Path optimization of open collaborative innovation of energy industry in urban agglomeration based on particle swarm optimization algorithm

Cao, Ziying; Yan, Yongcai; Tang, Kai

doi:10.1016/j.egyr.2022.04.020

Cited by 17 publications

(8 citation statements)

References 10 publications

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“… 32 found that the development model of urban agglomeration can effectively improve the carrying capacity of land resources and help to improve the economic performance of cities within urban agglomerations. Therefore, the urban agglomeration policies may have an impact on a larger range of spatial agglomeration 33 , and then produce policy spillovers to micro-market entities, that is to promote the reduction of enterprise transaction costs by means of the integration effect of urban agglomerations: firstly, the urban agglomerations improve the level of integrated markets, Ren et al . 34 believed that cities within urban agglomerations mainly promoted the improvement of the degree of integration of the urban agglomerations through the interconnection of transportation infrastructure and the diversification of economic connections.…”

Section: Mechanism Analysis and Research Hypothesismentioning

confidence: 99%

A quasi-natural experimental study on enterprise innovation driven by urban agglomeration policies in China

Song

2023

Sci Rep

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It is of great significance to carefully evaluate the actual impact of macro-policy formulation on promoting micro-enterprise innovation and implementing innovation-driven strategies. This study utilizes data from Chinese listed companies between 2012 and 2019 and takes the implementation of urban agglomeration policies as a natural experiment. By employing the multi-period differential method, the driving mechanism of urban agglomeration policies on enterprise innovation is investigated. The results show that: (1) Urban agglomeration policies effectively promote the enhancement of regional enterprises’ innovation capability. (2) Urban agglomeration policies reduce enterprise transaction costs through integration effects, mitigate the influence of geographical distance through spillover effects, and stimulate enterprise innovation. (3) Urban agglomeration policies have a regulatory effect on the siphon and spillover mechanism formed by the central city, thereby driving the innovation and development of peripheral micro-enterprises. (4) Further research from the perspectives of enterprises, industries, and locations reveals that the macro, medium, and micro effects of urban agglomeration policies differ, leading to heterogeneity in enterprise innovation responses. Therefore, it is necessary to continue promoting policy planning for urban agglomerations, enhance the coordination of urban policies within urban agglomerations, adjust the influence of the self-mechanism of urban agglomerations, and foster the formation of a multi-center innovation structure and network within urban agglomerations.

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Section: Mechanism Analysis and Research Hypothesismentioning

confidence: 99%

A quasi-natural experimental study on enterprise innovation driven by urban agglomeration policies in China

Song

2023

Sci Rep

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“…Te PSO algorithm randomly generates particles of a certain size as an efective solution to the search space of the problem and then performs an iterative search to obtain the optimization result [29]. Te solution process of particles is afected by the historical optimal solution of the particle and the optimal solution of the group so that the particle can search and develop in the search space, and fnally fnd the global optimal solution, which is a kind of global search.…”

Section: Optimization Of Csa By Psomentioning

confidence: 99%

Research on Risk Identification of Coal and Gas Outburst Based on PSO‐CSA

Peng

Shi

et al. 2023

Mathematical Problems in Engineering

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Aiming at the identification of coal and gas outburst risk, using the advantages of the clone selection algorithm (CSA), such as self-adaptation and robustness, and the characteristics of fast convergence of particle swarm optimization (PSO) algorithm, the complex decoding problem, and mutation process brought by CSA binary coding are used. It is difficult to control the problem. Using PSO optimization, the problem of abnormal detection and identification in coal and gas outburst monitoring is developed and studied, and a CSA coal and gas outburst risk anomaly detection and identification model based on PSO optimization variation is established. The model uses the coal and gas outburst index data as a collection of antigen-stimulated antibodies to achieve abnormal detection and identification of measured data. With the help of the measured data, the verification results show that the model can effectively detect and identify the risk of coal and gas outburst, and the identification results are consistent with the risk of coal and gas outburst in the field. It can be used as an effective risk identification model to guide coal mining work.

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“…He considered the city and surrounding countryside as a whole from the perspective of spatial organization and content dynamics and believed that the manifestation of a city included not only the area occupied by the city but also the garden areas surrounding it (Vasenev et al, 2021). Geddes studied urban agglomerations from the perspective of the aggregation of cities and believed that the rapid suburbanization of cities would produce aggregation patterns in space, emphasizing spatial overlap (Cao et al, 2022). Since then, Cottmann has put forward the concept of a megalopolis, studied the phenomenon of urban spatial agglomeration with New York City as the center, and began to pay attention to the spatial performance of urban agglomerations (Ramos-H et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Green urbanization efficiency of 18 urban agglomerations in China: Evidence from spatial–temporal evolution

Wang²

2023

Front. Earth Sci.

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As a highly developed spatial form of integrated cities, urban agglomeration has become an important fulcrum for promoting economic development and regional growth. Green urban efficiency is the key to achieving green growth in a country. This study propose a slack-based model with undesirable output to evaluate the green urbanization of 18 urban agglomerations in China. Analysis was performed using the integrated barycenter coordinate method, standard deviation ellipses, and the geographic detector model to determine the spatial–temporal characteristics of green urbanization efficiency and the factors that influence urban agglomerations. We found that the green urbanization efficiency of urban agglomerations in China, when plotted, revealed a curve with the shape of “∧,” which increased at first and then decreased. The spatial differentiation characteristics were not obvious as the gap was narrowing. The center of green urbanization efficiency in China’s urban agglomerations has always been located in the Central Plains, with a small overall span and a relatively fixed position. The barycenter coordinates showed a trend of shifting from east to north, but the transfer speed and rhythm were relatively slow. The explanatory power of the various factors influencing the spatial differentiation of green urbanization efficiency of urban agglomerations differed markedly. The magnitude of importance was in the order of: urban population scale > investment growth > technology level > economic development > industrial structure.

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Path optimization of open collaborative innovation of energy industry in urban agglomeration based on particle swarm optimization algorithm

Cited by 17 publications

References 10 publications

A quasi-natural experimental study on enterprise innovation driven by urban agglomeration policies in China

A quasi-natural experimental study on enterprise innovation driven by urban agglomeration policies in China

Research on Risk Identification of Coal and Gas Outburst Based on PSO‐CSA

Green urbanization efficiency of 18 urban agglomerations in China: Evidence from spatial–temporal evolution

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