How does urbanization affect carbon dioxide emissions? A cross-country panel data analysis

Zhang, Ning; Yu, Kun; Chen, Zhongfei

doi:10.1016/j.enpol.2017.03.072

Cited by 386 publications

(111 citation statements)

References 52 publications

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“…These results are in accordance with the findings of other scholars [10,42,49]. Although some existing research showed that technological improvements could reduce regional carbon emissions [29,36,44], this research indicated that the effect of technological improvements was not significant; this might be related to the insignificant changes in regional energy efficiency. There were significant differences between the influencing factors of ICEM and RCEM.…”

Section: Discussionsupporting

confidence: 91%

“…The quantity and proportion of industrial and residential energy consumption and carbon emissions vary at different stages of industrial and urban development. The stage-related variations are affected by various factors attributed to industrialization and/or urbanization [16,28,29]. In China, although IEC and ICEM still dominate TEC and TCEM, REC and RECM are expected to increase rapidly as higher-quality urbanization is further integrated into the country's development [22,30].…”

Section: Introductionmentioning

confidence: 99%

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Temporal Evolution and Influencing Factors of Energy Consumption and Related Carbon Emissions from the Perspective of Industrialization and Urbanization in Shanghai, China

Sun

2018

Sustainability

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Improvements of manufacturability and living standard driven by industrialization and urbanization typically cause a spike in total energy consumption (TEC) and related carbon emissions (TCEM). However, there have been few comparative studies to include industrial and residential energy consumption (IEC and REC, respectively) and related carbon emissions (ICEM and RCEM, respectively). Since China is a major emerging industrial country wherein urbanization is still ongoing, the present study was conducted in an attempt to analyze the temporal evolution of China’s continued energy consumption and related carbon emissions regarding both industrialization and urbanization. The influencing factors of TCEM, RCEM and ICEM are determined via the log-mean divisia index (LMDI) model. The results showed that both TEC and TCEM gradually increased (apart from a slight decrease in 2014); REC and RCEM increased steadily with no sharp peak; while IEC and ICEM declined sharply. TCEM was positively affected by economic output, consumption level, and population size; the influence of consumption level became more and more significant. Per capita GDP and per capita expenditure were the most significant driving factors for RCEM, while industrial added value (IAV) was the main driving factor for ICEM. The temporal evolution and influencing factors of energy consumption and carbon emissions had stage-related characteristics in accordance with Shanghai’s three stages of development. The Shanghai case study provided a comprehensive understanding of energy consumption and related carbon emissions from the dual perspective of industrialization and urbanization.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Temporal Evolution and Influencing Factors of Energy Consumption and Related Carbon Emissions from the Perspective of Industrialization and Urbanization in Shanghai, China

Sun

2018

Sustainability

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“…where I represents the environmental impact, P is the population, A is the economic development, T is the technical level, α is the model coefficient, b, c, d are the index of independent variables, e is the model error term. According to the relevant research results [19][20][21], based on the concept of residual-free decomposition of Kaya identity established by Yoichi Kaya, this study decomposes the influencing factors under the framework of IPAT model, which can be expressed as follow:…”

Section: Stirpat Model Extensionmentioning

confidence: 99%

Peaking Industrial Energy-Related CO2 Emissions in Typical Transformation Region: Paths and Mechanism

et al. 2020

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Reducing CO2 emissions of industrial energy consumption plays a significant role in achieving the goal of CO2 emissions peak and decreasing total CO2 emissions in northeast China. This study proposed an extended STIRPAT model to predict CO2 emissions peak of industrial energy consumption in Jilin Province under the four scenarios (baseline scenario (BAU), energy-saving scenario (ESS), energy-saving and low-carbon scenario (ELS), and low-carbon scenario (LCS)). We analyze the influences of various factors on the peak time and values of CO2 emissions and explore the reduction path and mechanism to achieve CO2 emissions peak in industrial energy consumption. The results show that the peak time of the four scenarios is respectively 2026, 2030, 2035 and 2043, and the peak values are separately 147.87 million tons, 16.94 million tons, 190.89 million tons and 22.973 million tons. Due to conforming to the general disciplines of industrial development, the result in ELS is selected as the optimal scenario. The impact degrees of various factors on the peak value are listed as industrial CO2 emissions efficiency of energy consumption > industrialized rate > GDP > urbanization rate > industrial energy intensity > the share of renewable energy consumption. But not all factors affect the peak time. Only two factors including industrial clean-coal and low-carbon technology and industrialized rate do effect on the peak time. Clean coal technology, low carbon technology and industrial restructuring have become inevitable choices to peak ahead of time. However, developing clean coal and low-carbon technologies, adjusting the industrial structure, promoting the upgrading of the industrial structure and reducing the growth rate of industrialization can effectively reduce the peak value. Then, the pathway and mechanism to reducing industrial carbon emissions were proposed under different scenarios. The approach and the pathway and mechanism are expected to offer better decision support to targeted carbon emission peak in northeast of China.

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“…Moreover, there exist many other factors related to the carbon emission, e.g., urbanization, income level, energy consumption intensity, and industrial structure. Xu et al (2018), Zhang et al (2017), and Zhao et al (2015) found that urbanization had a significant impact on energy consumption and carbon emissions [29][30][31]. Fan et al (2006) studied how the income level and age structure influencecarbon emission, showing that the proportion of active labor force (15-64 years) in total population inhibited the increase of carbon emission in high-income countries during1975-2005 [32].…”

Section: Influencing Factors Analysisof Carbon Emissionmentioning

confidence: 99%

Impact of Population Aging on Carbon Emission in China: A Panel Data Analysis

Xin

Xiao-min

2018

Sustainability

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The impact of population structure on carbon emission has always been a key area of research in modern society. In this paper, we propose a new expanded STIRPAT model and panel co-integration method to analyze the relationship between population aging and carbon emission, based on the provincial panel data in China from 1999 to 2014. Empirical results show that there exists a significant inverted U-shaped curve between the population aging and carbon emission. There also exist regional discrepancies, where the impact of the population aging on carbon emission in the eastern region is significantly positive. By contrast, a negative relationship arises in the central and western regions. Finally, several suggestions for low carbon development are provided.

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How does urbanization affect carbon dioxide emissions? A cross-country panel data analysis

Cited by 386 publications

References 52 publications

Temporal Evolution and Influencing Factors of Energy Consumption and Related Carbon Emissions from the Perspective of Industrialization and Urbanization in Shanghai, China

Temporal Evolution and Influencing Factors of Energy Consumption and Related Carbon Emissions from the Perspective of Industrialization and Urbanization in Shanghai, China

Peaking Industrial Energy-Related CO2 Emissions in Typical Transformation Region: Paths and Mechanism

Impact of Population Aging on Carbon Emission in China: A Panel Data Analysis

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