Decision Sciences Institute, Fuzhou University, 2 Wulongjiang North Avenue, Fuzhou, People's Republic of China.
Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, 2 Wulongjiang North Avenue, Fuzhou, People's Republic of China.
Environ Sci Pollut Res Int. 2021 Aug;28(31):41896-41911. doi: 10.1007/s11356-021-13128-w. Epub 2021 Apr 1.
Thanks to the booming industry, China has made a huge economic achievement during the past several decades. However, it is suffering severe environmental and sustainable problems now. To find a sustainable development path, it is necessary to assess Chinese industrial energy and environment productivity and explore the contributing reasons. It is known that the technical change is the one power that drives the growth of the industrial productivity. Nevertheless, the technical change bias of Chinese industrial energy and environment productivity has rarely been analyzed, such that the secrets of Chinese industrial energy and environment productivity cannot be further uncovered. Thus, in this paper, we first propose a global DEA-Malmquist productivity index to evaluate the industrial energy and environment productivity of China and then figure out the Chinese industrial technical change biases by relaxing the Hicks' neutral assumption and decomposing the industrial technical change. We find out that both the global DEA-Malmquist productivity and the technical change are increased. Furthermore, the technical change drives the improvement of the global Malmquist productivity, but the technical progress is mainly driven by labor, energy consumption and CO emission biases. A multinomial logistic model is employed to find out the reasons for these biases. It finds that (1) the economic foundation has a significant positive impact on labor bias, while the infrastructures have negative impacts on labor bias. (2) CO emission bias is influence by energy prices positively. (3) The energy prices and the energy consumption structure have a negative influence on labor and energy bias, but the cost of curbing air pollutants and the size of the firm influence labor and energy bias positively. (4) The infrastructures and energy prices affect energy and CO emission bias positively, and the economic foundation and the size of the firm have negative impacts on energy and CO emission bias.
由于工业的蓬勃发展，中国在过去几十年中取得了巨大的经济成就。然而，它现在正面临着严重的环境和可持续发展问题。为了找到可持续发展的道路，有必要评估中国工业能源和环境的生产力，并探讨其贡献的原因。众所周知，技术变革是推动工业生产力增长的动力之一。然而，中国工业能源和环境生产力的技术变革偏向很少被分析，因此，中国工业能源和环境生产力的秘密还没有被进一步揭示。因此，在本文中，我们首先提出了一个全局 DEA-Malmquist 生产力指数来评估中国的工业能源和环境生产力，然后通过放宽 Hicks 中性假设和分解工业技术变革，找出中国工业技术变革的偏向。我们发现，全局 DEA-Malmquist 生产力和技术变革都在增加。此外，技术变革推动了全局 Malmquist 生产力的提高，但技术进步主要是由劳动力、能源消耗和 CO 排放偏向驱动的。我们采用多项逻辑回归模型来找出这些偏向的原因。结果表明：（1）经济基础对劳动力偏向有显著的正向影响，而基础设施对劳动力偏向有负向影响。（2）CO 排放偏向受能源价格的正向影响。（3）能源价格和能源消费结构对劳动力和能源偏向有负向影响，而抑制空气污染物的成本和企业规模对劳动力和能源偏向有正向影响。（4）基础设施和能源价格对能源和 CO 排放偏向有正向影响，而经济基础和企业规模对能源和 CO 排放偏向有负向影响。
