Sustainable Development in China’s Coastal Area: Based on the Driver-Pressure-State-Welfare-Response Framework and the Data Envelopment Analysis Model

Wang, Song; Sun, Caizhi; Li, Xin; Zou, Wei

doi:10.3390/su8090958

Cited by 45 publications

(33 citation statements)

References 57 publications

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“…It is an important indicator of the economic activity of an enterprise and the overall performance of enterprise production, technology level, management level, technical proficiency, and labor enthusiasm of the workers. The formula for CLP (Wang, Sun, Lin, & Zou, ) is as follows:

C L P_{i} = \frac{G D P_{i} / \sum_{i = 1}^{n} G D P_{i}}{L a b o r_{i} / \sum_{i = 1}^{n} L a b o r_{i}}, false(i = 1, 2, \dots, n false)

…”

Section: Methodology and Datamentioning

confidence: 99%

Can regional collaborative innovation improve innovation efficiency? An empirical study of Chinese cities

2019

Self Cite

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The relationship between collaborative innovation and innovation efficiency has attracted the attention of researchers in recent years. However, few studies have integrated intra‐regional and inter‐regional collaborative innovations (IRCI) into a unified framework to analyze the overall impact of regional innovation efficiency. To fill this gap, this paper uses an improved Data Envelopment Analysis Model to measure the innovation efficiency of Chinese cities from 2003 to 2016 based on the regional innovation capability. Using the Coupling Coordination Degree Model to measure the degree of intra‐regional collaborative innovation, we constructed a Capability Structure Model to measure the degree of IRCI, then used the Spatial Durbin Model to empirically analyze the influence of intra‐regional and IRCI on regional innovation efficiency. The results show that: (a) both intra‐regional and IRCI promote regional innovation efficiency, but the internal factors are the primary influences on regional innovation efficiency; (b) intra‐regional collaborative innovation not only promotes local regional innovation efficiency but also promotes innovation efficiency in other regions effectively, although its driving effect on the local region is higher than on other regions; (c) there is a time lag in promoting regional innovation efficiency through the cooperation and interaction among innovation in knowledge, technological, industrial, and service, and environment capabilities. The regional innovation capacities can result in good collaborative innovation effects only after a certain period of cooperation.

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C L P_{i} = \frac{G D P_{i} / \sum_{i = 1}^{n} G D P_{i}}{L a b o r_{i} / \sum_{i = 1}^{n} L a b o r_{i}}, false(i = 1, 2, \dots, n false)

…”

Section: Methodology and Datamentioning

confidence: 99%

Can regional collaborative innovation improve innovation efficiency? An empirical study of Chinese cities

2019

Self Cite

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“…In the influence factor frame, each index unit and degree of importance is different, so the subjective and objective combination method was used to process the influence factor data. The subjective and objective weights were evaluated by the Analytic Hierarchy Process (AHP) and the entropy method, respectively, and the comprehensive weights were based on D-S evidence synthesis theory [38][39][40][41][42][43]. According to the calculation above, the corresponding weights of each method can be obtained from Table 1.…”

Section: Processing Of Indexesmentioning

confidence: 99%

Analysis of the Water-Energy Coupling Efficiency in China: Based on the Three-Stage SBM-DEA Model with Undesirable Outputs

Wang

Sun

Wang

2019

Water

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Although the relationships between water and energy systems have been widely researched globally, such studies have not properly considered the coupling and driving mechanisms of the nexus between water and energy. Based on panel data from 30 Chinese provinces and cities, we used a three-stage Slacks-Based Measure model for Data Envelopment Analysis (SBM-DEA) to estimate the Water-Energy coupling efficiency in China from 2003–2015. Using the Stochastic Frontier Approach (SFA) regression model, we constructed an index of environmental factors that affect the Water-Energy coupling efficiency from the four aspects of resource environment, social environment, economic environment, and ecological environment. The results indicate that the Water-Energy coupling efficiency scores in most provinces in China are high and stable, and that the coupling efficiency of water and energy in China has a distribution pattern of northeast > east > west > central. Compared to the results in the first stage of analysis, the efficiency values in the third stage (after removal of environmental and stochastic factors) were smaller, illustrating that the coupling efficiency of water and energy in China depends on a favorable external environment. In the sample period, we also found that the improvement of the resource and social environments was the most conducive way to improve the Water-Energy coupling efficiency. Overall, the management level of technological innovation in China still has some room for improvement.

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“…We used kernel density estimation to reveal the spatial and temporal trends in UER across 286 cities in China from 2004 to 2016. Kernel density estimation is a density function used to estimate unknown values based on the probability theory using a nonparametric test method [37][38][39][40]. This function was used in this study to produce a smooth surface of UER across the country based on values of the 286 cities.…”

Section: Kernel Density Estimationmentioning

confidence: 99%

Spatiotemporal Variation and Inequality in China’s Economic Resilience across Cities and Urban Agglomerations

Liu

et al. 2018

Sustainability

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Economic resilience is a critical indicator of the sustainable development of an urban economy. This paper measures the urban economic resilience (UER) of 286 major cities in China from six indicators—economic growth, opening up, social development, environmental protection, natural conditions, and technological innovation—using a subjective and objective weighting method and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods. Furthermore, kernel density estimation (KDE) was used to reveal the spatial and temporal trends in UER across cities, and a social opportunity function was applied to access the opportunity for economic resilience and the fairness of opportunities for economic resilience in 19 urban agglomerations in China. The results show that the UER was, in general, low across all cities but increased over time. Geographically, the UER disperses from the eastern coast to inland cities. Amongst urban agglomerations in China, the economic resilience opportunity index also varies spatially and increases over time. On the other hand, the opportunity fairness index of UER remained largely stable and substantial inequalities exist across all urban agglomerations, indicating the need for differentiated policy intervention to ensure equality and the sustainable development of the region. The methodology developed in this research can also be applied in other cities and regions to test its re-applicability and to understand the UER in different contexts.

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Sustainable Development in China’s Coastal Area: Based on the Driver-Pressure-State-Welfare-Response Framework and the Data Envelopment Analysis Model

Cited by 45 publications

References 57 publications

Can regional collaborative innovation improve innovation efficiency? An empirical study of Chinese cities

Can regional collaborative innovation improve innovation efficiency? An empirical study of Chinese cities

Analysis of the Water-Energy Coupling Efficiency in China: Based on the Three-Stage SBM-DEA Model with Undesirable Outputs

Spatiotemporal Variation and Inequality in China’s Economic Resilience across Cities and Urban Agglomerations

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