A systematic review of spatial disaggregation methods for climate action planning
Shruthi Patil,
Noah Pflugradt,
Jann M. Weinand,
Detlef Stolten,
Jürgen Kropp
Affiliations
Shruthi Patil
Institute of Energy and Climate Research, Techno-economic Systems Analysis (IEK-3), Forschungszentrum Jülich, Wilhelm-Johnen-Straße, Jülich, 52425, NRW, Germany; University of Potsdam, Institute for Environmental Science and Geography, Karl-Liebknecht-Str. 24-25, Potsdam-Golm, 14476, Brandenburg, Germany; Corresponding author at: Institute of Energy and Climate Research, Techno-economic Systems Analysis (IEK-3), Forschungszentrum Jülich, Wilhelm-Johnen-Straße, Jülich, 52425, NRW, Germany.
Noah Pflugradt
Institute of Energy and Climate Research, Techno-economic Systems Analysis (IEK-3), Forschungszentrum Jülich, Wilhelm-Johnen-Straße, Jülich, 52425, NRW, Germany
Jann M. Weinand
Institute of Energy and Climate Research, Techno-economic Systems Analysis (IEK-3), Forschungszentrum Jülich, Wilhelm-Johnen-Straße, Jülich, 52425, NRW, Germany
Detlef Stolten
Institute of Energy and Climate Research, Techno-economic Systems Analysis (IEK-3), Forschungszentrum Jülich, Wilhelm-Johnen-Straße, Jülich, 52425, NRW, Germany; Chair for Fuel Cells, RWTH Aachen University, c/o IEK-3, Forschungszentrum Jülich, Jülich, 52425, NRW, Germany
Jürgen Kropp
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60, 12 03, Potsdam, D-14412, Brandenburg, Germany; University of Potsdam, Institute for Environmental Science and Geography, Karl-Liebknecht-Str. 24-25, Potsdam-Golm, 14476, Brandenburg, Germany
National-level climate action plans are often formulated broadly. Spatially disaggregating these plans to individual municipalities can offer substantial benefits, such as enabling regional climate action strategies and for assessing the feasibility of national objectives. Numerous spatial disaggregation approaches can be found in the literature. This study reviews and categorizes these. The review is followed by a discussion of the relevant methods for the disaggregation of climate action plans. It is seen that methods employing proxy data, machine learning models, and geostatistical ones are the most relevant methods for the spatial disaggregation of national energy and climate plans. The analysis offers guidance for selecting appropriate methods based on factors such as data availability at the municipal level and the presence of spatial autocorrelation in the data.As the urgency of addressing climate change escalates, understanding the spatial aspects of national energy and climate strategies becomes increasingly important. This review will serve as a valuable guide for researchers and practitioners applying spatial disaggregation in this crucial field.
