in nine scenarios for biomass production). Also, a scenario with no dedicated production of biomass, but use of biomass residues is included, which does not entail emissions from land use and land-use change.
Since the conversion of areas for agriculture use entail large none-recurring emissions of CO₂, these have been calculated as the quotient of cumulated CO₂ emissions and cumulated bioenergy production. N₂O and CH₄ emission coefficients were calculated as the quotient of emissions and bioenergy use. Furthermore, only global values were used for the LULUC coefficients, as changes in regional production and trade could lead to skewed regional values. Note also that the LULUC coefficients in MAgPIE_LULUC_GHG_coefficients.csv are in terms of primary energy and are therefore converted to Mt CO₂eq/EJ_electricity using the generic efficiencies of 41% and 31% for Bioenergy without and with CCS.

Time Horizon

As LCA coefficients are only available for the years 2010, 2030 and 2050, they are linearly interpolated for the periods in between (i.e., C₂₀₃₅ = 3/4 C₂₀₃₀+1/4 C₂₀₅₀) and constant before and after at the level of the closest period available (i.e., C₂₀₇₀ = C₂₀₅₀). If values after 2050 are reported, they should be qualified by the information that possible future (after 2050) developments are not accounted for in the LCA coefficients.

Publications

• Anders Arvesen, Gunnar Luderer, Michaja Pehl, Benjamin Leon Bodirsky, and Edgar G. Hertwich. “Deriving Life Cycle Assessment Coefficients for Application in Integrated Assessment Modelling”. Environmental Modelling & Software 99 (January 2018): 111–25. https://doi.org/10.1016/j.envsoft.2017.09.010
• Michaja Pehl, Anders Arvesen, Florian Humpenöder, Alexander Popp, Edgar G. Hertwich, and Gunnar Luderer. “Understanding future emissions from low-carbon power systems by integration of life-cycle assessment and integrated energy modelling”. Nature Energy 2, no. 11 (8 December 2017). https://doi.org/10.1038/s41560-017-0032-9
• Gunnar Luderer, Michaja Pehl, Anders Arvesen, Edgar Hertwich, Anders Hammer Strømman, Ioanna Mouratiadou, Benjamin Leon Bodirsky, Robert Pietzcker, Alexander Popp, Harmen-Sytze de Boer, Oliver Fricko, Silvana Mima: "Distinctly different environmental impacts of alternative power sector decarbonization strategies" (in preparation)
• International Energy Agency (IEA): "Energy Technology Perspectives 2010: Scenarios & Strategies to 2050" (2010)