scholarly journals Analysis and Evaluation of the Regional Characteristics of Carbon Emission Efficiency for China

2020 ◽  
Vol 12 (8) ◽  
pp. 3138 ◽  
Author(s):  
Jinkai Li ◽  
Jingjing Ma ◽  
Wei Wei
Keyword(s):  
Carbon Dioxide Emissions ◽  
Carbon Emission ◽  
Government Regulation ◽  
Industrial Structure ◽  
Western China ◽  
Energy Structure ◽  
Analysis And Evaluation ◽  
Emission Efficiency ◽  
Technological Efficiency ◽  
The Impact

To promote economic and social development with reduced carbon dioxide emissions, the key lies in determining how to improve carbon emission efficiency (CEE). We first measured the CEE of each province by using the input-oriented three-stage Data Envelopment Analysis (DEA) and DEA-Malmquist model for the panel data of 30 provinces in China during 2000–2017. Then we explored the CEE differences and characteristics of different regions obtained by using hierarchical clustering of each province’s CEE. Finally, based on the regression model, we conducted an empirical analysis of the impact of each factor of total factor productivity (TFP) on CEE. The main findings of this research are as follows: (1) The industrial structure, energy structure, government regulation, technological innovation, and openness had a significant impact on CEE; (2) The variation trends of CEE and TFP in the eight regions we studied were convergent, while the variations of CEE among regions were diverse and all distributed stably in different ranges; (3) The eight regions’ efficiency basically showed a downward trend of eastern, central and western China; (4) Technological regression was the main reason for the decline in TFP. Technological progress and technological efficiency can contribute to an improvement in CEE. Based on the findings above, we provide decision-making references for comprehensively improving the efficiency of various regions and accelerating China’s energy conservation, emissions reduction, and coordinated development.

scholarly journals Does Emission Trading Improve Carbon Emission Efficiency of China’s Iron and Steel Industry? An Exploration Using a DEA-SBM and Difference-In-Differences Approach

2021 ◽  
Author(s):  
Xiping Wang ◽  
Sujing Wang
Keyword(s):  
Steel Industry ◽  
Carbon Emission ◽  
Emission Trading ◽  
Energy Structure ◽  
Iron And Steel Industry ◽  
Low Carbon ◽  
Difference In Differences ◽  
Iron And Steel ◽  
Emission Efficiency ◽  
The Impact

Abstract As an effective tool of carbon emission reduction, emission trading has been widely used in many countries. Since 2013, China implemented carbon emission trading in seven provinces and cities, with iron and steel industry included in the first batch of pilot industries. This study attempts to explore the policy effect of emission trading on iron and steel industry in order to provide data and theoretical support for the low-carbon development of iron and steel industry as well as the optimization of carbon market. With panel data of China’s 29 provinces from 2006 to 2017, this study adopted a DEA-SBM model to measure carbon emission efficiency of China’s iron and steel industry (CEI) and a difference-in-differences (DID) method to explore the impact of emission trading on CEI. Moreover, regional heterogeneity and influencing mechanisms were further investigated, respectively. The results indicate that: (1) China's emission trading has a significant and sustained effect on carbon abatement of iron and steel industry, increasing the annual average CEI by 12.6% in pilot provinces. (2) The policy effects are heterogeneous across diverse regions. Higher impacts are found in the western and eastern regions, whereas the central region is not significant. (3) Emission trading improves CEI by stimulating technology innovation, reducing energy intensity, and adjusting energy structure. (4) Economic level and industrial structure are negatively related to CEI, while environmental governance and openness degree have no obvious impacts. Finally, according to the results and conclusions, some specific suggestions are proposed.

scholarly journals Factors Affecting Energy-Related Carbon Emissions in Beijing-Tianjin-Hebei Region

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Jing-min Wang ◽  
Yu-fang Shi ◽  
Xue Zhao ◽  
Xue-ting Zhang
Keyword(s):  
Carbon Emissions ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Energy Structure ◽  
Factors Affecting ◽  
Economic Output ◽  
Level Model ◽  
The Impact ◽  
The Relationship

Beijing-Tianjin-Hebei is a typical developed region in China. The development of economy has brought lots of carbon emissions. To explore an effective way to reduce carbon emissions, we applied the Logarithmic Mean Divisia Index (LMDI) model to find drivers behind carbon emission from 2003 to 2013. Results showed that, in Beijing, Tianjin, and Hebei, economic output was main contributor to carbon emissions. Then we utilized the decoupling model to comprehensively analyze the relationship between economic output and carbon emission. Based on the two-level model, results indicated the following: (1) Industry sector accounted for almost 80% of energy consumption in whole region. The reduced proportion of industrial GDP will directly reduce the carbon emissions. (2) The carbon factor for CO2/energy in whole region was higher than that of Beijing and Tianjin but lower than that of Hebei. The impact of energy structure on carbon emission depends largely on the proportion of coal in industry. (3) The energy intensity in whole region decreased from 0.79 in 2003 to 0.40 in 2013 (unit: tons of standard coal/ten thousand yuan), which was lower than national average. (4) The cumulative effects of industrial structure, energy structure, and energy intensity were negative, positive, and negative, respectively.

scholarly journals Towards Carbon Neutrality: The Impact of Renewable Energy Development on Carbon Emission Efficiency

2021 ◽  
Vol 18 (24) ◽  
pp. 13284
Author(s):  
Feng Dong ◽  
Chang Qin ◽  
Xiaoyun Zhang ◽  
Xu Zhao ◽  
Yuling Pan ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Carbon Emission ◽  
Energy Development ◽  
Western China ◽  
Carbon Neutrality ◽  
Carbon Efficiency ◽  
Emission Efficiency ◽  
Renewable Energy Development ◽  
Reasonable Range ◽  
The Impact

The energy transition and carbon emission efficiency are important thrust and target functions, respectively, for achieving carbon neutrality in the future. Using a sample of 30 Chinese provinces from 2006 to 2018, we measured their carbon efficiency using the game cross-efficiency data envelopment analysis (DEA). Then, a random forest regression model was used to explore the impact of renewable energy development on regional carbon emission efficiency. The results are as follows. First, China’s carbon emission efficiency in the southeast coastal area was better than that in the northwest area. Second, renewable energy development first inhibited and then promoted carbon emission efficiency, and there existed a reasonable range. Third, through a regional heterogeneity analysis, the trend of the influence of renewable energy development on carbon emission efficiency was found to not be significantly different in eastern, central, and western China, but there was a certain gap in the reasonable range. Our study not only helps to promote the study of renewable energy development and the carbon neutral target, but also provides an important reference for Chinese policy-makers to design a reasonable carbon emissions reduction path.

scholarly journals Industrial Structure Transformation and Layout Optimization of Beijing-Tianjin-Hebei Region under Carbon Emission Constraints

2021 ◽  
Vol 13 (2) ◽  
pp. 643
Author(s):  
Yi Chai ◽  
Xueqin Lin ◽  
Dai Wang
Keyword(s):  
Carbon Emission ◽  
High Efficiency ◽  
Industrial Structure ◽  
Layout Optimization ◽  
Energy Structure ◽  
Structure Transformation ◽  
Low Carbon ◽  
Emission Efficiency ◽  
The Status ◽  
Low Efficiency

To address the issue of global warming, there is a trend towards low-carbon economies in world economic development. China’s rapid economic growth and high carbon energy structure contribute to its large carbon emissions. To achieve sustainable development, China must transform its industrial structure to conserve energy, reduce emissions, and adapt to climate change. This study measured the carbon entropy and carbon emission efficiency of 25 industries in the Beijing-Tianjin-Hebei region from 2000 to 2015 by building carbon entropy models and total factor industrial carbon emission efficiency evaluation models. The study showed that: (a) Priority development industries in the Beijing-Tianjin-Hebei region were expanding, the regional competitiveness of the moderate development industry was improving, and the proportion of restricted development industries had dropped significantly; (b) the spatial distribution of the three types of industries presented a pattern of concentric rings, with priority industries at the core, surrounded by moderate, then by restricted development industries; (c) the status of medium- and high-efficiency industries had improved, while the status of low-efficiency industries had decreased. Spatially, high- and low-efficiency industries were becoming concentrated, and medium-efficiency industries were becoming dispersed; (d) considering carbon entropy and carbon emission efficiency, the path of industrial structure transformation and upgrading and layout optimization in Beijing-Tianjin-Hebei region was proposed.

The Scenario Analysis of Carbon Emissions Based on Improved IPAT Model in China

2012 ◽  
Vol 616-618 ◽  
pp. 1484-1489 ◽  
Author(s):  
Xu Shan ◽  
Hua Wang Shao
Keyword(s):  
Carbon Dioxide ◽  
Economic Development ◽  
Carbon Dioxide Emissions ◽  
Industrial Structure ◽  
Energy Structure ◽  
Low Carbon ◽  
Policy Scenario ◽  
Social And Economic Development ◽  
Energy Environment ◽  
Development Goals

The coordination development of economy-energy-environment was discussed with traditional environmental loads model, combined with "decoupling" theory. Considering the possibilities of social and economic development, this paper set out three scenarios, and analyzed quantitatively the indexes, which affected carbon dioxide emissions, including population, per capita GDP, industrial structure and energy structure. Based on this, it forecasted carbon dioxide emissions in China in future. By comparing the prediction results, it held that policy scenario was the more realistic scenario, what’s more it can achieve emission reduction targets with the premise of meeting the social and economic development goals. At last, it put forward suggestions to implement successfully policy scenario, from energy structure, industrial structure, low-carbon technology and so on.

scholarly journals An Examination of Green Credit Promoting Carbon Dioxide Emissions Reduction: A Provincial Panel Analysis of China

2021 ◽  
Vol 13 (13) ◽  
pp. 7148
Author(s):  
Wenjie Zhang ◽  
Mingyong Hong ◽  
Juan Li ◽  
Fuhong Li
Keyword(s):  
Carbon Dioxide ◽  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Industrial Structure ◽  
Emissions Reduction ◽  
Green Finance ◽  
Carbon Emissions Reduction ◽  
Negative Effect ◽  
Emissions Intensity ◽  
The Impact

The implementation of green finance is a powerful measure to promote global carbon emissions reduction that has been highly valued by academic circles in recent years. However, the role of green credit in carbon emissions reduction in China is still lacking testing. Using a set of panel data including 30 provinces and cities, this study focused on the impact of green credit on carbon dioxide emissions in China from 2006 to 2016. The empirical results indicated that green credit has a significantly negative effect on carbon dioxide emissions intensity. Furthermore, after the mechanism examination, we found that the promotion impacts of green credit on industrial structure upgrading and technological innovation are two effective channels to help reduce carbon dioxide emissions. Heterogeneity analysis found that there are regional differences in the effect of green credit. In the western and northeastern regions, the effect of green credit is invalid. Quantile regression results implied that the greater the carbon emissions intensity, the better the effect of green credit. Finally, a further discussion revealed there exists a nonlinear correlation between green credit and carbon dioxide emissions intensity. These findings suggest that the core measures to promote carbon emission reduction in China are to continue to expand the scale of green credit, increase the technology R&D investment of enterprises, and to vigorously develop the tertiary industry.

scholarly journals Analysis of Regional Differences and Influencing Factors on China’s Carbon Emission Efficiency in 2005–2015

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3081 ◽  
Author(s):  
Zeng ◽  
Lu ◽  
Liu ◽  
Zhou ◽  
Hu
Keyword(s):  
Influencing Factors ◽  
Regional Differences ◽  
Carbon Emission ◽  
Industrial Structure ◽  
Tobit Model ◽  
Policy Implications ◽  
Dea Model ◽  
Emission Efficiency ◽  
Unit Root Testing ◽  
Technology Level

With the challenge to reach targets of carbon emission reduction at the regional level, it is necessary to analyze the regional differences and influencing factors on China’s carbon emission efficiency. Based on statistics from 2005 to 2015, carbon emission efficiency and the differences in 30 provinces of China were rated by the Modified Undesirable Epsilon-based measure (EBM) Data Envelopment Analysis (DEA) Model. Additionally, we further analyzed the influencing factors of carbon emission efficiency’s differences in the Tobit model. We found that the overall carbon emission efficiency was relatively low in China. The level of carbon emission efficiency is the highest in the East region, followed by the Central and West regions. As for the influencing factors, industrial structure, external development, and science and technology level had a significant positive relationship with carbon emission efficiency, whereas government intervention and energy intensity demonstrated a negative correlation with carbon emission efficiency. The contributions of this paper include two aspects. First, we used the Modified Undesirable EBM DEA Model, which is more accurate than traditional methods. Secondly, based on the data’s unit root testing and cointegration, the paper verified the influencing factors of carbon emission efficiency by the Tobit model, which avoids the spurious regression. Based on the results, we also provide several policy implications for policymakers to improve carbon emission efficiency in different regions.

Carbon Dioxide Emissions from Energy Consumption and Projections in Shaanxi Province

2014 ◽  
Vol 522-524 ◽  
pp. 176-180
Author(s):  
Jian Xu ◽  
Wei Zhang ◽  
Jin Suo Zhang
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Carbon Dioxide Emissions ◽  
Carbon Emission ◽  
Carbon Oxide ◽  
Industry Structure ◽  
Shaanxi Province ◽  
Energy Structure ◽  
Calculation Methodology ◽  
Secondary Industry

Using the calculation methodology based on energy consumption, the amount of carbon emissions due to energy consumption in Shaanxi province were calculated from 2000 to 2010. The decreasing trends in carbon emission were analyzed in terms of energy structure, economic growth and industry structure. The increasing of carbon oxide emission of energy consumption of Shaanxi province was mainly drove by economic increasing. The carbon emission of the Secondary industry is the biggest one in energy consumption which is 60% in the gross carbon emission, then, the Tertiary industry is 20% of the gross carbon emission.

scholarly journals A Factor Decomposition on China’s Carbon Emission from 1997 to 2012 Based on IPAT-LMDI Model

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Wei Li ◽  
Ya-Bo Shen ◽  
Hui-Xia Zhang
Keyword(s):  
Development Strategy ◽  
Carbon Dioxide Emissions ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Scale Effects ◽  
Assessment System ◽  
Energy Structure ◽  
Low Carbon ◽  
Economic Output ◽  
Population Scale

We probe into the key factors that possess significant effects on China’s CO2emissions during 1997–2012 on the basis of IPAT-LMDI model. Carbon dioxide emissions are specifically decomposed into CO2emission intensity, energy structure, energy intensity, industrial structure, economic output, and population scale effects. Results indicate that the paramount driving factors that resulted in the growth of CO2emissions are economic output, population scale, and energy structure. In contrast, energy intensity and industrial structure generally play an outstanding role in reducing emissions. This paper constructs a new weight assessment system by introducing “contribution value-significant factor-effect coefficient” to replace “contribution value-contribution rate” in the previous literature. According to the most significant positive effect and the most negative effect from the conclusion, we point out the effective policies that can not only accelerate the target of “China’s carbon emissions per unit of GDP could be cut down by 40–45% by 2020, from 2005 levels,” but also have crucial significance on the low-carbon economic development strategy of China.

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