An analysis of the relation between water pollution and economic growth in China by considering the contemporaneous correlation of water pollutants

Highlights

• Exploring correlation of pollutants by semi-parametric seemingly unrelated model.

• Semiparametric seemingly unrelated model reflects income-pollution relation better.

• Eight types of Environmental Kuznets Curve are found, including a new M-shape.

• Good EKCs’ proportion in underdeveloped area (76%) is higher than developed (71%).

Abstract

The study of the relationship between water pollution and economic growth holds great significance for sustainable development. Under Environmental Kuznets Curve (EKC) hypothesis, this paper focuses attention on a Chinese context and investigates the relationship between water pollution discharge--waste water (WW), chemical oxygen demand (COD) and ammonia nitrogen (NH₄–N) and economic growth--per capita Gross Domestic Production (GDPPC), based on a comparison of the results from two variable coefficient panel data models–-a Locally Weighted Smoothed Regression Estimator and Smoothing Scatterplots Model (LOWESSM) which is a nonparametric model, and a Semi-parametric Seemingly Unrelated Regression Model (SSURM) which considers the contemporaneous correlation of water pollutants that most previous studies have ignored. The empirical results indicate that there exist differences that can be represented by the characteristics of different EKC types, or different turning points under the same EKC type and that the SSURM may be more conducive to reflecting the real relationship between water pollution and economic growth. The study also finds that there are eight types of EKC which can be categorized as “good EKCs” (negative monotonic shape, inverted N-shape, inverted U-shape and M-shape), “bad EKCs” (positive monotonic shape, N-shape and U-shape) and “transition EKC” (positive monotonic and flat-tailed shape) and the proportion of “good EKCs” in economically developed areas (71.43%) is lower than that in the less economically developed areas (76.47%) in terms of COD discharge. Results suggest that addressing the state of water pollution will not occur automatically as GDPPC increases, but requires the regulatory power of government environmental policies.

Introduction

Most countries in the world are facing an increasingly serious water pollution problem. Water pollution has brought increasing risks to rivers, ecosystem services and sustainable development (Loucks, 2017) and has became a vital factor influencing the survival of human beings and the development of socio-economic systems (Lee et al., 2010). Being the world’s largest developing country, China has been confronted with the enormous pressure of water pollution caused by a rapid economic growth rate. According to the calculation of purchasing power parity, China has superseded the United States to become the world’s largest economic community in 2014 (Li et al., 2016). China’s Gross Domestic Production (GDP) has increased more than ten times since the country’s Open and Reform Policy in 1978 while the country’s water quality has been continuously deteriorating, with an amount of waste water discharge of over 70 Gt and a roughly average annual increase rate of 200 kt (ChinaNBoSo, 2018). According to the statistical data (CNEMC, 2018a) on the surface water quality of China’s seven major river basins, the proportion of water deemed unfit for direct human contact was 53.8% of the 160 water quality monitoring sections in the Haihe basin while this proportion in the Liaohe basin was 50.9%. The average pH of Haihe and Liaohe was 7.68 and 7.51. The alkalinity of Haihe ranged from 393.5 mg/L to 1627.3 mg/L (Shi, 2014) while that of Liaohe ranged from 56.83 mg/L to 187.2 mg/L (Shao et al., 2017). The main water pollution indicators in these rivers were COD, NH₄–N, etc. According to the statistical data (CNEMC, 2018a) on China’s groundwater quality, 76.1% of the 2833 shallow groundwater quality monitoring wells were categorized as bad to very bad with the main water pollution indicators being NH₄–N, Nitrate Nitrogen, etc. Given these findings alongside the fact of China’s rapid economic growth, it can be said that the study of the relationship between water pollution and such growth plays an important role in evaluating China’s water environmental policy, judging the country’s water pollution status and future evolution trend, as well holding great significance for sustainable development.

The remainder of this paper is arranged as follows. Section 2 provides an overview of the current literature on Environmental Kuznets Curve (EKC), specifically pertaining to the relationship between water contamination and economic development. Section 3 introduces the data sources and software used in the current study. Section 4 describes the analytical methods--Locally Weighted Smoothed Regression Estimator and Smoothing Scatterplots Model (LOWESSM) and Semi-parametric Seemingly Unrelated Regression Model (SSURM). Section 5 presents the results of the EKC between multiple water pollutants and Gross Domestic Production Per Capital (GDPPC) under two different varying coefficient panel data models (LOWESSM and SSURM). Section 6 discusses the results and Section 7 concludes.