Examining the impacts of socioeconomic factors, urban form, and transportation networks on CO2 emissions in China’s megacities

Wang, Shuanjin; Li, Xiaoping; Zhou, Chunshan; Hu, Jincan; Ou, Jinpei

doi:10.1016/j.apenergy.2016.10.052

Cited by 349 publications

(126 citation statements)

References 49 publications

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“…All of those researchers agreed that increasing the construction land use will increase the city's CO 2 emissions. The land-use type and urban morphology in the city are the basic elements of urban planning [47], and urban planning plays an important role in reducing urban CO 2 emissions [48,49]. Based on the previous studies, we select five types and 10 factors that may affect the spatial distribution of CO 2 emissions in Shanghai.…”

Section: Mechanism Of the Geographic Detectormentioning

confidence: 99%

Analyzing Temporal and Spatial Characteristics and Determinant Factors of Energy-Related CO2 Emissions of Shanghai in China Using High-Resolution Gridded Data

et al. 2019

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In this study, we create a high-resolution (1 km x 1 km) carbon emission spatially gridded dataset in Shanghai for 2010 to 2015 to help researchers understand the spatial pattern of urban CO 2 emissions and facilitate exploration of their driving forces. First, we conclude that high spatial agglomeration, CO 2 emissions centralized along the river and coastline, and a structure with three circular layers are the three notable temporal-spatial characteristics of Shanghai fossil fuel CO 2 emissions. Second, we find that large point sources are the leading factors that shaped the temporal-spatial characteristics of Shanghai CO 2 emission distributions. The changes of CO 2 emissions in each grid during 2010-2015 indicate that the energy-controlling policies of large point emission sources have had positive effects on CO 2 reduction since 2012. The changes suggest that targeted policies can have a disproportionate impact on urban emissions. Third, area sources bring more uncertainties to the forecasting of carbon emissions. We use the Geographical Detector method to identify these leading factors that influence CO 2 emissions emitted from area sources. We find that Shanghai's circular layer structure, population density, and population activity intensity are the leading factors. This result implied that urban planning has a large impact on the distribution of urban CO 2 emissions. At last, we find that unbalanced development within the city will lead to different leading impact factors for each circular layer. Factors such as urban development intensity, traffic land, and industrial land have stronger power to determine CO 2 emissions in the areas outside the Outer Ring, while factors such as population density and population activity intensity have stronger impacts in the other two inner areas. This research demonstrates the potential utility of high-resolution carbon emission data to advance the integration of urban planning for the reduction of urban CO 2 emissions and provide information for policymakers to make targeted policies across different areas within the city.

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Section: Mechanism Of the Geographic Detectormentioning

confidence: 99%

Analyzing Temporal and Spatial Characteristics and Determinant Factors of Energy-Related CO2 Emissions of Shanghai in China Using High-Resolution Gridded Data

et al. 2019

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“…Scientific evidence has shown that past emissions of GHGs are already affecting the earth's climate, and time appears to have already run out to completely prevent climate change . Technically, the most efficient way to understand the real causes of climate change is to figure out the factors that influence CO 2 emissions and the specific extent of their respective impacts …”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3] The consensus is that each country has the responsibility to reduce its emissions of greenhouse Original Research Article: Economic-related CO 2 emissions analysis of Ordos Basin based on a refined STIRPAT model S Liu et al CO 2 emissions and the specific extent of their respective impacts. 5,6 China has been among the largest and fastest growing economies for many years. During the period of 2005−2015, China's GDP increased by a factor of 3.83 from US$ 2714.49 billion to US$ 10 385.66 billion; the secondary industry increased by a factor of 3.22 from US$ 1318.89 billion to US$ 4250.91 billion, and the tertiary industry increased by a factor of 4.72 from US$ 1105.57 billion to US$ 5213.26 billion.…”

Section: Introductionmentioning

confidence: 99%

Economic‐related CO₂ emissions analysis of Ordos Basin based on a refined STIRPAT model

et al. 2019

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Identifying the impact factors of CO2 emissions is the basis of economic‐related CO2 emission analysis. In this study, we used the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model to investigate the extent of influence of each impact factor on the CO2 emissions of Ordos Basin, a typical heavy industrial region and the largest coal production area of China. A new parameter, exhaust gas treatment costs ratio (R), was introduced into the STIRPAT model to examine the relationship between abatement investment and CO2 emissions. The impact factors that were positively associated with the CO2 emissions of Ordos Basin (and the corresponding coefficient values, from the largest to the smallest) were the population count (1.0449), property of secondary industry (0.8366), energy intensity (0.7672), GDP per capita (0.7598), property of tertiary industry (0.6978), and urbanization level (0.4641). The only negatively associated parameter was the exhaust gas treatment costs ratio, which had a coefficient value of −0.1339. In addition, the technological factors (i.e., energy intensity and exhaust gas treatment costs ratio) could be used as indicators of CO2 emission control. This study systematically investigated and discussed the impact factors of CO2 emissions in coal‐producing areas, to gain insight into the emissions’ characteristics and to lay a foundation for CO2 reduction in such regions. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd.

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“…Provided that the exported production is increased to a more advanced economic level, about 90% of China's reduction in emissions would be counteracted. Thus, Wang et al [84] recognize that optimizing the country's industrial structure and developing the emerging low-carbon industry may become more powerful ways to lower CO 2 emissions for China's major cities compared to the scenario where reducing carbon emissions at the expense of economic growth.…”

Section: Economic Developmentmentioning

confidence: 99%

Peak Carbon Emissions in China: Status, Key Factors and Countermeasures—A Literature Review

Dong

Hua

2018

Sustainability

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China is continually increasing its efforts to reduce carbon emissions due to dual pressure domestically and internationally. Specifically, China has committed to achieving peak carbon emissions no later than 2030. As an international goal, carbon emission peak in China has generated considerable research interest. However, most scholars only focus on a partial aspect of peak carbon emissions. Through a review of literature from numerous sources, this paper provides a more systematic analysis than previously available of how China can reach its peak carbon emissions as early as possible. This study first elaborates the status of peak carbon emissions in China from regional and sectoral perspectives and summarizes the various predictions. Then, five main driving factors of carbon emission peak in China are investigated, i.e., economic development, urbanization, energy-related issues, foreign direct investment and technology, and transportation. Corresponding to these five factors, some specific and practical recommendations are proposed. Furthermore, regional differences and spatial "spillovers" must be taken into account, thereby achieving a "win-win" solution with respect to the integration of emission mitigation and economic development, as well as reaching the desired target of peak carbon emissions.

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Examining the impacts of socioeconomic factors, urban form, and transportation networks on CO2 emissions in China’s megacities

Cited by 349 publications

References 49 publications

Analyzing Temporal and Spatial Characteristics and Determinant Factors of Energy-Related CO2 Emissions of Shanghai in China Using High-Resolution Gridded Data

Analyzing Temporal and Spatial Characteristics and Determinant Factors of Energy-Related CO2 Emissions of Shanghai in China Using High-Resolution Gridded Data

Economic‐related CO₂ emissions analysis of Ordos Basin based on a refined STIRPAT model

Peak Carbon Emissions in China: Status, Key Factors and Countermeasures—A Literature Review

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Examining the impacts of socioeconomic factors, urban form, and transportation networks on CO2 emissions in China’s megacities

Cited by 349 publications

References 49 publications

Analyzing Temporal and Spatial Characteristics and Determinant Factors of Energy-Related CO2 Emissions of Shanghai in China Using High-Resolution Gridded Data

Analyzing Temporal and Spatial Characteristics and Determinant Factors of Energy-Related CO2 Emissions of Shanghai in China Using High-Resolution Gridded Data

Economic‐related CO2 emissions analysis of Ordos Basin based on a refined STIRPAT model

Peak Carbon Emissions in China: Status, Key Factors and Countermeasures—A Literature Review

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Product

Resources

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Economic‐related CO₂ emissions analysis of Ordos Basin based on a refined STIRPAT model