Spatial effects of dynamic comprehensive energy efficiency on CO2 reduction in China

Abudureheman, Maliyamu; Jiang, Qi; Dong, Xiucheng; Dong, Cong

doi:10.1016/j.enpol.2022.113024

Cited by 12 publications

(2 citation statements)

References 92 publications

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“…Notably, a spatial autocorrelation test must be performed before constructing the spatial econometric model. We applied the commonly used Moran's I and Geary's C index tests for spatial autocorrelation check (Liu and Yang, 2021;Abudureheman et al, 2022b). The specific calculation formulas of the Moran's I and Geary's C indexes are as follows:…”

Section: Spatial Model Settingmentioning

confidence: 99%

The impact of energy-saving R&D on urban carbon emission performance: evidence from 218 prefecture-level cities in China

Abudureheman,

Yiming

2024

Front. Environ. Sci.

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Different from the previous studies that mainly focus on the environmental and economic benefits of the total R&D, we specifically focus on the R&D activities in the energy field, namely, energy-saving R&D in this study, and explored the spatial impacts of energy-saving R&D on urban carbon emission performance and its internal transmission mechanisms for the first time. The heterogeneity analysis from the perspectives of different-types of R&D and different urban groups were also conducted. Results indicate that: Energy-saving R&D has obvious promoting effect on the improvement of urban carbon emission performance, among which the utility-type energy-saving R&D plays a more important role than the invention-type R&D. Energy-saving R&D promotes urban carbon performance mainly through the transmission mechanism of technical effects, while structural effect and urbanization effect negatively affects carbon emission performance due to the existence of energy rebound. Furthermore, there exists significant spatial spillover effects of energy-saving R&D on urban carbon emission performance, and economic factors play a more important role in this spatial effect. In addition, the impact of energy-saving R&D on carbon emission performance has obvious urban group heterogeneity. Finally, we proposed several policy suggestions based on the main conclusions of this study.

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Section: Spatial Model Settingmentioning

confidence: 99%

The impact of energy-saving R&D on urban carbon emission performance: evidence from 218 prefecture-level cities in China

Abudureheman,

Yiming

2024

Front. Environ. Sci.

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“…First, most of the studies adopt data envelopment analysis (DEA) for the measurement of TFP growth in China; however, the DEA method requires the construction of a production frontier for each cycle of the panel data, which requires high data accuracy [15] and is easily affected by sample outliers [16]. Moreover, since China is currently undergoing a rapid economic transition and is affected by some imperfectly controllable factors, such as institutional transitions and the international market environment, there are inevitably random disturbances and unobservable factors involved in the process of economic growth [17]. Therefore, a DEA method with deterministic boundaries may not be fully applicable to the TFP problem in China.…”

Section: Introductionmentioning

confidence: 99%

Estimating and Decomposing the TFP Growth of Service-Oriented Manufacturing in China: A Translogarithmic Stochastic Frontier Approach

et al. 2023

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By constructing a translogarithmic stochastic frontier production model, this study explores the total factor productivity (TFP) of service-oriented manufacturing in 30 provinces in China during 2004–2020. We carried out decomposition analysis to understand in greater depth the potential drivers of TFP growth. The results show that the overall TFP of service-oriented manufacturing continuously improved during the sample period; however, the overall growth rate showed a significant slowing trend, and the contribution of TFP growth to output growth is still low. The industrial growth of service-oriented manufacturing is mainly driven by capital input, and the transformation of its growth mode from extensive to intensive has not yet been realized. Furthermore, there exists significant regional and sub-sectoral heterogeneity in the TFP growth of the industry. The decomposition of TFP growth shows that technological progress and technical efficiency are the main sources of TFP growth, but the growth rate of technological progress is declining gradually, and its driving effect on TFP is weakening. The deterioration of both scale and allocation efficiency hinders the improvement of TFP in service-oriented manufacturing, and there is still room for the industry to improve its TFP level by improving scale efficiency and allocation efficiency.

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How does the digital economy affect urban CO2 emissions? Mechanism discussion and empirical test

Wu,

Liu

2024

Environ Dev Sustain

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Spatial effects of dynamic comprehensive energy efficiency on CO2 reduction in China

Cited by 12 publications

References 92 publications

The impact of energy-saving R&D on urban carbon emission performance: evidence from 218 prefecture-level cities in China

The impact of energy-saving R&D on urban carbon emission performance: evidence from 218 prefecture-level cities in China

Estimating and Decomposing the TFP Growth of Service-Oriented Manufacturing in China: A Translogarithmic Stochastic Frontier Approach

How does the digital economy affect urban CO2 emissions? Mechanism discussion and empirical test

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