Study of the impact of energy consumption structure on carbon emission intensity in China from the perspective of spatial effects

2018 ◽  
Vol 99 (3) ◽  
pp. 1365-1380 ◽  
Author(s):  
Hongwei Xiao ◽  
Zhongyu Ma ◽  
Peng Zhang ◽  
Ming Liu
Keyword(s):  
Energy Consumption ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Spatial Effects ◽  
Consumption Structure ◽  
Carbon Emission Intensity ◽  
The Impact

Energy technological innovation and carbon emissions mitigation: evidence from China

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shuping Cheng ◽  
Lingjie Meng ◽  
Lu Xing
Keyword(s):  
Technological Innovation ◽  
Carbon Emissions ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Fossil Energy ◽  
Content Type ◽  
Carbon Emission Intensity ◽  
Emissions Mitigation ◽  
The Impact ◽  
Positive Effect

PurposeThe purpose of this paper is to examine the effects of energy technological innovation on carbon emissions in China from 2001 to 2016.Design/methodology/approachConditional mean (CM) methods are first applied to implement our investigation. Then, considering the tremendous heterogeneity in China, quantile regression is further employed to comprehensively investigate the potential heterogeneous effect between energy technological innovation and carbon emission intensity.FindingsThe results suggest that renewable energy technological innovation has a significantly positive effect on carbon emission intensity in lower quantile areas and a negative effect in higher quantile areas. Contrarily, fossil energy technological innovation exerts a negative correlation with carbon emission intensity in lower quantile areas and a positive effect on carbon emission intensity in higher quantiles areas.Originality/valueConsidering that energy consumption is the main source of CO2 emissions, it is of great importance to study the impact of energy technological innovation on carbon emissions. However, the previous studies mainly focus on the impact of integrated technological innovation on carbon emissions, ignoring the impact of energy technological innovation on carbon emissions mitigation. To fill this gap, we construct an extended STIRPAT model to examine the effects of renewable energy technological innovation and fossil energy technological innovation on carbon emissions in this paper. The results can provide a reference for the government to formulate carbon mitigation policies.

scholarly journals Factors Affecting Wind Power Efficiency: Evidence from Provincial-Level Data in China

2021 ◽  
Vol 13 (22) ◽  
pp. 12759
Author(s):  
Xiaoyan Sun ◽  
Wenwei Lian ◽  
Hongmei Duan ◽  
Anjian Wang
Keyword(s):  
Spatial Distribution ◽  
Wind Power ◽  
Influencing Factors ◽  
Emission Intensity ◽  
Power Efficiency ◽  
Carbon Emission ◽  
Clean Energy ◽  
Spillover Effects ◽  
Spatial Effects ◽  
Carbon Emission Intensity

As a significant energy consumer, China is under tremendous pressure from the international community to address climate change issues by reducing carbon emissions; thus, the use of clean energy is imperative. Wind power is an essential source of renewable energy, and improving the efficiency of wind power generation will contribute substantially to China’s ability to achieve its energy-saving and emission reduction goals. This paper measured the wind power efficiency of 30 provinces in China from 2012 to 2017 using the data envelopment analysis (DEA) method. Moran’s I index and the spatial Durbin model were applied to analyse the spatial distribution of the wind power efficiency and the spatial effects of influencing factors. The results show obvious differences in the spatial distribution of wind power efficiency in China; specifically, the wind power efficiency in the eastern and western regions is higher than that in the central areas. Moreover, wind power efficiency has a significant positive spatial correlation between regions: the eastern and western regions show certain high-high clustering characteristics, and the central area shows certain low-low clustering characteristics. Among the influencing factors, the fixed asset investment and carbon emission intensity of the wind power property have a negative impact on the efficiency of regional wind power production, while the urbanization process and carbon emission intensity have significant spatial spillover effects. The optimization of the economic structure, technological innovation and the construction of energy infrastructure are expected to improve the regional wind power efficiency. The results present a new approach for accurately identifying the spatial characteristics of wind power efficiency and the spatial effects of the influencing factors, thus providing a reference for policymakers.

scholarly journals Can Financial Development Curb Carbon Emissions? Empirical Test Based on Spatial Perspective

2021 ◽  
Vol 13 (21) ◽  
pp. 11912
Author(s):  
Xueyang Liu ◽  
Xiaoxing Liu
Keyword(s):  
Structural Optimization ◽  
Financial Development ◽  
Technological Innovation ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Empirical Test ◽  
Total Carbon ◽  
Eastern Region ◽  
Carbon Emission Intensity ◽  
The Impact

To respond to global climate change and achieve a “carbon peak” and “carbon neutrality” as soon as possible has become a common goal around the world. Economic growth relies heavily on financial development; indeed, low-carbon economic development is inseparable from financial support. This paper studies the impact of financial development on carbon emission intensity and its mechanism from both theoretical and empirical aspects. Based on the 2005–2018 data on Chinese cities and the Spatial Durbin Model (SDM) research results, this paper finds that: (1) Financial development has significantly reduced China’s carbon emission intensity overall. After considering spatial effects, financial development increases local carbon emission intensity, although it may lead to a more significant decrease in the surrounding area. (2) The analysis of heterogeneity shows that only the financial development in the eastern region has a substantial detrimental impact on total carbon emission intensity and the carbon emission intensity of neighboring cities. The financial development in the central and western regions has no significant effect on carbon emission intensity. (3) The mechanism test shows that financial development mainly reduces carbon emission intensity through technological innovation and structural optimization, with the effect of technological innovation being 9.5%, and the effect of structural optimization being 12.15%. The expansion of the consumption effects of financial development has no significant impact on carbon emission intensity. Accordingly, this article believes that it is necessary to further support financial development, build large-scale financial centers, continue to optimize the structure of financial products, and encourage the development of green finance.

scholarly journals Can Financial Development Curb Carbon Emissions? —Empirical Test Based on Spatial Perspective

2021 ◽  
Author(s):  
Xueyang Liu ◽  
Xiaoxing Liu
Keyword(s):  
Structural Optimization ◽  
Financial Development ◽  
Technological Innovation ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Empirical Test ◽  
Total Carbon ◽  
Eastern Region ◽  
Carbon Emission Intensity ◽  
The Impact

Abstract To respond to global climate change and achieve "carbon peak" and "carbon neutrality" as soon as possible has become a common goal around the world. Economic growth relies heavily on financial development; indeed, low-carbon economic development is inseparable from financial support. This paper studies the impact of financial development on carbon emission intensity and its mechanism from both theoretical and empirical aspects. Based on the 2005–2018 data on Chinese cities and the Spatial Durbin Model (SDM) research results, this paper finds: (1) Financial development has significantly reduced China's carbon emission intensity overall. After considering spatial effects, financial development increased local carbon emission intensity, although it may lead to a more significant decrease in the surrounding area. (2) The analysis of heterogeneity shows that only the financial development in the eastern region has a substantial detrimental impact on total carbon emission intensity and the carbon emission intensity of neighboring cities. The financial development in the central and western regions has no significant effect on carbon emission intensity. (3) The mechanism test shows that financial development mainly reduces carbon emission intensity through technological innovation and structural optimization, with the effect of technological innovation 9.5%, and the effect of structural optimization 12.15%. The expansion of the consumption effects of financial development has no significant impact on carbon emission intensity. Accordingly, this article believes that it is necessary to further support financial development, build large-scale financial centers, continue to optimize the structure of financial products and encourage the development of green finance.

scholarly journals Construction and Application Analysis of Carbon Emission Influence Factor Model of Energy Consumption in Mining Industry

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Lili Wei ◽  
Xiwen Feng ◽  
Guangyu Jia
Keyword(s):  
Energy Consumption ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Mining Industry ◽  
Energy Structure ◽  
Low Carbon ◽  
Greenhouse Gas Inventory ◽  
Carbon Emission Intensity ◽  
Low Carbon Development

With the proposal of China’s “double carbon goal,” as a high energy-consuming industry, it is urgent for the mining industry to adopt a low-carbon development strategy. Therefore, in order to better provide reasonable suggestions and references for the low-carbon development of mining industry, referring to the methods and parameters of the 2006 IPCC National Greenhouse Gas Inventory Guidelines and China’s Provincial Greenhouse Gas Inventory Preparation Guidelines (Trial), a carbon emission estimation model is established to estimate the carbon emission of energy consumption of China's mining industry from 2000 to 2020. Then, using the extended Kaya identity, the influencing factors of carbon emission in mining industry are decomposed into energy carbon emission intensity, energy structure, energy intensity, industrial structure, and output value. On this basis, an LMDI model is constructed to analyze the impact of five factors on carbon emission from mining industry. The research shows that the carbon emission and carbon emission intensity of energy consumption in China’s mining industry first rise and then fall and then rise slightly. The carbon emission intensity in recent three years is about 2 tons/10000 yuan. The increase in output value is the main factor to increase carbon emission. The reduction in energy intensity is the initiative of carbon emission reduction. The current energy structure of mining industry is not conducive to carbon emission reduction.

scholarly journals Carbon Emission Reduction Performance of Green Biased Technology Progress: From the Perspective of Industrial Structure Adjustment

2021 ◽  
Vol 251 ◽  
pp. 02070
Author(s):  
Yang Li
Keyword(s):  
Emission Intensity ◽  
Carbon Emission ◽  
Industrial Structure ◽  
Low Carbon ◽  
Suppression Effect ◽  
Carbon Emission Intensity ◽  
Structure Adjustment ◽  
Industrial Structure Adjustment ◽  
Productivity Indicators ◽  
The Impact

This paper uses China’s provincial panel data from 1997 to 2015 to construct the Malmquist- Luenberger productivity indicators to measure the level of green biased technology progress, and measures the change in industrial structure based on indicators of low-carbon transformation, optimization and rationalization of industrial structure, and empirically tests the impact of green biased technology progress and industrial structure adjustment on China’s provincial carbon emission intensity. The results show that green biased technology progress can significantly exert the suppression effect of carbon emission intensity through the channel of low-carbon transformation of industrial structure.

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