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Public Policy and Decision Science: Difference between revisions
→Climate policy databases: add Climate Policy Radar
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* [[Data management, cleaning and imputation|'''Data management, cleaning and imputation''']]: ML can help with cleaning, merging and completing datasets that are relevant for policy-making.
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* '''[[Computational text analysis]]:''' Text documents are an important source of information to inform climate policy. Natural language processing can help to analyze large corpora of text.
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* '''Urban planning:''' With new techniques such as [[surrogate modeling]], ML can help with complex planning tasks.
* '''[[Power System Planning]]:''' Algorithms for planning new low-carbon energy infrastructure are often large and slow. ML can help speed up or provide proxies for these algorithms.
* '''[[Integrated Assessment Models (IAMs)|Integrated assessment models]]:''' IAMs are large simulations used to explore future societal pathways that are consistent with climate goals, which can be improved with ML.
=== Ex-post policy analysis ===
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=== Climate policy databases ===
* [https://app.climatepolicyradar.org Climate Policy Radar]: an open resource of climate laws, policies and action plans from every national government. The database offers full text search of documents via the website.
* [https://www.iea.org/policies Policies database of the International Energy Agency (IEA)]: one of the largest international climate and energy policy databases, integrating the IEA/IRENA Renewable Energy Policies and Measures Database, the IEA Energy Efficiency Database, the Addressing Climate Change database, and the Building Energy Efficiency Policies (BEEP) database.
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