Unfolding the spatial spillover effects of urbanization on interregional energy connectivity: Evidence from province-level data

Hong, Jiman; Gu, Jianping; He, Rongxiao; Wang, Xianzhu; Shen, Geoffrey Qiping

doi:10.1016/j.energy.2020.116990

Cited by 17 publications

(7 citation statements)

References 26 publications

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“…Song et al (2020) found the local energy intensity could influence the energy intensity of the surrounding regions. If the spatial spillover effect is ignored, it may cause deviation in estimation results (Hong et al 2020). The existing studies on the spatial spillover effect have mainly concentrated on a certain region or on industries than the construction industry, and few have focused on the construction industry.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This model considers carbon emissions as undesirable outputs by a non-linear measurement based on input-output analysis to estimate the efficiency value. Referring to studies by Hong et al (2020), Wang et al 2021, andZhang et al (2017), the super-SBM DEA method was constructed in this paper. Capital, labor, energy consumption, and machines were selected as inputs; industrial economic output was regarded as the desirable output; while carbon emissions were regarded as the undesirable output.…”

Section: Super-sbm Modelmentioning

confidence: 99%

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Spatial spillover effect of carbon emission efficiency in the construction industry of China

Deng

Zhou

et al. 2021

Environ Sci Pollut Res

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The construction industry plays an important role in energy saving and carbon emissions mitigation of China. Promoting carbon emission efficiency is seen as an efficient way to abate carbon emissions. Using 2005-2016 data, the carbon emission efficiency of the construction sector in 30 provinces is estimated, and the spatial distribution characteristics of the carbon emission efficiency of the construction industry is explored. The spatial Markov transition probability matrix is employed to investigate the influence of the spatial spillover effect on the regional distribution pattern of carbon emission efficiency. The results demonstrate that the carbon emission efficiency of the construction industry exhibits an unbalanced regional distribution, which is high in the east and low in the west. The spatial autocorrelation indicates that the carbon emission efficiency has a spatial dependence and is characterized by spatial agglomeration. Markov Chain results show a significant spatial spillover effect in carbon emission efficiency. The provinces with higher carbon emission efficiency have a positive effect on their neighbors, while the provinces with lower efficiency have a negative effect on neighbors. The findings are of great importance to understand the differences in and interactions of carbon emission efficiency between regions.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Super-sbm Modelmentioning

confidence: 99%

Spatial spillover effect of carbon emission efficiency in the construction industry of China

Deng

Zhou

et al. 2021

Environ Sci Pollut Res

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“…Further research is thus needed to analyze the spillover effects of information flows using the spatial econometric interaction model. The spillover effects show significant differences on different scales, such as at the prefectural level, provincial level, or urban agglomeration level [29][30][31]. Urban agglomeration is a highly developed spatial form of integrated cities and is one of the most important carriers for global economic development [32][33][34].…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial Spillover Effect and Influencing Factors of Information Flow in Urban Agglomerations—Case Study of China Based on Baidu Search Index

Chen

et al. 2021

Sustainability

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Cities in an urban agglomeration closely interact with each other through various flows. Information flow, as one of the important forms of urban interactions, is now increasingly indispensable with the fast development of informatics technology. Thanks to its timely, convenient, and spatially unconstrained transmission ability, information flow has obvious spillover effects, which may strengthen urban interaction and further promote urban coordinated development. Therefore, it is crucial to quantify the spatial spillover effect and influencing factors of information flows, especially at the urban agglomeration scale. However, the academic research on this topic is insufficient. We, therefore, developed a spatial interaction model of information flow (SIM-IF) based on the Baidu Search Index and used it to analyze the spillover effects and influencing factors of information flow in the three major urban agglomerations in China, namely Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) in the period of 2014–2019. The results showed that the SIM-IF performed well in all three agglomerations. Quantitative analysis indicated that the BTH had the strongest spillover effect of information flow, followed by the YRD and the PRD. It was also found that the hierarchy of cities had the greatest impact on the spillover effects of information flow. This study may provide scientific basis for the information flow construction in urban agglomerations and benefit the coordinated development of cities.

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“…The findings demonstrate that whether an economy has the potential for divergence depends on the size of cities and the transportation costs of industrial development [ 5 ]. Knowledge transfer and localized information, according to Hong et al, not only helped cities form economic agglomerations but also fueled their expansion economically [ 6 ]. This causes confusion in the RED model research.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] An Intelligent Analysis Model in the Environment of Regional Economic Development and Regional Economic Differences in China Using Big Data Analysis Technology

Luo

2022

Journal of Environmental and Public Health

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The current and long-term regional economic imbalance in China requires ongoing attention. To ensure the balanced development of China’s renewable energy, it is therefore important to examine the causes of the differences in China’s renewable energy from a variety of perspectives. The spatial distribution pattern and characteristics of China’s per capita GDP (gross domestic product) from 2012 to 2021 were examined in this study using the exploratory spatial data analysis tool. In addition, it conducts an empirical investigation into the spatial spillover effect of RED and the manufacturing agglomeration in China (regional economic development). The findings indicate that in the eastern region, the total backward link value of the feedback effect of 17 industrial sectors is 0.8524, and in the central region, the value is 0.8139. The real per capita GDP of neighboring provinces will increase by 0.118% for every 1% increase in manufacturing agglomeration level. According to the overall ranking, China’s RED level is very uneven due to a number of factors. We should direct and encourage the manufacturing industry to congregate in various regions, optimize the spatial pattern of manufacturing industry agglomeration, and fully exploit SSE in order to promote China’s RED and reduce the difference in RE.

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Unfolding the spatial spillover effects of urbanization on interregional energy connectivity: Evidence from province-level data

Cited by 17 publications

References 26 publications

Spatial spillover effect of carbon emission efficiency in the construction industry of China

Spatial spillover effect of carbon emission efficiency in the construction industry of China

Spatial Spillover Effect and Influencing Factors of Information Flow in Urban Agglomerations—Case Study of China Based on Baidu Search Index

[Retracted] An Intelligent Analysis Model in the Environment of Regional Economic Development and Regional Economic Differences in China Using Big Data Analysis Technology

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