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10 July 2020 Study on Spatial-Temporal Distribution and Dynamic Evolution of Eco-Efficiency in China's Coastal Provinces
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Abstract

Yu, W.; Chen, T.; Yu, S., and Wang, H., 2020. Study on spatial-temporal distribution and dynamic evolution of eco-efficiency in China's coastal provinces. In: Gong, D.; Zhang, M., and Liu, R. (eds.), Advances in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 106, pp. 454–458. Coconut Creek (Florida), ISSN 0749-0208.

The dynamic trends and convergence of eco-efficiency were analyzed using the super-efficiency data envelopment analysis model (US-DEA) with undesirable output and the σ convergence model based on the panel data of 11 coastal provinces in China from 2008 to 2017 in accordance with the eco-efficiency evaluation characteristics of coastal areas. A Markov transition probability model was introduced to analyze the transition probability of eco-efficiency in the time domain and predict the future eco-efficiency evolution of coastal areas in China. The results show that: (1) The eco-efficiency of each province in coastal areas is increasing year after year, presenting a convergence trend. (2) The eco-efficiency of coastal areas is transitioning gradually rather than suddenly. The upward transition probability is greater than the downward transition probability, indicating that the positive probability of eco-efficiency is greater than the negative probability. (3) The eco-efficiency of coastal areas in China will increase year after year in the future based on the prediction of the eco-efficiency transition probability.

©Coastal Education and Research Foundation, Inc. 2020
Weiyang Yu, Teng Chen, Shaodong Yu, and Hongyi Wang "Study on Spatial-Temporal Distribution and Dynamic Evolution of Eco-Efficiency in China's Coastal Provinces," Journal of Coastal Research 106(sp1), 454-458, (10 July 2020). https://doi.org/10.2112/SI106-102.1
Received: 20 December 2019; Accepted: 25 January 2020; Published: 10 July 2020
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