Provincial-level industrial CO2 emission drivers and emission reduction strategies in China: Combining two-layer LMDI method with spectral clustering

Wen, Lei; Li, Zhenkai

doi:10.1016/j.scitotenv.2019.134374

Cited by 85 publications

(30 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It shows that the effect of economic scale plays a dominant role in promoting the CO 2 emission, which is consistent with the result of Zhang et al [57]. Energy intensity effect has a significant impact on the CO 2 emission reduction, with the highest CO 2 emission reduction of 645.89 million tons in 2012-2013 [58]. Population size effect makes a relatively small contribution to promoting carbon emissions.…”

Section: Lmdi Analysis Based On Spatiotemporal Transition 41 Driving Factors Of National Industrial Co 2 Emissionsupporting

confidence: 89%

Social and Economic Factors of Industrial Carbon Dioxide in China: From the Perspective of Spatiotemporal Transition

Li¹,

Cheng²,

Diao³

et al. 2021

Sustainability

View full text Add to dashboard Cite

The reduction of CO2 emission has become one of the significant tasks to control climate change in China. This study employs Exploratory Spatial Data Analysis (ESDA) to identify the provinces in China with different types of spatiotemporal transition, and applies the Logarithmic Mean Divisia Index (LMDI) to analyze the influencing factors of industrial CO2 emissions. Spatial autocorrelation of provincial industrial CO2 emissions from 2003 to 2017 has been demonstrated. The results are as follows: (1) 30 provinces in China are categorized into 8 types of spatiotemporal transition, among which 24 provinces are characterized by stable spatial structure and 6 provinces show significant spatiotemporal transition; (2) For all types of spatiotemporal transition, economic scale effect is mostly contributed to industrial CO2 emission, while energy intensity effect is the most crucial driving force to reduce industrial carbon dioxide emission; (3) provinces of type HH-HH, HL-HL and HL-HH are most vital for CO2 emission reduction, while the potential CO2 emission increase of developing provinces in LL-LL, LH-LH and LL-LH should also be taken into account. Specific measures for CO2 emission reduction are suggested accordingly.

show abstract

Section: Lmdi Analysis Based On Spatiotemporal Transition 41 Driving Factors Of National Industrial Co 2 Emissionsupporting

confidence: 89%

Social and Economic Factors of Industrial Carbon Dioxide in China: From the Perspective of Spatiotemporal Transition

Li¹,

Cheng²,

Diao³

et al. 2021

Sustainability

View full text Add to dashboard Cite

show abstract

“…However, since a detailed CO 2 -related database is not yet available (at least considering the scope of this study), we manually calculated the emissions per state per year. This procedure is indeed common in STIRPAT studies (Wang et al 2017a ; Wang and Zhao 2015 ; Wen and Li 2020 ; Yang et al 2018 ).…”

Section: Methodsmentioning

confidence: 99%

The drivers of energy-related CO2 emissions in Brazil: a regional application of the STIRPAT model

Polloni‐Silva

Silveira

Ferraz

et al. 2021

Environ Sci Pollut Res

View full text Add to dashboard Cite

Since energy is one of the basic inputs for development, emerging economies should make an effort to investigate the environmental impacts of their fast economic growth. However, large emerging economies present significant regional heterogeneity that is usually uncounted for. This study uses the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model and regional data on the 27 Brazilian states to investigate the growth-CO 2 nexus under distinct development stages. To perform this analysis, we divided the states into three groups according to their average annual GDP (i.e., richer, intermediate, and poorer regions). The results suggest that richer and poorer regions, particularly, present economic and demographic developments that are environmentally costly. Also, population and per capita GDP have the largest influences on CO 2 emissions. The roles of the industrial sector and the ascending service sector are also subject to criticism. Moreover, Brazil arguably suffers from technological stagnation as its energy intensity is growing and boosting CO 2 emissions. We discuss the policy implications of these findings and suggest a future research agenda.

show abstract

“…Su et al (2020) found that the higher the level of urbanization, the greater the CO2 emissions in the urban areas of Fujian. Energy structure is considered to be an important factor affecting CO2 emissions (Zhang et al 2018;Wen and Li 2020). Li and Moubarak (2014) argued that China, which has abundant natural resources, could increase its investment in clean energy and renewable energy (solar energy), optimize the energy structure, and thus promote carbon emission reduction.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al (2018) found that the coal-based energy structure was the main cause of high ICEs and that adjustment of the energy structure was a powerful measure for reducing environmental pollution and promoting ICEs reduction. Wen and Li (2020) argued that the development of clean energy greatly improved the energy structure and reduced the consumption of fossil energy; additionally, adjustment of the energy structure could limit the increase in CO2 emissions. In other words, adjusting the energy structure has a significant effect on reducing CO2 emissions, which is of great significance for China's sustainable development strategy (Dong et al 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Technological innovation can improve energy utilization efficiency and reduce the energy intensity of China's industry, thus reducing the CO2 emissions (Poumanyvong and Kaneko 2010;Wen and Li 2020). For example, Dauda et al (2019) suggested that improving energy technology and related equipment could greatly improve energy efficiency, thereby reducing urban carbon dioxide emissions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Examining the driving factors of industrial CO2 emissions in Chinese cities using geographically weighted regression model

Wang

Zhang

2021

Clean Techn Environ Policy

View full text Add to dashboard Cite

The industrial sector is the sector with the largest CO2 emissions, and to reduce overall CO2 emissions, analysis of the impact factors holds significance. Based on the 2015 industrial CO2 emissions of 282 cities in China combined with economic and social data, and a geographically weighted regression (GWR) model, we analysed the characteristics of the spatial distribution of CO2 emissions and the influencing factors of spatial heterogeneity. The results show that China's urban industrial CO2 emissions present a significant spatial agglomeration state that includes Shandong, Beijing, Tianjin, Shanghai, Zhejiang, and Jiangsu, and the core of the coastal areas form a high-high (H-H) concentration; a low-low aggregation (L-L) is formed in less developed areas such as Guizhou, Yunnan, Sichuan and Guangxi. The influence of various factors on industrial CO2 emissions has significant spatial heterogeneity. The Industrial scale, industry share of GDP, and share of the service industry in GDP are factors that promote industrial CO2 emissions. The technological innovation, population density, and social investment in fixed assets are important factors that inhibit industrial CO2 emissions, but their impact on industrial CO2 emissions shows spatial differences. In contrast, the level of economic development, foreign direct investment, financial development and government intervention have a two-way impact on industrial CO2 emissions.

show abstract

Provincial-level industrial CO2 emission drivers and emission reduction strategies in China: Combining two-layer LMDI method with spectral clustering

Cited by 85 publications

References 54 publications

Social and Economic Factors of Industrial Carbon Dioxide in China: From the Perspective of Spatiotemporal Transition

Social and Economic Factors of Industrial Carbon Dioxide in China: From the Perspective of Spatiotemporal Transition

The drivers of energy-related CO2 emissions in Brazil: a regional application of the STIRPAT model

Examining the driving factors of industrial CO2 emissions in Chinese cities using geographically weighted regression model

Contact Info

Product

Resources

About