Abstract
This study investigates the evolution of provincial new energy policies and industries of China using a topic modeling approach. To this end, six out of 31 provinces in China are first selected as research samples, central and provincial new energy policies in the period of 2010 to 2019 are collected to establish a text corpus with 23, 674 documents. Then, the policy corpus is fed to two different topic models, one is the Latent Dirichlet Allocation for modeling static policy topics, another is the Dynamic Topic Model for extracting topics over time. Finally, the obtained topics are mapped into policy tools for comparisons. The dynamic policy topics are further analyzed with the panel data from provincial new energy industries. The results show that the provincial new energy policies moved to different tracks after about 2014 due to the regional conditions such as the economy and CO2 emission intensity. Underdeveloped provinces tend to use environment-oriented tools to regulate and control CO2 emissions, while developed regions employ the more balanced policy mix for improving new energy vehicles and other industries. Widespread hysteretic effects are revealed during the correlation analysis of the policy topics and new energy capacity.
Highlights
With a series of incentive new energy policies (NEPs) issued by governments, the past decade has witnessed the rapid development of new energy industries (NEIs) in China, especially the photovoltaic and wind power industry
Since coherence-oriented topics in an Latent Dirichlet Allocation (LDA) model are proved to be more interpretable than the perplexity-oriented ones [41], T = 29 corresponding to point A(29, 0.664) in Fig 5 with the highest coherence value is selected as the topic number for modeling policy topics
Focal topics like “environment governance”, “high-end manufacturing”, and “transportation and facilities” appear in the NEP because the aim of building NEIs is to protect the environment by replacing fossil energy with solar and wind power
Summary
With a series of incentive new energy policies (NEPs) issued by governments, the past decade has witnessed the rapid development of new energy industries (NEIs) in China, especially the photovoltaic and wind power industry. Text mining technology has become more and more popular in sociology, topic models are hardly used in policy text analysis, especially in the NEPs. in order to explore the discrepancy of provincial NEPs from multiple perspectives like policy tool usage, policy evolution, and the role of policies in NEIs, topic models and text mining methods are used to analyze large-scale NEI-related policy documents from several representative provinces in China. (1) An analytical framework based on topic models is proposed for extracting both static and dynamic policy topics from large-scale provincial new energy policies including legislation, regulation, and other official documents in the period of 2010 to 2019. (3) The interaction between provincial new energy policies and industries is compared by the correlation analysis of time-varying new energy policy topics and panel data.
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