A systematic review of spatial disaggregation methods for climate action planning  

作　　者：Shruthi Patil Noah Pflugradt Jann M.Weinand Detlef Stolten Jürgen Kropp 

机构地区：[1]Institute of Energy and Climate Research,Techno-economic Systems Analysis(IEK-3),Forschungszentrum Jülich,Wilhelm-Johnen-Straße,Jülich,52425,NRW,Germany [2]University of Potsdam,Institute for Environmental Science and Geography,Karl-Liebknecht-Str.24-25,Potsdam-Golm,14476,Brandenburg,Germany [3]Chair for Fuel Cells,RWTH Aachen University,c/o IEK-3,Forschungszentrum Jülich,Jülich,52425,NRW,Germany [4]Potsdam Institute for Climate Impact Research(PIK),Member of the Leibniz Association,P.O.Box 60,1203,Potsdam,D-14412,Brandenburg,Germany

出　　处：《Energy and AI》2024年第3期458-469,共12页能源与人工智能（英文）

基　　金：funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No.101036458.

摘　　要：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.

关 键 词：Spatial downscaling Proxy data Mass-preserving Climate action plans Spatial autocorrelation Machine learning Geostatistical models 

分 类 号：P46[天文地球—大气科学及气象学] TP39[自动化与计算机技术—计算机应用技术]