各地工业能源效率的影响因素及分位效应研究——基于环境规制视角
Research on Influencing Factors and Quantile Effects of Various Region’s Industrial Energy Efficiency—From the Perspective of Environmental Regulation
摘要: 本研究基于2016~2022年东部六个地区的面板数据,从环境规制视角考察其对省域工业能源效率的影响,并关注数字经济的调节作用。通过SBM-DEA模型测度工业能源效率,并运用分位数回归进行实证分析。结果表明,环境规制对工业能源效率具有正向影响,且存在明显分位异质性:在效率较低的地区提升作用更强,在效率较高的地区则相对有限,这印证了环境规制效果因地区发展阶段不同而呈现差异。同时,数字经济能够强化环境规制的能效提升效应,尤其在低效率地区,二者协同产生的“赋能追赶”效果最为显著。此外,研发投入、产业结构升级与对外开放等因素对能源效率的影响也随效率水平不同而呈现差异。研究表明,环境规制政策应避免“一刀切”,需结合各地效率水平与数字化基础,从而更精准有效地推动工业节能降碳。
Abstract: Based on the panel data of six eastern regions from 2016 to 2022, this study examines the impact of environmental regulation on provincial industrial energy efficiency from the perspective of environmental regulation, and focuses on the moderating role of the digital economy. The industrial energy efficiency is measured by the SBM-DEA model, and an empirical analysis is conducted using quantile regression. The results show that environmental regulation has a positive impact on industrial energy efficiency, with significant quantile heterogeneity: the enhancing effect is stronger in regions with lower efficiency, but relatively limited in regions with higher efficiency, which confirms that the effectiveness of environmental regulation varies depending on the development stage of the region. Meanwhile, the digital economy can strengthen the energy efficiency improvement effect of environmental regulation, especially in low-efficiency regions, where the synergistic “empowerment and catch-up” effect is most significant. In addition, the impacts of factors such as R&D investment, industrial structure upgrading, and openness on energy efficiency also vary with the level of efficiency. The study suggests that environmental regulation policies should avoid a “one-size-fits-all” approach and need to be combined with local efficiency levels and digital foundations to more precisely and effectively promote industrial energy conservation and carbon reduction.
文章引用:陈韦琦嘉. 各地工业能源效率的影响因素及分位效应研究——基于环境规制视角[J]. 统计学与应用, 2026, 15(1): 181-196. https://doi.org/10.12677/sa.2026.151018

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