Analysis of China's energy efficiency and influencing factors under carbon peaking and carbon neutrality goals

Zhang, Yongli

doi:10.1016/j.jclepro.2022.133604

Cited by 50 publications

(20 citation statements)

References 39 publications

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“…Since green finance and industrial-structure optimization are equally important, both

and

are therefore, assigned to a value of 0.5. The relevant levels of coupling degree and coupling coordination degree are determined through the combination of the existing research and actual situation [ 52 , 53 , 54 ], as shown in Table 2 .…”

Section: Methodology and Datamentioning

confidence: 99%

“…Due to the differences in water basis, resource endowment and inherent industrial structure of economic development in different regions of China, the development level of green finance in different regions will also be different [ 53 ]. In the theory of “pollution paradise Hypothesis”, due to the increase in production cost brought by environmental regulation, highly polluting enterprises will tend to invest and set up factories in areas with weak environmental regulation in order to save costs [ 54 ]. Similarly, green finance is essentially a kind of market-oriented environmental regulation, and highly polluting enterprises tend to produce in areas with slow development of green finance [ 55 ].…”

Section: Theoretical Analysis and Research Hypothesismentioning

confidence: 99%

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Coupling and Coordinative Development of Green Finance and Industrial-Structure Optimization in China: Spatial-Temporal Difference and Driving Factors

Nie

Chen

Liu

et al. 2022

IJERPH

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Although the literature has studied the direction and extent of the effect of green finance on industrial-structure optimization, quantitative analysis of the coupling coordination and spatial–temporal differences between green finance and industrial structure is relatively scarce. Therefore, in this paper, we built the theoretical framework of the coupling coordination relationship between green finance and industrial-structure optimization, and then we used the coupling coordination degree and geographic detector model to investigate the spatial–temporal evolution characteristics and influencing factors of the coupling coordination between the two based on the panel data of 31 provinces from 2012 to 2019. The results show that China’s green finance and industrial-structure optimization have basically reached the primary coupling, and the coupling coordination degree is from 0.40 to 0.43, which shows a “W”-type fluctuation trend of recovery. The regional gap of the coupling coordination degree firstly decreased and then increased, showing a change law of “agglomeration, equilibrium and agglomeration”. In the spatial dimension, the high-level coordination region showed an increasing trend of “fragmentation” fluctuation, while the low-level coordination region concentrated in the central and western regions with a tendency of “low value locking”. The trend surface showed a spatial characteristic of “high in the north and low in the south–high in the east and west and low in the middle”. We also found that the dependence of foreign trade and technological innovation are the main factors affecting the coupling coordination degree, and the interaction between government support and human capital synergistic is the crucial channel for the coevolution of green finance and industrial structure to promote green and low-carbon development.

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“…Since green finance and industrial-structure optimization are equally important, both

and

Section: Methodology and Datamentioning

confidence: 99%

Section: Theoretical Analysis and Research Hypothesismentioning

confidence: 99%

Coupling and Coordinative Development of Green Finance and Industrial-Structure Optimization in China: Spatial-Temporal Difference and Driving Factors

Nie

Chen

Liu

et al. 2022

IJERPH

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“…While SFA is often criticized due to difficulties in calculation and the reflection of multi-output, DEA is also questioned because it requires the same proportional and radial change on input and output factors Wang and Du, 2019;Luo et al, 2022). To reflect real-world carbon efficiency, scholars have proposed improved approaches to deal with undesirable output and flexible changes, such as the super efficiency DEA model (Tang et al, 2014), the SBM-undesirable output (Sun et al, 2016;Deng and Zhang, 2021;Zhang, 2022), and the global supper efficiency EBM model (Zhang W. et al, 2022). However, few have paid attention on the use of the non-radial directional distance function (NDDF).…”

Section: Graphical Abstractmentioning

confidence: 99%

“…investigated the effect of digital economy and its sub-indicators on carbon emissions and found a significant negative correlation when scale effects, structural effects, and technology effects were taken into account. Zhang et al (2022) and Lin and Zhou (2021) pointed out the emerging improvements in the effectiveness of energy systems brought about by digital technologies, such as accurate measurement and real-time detection of power supply and demand, and more efficient information transmission and analysis. They emphasized that the positive effects that the digital economy posed on energy structure and utilization could further exert a mitigation effect on carbon emissions.…”

Section: Graphical Abstractmentioning

confidence: 99%

“…At the same time, the growth of the digital economy is considered to be highly power-hungry and is predicted to account for half of global electricity consumption by 2030 (Andrae and Edler, 2015). The construction of digital facilities such as data centers, base stations, and network equipment will significantly increase the consumption of resources and energy (Zhang L. et al, 2022). After reaching a certain scale, the technology spillover effect and innovation progress from digitalization can help to accelerate the growth of productivity.…”

Section: Mediating Effect Through Industrial Electricity Productivitymentioning

confidence: 99%

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The influence of digital development on China’s carbon emission efficiency: In the view of economic and environmental balance

Yuan

Yang

2023

Front. Environ. Sci.

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Introduction: With the worsening global climate problem, carbon peak and carbon neutrality have become crucial objects to realize sustainable development. Regarded as the engine of economic development in the new era, it is worth exploring whether digitalization could contribute to carbon goals. Meanwhile, carbon reduction should not be advocated at the cost of economic recession and contains the risk of reversal when the economy renews in growth.Material and Methods: This paper evaluates carbon efficiency using the total factor non-radial directional distance function, which reflects the economic performance and environmental performance of 246 China’s prefecture-level cities during 2011–2019. Fixed effect and mediation effect models are used to explore the non-linear relationship and transmission channels between digital development and carbon efficiency.Results: It is found that: 1) digital development would hinder carbon efficiency first and then promote it after reaching a certain level; 2) digital development could indirectly affect carbon efficiency through industrial agglomeration, industrial structure upgrading, and industrial electricity productivity in non-linear ways. 3) Heterogeneity exists in the relationship between digital development and carbon efficiency due to different regions and development types.Discussion: Due to digital development itself having high carbon-negative externalities at the initial stage, its impact on carbon efficiency is complex and non-liner even when decomposing through multiple channels. A well-structured development strategy is needed during the digitalization process in order to prompt carbon efficiency.

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Is the Green Total Factor of Energy Efficiency Rising or Falling? Evidence from the Belt and Road Initiative Countries

Ma,

Guo,

Xiao

et al. 2024

Energy Tech

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“The Belt and Road Initiative” (BRI) seeks to attain high‐quality development and sustainability. This study examines the green total factor energy efficiency (GTFEE) of 58 BRI countries from 2005 to 2019 and its influence factors using the Super slacked‐based measurement and Tobit models. The findings indicate that: 1) Singapore and the other seven countries have GTFEEs above 0.9, which is better than other countries; seven out of 58 countries have GTFEEs below 0.5. 2) Due to regional disparities, Southeast Asia, Central, and Eastern Europe have the greatest GTFEE, followed by South Asia, West Asia, and the Commonwealth of Independent States, and the lowest is in Central Asia, China, and Mongolia. 3) Compared to developing economies, developed economies have a higher GTFEE. 4) Between 2005 and 2019, the overall BRI energy efficiency dropped from 0.75 to 0.56. 5) According to Tobit results, for every 1% increase in economic development and energy structure, GTFEE increases by 0.08% and 0.31% points, respectively, and for every 1% increase in trade and land area, GTFEE decreases by 0.04 and 0.05, respectively. Therefore, BRI countries should strengthen international cooperation, develop advanced technologies, and increase the proportion of renewable energy utilization.

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Analysis of China's energy efficiency and influencing factors under carbon peaking and carbon neutrality goals

Cited by 50 publications

References 39 publications

Coupling and Coordinative Development of Green Finance and Industrial-Structure Optimization in China: Spatial-Temporal Difference and Driving Factors

Coupling and Coordinative Development of Green Finance and Industrial-Structure Optimization in China: Spatial-Temporal Difference and Driving Factors

The influence of digital development on China’s carbon emission efficiency: In the view of economic and environmental balance

Is the Green Total Factor of Energy Efficiency Rising or Falling? Evidence from the Belt and Road Initiative Countries

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