Structural characteristics and determinants of an international green technological collaboration network

Li, Yaya; Zhang, Yuru; Lee, Chien‐Chiang; Li, Jing

doi:10.1016/j.jclepro.2021.129258

Cited by 104 publications

(37 citation statements)

References 59 publications

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“…Previous research has shown that QAP models based on the same data generally had lower coefficients of certainty than ordinary least square(OLS) models, and the values of R 2 in studies based on QAP methods ranged mostly between 12.5% to 40.3%. The R 2 data of the research were moderate, and the fit of the indicators was good, so it has good explanatory power [ 59 , 60 ].…”

Section: Resultsmentioning

confidence: 99%

Research on the evolution of spatial network structure of tourism eco-efficiency and its influencing factors in China’s provinces based on carbon emission accounting

Wang

Huang

et al. 2022

PLoS ONE

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In the context of global warming, although the coordinated development of tourism has led to regional economic growth, the high energy consumption-driven effects of such development have also led to environmental degradation. This research combines the undesired output of the Super-SBM model and social network analysis methods to determine the eco-efficiency of provincial tourism in China from 2010–2019 and analyzes its spatial correlation characteristics as well as its influencing factors. The aim of the project is to improve China’s regional tourism eco-efficiency and promote cross-regional tourism correlation. The results show that (1) the mean value of provincial tourism eco-efficiency in China is maintained at 0.405~0.612, with an overall fluctuating upward trend. The tourism eco-efficiency of eastern China is higher than that of central, western and northeastern China, but the latter three regions have not formed a stable spatial distribution pattern. (2) The spatial network of provincial tourism eco-efficiency in China is multithreaded, dense and diversified. Throughout the network, affiliations are becoming closer, and network structure robustness is gradually improving, although the “hierarchical” spatial network structure remains. In individual networks, Jiangsu, Guangdong and Shandong provinces in eastern China have higher centrality degrees, closeness centrality and betweenness centrality than other provinces, which means they are dominant in the network. Hainan Province, also located in eastern China, has not yet built a "bridge" for tourism factor circulation. In the core-periphery model, the core-periphery areas of China’s provincial tourism eco-efficiency are distributed in clusters, and the number of "core members" has increased. (3) The economic development level, information technology development level, and tourism technology level collectively drive the development and evolution of China’s provincial tourism eco-efficiency spatial network.

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Section: Resultsmentioning

confidence: 99%

Research on the evolution of spatial network structure of tourism eco-efficiency and its influencing factors in China’s provinces based on carbon emission accounting

Wang

Huang

et al. 2022

PLoS ONE

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“…Understanding the differences in influence factors of the barriers of the three typical production factors flow is greatly beneficial for regional and urban planners to make breakthroughs in the development path of the integration mechanism of urban agglomeration. Administrative division is the key to the barriers of the three typical production factors flow in the Chengdu-Chongqing urban agglomeration, and we should be aware of the difference of other influence factors between the three typical production flow and aim at the target in promoting the free and orderly flow of production factors (Li et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…QAP analysis can be divided into QAP correlation analysis and QAP regression model. QAP correlation analysis compares the lattice values corresponding to two or more square matrices to give the correlation coefficients between the matrices, and conducts non-parametric tests on the coefficients (Zhang et al, 2020;Li et al, 2021). Based on the permutation of matrix data, QAP regression model studies the regression relationship between multiple matrices and one matrix, and at the same time evaluates the significance of R-squared (Cranmer et al, 2017;Chong et al, 2018).…”

Section: Qap Regression Modelmentioning

confidence: 99%

Evaluating the barrier of typical production factor flow in the Chengdu-Chongqing Urban Agglomeration based on multi-source big data

Zhang

Wang

et al. 2022

Front. Environ. Sci.

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Promoting the free flow of production factors and improving the efficiency of resource allocation is a necessary requirement for China to achieve high-quality development. Therefore, it is significant to evaluate the barrier of production factors flow and analyze its influencing factors. This study, based on the flow space theory, takes the Chengdu-Chongqing urban agglomeration as an example, constructs the factor flow barrier index (FFBI) with multi-source big data (Baidu Migration data, investment data of listed companies and patent transfer data) and statistical data to evaluate the barriers of three typical product factors flow (labor flow, technology flow and capital flow). Moreover, quadratic assignment procedure regression model is used to analyze how system, economic, society, culture, policy and facility factors affect the barrier of the three typical production factors flow. The results demonstrate that: 1) The intensity of the three typical production factors flow of Chengdu-Chongqing are the highest, and the intensity of three typical production factors flow between the two cities and other cities are higher than that between other 14 cities. 2) In 120 city pairs, 87, 100 and 106 city pairs have positive FFBI of labor flow, capital flow and technology flow, respectively. The FFBI of Chongqing between other cities are mostly positive, indicating the three typical production factors flow are hindered to some extent. Labor flow is less hindered than capital flow and technology flow. 3) Administrative division relationships and administrative level differences have important impact on the barriers of the three typical production factors flow. City pairs belonging to different provincial administrative regions or within the same administrative level have larger FFBI. The barrier of labor and capital are positively affected by the transportation cost and the similarity of the industrial structure, respectively. The framework and findings are of great significance for revealing the formation mechanism of the barrier of production factors flow and provide some guidance for promoting the free flow of production factors and forming a new pattern of high-quality development in the Chengdu-Chongqing Urban Agglomeration.

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“…On the other hand, green technology transfer is a kind of market behavior; driven by the spontaneous cooperation model and innovation agglomeration, green technology tends to cluster in regions with high benefit on investment, and developed regions will have a “siphon effect” on green technology [ 7 ]. On the contrary, the underdeveloped regions will be unable to fully obtain external green technologies and fall into low-tech lock-in and high-carbon development lock-in [ 59 ]. In turn, green technology transfer may exacerbate the green technology gap and development chasm between regions.…”

Section: Theoretical Mechanismsmentioning

confidence: 99%

Green Technology Transfer, Environmental Regulation, and Regional Green Development Chasm: Based on the Empirical Evidence from Yangtze River Delta

Shang

Lyu

2022

IJERPH

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In the face of achieving the overall goal of emission peak and carbon neutrality, strengthening green technology transfer and environmental regulation is the key to narrowing the green technology gap and green development chasm between regions. This paper integrates green technology transfer, environmental regulation, and the green development chasm into one model, and analyzes the mechanism by which green technology transfer and environmental regulation impact the green development chasm. An empirical test was conducted by employing green technology transfer patent and panel data of the Yangtze River Delta from 2005–2019. The results are as follows: (1) Although the green development chasm still exists in the Yangtze River Delta, green technology transfer and environmental regulation have a positive impact on narrowing the regional green development chasm. Especially, the superposition of green technology transfer and environmental regulation can effectively make up for the lack of government and market regulation, and significantly promote the narrowing of the green development chasm. (2) Regional heterogeneity exists and developed regions can achieve the goal of narrowing the green development chasm by relying on green technology transfer or environmental regulation, while less developed regions must rely on the synergy of two dimensions. Thus, the coordination of green technology transfer and environmental regulation must be strengthened. Based on the above research, the main contributions of this paper are to analyze the theoretical mechanism of green technology transfer, environmental regulation, and regional green development chasm, to provide a theoretical and empirical basis for realizing the overall goal of regional green development, and suggestions for optimizing China’s current policies.

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Structural characteristics and determinants of an international green technological collaboration network

Cited by 104 publications

References 59 publications

Research on the evolution of spatial network structure of tourism eco-efficiency and its influencing factors in China’s provinces based on carbon emission accounting

Research on the evolution of spatial network structure of tourism eco-efficiency and its influencing factors in China’s provinces based on carbon emission accounting

Evaluating the barrier of typical production factor flow in the Chengdu-Chongqing Urban Agglomeration based on multi-source big data

Green Technology Transfer, Environmental Regulation, and Regional Green Development Chasm: Based on the Empirical Evidence from Yangtze River Delta

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