Factors Affecting Energy‐Related Carbon Emissions in Beijing‐Tianjin‐Hebei Region

Wang, Jingmin; Yufang, Shi; Zhao, Xue; Zhang, Xueting

doi:10.1155/2017/1524023

Cited by 7 publications

(6 citation statements)

References 43 publications

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“…This region is facing serious environmental issues, which makes it very important in analyzing how the socioeconomic factors affect EICE divergently. In this paper, we find that traditional factors, such as energy intensity effect, contributed greatly to the decrease of EICE change across this region, which is in accordance with previous studies [43,44]. In terms of R&D factors, there was a paper [45] using the STIRPAT (stochastic impacts by regression on population, affluence and technology) model to identify that the increase of R&D output was an efficient way to reduce CO2 emissions.…”

Section: Discussionsupporting

confidence: 86%

Divergent Leading Factors in Energy-Related CO2 Emissions Change among Subregions of the Beijing–Tianjin–Hebei Area from 2006 to 2016: An Extended LMDI Analysis

Zou¹,

Tang

2019

Sustainability

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In recent decades, the Beijing–Tianjin–Hebei (BTH) region has experienced rapid economic growth accompanied by increasing energy demands and CO2 emissions. Understanding the driving forces of CO2 emissions is necessary to develop effective policies for low-carbon economic development. However, because of differences in the socioeconomic systems within the BTH region, it is important to investigate the differences in the driving factors of CO2 emissions between Beijing, Tianjin, and Hebei. In this paper, we calculated the energy-related industrial CO2 emissions (EICE) in Beijing, Tianjin, and Hebei from 2006 to 2016. We then applied an extended LMDI (logarithmic mean Divisia index) method to determine the driving forces of EICE during different time periods and in different subregions within the BTH region. The results show that EICE increased and then decreased from 2006 to 2016 in the BTH region. In all subregions, energy intensity, industrial structure, and research and development (R&D) efficiency effect negatively affected EICE, whereas gross domestic product per capita effect and population had positive effects on EICE. However, R&D intensity and investment intensity had opposite effects in some parts of the BTH region; the effect of R&D intensity on EICE was positive in Beijing and Tianjin but negative in Hebei, while the effect of investment intensity was negative in Beijing but positive in Tianjin and Hebei. The findings of this study can contribute to the development of policies to reduce EICE in the BTH region.

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Section: Discussionsupporting

confidence: 86%

Divergent Leading Factors in Energy-Related CO2 Emissions Change among Subregions of the Beijing–Tianjin–Hebei Area from 2006 to 2016: An Extended LMDI Analysis

Zou¹,

Tang

2019

Sustainability

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“…Román-Collado et al [ 20 ] found that the key driving factors were economic activities and population effects in Latin America, followed by fossil fuels and carbonization effects, while intensity effect was an inhibitory factor. Furthermore, some researchers analyzed the drivers of CO 2 emissions from aspects of energy structure, population size, energy intensity, economic growth, and other factors in Eastern and central China, Yangtze River Delta, and Beijing–Tianjin–Hebei Region, respectively [ 21 , 22 , 23 ]. The above studies mainly focused on countries or large-scale areas and achieved good results by analyzing the factors of CO 2 emissions.…”

Section: Literature Reviewmentioning

confidence: 99%

Decomposition and Decoupling Analysis of CO2 Emissions Based on LMDI and Two-Dimensional Decoupling Model in Gansu Province, China

Xin

Jia

et al. 2021

IJERPH

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Currently, little attention has been paid to reducing carbon dioxide (CO2) emissions of Gansu, and the two-dimensional decoupling model has been rarely used to study the relationship between the economic development and CO2 emissions, especially in western China (e.g., Gansu). Thus, here, we first used the Logarithmic Mean Divisia Index (LMDI) to decompose the driving factors of Gansu’s CO2 emissions between 2000–2017 and then analyzed the decoupling relationship by using the two-dimensional model. Results showed: (1) Gansu’s CO2 emissions increased from 7805.70 × 104 t in 2000 to 19,896.05 × 104 t in 2017. The secondary industry accounted for the largest proportion in Gansu’s CO2 emissions, followed by the tertiary industry and the primary industry. (2) The economic output showed the dominant driving effect on Gansu’s CO2 emissions growth with the cumulative contribution rate of 201.94%, followed by the effects of industrial structure, population size, and energy structure, and their cumulative contribution rates were 9.68%, 7.81%, and 3.05%, respectively. In contrast, the energy intensity effect presented the most obvious mitigating effect with the cumulative contribution rate of −122.49%. (3) The Environmental Kuznets Curve (EKC) between CO2 emissions and economic growth was demonstrated the inverted U-shape in Gansu. The two-dimensional decoupling status was the low level-weak decoupling (WD-LE) during 2000–2017. Thus, dropping the proportion of the secondary industry, reducing the use of carbon-intensive fuel like coal, introducing advanced technologies, and increasing the investment of new energy might effectively restrain the growth of Gansu’s CO2 emissions.

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“…The results showed that the relative decoupling occurred at a high level in China. Wang et al 16 compared the decoupling states between carbon emissions and GDP in Beijing-Tianjin-Hebei between 200 and 2013. According to Vehmas et al 17 and Tapio, 13 China and the United States are the top two CO 2 emitters in the world, thus the simultaneous relationship between their economic development level and CO 2 emissions has attracted the interest of scholars.…”

Section: Literature Reviewmentioning

confidence: 99%

Decomposition analysis of the decoupling process between economic growth and carbon emission in Beijing city, China: A sectoral perspective

Wang

et al. 2019

Energy & Environment

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Cities play a major role in decoupling economic growth from carbon emission for their significant role in climate change mitigation from national level. This paper selects Beijing (economic center and leader of emission reduction in China) as a case to examine the decoupling process during the period 2000–2015 through a sectoral decomposition analysis. This paper proposes the decoupling of carbon emission from economic growth or sectoral output by defining the Tapio decoupling elasticity, and combined the decoupling elasticity with decomposition technique such as Logarithmic Mean Divisia Index approach. The results indicate that agriculture and industrial sectors presented strong decoupling state, and weak decoupling is detected in construction and other industrial sectors. Meanwhile, transport sector is in expansive negative decoupling while trade industry shows expansive coupling during the study period. Per-capita gross domestic product, industrial structure, and energy intensity are the most significant effects influencing the decoupling process. Agriculture and industry are conducive to decoupling of carbon emissions from economic output, while transport and trade are detrimental to the realization of strong decoupling target between 2000 and 2015. However, construction and other industrial sectors exerted relatively little minor impact on the whole decoupling process. Improving and promoting energy-saving technologies in transport sector and trade sector should be the key strategy adjustments for Beijing to reduce carbon emissions in the future. The study aims to provide effective policy adjustments for policy makers to accelerate the decoupling process in Beijing, which, furthermore, can lay a theoretical foundation for other cities to develop carbon emission mitigation polices more efficiently.

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Factors Affecting Energy‐Related Carbon Emissions in Beijing‐Tianjin‐Hebei Region

Cited by 7 publications

References 43 publications

Divergent Leading Factors in Energy-Related CO2 Emissions Change among Subregions of the Beijing–Tianjin–Hebei Area from 2006 to 2016: An Extended LMDI Analysis

Divergent Leading Factors in Energy-Related CO2 Emissions Change among Subregions of the Beijing–Tianjin–Hebei Area from 2006 to 2016: An Extended LMDI Analysis

Decomposition and Decoupling Analysis of CO2 Emissions Based on LMDI and Two-Dimensional Decoupling Model in Gansu Province, China

Decomposition analysis of the decoupling process between economic growth and carbon emission in Beijing city, China: A sectoral perspective

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