State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing, 100084, China.
School of Public Policy and Management, Tsinghua University, Beijing, 100084, China.
Sci Data. 2024 Feb 16;11(1):213. doi: 10.1038/s41597-024-03033-5.
Low-carbon policies are essential for facilitating manufacturing industries' low-carbon transformation and achieving carbon neutrality in China. However, recent studies usually apply proxy variables to quantify policies, while composite indices of policy intensity measured by objectives and instruments focus more on the national level. It is deficient in direct and comprehensive quantification for low-carbon policies. Hence, having extended the meaning of policy intensity, this paper constructs a low-carbon policy intensity index quantified by policy level, objective and instrument via phrase-oriented NLP algorithm and text-based prompt learning. This process is based on the low-carbon policy inventory we built for China's manufacturing industries containing 7282 national-, provincial- and prefecture-level policies over 2007-2022. Lastly, we organize the dataset in two formats (.dta and .xlsx) for multidiscipline researchers. Apart from the inventory and intensity for each policy, the policy intensity is also aggregated to national-, provincial- and prefecture-level with sub-intensity for four objectives and three instruments. This dataset has potential uses for future studies by merging with macro and micro data related to low-carbon performances.
低碳政策对于促进制造业的低碳转型和实现中国碳中和至关重要。然而,最近的研究通常使用代理变量来量化政策,而通过目标和工具衡量的政策强度综合指数则更侧重于国家层面。对于低碳政策,缺乏直接和全面的量化。因此,本文扩展了政策强度的含义,通过面向短语的自然语言处理算法和基于文本的提示学习,构建了一个由政策级别、目标和工具量化的低碳政策强度指数。这一过程基于我们为中国制造业构建的低碳政策清单,其中包含 2007-2022 年 7282 项国家级、省级和地市级政策。最后,我们以.dta 和.xlsx 两种格式组织数据集,供多学科研究人员使用。除了每项政策的清单和强度外,政策强度还按国家、省和地市级进行汇总,并按四个目标和三个工具进行细分强度汇总。通过与低碳绩效相关的宏观和微观数据合并,该数据集具有未来研究的潜在用途。