Effect of national-level spatial distribution of cities on national transport CO2 emissions

Lim, Jaebin; Kang, Myounggu; Jung, Chang-Mu

doi:10.1016/j.eiar.2019.04.006

Cited by 37 publications

(10 citation statements)

References 46 publications

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“…Referring to Lim et al (2019) [ 45 ] and Lv et al (2019) [ 46 ], it is necessary to construct a model that allows the flexibility to select other factors to be added according to the research object under the consideration of the original macro conditions, while micro-influencing factors need to be identified when adding them to the model to avoid repeated explanations. According to the basic theory set by the theoretical model, this article discusses the basic conclusions of the theoretical model from the use of the STIRPAT model, including wealth and technological innovation.…”

Section: Methodsmentioning

confidence: 99%

Greening China’s Growth: Assessing the Synergistic Impact of Financial Development and Technological Innovation on Environmental Pollution Reduction—A Spatial STIRPAT Analysis

Peng

Gao

et al. 2023

IJERPH

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To achieve sustainable economic development in China, it is crucial to balance economic growth and environmental protection. Financial capital and technology can contribute positively to environmental pollution control. This study employs the Cournot model to examine the impact of financial development and technological innovation on environmental pollution at the micro level. It utilizes the spatial STIRPAT model to analyze inter-provincial panel data from China between 2005 and 2020. The results show that China’s ecological environment pollution exhibits significant spatial dependence, and heavily polluted areas tend to agglomerate. While improving financial development can increase regional environmental pressure, positive spatial spillover improves environmental quality in neighboring areas. Conversely, technological innovation reduces local ecological pressure, with negative spatial spillover effectively curbing environmental pollution in surrounding regions. The results support the environmental Kuznets curve (EKC) hypothesis, which posits an inverted U-shaped relationship between economic growth and environmental pressure, while population growth increases environmental pressure. The findings are robust and have important policy implications.

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Section: Methodsmentioning

confidence: 99%

Greening China’s Growth: Assessing the Synergistic Impact of Financial Development and Technological Innovation on Environmental Pollution Reduction—A Spatial STIRPAT Analysis

Peng

Gao

et al. 2023

IJERPH

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“…Lim et al [25] Moran index and STIRPAT model The national transportation CO 2 emissions in the spatially dispersed urbanized countries have a high probability of being higher than those in the spatially polarized urbanized countries.…”

Section: Zhang Andmentioning

confidence: 99%

Spatial Non-Equilibrium and Its Solidification Effect of China’s Per Capita Transportation Carbon Emissions

Bai¹,

Kang²,

Wang³

2021

Pol. J. Environ. Stud.

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It is of great significance to understand the spatial non-equilibrium and the solidification degree of transportation carbon emissions, so that differentiated measures can be taken to reduce transportation carbon emissions. Based on China's provincial data from 2005 to 2015, this article uses the Dagum Gini coefficient to decompose the spatial non-equilibrium of per capita transportation carbon emissions from static viewpoint. The results indicate that the overall spatial non-equilibrium shows a significant downward trend; the intensity of transvariation has had an increased contribution rate year by year since 2008, and replaces the interregional differences as the main source of the overall spatial nonequilibrium. Next, Kernel density estimation is employed to analyze the evolution laws of spatial non-equilibrium from dynamic viewpoint. The results show that the per capita transportation carbon emissions in the eastern region are successively higher than those at the national level, and in other regions. Meanwhile, some provinces are concentrated at a low level, while others at a high level. Finally, Markov chains approach is applied to identify the solidification effect of the spatial non-equilibrium. The results indicate that the effect is obvious and the state transitions occur only between adjacent types. Moreover, the solidification effects of the provinces with higher per capita transportation carbon emissions are stronger than those provinces with a lower level.

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“…While research within Topic 6 is also for policy analysis, it is concerned primarily with high-frequency spatial or temporal data (often in the urban context). Illustratively, Lim et al (2019) combined spatial data on population distribution with national statistics to examine how patterns of urbanization influence greenhouse gas emissions. Similarly, Ahn and Sohn (2019) used information on energy consumption with geographic information system data in Seattle to investigate the effect of urban form at the neighborhood level on building energy consumption.…”

Section: Topics Analyzed Using Big Data In the Policy Sciencesmentioning

confidence: 99%

Embracing the future of the policy sciences: big data in pedagogy and practice

Goyal

El-Taliawi²,

Howlett³

2021

The Future of the Policy Sciences

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Effect of national-level spatial distribution of cities on national transport CO2 emissions

Cited by 37 publications

References 46 publications

Greening China’s Growth: Assessing the Synergistic Impact of Financial Development and Technological Innovation on Environmental Pollution Reduction—A Spatial STIRPAT Analysis

Greening China’s Growth: Assessing the Synergistic Impact of Financial Development and Technological Innovation on Environmental Pollution Reduction—A Spatial STIRPAT Analysis

Spatial Non-Equilibrium and Its Solidification Effect of China’s Per Capita Transportation Carbon Emissions

Embracing the future of the policy sciences: big data in pedagogy and practice

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