Chen Hongfei, Niu Dongxiao
School of Economics and Management, North China Electric Power University, Beijing, 102206, China; Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing, 102206, China.
School of Economics and Management, North China Electric Power University, Beijing, 102206, China; Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing, 102206, China.
J Environ Manage. 2025 Aug;389:126187. doi: 10.1016/j.jenvman.2025.126187. Epub 2025 Jun 16.
There is a general consensus on the importance of energy transition in society; however, the synergistic interactions of energy policies and their effects on enterprise energy transitions (EETs) have yet to be fully elucidated. In this study, we develop a PMC index model and a synergy model utilizing text mining technology to assess the efficiency and synergy levels of various types of energy policies in China. Furthermore, we employ a fixed-effects model to investigate the impact and underlying mechanisms of energy policy synergy (EPS) on the ET of high-energy-consuming enterprises. The results show that the PMC index of technology energy policies (TEP) is significantly higher than that of supervisory energy policies (SEP), with TEP accounting for 75.88 % compared to SEP's 23.81 % among excellent policies. China's energy policies exhibit a high degree of synergy and fall under the category of synergistic development. The fluctuation curve of these policies aligns with that of SEP, demonstrating the "barrel principle," wherein the overall effectiveness is determined by the least effective element. Both TEP and EPS significantly and positively influence EET, with the facilitating effect of EPS being substantially greater than that of individual policies (0.1477 > 0.0202). This highlights the synergistic characteristic, encapsulated by the adage "1 + 1 > 2." Mechanism tests reveal that enhancing clean energy supply capacity further amplifies the impact of EPS on EET. Heterogeneity analysis shows that the contribution of EPS to EET is more pronounced among high-carbon and non-new energy enterprises. Moving forward, it is crucial to advance EPS and support EET by improving policy comprehensiveness and complementarity, strengthening synergies, and establishing both incentive and penalty frameworks.
社会对能源转型的重要性已达成普遍共识；然而，能源政策的协同相互作用及其对企业能源转型（EETs）的影响尚未得到充分阐明。在本研究中，我们利用文本挖掘技术开发了一个PMC指数模型和一个协同模型，以评估中国各类能源政策的效率和协同水平。此外，我们采用固定效应模型来研究能源政策协同（EPS）对高耗能企业能源转型（ET）的影响及潜在机制。结果表明，技术能源政策（TEP）的PMC指数显著高于监管能源政策（SEP），在优秀政策中，TEP占75.88%，而SEP占23.81%。中国的能源政策呈现出高度的协同性，属于协同发展类别。这些政策的波动曲线与SEP的波动曲线一致，体现了“木桶原理”，即整体有效性由最无效的要素决定。TEP和EPS均对EET有显著的正向影响，且EPS的促进作用远大于单个政策（0.1477 > 0.0202）。这突出了协同特征，正如“1 + 1 > 2”这句格言所概括的。机制测试表明，提高清洁能源供应能力进一步放大了EPS对EET的影响。异质性分析表明，EPS对EET的贡献在高碳和非新能源企业中更为显著。展望未来，通过提高政策的全面性和互补性、加强协同作用以及建立激励和惩罚框架来推进EPS并支持EET至关重要。
