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Journal of ZheJiang University (Engineering Science)  2024, Vol. 58 Issue (1): 188-196    DOI: 10.3785/j.issn.1008-973X.2024.01.020
    
Choice of innovation type for China's industrial green transformation under environmental regulation
Haiying LIU(),Xianzhe CAI
School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China
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Abstract  

A super-efficient SBM model including non-desired outputs was used to measure industrial environmental efficiency in 30 Chinese provinces from 2008 to 2020 in order to solve the problem of how industrial enterprises can pick appropriate green technology innovations to accomplish industrial green transformation under the background of strict environmental regulations. The efficiency was used to characterize the level of industrial green transformation. A panel threshold model was used to explore the mechanism of the impact of different green technology innovations on industrial green transformation under different environmental regulation intensities. Results show that China's industrial environmental efficiency fluctuates and rises from 2008 to 2020 as a whole, and the efficiency gap between regions shows a slightly decreasing trend. The environmental impacts of various green technology innovations significantly differ, among which process-oriented green technology innovations emphasizing on processes and products is the key to achieving industrial green transformation. The positive environmental effect of process-oriented green technology innovation increases, while the negative environmental effect of result-oriented green technology innovation decreases as environmental regulations become more stringent.



Key wordsgreen technology innovation      environmental regulation      industrial green transformation      super-efficient SBM model      panel threshold model     
Received: 28 February 2023      Published: 07 November 2023
CLC:  F 062  
  F 424  
Fund:  国家社会科学基金资助项目(20BJY102)
Cite this article:

Haiying LIU,Xianzhe CAI. Choice of innovation type for China's industrial green transformation under environmental regulation. Journal of ZheJiang University (Engineering Science), 2024, 58(1): 188-196.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2024.01.020     OR     https://www.zjujournals.com/eng/Y2024/V58/I1/188


环境规制下中国工业绿色转型的创新类型选择

为了解决严格环境规制背景下工业企业如何选择适宜的绿色技术创新类型来实现工业绿色转型的问题,采用包含非期望产出的超效率与基于松弛值测算(SBM)相结合的SBM模型,测算中国30个省份2008—2020年的工业环境效率,以该效率表征工业绿色转型水平. 通过面板门槛模型,探究不同环境规制强度下绿色技术创新类型对工业绿色转型的影响机理. 结果表明,整体上,2008—2020年中国工业环境效率处于波动上升态势,区域间的效率差距整体上呈微幅减小的趋势. 不同绿色技术创新的环境效应存在显著差异,其中强调工艺和产品的过程导向型绿色技术创新是实现工业绿色转型的关键. 随着环境规制强度的加大,过程导向型绿色技术创新的正向环境效应会加大,结果导向型绿色技术创新的负向环境效应会降低.


关键词: 绿色技术创新,  环境规制,  工业绿色转型,  超效率SBM模型,  面板门槛模型 
总体指标 目标指标 具体指标 单位
投入 人力投入 规模以上工业企业平均从业人数 万人
资本投入 规模以上工业企业固定资产净值 亿元
工业污染治理完成投资额 万元
能源投入 工业能源消耗量 万吨标准煤
产出 期望产出 工业企业增加值 亿元
非期望产出 工业化学需氧量(COD)和氨氮排放量
工业二氧化硫排放量
一般工业固体废物产生量 万吨
工业二氧化碳排放量 万吨
Tab.1 Selection of input-output indicators for industrial environmental efficiency measurement
变量 变量标签 容差 VIF
结果导向型绿色技术创新 RGTI 0.308 3.244
过程导向型绿色技术创新 PGTI 0.736 1.359
固定资产投资 IFA 0.539 1.857
政府技术支持 GTS 0.329 3.042
政府环保支持 GES 0.814 1.228
政府财政支持 GFS 0.427 2.343
环境规制强度 ERI 0.484 2.064
产业升级 UI 0.389 2.572
产业调整 RI 0.712 1.405
Tab.2 Multicollinearity test results
Fig.1 Temporal changes in mean value of industrial environmental efficiency in China, 2008—2020
Fig.2 Kernel density curves of industrial environmental efficiency in China in 2008, 2014 and 2020
变量 模型(1) 模型(2) 模型(3) 模型(4) 模型(5)
ln x1 0.179***(6.53) 0.190***(5.24) 0.217***(4.97) 0.120***(3.37) 0.120**(2.12)
ln x2 ?0.174***(?3.19) ?0.189***(?3.52) ?0.204***(?3.74) ?0.207***(?4.39) ?0.207***(?2.99)
控制变量 控制 控制 控制 控制 控制
C ?2.561***(?6.31) ?2.836***(?4.58) ?3.651***(?4.18) ?3.614***(?5.09) ?3.614***(?3.59)
样本量 330 330 330 330 330
拟合优度 0.563 0.457 0.479 0.693 0.693
地区固定 YES YES YES YES
年份固定 YES YES
省级聚类 YES
Tab.3 Regression result of baseline model
模型 解释变量 门槛变量 门槛数量 F统计量 P
模型(9) ln x1 ln ERI 单门槛 18.70** 0.038
模型(9) ln x1 ln ERI 双门槛 5.77 0.493
模型(10) ln x2 ln ERI 单门槛 14.06* 0.095
模型(10) ln x2 ln ERI 双门槛 8.99 0.263
Tab.4 Results of threshold effect test
模型 第一门槛
估计值 95% 置信区间
模型(9) ?6.561 (0.001) [?6.603,?6.533]
模型(10) ?6.561 (0.001) [?6.577,?6.533]
Tab.5 Threshold estimates and confidence intervals
变量 变量值
模型(9) 模型(10)
ln x1(ln ERI≤σ1) 0.171***(0.046)
ln x1(ln ERI>σ1) 0.242***(0.044)
ln x2(ln ERI≤ξ1) ?0.360***(0.076)
ln x2(ln ERI>ξ1) ?0.162***(0.056)
控制变量 控制 控制
C ?1.576***(0.512) ?1.337***(0.498)
样本量 390 330
拟合优度 0.490 0.491
Tab.6 Regression results of threshold model
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