Abstract
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.