Measuring China’s Policy Stringency on Climate Change for 1954–2022
Abstract Efforts on climate change have demonstrated tangible impacts through various actions and policies. However, a significant knowledge gap remains: comparing the stringency of climate change policies over time or across jurisdictions is challenging due to ambiguous definitions, the lack of a u...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04476-0 |
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| _version_ | 1832571987917012992 |
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| author | Bo Li Enxian Fu Shuhao Yang Jiaying Lin Wei Zhang Jian Zhang Yaling Lu Jiantong Wang Hongqiang Jiang |
| author_facet | Bo Li Enxian Fu Shuhao Yang Jiaying Lin Wei Zhang Jian Zhang Yaling Lu Jiantong Wang Hongqiang Jiang |
| author_sort | Bo Li |
| collection | DOAJ |
| description | Abstract Efforts on climate change have demonstrated tangible impacts through various actions and policies. However, a significant knowledge gap remains: comparing the stringency of climate change policies over time or across jurisdictions is challenging due to ambiguous definitions, the lack of a unified assessment framework, complex causal effects, and the difficulty in achieving effective measurement. Furthermore, China’s climate governance is expected to address multiple objectives by integrating main effects and side effects, to achieve synergies that encompass environmental, economic, and social impacts. This paper employs an integrated framework comprising lexicon, text analysis, machine learning, and large-language model applied to multi-source data to quantify China’s policy stringency on climate change (PSCC) from 1954 to 2022. To achieve effective, robust, and explainable measurement, Chain-of-Thought and SHAP analysis are integrated into the framework. By framing the PSCC on varied sub-dimensions covering mitigation, adaptation, implementation, and spatial difference, this dataset maps the government’s varied stringency on climate change and can be used as a robust variable to support a series of downstream causal analysis. |
| format | Article |
| id | doaj-art-32f0486c8da741d6a24a886b1e3288ca |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-32f0486c8da741d6a24a886b1e3288ca2025-02-02T12:07:58ZengNature PortfolioScientific Data2052-44632025-01-0112111810.1038/s41597-025-04476-0Measuring China’s Policy Stringency on Climate Change for 1954–2022Bo Li0Enxian Fu1Shuhao Yang2Jiaying Lin3Wei Zhang4Jian Zhang5Yaling Lu6Jiantong Wang7Hongqiang Jiang8Chinese Academy of Environmental Planning, State Environmental Protection Key Laboratory of Environmental Planning and Policy SimulationHarvard University, Harvard Kennedy SchoolChinese Academy of Environmental Planning, Centre of Situation Analysis and Planning AssessmentWorld Resources Institute China, Sustainable Cities ProgramChinese Academy of Environmental Planning, State Environmental Protection Key Laboratory of Environmental Planning and Policy SimulationSchool of Government, Central University of Finance and EconomicsChinese Academy of Environmental Planning, State Environmental Protection Key Laboratory of Environmental Planning and Policy SimulationChinese Academy of Environmental Planning, State Environmental Protection Key Laboratory of Environmental Planning and Policy SimulationChinese Academy of Environmental Planning, State Environmental Protection Key Laboratory of Environmental Planning and Policy SimulationAbstract Efforts on climate change have demonstrated tangible impacts through various actions and policies. However, a significant knowledge gap remains: comparing the stringency of climate change policies over time or across jurisdictions is challenging due to ambiguous definitions, the lack of a unified assessment framework, complex causal effects, and the difficulty in achieving effective measurement. Furthermore, China’s climate governance is expected to address multiple objectives by integrating main effects and side effects, to achieve synergies that encompass environmental, economic, and social impacts. This paper employs an integrated framework comprising lexicon, text analysis, machine learning, and large-language model applied to multi-source data to quantify China’s policy stringency on climate change (PSCC) from 1954 to 2022. To achieve effective, robust, and explainable measurement, Chain-of-Thought and SHAP analysis are integrated into the framework. By framing the PSCC on varied sub-dimensions covering mitigation, adaptation, implementation, and spatial difference, this dataset maps the government’s varied stringency on climate change and can be used as a robust variable to support a series of downstream causal analysis.https://doi.org/10.1038/s41597-025-04476-0 |
| spellingShingle | Bo Li Enxian Fu Shuhao Yang Jiaying Lin Wei Zhang Jian Zhang Yaling Lu Jiantong Wang Hongqiang Jiang Measuring China’s Policy Stringency on Climate Change for 1954–2022 Scientific Data |
| title | Measuring China’s Policy Stringency on Climate Change for 1954–2022 |
| title_full | Measuring China’s Policy Stringency on Climate Change for 1954–2022 |
| title_fullStr | Measuring China’s Policy Stringency on Climate Change for 1954–2022 |
| title_full_unstemmed | Measuring China’s Policy Stringency on Climate Change for 1954–2022 |
| title_short | Measuring China’s Policy Stringency on Climate Change for 1954–2022 |
| title_sort | measuring china s policy stringency on climate change for 1954 2022 |
| url | https://doi.org/10.1038/s41597-025-04476-0 |
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