产业集聚和技术创新如何影响碳排放效率:来自中国的证据

IF 4.6 0 ENERGY & FUELS
Qinghua Pang , Huilin Zhai , Tianxin Zhao , Lina Zhang , Yung-ho Chiu
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引用次数: 0

摘要

尽管产业集聚对碳排放效率的非线性效应越来越受到关注,但技术创新作为阈值变量在形成这种非线性动态中的关键作用尚未得到充分探讨。本文采用空间Durbin模型(SDM)揭示了2006 - 2019年中国30个省区产业集聚对碳排放效率的影响。还进一步讨论了区域和行业的异质性。随后,采用阈值面板模型检验技术创新在碳排放效率与产业集聚关系中的阈值作用。结果表明:(1)产业集聚与碳排放效率之间存在显著的倒“u”型关系。②东部地区的产业集聚拐点大于中西部地区。资本密集型和技术密集型产业比资源密集型和劳动密集型产业具有更强的可持续集聚优势。(3)随着技术创新的进一步发展(TEC>0.067),产业集聚对碳排放效率的正向影响由强向弱转变。该研究使政策制定者能够利用技术创新门槛作为一个可操作的基准,战略性地选择产业集聚干预的时机,以最大限度地提高脱碳回报,同时避免规模驱动的效率陷阱。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How industrial agglomeration and technological innovation affect carbon emission efficiency: Evidence from China
Despite growing attention to the nonlinear effect of industrial agglomeration on carbon emission efficiency, the critical role of technological innovation as a threshold variable in shaping the nonlinear dynamic remains underexplored. Herein, revealing the influence of industrial agglomeration on carbon emission efficiency in 30 provinces spanning 2006 to 2019 using a spatial Durbin model (SDM). Regional and industrial heterogeneities are also further discussed. Subsequently, the threshold role of technological innovation in the relationship between carbon emission efficiency and industrial agglomeration was tested by adopting a threshold panel model. The results showed: (1) A significant inverted “U-shaped” relationship was found to exist between industrial agglomeration and carbon emission efficiency. (2) The eastern region had a greater industrial agglomeration inflection point than the central and western regions. Capital and technology-intensive industries had more sustainable agglomeration advantages than resource-intensive and labor-intensive industries. (3) With the further development of technological innovation (TEC>0.067), the positive influence of industrial agglomeration on carbon emission efficiency shifted from strong to weak. This study enables policymakers to leverage the technological innovation threshold as an actionable benchmark, strategically timing industrial agglomeration interventions to maximize decarbonization returns while avoiding scale-driven efficiency traps.
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