Spatial-temporal characteristics of industrial land green efficiency in China: Evidence from prefecture-level cities

https://doi.org/10.1016/j.ecolind.2020.106256Get rights and content

Highlights

  • This study calculates industrial land green efficiency using SBM-Undesirable model.

  • Average ILGE in the eastern region is higher than central and western regions.

  • More cities with high ILGE appear to be concentrated in the eastern region.

  • Regional internal ILGE differences are gradually decreasing.

Abstract

In the context of the construction of an ecologically sustainable civilization and green development, the continuous improvement in industrial land green use has played an important role in China. Only a few studies have evaluated industrial land-use efficiency from the perspective of ecological economy. Considering energy consumption and undesirable outputs simultaneously, this study evaluated the industrial land green efficiency (ILGE) of 198 cities in China from 2007 to 2015 using the Slacks-based Measure (SBM)-Undesirable model, and spatial convergence methods were used to analyze the trend of ILGE. The study found clear regional differences in ILGE. The average ILGE in the eastern region was much higher than that in the central and western regions. From a temporal perspective, the average ILGE in all cities has gradually decreased since 2007 and fell to its lowest value in 2013. However, the ILGE showed a continuous upward trend in the final two years. Most cities with high ILGE values appeared to be concentrated in the eastern region. Fifteen cities with scores of 1.00 accounted for 8.6% of the total, indicating that they are the most efficient in the sample over all the years studied. Heilongjiang Province had the lowest ILGE, with 75% of the cities located in the lowest 30 places of the ILGE ranking. The national, eastern, and central regions showed an unstable short-term characteristic of σ-convergence since 2013. Absolute and conditional β-convergence existed in all regions, indicating that cities with poorer efficiency have a faster ILGE growth rate than leading cities.

Introduction

China has established the most complete industrial system and become the largest manufacturing country in the world since it implemented economic reform and opening-up in 1978 (Sheng, 2019). In response to rapid industrialization, a continuous supply of large areas of industrial land is needed to support further development of industry (Zhang et al., 2017, Chen et al., 2018a). China’s industrial land area is 10,298.65 km2 (MOHURD, 2015), accounting for nearly 20% of built-up land in cities in 2015. Rapid expansion and low density industrial land development lead to low industrial land use efficiency in China. In comparison, the proportion of industrial land in most developed countries is less than 10%, but industrial land-use efficiency is much higher than that of China (Xie et al., 2015). Moreover, the high speed of the industrial economy has exacerbated low resource utilization efficiency and caused environmental deterioration (He et al., 2014, Zhang et al., 2016, Wang et al., 2018). Moreover, industry has become the largest energy consumer in China, accounting for 68% to 72% of national energy consumption (Mao et al., 2010). Reducing resource consumption and pollution emissions and realizing a circular industrial economy and green production will be conducive to environmental improvements and ecological stability. Therefore, reducing the impact of industrial land use on ecological environment is an important guarantee for sustainable industry development and the green economy.

Industrial production generates desirable outputs for the economy, but produces environmental pollution, that is, undesirable outputs, at the same time. Therefore, it is necessary to consider both desirable and undesirable outputs when evaluating industrial land-use efficiency. Generally speaking, the connotations of eco-efficiency, green efficiency and environmental efficiency are essentially the same, and all means the optimal efficiency gaining the largest economic and ecological effects with fewer resources (Sorvari et al., 2009, Xie et al., 2018, Yang and Yang, 2019). Therefore, the term “green efficiency” is used in this paper. Green efficiency is an important analytical tool for measuring the degree of coordination between human economic development and environmental impact (Wang et al., 2014, Tang, 2015). Green efficiency refers to a gradual reduction in ecological impact and resource intensity over the entire industrial life cycle, to a level that is at least consistent with the earth’s estimated carrying capacity while providing competitively priced goods and services (Zhang et al., 2018). Industrial land use should maximize the combined benefits to the economy, society, and the ecological systems (Wey and Hsu, 2014). Therefore, it is very important to evaluate industrial land-use efficiency to achieve sustainable economic development of urban land use (Zhang et al., 2015, Chen et al., 2017).

At present, developed countries pay more attention to ecological planning of industrial land (Louw and Bontekoning, 2010, Jensen et al., 2011), brownfield regeneration (Jamecny and Husar, 2016), and space conversion and redevelopment (Mcgrath, 2000, Lester et al., 2013). According to various research purposes, there are multiple methods from what to choose. From a single-factor perspective, productivity can directly reflect the relationship between land input and economic output, and it is easy to make regional comparisons (Chen et al., 2018b). Data envelopment analysis can objectively determine parameter weights, and evaluate efficiency scientifically and systematically (Chen et al., 2017), but such evaluations are constrained by static analysis (Sun et al., 2012). The Malmquist index can decompose efficiency into the catch-up effect and technological progress to explore the specific reasons for efficiency changes (Zhang and Choi, 2013). Efficiency research in relation to ecology or environmental aspects, require an accounting of necessary to take undesirable outputs of the efficiency evaluation model (Martin et al., 2018). The Slacks-based Measure (SBM) model was proposed to measure efficiency with undesirable outputs (Wang et al., 2017, Zhou et al., 2017). Liu et al. (2018) measured the allocative efficiency of construction land using an extended Cobb-Douglas production function.

Some studies have focused on factors influencing industrial land-use efficiency. Chen et al. (2017) established that industrial land-use efficiency is influenced by regional industrial development. The characteristics of industrial land green efficiency differ among regions (Wang and Xiao, 2017). Because of the influence of knowledge spillover, labor mobility and the common value, enterprises of industrial parks were found to be more efficient than those outside the industrial park (Cainelli, 2008, Huang et al., 2017). Environmental quality has an obvious “crowding out effect” on urban land-use efficiency (Peng et al., 2017). A large number of foreign direct investment and international trade promote globalization to become an important driving force for China’s urban land expansion. Marketization have changed the land relocation system and realized land value-added (Wu et al., 2017). Meng et al. (2008) pointed out that quantity, arrangement and scheduling are the three most important aspects of industrial land use efficiency and planning. Traffic accessibility can promote the development of regional integration. The implementation of land planning and arrangement by the government can restrict the scale and speed of industrial development. Industrial land-use was most affected by industrial sectors and industrial land lease policies did not achieve the goal to improving land-use efficiency (Tu et al., 2014). Wang et al. (2013) demonstrated that the output elasticity of industrial land under a market supply approach was higher than that under a planned supply approach, indicating that the market supply approach was conducive to providing industrial land allocation efficiency. Foreign advanced management experience in industrial land use has demonstrated that a combination of market approaches with government planning and control was an effective way to improve industrial land-use efficiency (Wu et al., 2017).

Previous studies on economic efficiency have shown that industrial land-use efficiency is highly related to industrial development. At present, energy consumption and undesirable outputs have been taken into account in evaluating industrial land-use efficiency, however, most research has focused on industrial parks (Lu et al., 2014, Tian et al., 2014, Liu et al., 2017), while research from regional and urban perspectives has been lacking. Moreover, China has a vast territory with an unbalanced spatial development. There are obvious differences in resource endowments, industrial structure, and development level across China, which will inevitably lead to differences in the spatial distribution of ILGE. To fill these gaps, this study used the non-radial and non-oriented SBM-Undesirable model to evaluate and analyze the ILGE in 198 cities at the prefecture level in China during 2007–2015.

The objectives of this study were: (1) to evaluate industrial land green efficiency, considering energy consumption as an input and industrial waste as an undesirable output; (2) to reveal regional differences among cities and to forecast development trends using convergence analysis; (3) to show the temporal and spatial characteristics of industrial land green use in China, thereby helping in further formulation of regional development strategies against the background of green development.

Section snippets

Calculation of industrial land green efficiency

Industrial production activities not only produce desirable economic outputs, but also emit undesirable pollutants. Based on the objective of realizing sustainable economic development, this paper proposes the concept of ILGE, which calculates only the land factor instead of the whole city. The aim of efficiently using industrial land is to achieve as much economic output as possible, with fewer undesirable outputs, within a limited land area. Hence, an ILGE evaluation can balance economic

Annual changes in industrial production activities

Industrial land use, energy consumption, and undesirable outputs exhibited various changes at the national level from 2007 to 2015. The amount of industrial land has generally maintained an upward trend, although it experienced a slight decline between 2010 and 2013. The growth rate has slowed from the previous period by approximately 2.7% from 2013 to 2015, but industrial land area remained increasing. Similarly, energy consumption increased with economic growth, although the two trends were

Conclusions

This paper considers undesirable outputs and energy consumption in industrial land use analysis using the SBM-Undesirable model to evaluate industrial land green efficiency (ILGE) for 198 cities at the prefecture level in China. The conclusions are as follows:

  • (1)

    There exist obvious regional differences in industrial land green efficiency. The average ILGE in the eastern region is much higher than in central and western regions. The central region has the lowest ILGE value. The average of 116

CRediT authorship contribution statement

Wei Chen: Conceptualization, Methodology, Software. Siyin Ning: Writing - original draft, Writing - review & editing. Wenjun Chen: Investigation, Resources. Er-na Liu: Investigation, Resources. Yanan Wang: Conceptualization, Methodology, Writing - review & editing. Minjuan Zhao: Supervision, Project administration.

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.

Acknowledgments

This study was funded by the National Natural Science Foundation of China (71503200, 41602336); Project of Humanities and Social Sciences of the Ministry of Education of China (18XJC790014); Research Program of Shaanxi Soft Science (2019KRZ009); Natural Science Basic Research Program of Shaanxi Province (2019JQ-350); Social Science Foundation of Shaanxi Province (2019S001, 2019S010); China Postdoctoral Science Foundation (2019M653780, 2019M663846). The authors would like to thank the anonymous

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