scholarly journals Industrial Carbon Emission Efficiency in the Yangtze River Economic Belt and Its Influencing Factors

2020 ◽  
Vol 15 (1) ◽  
pp. 25-32 ◽  
Author(s):  
Yiqiong Lu ◽  
Miao Li
Keyword(s):  
Influencing Factors ◽  
Yangtze River ◽  
Carbon Emission ◽  
The Yangtze River ◽  
Industrial Carbon ◽  
Emission Efficiency

scholarly journals Study on Total Factor Carbon Emission Efficiency in the Yangtze River Economic Zone, China

2020 ◽  
Vol 143 ◽  
pp. 02026
Author(s):  
Jiwen Chen ◽  
Zuxu Zou
Keyword(s):  
Emission Reduction ◽  
Yangtze River ◽  
Carbon Emission ◽  
Malmquist Index ◽  
The Yangtze River ◽  
Growth Trend ◽  
Reduction Model ◽  
Emission Efficiency ◽  
Economic Zone ◽  
Modernization Process

With the continuous acceleration of the modernization process, the Eco-environmental problems of the Yangtze River Economic Zone in China have become increasingly prominent, which makes the study of carbon emission efficiency become a long-term concern. Based on the panel data of 11 provinces and cities of the Yangtze River Economic Zone in 2009～2016, this paper calculates the DEA-Malmquist index of the Total Factor Carbon Emission Efficiency containing undesirable output in various provinces and cities and three major regions. By studying the DEA-Malmquist index and its decomposition, the results show that the Total Factor Carbon Emission Efficiency of various regions in the Yangtze River Economic Zone presents a growth trend, and its main contribution comes from technological progress. In the future, the emission reduction rules of the Yangtze River Economic Zone will be transformed from the traditional top-down emission reduction model to the bottom-up “independent contribution” emission reduction model.

scholarly journals Influence of Urbanization and Foreign Direct Investment on Carbon Emission Efficiency: Evidence from Urban Clusters in the Yangtze River Economic Belt

2021 ◽  
Vol 13 (5) ◽  
pp. 2722
Author(s):  
Shijian Wu ◽  
Kaili Zhang
Keyword(s):  
Economic Development ◽  
Foreign Direct Investment ◽  
Carbon Emissions ◽  
Direct Investment ◽  
Yangtze River ◽  
Carbon Emission ◽  
The Yangtze River ◽  
Urban Agglomerations ◽  
Emission Efficiency ◽  
The Impact

Reducing carbon emissions and realizing green, circular, and low-carbon development is essential for high-quality economic development. Following the construction of a superefficiency SBM model and combining the panel data of three major urban agglomerations in the Yangtze River Economic Belt from 2003 to 2017, carbon emission efficiency was measured and analyzed. A spatial Durbin model (SDM) was incorporated to analyze the urban agglomerations in the Yangtze River Economic Belt and the impact of urbanization quality and foreign direct investment (FDI) on carbon emission efficiency. Finally, the SDM model was used to decompose the spillover effect. Generally, carbon emission efficiency in the three major urban agglomerations in the Yangtze River Economic Belt is low, with regional differences. FDI only has a positive impact on the carbon emissions of the Yangtze River Delta and the middle reaches of the Yangtze River. Furthermore, urbanization and population density have led to high levels of carbon emission in the region; however, the industrial structure and energy intensity factors have inhibited the improvement of regional carbon emission efficiency. Improving the quality of urbanization and trade structure is important to achieve energy conservation and emission reductions, which are pillars of sustainable economic development.

scholarly journals Random forest analysis of factors affecting urban carbon emissions in cities within the Yangtze River Economic Belt

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252337
Author(s):  
Zhaohan Wang ◽  
Zijie Zhao ◽  
Chengxin Wang
Keyword(s):  
Random Forest ◽  
Influencing Factors ◽  
Carbon Emissions ◽  
Regional Differences ◽  
Yangtze River ◽  
Carbon Emission ◽  
Machine Learning Algorithms ◽  
Population Characteristics ◽  
The Yangtze River ◽  
Real World Data

China became the country with the largest global carbon emissions in 2007. Cities are regional population and economic centers and are the main sources of carbon emissions. However, factors influencing carbon emissions from cities can vary with geographic location and the development history of the cities, rendering it difficult to explicitly quantify the influence of individual factors on carbon emissions. In this study, random forest (RF) machine learning algorithms were applied to analyze the relationships between factors and carbon emissions in cities using real-world data from Chinese cities. Seventy-three cities in three urban agglomerations within the Yangtze River Economic Belt were evaluated with respect to urban carbon emissions using data from regional energy balance tables for the years 2000, 2007, 2012, and 2017. The RF algorithm was then used to select 16 prototypical cities based on 10 influencing factors that affect urban carbon emissions while considering five primary factors: population, industry, technology levels, consumption, and openness to the outside world. Subsequently, 18 consecutive years of data from 2000 to 2017 were used to construct RFs to investigate the temporal predictability of carbon emission variation in the 16 cities based on regional differences. Results indicated that the RF approach is a practical tool to study the connection between various influencing factors and carbon emissions in the Yangtze River Economic Belt from different perspectives. Furthermore, regional differences among the primary carbon emission influencing factors for each city were clearly observed and were related to urban population characteristics, urbanization level, industrial structures, and degree of openness to the outside world. These factors variably affected different cities, but the results indicate that regional emission reductions have achieved positive results, with overall simulation trends shifting from underestimation to overestimation of emissions.

scholarly journals Evaluation and Factor Analysis of Industrial Carbon Emission Efficiency Based on “Green-Technology Efficiency”—The Case of Yangtze River Basin, China

Land ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1408
Author(s):  
Jingyi Wang ◽  
Kaisi Sun ◽  
Jiupai Ni ◽  
Deti Xie
Keyword(s):  
River Basin ◽  
Yangtze River ◽  
Carbon Emission ◽  
Stochastic Frontier Analysis ◽  
Yangtze River Basin ◽  
Green Technology ◽  
Frontier Analysis ◽  
Industrial Carbon ◽  
Emission Efficiency ◽  
Carbon Productivity

In the context of low-carbon development, effectively improving carbon emission efficiency is an inevitable requirement for achieving sustainable economic and social development. Based on panel data of 11 provinces and municipalities in the Yangtze River Basin (YRB), ranging from 2000 to 2019, this paper uses green-technology efficiency to measure industrial carbon emission efficiency via stochastic frontier analysis (SFA) incorporated with carbon productivity. This provides a comprehensive analytical framework for assessing the carbon emission efficiency, quantitatively measuring the reduction potential, and clarifying the incentive channels. The results are as follows: (1) The industrial carbon emission efficiency (ICEE) of YRB presents an increasing trend. Although differences in emission efficiency among provinces and municipalities are narrowing, their emission efficiency is still prominently imbalanced. (2) The potential for reducing industrial carbon emissions in this region shows an upward-to-downward trend. The decline in such potential of each province and municipality in recent years indicates that further reduction is becoming more difficult. (3) Effective means to improve ICEE are to improve the level of industrialization, promote technological innovation in industrial low-carbonization, and raise industrial productivity. Meanwhile, the significant spatial spillover effect of ICEE further emphasizes the necessity of strengthening the coordination of carbon reduction policies in YRB. The research in this paper adds a new perspective to the evaluation of ICEE and also provides reference and technical support for the government to enhance ICEE and formulate green and sustainable development policies.

scholarly journals Multi-Perspective Influence Mechanism Analysis and Multi-Scenario Prediction of Carbon Emissions——A Case of the Yangtze River Delta, China

2019 ◽  
Vol 118 ◽  
pp. 04014
Author(s):  
Tao Yi ◽  
Mohan Qiu ◽  
Zhengang Zhang ◽  
Song Mu ◽  
Yu Tian
Keyword(s):  
Carbon Emissions ◽  
Emission Reduction ◽  
Yangtze River ◽  
Carbon Emission ◽  
Yangtze River Delta ◽  
Mechanism Analysis ◽  
The Yangtze River ◽  
Carbon Emission Reduction ◽  
Influence Mechanism ◽  
The Yangtze River Delta

Under the mandatory push of meeting carbon emission reduction commitments proposed in the Paris Agreement, the analysis on the peaking time of China’s carbon emissions deserves enough attention. This paper focuses on the peaking times of total carbon emissions (TCE) and carbon emission intensity (CEI) in the Yangtze River Delta (YRD). According to the development of carbon emissions in YRD and related targets in the 13th Five-Year Plan, the peaking times of TCE and CEI in different scenarios are predicted based on the influence mechanism analysis of carbon emissions in YRD from the perspective of energy, economy and society. Considering the development characteristics of China at this stage, this paper introduces several new indicators such as full-time equivalent of research and development (R&D) personnel and investment in environmental pollution control. Based on the study results, several policy recommendations are put forward to fulfil China’s carbon emission reduction commitments.

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