scholarly journals Analysis of Influencing Factors on Regional Carbon Emission Intensity in China-Based on Empirical Research with Provincial Panel Data

2014 ◽  
Vol 7 (3) ◽  
Author(s):  
Shih- Feng Tsai
Keyword(s):  
Panel Data ◽  
Empirical Research ◽  
Influencing Factors ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Carbon Emission Intensity

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.

On Carbon Emissions Measure and its Influencing Factors of Yunnan Province: 1998-2008

2011 ◽  
Vol 99-100 ◽  
pp. 539-545
Author(s):  
Ya Zhang ◽  
You Liang Mao
Keyword(s):  
Influencing Factors ◽  
Carbon Emissions ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Yunnan Province ◽  
Low Carbon ◽  
Low Carbon Economy ◽  
Carbon Emission Intensity ◽  
Administrative Area ◽  
Executive Agencies

Coming up with the idea of low-carbon economy, numerous studies both at home and abroad on carbon emissions have emerged, nonetheless of which seldom are studies aiming at specific executive agencies and supervisory authorities of government development plan at provincial administrative area level. This paper, by using calculation formulas in carbon emission calculation guide of IPCC and carbon emission coefficient default value, measured the carbon emissions of Yunnan Province during 1998 and 2008 and analyzed relative influencing factors. The study shows economic growth and industrial restructuring increase the carbon emission intensity which is not remarkably affected by energy restructuring. The key to decrease carbon emission intensity is enhancing energy efficiency.

scholarly journals The Changes of Carbon Emission in China’s Industrial Sectors from 2002 to 2010: A Structural Decomposition Analysis and Input-Output Subsystem

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Guoxing Zhang ◽  
Mingxing Liu
Keyword(s):  
Carbon Emissions ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Decomposition Analysis ◽  
Final Demand ◽  
Good Effect ◽  
Input Output ◽  
Carbon Emission Intensity ◽  
Industrial Sectors ◽  
Three Components

Based on 2002–2010 comparable price input-output tables, this paper first calculates the carbon emissions of China’s industrial sectors with three components by input-output subsystems; next, we decompose the three components into effect of carbon emission intensity, effect of social technology, and effect of final demand separately by structure decomposition analysis; at last, we analyze the contribution of every effect to the total emissions by sectors, thus finding the key sectors and key factors which induce the changes of carbon emissions in China’s industrial sectors. Our results show that in the latest 8 years five departments have gotten the greatest increase in the changes of carbon emissions compare with other departments and the effect of final demand is the key factor leading to the increase of industrial total carbon emissions. The decomposed effects show a decrease in carbon emission due to the changes of carbon emission intensity between 2002 and 2010 compensated by an increase in carbon emissions caused by the rise in final demand of industrial sectors. And social technological changes on the reduction of carbon emissions did not play a very good effect and need further improvement.

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