Decennary spatial pattern changes and scaling effects of CO2 emissions of urban agglomerations in China

Cui, Can; Cai, Bofeng; Bin, Guoshu; Wang, Zhen

doi:10.1016/j.cities.2020.102818

Cited by 27 publications

(10 citation statements)

References 20 publications

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“…To compare spatial disparities of the urban scaling exponent, we identified four major regions (Figure 1), namely Western, Central, Eastern and Northeast China (National Bureau of Statistics of China, 2011). Urban agglomeration is created as an emerging state-space to manage inter-city cooperation and competition (Cui et al, 2020;Fang, 2015;Wu, 2015) and will be the ultimate urban spatial form of China's new urbanisation (Fang and Yu, 2017). Following The 13th Five-Year Plan for Economic and Social Development, we also selected 19 urban agglomerations as research samples, including five national-level urban agglomerations, eight regional medium-sized urban agglomerations and six regional economic cooperation organisations, namely a '5 + 8 + 6' spatial structure for China's urban agglomerations, to provide a larger sample to test urban scaling effects.…”

Section: Study Area and Data Sourcesmentioning

confidence: 99%

Urban scaling in rapidly urbanising China

Lei

Jiao

et al. 2021

Urban Studies

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Understanding the scaling characteristics in China is critical for perceiving the development process of rapidly urbanising countries. This paper conducts a comprehensive scaling analysis with quantitative assessment of a large number of diverse urban indicators of 275 Chinese cities. Our findings confirm that urban scaling laws can also be applied to rapidly urbanising China but demonstrate some unique features echoing its distinct urbanisation. Chinese urban population agglomeration results in more effective economic production but the economies of scale for infrastructure are less obvious. Some urban indicators associated with infrastructure and living facilities surprisingly scale super-linearly with urban population size, contrary to expected sublinear scaling behaviours. In developing countries, different-sized cities have diverse agglomeration, industrial and resource allocation advantages, which can be reflected by scaling exponents. We characterise these unique features in detail, exploring the spatial disparities and temporal evolution of scaling exponents ( β). Strong regional variations and differences are particularly pronounced in Northeast China and the Beijing-Tianjin-Hebei Urban Agglomeration. Scaling exponent variations over time reflect the temporal evolution of the urban system and measure the coordination and balance of urbanisation. Economic output was most efficient in 2009 and β of GDP was slightly greater than 1.15 in recent years. Urban land expansion has been accelerating since 2000 with β remaining around 0.85–0.90. The study of urban scaling in China is enlightening in elaborating the uniqueness and coordination of urban development in rapidly urbanising countries and provides support in formulating differentiated urban planning for different-sized cities to promote coordinated development.

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Section: Study Area and Data Sourcesmentioning

confidence: 99%

Urban scaling in rapidly urbanising China

Lei

Jiao

et al. 2021

Urban Studies

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“…Theoretical studies are the basis of practical studies, and some scholars focus on the strong industry characteristics of carbon emissions, so they have gradually launched studies on carbon emissions from transportation (Xie et al, 2017;Zhu et al, 2020;Dujmovic et al, 2022), agriculture (Ali et al, 2022;Huang and Gao, 2022), aviation (Han et al, 2022), construction (Du et al, 2021;Du et al, 2022), logistics (Zhang Y. et al, 2021), and tourism. Further, related scholars have incorporated the topic of carbon reduction into the analysis of specific regional issues in order to improve the practical application value, and the carbon emission problems of some special regions have been widely studied, such as river basins (Sun et al, 2021;Wang et al, 2021), large cities (Qian et al, 2022), industrial cities (Zhang C. et al, 2021), urban agglomerations (Cui et al, 2020), metropolitan areas (Benjamin and Marilyn, 2009;Thomas, 2013), rural areas (Zhang et al, 2014), ports (Gian et al, 2020), etc.…”

Section: Introductionmentioning

confidence: 99%

County carbon emissions in the Yangtze River Delta region: Spatial layout, dynamic evolution and spatial spillover effects

Wei

Wang

et al. 2022

Front. Environ. Sci.

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The Yangtze River Delta region contributes nearly 16% of the national carbon emissions and is the key area for carbon emission reduction in China. Accurately grasping the spatial evolution characteristics of carbon emissions and the interaction between counties and regions is of great practical significance for precise and collaborative carbon reduction. This study firstly explores the spatial layout and dynamic evolution characteristics of county carbon emissions in the Yangtze River Delta region from 2000 to 2018 by using spatial statistical analysis, secondly identifies the influencing factors of county carbon emissions (CAR) in the Yangtze River Delta region from dynamic and static dimensions respectively by using static and dynamic Spatial Dubin Model, and finally judges the spatial spillover effects of each factor. We find that county carbon emissions are more complex and more diverse in non-synchronous state compared to provinces and cities. The high carbon areas in the Yangtze River Delta region are concentrated in Shanghai and its neighboring cities, as well as industrial counties under the jurisdiction of other sub-core cities, which are continuously clustered towards the center. We have made some theoretical discussions on the results of the spillover effects of various factors on carbon emissions, and concluded that economic of scale (ECO) and industrial structure (IND) have a “polarization effect”, population size (POP) is consistent with the Malthusian view, technological advance (TEC) has a “cumulative effect”, and environmental quality (ENV) The “pollution paradise effect” is mitigated. Finally, we believe that the main unit of precise carbon reduction can take the form of “city-county” combination, and the government should implement differentiated and coordinated carbon reduction policies.

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“…In east and central China, the center and surroundings featured high levels (high-high cluster) of total CO 2 emissions and low levels (lowlow-cluster) of per-unit-GDP CO 2 emission in urban agglomerations. The Yangtze-River-Delta, the Beibu-Gulf, and the Guangdong-Hong Kong-Macao UAs were more efficient at emission reduction with the cities' rising scales, while cities of the Beijing-Tianjin-Hebei UA and the Chengdu-Chongqing UA performed less efficiently (Cui et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 94%

Driving Factors of CO2 Emissions: Further Study Based on Machine Learning

Siu

Zhao

2021

Front. Environ. Sci.

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Greenhouse gases, especially carbon dioxide (CO2) emissions, are viewed as one of the core causes of climate change, and it has become one of the most important environmental problems in the world. This paper attempts to investigate the relation between CO2 emissions and economic growth, industry structure, urbanization, research and development (R&D) investment, actual use of foreign capital, and growth rate of energy consumption in China between 2000 and 2018. This study is important for China as it has pledged to peak its carbon dioxide emissions (CO2) by 2030 and achieve carbon neutrality by 2060. We apply a suite of machine learning algorithms on the training set of data, 2000–2015, and predict the levels of CO2 emissions for the testing set, 2016–2018. Employing rmse for model selection, results show that the nonlinear model of k-nearest neighbors (KNN) model performs the best among linear models, nonlinear models, ensemble models, and artificial neural networks for the present dataset. Using KNN model, sensitivity analysis of CO2 emissions around its centroid position was conducted. The findings indicate that not all provinces should develop its industrialization. Some provinces should stay at relatively mild industrialization stage while selected others should develop theirs as quickly as possible. It is because CO2 emissions will eventually decrease after saturation point. In terms of urbanization, there is an optimal range for a province. At the optimal range, the CO2 emissions would be at a minimum, and it is likely a result of technological innovation in energy usage and efficiency. Moreover, China should increase its R&D investment intensity from the present level as it will decrease CO2 emissions. If R&D reinvestment is associated with actual use of foreign capital, policy makers should prioritize the use of foreign capital for R&D investment on green technology. Last, economic growth requires consuming energy. However, policy makers must refrain from consuming energy beyond a certain optimal growth rate. The above findings provide a guide to policy makers to achieve dual-carbon strategy while sustaining economic development.

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Decennary spatial pattern changes and scaling effects of CO2 emissions of urban agglomerations in China

Cited by 27 publications

References 20 publications

Urban scaling in rapidly urbanising China

Urban scaling in rapidly urbanising China

County carbon emissions in the Yangtze River Delta region: Spatial layout, dynamic evolution and spatial spillover effects

Driving Factors of CO2 Emissions: Further Study Based on Machine Learning

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