Comparisons of CO2 emission performance between secondary and service industries in Yangtze River Delta cities

Xiao, Huijuan; Shan, Yuli; Zhang, Ning; Zhou, Yong‐Wu; Wang, Dao-Ping; Duan, Zhiyuan

doi:10.1016/j.jenvman.2019.109667

Cited by 56 publications

(13 citation statements)

References 54 publications

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“…Xiao et al revealed that the performance of the total-factor CO 2 emissions of the tertiary industry is better than that of the secondary industry. However, prospects for the low-carbon development of the tertiary industry, in the Yangtze River Delta, are not optimistic [37]. erefore, we propose Hypothesis 2.…”

Section: Hypothesismentioning

confidence: 84%

Does Industrial Agglomeration Promote Carbon Efficiency? A Spatial Econometric Analysis and Fractional-Order Grey Forecasting

Sun

Liu

2021

Journal of Mathematics

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In theory, the industrial agglomeration is a double-edged sword as there are both positive and negative externalities. China’s cities, with great disparities on degrees of the industrial agglomeration, often face different energy and carbon dioxide emission problems, which raise the question whether the industrial agglomeration promotes or inhibits energy efficiency and carbon dioxide emission. This paper explored the effects of the industrial agglomeration on carbon efficiency in China. Spatial econometric methods were implemented using panel data (2007–2016) of 285 cities above the prefecture level. The results revealed that industrial agglomerations have significant impacts on the urban carbon efficiency with significant spatial spillover effects. The agglomerations of the manufacturing and high-end productive service industries take positive effects on carbon efficiency while the low-end productive and living service industries take negative effects. As a comparison, we found that the agglomeration effects at the level of the megalopolis are greater than those at the national level, especially for the living services industry, in which the higher levels of agglomeration make the effects on carbon efficiency change from negative to positive. The divisions of labor for the central and common cities in the megalopolises are integrated into the industrial agglomeration. Furthermore, the fractional-order grey forecasting model is used in this paper. By the virtue of its advantage in dealing with small sample data which lack statistical rules, this paper makes an out-of-sample prediction of carbon efficiency and industrial agglomeration degree of Chinese cities. By adding the predicted results to the spatial correlation test, new evidence on the spatial correlation of carbon efficiency and spatial division of labor between cities is obtained. Based on the empirical results of the present study, we have proposed some policy recommendations.

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Section: Hypothesismentioning

confidence: 84%

Does Industrial Agglomeration Promote Carbon Efficiency? A Spatial Econometric Analysis and Fractional-Order Grey Forecasting

Sun

Liu

2021

Journal of Mathematics

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“…Dong et al [ 22 ] developed a comprehensive system for assessing regional carbon emission efficiency and proved that the eastern region ranks highest and the Western lowest among China’s three major geographical regions. Xiao et al [ 23 ] investigated carbon emission performance in the Yangtze River Delta, revealing that upgrading the industrial structure is more carbon-friendly to the environment, especially the development of the tertiary industry. Shen et al [ 24 ] identified a number of unbalanced characteristics in industrial carbon efficiency, using a low-carbon transformation index from the perspective of regional and industrial disparity.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In addition to the above focus on environmental regulation, a number of scholars have also addressed the influencing factors driving green innovation efficiency from micro and macro perspectives. From the micro perspective, factors such as enterprise scale, the effects of technology introduction, capital density, the industry profit rate, the environmental protection awareness of senior executives, consumer orientation, etc., have all be considered [ 23 , 35 , 36 ]. From the micro perspective, factors such as urban scale, economic openness, R&D input intensity, FDI, the industrial structure, energy intensity, government quality, etc., have also been examined [ 37 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating Green Innovation Efficiency and Its Socioeconomic Factors Using a Slack-Based Measure with Environmental Undesirable Outputs

Guo

Xie

2021

IJERPH

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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 CO2 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.

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“…Eco-efficiency, proposed by Schaltegger and Sturm (1990), is the ability to produce goods and services with minimal natural resource consumption and negative environmental impacts (Kuosmanen, 2005;Long et al, 2017;Oggioni et al, 2011). Improving the eco-efficiency in a precise manner provides a solution for this transformation towards sustainable development, as efficiency enhancement can mitigate the negative economic impact of emission constraints (Xiao et al, 2019b;Zhang et al, 2016a). To measure the eco-efficiency, data envelopment analysis (DEA) has attracted great attention based on a total-factor production process (Wang et al, 2016;Wu et al, 2020;Xiao et al, 2019b;Zhou et al, 2008a;Zhou and Ang, 2008).…”

Section: Introductionmentioning

confidence: 99%

The governance-production nexus of eco-efficiency in Chinese resource-based cities: A two-stage network DEA approach

Xiao

Wang

et al. 2021

Energy Economics

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Comparisons of CO2 emission performance between secondary and service industries in Yangtze River Delta cities

Cited by 56 publications

References 54 publications

Does Industrial Agglomeration Promote Carbon Efficiency? A Spatial Econometric Analysis and Fractional-Order Grey Forecasting

Does Industrial Agglomeration Promote Carbon Efficiency? A Spatial Econometric Analysis and Fractional-Order Grey Forecasting

Evaluating Green Innovation Efficiency and Its Socioeconomic Factors Using a Slack-Based Measure with Environmental Undesirable Outputs

The governance-production nexus of eco-efficiency in Chinese resource-based cities: A two-stage network DEA approach

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