1. Introduction
With the continuous transformation and upgrading of the consumption structure of agricultural products, the demand for animal husbandry products such as meat, dairy products, and eggs has been continuously rising, which has greatly promoted the rapid development of the livestock and poultry breeding industry (Department of Natural Ecological Protection, State Environmental Protection Administration, 2002) [
1]; since 1991, China’s meat, eggs, and poultry output have been ranked first in the world [
2,
3]. However, with the development of the livestock and poultry industry, livestock and poultry breeding pollution has become one of the important causes of agricultural non-point source pollution [
4,
5]. Research on pollution in China is unavoidable as a big consumption of animal husbandry products. Experience shows that the development of the livestock and poultry industry has mainly brought water pollution and soil pollution. According to the Second National Pollution Sources Census Bulletin issued by China in 2020, the chemical–oxygen demand of water pollutant discharge in China was 100,053 million tons, of which 6.0483 million tons comes from large-scale livestock and poultry farms. The total nitrogen emissions is 596,300 tons, 370,000 tons of which came from the livestock and poultry industry; the total phosphorus emissions reached 119,700 tons, of which 80,400 tons came from the livestock and poultry industry. Census results show that the discharge of livestock manure has been the main source of water pollution in rural areas. In watersheds with serious water pollution in China, rural livestock and poultry breeding is one of the main causes of nitrogen and phosphorus eutrophication in water bodies, and its contribution rate is much higher than that of urban domestic sewage point source pollution and industrial point source pollution [
6]. As the carrier of the development of the livestock and poultry industry, the pollution problem of cultivated land is also inevitable. Soil pollution of cultivated land refers to the harmful substances in cultivated land exceeding the purification capacity of cultivated land, which changes the composition and character of cultivated land, leading to the deterioration of cultivated land soil quality and dysfunction. According to the survey results from the China Environmental Protection Administration, the production of livestock and poultry manure in China is twice that of industrial solid waste [
7]. With the rapid development of the livestock and poultry industry in China, a large amount of manure discharge is likely to have exceeded the range that the land can bear, causing pollution to the country’s water and soil, which deserves great attention.
China’s livestock and poultry breeding industry is gradually transferring to resource-rich regions with less developed economies [
8], and with the expansion of industrial agglomeration [
9], the transfer continues to intensify. The transfer of pig production area is a typical example: according to the research of [
10], the pig breeding in Shanghai, Beijing, and some other big cities has been transferred to surrounding or remote areas. Some scholars have verified the “pollution paradise effect” in pig production through empirical analysis and pointed out the transfer of pig industry due to environmental cost savings, that is, the transfer of pig production from areas with strict environmental regulations to areas with looser environmental regulations; so, that is unsustainable and has high environmental risks [
11]. The existing layout of livestock and poultry breeding reduces the fairness of resources and environment between regions, and with the gradual transfer of the breeding layout, this asymmetry will continue to expand and result in unfair environmental burdens [
12]. The Chinese government have attached a great level of importance to this, and in 2010 issued a “livestock and poultry breeding pollution control technology policy” (2013), that issued the scale of livestock and poultry breeding pollution control ordinance, and then introduced another policy in 2017 to “accelerate the breeding livestock and poultry waste resource utilization”, and so on, as a series of regulations to manage the pollution of livestock and poultry industry in China. These policies mainly stipulated how to prevent and control the pollution of the livestock and poultry industry in the region, but did not give clear management methods for the pollution transfer and the unfair environmental burden caused by the livestock and poultry industry.
Based on an extensive review of existing studies, empirical studies on environmental burden inequality are mainly divided into two categories. One is to use income distribution methods. The study [
13] verified that pollution transfer would bring serious environmental inequality by simulating China’s environmental Kuznets curve. The study [
14] used the gene coefficient to measure regional environmental equity in China and proved that environmental inequality in China was on the rise. Another type of study focuses on measuring the emission and transfer of pollution between different regions, such as in this paper, the pollutant discharge coefficient method has been used to calculate the amount of manure produced by livestock and poultry breeding in China, and the current situation and problems of its resource utilization have been analyzed [
15]. The metal enrichment factor (EF) was used to measure the watershed distribution of Cu, Zn, Pb, and Cd in sediments in the Wen-Rui Tang urban river system in Wenzhou, Eastern China [
16]. This study analyzes pollution emissions and spatial-temporal variations in China’s cattle breeding industry [
17]. In the study [
18], the inequity factors were measured and it was found that the inequity factors of China’s resources and environment were mainly concentrated in the economically underdeveloped areas of western China. All these studies have contributed to the study of environmental burden inequality, but pollution has significant spatial transitivity, and environmental inequality itself reflects spatial crowding out from surrounding areas. Unfortunately, little has been written about the extent to which environmental inequity is affected by the surrounding area.
This paper aims to explore whether the pollution caused by the livestock and poultry industry in China has been exceeded by the environmental burden. More importantly, given that pollution is spatially transitive, the study tries to understand how unfairly the environmental load in the surrounding areas affect the region. This study focuses on the unfairness of environmental burdens in different provinces in China and takes the regional economic development level as an entry point to explore the environmental burden and pollution of local and other regions caused by the development of the economic level of each region. Specifically, the environmental burden refers to the pollution of water and soil caused by nitrogen, phosphorus, and chemical oxygen demand emissions from the livestock and poultry industries.
The premise of the application of the spatial model to study space pollution is to build a relatively reasonable and accurate spatial matrix, which is the premise of all spatial research. However, since the construction of the spatial matrix has not formed a unified construction standard in the academic community, most of the existing research mainly adopts a static and cross-sectional spatial matrix. Even if the geographical location of the two regions will not change significantly in a short period of time, the closeness of their relationship is still constantly changing. For example, due to the closed traffic, region A rarely traded with other surrounding areas, but with its economic development, the traffic gradually improved, and then it also starts frequent trade with the surrounding areas. At this time, if the static and cross-sectional spatial matrix was still used, the change in this spatial relationship could not be reflected. Considering that the duration of this study is as long as 13 years, a dynamic and variable endogenous spatial matrix is more in line with the research needs. Therefore, this paper adopts a more cutting-edge spatial matrix construction method to conduct the research, which will be detailed in the following paper.
The main contribution of this study is mainly as follows. First, it discusses the pollution transfer of the livestock and poultry industry in China from the perspective of an unfair environmental burden. It also provides a decision-making basis and pollution-treatment experience for the development of the livestock and poultry industry in China and even other countries and regions. The second is to apply the cutting-edge spatial matrix construction method and prove its applicability in the spatial change relationship, which provides empirical evidence for the subsequent spatial metrology research.
3. Results
3.1. Pollution Analysis of Livestock and Poultry Industry
The pollution caused by livestock and poultry mainly includes the pollution of cultivated land and the pollution of water resources. The pollution of cultivated land mainly refers to the discharge of manure exceeding the suitable amount of organic fertilizer. The pollution of water sources mainly refers to the total amount of surface water resources needed to dilute pollutants in a certain area after livestock and poultry manure enters the water body under the established water quality and environmental standards (refer to the Class III standard of the “Surface Water Environmental Quality Standards”) and the area. The ratio of the total amount of surface water resources that can be used to dilute pollutants (calculated at 30% for livestock manure intake) [
22].
3.1.1. Measurement Results of Cultivated Land Pollution
The calculation results show that manure discharge exceeding the load of cultivated land is more common in all regions of China. There are 13 regions in the country where a load of livestock and poultry manure poses a serious pollution threat to the environment. Among them, due to the small area of arable land and the large discharge of livestock and poultry manure in Tibet, the livestock and poultry industry poses a serious threat of environmental pollution, the Tibet region ranking first in the country.
3.1.2. Measurement Results of Cultivated Water Pollution
In
Table 4 the water pollution caused by livestock and poultry industries in each region is calculated. To make the text concise, only the areas that have caused water pollution and their specific calculation results are listed in the table. The results showed that the W value of chemical oxygen demand (COD) in Beijing, Hebei, Shandong, and Ningxia has been greater than 1, and the phosphorus emission of the livestock and poultry industry in Hebei Province has also been greater than 1. Due to the livestock and poultry industries in this region, the polluted water situation is more serious. In addition, the W values of COD in Beijing and Henan provinces are 0.975 and 0.977, respectively, closer to 1.
3.2. Unfair Analysis of Environmental Loads
3.2.1. Environmental Inequity Analysis of Chemical Oxygen Demand Emissions
According to Equation (4), the fair distribution index of the chemical oxygen demand emission in China’s provinces and cities from 2007 to 2019 has been calculated. The annual average value of each region has been calculated to obtain the results in
Table 5. The results show that the fair distribution index of chemical oxygen demand emissions from the livestock and poultry industries in the central and western regions, especially in many provinces in the western region, is less than 1.
3.2.2. Environmental Inequity Analysis of Nitrogen Emissions
The fair distribution index of nitrogen emissions from livestock and poultry industries in various regions is shown in
Table 6. The results show that the data on the fair distribution of nitrogen emissions from livestock and poultry industries in various regions are not significantly different from COD emissions. Emissions are more equitable than chemical oxygen demand emissions.
3.2.3. Environmental Inequity Analysis of Phosphorus Emissions
Table 7 shows the fairness index of phosphorus emissions from the livestock and poultry industries in each region. The comparison shows that the overall fair distribution index of the three types of pollution sources in the livestock and poultry industry in all regions in China is relatively consistent. Among them are Liaoning, Fujian, Shandong, Guangdong, and Hainan in the eastern region; Jilin, Jiangxi, Henan, Hubei, and Hunan in the central region; and Guangxi, Sichuan, Yunnan, Tibet, and Qinghai in the western region are all three types of pollution source fair distribution index less than 1. The livestock and poultry industries in these areas discharge more than their own environmental capacity and are seeking the development of the livestock and poultry industries at the expense of the environment.
3.3. Analysis of Unfair Influence of Economic Development on Environmental Load
3.3.1. Space-Time Weight Matrix Selection
According to conventional experiences combined with the needs of this research, eight basic space matrices are constructed, namely W1 (01 matrix based on Queen space adjacency relationship), W2 (reciprocal matrix of provincial capital city distance), W3 (reciprocal square matrix of provincial capital city distance), W4 (reciprocal matrix of the distance between regional center points), W5 (reciprocal matrix of the distance between regional center points), W6 (combination matrix of highway distance of provincial capital cities and total population), W7 (matrix of provincial capital city highway distance), and W8 (reciprocal square of provincial capital cities) combined with the total population matrix. Among them, W2, W3, W4, and W5 are calculated according to the Euclidean distance and according to the latitude and longitude of the region or capital city. The eight fundamental matrices are all row-random normalized. Based on these eight basic matrices, according to the above-mentioned principle of constructing the space-time weight matrix. The explained variables are first processed into stacking sequences, and the structure of the processed explained variables is shown in
Figure 1. Then, the eight global Moran indices for each year are calculated according to the eight fundamental matrices, and then according to Equations (6) and (7), eight variable endogenous space-time weight matrices are generated, the structure of which is shown in
Figure 2. Among them, in the coordinate axis, 1~403 represents the accumulation sequence of the 31 provinces in the country from 2007 to 2019, for example, 1~31 represents the 31 provinces and cities in the country in 2007.
To avoid the randomness brought about by subjective selection, Fisher’s
t-test [
27] has been carried out on the eight variable space-time weight matrices according to the principle of effective correlation. The test results are shown in
Table 8. The test results show that the eight space-time matrices all pass the Fisher
t-test at the 1% level. However, the effective correlation coefficient of the space-time matrix constructed based on the provincial capital city highway distance is 0.807. The correlation coefficient is the highest among the eight matrices, which can be considered the most ideal matrix among the eight basic matrices constructed in this paper. Therefore, the following spatial econometric analysis chooses the provincial capital city highway distance as the spatial matrix.
3.3.2. Spatial Model Construction and Selection
Taking the highway distance of the provincial capital as the spatial matrix. The selection of the spatial model was carried out before the study. The research data was brought into the spatial autoregressive model (SAR) which has been fitted with the double fixed individual period, the double fixed spatial error model in the individual period (SEM), fixed-effects spatial Durbin model (SDM), random-effects spatial Durbin model, and carried out the Wald test and the LR test. The test results are shown in
Table 9. The results show that, according to the research data, the model cannot degenerate into a spatial autoregressive model or a spatial error model. The research objective of this paper is suitable to choose the spatial Durbin model with double fixed individual periods.
3.4. Variable Selection
Dependent variable: In this paper, the emission of chemical oxygen demand, nitrogen, and phosphorus from the livestock and poultry industry are selected to measure the unfairness of the environmental burden. Item index, as the dependent variable of this paper.
Independent variables: The unfair environmental burden caused by the discharge of pollutant sources such as chemical oxygen demand, nitrogen, and phosphorus in the livestock and poultry industry is determined by a series of social and economic factors. There are certain differences in management level and residents’ awareness of environmental protection. Therefore, the study selects the level of economic development as an independent variable to measure the stage of economic development in different regions and uses the per capita GDP to represent the level of economic development.
Control variables: The study selected six indicators as control variables: industry, comparison of income levels of urban and rural residents, local financial science and technology expenditure, number of agricultural meteorological observation sites, per capita cultivated land area, and per capita education years of rural residents. The specific calculation method and data structure are shown in
Table 10.
From the descriptive statistical results of the variables, the mean values of the three types of fair distribution indexes are all greater than 1, but the gap between the minimum value and the maximum value is large, indicating that there are large differences in the fair distribution indexes between regions and years, and the inequity in some regions was prominent. In the same way, there are also large differences in per capita GDP between regions. Animal husbandry output value in the whole agricultural output value accounted for about 30%, accounting for a relatively large amount. The income comparison of urban and rural residents is 2.805, indicating that the income level of urban residents has 2.8 times that of rural residents. The annual local government expenditure on science and technology has 9.279 billion yuan on average, with a large gap between different years, but the overall investment has constantly increased. Each province has an average of 22.37 agricultural meteorological observation stations. The per capita cultivated land area in the survey area has 291,198 people/10,000 hac, and the difference between different provinces was great. The average length of education of rural residents was 8.862 years, they have mainly completed junior high school education. However, with the passage of time, the level of education of Chinese farmers is constantly improving, and the average length of education in some areas has reached 12 years.
3.5. Outcome of Practice
In the table, Models 1 and 2 use an invariant exogenous spatial weight matrix, based on a maximum likelihood estimation. Where Model 2 is corrected for fixed effects bias, Model 3 uses a variable endogeneity spatiotemporal matrix, based on Bayesian estimation. When the spatial spillover effect of the spatial Durbin model is significantly non-zero, the directly obtained main regression results are biased, and its influence effect needs to be decomposed. Available on request.
From the perspective of the economic development level, the regression results show that the spatial effects of each model are significantly negative at the 1% level, indicating the unfairness of the environmental burden among regions has a significant spatial crowding out effect. In Models 1 and 2, both the direct and indirect effects of GDP on the unfair impact of environmental burdens show a U-shaped relationship, that is, in the early stage of economic development, with the increase in GDP, the unfairness of environmental burdens have intensified. After crossing the inflection point, this situation has been reversed. As GDP continues to rise, the environmental burden of the livestock and poultry industry pollution between regions has tended to develop in a more equitable direction. The impact relationship also exists in the impact of neighboring regions on the region. In Model 3, after considering the temporal change of the spatial spillover effect, the situation has changed. The GDP has no significant impact on the inequity of the environmental burden in the region but the inequity in the local environmental burden is significantly affected by the neighboring regions. The specific performance is an inverted U-shaped relationship. That is to say, the increase in the GDP of the surrounding areas has promoted the fairness of the environmental pollution burden in the area in the early stage, but as the GDP of the surrounding areas continues to rise after the inflection point is crossed, it has begun to squeeze the environmental pollution capacity of the area, through pollution transfer and overloading. Pollution and other methods have a significant negative impact on the unfairness of the environmental burden in the region. Similarly, the spatial spillover effects of the three models all show that, on the whole, the inequity of the environmental burden in the region has a significant negative impact on the inequity in the surrounding areas.
From the perspective of other influencing factors: First, the industrial structure. The higher the output value of animal husbandry in the total output value of agriculture and forestry, the more serious the pollution to the environment. From the empirical results of the three models, whether it is a local impact or a neighboring area, the industrial structure has a significant negative impact on the unfairness of the environmental burden, and the impact of the neighboring area is higher than the local one. This also supports the research hypothesized pollution transfer situation of the livestock and poultry industry. Second, the income levels of urban and rural residents are compared. The research shows that the increase in the income ratio of urban residents, is conducive to promoting the fairness of the environmental burden in the region, and it is significant at the 1% level. However, it has a significant negative impact on the surrounding areas. Third, local financial science and technology expenditures. The results of research model 3 show that the local financial science and technology expenditure has no significant impact on the environmental burden. The reason may be that in China, the livestock and poultry industry still adopts a more traditional way of breeding and production, and does not directly utilize related science and technology. Therefore, concerning local financial expenditure on science and technology, the Chinese government should more fully consider supporting the development of science and technology related to livestock and poultry pollution. However, according to the results of Models 1 and 2, the local environmental burden inequity index has been affected by the scientific and financial expenditures of the neighboring areas, which also supports the speculation that the economically developed areas have transferred pollution to the surrounding areas. Fourth, is the per capita arable land area. The results of Model 3 show that the per capita arable land area has no significant impact on the environmental pollution burden. However, the results of Models 1 and 2 show that the per capita cultivated land area in the surrounding area has a significant negative impact on the environmental burden of the area, but the impact is very small. Fifth is the number of years of education per capita. According to the results of Models 1 and 2, the per capita years of education in the region have a significant positive impact on the environmental burden, but the results of Model 3 show that the per capita years of education in the surrounding areas have a significant negative impact on the local environmental burden. Research speculates that this is also related to the level of regional economic development and pollution transfer.
4. Discussion
According to the above calculation of the cultivated land load and fairness index of the livestock and poultry industries in various regions, in order to further explore the impact of economic development on the unfair environmental burden, the trend of the R-value in the three major regions from 2007 to 2019 and the fair distribution represented by COD have been calculated. The trend of the index has been plotted, and
Figure 3 and
Figure 4 are obtained.
Figure 3 and
Figure 4 show that, although the alarm R-value of the cultivated land load has a downward trend, the cultivated land load in the eastern region (economically developed region) is at a relatively high level each year, that is, the cultivated land load of the livestock and poultry industry in the more economically developed eastern region has a negative impact on the environment and serious pollution has been produced. The western region (economically backward region) has greatly increased the average R-value of the entire western region because the R-value of Tibet is too high. If Tibet is not included in the calculation, the arable land load of other provinces in the western region is significantly lower than that of other regions.
However, it is worth noting that in the context of the arable land load in the western region, where economic development is relatively lagging, is lower than that in the central and eastern regions with higher economic development levels, the environmental fair distribution index in the western region has always been at the lowest level in the 13 years of statistical research. Even after removing the Tibet region with the lowest equitable distribution index value, this situation did not change. Therefore, among the three models that use different spatial matrices to obtain diametrically opposed results, this paper believes that the results of the model (Model 3) constructed by using variable endogenous spatiotemporal weights are more appropriate to the above analysis results.
Therefore, this paper has reason to believe that the level of economic development has no significant direct impact on the environmental equity distribution index of the region, but it will form an inverted U-shaped indirect impact on the surrounding areas. According to the specific analysis of this paper, the western region (the level of economic development that is relatively backward) plays a significant role in promoting the environmental equitable distribution index in the surrounding areas. On the other hand, the central and eastern regions (with a relatively high level of economic development) have a significant inhibitory effect on the environmental equitable distribution index in the western region. However, as per capita GDP in the western region is lower than the national average level, the overall spatial spillover effect is still manifested as the spatial “crowding out effect”.
According to the above analysis results, this paper further calculates the inflection point of the non-linear effect of the per capita GDP on the unfair environmental burden. 33,500 yuan per person (
Table 11). According to the
Figure 5, from 2017 to 2018, the central and eastern regions reached the inflection point of per capita GDP with direct effect, while the western region has not yet reached the inflection point by 2019. The eastern, central, and western regions reached the inflection point of per capita GDP of indirect effect between 2009 and 2011. This result also confirms that Model 3 believes that the result that per capita GDP has no significant direct impact on the research object is more reliable.