The spatial convergence and drivers of environmental efficiency under haze constraints - Evidence from China
Introduction
Since the economic reform and opening-up that began 42 years ago, China has developed its economy tremendously by adopting an extensive model of growth dominated by traditional resources, which brought on substantial energy consumption problems and environmental damage. Particularly in recent years, there have been frequent outbreaks of haze, the pollution with PM (particulate matter) 2.5 as the major composite, hovering over Chinese cities. It has seriously threatened the ecological environment and public health and has drawn great attention from government agencies and scholars. In the 2014 Report on the Work of the Government, Chinese Premier Li Keqiang pointed out that haze is a natural warning that the extended economic growth model is not sustainable, and mankind should resolutely declare war on pollution, just like fighting war on poverty (Report on the Work of the Government, 2014). According to the statistics from the Ministry of Environmental Protection, approximately 350,000–500,000 people die prematurely every year because of haze and other air pollution.It seriously threat to the normal operation of the economy and human health (Apte et al., 2018). These consequences have greatly dampened the benefits brought by economic development. Therefore, researching environmental efficiency under haze constraints is significant in managing pollution and evaluating the balance between economic development and environmental health. In addition, China is a large country that has tremendous geographical diversity wherein development is unbalanced across regions, and each region has distinct factor endowment. This inevitably results in discrepancy in the spatial distribution of environmental efficiency. How great is the discrepancy among different regions? Will it decrease over time and demonstrate convergence? If there is convergence, will the convergence of environmental efficiencies of different cities and regions share the same characteristics? What are the drivers of convergence? The answers to these questions have real-life significance in guiding effective policymaking that treats and prevents haze pollution and strikes a balance between environmental and economic development.
Earlier environmental efficiency evaluations were based on double input variables and economic output (Kaneko and Managi, 2004). With the increasing deterioration of the environment because of energy consumption, some scholars have factored it into input variables (Gómez-Calvet et al., 2013) featured by a selection of different pollutants. Watanabe (Watanabe and Tanaka, 2007) and Nourry (Nourry, 2009) selected sulfur dioxide as an undesirable output, whereas Wang et al. (Wang et al., 2013) selected both sulfur dioxide and carbon dioxide, and Marconi (Marconi, 2012) and Song et al. (Song et al., 2013) found industrial gas, water, and solid wastes to be undesirable outputs. Besides the “three wastes,” Kaneko and Managi (Kaneko and Managi, 2004) also selected industrial dust, smoke, and the emissions of chemical oxygen demand as input variables. After adding environmental factors, the computed environmental efficiency is possibly closer to its actual value. However, these scholars failed to include haze pollution as a variable. Presently, studies on the influence factors of environmental efficiency have focused on population density, foreign direct investment (FDI), degree of trade dependence, industrial structure, technological innovation, among other factors. For example, Yang.,et al. (Yang et al., 2018) believed that the higher the population density, the higher the residents' requirements of the environment and, thus, the higher the environmental efficiency. Zugravu-soilita (Zugravu-Soilita, 2017) discovered that the impact of FDI on environmental efficiency is insignificant. However, Doytch. (Doytch and Uctum, 2016) believed that the effect of FDI on environmental efficiency is uncertain. Mulatu.,et al. (Mulatu et al., 2010) and Tukker (Tukker et al., 2018) suggested that the greater the degree of trade dependence, the lower the environmental efficiency. Shen.,et al. (Shen et al., 2019) discovered that the influence of regional industrial structure on environmental efficiency is different because of its development level. Aldieri.,et al. (Aldieri et al., 2019) believed that accelerating technological innovation could effectively enhance environmental efficiency.
During the literature review, the existing papers rarely consider haze pollution as a variable, and their measurement of environmental efficiency might contain distortion or deviation. Haze, as a type of pollutant, has greatly impacted the economy, society, and ecological environment in recent years. Therefore, the possible marginal contribution of this paper is to measure the environmental efficiency in Chinese cities and to analyze the differences across regions by taking the Haze as a new constraint indication into the framework of comprehensive environmental efficiency evaluation.The results of this paper are closer to real-life production. Moreover, because China is geographically huge, its population, economy, and technological innovations demonstrate relatively large differences from region to region. This may lead to differences in environmental efficiency across regions and spatial convergence, therefore, leading to differences in its drivers across regions. In light of this, the present paper conducts empirical research on the spatial convergence and its drivers of environmental efficiency using EBM model-based measurement of the value of environmental efficiency under haze constraints with data from 99 cities in China.
Section snippets
Measurement of environmental efficiency
According to Manuela's (Manuela et al., 2019) thought,and based on the comprehensive radial and non-radial EBM model put forward by Tone, (Tone and Tsutsu, 2010) this measures environmental efficiency under haze constraints. For D number of decision units that consist of q number of input (x) and s number of output (y), the CCR model analysis is as follows:
γi ≥0,i = 0,1,2,3, …n.
the EBM model analysis is as follows:
Empirical analysis
This paper uses 99 cities in China as research units and measures the environmental efficiency of the 99 cities under haze constraints with the EBM model. Fig. 1 is the line chart of the annual means of environmental efficiency from 2005 to 2017. From Fig. 1, it can be found that during the observation period, Eastern, Central, and Western China and the overall country demonstrate similar evolving trends in terms of environmental efficiency, characterized by the dumbbell curve. Between 2005 and
Conclusions and policy proposals
Based on the EBM model with comprehensive radial and non-radial characteristics, this paper measures and analyzes the environmental efficiency of 99 cities in China under haze constraints between 2005 and 2017. The α- and β-convergence models were applied to analyze the spatial convergence characteristics and its drivers of the China's environmental efficiency under haze constraints in Eastern, Central, and Western China as well as China as a whole. Our main conclusions are as follows.
- (1)
The
Funding
This study was funded by the High Level Talents Project of Hainan Basic and Applied Basic Research Plan (Natural Science Field) in 2019, China, grant number 2019rc131,the National Social Science Fund of China, China, grant number 18BJL056, the Research Initiation Fund of Hainan University, China, grant number kyqdsk201903.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
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