Updated Decomposition Tools Can Help Integrated Assessment Modelers Better Analyze and Compare Their Results
Researchers and practitioners participating in the seventh assessment cycle of the Intergovernmental Panel on Climate Change (IPCC) can now rely on updated scenario decomposition tools to accelerate scenario troubleshooting and analysis. These tools provide enhanced visibility and deeper understanding of key drivers affecting projected greenhouse gas (GHG) emissions.
The IPCC mitigation report relies heavily on outputs from integrated assessment models (IAMs), which use different assumptions for drivers of emissions, such as GDP, population growth, and technology availability and costs. Unfortunately, modeling exercises rely on so many inputs and produce so many outputs that fully assessing results is slow and difficult.
The main resource for systematizing outputs of these models is the PYAM project, supported by the International Institute for Applied Systems Analysis (IIASA). PYAM provides tools that aid the visualization and analysis of any IAM outputs that follow the standard data format developed by the Integrated Assessment Modeling Consortium (IAMC).
The updated software tools described here are fully compatible with PYAM and IAMC data formats and have undergone testing for integration into the PYAM framework, though they have not yet been incorporated into that software.
Methods
Many analysts most commonly rely on the four-factor Kaya identity1 for analyzing and comparing scenario results from IAMs. This tool is often used to decompose drivers of GHG emissions in the energy sector. The Kaya identity enables deeper understanding of the energy-sector outputs from IAMs by plotting key drivers affecting energy use and carbon dioxide (CO2) emissions in long-term greenhouse gas emissions scenarios.
We show the four-factor version of the Kaya identity in Equation 1:
As many researchers have realized, the original Kaya identity masks complex system dynamics in energy scenarios. In 2019, a more comprehensive version of the Kaya identity appeared in the journal Environmental Modeling and Software that incorporated an energy supply loss factor, the carbon intensity of fossil fuel supplied and the net emissions from the energy sector after sequestration2. In its fully developed form shown in Equation 2, the expanded version becomes:
Of course, the energy sector is not the only important source of GHG emissions. We created another equation to summarize emissions for all sectors, adding land use, industrial process CO2 emissions, biomass carbon capture and storage (CCS), other forms of carbon capture (direct air capture in most scenarios, and emissions of other gases than CO2 (i.e., methane, nitrous oxide and F-gases). Carbon capture for fossil fuels is embedded in the CFossil fuels term
This fully expanded, all-sector decomposition tool, which characterizes total CO2 equivalent GHG emissions (in carbon equivalent emissions or CO2e), is summarized in Equation 3:
Biomass carbon sequestration interacts with both the energy sector and the land-use sector, so we split that form of carbon capture from direct air capture to make sure any such interactions are correctly tracked.
To illustrate the usefulness of the fully expanded decomposition, we applied it to two scenarios in a 2022 Environmental Modeling and Software journal article2.
We credit the foundation of these tools to Holmes Hummel’s 2006 dissertation3, Interpreting Global Energy and Emission Scenarios: Methods for Understanding and Communicating Policy Insights. Hummel built the initial tools in Excel workbooks, which served well for years. However, it proved hard to convince modelers to integrate spreadsheets into their workflows, which were largely automated using Python and other more modern tools. With that experience in mind, we set out to recreate those tools as a Python package that modelers could easily upload and use.
We have now released that Python package for general use. Virtually all IAMs generate the required data to use our tools, and we stuck closely to the terminology, definitions and data structures embodied in IIASA's PYAM tools.
Users can download the Python package directly from PyPI or from the GitHub project page, and view an example notebook on GitHub showing how to use the tools.
The Python package is licensed under Apache 2.0, which is an open-source license that allows free use, modification and distribution for commercial or private use. Any contributors automatically grant a royalty-free license to any patented algorithms they add to the software.
We are confident that these tools will facilitate analysis and comparison of IAM-based scenarios, assist in troubleshooting those scenarios and increase understanding of key drivers affecting GHG emissions
Example Dashboards of Decomposition Tools for Long-Term Emissions Scenarios
There are three main dashboards, as documented in the 20194 and 20222 Environmental Modeling and Software journal articles. The first shows what we call "Kaya factors," like population, gross world product, final energy, primary energy, fossil fuel primary energy, total fossil carbon emissions and net fossil carbon emissions after accounting for sequestration. We use runs from IMAGE 3.0.1. The baseline is SSP2 and the intervention case is IMA15-TOT, a scenario that keeps global temperatures from exceeding 1.5 degrees C. The runs are documented in a 2018 Nature Climate Change article5.
Blue lines are reference case values and orange lines are intervention case values.
The second dashboard shows what we call the "Kaya ratios," which are the terms in the expanded Kaya identity. These include population, economic activity per person, final energy per dollar of economic activity, primary energy per unit of final energy, the fossil fuel fraction of primary energy, total fossil carbon per unit of primary energy and the ratio of fossil carbon reaching the atmosphere to the fossil carbon combusted in the energy system.
The third dashboard summarizes our "fully expanded decomposition," which includes the energy sector results in one pane, along with additive results for biomass carbon sequestration, direct air capture carbon sequestration, land use, industrial process carbon dioxide emissions and emissions of other gases than CO2 (other agents). The intervention scenario in this case has no biomass CCS and little change in industrial process emissions.
These dashboards together give a complete and conceptually clear high-level picture of the evolving emissions of the global economy for a business-as-usual scenario and an emissions reduction scenario. Of course it's always possible to dig deeper, but these three dashboards are a great place to start. We hope that automating the creation of such dashboards will enable much faster troubleshooting and high-level analysis and comparison of scenarios.
View the notebook that explains how to make these graphs. Download the Excel workbook that contains the original data for the scenario pictured above.
We welcome questions or suggestions by email at [email protected] and [email protected].
References
1 Kaya, Yoichi. 1990. Impact of Carbon Dioxide Emission Control on GNP Growth: Interpretation of Proposed Scenarios. Proceedings of the IPCC Energy and Industry Subgroup of the Response Strategies Working Group. Paris, France.
2 Koomey, Jonathan, Zachary Schmidt, Karl Hausker, and Dan Lashof. 2022. "Exploring the black box: Applying macro decomposition tools for scenario comparisons." Environmental Modeling and Software. vol. 155, September. [https://doi.org/10.1016/j.envsoft.2022.105426]
3 Hummel, Holmes. 2006. Interpreting Global Energy and Emission Scenarios: Methods for Understanding and Communicating Policy Insights. Thesis, Interdisciplinary Program on Environment and Resources, Stanford University. [https://profiles.stanford.edu/holmes-hummel]
4 Koomey, Jonathan, Zachary Schmidt, Holmes Hummel, and John Weyant. 2019. "Inside the Black Box: Understanding Key Drivers of Global Emission Scenarios." Environmental Modeling and Software. vol. 111, no. 1. January. pp. 268–281. [https://www.sciencedirect.com/science/article/pii/S1364815218300793]
5 van Vuuren, Detlef P., Elke Stehfest, David E. H. J. Gernaat, Maarten van den Berg, David L. Bijl, Harmen Sytze de Boer, Vassilis Daioglou, Jonathan C. Doelman, Oreane Y. Edelenbosch, Mathijs Harmsen, Andries F. Hof, and Mariësse A. E. van Sluisveld. 2018. "Alternative pathways to the 1.5 °C target reduce the need for negative emission technologies." Nature Climate Change. 2018/04/13. [https://doi.org/10.1038/s41558-018-0119-8]
