Once we have developed modules, methods and tools in the project, they can be found on this page.
The detailed descriptions of the methodologies respond to the widespread need in the modelling community to share methodologies and input data for the further improvement of integrated assessment and energy‐economy modelling. The toolbox includes new model components, mathematical formulae, algorithmic approaches, examples of model code, and generic input datasets and instructions for implementation.
Long-term marginal abatement cost curves of non-CO2 greenhouse gases
This dataset represents long-term marginal abatement cost (MAC) curves of all major emission sources of non-CO2 greenhouse gases (GHGs); methane (CH4), nitrous oxide (N2O) and fluorinated gases (HFCs, PFCs and SF6). The work is based on existing short-term MAC curve datasets and recent literature on individual mitigation measures. The data represent a comprehensive set of MAC curves, covering all major non-CO2 emission sources for 26 aggregated world regions. They are suitable for long-term global mitigation scenario development, as dynamical elements (technological progress, removal of implementation barriers) are included. The data is related to the research article Harmsen et al. (2019) “Long-term marginal abatement cost curves of non-CO2 greenhouse gases” (https://doi.org/10.1016/j.envsci.2019.05.013)
The datasets contain CH4 and N2O (Data_MAC_CH4N2O_Harmsen et al._PBL) and fluorinated gas (Data_MAC_F-gases_Harmsen et al._PBL) marginal abatement cost (MAC) curves for all major global emission sources. Values represent relative emission reductions for the different emission sources at different marginal cost levels for the period 2015e2100 (not all intermediate years are provided, but values between subsequently provided years can be linearly interpolated). Two sets are made available: 1) One baseline-independent set with relative reductions compared to the global average emission factor in 2015 for the emission source concerned. Negative values represent a higher emission factor than the global average in 2015. 2) One set compatible with the IMAGE SSP2 baseline scenario (with “SSP200 in the name of the sheet). Source-specific emission reductions in SSP2 are deducted from the reductions in the baseline-independent MACs. Implementation costs are provided in (2005/2010) $/tonne of C equivalents, assuming the use of the AR4 100 yr GWP potential.
The MAC curves represent the combined reduction potential of all relevant mitigation measures at specific marginal costs for a specific emission source and country or region. In order to be relevant for long term climate policy projections, they account for future changes in reduction potential and costs, due to 1) technological learning and 2) removal of implementation barriers. The MAC curves developed in this study are based on a combination of existing datasets and an assessment of individual mitigation options described in literature.
Link to code/data
The data files can be downloaded here: https://doi.org/10.1016/j.dib.2019.104334
How to use the dataset
For integrated assessment modelling teams, the recommendation is to use the first dataset: baseline-independent set with relative reductions compared to the global average emission factor in 2015 for the emission source concerned. Note that, when implementing these MACs in an IAM, relative emission reductions as represented in the dataset cannot simply be deducted from the baseline emissions. The model should take into account emission reductions already taking place in the baseline.
The study has been published open access under a Creative Commons licence. The data can be freely used by all parties.
Mathijs J.H.M. Harmsen, Detlef P. van Vuuren, Dali R. Nayak, Andries F. Hof, Lena H€oglund-Isaksson, Paul L. Lucas, Jens B. Nielsen, Pete Smith, Elke Stehfest (2019) Long-term marginal abatement cost curves of non-CO2 greenhouse gases, Environ. Sci. Policy 99 136e149. https://doi.org/10.1016/j.envsci.2019.05.013
Mathijs J.H.M. Harmsen, D.P. van Vuuren, D.R. Nayak, A.F. Hof, L. Höglund-Isaksson, P.L. Lucas, J.B. Nielsen, P. Smith, E. Stehfest (2019) Data for long-term marginal abatement cost curves of non-CO2 greenhouse gases, Data Brief, 25, p. 104334, https://doi.org/10.1016/j.dib.2019.104334
This module includes updated SSP GDP per capita scenarios (covering the Covid shock), related PPP/MER conversion rates, and a set of structural change scenarios. The latter is represented by sectoral shares on employment, value added and final energy use. The sectoral resolution contains agriculture, manufacturing and services. The development of these key variables of economic activity is projected until 2050 and is available for each the five SSP scenarios. GDP per capita scenarios are available until 2100. The data set also includes historical data (from 1998 onwards) for GDP, population and sectoral shares.
The scenarios are available on a country and region level. A more in-depth description of the scenarios is available here.
Link to code/data
The data set is provided in this excel file.
How to use the scenarios
The structural change scenarios are directly linked to the updated set of SSP GDP scenarios. Updated GDP scenarios can be used as basic driver and the structural change scenarios as an additional driver in all mitigation and impact analyses.
Leimbach, M., Marcolino, M., Koch, J. (2021). Structural change scenarios within the SSP framework. Futures (submitted).
Koch, J. and Leimbach, M. (2021). Update of SSP GDP projections: capturing recent changes in national accounting, PPP conversion and Covid 19 impacts. Working paper.
Aviation Integrated Model
The Aviation Integrated Model (AIM) is a global aviation systems model which simulates interactions between passengers, airlines, airports and other system actors into the future, with the goal of providing insight into how policy levers and other projected system changes will affect aviation’s externalities and economic impacts. The model was originally developed in 2006-2009 with UK research council funding (e.g. Reynolds et al., 2007; Dray et al. 2014), and was updated as part of the ACCLAIM project (2015-2018) between University College London, Imperial College and Southampton University (e.g. Dray et al., 2019), with additional input from MIT regarding electric aircraft (e.g. Schäfer et al., 2018). The model is open-source, with code, documentation and a simplified version of model databases which omit confidential data available from the UCL Air Transportation Systems Group website (note that the website code and databases are slightly simplified from the full version used at UCL to remove confidential data).
In the context of NAVIGATE, a metamodeling approach is taken. We consider the most important factors affecting future aviation demand and emissions to be:
- Socioeconomic scenario (e.g. population, GDP, and potentially changes in attitudes to flying),
- Oil price,
- Carbon price, and
- Technology characteristics.
For each of these factors, we define a range of model inputs and carry out a grid of model runs using those inputs. More in-depth information on the model inputs can be found here.
Link to code/data
The metamodel and data needed to use it are available from the GitHub public repository: https://github.com/ODessens/NAVIGATE_T3.3
How to use the model
Currently, the model is supplied as two Python code files and a set of associated data tables. These routines also contain extensive comments on how they function and on the definition of different variables. Each model run requires two separate components. First, the data tables are read in. Second, each time the main IAM using the aviation metamodel requires aviation metrics, the interpolation model is run.
The different functions are elaborated upon in this document, containing more in-depth information about the model.
The data and program can be freely used by all parties.
Reynolds, T., Barrett, S., Dray, L., Evans, A., Köhler, M., Vera-Morales, M., Schäfer, A., Wadud, Z., Britter, R., Hallam, H., Hunsley, R., 2007. Modelling Environmental and Economic Impacts of Aviation: Introducing the Aviation Integrated Modelling Tool. In: Proceedings of the 7th AIAA Aviation Technology, Integration and Operations Conference, Belfast, 18–20 September 2007, AIAA-2007-7751;
Dray, L., Evans, A., Reynolds, T., Schäfer, A., Vera-Morales, M. and Bosbach, W., 2014. Airline fleet replacement funded by a carbon tax: an integrated assessment. Transport Policy, 34, 75-84.
Dray L., Krammer P., Doyme K., Wang B., Al Zayat K, O’Sullivan A., Schäfer A., 2019. “AIM2015: Validation and initial results from an open-source aviation systems model”, Transport Policy, 79, 93-102.
Schäfer A., Barrett, S., Doyme, K., Dray, L., Gnadt, A., Self, R., O’Sullivan, A., Synodinos, A., & Torija, A., 2018. Technological, economic and environmental prospects of all-electric aircraft. Nature Energy, 4, 160-166.
Other relevant publications are cited in this document, which contains more in-depth information.