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<p>Amid global energy transition and China’s “dual carbon” goals, green technological innovation (GTI) is pivotal to reconciling economic expansion with environmental stewardship. Existing scholarship predominantly examines the standalone impacts of individual policies, overlooking their synergistic interactions, optimal implementation sequencing, and spatial spillover effects—gaps this study seeks to fill. Leveraging panel data spanning 2005–2023 from 294 Chinese prefecture-level cities, we employ a double machine learning (DML) framework to robustly estimate the causal impact of New Energy Demonstration City (NEDC)-Innovative Urban City (IUC) policy synergy on GTI, adopt a Spatial Durbin Difference-in-Differences (SDM-DID) model to assess spatial effects. Results show that joint NEDC-IUC implementation generates a synergistic effect, boosting GTI by 15.2%, outperforming single-policy impacts (NEDC: 8.7%; IUC: 9.3%). Mechanisms include fiscal support (mediating effect: 23.5%), talent agglomeration (18.9%), and energy structure optimization (16.7%). Environmental regulation positively moderating the effect. Policy sequencing matters: implementing IUC first, then NEDC, yields the strongest effect. Spatial spillovers are significant, with neighboring cities’ GTI increasing by 6.8%. This study enriches the literature on policy mixes for GTI by unpacking NEDC-IUC synergy mechanisms, provides a methodological reference for addressing endogeneity via DML, and offers actionable guidance for optimizing regional green collaborative governance to advance the “dual carbon” goals.</p>

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Energy transition, innovation-driven, and green technological innovation—an empirical study based on double machine learning

  • 期刊论文

  • 发表日期：

    2026年1月10日

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  • Wen Li,
  • Rui Gao,
  • Qingjun Zeng,
  • Xianpeng Guo

摘要

Amid global energy transition and China’s “dual carbon” goals, green technological innovation (GTI) is pivotal to reconciling economic expansion with environmental stewardship. Existing scholarship predominantly examines the standalone impacts of individual policies, overlooking their synergistic interactions, optimal implementation sequencing, and spatial spillover effects—gaps this study seeks to fill. Leveraging panel data spanning 2005–2023 from 294 Chinese prefecture-level cities, we employ a double machine learning (DML) framework to robustly estimate the causal impact of New Energy Demonstration City (NEDC)-Innovative Urban City (IUC) policy synergy on GTI, adopt a Spatial Durbin Difference-in-Differences (SDM-DID) model to assess spatial effects. Results show that joint NEDC-IUC implementation generates a synergistic effect, boosting GTI by 15.2%, outperforming single-policy impacts (NEDC: 8.7%; IUC: 9.3%). Mechanisms include fiscal support (mediating effect: 23.5%), talent agglomeration (18.9%), and energy structure optimization (16.7%). Environmental regulation positively moderating the effect. Policy sequencing matters: implementing IUC first, then NEDC, yields the strongest effect. Spatial spillovers are significant, with neighboring cities’ GTI increasing by 6.8%. This study enriches the literature on policy mixes for GTI by unpacking NEDC-IUC synergy mechanisms, provides a methodological reference for addressing endogeneity via DML, and offers actionable guidance for optimizing regional green collaborative governance to advance the “dual carbon” goals.