Exploring the path of carbon emissions reduction in China’s industrial sector through energy efficiency enhancement induced by R&amp;D investment

He, Yong; Fu, Feifei; Liao, Nuo

doi:10.1016/j.energy.2021.120208

Cited by 58 publications

(28 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Continued R&D investment will enable the acquisition of new knowledge and enable effective improvement of production processes and energy structure, which in turn will have an impact on GTFEE (He et al, 2021). This study selects the ratio of R&D expenditure to GDP to characterize R&D investment.…”

Section: Other Variablesmentioning

confidence: 99%

Assessing the Impact of the Digital Economy on Green Total Factor Energy Efficiency in the Post-COVID-19 Era

Zhang

Cui

2021

Front. Energy Res.

View full text Add to dashboard Cite

Digital transformation in the energy sector is an essential tool for promoting the construction of a clean energy system in the post-COVID-19 era. Under the background of digital China strategy and sustainable energy transformation in the post-COVID-19 era, it is meaningful to investigate the relationship between the digital economy and green total factor energy efficiency (GTFEE) to better drive the development of the digital economy and improve GTFEE. For this purpose, this study estimates deeply the impact of the digital economy on GTFEE by applying ordinary least squares (OLS), panel vector autoregression (PVAR), panel threshold, and mediation effect models based on panel data of 30 Chinese provinces from 2006 to 2018. The statistical results indicate that digital economy is conducive to improving GTFEE. Digital economy can significantly contribute to GTFEE by improving economic growth level, urbanization level, R&D investment, and human capital. The most interesting finding was that there is also a non-linear relationship between digital economy and GTFEE. The effect of digital economy on GTFEE is shown to be first promoted and then inhibited as digital economy level continues to increase. Further, the positive impact of the digital economy on GTFEE is strengthened with increasing levels of economic growth, urbanization, R&D input, and human capital. Finally, A positive correlation was found between digital economy and GTFEE in the eastern and central regions, but insignificantly in other regions.

show abstract

Section: Other Variablesmentioning

confidence: 99%

Assessing the Impact of the Digital Economy on Green Total Factor Energy Efficiency in the Post-COVID-19 Era

Zhang

Cui

2021

Front. Energy Res.

View full text Add to dashboard Cite

show abstract

“…As a new carbon market model, the healthy operation of carbon trading can drive market entities to actively participate in carbon trading [ 75 ], increase the activity of carbon market trading, realize the rationality of carbon allowance pricing, and give full play to the guidance mechanism of carbon price on emission reduction strategies [ 76 ]. It forces enterprises to increase R&D expenditures [ 77 ], develop emission reduction technologies, reduce the cost of carbon emission reduction, urge enterprises to actively reduce carbon emission, and form a benign interaction in the carbon trading market. The vitality of enterprises to reduce carbon emissions should be enhanced.…”

Section: Discussionmentioning

confidence: 99%

Evolutionary Game Analysis of Carbon Emission Reduction between Government and Enterprises under Carbon Quota Trading Policy

Chen

Liu

2022

IJERPH

View full text Add to dashboard Cite

As one of the most efficient means of emission reduction policies, carbon quota trading has a far-reaching impact on the carbon emission reduction of enterprises. Firstly, a two-party evolutionary game model of enterprise and government and a three-party evolutionary game model of enterprise–enterprise–government are constructed based on the multi-agent driving mechanism, evolutionary game theory, scenario simulation, and other methods. Then, we conduct a series of policy simulations for carbon emission under different scenario models and various enforcement strengths. Lastly, the behavioral strategies and system evolution trajectories in enterprises and government carbon trading are comprehensively investigated. The results show that in the two-party and three-party evolutionary game models, the carbon trading behavior is affected by the joint action of the enterprise and the government. The difference in initial willingness mainly affects the speed of the subject’s convergence to the steady state. Based on this, policy suggestions are proposed, such as reducing the cost of carbon emission of enterprises, enhancing the vitality of carbon emission reduction of enterprises, and stimulating the power of government regulation and responsibility performance, which can provide suggestions for the development of the carbon market.

show abstract

“…Therefore, how to manage the energy demand of Chinese manufacturing enterprises and improve their energy efficiency is very important for achieving regional and global reductions in greenhouse gas emissions and reducing corporate energy intensity [ 5 ]. Studies to date have pointed out that technology can indeed improve energy efficiency and reduce energy consumption [ 6 , 7 ], but current industrial energy efficiency is far below the best technically feasible levels.…”

Section: Introductionmentioning

confidence: 99%

Can Artificial Intelligence Improve the Energy Efficiency of Manufacturing Companies? Evidence from China

Liu

et al. 2022

IJERPH

View full text Add to dashboard Cite

Improving energy efficiency is an important way to achieve low-carbon economic development, a common goal of most nations. Based on the comprehensive survey data of enterprises above a designated size in Guangdong Province, this paper studies the impact of artificial intelligence on the energy efficiency of manufacturing enterprises. The results show that: (1) artificial intelligence, as measured by the use of industrial robots, has significantly improved the energy efficiency of manufacturing enterprises. This conclusion is still robust after introducing data on industrial robots in the United States over the same time period as the instrumental variable for the endogeneity test. (2) The mechanism test shows that artificial intelligence mainly promotes the improvement in energy efficiency by promoting technological progress; the impact of artificial intelligence on the technological efficiency of enterprises is not significant. (3) Heterogeneity analysis shows that the age of the manufacturing enterprises inhibits a promoting effect of artificial intelligence on energy efficiency; manufacturing enterprises’ performance can enhance the promoting effect of artificial intelligence on energy efficiency, but this promoting effect can only be shown when the enterprise performance is positive. The paper clarifies both the impact of artificial intelligence on the energy efficiency of manufacturing enterprises and its mechanism of action; this will help provide a reference for future decision-making designed to improve manufacturing enterprises’ energy efficiency.

show abstract

Exploring the path of carbon emissions reduction in China’s industrial sector through energy efficiency enhancement induced by R&D investment

Cited by 58 publications

References 36 publications

Assessing the Impact of the Digital Economy on Green Total Factor Energy Efficiency in the Post-COVID-19 Era

Assessing the Impact of the Digital Economy on Green Total Factor Energy Efficiency in the Post-COVID-19 Era

Evolutionary Game Analysis of Carbon Emission Reduction between Government and Enterprises under Carbon Quota Trading Policy

Can Artificial Intelligence Improve the Energy Efficiency of Manufacturing Companies? Evidence from China

Contact Info

Product

Resources

About