Multi-Perspectives’ Comparisons and Mitigating Implications for the COD and NH₃-N Discharges into the Wastewater from the Industrial Sector of China

Abstract

Taking China as a case study, we analyzed the underlying driving forces of two discharges—chemical oxygen demand (COD) and ammonia nitrogen (NH₃-N)—from both periodic and structural perspectives by the Logarithmic Mean Divisia Index (LMDI) method. Changes in the two discharges were decomposed into three effects: the economic output effect, the industrial structure effect and the discharge intensity effect. The discharge intensity effect could be further decomposed into the cleaner production (technologies’) effect and the pollution abatement (technologies’) effect. Results showed that the economic output effect was mainly responsible for the growth of the two discharges; the average annual contribution rates were 10.77% and 10.39%, respectively. Inversely, the pollution abatement (technologies’) effect presented the most obvious mitigating effects (−9.71% and −9.52%, respectively). Furthermore, the clean production (technologies’) effect followed it (−4.36% and −5.22%). So, we found that the discharge intensity effect played a crucial role in the reduction of the two discharges. Then, the mitigation effect of industrial structure adjustment was the weakest (−0.19% and 0.47%). However, we could still not ignore the potential impact of industrial structure optimization for reducing the absolute amount of discharges in the long run. In addition, to simultaneously reduce the COD and NH₃-N discharges, the sub-sectors of “Processing of Food from Agricultural Products (I7)”, “Manufacture of Foods (I8)”, “Manufacture of Raw Chemical Materials and Chemical Products (I20)”, “Manufacture of Non-metallic Mineral Products (I24)” and “Smelting and Pressing of Non-ferrous Metals (I26)” were suggested to be given prior consideration for the design of related mitigation policies. Finally, some particular policy implications were also recommended for reducing the two discharges.

1. Introduction

Organic carbon, nitrogen and phosphorus have always been considered major water pollutants, but they are attracting increasing attention around the world [1,2]. However, chemical oxygen demand (COD) and ammonia nitrogen (NH₃-N) are often considered the best indicators for preventing or reducing water pollution [3,4,5]. For example, some scientists have studied the effects of electrochemical oxidation [6,7,8,9] and ozone oxidation [10] as well as bioremediation engineering methods [4] on the simultaneous removal of these two indicator pollutants (COD and NH₃-N) from different types of wastewater. Additionally, other scholars have used response surface methodology to analyze the optimal conditions for the removal of the two indicators [11,12,13] for the following reasons. The COD can, to some extent, reflect the organic carbon content of wastewater [14,15], and NH₃-N can describe the nitrogen content [16]. However, there are no indicators of phosphorus pollution in the relevant databases and the National Controlling Plan of China [17]. Therefore, the above two indicator pollutants (COD and NH₃-N) in China are the focus of this paper. In fact, reducing the discharge of these two pollutants is important for maintaining cleaner aquatic environments.

To realize this goal, it is essential to clarify the relationship between the macroeconomic drivers and the two pollutants and to then identify the main drivers and formulate effective measures to reduce them. Grossman et al. [18] advanced the environmental Kuznets curve (EKC) to explain the relationship between pollution and economic growth, and based on this concept, the discharge of these two pollutants should show an inverted-U shape in response to the rapid development of wealth. However, the inverted-U shape is not the only possible relationship between economic development and the two pollutants [19,20]; in other words, the economy may be not the exclusive driver of their discharge [21,22]. For example, Lei et al. [23] attributed the discharge dynamics of these pollutants (Y) to the three drivers of economic output (G), industrial structure (S) and discharge intensity (I). Here, G refers to the gross domestic product (GDP) from all studied industrial sub-sectors, and S indicates that the different industrial sub-sectors are responsible for different shares of the output and have greatly different impacts on the discharge of the pollutants. I denotes the amount of discharge per unit of GDP, which can, to some extent, reflect the level of technological progress. Moreover, the effect of I can be further divided into the influence of cleaner production (C) and pollution abatement (A), where C indicates the production of the two pollutants per unit of GDP, and A is the ratio of the pollutants being produced. Therefore, C and A can reflect the development of cleaner production technologies (CPT) and pollution abatement technologies (PAT), respectively [23]. The CPT focuses on the management of these pollutants’ sources and the reuse of resources during the process of production, whereas PAT focuses on the controlling output of pollution.

Thus, taking China as a case study, we attempt to analyze the impacts of these macroeconomic drivers on the two pollutants (COD and NH₃-N) by using an appropriate model. In the previous literature, two models have commonly been used [24,25]: structural decomposition analysis (SDA) and index decomposition analysis (IDA). The SDA model often requires input–output economic data, while only aggregate data for each industrial category is needed for the IDA model [26,27,28]. Moreover, SDA can only analyze change between a limited number of years, while IDA can usually analyze change over any time period [24,29], so the IDA model was adopted based on the available data. Additionally, there are many indices available in the IDA model to quantify the impacts of factorial changes on the aggregate industrial sector, such as the Laspeyres index [30], the Paasche index [24], the Arithmetic Mean Divisia Index [31] and the Logarithmic Mean Divisia Index (LMDI) [32,33,34,35]. Among these indexes, the LMDI has become the most popular because of its incomparable advantages [36,37,38,39,40,41]; for example, it has some outstanding advantages in terms of its theoretical foundation (e.g., no unexplained residuals), adaptability and the interpretation of results [42,43,44,45]. Therefore, this LMDI (or “IDA-LMDI”) model was ultimately chosen. It should be noted that few scientists have studied the drivers of the two indicator discharges (COD or NH₃-N) in China to date from the perspectives of either the planning period or industrial structure, so this work is innovative.

The paper is organized as follows. The LMDI decomposition method and the corresponding data sources are described in Section 2, and the results of the analysis and the related discussion are presented in Section 3. Some conclusions and particular strategies and policy implications for mitigating the discharge of COD and NH₃-N in China, especially in the industrial sector, are proposed in Section 4.

2. Data and Methodology

2.1. Description of the Data

All the economic and pollutant data (COD or NH₃-N) were derived from the China Statistical Yearbook (CSY) and the China Statistical Yearbook on the Environment (CSYE) for the 2004–2015 years. To eliminate the influence of price changes on the raw economic data, we deflated the current prices to 2010 prices using the corresponding price indices, and since the price indices were not available at the industrial sub-sector levels, we instead used the price indices for the entire industrial sector. To a certain extent, this replacement may have resulted in some errors, but it did not affect the overall trend and validity of the results of this study. The amount and share of ancillary activities for exploitation (AAE) were marginal, i.e., the industrial COD or NH₃-N discharges from AAE were close to 0 in most years. Therefore, the industrial AAE was excluded, as were some industries. In the end, 38 industrial sub-sectors were investigated in this study (

Table A1. Classification of industrial sub-sectors studied in this paper and their respective GDP (10¹² CNY), COD (10⁴ t) and NH₃-N (10⁴ t) during the period 2003–2014.

No.	Sector	GDP			COD			NH3-N
		Max	Min	Mean	Max	Min	Mean	Max	Min	Mean
I1	Mining and Washing of Coal	3.10	0.35	1.76	12.4	5.3	8.8	0.57	0.10	0.34
I2	Extraction of Petroleum and Natural Gas	1.19	0.48	0.93	3.3	1.0	1.9	0.24	0.05	0.14
I3	Mining and Processing of Ferrous Metal Ores	0.88	0.05	0.44	1.5	0.8	1.1	0.18	0.02	0.06
I4	Mining and Processing of Non-Ferrous Metal Ores	0.55	0.08	0.32	6.0	3.2	4.4	0.26	0.02	0.14
I5	Mining and Processing of Nonmetal Ores	0.46	0.07	0.24	2.0	0.6	0.9	0.16	0.01	0.04
I6	Mining of Other Ores	0.00	0.00	0.00	0.1	0.0	0.1	0.01	0.00	0.00
I7	Processing of Food from Agricultural Products	5.51	0.83	2.95	67.7	44.1	55.6	4.93	1.88	2.54
I8	Manufacture of Foods	1.77	0.31	0.95	15.5	10.9	12.4	2.57	0.66	1.28
I9	Manufacture of Beverages	1.42	0.30	0.80	25.1	18.7	21.6	1.06	0.39	0.77
I10	Manufacture of Tobacco	0.78	0.31	0.52	0.9	0.2	0.4	0.03	0.01	0.02
I11	Manufacture of Textile	3.31	1.06	2.33	34.5	23.9	29.1	2.02	1.17	1.64
I12	Manufacture of Textile Wearing Apparel, Footware and Caps	1.82	0.46	1.07	2.1	0.8	1.6	0.17	0.04	0.11
I13	Manufacture of Leather, Fur, Feather and Related Products	1.20	0.30	0.70	7.5	4.9	6.4	0.86	0.37	0.64
I14	Processing of Timber, Manufacture of Wood, Bamboo, Rattan, Palm, and Straw Products	1.15	0.13	0.59	3.5	1.1	1.9	0.19	0.03	0.07
I15	Manufacture of Furniture	0.63	0.10	0.35	0.5	0.1	0.2	0.03	0.00	0.01
I16	Manufacture of Paper and Paper Products	1.20	0.34	0.83	159.7	47.8	112.1	4.14	1.63	2.73
I17	Printing, Reproduction of Recording Media	0.59	0.14	0.31	0.5	0.2	0.2	0.10	0.01	0.02
I18	Manufacture of Articles For Culture, Education and Sport	1.29	0.13	0.45	0.2	0.1	0.1	0.02	0.00	0.01
I19	Processing of Petroleum, Coking, Processing of Nuclear Fuel	3.64	0.90	2.45	9.0	6.1	7.7	1.63	0.73	1.25
I20	Manufacture of Raw Chemical Materials and Chemical Products	7.19	1.28	4.05	56.9	3.3	40.4	22.0	6.65	11.88
I21	Manufacture of Medicines	2.02	0.39	1.02	18.8	9.6	11.9	0.94	0.51	0.73
I22	Manufacture of Chemical Fibers	0.65	0.20	0.45	15.7	9.0	12.2	0.48	0.23	0.38
I23	Manufacture of Rubber & Plastics	2.59	0.59	1.60	5.1	1.0	1.7	0.30	0.06	0.12
I24	Manufacture of Non-metallic Mineral Products	4.97	0.75	2.61	7.4	3.0	4.4	0.56	0.14	0.22
I25	Smelting and Pressing of Ferrous Metals	6.82	1.45	4.54	17.6	2.9	11.2	1.91	0.57	1.11
I26	Smelting and Pressing of Non-ferrous Metals	4.44	0.50	2.42	4.9	2.7	3.2	1.87	0.27	0.89
I27	Manufacture of Metal Products	3.15	0.53	1.71	3.5	1.1	2.3	0.27	0.04	0.14
I28	Manufacture of General Purpose Machinery	4.07	0.77	2.58	2.4	0.8	1.5	0.12	0.04	0.08
I29	Manufacture of Special Purpose Machinery	3.01	0.52	1.70	2.1	0.6	1.2	0.56	0.06	0.15
I30	Manufacture of Transport Equipment	7.44	1.56	4.22	6.0	1.6	3.8	0.79	0.09	0.30
I31	Manufacture of Electrical Machinery and Equipment	5.80	1.06	3.40	1.3	0.7	1.0	0.08	0.03	0.05
I32	Manufacture of Communication Equipment, Computers and Other Electronic Equipment	7.40	2.25	4.89	3.7	1.2	2.6	0.32	0.08	0.22
I33	Manufacture of Measuring Instruments and Machinery for Cultural Activity and Office Work	0.72	0.23	0.52	1.3	0.1	0.6	0.07	0.01	0.03
I34	Manufacture of Artwork and Other Manufacturing	0.67	0.17	0.34	0.9	0.2	0.5	0.05	0.01	0.03
I35	Recycling and Disposal of Waste	0.32	0.01	0.15	0.3	0.0	0.2	0.02	0.00	0.01
I36	Production and Supply of Electric Power and Heat Power	4.94	1.58	3.47	13.2	2.5	6.5	0.55	0.13	0.29
I37	Production and Supply of Gas	0.45	0.06	0.21	11.8	0.1	1.6	0.63	0.02	0.20
I38	Production and Supply of Water	0.15	0.06	0.10	2.5	0.0	1.2	0.28	0.00	0.10

Note: Max, Min and Mean denoted the maximum value, the minimum value and the average value of the corresponding indicators among the 38 sub-sectors, respectively.

).

2.2. The IDA-LMDI Decomposition Model

The IDA-LMDA model has been developed within the energy field but not in environment or water-related fields [42]. Therefore, it is worth discussing the effectiveness of this method for water pollutant parameters. Particularly, the two indicator discharges (COD or NH₃-N) were first expressed as Y in Equation (1). Then, Y was decomposed into A, C, S and G (or I, S and G) [23,46,47,48].

$$\begin{array}{ll}Y={\displaystyle \sum _{i=1}^{38}\frac{Y_i}{G}}\times G={\displaystyle \sum _{i=1}^{38}\frac{Y_i}{G_i}\times \frac{G_i}{G}}\times G& ={\displaystyle \sum _{i=1}^{38}\frac{Y_i}{P_i}}\times \frac{P_i}{G_i}\times \frac{G_i}{G}\times G\\ & ={\displaystyle \sum _{i=1}^{38}{A}_i\times C_i\times S_i\times G}\\ & ={\displaystyle \sum _{i=1}^{38}{I}_i\times S_i\times G}\end{array}$$

(1)

where i is the number of industrial sub-sectors, i.e., 1, 2, … , 38; P is the industrial production of the two indicator pollutants; and Y_i, P_i, G_i, A_i, C_i, S_i and I_i are the Y, P, G, A, C, S and I of the i industrial sub-sectors, respectively. The unit of Y is the metric ton (t) and the unit of G is Chinese Yuans (CNY).

Basing the logarithmic differentiation of Equation (1) on time, we get:

$$\frac{d\ln Y}{dt}={\displaystyle \sum _{i=1}^{38}\left[{\mathsf{\phi }}_i(t)\times \left(\frac{d\ln A_i}{dt}+\frac{d\ln C_i}{dt}+\frac{d\ln S_i}{dt}+\frac{d\ln G}{dt}\right)\right]}$$

(2)

where ${\mathsf{\phi }}_i(t)=\frac{A_i\times C_i\times S_i\times G}{Y}=\frac{Y_i}{Y}$.

Integrating Equation (2) over the time interval [0, T], we get:

$$\ln \frac{Y_T}{Y_0}={\displaystyle \sum _{i=1}^{38}{\displaystyle {\int }_0^T{\mathsf{\phi }}_i(t)\times \left(\frac{d\ln A_i}{dt}+\frac{d\ln C_i}{dt}+\frac{d\ln S_i}{dt}+\frac{d\ln G}{dt}\right)}}\times dt$$

(3)

Next, we exponentiate Equation (3):

$$\begin{array}{r}\frac{Y_T}{Y_0}=\exp \left({\displaystyle \sum _{i=1}^{38}{\displaystyle {\int }_0^T{\mathsf{\phi }}_i}(t)\frac{d\ln A_i}{dt}dt}\right)\times \exp \left({\displaystyle \sum _{i=1}^{38}{\displaystyle {\int }_0^T{\mathsf{\phi }}_i}(t)\frac{d\ln C_i}{dt}dt}\right)\\ \times \exp \left({\displaystyle \sum _{i=1}^{38}{\displaystyle {\int }_0^T{\mathsf{\phi }}_i}(t)\frac{d\ln S_i}{dt}dt}\right)\times \exp \left({\displaystyle \sum _{i=1}^{38}{\displaystyle {\int }_0^T{\mathsf{\phi }}_i}(t)\frac{d\ln G}{dt}dt}\right)\end{array}$$

(4)

In accordance with the definite integral middle value theorem, Equation (4) can be transformed as follows:

$$\begin{array}{r}\frac{Y_T}{Y_0}\cong \exp \left({\displaystyle \sum _{i=1}^{38}{\mathsf{\phi }}_i(t^{\ast })\ln \frac{A_{i,T}}{A_{i,0}}}\right)\times \exp \left({\displaystyle \sum _{i=1}^{38}{\mathsf{\phi }}_i(t^{\ast })\ln \frac{C_{i,T}}{C_{i,0}}}\right)\\ \times \exp \left({\displaystyle \sum _{i=1}^{38}{\mathsf{\phi }}_i(t^{\ast })\ln \frac{S_{i,T}}{S_{i,0}}}\right)\times \exp \left({\displaystyle \sum _{i=1}^{38}{\mathsf{\phi }}_i(t^{\ast })\ln \frac{G_T}{G_0}}\right)\end{array}$$

(5)

where ${\mathsf{\phi }}_i(t^{\ast })$ is a weight function given by ${\mathsf{\phi }}_i(t)=\frac{Y_i}{Y}$ at point $t^{\ast }\in [0,T]$.

According to [42], the IDA-LMDI model has the desirable properties of no residual term and aggregation consistency, which allows sub-sector estimates to be consistently aggregated. Correspondingly, the ${\mathsf{\phi }}_i(t^{\ast })$ weight function could be expressed as:

$${\mathsf{\phi }}_i(t^{\ast })=\frac{L\left(Y_{i,T},Y_{i,0}\right)}{L\left(Y_T,Y_0\right)}$$

(6)

where the logarithmic mean of two positive numbers is:

$$L\left(x,y\right)=\{\begin{array}{cc}(x-y)/(\ln x-\ln y),& x\ne y>0\\ x& x=y>0\end{array}$$

(7)

Thus, Equation (5) could be simplified to:

$$\mathsf{\Psi }{Y}_{TOT}=Y_T/Y_0=\mathsf{\Psi }{Y}_I\times \mathsf{\Psi }{Y}_S\times \mathsf{\Psi }{Y}_G=\mathsf{\Psi }{Y}_A\times \mathsf{\Psi }{Y}_C\times \mathsf{\Psi }{Y}_S\times \mathsf{\Psi }{Y}_G$$

(8)

where $\mathsf{\Psi }{Y}_{TOT}$ means the total multiplicative changes of the discharges from year 0 ($Y_0$) to T ($Y_T$). $\mathsf{\Psi }{Y}_{\mathsf{\zeta }}=\exp \left({\displaystyle \sum _{i=1}^{38}\frac{(Y_{i,T}-Y_{i,0})/(\ln Y_{i,T}-\ln Y_{i,0})}{(Y_T-Y_0)/(\ln Y_T-\ln Y_0)}\times \ln \frac{{\mathsf{\zeta }}_{i,T}}{{\mathsf{\zeta }}_{i,0}}}\right)$, and $\mathsf{\zeta }$ denotes I, A, C, S and G.

Equation (8) is the multiplicative formulation of LMDI decomposition for the discharge of COD or NH₃-N in China. The corresponding additive formulation of the LMDI model can be written as follows based on [42]:

$$\mathrm{\Delta }{Y}_{TOT}=Y_T-Y_0=\mathrm{\Delta }{Y}_I+\mathrm{\Delta }{Y}_S+\mathrm{\Delta }{Y}_G=\mathrm{\Delta }{Y}_A+\mathrm{\Delta }{Y}_C+\mathrm{\Delta }{Y}_S+\mathrm{\Delta }{Y}_G$$

(9)

where $\mathrm{\Delta }{Y}_{TOT}$ means the total additive changes of the discharges from year 0 ($Y_0$) to T ($Y_T$). $\mathrm{\Delta }{Y}_{\mathsf{\zeta }}={\displaystyle \sum _{j=1}^{38}\frac{Y_{i,T}-Y_{i,0}}{\ln Y_{i,T}-\ln Y_{i,0}}\times \ln \frac{{\mathsf{\zeta }}_{i,T}}{{\mathsf{\zeta }}_{i,0}}}$. The unit of $\mathrm{\Delta }{Y}_{\mathsf{\zeta }}$ is t, but $\mathsf{\Psi }{Y}_{\mathsf{\zeta }}$ in Equation (8) has no unit.

In this paper, all the decompositions were based on [42], and the $\mathsf{\Psi }{Y}_{TOT}$ and $\mathrm{\Delta }{Y}_{TOT}$ were decomposed into several different effects associated with the following factors: economic output ($\mathsf{\Psi }{Y}_G$ and $\mathrm{\Delta }{Y}_G$), industrial structure ($\mathsf{\Psi }{Y}_S$ and $\mathrm{\Delta }{Y}_S$), and discharge intensity ($\mathsf{\Psi }{Y}_I$ and $\mathrm{\Delta }{Y}_I$). Moreover, the effects of discharge intensity could be further decomposed into two different effects, namely, the effects of clean production technologies ($\mathsf{\Psi }{Y}_C$ and $\mathrm{\Delta }{Y}_C$) and pollution abatement technologies ($\mathsf{\Psi }{Y}_A$ and $\mathrm{\Delta }{Y}_A$).

3. Results and Discussion

3.1. Comparisons of Various Stages from a Periodic Perspective

It is well known that a plan for national economic and social development has been proposed by the Chinese government every 5 years beginning in 1953, with the exception of 1963–1965, that is known as the “Five-Year Plan” [31]. Moreover, since 1978, China has experienced rapid economic growth [49]; it is the fastest growing country, with a remarkable increase in industrial GDP from 7.47 trillion CNY in 2003 to 22.34 trillion CNY in 2014 and a 10.47% annual average rate of increase (Figure 1a). This growth, coupled with industrialization, urbanization and inadequate investment in a basic pollutant treatment infrastructure, has resulted in widespread water pollution [23]. Therefore, people are increasingly asking whether the country’s development is sustainable and when and how its aquatic environments will be improved. For example, according to the 12th Five-Year Plan (2011–2015), the government of China decided to conduct some measures to reduce its annual discharges of COD and NH₃-N by no less than 8% and 10%, respectively, while still maintaining an annual average rate of GDP growth of at least 7% [17]. As a result, the industrial GDP growth rate obviously decreased, especially in 2014 (6.93%, Figure 1a), and correspondingly, the discharges of COD and NH₃-N almost steadily decreased from 2011 to 2014 (COD discharge slightly increased in 2012, Figure 1b), which showed that the government mitigation efforts were starting to be effective. These trends were obviously different from the respective changes during the period 2003–2010; e.g., the discharges of both COD and NH₃-N prominently increased in 2005, but the discharge of NH₃-N sharply decreased in 2006 (Figure 1b).

To more deeply analyze the potential reasons, we divided the study period (2003–2014) into three stages: 2003–2005, 2005–2010 and 2010–2014. China’s COD discharge obviously increased by 0.254 million metric tons (Mt) with a growth rate of 6% in the period 2003–2005 (Figure 1b and Figure 2a;

Table A2. Detailed multiplicative decomposition results of COD and NH₃-N changes.

Stage	COD					NH3-N
	ΨYTOT	ΨYG	ΨYS	ΨYC	ΨYA	ΨYTOT	ΨYG	ΨYS	ΨYC	ΨYA
2003–2004	0.9848	1.4235	0.9957	0.71	0.9785	1.0295	1.4247	0.9936	0.7254	1.0026
2004–2005	1.0737	1.0948	0.9841	1.018	0.979	1.2375	1.0942	1.0222	0.952	1.1622
2005–2006	0.9508	1.2151	0.9829	0.8353	0.9531	0.7592	1.2142	0.9923	0.8181	0.7702
2006–2007	0.9813	1.1839	0.9981	0.955	0.8696	0.8154	1.1837	1.0092	0.8743	0.7807
2007–2008	0.8997	1.1607	0.9961	0.8676	0.897	0.8666	1.1605	1.0021	0.8451	0.8817
2008–2009	0.9403	1.0915	1.0115	0.8773	0.9707	0.914	1.0912	1.0067	0.8878	0.9372
2009–2010	0.9625	1.2058	0.9924	0.852	0.9441	0.9936	1.2053	1.0023	0.8505	0.967
2010–2011	0.8162	1.1157	1.008	2.0433	0.3552	1.1502	1.1179	1.0231	2.3157	0.4343
2011–2012	1.0398	1.0776	1.002	0.903	1.0665	0.9234	1.0789	1.0102	0.8685	0.9755
2012–2013	0.9385	1.0891	0.9972	1.3047	0.6623	0.9276	1.0891	1.0044	1.4697	0.577
2013–2014	0.9634	1.0409	0.978	0.6323	1.4968	0.9368	1.0406	0.9909	0.5066	1.7935
2003–2005	1.0573	1.5569	0.9836	0.7215	0.957	1.274	1.5585	1.018	0.6826	1.1764
2005–2010	0.7598	2.1866	0.9811	0.5171	0.6849	0.4872	2.1651	1.0173	0.4657	0.4749
2010–2014	0.7673	1.3627	0.9889	1.4844	0.3836	0.9229	1.3637	1.022	1.4897	0.4445
2003–2014	0.6164	4.4543	0.9742	0.5464	0.26	0.5728	4.4404	1.0698	0.4729	0.255

and

Table A3. Detailed additive decomposition results of COD and NH₃-N changes (unit: Mt).

Stage	COD					NH3-N
	△YTOT	△YG	△YS	△YC	△YA	△YTOT	△YG	△YS	△YC	△YA
2003–2004	−0.067	1.551	−0.019	−1.505	−0.095	0.011	0.132	−0.002	−0.119	0.001
2004–2005	0.321	0.409	−0.073	0.081	−0.096	0.090	0.038	0.009	−0.021	0.063
2005–2006	−0.230	0.889	−0.079	−0.822	−0.219	−0.112	0.079	−0.003	−0.082	−0.107
2006–2007	−0.083	0.744	−0.008	−0.203	−0.616	−0.065	0.054	0.003	−0.043	−0.079
2007–2008	−0.438	0.618	−0.016	−0.589	−0.450	−0.039	0.040	0.001	−0.045	−0.034
2008–2009	−0.235	0.334	0.044	−0.499	−0.113	−0.022	0.021	0.002	−0.029	−0.016
2009–2010	−0.138	0.678	−0.028	−0.581	−0.208	−0.001	0.043	0.001	−0.037	−0.008
2010–2011	−0.654	0.352	0.026	2.300	−3.332	0.034	0.027	0.006	0.205	−0.204
2011–2012	0.116	0.221	0.006	−0.302	0.191	−0.020	0.019	0.003	−0.035	−0.006
2012–2013	−0.186	0.250	−0.008	0.778	−1.205	−0.017	0.020	0.001	0.090	−0.128
2013–2014	−0.104	0.111	−0.062	−1.275	1.121	−0.014	0.009	−0.002	−0.148	0.127
2003–2005	0.254	2.016	−0.075	−1.486	−0.200	0.100	0.184	0.007	−0.158	0.067
2005−2010	−1.125	3.203	−0.078	−2.699	−1.550	−0.239	0.257	0.006	−0.254	−0.248
2010–2014	−0.828	0.967	−0.035	1.234	−2.994	−0.018	0.068	0.005	0.087	−0.177
2003–2014	−1.698	5.244	−0.092	−2.121	−4.729	−0.157	0.419	0.019	−0.210	−0.384
Average	−0.154	0.477	−0.008	−0.193	−0.430	−0.014	0.038	0.002	−0.019	−0.035

) and then decreased by 1.125 Mt at a rate of −24% in the years 2005–2010 (Figure 1b and Figure 2b; Table A2 and Table A3). In the period 2010–2014, it decreased again by 0.828 Mt at a rate of −23% (Figure 1b and Figure 2c; Table A2 and Table A3). Overall, COD discharge exhibited an overall decreasing trend (Figure 1b); the amount and rate of change were −1.698 Mt and −38%, respectively (Table A2 and Table A3). Similarly, the discharge of NH₃-N in China increased by 0.100 Mt, with an overall growth rate of 27% during the 2003–2005 period (Figure 1b and Figure 2d; Table A2 and Table A3). It then decreased by 0.239 Mt at an overall rate of change of −51% in the period 2005–2010 (Figure 1b and Figure 2e; Table A2 and Table A3), and in the years 2010–2014, it decreased again by 0.018 Mt at a rate of −8% (Figure 1b and Figure 2f; Table A2 and Table A3). Overall, the discharge of NH₃-N in China showed a decreasing trend (Figure 1b); the amount and rate of change were −0.157 Mt and −43%, respectively (Table A2 and Table A3). A major difference was apparent in the second stage (2005–2010), when the discharge of NH₃-N fluctuated more drastically while decreasing overall, but the discharge of COD steadily decreased.

Here, the contributions of various factors to the changes in the discharge of the two pollutants are discussed. “Contributions” refer to the proportion of the change in discharge caused by each factor at time T (i.e., the additive decomposition result of each factor) compared to the total discharge at time 0 (

Table 1. Contributions of various factors to the changes in COD and NH₃-N discharge (unit: %).

Period	COD					NH3-N
	ΔYTOT	ΔYG	ΔYS	ΔYC	ΔYA	ΔYTOT	ΔYG	ΔYS	ΔYC	ΔYA
2003–2004	−1.52	34.98	−0.42	−33.93	−2.15	2.95	35.93	−0.65	−32.59	0.26
2004–2005	7.37	9.39	−1.66	1.85	−2.20	23.75	10.04	2.45	−5.49	16.75
2005–2006	−4.92	18.99	−1.68	−17.55	−4.68	−24.08	16.97	−0.67	−17.55	−22.83
2006–2007	−1.87	16.77	−0.19	−4.57	−13.87	−18.46	15.26	0.83	−12.15	−22.40
2007–2008	−10.03	14.14	−0.37	−13.49	−10.32	−13.34	13.86	0.20	−15.68	−11.72
2008–2009	−5.97	8.49	1.11	−12.69	−2.88	−8.60	8.35	0.64	−11.38	−6.20
2009–2010	−3.75	18.37	−0.75	−15.73	−5.64	−0.64	18.56	0.23	−16.10	−3.33
2010–2011	−18.38	9.91	0.72	64.66	−93.66	15.02	11.97	2.45	90.15	−89.54
2011–2012	3.98	7.62	0.20	−10.41	6.57	−7.66	7.30	0.97	−13.55	−2.38
2012–2013	−6.15	8.27	−0.27	25.78	−39.93	−7.24	8.22	0.42	37.10	−52.99
2013–2014	−3.66	3.93	−2.18	−44.99	39.58	−6.32	3.85	−0.89	−65.83	56.55
2003–2005	5.73	45.49	−1.70	−33.54	−4.52	27.40	50.21	2.02	−43.21	18.39
2005–2010	−24.02	68.41	−1.67	−57.66	−33.10	−51.28	55.08	1.22	−54.49	−53.09
2010–2014	−23.27	27.19	−0.98	34.71	−84.18	−7.71	29.81	2.09	38.30	−77.91
2003–2014	−38.36	118.44	−2.07	−47.92	−106.81	−42.72	114.28	5.17	−57.41	−104.77

Note: “−“ denotes a negative (mitigating) contribution (effect) to COD or NH₃-N discharge.

). For the period 2003–2014, the contributions of the factors mitigating COD discharge in China, ranked from high to low, were the pollution abatement effect (−106.81%), the clean production effect (−47.92%), and the industrial structure effect (−2.07%), while the only promoting factor was the economic output effect (118.44%). The promotion effect (118.44%) was less than the combined effect of the mitigating factors (−106.81% − 47.92% − 2.07% = −156.80%), which caused a 38.36% decrease in the total COD discharge in the 2003–2014 period (

Table 2. Average annual contribution rates at various stages and for the entire study period (%).

Variable	Effect	COD				NH3-N
		Stage 1 a	Stage 2 a	Stage 3 a	Whole	Stage 1 a	Stage 2 a	Stage 3 a	Whole
Discharge	/	2.865 b	−4.804 b	−5.818 b	−3.487 b	13.7 b	−10.256 b	−1.928 b	−3.884 b
Output	G	22.747	13.681	6.796	10.767	25.106	11.016	7.452	10.389
Structure	S	−0.851	−0.334	−0.245	−0.188	1.008	0.244	0.523	0.47
Intensity	C	−16.772	−11.532	8.676	−4.356	−21.607	−10.897	9.576	−5.219
	A	−2.259	−6.62	−21.046	−9.71	9.193	−10.619	−19.478	−9.524

Notes: ^a Stages 1, 2, and 3 were the periods 2003–2005, 2005–2010 and 2010–2014, respectively; ^b indicates the average annual rate of change of the two discharges; “/” represents a null values.

). Similarly, the factors mitigating NH₃-N discharge were the pollution abatement effect (−104.77%) and the clean production effect (−57.41%), while the promoting factors were the economic output effect (114.28%) and the industrial structure effect (5.17%). However, the combined influence of the promotion effects (114.28% + 5.17% = 119.45%) was less than the influence of the mitigating effects (−104.77% − 57.41% = −162.18%), which caused an obvious 42.73% decrease in NH₃-N discharge during the 2003–2014 period (Table 1).

3.1.1. Output Effect

To smooth the short-term volatile effects of various factors [31,42], the cumulative decomposition results were converted from the multiplicative decomposition results in Table A2, listed in

Table A4. Cumulative decomposition results converted from the multiplicative results (2003 = 1).

Year	COD					NH3-N
	ΨYTOT	ΨYG	ΨYS	ΨYC	ΨYA	ΨYTOT	ΨYG	ΨYS	ΨYC	ΨYA
2004	0.9848	1.4235	0.9957	0.7100	0.9785	1.0295	1.4247	0.9936	0.7254	1.0026
2005	1.0574	1.5584	0.9799	0.7228	0.9580	1.2740	1.5589	1.0157	0.6906	1.1652
2006	1.0054	1.8937	0.9631	0.6037	0.9130	0.9672	1.8928	1.0078	0.5650	0.8975
2007	0.9866	2.2419	0.9613	0.5766	0.7940	0.7887	2.2405	1.0171	0.4939	0.7006
2008	0.8876	2.6022	0.9575	0.5002	0.7122	0.6835	2.6001	1.0192	0.4174	0.6178
2009	0.8346	2.8403	0.9685	0.4389	0.6913	0.6247	2.8373	1.0261	0.3706	0.5790
2010	0.8033	3.4248	0.9612	0.3739	0.6527	0.6207	3.4198	1.0284	0.3152	0.5599
2011	0.6557	3.8211	0.9689	0.7640	0.2318	0.7139	3.8230	1.0522	0.7299	0.2431
2012	0.6818	4.1176	0.9708	0.6899	0.2472	0.6592	4.1246	1.0629	0.6339	0.2372
2013	0.6398	4.4845	0.9681	0.9001	0.1638	0.6115	4.4921	1.0676	0.9317	0.1369
2014	0.6164	4.6679	0.9468	0.5691	0.2451	0.5729	4.6745	1.0579	0.4720	0.2455

Note: “2003 = 1” indicated the corresponding values in 2003 were supposed as 1, and the values in the other years were the relative values divided by the values of 2003.

and depicted in Figure 3. The average annual contributions and the trends of the changes in each effect at different stages are shown in Figure 4 and Table 2, respectively.

It was apparent that only economic output always had a positive effect on the discharge of the two pollutants, and it trended sharply upward during the period 2003–2014 (ΨY_G in Figure 3), which is consistent with the results in Figure 4 and Table 2. It can be seen that the dominant factor influencing the increase in the two pollutants was the gross output expansion (Figure 4).

The additive decomposition effect of economic output on the discharge of COD was 2.016 Mt in the years 2003–2005 (Figure 2a), indicating that economic output was the largest driver of COD growth during the period 2003–2005, and in the years 2005–2010 and 2010–2014, the decomposition effects were 3.203 and 0.967 Mt (Figure 2b,c), respectively. In total, the effect of economic output made the greatest contribution to the total annual rate of COD discharge (10.767%) (Table 2), and for each of the three stages, the average annual contribution rates were 22.747%, 13.681% and 6.796%, respectively (Table 2). Simply stated, the growth in economic output growth was always the most prominent factor driving the increase in COD discharge.

Similarly, the additive decomposition effect of economic output on NH₃-N discharge was 0.184 Mt in the years 2003–2005 (Figure 2d), and in the periods 2005–2010 and 2010–2014, the effects were 0.257 and 0.068 Mt (Figure 2e,f), respectively. Moreover, the effect of economic output on the annual rate of NH₃-N discharge (10.389%) made a greater contribution than the other factors (Table 2), and during the three stages, the corresponding average annual contribution rates were 25.106%, 11.016% and 7.452%, respectively (Table 2). Additionally, these results mean that the growth in economic output was always the most prominent factor driving the growth in NH₃-N discharge.

3.1.2. Structure Effect

Similar to some previous studies [32,34], we also considered industrial structural adjustment to be an important factor lowering the discharge of pollutants; indeed, it had an overall mitigating effect on COD discharge during the period 2003–2014. For example, the average annual contribution rate of industrial structure to COD discharge was −0.188% (Table 2), and in the three stages, the corresponding average annual contribution rates were −0.851%, −0.334%, and −0.245%, respectively (Table 2). This regularity indicated that the industrial structure adjustment in China effectively mitigated COD discharge (Figure 4).

However, the effect of industrial structure adjustment on NH₃-N discharge was inverted during the period 2003–2014; the average annual contribution rate was 0.470% (Table 2), and in the three stages, the corresponding average annual contribution rates were 1.008%, 0.244%, and 0.523%, respectively (Table 2), indicating that the industrial structure adjustment did not effectively mitigate NH₃-N discharge in China (Figure 4). Therefore, to some extent, the effect of the industrial structure adjustment efficiently mitigated the discharge of some pollutants, such as COD, into the water, but it might inefficiently mitigate the discharge of some other pollutants, such as NH₃-N. Therefore, we should more deeply examine the effect of industrial structure from the perspective of different sub-sectors (the results are presented in Section 3.2).

3.1.3. Intensity Effects (Pollution Abatement Effect and Clean Production Effect)

Overall, the mitigation impacts of the intensity effects on the discharge of both COD and NH₃-N were stronger than those of the structural effects (Figure 2, Figure 3 and Figure 4; Table 1 and Table 2). These results indicated that the reductions in COD and NH₃-N discharges in China should mainly depend on the utilization and improvement of intensity effects, especially in industrial sectors, because the intensity effect reflected the overall technological improvement under the guidelines of various environment laws, regulations, tax policies and other measures [23].

As recommended by Lei et al. [26], the intensity effect was equal to the pollution abatement effect plus the clean production effect ($\mathrm{\Delta }{Y}_I=\mathrm{\Delta }{Y}_A+\mathrm{\Delta }{Y}_C$), so we considered them separately. In most years, the pollution abatement effect had the dominant mitigation impact on the discharge of COD and NH₃-N (Table 1). The largest average annual contribution to the rate of COD discharge (−9.710%, Table 2) came from the pollution abatement effect; in the three stages, the average annual contribution rates were −2.259%, −6.620% and −21.046%, respectively (Table 2). The corresponding decreases in COD discharge due to pollution abatement were −0.200, −1.550 and −2.994 Mt, respectively (Figure 2). Similarly, the average annual contribution of the pollution abatement effect to the rate of NH₃-N discharge was also the largest (−9.524%, Table 2), and in the three stages, the average annual contribution rates were 9.193%, −10.619% and −19.478%, respectively (Table 2). The corresponding reductions in COD discharge due to the pollution abatement effect were 0.067, −0.248 and −0.177 Mt, respectively (Figure 2). Thus, the pollution abatement effect was the most important factor mitigating water pollution due to reductions in the discharges of COD and NH₃-N in China.

In terms of the clean production effect, it also had an overall mitigation impact on the discharge of COD and NH₃-N (Table 1), but the average annual contribution of the clean production effect on the COD discharge rate was the second largest (−4.356%) during the period 2003–2014 (Table 2). In the three stages, the corresponding declines in COD discharge due to the clean production effect were −1.486, −2.699 and 1.234 Mt with average annual contribution rates of −16.772%, −11.532% and 8.676%, respectively (Figure 2 and Table 2). Similarly, the average annual contribution rate of the clean production effect to the mitigation of NH₃-N discharge was also the second largest (−5.219%, Table 2). From stage 1 to stage 3, the corresponding declines in NH₃-N discharge due to the clean production effect were −0.158, −0.254 and 0.087 Mt with average annual contribution rates of −21.607%, −10.897% and 9.576%, respectively (Figure 2 and Table 2). Thus, we could say that after the pollution abatement effect, the clean production effect was the next most important factor mitigating the discharge of COD and NH₃-N in China.

3.2. Comparisons of Different Sub-Sectors from a Structural Perspective

As mentioned above, there were 38 industrial sub-sectors, so it was essential for us to separate the contribution of each sub-sector to the four effects (economic output, industrial structure, clean production and pollution abatement) based on the suggestion in Section 3.1.2, and the corresponding results were as follows. Figure 5 shows the contributions of the four decomposition effects and their total effect on the two pollutants (COD and NH₃-N) for all 38 sub-sectors. The top ten sub-sectors contributing to the discharge of COD and NH₃-N from the perspective of the different decomposition effects are shown in

Table 3. The top ten sub-sectors contributing to COD discharge by their different effects (10⁴ t).

No.	Output Effect	Structure Effect	Clean Production	Pollution Abatement	Intensity Effect
1	12.72 (I16)	−2.68 (I16)	−4.48 (I7)	−15.39 (I16)	−19.57 (I16)
2	7.50 (I7)	1.67 (I7)	−4.18 (I16)	−6.48 (I7)	−10.96 (I7)
3	5.57 (I20)	−0.91 (I11)	−3.54 (I20)	−3.81 (I20)	−7.35 (I20)
4	3.41 (I11)	0.64 (I20)	−2.11 (I37)	−2.81 (I9)	−3.18 (I9)
5	2.87 (I9)	−0.46 (I22)	−1.08 (I25)	−2.28 (I11)	−2.88 (I21)
6	1.92 (I21)	0.34 (I1)	1.03 (I24)	−2.17 (I21)	−2.56 (I11)
7	1.74 (I8)	−0.26 (I36)	−0.71 (I21)	−1.69 (I24)	−2.25 (I8)
8	1.71 (I22)	0.22 (I8)	−0.70 (I22)	−1.66 (I8)	−1.90 (I25)
9	1.13 (I1)	0.20 (I26)	−0.64 (I30)	−0.98 (I36)	−1.66 (I37)
10	1.05 (I19)	0.17 (I24)	−0.62 (I36)	−0.82 (I25)	−1.60 (I36)
Sum of ten	39.62	−1.07	−17.03	−38.09	−53.90
All subsectors	47.67	−0.83	−19.29	−42.99	−62.28
Share of ten	83.12%	128.64%	88.30%	88.61%	86.55%

Note: The orders were sorted by their corresponding absolute value; the values in parentheses are the corresponding industrial numbers, which are consistent with those in Table A1.

and

Table 4. The top ten sub-sectors contributing to NH₃-N discharge by their different effects (10⁴ t).

No.	Output Effect	Structure Effect	Clean Production	Pollution Abatement	Intensity Effect
1	1.60 (I20)	0.18 (I20)	−1.02 (I20)	−1.79 (I20)	−2.81 (I20)
2	0.35 (I7)	0.08 (I7)	−0.21 (I7)	−0.34 (I7)	−0.55 (I7)
3	0.31 (I16)	−0.07 (I16)	−0.21 (I25)	−0.28 (I8)	−0.37 (I16)
4	0.21 (I19)	−0.05 (I11)	−0.10 (I16)	−0.27 (I16)	−0.35 (I8)
5	0.20 (I11)	0.03 (I26)	−0.07 (I37)	−0.13 (I19)	−0.20 (I25)
6	0.20 (I8)	0.03 (I8)	−0.07 (I8)	−0.12 (I26)	−0.18 (I26)
7	0.19 (I25)	−0.02 (I19)	0.06 (I24)	−0.08 (I24)	−0.18 (I19)
8	0.09 (I21)	−0.01 (I22)	−0.06 (I30)	−0.08 (I11)	−0.10 (I11)
9	0.08 (I9)	−0.01 (I36)	−0.06 (I26)	−0.07 (I13)	−0.09 (I30)
10	0.08 (I26)	0.01 (I24)	−0.04 (I19)	−0.06 (I29)	−0.09 (I29)
Sum of ten	3.31	0.16	−1.77	−3.24	−4.91
All subsectors	3.81	0.17	−1.91	−3.49	−5.40
Share of ten	86.94%	95.29%	92.63%	92.69%	90.84%

, respectively. The changes in the contributions of the economic output of some special sub-sectors from 2003 to 2014 are shown in

Table 5. The increase in the share of the output of I1, I7, I8, I20, I24 and I26 from 2003 to 2014 (%).

No.	Sector	2003	2014
I1	Mining and Washing of Coal	1.73	2.75
I7	Processing of Food from Agricultural Products	4.09	5.77
I8	Manufacture of Foods	1.51	1.85
I20	Manufacture of Raw Chemical Materials and Chemical Products	6.30	7.53
I24	Manufacture of Non-metallic Mineral Products	3.71	5.20
I26	Smelting and Pressing of Non-ferrous Metals	2.47	4.65

.

3.2.1. COD

For the period 2003–2014 overall, the average annual change in the industrial COD discharge in China was −15.44 × 10⁴ t, and the effects of economic output, industrial structure, clean production and pollution abatement were −42.99 × 10⁴, −19.29 × 10⁴, −0.83 × 10⁴ and 47.67 × 10⁴ t, respectively (Table A3). Thus, there were twelve industrial sub-sectors that had a combined positive effect on the discharge of COD, e.g., “Mining and Washing of Coal (I1)”, “Manufacture of Rubber and Plastics (I23)”, “Manufacture of Chemical Fibers (I22)”, etc. (Figure 5a). The total effect of these sectors could be calculated as 2.34 × 10⁴ t, which accounts for −15.2% of the change in COD discharge. Conversely, there were twenty-six industrial sub-sectors with a total negative effect on COD discharge, e.g., “Manufacture of Paper and Paper Products (I16)”, “Processing of Food from Agricultural Products (I7)”, etc. (Figure 5a). The total effect of these sectors was −17.78 × 10⁴ t, accounting for 115.2% of the change in COD discharge.

In terms of the effect of economic output on the discharge of COD, the main contributing sub-sectors were I16, I7, “Manufacture of Raw Chemical Materials and Chemical Products (I20)”, “Manufacture of Textiles (I11)”, “Manufacture of Beverages (I9)”, “Manufacture of Medicines (I21)”, “Manufacture of Foods (I8)”, I22, I1 and “Processing of Petroleum, Coking, and the Processing of Nuclear Fuel (I19)”. These ten sub-sectors accounted for 83.12% of the output effect from all sub-sectors over the period 2003–2014 (Figure 5a; Table 3) and should therefore be the top sectors targeted for reducing industrial COD discharge. One potential option based on this result could be to attempt to slow the expansion of the economic scale of these sub-sectors or to encourage them to convert to other sub-sectors with less COD discharge. It should be noted that these sub-sectors (except I19) also exerted the greatest mitigation effects (pollution abatement or industrial structure or clean production effects, Table 3), further indicating their significance for mitigating COD discharge. In addition, the sub-sectors “Manufacture of Non-metallic Mineral Products (I24)”, “Smelting and Pressing of Ferrous Metals (I25)”, “Manufacture of Transport Equipment (I30)”, “Production and Supply of Electric Power and Heat Power (I36)” and “Production and Supply of Gas (I37)” also had obvious clean production, pollution abatement or intensity effects on COD discharge mitigation (Table 3), therefore much attention should be paid to these five sub-sectors.

The effect of clean production technology accounted for 30.97% (= −19.29/−62.28) of the intensity effect, which mitigated COD discharge. However, the I24 sub-sector had an opposite effect (Table 3), indicating that it might include some sub-sectors lacking clean technology. Similarly, the structure effect mitigated 0.83 × 10⁴ t of COD discharge, but the sub-sectors I7, I20, I1, I8, “Smelting and Pressing of Non-ferrous Metals (I26)” and I24 had the opposite impacts of 1.67 × 10⁴, 0.64 × 10⁴, 0.34 × 10⁴, 0.22 × 10⁴, 0.20 × 10⁴ and 0.17 × 10⁴ t of COD discharge, respectively. This fact could be attributed to the increases in the output shares of these sectors (Table 5), so the I1, I7, I8, I20, I24, I25 and I26 sub-sectors are extremely important and should be considered when designing mitigation policies.

3.2.2. NH₃-N

During the period 2003–2014, the average annual change in the discharge of industrial NH₃-N in China was −1.42 × 10⁴ t, and the four effects due to economic output, industrial structure, clean production and pollution abatement were 3.81 × 10⁴, 0.17 × 10⁴, −1.91 × 10⁴ and −3.49 × 10⁴ t, respectively (Table A3). Similarly, there were nineteen industrial sub-sectors with positive (driving) effects on NH₃-N discharge, e.g., I11, I9, I24, etc. (Figure 5b). The total promotional effect of these sectors was 0.26 × 10⁴ t, accounting for −18.19% of the change in NH₃-N discharge. Conversely, there were also nineteen industrial sub-sectors with negative (mitigation) impacts on NH₃-N discharge, e.g., I20, I7, I8, etc. (Figure 5b), and the total mitigating effect of these sectors was −1.68 × 10⁴ t, accounting for 118.19% of the change in NH₃-N discharge. The main sub-sectors contributing to the effect of economic output on NH₃-N discharge were I20, I7, I16, I19, I11, I8, I25, I21, I9 and I26, and they accounted for 86.94% of the output effect of all sub-sectors during the period 2003–2014 (Figure 5b; Table 4). Therefore, these ten sub-sectors should also be priorities for reducing industrial NH₃-N discharge. Similarly, these sub-sectors (except I21 and I9) also had the top mitigation effects (pollution abatement or industrial structure or clean production effects, Table 4) on the NH₃-N discharge, and the sub-sectors I22, I36, I37, I30, “Manufacture of Leather, Fur, Feather and Related Products (I13)” and “Manufacture of Special Purpose Machinery (I29)” had obvious industrial structure, clean production, pollution abatement or intensity mitigation effects on NH₃-N discharge (Table 4). Therefore, these six sectors should also be continuously encouraged.

Similarly, the industrial structure effects of the sub-sectors I20, I7, I26, I8 and I24 mitigated 0.18 × 10⁴, 0.08 × 10⁴, 0.03 × 10⁴, 0.03 × 10⁴ and 0.01 × 10⁴ t of NH₃-N discharge, respectively, which could be due to increases in the output shares of these sectors (Table 5). Additionally, the effect of clean production technology accounted for 35.37% (= −1.91/−5.40) of the intensity effect, which mitigated NH₃-N discharge, but the sub-sector I24 had an opposite driving effect (Table 4). Thus, similar to COD, I7, I8, I20, I24 and I26 should receive attention and prior consideration in designing NH₃-N mitigation policies. In addition, it should be noted that sector I16 had an obvious overall mitigation effect on COD discharge, and sector I20 had an obvious overall mitigation effect on NH₃-N discharge (Figure 5). The reason could be due to the dramatic improvement in the production pattern of the two sub-sectors compared to previous energy-intensive and high pollution-emitting modes in China.

By comparing prior COD and NH₃-N discharges from sub-sectors, we could conclude that, because they simultaneously mitigate both the COD and NH₃-N, sectors I7, I8, I20, I24 and I26 should be given priority when designing mitigation policies. The sectors I1, I9, I11, I16, I19, I21, I22 and I25 should be given second priority because of their powerful driving effects on the discharges of the two pollutants due to the growth in outputs. Finally, the I13, I29, I30, I36 and I37 sectors should also be given prior consideration, or their development should be encouraged because of their obvious mitigation effects. Additionally, we could also speculate that, the obtained results and conclusions should be rich and reliable by still using the LMDI method when the data of phosphorus and some bacterial indicators such as the index of coliform organisms were available. However, there were still some detailed problems for which good answers could not be obtained by using only the LMDI: Why were the clean production effects positive for both COD and NH₃-N in the period 2010–2014? Were there any specific reasons for these increases of COD in 2012 and NH₃-N in 2011? These problems may be explained from the relative theories of specialized economic sciences, which are also the focus of our research direction in the future.

4. Conclusions and Implications

During the period 2003–2014, China’s industrial COD discharge decreased by 1.698 Mt, and the contributions of the economic output effect, industrial structural effect, clean production effect and pollution abatement effect were 5.244, −0.092, −2.122 and −4.729 Mt, respectively. Likewise, industrial NH₃-N discharge decreased by 0.157 Mt, and the corresponding contributions of the four effects were 0.419, 0.019, −0.210 and −0.384 Mt, respectively. These results indicated that the growth in economic output was always the most prominent driver of the changes of both the COD and NH₃-N discharges. The average annual contribution rates were 10.77% for COD and 10.39% for NH₃-N, respectively. However, pollution abatement (technology) presented the most obvious mitigation effects, and the corresponding average annual contribution rates were −9.71% for COD and −9.52% for NH₃-N. The impacts of clean production (technology) followed, and corresponding average annual contribution rates were −4.36% for COD and −5.22% for NH₃-N. Therefore, the intensity effect, which was composed of the pollution abatement effect and the clean production effect, played a crucial role in reducing COD and NH₃-N. The industrial structure effect played a minor role in the changes in the two discharges; the average annual contribution rates of the industrial structure effect on the COD and NH₃-N discharges were −0.19% and 0.47%, respectively. The sectors I7, I8, I20, I24 and I26 should be given the highest priority when designing mitigation policies to simultaneously reduce the discharge of both COD and NH₃-N, and the sectors I1, I9, I11, I16, I19, I21, I22 and I25 should be the second priority. In addition, the sectors I13, I29, I30, I36 and I37 should also be given prior consideration or be encouraged to develop as soon as possible due to their obvious mitigation effects.

Therefore, the Chinese government must seek a trade-off between economic development and pollutant discharge reduction; discharge-reduction policies should gradually counteract the economic output effect by activating structural and technology effects. For example, a circular economy should be promoted so that the total consumption of raw materials and fossil fuels and the corresponding discharge of pollutants can be minimized. Second, the government should pay more attention to improving corresponding clean production and pollution abatement technologies. For example, some regulatory policy instruments (i.e., discharge reduction liability, discharge audits, and discharge rights trading) should be more aggressively implemented to encourage industrial enterprises to improve their resource use efficiency and pollutant discharge performance. Third, adjusting the industrial structure should also be the focus of pollutant discharge reduction policy, especially in the sub-sectors of I1, I7, I8, I9, I11, I13, I16, I19, I20, I21, I22, I24, I25, I26, I29, I30, I36 and I37. For example, in the “Mining and Washing of Coal (I1)” sector, some advanced clean and renewable energy technologies, such as photovoltaic batteries and solar thermal power generation, should continue to be promoted to reduce or inhibit the use of coal as much as possible.