Research on the Spatial Differentiation and Driving Forces of Eco-Efficiency of Regional Tourism in China

Wang, Rui; Xia, Bing; Dong, Suocheng; Yu, Li; Li, Zehong; Ba, Duoxun; Zhang, Wenbiao

doi:10.3390/su13010280

Cited by 38 publications

(31 citation statements)

References 71 publications

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“…The application of an output-focused model also facilitates the definition of current efficiency gaps based on the correspondence between the input resources available and the final product. This efficiency resource is called "slacks" [87][88][89]. Slacks (slacks or input (output) excess) demonstrate "the unused efficiency" of the regions and that part of the resources requires changing inputs (decrease) and outputs (increase) to reach the efficiency limit [90].…”

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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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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show abstract

“…The Getis-Ord G i * index is put forward by Getis and Ord, and we can find subregions with different attribute values in the study area. This can be expressed as follows [55]:…”

Section: Analysis Methods Of Spatial Pattern: Analysis Of Hot Spot and Center Of Gravitymentioning

confidence: 99%

“…Therefore, we selected panel random effect Tobit regression model to identify the influencing factors of regional eco-efficiency in Mongolia. The model expression is as follows [55]:…”

Section: Influencing Factors Analysis Method: Panel Tobit Analysismentioning

confidence: 99%

Evolution Characters and Influencing Factors of Regional Eco-Efficiency in a Developing Country: Evidence from Mongolia

Xia

Dong

et al. 2021

IJERPH

Self Cite

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The sandstorm in 2021 in East Asia demonstrated the ecological issues that culminated for decades in Mongolia. Mongolia is facing challenges to realize green and sustainable development. This article aims to increase the understanding of eco-efficiency and its influencing factors in Mongolia and to provide a reference for similar developing countries and regions to achieve green and sustainable development. This article used the Slacks-Based Measure of Efficiency (SBM) model with advantages of dimension freedom and unit variable to estimate the economic efficiency and eco-efficiency of 22 provinces in Mongolia from 2007 to 2016; energy consumption and undesirable environmental outputs were taken as ecological/environmental indicators in the input and output system of regional eco-efficiency in Mongolia, combining traditional indicators of economic efficiency to build Mongolia’s eco-efficiency input–output framework. This article applied hot spot analysis and gravity center analysis to reveal the temporal and spatial evolution characters of eco-efficiency in Mongolia. Finally, the article applied panel Tobit regression to analyze the influencing factors of eco-efficiency. We were found that Mongolia’s eco-efficiency slightly improved from 0.7379 in 2007 to 0.7673 in 2016, lower than the economic efficiency. The high eco-efficiency provinces appeared in the capital Ulaanbaatar and its surrounding areas, showing an obvious spatial spillover effect. The low eco-efficiency provinces were mainly in the undeveloped western region. The relationship between per capita GDP and eco-efficiency was U-shaped and consistent with environmental Kuznets theory. Accelerating economic growth, optimizing population distribution, and improving energy structure and green technology can improve Mongolia’s eco-efficiency.

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“…(1) DEA-Malmquist Index The DEA-Malmquist index, which can avoid the difficulties in dealing with the crosssectional data faced by traditional DEA models [19], was used to analyze the fluctuations of the forestry output efficiency of China from 1998 to 2017 and explore its temporal fluctuation trends. In the analysis process, the DEA-Malmquist index was used to investigate the total-factor productivity (TFP) of 31 provinces of China and divided into the technical efficiency change (EC) and technological progress change (TC).…”

Section: Methodsmentioning

confidence: 99%

Research on the Temporal and Spatial Distribution and Influencing Factors of Forestry Output Efficiency in China

2021

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Forestry output efficiency is key to forestry development. China is now promoting the development of forestry, and thus the research on forestry output efficiency is of practical significance. Through the data envelopment analysis (DEA)-Malmquist index, spatial autocorrelation model, and fixed effect model of panel data, in this study, we analyzed the forestry output efficiency of China with indicators, such as the fixed asset input, employed personnel, total output value, and timber output, and drew the following conclusions. In the time series, the forestry total-factor productivity (TFP) in China saw a rapid increase, which is attributed to the technological progress change (TC), whereas the efficiency change (EC) imposed negative influences upon the forestry TFP. In the spatial distribution, there was a difference in the increase in the forestry output efficiency among the eastern, central, and western regions of China, with the eastern region having the fastest growth and the central region having the slowest growth. According to the spatial autocorrelation, there was spatial aggregation (high–high (HH) and low–low (LL)) with a significant positive correlation. Through the optimized fixed effect regression model, the fixed asset input, employed personnel, total output value, and timber output all had significant influences on the comprehensive technical efficiency of the forestry output, wherein the input indicators had negative influences, and the output indicators had positive influences.

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Research on the Spatial Differentiation and Driving Forces of Eco-Efficiency of Regional Tourism in China

Cited by 38 publications

References 71 publications

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

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

Evolution Characters and Influencing Factors of Regional Eco-Efficiency in a Developing Country: Evidence from Mongolia

Research on the Temporal and Spatial Distribution and Influencing Factors of Forestry Output Efficiency in China

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