Project

SuCCESs

Goal: We develop a new open-source integrated assessment model for Sustainable Climate Change Mitigation Strategies in Energy-Land-Material Systems scenario analysis (called SuCCESs). The model should help answer urgent questions about climate change mitigation and adaption strategies.

Integrated Assessment Models combine modeling of different disciplines, usually integrating the social, energy and climate systems. The models generate future scenarios on greenhouse gas emissions, energy consumption, climate change impacts and other environmental issues based on societal development. They are widely used in climate change assessments, such as the IPCC Synthesis report.

Our new model integrates land and materials systems in addition to the energy and climate systems, and thereby considers mutual dependencies and feedbacks between the systems more realistically.
The model is aimed to be a tool to approximate complex feedbacks of climate change mitigation attempts in different scenarios.

Date: 1 February 2021

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Project log

Nadine-Cyra Freistetter
added an update
The SuCCESs team will be present at the IAMC 2022 and will be presenting two posters about the progress of the soon-to-be-published SuCCESs IAM and it's standalone Landuse Module.
See you there!
 
Nadine-Cyra Freistetter
added a research item
Integrated Assessment Models (IAMs) provide foresight for long-term climate change mitigation strategies. The majority of IAMs focus on the transformation of the energy system; away from fossil fuels towards low-carbon technologies. Focusing on one mitigation sector alone, however, ignores the competition for land and materials as well as amplifying and inhibiting feedback mechanisms between the systems. Therefore, an increasing number of scientists are calling for a better understanding and exploitation of mitigation potentials immanent in land-based carbon sequestration, low-carbon materials, climate-safe technologies, and their mutual interdependencies (Pauliuk et al. 2017; Schandl et al. 2020; Harper et al. 2018; Griscom et al. 2017). We present a new, open-source IAM called SuCCESs (Sustainable Climate Change mitigation strategies in Energy-land-materials Systems) that calculates long-term scenarios in energy, land-use and materials systems. The representation of each system is condensed in comparison to dedicated land and energy models, to focus the computational power on the full integration and coupling of the subsystems, which allows joint optimization of mitigation measures while accounting for the interdependent feedback effects between the systems. Through that, SuCCESs also offers a broader scope than existing models by integrating multiple systems into a single package, which is then solved through intertemporal optimization. The partial equilibrium approach of SuCCESs simulates efficient markets and policies by finding a cost-minimizing solution that satisfies the projected global demand for energy, materials and food under available resources, assumed technological progress and prescribed environmental policies.
Nadine-Cyra Freistetter
added a project goal
We develop a new open-source integrated assessment model for Sustainable Climate Change Mitigation Strategies in Energy-Land-Material Systems scenario analysis (called SuCCESs). The model should help answer urgent questions about climate change mitigation and adaption strategies.
Integrated Assessment Models combine modeling of different disciplines, usually integrating the social, energy and climate systems. The models generate future scenarios on greenhouse gas emissions, energy consumption, climate change impacts and other environmental issues based on societal development. They are widely used in climate change assessments, such as the IPCC Synthesis report.
Our new model integrates land and materials systems in addition to the energy and climate systems, and thereby considers mutual dependencies and feedbacks between the systems more realistically.
The model is aimed to be a tool to approximate complex feedbacks of climate change mitigation attempts in different scenarios.