1. Introduction
Pollution spillovers of trans-boundary rivers are one of the most important research questions in environmental economics because the negative externalities of pollution are amplified by the flow of water. The emissions from upstream exceed the capacity of the river to self-purify, which means that the downstream pollution levels are more serious than are the upstream levels. Rivers across jurisdictional boundaries are more polluted because of the decentralization of regional finance and environmental management, which leads to more serious boundary pollution phenomena. Sigman [
1] found that the pollution upstream of jurisdictional boundaries will be 40% more serious when rivers cross national borders. The closer the border is, the more serious the pollution is. The harm of boundary pollution is very serious because it seriously damages the health of border residents [
2], causing problems such as diarrheal diseases and digestive system cancers. The poor people who live on the border are disproportionately affected by boundary pollution. China is one of the countries with the lowest per capita freshwater resources, but its water quality has deteriorated over the past 30 years. Seventy-five percent of the nation’s lake water cannot be used as drinking water [
3], and half of all rivers are unsafe for human contact [
4]. Ebenstein et al. [
5] proved that the pollution has seriously hindered the improvement of health in China over the last two decades. The deterioration of water quality is an important cause of the high incidence of cancer in China [
6]. The deterioration in water quality will increase the incidence of gastrointestinal cancer by 9.7% [
7]. Pollution crossing a river’s jurisdictional boundaries can also cause environmental inequality, which has been particularly serious over the past few years [
8]. More seriously, it affects the residents’ environmental attitudes. He et al. [
9] studied the impact of boundary water pollution on people’s willingness to pay (WTP) and found that WTP is negatively affected by the water quality from the upstream. This phenomenon is more pronounced when the downstream has a weak negotiation capability. The system for controlling the quantity of pollutants, which environmental protection departments develop based on environmental carrying capacity, is weakened. This is a threat to ecosystem security.
Existing papers have examined the existence of boundary environmental pollution and pollution spillovers in China from different perspectives. Upstream regions locate polluting enterprises close to borders so that pollution is carried downstream. Cai et al. [
10] studied industry-level activities along 24 major rivers in China and found that downstream areas of a province have 20% more pollutions. The enforcement of pollution reduction mandates is more lenient in the most downstream county of a province. As the boundary of the city, rural areas are often the focus of pollution emission. Wang et al. [
4] found that water pollution in small rural firms is a growing problem throughout China. He et al. [
11] found that the pollution from rural small and medium-sized chemical enterprises is very serious, using a case study in the Hebei province, because rural residents have no ability to exercise political power. The existence of boundary pollution has made the issue of local government governance more complex. There is also a branch of the existing literature whose findings are inconsistent with the above results. Yang and He [
12] concluded that polluting enterprises do not choose to locate near jurisdictional borders because local governments tend to agglomerate their effects to boost economic growth.
Studies have found that institutional factors affect pollution abatement. A foundational model of Oates and Schwab [
13] suggests that decentralization may increase inter-jurisdictional variation in pollution levels. Destructive regulatory competition, known as the “race to the bottom”, indicates that decentralization may lower environmental quality. Fell and Kaffine [
14] proved that the pollution levels of decentralization generally differ from that of a centralized planner’s social welfare-maximizing problem if we permit capital retirement and abatement activities. Related empirical research has found strong evidence that the decentralization of environmental governance leads to more serious boundary pollution around the world. It is difficult to negotiate successfully between upstream and downstream, which leads to pollution occurring “not in my backyard”. Sigman [
15] studied the impact of decentralization on water quality by studying pollution in rivers around the world and found that federal countries exhibit greater inter-jurisdictional variation in pollution levels. Lipscomb and Mobarak [
16] found an inverted U-shaped curve in the quality of river water crossing the border in Brazil. Trans-boundary environmental pollution may worsen under fiscal decentralization in developing countries with growth-driven governments. Greenstone and Jack [
17] explored the puzzle of poor environmental quality and high health burdens in developing countries and concluded that political factors undermine efficient policymaking. Regional fiscal competition in China exacerbates the consequences of environmental decentralization. Local officials can manipulate the distribution of polluting activities through project permits and the implementation of pollution reduction mandates. Growth-driven provincial governments prefer to develop the economy, which leads to a race to the bottom in environmental pollution. Cat et al. [
10] showed that the overall pollution reduction targets may increase the total emissions of pollution in China. Chinese local leaders’ behavior is driven by a career incentive structure, and local leaders lessen the enforcement of environmental regulations to attract firms [
18]. When environmental pollution became too severe to ignore, local officials began to control pollution under pressure to meet pollution reduction mandates. Zheng and Kahn [
19] concluded that institutional factors affect the decisions of governments to tackle local pollution externalities in China by analyzing the current literature, as proved by Zheng and Shi [
20] from the inter-country relocation of polluting industries.
Due to the deteriorating water quality, China’s central government began to control the total amount of pollutants in major basins beginning in the 10th Five-Year Plan (10th FYP), but the river water quality has not improved significantly. The Five-Year Plans are the most important policy instruments in China. The central government has incorporated emissions reduction targets into the scope of official assessments from the 11th FYP, which led to a decline in pollutant emissions from various regions. One motivation for the Chinese Communist Party to eliminate pollution is to build legitimacy by signaling [
21]. The problem is that border pollution disputes and environmental violence increased dramatically during this period. The first reason for this increase in violence may be that people’s awareness of environmental protection has been gradually awakened with the improvement of living standards. However, the conflicts also indicate that environmental policy is not able to meet the needs of the public and may be ineffective. The formal implementation of water quality assessment was covered in the period of the 12th FYP; the emission reduction effect has still not been sufficiently studied. In view of the harm of boundary pollution, it is necessary to further study the effect of regulation on water quality improvement and boundary pollution. In particular, we must pay attention to the change of boundary pollution in the process of institutional evolution. China is in an economic transition period, and the tolerance for treating water pollution across the whole society is declining. Various pollution reduction mandates are being perfected to account for the public’s environmental rights, environmental justice and general justice.
This paper examines the existence of boundary pollution as captured by the values of hydrogen ion concentration (pH), dissolved oxygen (DO), chemical oxygen demand (COD) and ammoniacal nitrogen (NH3-N) using weekly water quality data from monitoring stations from 2004 to 2014. We also investigate the effects and consequences of the 2006 and 2011 policy changes. Considering the heterogeneity of the monitoring stations, a propensity score-matching approach is used to mitigate selection based on unobservable variables. To confirm the net effect of the 2006 and 2011 policy changes from other factors, our identification strategy uses the difference in difference (DID) approach of constructing control groups by using internal monitoring stations. The pollution levels of monitoring stations on provincial borders are significantly higher than are those of interior monitoring stations, which varies greatly accompanied by policy changes. The water quality in the tributaries is better than that in the main streams before the water quality assessment, which reverses after the water quality assessment. The improvement of the water environment with the change of environmental policy showed great difference when we compare the water quality of monitoring site stations in the upper reaches of provincial borders with that downstream of provincial borders. The overall pollution reduction target aggravated border pollution, whereas water quality assessment eliminates boundary pollution. The conclusion of DID is consistent; the difference of policy effects is obvious. We confirmed the strategic behavior of local government in pollution reduction when studying only the four parameters, which may weaken the effectiveness an emission reduction policy.
This paper contributes to the existing research in three aspects. First, this paper uses weekly data on water quality from monitoring stations to study boundary pollution and the impact of environmental policy on boundary pollution in China. The existing studies have verified the existence of boundary pollution in China from the distribution of heavy industries, relocation of pollution companies and enforcement of environmental regulation. This article contributes to boundary environmental pollution and pollution spillover-related research in China. Second, unlike the existing papers, this paper finds significant differences between the impact of overall pollution reduction targets and water quality assessments on water quality by using a more rigorous approach. Little attention has been paid to the changes of boundary pollution accompanied by the strengthening in pollution reduction mandates. Further, existing studies often adopt pollutant emission intensity as a proxy for environmental regulation, which may be an endogenous variable. Our final contribution is to confirm strategic polluting by local officials’ behavior in China. The existing studies may overestimate the efforts of local governments to reduce emissions.
The main objective of this paper is to examine the existence of boundary pollution in China and investigate the effects of the 2006 and 2011 policy changes. The remainder of this paper is organized as follows.
Section 2 describes the institutional background and changes in China’s water polluting regulations and hypothesis development.
Section 3 describes the methodology used in the empirical analysis, including the specification of regression models.
Section 4 provides the specification of variables, data source and descriptive statistics.
Section 5 presents the empirical results and further discusses whether boundary pollution varies with environmental policies.
Section 6 summarizes the findings of this paper.
4. Data
Water quality data come from weekly reports of automatic water quality monitoring stations from 2004 to 2014, which can be obtained from the Ministry of Environmental Protection’s data center. The monitoring stations are located in the main stream of the important rivers, the entrance and river mouths of the important tributaries, the important lakes and reservoirs, the national border rivers and major water conservancy projects in China (
Figure 1). The monitoring stations studied in this paper include all stations, considering the possible bias caused by the artificial selection of the sites. China’s automatic water quality monitoring system is constantly improving with changes in the monitoring sites. There were 73 monitoring points at the beginning of 2004, which increased to 82 in the eighth week of 2005. Two stations were added in the forty-eighth week of 2006; the monitoring points reached 100 in the twenty-second week of 2007, 115 in the twenty-third week of 2011, 131 in the twenty-third week of 2012, and grew to 145 stations in the twenty-third week of 2014. The monitoring station adopts the method of continuous online monitoring, and the monitoring frequency is once every 4 h. The monitoring data are transmitted to the provincial monitoring center and the China Environmental Monitoring Station at the same time. Monitoring station management and data release are the responsibility of the Ministry of Environmental Protection, which can avoid the problem of self-reporting to some degree. The authenticity of the monitoring data is essential, as has been emphasized by Bernauer and Kuhn [
37]. Weekly reports provide a summary of the data for the week including 4 pollution indicators such as pH, DO, COD and NH3-N. Monitoring data may be missing due to power supply, flood, or disconnection, so the data set is unbalanced panel data. An overview of the water quality data is provided in
Table 1 and
Figure 2.
In
Table 1, water pollution has improved from 2006 for COD, but we do not find this phenomenon in other indicators. All water pollution indicators begin to improve significantly after 2010. There is a significant difference in water quality between monitoring stations on provincial borders and interior monitoring stations, as shown in
Figure 2. We construct a range of socioeconomic, water resources, environment, demographic, seasonal effect and other characteristics to mitigate selection based on unobservable variables. Specific indicators, data sources of characteristic variables and descriptive statistics are listed below as
Table 2 and
Table 3. All monetary variables are deflated to 2005 yuan using provincial GDP deflators because some data were missing in 2004. The aggregate data are processed by natural logarithm, which is comparable to different dimensions.