Spatiotemporal pattern evolution and influencing factors of green innovation efficiency: A China’s city level analysis

Wang, Keliang; Zhang, Fuqin; Xu, Ru-Yu; Zhang, Miao; Cheng, Yun-He; Sun, Huaping

doi:10.1016/j.ecolind.2023.109901

Cited by 54 publications

(23 citation statements)

References 77 publications

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“…Unlike OLS models, GWR models effectively analyze the spatial non-stationarity present in spatial data. The GWR model has demonstrated promising applications across various disciplines ( 37 , 38 , 40 ). However, OLS models used in similar studies have neglected spatial correlations among variables, which can lead to estimation bias ( 41 ).…”

Section: Methodsmentioning

confidence: 99%

Analysis of the situations and influencing factors of public anxiety in China: based on Baidu index data

Xie,

Huang,

Tan

et al. 2024

Front. Public Health

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BackgroundAnxiety disorders have emerged as one of the most prevalent mental health problems and health concerns. However, previous research has paid limited attention to measuring public anxiety from a broader perspective. Furthermore, while we know many factors that influence anxiety disorders, we still have an incomplete understanding of how these factors affect public anxiety. We aimed to quantify public anxiety from the perspective of Internet searches, and to analyze its spatiotemporal changing characteristics and influencing factors.MethodsThis study collected Baidu Index from 2014 to 2022 in 31 provinces in mainland China to measure the degree of public anxiety based on the Baidu Index from 2014 to 2022. The spatial autocorrelation analysis method was used to study the changing trends and spatial distribution characteristics of public anxiety. The influencing factors of public anxiety were studied using spatial statistical modeling methods.ResultsEmpirical analysis shows that the level of public anxiety in my country has continued to rise in recent years, with significant spatial clustering characteristics, especially in the eastern and central-southern regions. In addition, we constructed ordinary least squares (OLS) and geographically weighted regression (GWR) spatial statistical models to examine the relationship between social, economic, and environmental factors and public anxiety levels. We found that the GWR model that considers spatial correlation and dependence is significantly better than the OLS model in terms of fitting accuracy. Factors such as the number of college graduates, Internet traffic, and urbanization rate are significantly positively correlated with the level of public anxiety.ConclusionOur research results draw attention to public anxiety among policymakers, highlighting the necessity for a more extensive examination of anxiety issues, especially among university graduates, by the public and relevant authorities.

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Section: Methodsmentioning

confidence: 99%

Analysis of the situations and influencing factors of public anxiety in China: based on Baidu index data

Xie,

Huang,

Tan

et al. 2024

Front. Public Health

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“…This approach addresses the issues associated with variables and mixed radial characteristics, enabling a thorough analysis of trends and differences among evaluation units. The chosen model aligns with the present state of green development [ 44 , 45 ], which can be articulated as follows.…”

Section: Methodology and Datamentioning

confidence: 99%

The impact and mechanism of vertical fiscal imbalance on green development efficiency: An empirical analysis based on city-level samples in China

Liu,

Sun,

Cao

2024

Heliyon

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“…Wang et al (2022) evaluated the GIE (GIE) of 285 cities in China using the EBM-DEA model [20]. Wang et al (2023) used the GSE-EBM model to measure the GIE of 285 cities in China [21]. The results showed that the GIE of Chinese cities showed a fluctuating upward trend, with pronounced regional differences.…”

Section: Literature Reviewmentioning

confidence: 99%

The impact of foreign direct investment on green innovation efficiency: Evidence from Chinese provinces

Zhong,

Zhou,

Jing

2024

PLoS ONE

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Improving green innovation efficiency (GIE) is the key to achieve high-quality economic development in China, and the introduction of foreign direct investment (FDI) has become an important path choice to promote the GIE. Based on the data of 30 provinces in China, this paper explores the linear and nonlinear effects of FDI on GIE from both quantity and quality perspectives, and further analyzes the mediating role of environmental regulation level. The results show that: (1) From 2011 to 2020, the GIE of all provinces in China generally shows an upward trend. (2) The quantity and quality of FDI have a significant positive impact on the improvement of GIE in China’s provinces, and this impact has regional heterogeneity. (3) The quantity and quality of FDI can promote the improvement of GIE in China through the level of environmental regulation (ER). (4) With the level of knowledge accumulation and GIE as the threshold variables, the quantity and quality of FDI have a single threshold effect on the GIE of China’s provinces. The conclusions of this study provide some policy implications for local governments to make full use of FDI to perform green innovation activities.

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Spatiotemporal pattern evolution and influencing factors of green innovation efficiency: A China’s city level analysis

Cited by 54 publications

References 77 publications

Analysis of the situations and influencing factors of public anxiety in China: based on Baidu index data

Analysis of the situations and influencing factors of public anxiety in China: based on Baidu index data

The impact and mechanism of vertical fiscal imbalance on green development efficiency: An empirical analysis based on city-level samples in China

The impact of foreign direct investment on green innovation efficiency: Evidence from Chinese provinces

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