Elsevier

Journal of Cleaner Production

Volume 139, 15 December 2016, Pages 1157-1167
Journal of Cleaner Production

Spatial linkage analysis of the impact of regional economic activities on PM2.5 pollution in China

https://doi.org/10.1016/j.jclepro.2016.08.152Get rights and content

Highlights

  • The spatial spillover effect of economic activities on PM2.5 is analysed.

  • An economic weight matrix is measured to analyse the spatial economic interdependence.

  • A SAR model is constructed to identify the main driving factors of PM2.5.

  • The relationship between PM2.5 and economic development supports EKC hypothesis.

  • Various economic factors have significant impacts on PM2.5 in different way.

Abstract

Fog and haze represent the most hazardous type of weather affecting the life and health of residents in China since 2009; as a result, many policymakers and economists have focussed on this issue. This study analyses the driving factors of fog and haze in 152 cities in China from the perspective of the economic effect. Considering the spatial attributes of fog and haze alongside the regional economic association, a spatial economic weight matrix is constructed and introduced into a spatial autoregressive model to analyse the spatial diffusion effect of PM2.5 pollution. The results show that PM2.5 pollution has significant spatial aggregation and diffusion effects that are significantly influenced by geospatial attributes and regional economic association in China. Furthermore, the effects of economic, social, and technological factors exhibit significant differences in relation to PM2.5 in different cities, and all of these factors present regional spillover effects. Specifically, the relationship between PM2.5 and economic development in China is consistent with the Environmental Kuznets Curve (EKC) hypothesis, in which different regions are located at different positions on the EKC curve due to the different situations related to economic development. As a result, the regional coordination of environmental policies and pollution-intensive industry transfer are needed to hold China's air pollution in check.

Introduction

Since 2009, fog and haze have swept across most of the provinces of China and become a disastrous weather phenomenon. As the main component of the fog and haze pollution, PM2.5 (fine particles with diameter of 2.5 μm or less) is tiny enough to enter the lungs and the bloodstream; moreover, it can persist in the air for a long time and spreads easily, which can threaten people's health and life. In particular, China's fog and haze have come to represent an increasingly serious problem in recent years. The PM2.5 pollution figure exceeded the maximum value set by the air-quality detection system in many areas of China at the end of 2015, and the meteorological centre repeatedly issued a red alert within a month across parts of China.

With increasingly serious air pollution creating fog and haze, it is an urgent problem for the Chinese government to deal with at present. To this end, the PM2.5 index has been included in the air-quality monitoring system in China, and it is the current primary control objective to improve air quality. At the same time, the government has introduced a series of policies to control PM2.5. For example, in 2013, the government introduced subsidies for new, energy-efficient automobiles to encourage their development and to reduce automobile exhaust emission. Moreover, the government revised the Air Pollution Control Act in August 2015, which was the first time that the government focussed on the elements of China's energy issues; furthermore, this act added the special stipulation for controlling atmospheric pollution from coal combustion.

The formation of fog and haze is not only related to physical and chemical factors (such as atmospheric conditions, climate change, etc.) but also human economic activities. To effectively deal with fog and haze pollution, it is important to clearly understand the mechanisms behind its formation. Hence, this study attempts to identify the mechanisms of the main economic factors affecting fog and haze at the city level in China from the economic perspective. To overcome this issue, this study analyses the dynamic relationships between economic activities and PM2.5; in addition, it explores its economic diffusion effect, which can help the Chinese government to understand the economic mechanisms related to the fog and haze phenomenon and put forward specific policy measures.

Exploration of the relationship between socioeconomic development and environmental pollution in the environmental economics field has always attracted a great deal of research attention. Most empirical studies on this topic have shown that there is a complicated relationship between environmental pollution and the level of socioeconomic development (Suri and Chapman, 1998, Rupasingha et al., 2004, Paudel and Schafer, 2009, Zapata and Pandel, 2009).

The Environmental Kuznets Curve (EKC) hypothesis, which was first proposed by Grossman and Krueger (1995), provides a theoretical basis for studying the relationship between socioeconomic development and the environment. These researchers identified an inverted U-shaped relationship between per capita income and urban air pollution. It means that in the early stages of socioeconomic development, economic activities have often been accompanied by the destruction of the environment. When the economy develops to a certain level, people will demand more measures to protect the environment, and new technological developments will help to optimise the industrial structure and increase the proportion of the third industry. Hence, the environmental problems that the society faces will be alleviated. Since the 1990s, a large number of researchers have studied a wide variety of pollutants based on the EKC. Most of the existing research focusses on suspended particulate matter, such as SO2, CO, CO2, NOx, PM2.5 and PM10 (Selden and Song, 1994, Roca et al., 2001, Burnett, 2009). In the results of these studies, researchers have demonstrated different relationships between these pollutants and income, such as a U-shaped relationship (Deacon and Norman, 2004) and a linear relationship (Stern and Common, 2001, Burnett, 2009). Park and Lee (2011) found that the shape of the EKC curve greatly depends on the type of pollutants.

In addition to economic development, Grossman and Kuznets (1995) stressed that technological change, the level of education and political development may lead to a decrease in environmental pollution. Moreover, some researchers emphasise that the scale of the population and urbanization are key factors affecting energy consumption and pollutant emission (York et al., 2003, Cole and Neumayer, 2004, Liddle, 2014, Kouchaki-Penchah et al., 2016). Rupasingha et al. (2004) included economic variables (such as population density, the level of education, race, etc.) into the model to examine the relationship between toxic pollutants and per capita income in the United States. Furthermore, Keene and Deller (2013) reported an inverted EKC for PM2.5 in the US from the perspective of social capital. Expanding the independent variables helps to explain the environmental changes, and this can avoid the inconsistency in parameter estimation, which is caused by the lack of sample and explanatory variables. In addition, some studies tend to include the spatial pattern of pollutants to analyse the relationship between environmental pollutants and income (Rupasingha et al., 2004, Keene and Deller, 2013, Hao and Liu, 2016).

The above studies investigated the relationship between environmental pollutants and economic development from different angles. However, most of the existing research has focused on SO2, CO2, NOx, water pollution and hazardous waste, while research on the relationship between PM2.5 and socioeconomic development is limited. Existing studies have mainly focussed on identifying the physical components of PM2.5 (Koutrakis et al., 2005, Huang et al., 2014). In China, PM2.5 only begun to receive more attention in 2012, while nearly no related economic analysis of PM2.5 has been carried out. To the best of our knowledge, only Hao and Liu (2016) have analysed the relationship between fog and haze and economic development from the perspective of the energy structure. However, they took a single factor into account; thus, they did not consider the aggregation and diffusion effects of regional economic activities.

In relation to previous research, this study has three main contributions. First, various macroeconomic variables – including economic, social and technological dimensions – are included. The main driving factors of fog and haze pollution are investigated from the viewpoint of economic activities; this could offer new evidence for developing policy to tackle fog and haze pollution. Second, a new spatial economic weight matrix is constructed in combination with a spatial autoregressive model to analyse the spatial impact of regional economic activities in China on PM2.5 pollution. Specifically, influencing factors are further decomposed to identify the spatial diffusion effects in different regions. This analysis not only confirms the importance of enhancing cooperation between regions to solve the regional pollution problems but also delineates the direction for change in high energy-consuming industries in high pollution areas. Third, the relationship between PM2.5 and socioeconomic development is investigated to support the EKC hypothesis.

Section snippets

Methodology and indicators

Given that fog and haze are nationwide weather occurrences, they are apparently connected to geospatial information. In addition, the space distribution of fog and haze is closely related to the position of polluting sources. Thus, this paper focusses on economic activities. There are three main parts in this section. First, a spatial autoregressive model (SAR model) is built to identify the main driving factors of PM2.5. Second, a spatial economic weight matrix is constructed to measure the

Empirical results

To show the spatial characteristics of PM2.5, Fig. 1 presents the average annual PM2.5 concentration in all 152 sample cities. We use different colours to represent different levels of PM2.5 concentrations. As shown in Fig. 1, fog and haze swept through half of China. Three provinces in Northeast China, the North China Plain, Coastal provinces (including Shandong, Jiangsu and Zhejiang) and the Central provinces of China (including Henan, Anhui, Hubei, Hunan and Shanxi) were all suffering from

Conclusions

In this paper, a spatial economic weight matrix was constructed to measure the spatial diffusion effect caused by interregional economic activities. Different SAR models were also built to identify the influence mechanisms of various economic variables on PM2.5. The main findings were as follows:

  • (1)

    In addition to economic growth, the estimation results suggest that urbanization, population density, vehicle population, energy intensity and the price of refined oil are essential driving factors

Acknowledgements

Supports from the National Natural Science Foundation of China under Grant No. 91546109, No. 71133005 and No. 71203210 are acknowledged. The authors appreciate the weekly seminars at CEEP in CAS, from where the earlier draft of the paper got improved.

Cited by (0)

View full text