Research on the Regional Differences and Influencing Factors of the Innovation Efficiency of China’s High-Tech Industries: Based on a Shared Inputs Two-Stage Network DEA

Chen, Huangxin; Lin, Hang; Zou, Wenjie

doi:10.3390/su12083284

Cited by 67 publications

(36 citation statements)

References 36 publications

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“…ese factors will affect the development level of the subsystem invested in the regional green innovation subsystem and then affect the coordinated development level of the regional green innovation system. However, these factors cannot be used as the direct investment of each subsystem, and these factors are often not directly measured [47]. erefore, in order to systematically and comprehensively study the driving factors of regional green innovation system, the factors that affect the efficiency of green innovation in the YREB are summarized as direct and indirect factors, including environmental regulation and industrial structure, and the indirect factors include the level of economic development, the strength of government support, and the level of opening to the outside world [48].…”

Section: Influential Factors On Green Innovation Efficiencymentioning

confidence: 99%

Research on the Measurement, Evolution, and Driving Factors of Green Innovation Efficiency in Yangtze River Economic Belt: A Super-SBM and Spatial Durbin Model

et al. 2020

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With the shortage of resources and the increasingly serious environmental pollution in China, green innovation has become a sustainable competition for a region. The Yangtze River Economic Belt (YREB) strategy is one of the most important strategies for the sustainable development of China’s economy under the new normal. Green innovation plays a linking role in the resources exchange and trade flow in YREB, and it is also the foundation and guarantee to implement the YREB strategy. The global environmental pollution and the weak recovery of world economy make the traditional extensive economic growth model unsustainable. Sustainable economic growth should focus on the quality of development and its external costs to the environment. In order to implement the concept of sustainable development, the improvement of logistics ecological efficiency is related to the quality of ecological civilization construction. Therefore, it is of theoretical and practical significance to study the measurement, evolution, and driving factors of coordinated development level of regional green innovation system. This paper proposes a super-slack-based measure (super-SBM) data envelopment analysis (DEA) model to measure the green innovation efficiency of 11 provinces and cities in YREB from 2008 to 2017, mastering its spatial and evolutionary characteristics, and conduct empirical analysis on the influencing factors. The empirical results indicate that economic development, government support, and industrial structure upgrading are the leading forces to directly enhance the green technology innovation ability of cities in the Yangtze River Economic belt and play the core driving role of green innovation. To further enhance the capacity of urban green innovation in the Yangtze River Economic belt, we will increase the government’s support for green innovation, optimize the environmental governance model, promote the green upgrading of industrial structure, and enhance the enthusiasm of enterprises for green innovation.

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Section: Influential Factors On Green Innovation Efficiencymentioning

confidence: 99%

Research on the Measurement, Evolution, and Driving Factors of Green Innovation Efficiency in Yangtze River Economic Belt: A Super-SBM and Spatial Durbin Model

et al. 2020

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“…The standard formulation of the DEA analysis task ignores the fact that the condition for achieving the regional innovation production system's efficiency is often its coordination with other systems-at least, the knowledge production system (science) and the human capital production system (education). Therefore, this point should also be considered when selecting the variables to be analysed [73,80,81]; − Model focus selection: In this case, the model can be input-or output-focused. The choice will impact how the results are interpreted further, either in terms of maximising outputs with the given set of inputs or somehow differently [62].…”

Section: Methodsmentioning

confidence: 99%

Regional Open Innovation Systems in a Transition Economy: A Two-Stage DEA Model to Estimate Effectiveness

Rudskaya

Kryzhko

Skhvediani

et al. 2022

Journal of Open Innovation: Technology, Market, and Complexity

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The development of innovation at a regional level in a transition economy is characterised by complex multidirectional processes of generating and commercialising innovation, indicating the need for systematic research and rethinking of the existing methods of managing such territorial entities to stimulate innovation. For the successful introduction and implementation of innovative solutions, the deployment of appropriate amounts of intellectual, material and financial resources as well as their concentration in space and time is important. This article aims to develop a model for assessing the effectiveness of regional innovation systems (RISs) during the shift from the transition economy to the market economy. The authors developed a two-stage data envelopment analysis (DEA) model connected with patent activities and the output of innovative goods and services. The model’s application made it possible to build maps describing the rating of regions concerning the performance indicator and to identify the availability of unutilised resources. For example, we identified efficient and inefficient regions in terms of producing innovative products, which is especially important for developing additional measures for developing the institutional environment of regions with considerable resources but very low utilisation efficiency. The data obtained will allow for more effective management of the structural elements of RISs as well as the detection of changes in the dynamics of key development indicators by identifying the size of efficiency reserves and the causes of their occurrence at the individual subject level.

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“…Among them, the DEA method can realize the measurement of innovation efficiency with multiple input and multiple output variables. Although the SFA method can avoid the random error problem that the DEA method cannot solve, it is necessary to set a suitable frontier production function form in advance when applying it [24][25][26]. The DEA method does not require setting a specific function form and is a non-parametric estimation method.…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of Green Innovation Efficiency on Carbon Emission Reduction in the Guangdong-Hong Kong-Macao GBA

Chen

Huo

2021

Sustainability

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Climate change has become a global issue of general concern to human society. It is not only an environmental issue, but also a development issue. As the second largest economy in the world, China has adhered to its commitments in the Paris Agreement and formulated a series of autonomous action targets. In this context, scholars have done a lot of research focusing on carbon emission reduction, but have neglected the spatial correlation of carbon emission, and lack of research on carbon emission reduction in urban agglomerations. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has been at the forefront of China in terms of economy, politics, ecology, and civilization by taking advantage of the “one country, two systems” policy. This article innovatively proposes that there is a non-linear relationship between the efficiency of green innovation and the carbon emission intensity of the Guangdong-Hong Kong-Macao GBA, and has passed quantitative verification. Based on the panel data of the Guangdong-Hong Kong-Macao GBA from 2009 to 2019, we used the super-efficiency slacks-based measure (SBM) model to measure the efficiency of green innovation. We used the global Moran index and Theil index to discuss the spatial correlation of carbon emissions and regional differences in carbon emission intensity in the Guangdong-Hong Kong-Macao GBA, respectively. Then, we used the threshold model to verify the nonlinear relationship between the efficiency of green innovation and the intensity of carbon emissions in the Guangdong-Hong Kong-Macao GBA. The results of the study found that the green innovation efficiency of the Guangdong-Hong Kong-Macao GBA is increasing overall, carbon emissions have a certain spatial correlation, and the correlation is low overall. The impact of green innovation efficiency on carbon emission intensity has a non-linear relationship and there is an “inverted U” pattern between the two, and there is an inflection point in green innovation efficiency. Based on this, this article proposes carbon emission reduction measures within a reasonable range, and looks forward to future research directions and complement the research deficiencies.

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Research on the Regional Differences and Influencing Factors of the Innovation Efficiency of China’s High-Tech Industries: Based on a Shared Inputs Two-Stage Network DEA

Cited by 67 publications

References 36 publications

Research on the Measurement, Evolution, and Driving Factors of Green Innovation Efficiency in Yangtze River Economic Belt: A Super-SBM and Spatial Durbin Model

Research on the Measurement, Evolution, and Driving Factors of Green Innovation Efficiency in Yangtze River Economic Belt: A Super-SBM and Spatial Durbin Model

Regional Open Innovation Systems in a Transition Economy: A Two-Stage DEA Model to Estimate Effectiveness

Impact of Green Innovation Efficiency on Carbon Emission Reduction in the Guangdong-Hong Kong-Macao GBA

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