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  A systematic review of spatial disaggregation methods for climate action planning

Patil, S., Pflugradt, N., Weinand, J. M., Stolten, D., Kropp, J. P. (2024): A systematic review of spatial disaggregation methods for climate action planning. - Energy and AI, 17, 100386.
https://doi.org/10.1016/j.egyai.2024.100386

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 Creators:
Patil , Shruthi1, Author
Pflugradt, Noah2, Author
Weinand, Jann M.2, Author
Stolten, Detlef2, Author
Kropp, Jürgen P.1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Abstract: 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.

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Language(s): eng - English
 Dates: 2024-01-082024-06-062024-06-172024-09-01
 Publication Status: Finally published
 Pages: 12
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.egyai.2024.100386
Organisational keyword: RD2 - Climate Resilience
PIKDOMAIN: RD2 - Climate Resilience
Working Group: Urban Transformations
MDB-ID: No data to archive
Research topic keyword: Energy
OATYPE: Gold Open Access
 Degree: -

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Title: Energy and AI
Source Genre: Journal, Scopus, oa
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Pages: - Volume / Issue: 17 Sequence Number: 100386 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/energy-and-ai
Publisher: Elsevier