School of Architecture and Urban Planning, Guangdong University of Technology, 729 East Dongfeng Road, Guangzhou 510090, China.
Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China.
Int J Environ Res Public Health. 2021 Dec 7;18(24):12880. doi: 10.3390/ijerph182412880.
Understanding green innovation efficiency (GIE) is crucial in assessing achievements of the current development strategy scientifically. Existing literature on measuring green innovation efficiency with considering environmental undesirable outputs at the city level is limited. Consulting existing studies, this paper constructs an evaluation index system to measure green innovation efficiency and its socioeconomic impact factors. Employing a super slacks-based measure (Super-SBM) model, which takes into account undesirable outputs (industrial wastewater emissions, industrial exhaust emissions and CO emissions), and a Global Malmquist-Luenberger index (GML), we calculate the green innovation efficiency of 15 cities in the Pearl River Delta (PRD) urban agglomeration from 2009 to 2017, exploring the impact factors behind green innovation efficiency using a Tobit panel regression model. The empirical results are as follows: Due to the heterogeneity of urban functional division and economic development in the Pearl River Delta, more than half of the region's cities were found to be in ineffective or transitional states with respect to their green innovation efficiency. A GML decomposition index shows that technological efficiency and technological progress are out of step with one another in the Pearl River Delta, an asymmetry which is restricting regional green innovation growth. The influencing factors of industrial structure, the level of economic openness, and the urban informationization level are shown to have promoted green innovation efficiency in the Pearl River Delta's cities, while government R&D expenditure and education expenditure exerted negative effects. This paper concludes by highlighting the importance of cooperation between the government and enterprises in achieving green innovation.
理解绿色创新效率（GIE）对于科学评估当前发展战略的成果至关重要。现有文献中关于在城市层面考虑环境非期望产出的绿色创新效率测度的研究较少。本文参考现有研究，构建了一个评价指标体系，用以衡量绿色创新效率及其社会经济影响因素。利用超效率数据包络分析模型（Super-SBM），该模型考虑了非期望产出（工业废水排放量、工业废气排放量和 CO 排放量），以及全局 Malmquist-Luenberger 指数（GML），我们计算了 2009 年至 2017 年珠江三角洲（PRD）城市群 15 个城市的绿色创新效率，利用 Tobit 面板回归模型探讨了绿色创新效率背后的影响因素。实证结果表明：由于珠江三角洲城市功能划分和经济发展的异质性，该地区一半以上的城市在绿色创新效率方面处于无效或过渡状态。GML 分解指数显示，珠江三角洲的技术效率和技术进步不同步，这种不对称性限制了区域绿色创新的增长。产业结构、经济开放水平和城市信息化水平的影响因素促进了珠江三角洲城市的绿色创新效率，而政府研发支出和教育支出则产生了负面影响。本文最后强调了政府和企业在实现绿色创新方面合作的重要性。
