作　者：陈景华 刘展豪
关键词：沿黄城市群；绿色创新转化效率；时空格局；空间分位数回归
摘　要：

提升绿色创新转化效率是黄河流域各地加快发展新质生产力的必然要求。基于沿黄城市群70 个城市面板数据，运用超效率网络SBM 模型、空间核密度估计及空间分位数回归模型，可以探究沿黄城市群绿色创新转化效率的时空格局与驱动因素。研究发现：沿黄城市群绿色创新转化效率稳步提升，科技研发阶段效率波动式上升，绿色转化阶段效率较低但增长显著，呈现出以山东半岛城市群、呼包鄂榆城市群为引领，各城市群竞相发展的良好态势。沿黄城市群绿色创新转化效率的稳定性较强，存在明显的空间分异特征与收敛趋势，济南、青岛、西安等城市绿色创新转化高地逐步形成，并具有显著的空间溢出效应。完善基础设施、发展数字技术、优化产业结构、加强环境规制能显著提升绿色创新转化效率，随着分位点的提升，影响效应持续增强，一定程度上导致区域差异的形成；促进金融发展、加强市场自由度、强化政策支持在各分位点处的影响效应不一致。在空间层面，仅有基础设施建设具有正向溢出效应，政策支持、环境规制、市场自由度、产业结构与数字技术呈现“逐底竞争” “以邻为壑”的特征，产生了负向溢出效应，需着力强化驱动因素的空间协同效应。

 

The Spatial Patterns and Drivers of Green Innovation Transformation Efficiency in Urban Agglomerations along the Yellow River Basin

Chen Jinghua（Shandong University of Finance and Economics） Liu Zhanhao（Hunan University）

Abstract Enhancing the green innovation transformation efficiency is vital for accelerating new quality productive forces in the Yellow River Basin. Using panel data from 70 cities within the urban agglomerations along the Yellow River, this study examines the spatiotemporal evolution and drivers of green innovation transformation efficiency via a super-efficiency network SBM model, spatial kernel density estimation, and spatial quantile regression model. The results reveal a steady improvement across the region—with fluctuating gains in R&D efficiency and modest yet significant progress in the transformation stage. Notably, the Shandong Peninsula and Hohhot-Baotou-Ordos-Yulin urban agglomerations lead this advancement, while all urban agglomerations trend toward better development. Moreover, green innovation transformation efficiency exhibits robust stability, significant spatial heterogeneity and convergence, with core cities such as Jinan, Qingdao, and Xi'an emerging as innovation hubs with pronounced spatial spillover effects. Infrastructure, digitalization, industrial upgrading, and stringent environmental regulations significantly boost efficiency—effects that intensify at higher quantiles and partly explain regional disparities. In contrast, financial development, market liberalization, and policy interventions have uneven impacts. Spatial analysis further identifies that policy interventions, environmental regulation, market freedom, industrial structure, and digitalization pro‐ duce negative spillovers, leading to ''race-to-the-bottom'' and ''beggar-thy-neighbor'' competitions; only infra‐ structure offers positive spatial externalities, underscoring the need for coordinated spatial governance of in‐ novation drivers.

Key words urban agglomerations along the Yellow River Basin; green innovation transformation efficiency; spatial patterns; spatial quantile regression model

 

■    作者简介  陈景华，山东财经大学经济学院教授，山东 济南 250014；

刘展豪（通讯作者），湖南大学经济与贸易学院博士研究生，湖南 长沙410079。

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