Abstract
Based on the Driver-Pressure-State-Impact-Response (DPSIR) framework, this paper constructs the input, expected output, and unexpected output of China’s sustainable development. This paper calculates the sustainable development efficiency of 31 provinces and cities in China using a super-slack-based measure (SBM) model that eliminates the influence of scale factors through a three-stage data envelope analysis (DEA) approach. Taking the level of science and technology as the control variable, and the relative scale efficiency as the threshold variable, this paper calculates the effects of the absolute scale of labor force, education, economy, enterprise, and transportation on sustainable development efficiency. The results show that: (1) there is an upward trajectory for sustainable development efficiency of China’s provinces and municipalities overall from 0.65 in 2004 to 0.68 in 2017, with significant regional differences in which the ecological efficiency in the Eastern region is 0.26 higher than that of the Central region; (2) it enhances the absolute scale of the labor force, education, and transportation, in order to reduce the inhibition on sustainable development efficiency; and (3) shifts our attention to the promotion of absolute economic scale to the promotion of green economic development, and increases control of polluting enterprises.
Highlights
In the 200 years since the Industrial Revolution, the unprecedented speed and scale of population growth has changed the Earth’s life support system
Among the five scale indicators, we divide these indicators into three categories when we formulate policies to improve the efficiency of sustainable development; these involve the positive and negative influence coefficients of each indicator on the efficiency of sustainable development under different thresholds: (i) Indicators that need to be further improved, including the scale of labor force, education, and transportation
The sustainable development efficiency of 31 provinces and cities in China was calculated using a super-slack-based measure (SBM)-data envelope analysis (DEA) model and the efficiency was analyzed through different time periods
Summary
In the 200 years since the Industrial Revolution, the unprecedented speed and scale of population growth has changed the Earth’s life support system. Science and technology are developing rapidly, with growth in the scale and sophistication of human society, and the agglomeration of economies. Human society is facing arduous challenges because of rapid global population growth, the shortage of natural resources, and the deterioration of the ecological environment. Research focus on sustainable development has expanded from evaluation and measurements of efficiency to a comprehensive investigation into social, economic, and ecological impacts, influenced by variables including science, technology, and scale. It is important to establish a framework that includes economic, social, and ecological impacts from sustainable development efficiency concerns and analyze the impact of technology and scale
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