Abstract
The economic development of China’s coastal areas is being constrained by resources and the environment, with sustainable development being the key to solving these problems. The data envelopment analysis (DEA) model is widely used to assess sustainable development. However, indicators used in the DEA model are not selected in a scientific and comprehensive manner, which may lead to unrepresentative results. Here, we use the driver-pressure-state-welfare-response (DPSWR) framework to select more scientific and comprehensive indicators for a more accurate analysis of efficiency in China’s coastal area. The results show that the efficiencies of most provinces and cities in China’s coastal area have a stable trend. In the time dimension, efficiency was rising before 2008, after which it decreased. In the spatial dimension, China’s coastal provinces and cities are divided into three categories: high efficiency, low efficiency, and greater changes in efficiency. By combining DPSWR and DEA, we produce reliable values for measuring efficiency, with the benefit of avoiding the incomplete selection of DEA indicators.
Highlights
With rapid economic development exhausting land resources, people have begun focusing on the sea [1], which is becoming increasingly important for sustaining the economy [2,3], especially in China [4]
The data envelopment analysis (DEA) model is suitable for assessing the sustainable development of marine ecological economy, which has both environmental and economic problems [6]
DPSWR Framework The DPSWR framework was established based on the DPSIR framework, which was developed from the pressure-state-response (PSR) framework of the Organization for Economic Cooperation and Development [26] by the European Environmental Agency [27]
Summary
With rapid economic development exhausting land resources, people have begun focusing on the sea [1], which is becoming increasingly important for sustaining the economy [2,3], especially in China [4]. The data envelopment analysis (DEA) model constructs the objective function skillfully, and transforms the fractional programming problem to a linear programming problem through the Charnes-Cooper transform (C2—Transform), without the requirement of the dimension of a uniform index and input-output weights. This capability improves the objectivity of the evaluation of decision-making units. Yuan and Qiu used principal component analysis (PCA) to reduce the overlap of information between indicators when calculating the development efficiency of Tianjin, China [18] These authors focused on the scientific nature of indicator selection, but overlooked comprehensiveness. We used kernel density estimation and hierarchical clustering to analyze the time and space efficiency of 11 provinces and cities in China, to provide suggestions for management
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