Green innovation efficiency and multiple paths of urban sustainable development in China: multi-configuration analysis based on urban innovation ecosystem

Guo, Jinguang; Fu, Yu; Sun, Xuefu

doi:10.1038/s41598-023-40084-x

Cited by 10 publications

(3 citation statements)

References 89 publications

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“…And the results of this paper, which analyzed the regional differences of GIE on the city level, also confirms this. Meanwhile, we found that GIE in the Eastern region, which is better supported by open economies and innovation [42], was continuously improving at the fastest speed among the three regions. This is inconsistent with Zhao's research [11], which pointed out that GIE in the Eastern region decreased slightly based on provincial analysis.…”

Section: Discussionmentioning

confidence: 91%

Regional Differences and Influencing Factors of Green Innovation Efficiency in China’s 285 Cities

Shang,

Niu,

Song

2023

Sustainability

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Green innovation is an important concept of high-quality development to achieve resource conservation and ecological protection. To explore whether there is an imbalance in the development of green innovation in China and find the reasons for this phenomenon, it is of vital importance to investigate the regional differences of green innovation efficiency (GIE) and its influencing factors. Many scholars have studied the performance of green innovation from the efficiency perspective but concentrated on provincial-level analysis and lacked heterogeneity analysis of the influencing factors. To fill this gap, this study explores the regional differences of GIE of 285 prefecture-level and above cities in China during the period 2003–2019, and then employs the spatial error model (SEM) to explore the heterogeneity of influencing factors. The results show that: (1) The GIE in China and its three regions underwent an overall increasing process, revealing regional heterogeneity, with most efficient cities agglomerated in the Eastern region. (2) The spatial difference of GIE in China was narrowing, and the within-region Gini coefficient in the three regions presented a similar trend. Between-region difference contributed the largest to the regional differences, especially between the Central and Western regions. The kernel density estimation results showed that GIE presents significant spatial characteristic of polarization. (3) The SEM model analysis indicated that economic development, government motivation, industrial structure, financial support, and population scale affected GIE profoundly in China, and there was significant spatial heterogeneity in the impact of each influencing factor. Western cities were mainly driven by governmental support in green innovation, while Eastern and Central cities were driven by economic development and improved industrial structure.

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Section: Discussionmentioning

confidence: 91%

Regional Differences and Influencing Factors of Green Innovation Efficiency in China’s 285 Cities

Shang,

Niu,

Song

2023

Sustainability

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“…By listing several control variables, other factors that may affect the innovation capability of a city can be controlled to ensure the accuracy of the analysis. The following is an explanation of each control variable and an explanation of the reasonableness of the selection: (i) Level of economic development (rpgdp) [25,26]: The per capita gross national product (GNP) is an important index for measuring the economic prosperity of a region or city. It reflects the average economic value created by each resident and is a representative indicator of the overall economic level of a city or region.…”

Section: Control Variablesmentioning

confidence: 99%

Evaluation of innovation primacy in cross-regional central cities: Evidence from the Huaihai Economic Zone in China (2010–2020)

Liu

2024

PLoS ONE

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With the intensification of global economic competition, innovation has become one of the core elements of vigorous development in various regions. Improving the innovation ability of cross-regional central cities is the main factor influencing whether a region can achieve economic and social development. In this paper, an innovation primacy index system is designed according to the links of the innovation value chain, and the innovation advantages and empirical effects are comprehensively analyzed by using the point-to-point method, entropy weight method, gravity model and two-way fixed effect model. Based on the data of 8 cities in the core area of the Huaihai Economic Zone from 2010 to 2020 in China, Xuzhou’s innovation primacy ranks first in the core area of the Huaihai Economic Zone, which accords with its status as the central city of the region. Its index has been rising, and its development trend is also good. However, the overall innovation ability of the core area of the Huaihai Economic Zone is unbalanced; the overall economic foundation is not solid enough. In terms of knowledge innovation, R&D innovation and industrial innovation, the industrialization level, around the activation of Xuzhou city vitality, enhances the Xuzhou innovation radiation drive, leading to a decrease in the Huaihai Economic Zone. Finally, some corresponding suggestions on innovation primacy have been proposed for the city of the Huaihai Economic Zone.

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“…The flow of innovation factors will be accelerated further, leading to a rise in innovation outputs and a further expansion of innovation inputs as there are more people and resource inputs. In accordance with Guo et al's methodology, this article chooses human, material, and capital inputs as the secondary indicators of innovation inputs[73][74][75]. 2.…”

mentioning

confidence: 99%

The Impact of Green Innovation Capacity on Urban Economic Resilience: Evidence from China’s Yangtze River Delta Region

Lv,

Zeng,

Liu

2023

Sustainability

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Urban green innovation plays a crucial role in achieving sustainable urban economic development, and urban economic resilience is an important manifestation of urban economic development. This prese nt study aims to investigate how green innovation contributes to urban economies’ resilience, which is essential for long-term urban agglomeration expansion and sustainable economic development. To decrease subjectivity and achieve comprehensive evaluation, this study first constructs an index system for evaluating green innovation capability on urban economic resilience, which contains 21 indicators in three areas, including innovation input, innovation output, and green innovation foundation, and the performs a scientific evaluation using the TOPSIS method. On this basis, using the Super-SBM model, the green innovation efficiency value of cities in the Yangtze River Delta (YRD) is calculated. Finally, ArcGIS 10.8 software is used to classify the economic resilience of the 26 cities in the YRD city cluster and analyze the spatial layout characteristics of urban economic resilience. The results show that: (1) the decision evaluation model used in this study is stable and effective, and it can effectively address the issues of subjective assessment processes and information redundancy; (2) green innovation capacity has a positive contribution to urban economic resilience, and its contribution is more significant for cities with strong economic strength; and (3) the green innovation capacity of the YRD city cluster is unevenly distributed, with Shanghai, Suzhou, Hangzhou, and Nanjing having high levels of green innovation capacity and strong urban economic resilience, thus forming the core area of cities radiating outward, showing a “core-edge” spatially. Finally, suggestions for improving the overall economic resilience of urban agglomerations are provided.

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Green innovation efficiency and multiple paths of urban sustainable development in China: multi-configuration analysis based on urban innovation ecosystem

Cited by 10 publications

References 89 publications

Regional Differences and Influencing Factors of Green Innovation Efficiency in China’s 285 Cities

Regional Differences and Influencing Factors of Green Innovation Efficiency in China’s 285 Cities

Evaluation of innovation primacy in cross-regional central cities: Evidence from the Huaihai Economic Zone in China (2010–2020)

The Impact of Green Innovation Capacity on Urban Economic Resilience: Evidence from China’s Yangtze River Delta Region

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