摘要
精准推进绿色偏向技术进步,是聚焦节能降碳减污目标、实现我国工业部门全面绿色低碳转型与高质量发展的重要驱动力。本文将能源与CO_(2)、VOC_(S)、NO_(X)要素纳入全要素生产率的框架,结合非角度、非径向的BAM-DEA模型和Luenberger生产率指标分解模型,科学测度2000—2020年我国省际工业绿色偏向技术进步的要素贡献,识别绿色技术进步的要素偏向,并进一步探究环境要素绿色偏向技术进步的影响因素。研究表明,绿色技术进步是我国工业绿色全要素生产率提升的主要驱动力,但促进作用力正渐趋弱化,其中劳动、能源与环境要素对工业绿色偏向技术进步的贡献显著。从偏向类型上看,投入要素和环境要素在技术进步中分别属于资本与劳动节约型、NO_(X)与CO_(2)减排型偏向技术进步。此外,绿色技术创新能力提升、资本深化与能源结构优化对我国工业环境绿色偏向技术进步的促增效应显著。本文为精准测度不同能源与环境要素的绿色偏向技术进步水平提供新的研究范式和方法,为寻求我国工业减污降碳协同治理和绿色低碳转型的技术创新路径提供科学依据。
Accurately advancing green-biased technical progress represents a crucial impetus in achieving the objectives of energy conservation,carbon reduction and pollution reduction.It is pivotal for the holistic green and low-carbon transformation and the high-quality development of China’s industrial sector.This paper incorporates energy consumption,CO_(2),VOC_(S),NO_(X) emission into the framework of total factor productivity.Employing the non angular and non radial BAM-DEA model and Luenberger productivity indicator decomposition model,the factor contribution of green-biased technical progress in China’s industrial sector between provinces from 2000 to 2020 is scientifically measured and the factor bias of green technical progress is identified.Then the influencing factors of environmental green-biased technical progress are further explored.The principal findings of this study are as follows.Green technical progress is the main driving force for the improvement of China’s industrial green total factor productivity,albeit with a diminishing momentum.Factors such as labor,energy,and environment significantly influence industrial green-biased technical progress.In terms of bias types,the input factors predominantly exhibits capital and labor conservation bias in technical progress,and the environmental factors demonstrates a bias towards reducing NO_(X) and CO_(2) emission in technical progress.Moreover,the enhancement of green technology innovation capability,capital deepening,and the optimization of the energy structure exert a remarkable promoting influence on environmental green-biased technical progress.This article provides a new research paradigm and method for accurately measuring the green-biased technical progress in different energy and environmental contexts.It offers a scientific basis for seeking innovative technological pathways for China’s industrial pollution reduction and carbon reduction collaborative governance,and green and low-carbon transformation.
作者
吴戈
张月池
苗壮
Wu Ge;Zhang Yuechi;Miao Zhuang
出处
《统计研究》
CSSCI
北大核心
2024年第5期3-14,共12页
Statistical Research
基金
国家自然科学基金面上项目“要素流动视角的中国工业部门‘节能–降碳’协同:足迹核算、绩效评价与路径优化”(72373120)
国家自然科学基金面上项目“华北‘2+26’城市及拓展区域工业大气污染防控:绩效评价、驱动因素与治理关键”(72074183)
国家自然科学基金青年项目“基于结构效率的‘能–碳–污’协同治理研究:绩效评价、影响因素与成本评估”(72204202)。
关键词
绿色偏向技术进步
Luenberger生产率指标
要素识别
减污降碳
Green-biased Technical Progress
Luenberger Productivity Indicator
Factor Identification
Reduction of Pollution and Carbon Emissions
