科研管理

科研管理 ›› 2022, Vol. 43 ›› Issue (9): 127-138.

• 论文 • 上一篇    下一篇

技术异质下中国企业绿色创新效率及损失来源分析

钱丽1,2，王文平2，肖仁桥1   

  1. 1.安徽财经大学工商管理学院，安徽 蚌埠233030； 2.东南大学经济管理学院，江苏 南京210096
  • 收稿日期:2019-10-28 修回日期:2020-06-16 出版日期:2022-09-20 发布日期:2022-09-19
  • 通讯作者: 钱丽
  • 基金资助:
    国家自然科学基金面上项目(71973023,2020—2023);国家社会科学基金项目(15CGL010,2015—2020);安徽财经大学科研重点项目(ACKYB21015,2021—2023)。

An analysis of the regional differences and loss sources of green innovation efficiency of Chinese enterprises under technology heterogeneity

Qian Li1,2, Wang Wenping2, Xiao Renqiao1   

  1. 1. School of Business Administration, Anhui University of Finance and Economics, Bengbu 233030, Anhui, China; 
    2. School of Economics and Management, Southeast University, Nanjing 210096, Jiangsu, China
  • Received:2019-10-28 Revised:2020-06-16 Online:2022-09-20 Published:2022-09-19

摘要：    绿色创新是缓解当前资源环境压力，实现中国工业高质量发展的重要途径。本文基于区域技术异质性与创新投入共享关联特征，构建共同前沿-共享投入关联型两阶段DEA模型以测算2008—2017年中国省际工业企业绿色创新两阶段效率及整体效率。运用“技术落差比率”指标衡量东部、中部与西部地区企业之间的技术差距，并对创新无效率值进行分解以挖掘效率损失根源，定位各地区企业效率提升的关键环节与重点策略。研究结果表明：①共同前沿下中国工业企业绿色研发效率、成果转化效率和整体效率均值分别为0.774、0.831和0.647，共同前沿下各效率值均未超过群组前沿下效率值；考察期内全国及三大地区绿色创新两阶段效率并未有明显改善，呈稳中略降趋势。②三大地区间企业绿色研发与成果转化的技术落差比率差异均显著，绿色研发阶段东-中地区企业间技术差距徘徊于0.2左右，而成果转化阶段东-西地区企业间技术差距最为明显，并呈不断扩大趋势。③东部地区企业绿色创新两阶段效率损失主要源自区域内企业自身管理不当，而中西部地区企业则因技术差距无效和管理无效均造成效率损失，且由区域技术落后引起的效率损失占比超过50%。

关键词: 技术异质性, 绿色创新效率, 共同前沿-共享投入关联型两阶段DEA模型, 无效率分解

Abstract:

    Green innovation is an important way to ease the current pressure on resources and the environment and achieve high-quality development of the Chinese industry. In recent years, China′s industrial enterprises have continued to increase R&D investment, but the technological, economic, and environmental benefits have not improved significantly. It is necessary to improve the green innovation efficiency of enterprises and realize the transformation of industrial enterprises into a green development mode. Due to differences in resource endowments, economic development, investment in innovative resources, and market environment in different regions of China, it means that the production technology sets available for green innovation are different. Scientifically measuring the green innovation efficiency and technology gap of industrial enterprises in different regions, and clarifying the source of efficiency loss, is conducive to the formulation of differentiated green innovation policies in different regions of China, so as to improve the enterprise′s green innovation ability.

     Based on the characteristics of regional technology heterogeneity and green innovation input sharing, this paper builds a meta-frontier-shared input related two-stage DEA model to measure the green R&D efficiency, achievement conversion efficiency and overall efficiency of Chinese provincial industrial enterprises from 2008 to 2017.Under the meta-frontier framework, the "technical gap ratio" index is used to measure and analyze the green innovation technology gap and its changing trends among enterprises in eastern, central and western regions of China. Kruskal-Wallis was conducted on the difference between the two-stage TGR values of the three major regional enterprises test. From the perspective of the ineffectiveness of regional technology gaps and ineffectiveness of business management, the two-stage green innovation inefficiency value is decomposed to dig out the true root cause of efficiency losses, and locate the key links and key strategies for improving the efficiency of enterprises in various regions. The results show that: 

     (1) The average efficiency of green innovation of Chinese industrial enterprises under the meta-frontier is 0.647, and the average efficiency of green R&D and achievement transformation is 0.774 and 0.831 respectively. The efficiency under the meta-frontier does not exceed the corresponding efficiency under the group frontier. The green R&D efficiency of industrial enterprises in the eastern, central and western regions of the meta-frontier is 0.804, 0.669 and 0.834 respectively. The efficiency of enterprises in the eastern and western regions is relatively high, while the efficiency in the central region is significantly lower. In the stage of green achievement transformation, the efficiency of enterprises in eastern, central and western regions was 0.948, 0.784 and 0.763 in order. During the period of investigation, the efficiency of the two stages of green innovation in the country and the three major regions has not improved significantly, showing a slight downward trend. 
     (2) The Kruskal-Wallis test showed that the difference in the technology gap between green R&D and achievement transformation among the three major regions was significant. In the green technology R&D stage, the average TGR of the eastern, central, and western enterprises were 0.979, 0.790, and 0.919, respectively. The average TGR of enterprises in eastern, central and western regions were 0.989, 0.877 and 0.847 respectively. In the green R&D stage, the technology gap between the enterprises in the east and middle regions hovered around 0.2, while the technology gap between the enterprises in the east and west regions in the achievement transformation stage was the most obvious, and it showed a trend of continuous expansion.
     (3) There are obvious differences in the root causes of green innovation efficiency losses of enterprises in different regions. Inefficiency decomposition finds that the two-stage efficiency losses of green innovation of enterprises in the eastern region are mainly due to the improper management of the enterprises in the region. Enterprises in the region need to focus on employee innovation mission, responsibility training and corporate culture construction to improve the level of corporate green innovation management. While the enterprises in the central and western regions are due to ineffective technology gaps and ineffective management. They all cause efficiency losses, and the efficiency losses caused by backward regional technology account for more than 50%. Enterprises in the region need to have both internal and external training, focusing on improving the regional technical level and optimizing the innovation environment.

Key words: technology heterogeneity, green innovation efficiency, two-stage DEA model of meta-frontier-shared input, inefficiency decomposition