How do institutional changes facilitate university-centric networks in Taiwan? The Triple Helix model of innovation view

Chang, Yuan‐Chieh; Chen, Po-Hsuan; Teng, Min-Jun

doi:10.1093/scipol/scaa076

Cited by 12 publications

(11 citation statements)

References 99 publications

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“…The dependent variable is water pollution, represented by industrial wastewater discharge. This study assumes that innovation input and patent innovation output have a certain impact on water pollution, which are represented by scientific and technological expenditure and the number of patent grants, respectively [ 94 , 95 , 96 , 97 , 98 , 99 ]. In addition, according to relevant research, water pollution is also affected by factors such as industrial structure, urban greening, environmental governance, population size, FDI factors, and innovation foundations.…”

Section: Methods and Datamentioning

confidence: 99%

Spatial Pattern of Technological Innovation in the Yangtze River Delta Region and Its Impact on Water Pollution

Zhang

Jiao

et al. 2022

IJERPH

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The impact of technological innovation on water pollution is an important parameter to determine and monitor while promoting and furthering a region’s economic development. Here, exploratory spatial data analysis was used to analyze: the spatial patterns of technological innovation and water pollution in the Yangtze River, the changes in technical innovation and the resulting changes in water pollution, and the impact of technological innovation on water pollution. The following major inferences were drawn from the obtained results: (1) The spatial pattern of innovation input has a single-center structure that tends to spread. The patent innovation output has evolved, from a single spatial pattern with Shanghai as the core to a diffusion structure with three cores-Hangzhou, Shanghai, and Nanjing. (2) The aggregation mode of water pollution has evolved from the original “Z” mode to a new mode of core agglomeration, and water pollution is constantly being reduced. (3) The trends of change in patent innovation output and innovation input are roughly the same, while the trends of both and that of water pollution are contrary to each other. (4) The correlations between innovation input, patented innovation output, and water pollution are relatively low. From the perspective of spatial distribution, the number of cities with medium and high levels of gray correlation with water pollution is the same.

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Section: Methods and Datamentioning

confidence: 99%

Spatial Pattern of Technological Innovation in the Yangtze River Delta Region and Its Impact on Water Pollution

Zhang

Jiao

et al. 2022

IJERPH

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“…Under different thresholds, its elasticity coefficient shows that with the increase of per capita GDP, its effect on urban GIE varies in size and direction. When per capita GDP is low, total environmental quality (E) has a more obvious inhibitory effect on urban innovation, and with the continuous increase of per capita GDP, The total environmental quality (E) has a relatively obvious promoting effect on the urban innovation, but this promoting effect shows a further weakening trend with the increase of GDP per capita (Chang et al, 2021;Adhikari et al, 2022;Lan et al, 2022;Tang et al, 2022;Zhang et al, 2022a). Compare the impact of three wastes pollution treatment (EP), green environment (EG) and total environmental quality (E) on urban GIE under different threshold values of GDP per capita.…”

Section: Analysis Of Impact Mechanism Of Environmental Factor Combina...mentioning

confidence: 99%

Can regional environmental quality improve green innovation performance? an empirical analysis from China

Ding,

2023

Front. Environ. Sci.

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Introduction: Green innovation is an important driving force for reducing pollution and achieving high-quality development. Environmental factors are important external variables that affect innovation and are crucial to innovation development. There is a close relationship between environmental quality and green innovation performance. This paper takes the single environmental factor and combination of environmental factors of environmental quality heterogeneity as the explanatory variables, the traffic scale, economic scale, industrial scale and consumption scale as the control variables, and the per capita GDP of each city as the threshold variable, and calculates the impact of the single environmental factor and combination of environmental quality factors on the green innovation performance of 286 cities in China under different per capita GDP thresholds.Methods: We used Data Envelopment Analysis (DEA) and Threshold Regression model to measure the relationship between environmental quality and green innovation.Results: 1) under different thresholds, a single environmental factor has different impacts on the comprehensive level of innovation drive in Chinese cities. Comparing the single environmental factor under different threshold values of per capita GDP, when the per capita GDP is low, attention should be paid to improving the centralized treatment rate of urban sewage treatment plants and the comprehensive utilization rate of general industrial solid waste; When the per capita GDP is high, the harmless treatment rate of domestic waste, the centralized treatment rate of sewage treatment plants, the green coverage rate of the built-up area, and the green area can all have different promotion effects on the driving capacity of cities along the line. 2) Under different thresholds, the elasticity coefficient of the total ecological environment, the total ecological environment has a relatively obvious promoting effect on the innovation-driven development of cities along the line, but this promoting effect shows a further weakening trend with the increase of per capita GDP.Conclusion: This study helps to explain environmental quality and green innovation, which is important for promoting sustainable economic development. The government should control environmental pollution and introduce laws and policies to ensure innovation.

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“…The mediating variable is PM2.5. Analyzed from the perspective of environmental carrying capacity, air pollutants can be divided into primary and secondary pollutants according to the formation process [ 76 , 77 , 78 ]. The so-called primary pollutants refer to pollutants emitted directly from the sources, such as carbon monoxide and sulfur dioxide.…”

Section: Model Construction and Data Selectionmentioning

confidence: 99%

Can Green Innovation Improve Regional Environmental Carrying Capacity? An Empirical Analysis from China

Cheng-jin

2022

IJERPH

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Green innovation has become an important driving force for China’s economic transformation and development. This paper selects the 2010–2020 provincial-level regions in China as samples, and adopts a multi-indicator comprehensive evaluation method to comprehensively, objectively and scientifically evaluate the environmental carrying capacity of air pollution in two dimensions: natural resource endowment and human activity impact, and also measures and calculates the green innovation in each province, city and autonomous region to explore the specific impact of green innovation on environmental carrying capacity and its spatial spillover effect; it also explores the heterogeneous effects of green innovation on environmental carrying capacity under different pollution environments. The conclusions show that: (1) Green innovation has a positive impact on environmental carrying capacity. (2) There is a spatial spillover effect of green innovation on environmental carrying capacity. In other words, in areas with higher PM2.5 concentration, that is, lower environmental quality, green innovation has a weaker ability to improve environmental carrying capacity; in areas with lower PM2.5 concentration, that is higher environmental quality, green innovation has a stronger ability to improve environmental carrying capacity. (3) In the process of green innovation affecting environmental carrying capacity, PM2.5 plays the part of a mediating effect, indicating that green innovation is an intermediate transmission mechanism affecting environmental carrying capacity, and the results show that the absolute value of the short-term indirect effect is greater than the absolute value of the short-term direct effect, and the long-term direct effect is greater than the long-term indirect effect.

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How do institutional changes facilitate university-centric networks in Taiwan? The Triple Helix model of innovation view

Cited by 12 publications

References 99 publications

Spatial Pattern of Technological Innovation in the Yangtze River Delta Region and Its Impact on Water Pollution

Spatial Pattern of Technological Innovation in the Yangtze River Delta Region and Its Impact on Water Pollution

Can regional environmental quality improve green innovation performance? an empirical analysis from China

Can Green Innovation Improve Regional Environmental Carrying Capacity? An Empirical Analysis from China

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