Analysis on the variation rule and influencing factors of energy consumption carbon emission intensity in China's urbanization construction

Han, Xiuyan; Cao, Tianyi; Sun, Tao

doi:10.1016/j.jclepro.2019.117958

Cited by 91 publications

(30 citation statements)

References 15 publications

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“…The effect of energy structure (Δ CI ES ) on carbon intensity fluctuated over time in most provinces, and the change in energy structure had the least impact on carbon intensity compared to other factors, which was similar to the results of Jiang [ 62 ]. The effects of energy consumption per capita on carbon intensity (Δ CI EP ) were mainly positive in the increase in carbon intensity, which was in accordance with the results of Han et al [ 63 ], and its degree of influence varied in different provinces. In some cities, such as Tianjin, the degree of influence of energy consumption per capita on carbon intensity tended to decrease, while in Shaanxi and Shanghai, its degree of influence exhibited the opposite trend, with an upward tendency.…”

Section: Resultssupporting

confidence: 89%

Examining the Driving Factors of Urban Residential Carbon Intensity Using the LMDI Method: Evidence from China’s County-Level Cities

Zhao

Liu

2021

IJERPH

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Improving carbon efficiency and reducing carbon intensity are effective means of mitigating climate change. Carbon emissions due to urban residential energy consumption have increased significantly; however, there is a lack of research on urban residential carbon intensity. This paper examines the spatiotemporal variation of carbon intensity in the residential sector during 2001–2015, and then identifies the causes of the variation by utilizing the logarithmic mean Divisia index (LMDI) with the help of Microsoft Excel 2016 for 620 county-level cities in 30 Chinese provinces. The results show that high carbon intensity is mainly found in large cities, such as Beijing, Tianjin, and Shanghai. However, these cities showed a downward trend in carbon intensity. In terms of influencing factors, the energy consumption per capita, urban sprawl, and land demand are the three most influential factors in determining the changes in carbon intensity. The effect of energy consumption per capita mainly increases the carbon intensity, and its impact is higher in the municipal districts of provincial capital cities than in other types of cities. Similarly, the urban sprawl effect also promotes increases in carbon intensity, and a higher degree of influence appears in large cities. However, as urban expansion plateaus, the effect of urban sprawl decreases. The land-demand effect reduces the carbon intensity, and the degree of influence of the land-demand effect on carbon intensity is also clearly stronger in big cities. Our findings show that lowering the energy consumption per capita and optimizing the land-use structure are a reasonable direction of efforts, and the effects of differences in influencing factors should be paid more attention to reduce carbon intensity.

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Section: Resultssupporting

confidence: 89%

Examining the Driving Factors of Urban Residential Carbon Intensity Using the LMDI Method: Evidence from China’s County-Level Cities

Zhao

Liu

2021

IJERPH

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“…Both theoretical and empirical studies are showing their discoveries for energy consumption and climate change relationship. Han et al (2019) the energy consumption carbon emission and its intensity in China. It is observed that for the Chinese economy, the key source of carbon emission is energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Climate Change and Energy Consumption Patterns in Thailand: Time Trends During 1988-2013

Yuaningsih¹,

Febrianti²,

Kamran³

2020

IJEEP

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In traditional context, fossil fuel energy consumption is observed as non-renewable sources and contributing its major part for the energy source in the world economy. However, a new term under the title of renewable energy source is also examined and studied too. The focus of this paper is to investigate the impact of fossil fuel energy and renewable energy sources on the factor of climate change. For the measurement of climate change, overall six dimensions were added in the empirical analyses. The time period for the study was 1987 to 2013 and data was collected from World Development Indicator WDI. Findings for the regression analyses reveal that climate change in terms of CO 2 emissions from liquid fuel consumption (kt) or C 1 is directly influenced by both fossil and renewable energy, and same trend is found for CO 2 emissions (kt); C 2 . Meanwhile, the third indicator of climate change C 3 or CO 2 emissions (kg per 2010 US$ of GDP) is negatively affected by renewable energy. Furthermore, C 4 (CO 2 emissions from gaseous fuel consumption [% of total]) is directly influenced by both of the energy sources in Thailand. Additionally, the trends in C 5 is found to be positively determined by both fossil fuel and renewable energy, while their negative and significant impact is observed for C 6 ; CO 2 emissions from transport (% of total fuel combustion). Future research studies may consider the sub division of energy sources in both public and private sectors.

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“…A series of NEV industry support policies issued by the government have a greater incentive effect on production enterprises and vehicle carbon emission reduction (Li, 2020;Wu et al, 2021). Free cash flow, debt constraint, energy intensity, and enterprise size also have important effects on enterprise financial performance and vehicle carbon emissions (Cui et al, 2021;Braune et al, 2020;Han et al, 2019).…”

Section: Influencing Factors and Variablesmentioning

confidence: 99%

A study on evaluation and influencing factors of carbon emission performance in China’s new energy vehicle enterprises

Min

Sun

Ren

2021

Environ Sci Pollut Res

Self Cite

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Vehicle industry has made great contribution to human progress. However, in the process of vehicle operation, a large number of carbon compounds are emitted, which brings serious environmental problems. As one of the important means of vehicle carbon emission governance, the development of new energy vehicles(NEVs) has attracted much attention. The behavior and performance of NEV enterprises are highly concerned. Using Chinese 23 NEV vehicle enterprises' data from 2011 to 2018, this paper evaluates the carbon emission performance with the super-efficiency slacks-based measure (SE-SBM) model based on unexpected output, and then constructs STIRPAT model to analyze the influencing factors of carbon emission performance.The results indicate that, firstly, the carbon emission performance of China's NEV enterprises is not ideal at present, but it is increasing year by year. Secondly, the carbon emission performance of different NEV enterprise is distinct in the same year, and the carbon emission performance of the same NEV enterprise is distinct in different year. Thirdly, technological innovation, government support and free cash flow have significant positive impact on the carbon emission performance of NEV enterprises, while debt constraint, energy intensity and enterprise size have a significant negative impact on the carbon emission performance of NEV enterprises.

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Analysis on the variation rule and influencing factors of energy consumption carbon emission intensity in China's urbanization construction

Cited by 91 publications

References 15 publications

Examining the Driving Factors of Urban Residential Carbon Intensity Using the LMDI Method: Evidence from China’s County-Level Cities

Examining the Driving Factors of Urban Residential Carbon Intensity Using the LMDI Method: Evidence from China’s County-Level Cities

Climate Change and Energy Consumption Patterns in Thailand: Time Trends During 1988-2013

A study on evaluation and influencing factors of carbon emission performance in China’s new energy vehicle enterprises

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