A new intermediate network data envelopment analysis model for evaluating China's sustainability

Zhang, Ruchuan; Wei, Qian; Li, Aijun; Chen, Shanshan

doi:10.1016/j.jclepro.2022.131845

Cited by 18 publications

(5 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The number of patents granted in the lower 10th, 10th-25th, 25th-50th, 50th-75th, and 75th-90th quantile groups was 22991, 15576, 18516, 15359, and 26418 pieces respectively. The new energy industry is a technology-intensive industry (Zhang et al, 2022b). Obtaining a large number of patented technologies is the key to promoting the new energy industry.…”

Section: The Contribution Of Technological Progress To the New Energy...mentioning

confidence: 99%

Investigating the Determinants of the Growth of the New Energy Industry: Using Quantile Regression Approach

Xu¹,

Lin²

2023

The Energy Journal

View full text Add to dashboard Cite

Expanding the supplies of new energy can not only reduce CO2 emissions, but also alleviate energy shortage. This paper applies the quantile regression to investigate the new energy industry in China. The results show that economic growth exerts the greatest effect on the new energy industry in the lower 10th quantile province. This is because these provinces have the developed economies, demand for a higher ecological environment and new energy resources. Foreign energy dependence has a minimal impact on the new energy industry in the 25th–50th quantile province, due to their minimal oil importation. The contribution of technological progress to the upper 90th quantile province is the lowest, because their R&D capabilities are the weakest. The impact of energy consumption structure decreases in steps from the lower 10th quantile provinces to the upper 90th quantile provinces. The agricultural sector promotes the new energy industry in most provinces.

show abstract

Section: The Contribution Of Technological Progress To the New Energy...mentioning

confidence: 99%

Investigating the Determinants of the Growth of the New Energy Industry: Using Quantile Regression Approach

Xu¹,

Lin²

2023

The Energy Journal

View full text Add to dashboard Cite

show abstract

“…The links PM2.5, SO 2 , and NO x are from (Zhang et al, 2018a;Lin et al, 2021). The carry-over variable for multiple periods is government health expenditures, referring to (Zhang et al, 2022).…”

Section: Variables Selectionmentioning

confidence: 99%

The dynamic relationship among economic development, air pollution, and health production in China: the DNSBM efficiency model

Qun-shan

2023

Front. Environ. Sci.

View full text Add to dashboard Cite

China’s economy has grown rapidly in response to the adoption of a high-energy, high-emissions development model, which has led to varying degrees of air pollution; moreover, the corresponding health damage has become a major concern for the public. Studies of the interrelationships between the economy, air pollution, and health often use empirical methods such as regression analysis, to explore the impacts of economic growth and air pollution, human health or air pollution on human health in isolation, and they rarely explore the interactions between the economy, air pollution, and health in terms of efficiency (i.e., the maximum output per unit of input resources that can be produced). Thus, this study constructed a Dynamic Network SBM efficiency model that unifies the production of economic development, air pollution, and health into a single framework. The article reports the findings of a comprehensive study of economic development, air pollution, and health management data for 30 Chinese provinces from 2015 to 2020. The results show that: 1) the overall efficiency (0.693) of China’s two stages from economic production input to healthy output from 2015 to 2020 is low, and there is much room for improvement; 2) the efficiency of economic development (0.729) is higher than the efficiency of health production (0.657), indicating that the health production stage needs to be strengthened; 3) during the study period, China’s PM2.5, SO2, and NOx control efficiency was low, at 0.786, 0.710, and 0.718, respectively, indicating that more effective measures are needed to improve environmental efficiency; and 4) there are significant differences in economic, environmental, and health efficiency across regions. China’s developed eastern provinces are more efficient in terms of economic development, health production, and air pollution control. In contrast, the central and western provinces, which are relatively backward in their economic development, are less efficient. This means that each province should implement sound policies based on its own assessment to promote sustainable economic development while enhancing air pollution mitigation and health promotion.

show abstract

“…The production activities of the heavy industries often required a large amount of energy input [20]. Rising fossil energy prices turned to increase cost for heavy industry producers [21]. To reduce production costs and enhance market competitiveness, heavy industry production companies had begun to focus on improving energy efficiency and reducing carbon intensity [22].…”

Section: Literature Reviewmentioning

confidence: 99%

How to Efficiently Reduce the Carbon Intensity of the Heavy Industry in China? Using Quantile Regression Approach

2022

IJERPH

View full text Add to dashboard Cite

This decoupling between carbon dioxide emissions and the heavy industry is one of the main topics of government managers. This paper uses the quantile regression approach to investigate the carbon intensity of China’s heavy industry, based on 2005–2019 panel data. The main findings are as follows: (1) incentive-based environmental regulations have the greater impact on the carbon intensity in Jiangsu, Shandong, Zhejiang, Henan, Liaoning, and Shaanxi, because these provinces invest more in environmental governance and levy higher resource taxes; (2) the impact of mandatory environmental regulations on carbon intensity in Beijing, Tianjin, and Guangdong provinces is smaller, since these three provinces have the fewest enacted environmental laws and rely mainly on market incentives; (3) conversely, foreign direct investment has contributed most to carbon intensity reduction in Tianjin, Beijing, and Guangdong provinces, because these three have attracted more technologically advanced foreign-funded enterprises; (4) technological progress contributes more to the carbon intensity in the low quantile provinces, because these provinces have more patented technologies; (5) the carbon intensity of Shaanxi, Shanxi, and Inner Mongolia provinces is most affected by energy consumption structures because of their over-reliance on highly polluting coal.

show abstract

A new intermediate network data envelopment analysis model for evaluating China's sustainability

Cited by 18 publications

References 40 publications

Investigating the Determinants of the Growth of the New Energy Industry: Using Quantile Regression Approach

Investigating the Determinants of the Growth of the New Energy Industry: Using Quantile Regression Approach

The dynamic relationship among economic development, air pollution, and health production in China: the DNSBM efficiency model

How to Efficiently Reduce the Carbon Intensity of the Heavy Industry in China? Using Quantile Regression Approach

Contact Info

Product

Resources

About