School of Management, Lanzhou University, Lanzhou, 730000, China.
Institute of Green Finance, Lanzhou University, Lanzhou, 730000, China.
Sci Data. 2022 Mar 11;9(1):75. doi: 10.1038/s41597-022-01183-y.
Improving the measurement of environmental policy intensity would affect not only the selection of variables in environmental policy research but also the research conclusions when evaluating policy effects. Because direct evaluation is lacking, the existing research usually applies data such as pollutant emission data, or the number of policies to construct proxy variables. However, these proxy variables are affected by many assumptions and different selection criteria, and they are inevitably accompanied by endogeneity problems. In this study, China's environmental policy is comprehensively collected for the first time, and a machine learning algorithm is applied to evaluate the policy intensity. We provide all the policies issued by the Chinese government from 1978 to 2019 and the quantified intensity for each policy. We also distinguish all policies into three types according to their attributes. This dataset can help researchers to further understand China's environmental policy system. In addition, it provides a valuable dataset for related research on evaluating environmental policy and recommending actions for further improvement.
改进环境政策强度的衡量标准不仅会影响环境政策研究中变量的选择,还会影响评估政策效果时的研究结论。由于缺乏直接的评估,现有研究通常应用污染物排放数据或政策数量等数据来构建代理变量。然而,这些代理变量受到许多假设和不同选择标准的影响,并且不可避免地伴随着内生性问题。在这项研究中,我们首次全面收集了中国的环境政策,并应用机器学习算法来评估政策强度。我们提供了 1978 年至 2019 年中国政府发布的所有政策及其量化强度,并根据政策属性将其分为三类。这个数据集可以帮助研究人员进一步了解中国的环境政策体系。此外,它还为评估环境政策和提出进一步改进建议的相关研究提供了有价值的数据集。