Linking ecological efficiency and the economic agglomeration of China based on the ecological footprint and nighttime light data

Highlights

• Economic agglomeration (EA) was quantified based on nighttime light data.

• All provinces are facing ecological overload excluding Qinghai.

• A positive correlation between EA and ecological efficiency (EE) was found.

Abstract

As natural resources are becoming one of the factors hindering economic development, the utilization efficiency of natural resources should be improved and ecological stress should be reduced to ensure sustainable economic development. In this study, the ecological stress index and ecological efficiency were quantified using the ecological footprint model to describe the current status of sustainability and utilization efficiency of natural resources. The economic agglomeration was determined by conducting global spatial autocorrelation analysis using Defense Meteorological Program/Operational Line-Scan System (DMSP/OLS) nighttime light data. The relationship between economic agglomeration and ecological efficiency of China was then illustrated in a four-partite graph, and the results show that (1) the per-capita ecological footprints vary between the different provinces; however, the composition of the ecological footprint is similar between the provinces, and demonstrates the demand for fossil energy land is highest, while that for fishing ground is lowest. (2) All provinces are facing ecological overload, excluding Qinghai, and Shanghai, Tianjin, and Beijing are enduring the most severe ecological overload. (3) The ecological efficiency differs greatly between the different provinces of China, and the ecological efficiency values tend to decrease from the eastern coast to the inland region. (4) The economic activities in every province of China are spatially agglomerated, and the degree of agglomeration differs significantly. (5) There is a positive correlation between economic agglomeration and ecological efficiency, indicating that promoting economic agglomeration is an effective method of improving the utilization efficiency of natural resources. The relationship between economic agglomeration and ecological efficiency observed in this paper will provide a reference for optimizing the spatial distribution of economic activities and a theoretical basis for synchronizing environmental protection with economic development in China.

Introduction

As the ecosystem offers products and services to support the survival and development of human society, ecological sustainability is the primary goal of sustainable development (Eustachio et al., 2019, Peng et al., 2018a, Peng et al., 2018b). With the continuous development of the global economy since the industrial revolution, humans have gained enormous economic benefits; however, this has been to the detriment of resources and the environment. The frequent occurrence of pollution incidents worldwide has brought attention to the concept of sustainable development since the 1930s. In Silent Spring (Carson, 1962), “the other path” was proposed to protect the Earth for the first time and inspired environmental awareness among the public. At the start of the 21st Century, natural resources began to impede economic development (Nilashi et al., 2019, Yang and Song, 2019). In 2001, the United Nations launched the Millennium Ecosystem Assessment Project to improve ecosystem management and ensure sustainable socioeconomic development. Human society continuously faces the challenging conflict between environmental protection and economic development, and quantifying human demand and supply for natural capital and coordinating environmental protection and economic development is a core theme in the field of sustainable development research (Fan et al., 2019, Peng et al., 2018a, Peng et al., 2018b, Peng et al., 2019a, Peng et al., 2019b).

Under the requirements of sustainable development, resource exhaustion and environmental deterioration cannot be ignored (Huang et al., 2016). However, allowing a decline in economic growth by limiting resource consumption is unrealistic. Therefore, many countries have focused on exploring renewable energy sources, (i.e., hydro, wind, solar, tides, biomass, biofuels and geothermal), among which hydropower is the most common and represents the highest proportion (Kuriqi et al., 2017, Kuriqi et al., 2019a, Kuriqi et al., 2019b). Inexhaustible and clean renewable energy aids in mitigating the effects of fuel shortage and pollution, but more efforts should be devoted to relieving environmental pressure. To promote economic growth, ecological efficiency (EE) should be improved, which would not only maximize economic benefits, but also minimize ecological problems (Yue et al., 2016). Therefore, methods of enhancing EE should be developed. Agglomerated economies can produce positive externalities, such as reducing transportation and information communication costs, and have spatial spillover effects, such as improved sewage technologies and the development models of enterprises (Zeng and Zhao, 2009). However, agglomerated economies also have negative externalities, such as causing environmental pollution and their potential for environmental unsustainability (Cheng, 2016).

In 1992, Rees initially proposed the concept of the ecological footprint (EF), and his student, Wackernagel, completed an algorithm for measuring it (Rees, 1992, Wackernagel and Rees, 1997). They compared the consumption of natural resources by humans to a footprint, where the size of the footprint represents the human demand for natural resources and dependence on nature, and then calculated the EF of 52 countries (Wackernagel et al., 1999). Considering its robustness and global comparability, this method has been broadly applied in natural capital accounting and sustainable development assessment in China and overseas (Yang and Hu, 2018). The EF can be widely applied in assessing regional sustainable development at different scales, from global (Toth and Szigeti, 2016), national (Galli, 2015, Solarin and Bello, 2018), and regional (Gu et al., 2015, Peng et al., 2019a, Peng et al., 2019b) to single cities (Pan et al., 2019). Since its inception, many researchers have improved the basis of the EF. For example, Niccolucci proposed a three-dimensional (3D) EF model that introduced the concepts of footprint size and depth (Niccolucci et al., 2009, Niccolucci et al., 2011). Using the derived footprints, such as the water footprint developed by Hoekstra and Hung (2002) and the carbon footprint developed by Weidema et al., 2008, Galli et al., 2012 first explored the independent concept of a “Footprint Family” as a suite of indicators and described the hypothesis, rationale, and methodology of calculating the ecological, carbon, and water footprints. These indicators have since been used to evaluate the influence of human consumption and emissions on the planet from different angles (Solarin and Bello, 2018).

The EF has been used to construct regional EE models (Fu et al., 2015, Yang and Yang, 2019). The EF is linked to economic indicators, allowing it to be applied across domains, and has also been applied to evaluate the coordination state between environmental protection and economic development. A high EE indicates that more is achieved using fewer resources, or economic output is generated with little resource consumption and environmental degradation (Cheng, 2016, Kuosmanen and Kortelainen, 2005). EE can be used to measure regional coordination and sustainability. From an input-output perspective, the goal of EE is to minimize the impact on resources and the environment while maximizing economic output. It also aims to ensure that economic development does not sacrifice ecological benefits, and that, through the rational allocation of natural capital, a harmonious and mutually beneficial situation for the economy and environment within the region can be achieved (Mickwitz et al., 2006, Zhu et al., 2019). GDP cannot currently be considered as a real indicator to measure the development quality (Kunanuntakij et al., 2018), and the “Black Development” mode, which facilitates rapid economic growth alongside great resource consumption and high pollution emissions, is being abandoned. Under the appeal for the efficient utilization of natural capital, EE is an appropriate indicator to assess the regional development quality.

To describe the spatial agglomeration characteristics of economic activities, which refer to as economic agglomeration (EA), some researchers have taken the output value per 100 square kilometers of land area as an indicator (Liu et al., 2019), which is also known as “land economic density”. There are also other common indicators, such as the nonagricultural output value per unit, GDP per unit of a county, which accounts for the proportion throughout the province, and employment density (Baumont et al., 2004, Brülhart and Mathys, 2008). Defense Meteorological Program/Operational Line-Scan System (DMSP/OLS) nighttime light data provide a powerful remote sensing tool for modeling the spatiotemporal dynamics of GDP at a large spatial scale when exploring the spatial differences of economic activities. Nighttime light remote sensing is an active branch of remote sensing application research. Since the DMSP/OLS nighttime light data were first released by the National Oceanic and Atmospheric Administration’s National Geophysical Data Center (NOAA/NGDC), they have received increasing attention from researchers in natural and socioeconomic science, and have been widely applied in many fields, such as population estimation (Chen et al., 2019), urbanization (Gao et al., 2015), impervious surfaces detection (Ma et al., 2019, Pok et al., 2017), socioeconomic indicator evaluation (Bennett and Smith, 2017), and pollution monitoring (Ji et al., 2019, Zhao et al., 2019). Many researchers have found a strong correlation between the brightness of DMSP/OLS nighttime imagery and the regional GDP (He et al., 2014, Wu et al., 2013, Zhao et al., 2017). Therefore, nighttime light data have become crucial substitutes for socioeconomic indicators (Dai et al., 2017, Wu et al., 2013).

In this study, we calculated the EA and EE of the provinces of China to explore the impacts of economic agglomeration on the utilization efficiency of natural resources. Using ArcGIS and OpenGeoda, we derived the global Moran’s I by conducting global spatial autocorrelation analysis to calculate the value of EA. The EF was applied to measure the human demand for natural resources in economic activities, and we used the GDP per unit of EF to measure the EE. Finally, we presented suggestions for mitigating the ecological stress and resolving the conflict between environmental protection and economic development under China’s current situation.