img

官方微信

高级检索

中国沙漠 ›› 2026, Vol. 46 ›› Issue (5): 64-77.DOI: 10.7522/j.issn.1000-694X.2026.00065

• • 上一篇    下一篇

黄河流域城市能源转型赋能数字化绿色化协同发展:影响机制与空间溢出

马永刚1(), 臧家根2(), 范为3, 燕玲4   

  1. 1.兰州财经大学,工商管理学院,甘肃 兰州 730020
    2.兰州财经大学,信息工程与人工智能学院,甘肃 兰州 730020
    3.中国人事科学研究院,北京 100101
    4.大连民族大学 国际商学院,辽宁 大连 116650
  • 收稿日期:2026-03-25 修回日期:2026-05-02 出版日期:2026-09-20 发布日期:2026-11-02
  • 通讯作者: 臧家根
  • 作者简介:马永刚(1981—),男,宁夏银川人,博士,副教授,主要从事区域经济与人文地理研究。E-mail: ygma2014@gmail.com
  • 基金资助:
    甘肃省软科学项目(24JRZA173)

Urban energy transformation empowers digital-green synergistic development in the Yellow River Basin: mechanisms and spatial spillover effects

Yonggang Ma1(), Jiagen Zang2(), Wei Fan3, Ling Yan4   

  1. 1.School of Business Administration /, Lanzhou University of Finance and Economics,Lanzhou 730020,China
    2.School of Information Engineering and Artificial Intelligence, Lanzhou University of Finance and Economics,Lanzhou 730020,China
    3.Chinese Academy of Personnel Science Research,Beijing 100101,China
    4.International Business School,Dalian Minzu University,Dalian 116650,Liaoning,China
  • Received:2026-03-25 Revised:2026-05-02 Online:2026-09-20 Published:2026-11-02
  • Contact: Jiagen Zang

摘要:

本文立足黄河流域生态保护和高质量发展战略背景,考察城市能源转型对数字化绿色化协同发展的影响机制与空间溢出效应。基于2011—2021年76个地级市的面板数据,将新能源示范城市政策作为能源转型的准自然实验,运用耦合协调度模型测度数字化绿色化协同发展水平,并采用双重机器学习与空间杜宾模型进行因果识别与空间效应分析。结果表明:黄河流域城市能源转型显著促进数字化绿色化协同发展,该结论在经过一系列稳健性与内生性检验后依然成立。机制分析显示,城市能源转型通过推动技术创新、提升城市韧性与降低碳排放强度3条路径发挥作用。异质性分析显示,政策效应在非资源型城市、环保重点城市和非老工业基地城市表现更为强劲。空间杜宾模型显示,城市能源转型对数字化绿色化协同发展具有显著正向空间溢出效应,本地政策能够辐射带动邻近城市协同水平提升。

关键词: 能源转型, 数字化绿色化协同发展, 黄河流域, 双重机器学习, 空间溢出效应

Abstract:

Under the strategy of ecological protection and high-quality development in the Yellow River Basin, this study explores the mechanisms and spatial spillover effects of urban energy transformation on digital-green synergistic development (DGSD). Using panel data from 76 prefecture-level cities (2011-2021) and employing a quasi-natural experiment based on the New Energy Demonstration City policy, we apply a coupling coordination degree model, double machine learning, and the spatial Durbin model. The results show that urban energy transformation significantly promotes DGSD, a finding that holds after robustness and endogeneity tests. Mechanism analysis reveals three transmission pathways: technological innovation, urban resilience, and carbon emission intensity. Heterogeneity analysis indicates stronger policy effects in non-resource-based cities, key environmental protection cities, and non-old industrial bases. The spatial Durbin model shows positive and significant spatial spillover effects, where local policies enhance DGSD in neighboring cities.

Key words: energy transformation, digital-green synergistic development, Yellow River Basin, double machine learning, spatial spillover effects

中图分类号: