ijerph International Journal of Environmental Research and Public Health Int. J. Environ. Res. Public Health International journal of environmental research and public health 1660-4601 MDPI 10.3390/ijerph9062226 ijerph-09-02226 Article Decomposition Analysis of Wastewater Pollutant Discharges in Industrial Sectors of China (2001-2009) Using the LMDI I Metho Lei Hongjun 1 2 Xia Xunfeng 1 Li Changjia 2 Xi Beidou 1 * State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, No. 8 Dayangfang Road, Chaoyang District, Beijing 100012, China; Email: hj_lei2002@163.com (H.L.); xiaxunfengg@sina.com (X.X.) School of Water Conservancy, North China University of Water Conservancy and Hydroelectric Power, No. 36 Beihuan Road, Zhengzhou, Henan 450011, China; Email: lcjyzh@gmail.com Author to whom correspondence should be addressed; Email: xibeidou@yeah.net; Tel./Fax: +86-10-8491-3133. 14 06 2012 06 2012 9 6 2226 2240 11 05 2012 05 06 2012 11 06 2012 © 2012 by the authors; licensee MDPI, Basel, Switzerland. 2012

This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).

China’s industry accounts for 46.8% of the national Gross Domestic Product (GDP) and plays an important strategic role in its economic growth. On the other hand, industrial wastewater is also the major source of water pollution. In order to examine the relationship between the underlying driving forces and various environmental indicators, values of two critical industrial wastewater pollutant discharge parameters (Chemical Oxygen Demand (COD) and ammonia nitrogen (NH4-N)), between 2001 and 2009, were decomposed into three factors: i.e., production effects (caused by change in the scale of economic activity), structure effects (caused by change in economic structure) and intensity effects (caused by change in technological level of each sector), using additive version of the Logarithmic Mean Divisia Index (LMDI I) decomposition method. Results showed that: (1) the average annual effect of COD discharges in China was −2.99%, whereas the production effect, the structure effect, and the intensity effect were 14.64%, −1.39%, and −16.24%, respectively. Similarly, the average effect of NH4-N discharges was −4.03%, while the production effect, the structure effect, and the intensity effect were 16.18%, −2.88%, and −17.33%, respectively; (2) the production effect was the major factor responsible for the increase in COD and NH4-N discharges, accounting for 45% and 44% of the total contribution, respectively; (3) the intensity effect, which accounted for 50% and 48% of the total contribution, respectively, exerted a dominant decremental effect on COD and NH4-N discharges; intensity effect was further decomposed into cleaner production effect and pollution abatement effect with the cleaner production effect accounting for 60% and 55% of the reduction of COD and NH4-N, respectively; (4) the major contributors to incremental COD and NH4-N discharges were divided among industrial sub-sectors and the top contributors were identified. Potential restructuring and regulation measures were proposed for pollutant reduction.

 decomposition analysis industrial wastewater pollutant discharges LMDI I method China 1. Introduction

If any region can be seen as a microcosm with both the environmental and developmental problems and opportunities facing the World, particularly the developing countries, it is China. Since 1978 China’s gross domestic product (GDP) has experienced an annual growth of ten percent, making China the fastest growing country [1]. China’s industrial GDP showed remarkable growth rate of over 10% between 2001 and 2009 (see Figure 1). In 2007, China’s GDP stood at US$3.38 trillion, which made China the world’s third largest economy as far as GDP is concerned [2]. China’s economic growth, industrialization, and urbanization coupled with inadequate investment in basic water supply and treatment infrastructure have resulted in widespread water pollution, and China is quickly moving in the direction of becoming one of the world’s biggest polluters. A report from United Nations Environmental Programme (UNEP) demonstrated that environmental damages were resulting in significant losses of GDP in China each year [3]. More and more attention has been paid to whether or not the country’s development is sustainable and when and how its environmental conditions will be improved.

Changes of China’s industrial gross domestic product during 2001–2009 (Data resources: Annual Statistic Report on Environment in China—ASREC [4]).

In 1991, Grossman innovatively put forward the concept of Environmental Kuznets Curves (EKC) to explain the relationship between economic development and environmental quality. The process can be expressed as an inverted “U”-shape i.e., three stages from harmony to disorder, followed by coordination [5]. However, the inverted “U”-shape is not the sole possible relationship between the two, so economic development is not the exclusive solution to environmental problems [6,7]. To identify the underlying mechanisms of economic progress and environmental degradation, several theories and empirical methods were put forward during the past three decades, i.e., the econometric regression, the structural decomposition analysis (SDA) and the index decomposition analysis (IDA) [8]. IDA is a useful technique for quantifying a large number of underlying factors that contribute to the changes in environmental quality by decomposing these changes at the sector level. The advantage of IDA is that it can be readily applied to various available data at different levels of aggregation [9]. With respect to IDA, there have been a variety of different index methods available, such as Laspeyres index [10] and the Divisia index [11,12]. In 1997, Ang and Choi [13] first put forward the multiplication version of the Logarithmic Mean Divisia Index (namely LMDI II), which resolved the residual problem in the Divisia index method but led to a new problem of discontinuity in aggregation. Ang and Liu proposed the additive version of the Logarithmic Mean Divisia Index (LMDI I), which was complete in decomposition and consistent in aggregation, and efficient in handling zero values [14]. LMDI I is widely used in the industrial energy conservation, and SO2 & CO2 emission reduction analysis [15,16]. However, few attempts have focused on the interaction analysis between the industry wastewater pollutant discharges and the economic growth.

China’s 12th Five-Year Plan (2011–2015) aims to achieve an average annual GDP growth of 7% while protecting the environment with an aim to reduce emissions of chemical-oxygen demand (COD) and ammonia nitrogen (NH4-N) by 8% and 10%, respectively, over the five-year period. In order to achieve harmonized development between economic growth and environmental sustainability, China must strengthen its environmental regulations.

During the period of 2001–2009, industrial COD and NH4-N discharges in wastewater showed an overall declining tendency (see Figure 2a)and the average annual decreases were 148,927.7 t and 14,580.3 t, respectively. The discharge efficiency (amount of pollutant discharges per 104 CNY, in Figure 2b) of COD and NH4-N shows an obvious decline trend from the year 2001 to the year 2009 with the exception of 2005. It is important to know the cause of this change, and to achieve a clear understanding of the relationships among the economic development, the technology improvement, the structure adjustment and the wastewater pollutant discharges. This will make contribution for the policy and regulations making to ensure the realization of COD and NH4-N reduction goal.

This paper analyzes the impact of different factors on the change of China’s industrial COD and NH4-N discharges. The aim of the paper is to identify the dominant factors driving the change of these COD and NH4-N discharges. Based on the two critical pollutants emission from industrial wastewater, the representative IDA method LMDI I is used to demonstrate the interaction mechanism. The changes in industrial pollutant discharges were decomposed into the scale of economic activities (production effect), the technological level of each sector (intensity effect), and the economic structure (structure effect).

Changes of the wastewater discharges in industrial wastewater during 2001–2009, (a) pollutant discharges; (b) pollutant discharge efficiency (Data resources: ASREC [4]).

This paper is organized as follows: Section 2 describes the LMDI I method and the data used. Section 3 presents the impacts of different factors on the changes of China’s industrial COD and NH4-N discharges based on LMDI I. Section 4 presents the conclusions, and the last section offers some policy implications.

 2. Methodology and Data 2.1. Principle and Basic Model

IDA is a useful and meaningful tool for quantifying a large number of underlying driving forces that contribute to changes. The underlying theory of IDA is the definition of a governing function relating an aggregate indicator to a number of pre-defined factors of interest, thus identifying the respective effects of the factors on the aggregate [17]. According to Sun [18], changes in China’s industrial wastewater discharges can be attributed to production effects, intensity effects, and structure effects. In addition, the intensity effect can be divided into cleaner production effect and pollution abatement effect. To illustrate how the IDA approaches work, some related variables are listed in Table 1.

ijerph-09-02226-t001_Table 1

Summary of notations and definitions.

Variables Meaning of variables
Ei,t, Ei,0 Industrial pollutant discharge in sector i in year t and year 0, respectively
Et, E0 Total industrial pollutant discharge in year t(Et= ∑i Ei,t) and year 0, respectively
ei,t, ei,0 Industrial pollutant production in sector i in year t and year 0, respectively
et, e0 Total industrial pollutant productionin year t(et= ∑i ei,t) and year 0, respectively
Gi,t, Gi,0 Production of industrial sector i in year t and year 0, respectively
Gt, G0 Total industrial production in year t(Gt= ∑i Gi,t) and year 0, respectively
Si,t, Si,0 Production share of industrial sector i in year t(Si,t= Gi,t/Gt) and year 0, respectively
Ii,t, Ii,0 Discharge intensity in sector i in year t(Ii,t = Ei,t/Gi,t) and year 0, respectively
Ti,t, Ti,0 Production intensity in sector i in year t(Ti,t= ei,t/Gi,t) and year 0, respectively
Ai,t, Ai,0 Discharge rate in sector i in year t(Ai,t= Ei,t/ei,t) and year 0, respectively
Δ Etot Total change in industrial pollutant discharge from year 0 to t(ΔEtot= Et −E0)
Δ Epdn Impact of production change on the aggregate industrial pollutant discharge
Δ Estr Impact of structural change on the aggregate industrial pollutant discharge
Δ Eity Impact of intensity change on the aggregate industrial pollutant discharge
Δ Etec Impact of clean technologies change on the aggregate industrial pollutant discharge
Δ Eaba Impact of pollution abatement change on the aggregate industrial pollutant discharge
Δ Ersd Residual terms

Grossman and Krueger [19] and De Bruyn [20] adopted the basic IDA model in the environmental quality analysis. In this paper, we express the aggregate industrial wastewater pollutant discharges as a summation of all sectors, and decompose the impact index of industrial wastewater pollutant discharges as follows:

 2.2. Methodology

Decomposition can be carried out either multiplicatively or additively with the same data of i industrial sectors. The additive case is the direct decomposition of a quantity change, which is provided in the chosen measurement unit as the factorized effects. The multiplicative case measures the change by dividing the aggregate intensity of one year, with the factorized effects provided in indices [21]. In summary, different decomposition methods involve approaches formulated differently, which lead to different estimates on the right hand side of the equations. Among the two methods, the results of additive decomposition can be easily to understand and “difference” change is decomposed, whereas the results of multiplicative decomposition can be graphically presented and the “ratio” of an aggregate is obtained.

In additive decomposition, the total change (ΔEtot) is decomposed into three different effects associated with the following factors: ΔEpdn (production effect), ΔEstr (structure effect), and ΔEity (intensity effect). Moreover, the intensity effect (ΔEity) is decomposed into two different effects, i.e., ΔEtec (clean-production effect), and ΔEaba (pollution abatement effect). The additive decomposition takes the form below:

If ΔErsd = 0, the method is considered as a perfect decomposition with no residual terms. Here, the additive LMDI I is presented below:

 2.3. Data Source

Since 2001, China’s industrial sectors have been divided into 40 sub-sectors (see appendix Table A1) in the China Statistical Yearbook (CSY) [22]. Considering the data availability, consistency and the detailed classification of industrial sub-sectors, the time-series data from 2001 to 2009 issues of ASREC [4] and CSY [22] were obtained. In this study, industrial added values at current prices are converted into 2001 constant prices. Since the price indices are not available at the levels of industrial sub-sectors, the added values at constant prices for each sector are derived using the price indices of the industrial sector.

 3. Results and Discussion

Based on LMDI I, the influencing factors of China’s industrial COD and NH4-N discharges in wastewater were analyzed between 2001 and 2009. A positive value of contribution indicates an incremental effect on industrial pollutant discharges, while a negative value indicates a decremental effect.

3.1. Yearly Comparisons

The yearly contribution of different effects to China’s industrial COD and NH4-N discharge were given in Table 2 and Table 3. The average change of industrial NH4-N discharges decreased year by year and the results were given in Table 2. As shown in Table 2, from 2001 to 2009 the industrial COD discharges decreased on average 14.89 × 104 t, and the effects brought about by the production scale, structural scale, intensity scale, cleaner production scale and pollution abatement scale were 72.97 × 104 t, −6.93 × 104 t, −80.94 × 104 t, −53.37 × 104 t and −27.57 × 104 t, respectively. Similarly, the average change of industrial NH4-N discharges was −1.46 × 104 t, and the ΔEpdn,, ΔEstr,, ΔEity,, ΔEtec, and ΔEaba were 5.85 × 104 t, −1.04 × 104 t, −6.27 × 104 t, −3.43 × 104 t and −2.84 × 104 t, respectively. Among the change effect of the different influencing factors, the average value of intensity scale ranked first and the value was mostly negative, indicating that intensity scale was the most effective measures to control pollutants emission, which were confirmed by the study of SO2 emissions in China [12] and SO2 emissions in Germany and Holland [20].

Table 3 suggests that the average annual change effect of COD discharges was −2.99%, and the effects brought by production scale, structural scale, intensity scale, cleaner production and pollution abatement were 14.64%, −1.39%, −16.24%, −10.71% and −5.53%, respectively. Likewise, the average effect changes of NH4-N discharges was −4.03%, and the ΔEpdn,, ΔEstr,, ΔEity,, ΔEtec, and ΔEaba were 16.18%, −2.88%, −17.33%, −9.48% and −7.85%, respectively.

3.1.1. Production Effect

Between 2001 and 2009, China’s industrial sectors experienced rapid growth. As a result, the production effects (ΔEpdn) of China’s industrial pollutants (including COD and NH4-N) discharges were always positive with COD ranging from 46.40 × 104 t to 103.29 × 104 t and NH4-N ranging from 3.03 × 104 t to 9.16 × 104 t (shown in Table 2).

ijerph-09-02226-t002_Table 2

Contribution of different effects to China’s industrial COD and NH4-N discharges (Unit: 104 t).

Period COD NH4-N
Δ Epdn Δ Estr Δ Eity Δ Etec Δ Eaba Δ Etot Δ Epdn Δ Estr Δ Eity Δ Etec Δ Eaba Δ Etot
2001–2002 68.17 −16.27 −86.53 74.03 −160.56 −34.63 5.23 −3.58 0.02 2.87 −2.84 1.68
2002–2003 57.22 14.69 −93.93 −76.66 −17.28 −22.03 4.73 3.61 −9.09 −8.95 −0.15 −0.76
2003–2004 77.13 −17.49 −49.63 −54.14 4.51 10.01 6.52 −3.56 −1.48 1.78 −3.26 1.47
2004–2005 80.63 −15.83 −23.29 −47.14 23.84 41.51 7.33 −1.12 3.25 0.63 2.62 9.46
2005–2006 103.29 9.08 −142.98 −126.66 −16.32 −30.61 9.16 0.51 −20.11 −11.90 −8.21 −10.43
2006–2007 99.14 17.78 −126.41 −121.65 −4.76 −9.49 7.35 1.82 −16.13 −13.00 −3.13 −6.96
2007–2008 51.82 −17.06 −82.98 −50.68 −32.30 −48.22 3.46 −4.41 −2.92 2.68 −5.60 −3.86
2008–2009 46.40 −30.31 −41.78 −24.10 −17.68 −25.68 3.03 −1.60 −3.69 −1.56 −2.13 −2.26
Average 72.97 −6.93 −80.94 −53.37 −27.57 −14.89 5.85 −1.04 −6.27 −3.43 −2.84 −1.46
ijerph-09-02226-t003_Table 3

Contribution of different effects to China’s industrial COD and NH4-N discharges (%).

Period COD NH4-N
Δ Epdn Δ Estr Δ Eity Δ Etec Δ Eaba Δ Etot Δ Epdn Δ Estr Δ Eity Δ Etec Δ Eaba Δ Etot
2001–2002 13.68 −3.26 −17.36 14.86 −32.22 −6.95 14.47 −9.89 0.07 7.93 −7.86 4.65
2002–2003 11.48 2.95 −18.85 −15.38 −3.47 −4.42 13.07 9.98 −25.15 −24.73 −0.41 −2.09
2003–2004 15.48 −3.51 −9.96 −10.86 0.91 2.01 18.04 −9.86 −4.10 4.92 −9.02 4.08
2004–2005 16.18 −3.18 −4.67 −9.46 4.79 8.33 20.26 −3.09 8.98 1.74 7.24 26.14
2005–2006 20.73 1.82 −28.69 −25.42 −3.28 −6.14 25.34 1.41 −55.59 −32.89 −22.70 −28.85
2006–2007 19.90 3.57 −25.37 −24.41 −0.96 −1.90 20.33 5.03 −44.61 −35.94 −8.67 −19.24
2007–2008 10.40 −3.42 −16.65 −10.17 −6.48 −9.68 9.57 −12.20 −8.06 7.42 −15.48 −10.68
2008–2009 9.31 −6.08 −8.38 −4.84 −3.55 −5.15 8.36 −4.42 −10.19 −4.31 −5.88 −6.25
Average 14.64 −1.39 −16.24 −10.71 −5.53 −2.99 16.18 −2.88 −17.33 −9.48 −7.85 −4.03

The average production effects on the changes of COD and NH4-N discharges were 72.97 × 104 t and 5.85 × 104 t, respectively, indicating the expansion in economic scale has caused an increase in water pollutant discharges. Year 2001–2006 witnessed a rapid increase in economic scale, and the production effect showed a rising tendency with an average annual value of 12.84% (see Table 3). This may be caused by the implementation of macroeconomic policies like expansion of domestic demand and increase of investment, which encouraged a large number of infrastructure construction and industrial projects. Industrial GDP growth rate remained at an average value of 18.11% (see Figure 1), and it made the economic development a major contributor to industrial COD and NH4-N discharges. From 2007 to 2009, the industrial GDP kept declining, finally down to 12.59% in 2009, and the corresponding production effect also fell to a certain extent. This is probably because in the Seventeenth National Congress of the Communist Party of China in 2007, China promoted the conservation culture by basically forming an energy- and resource-efficient and environment-friendly structure of industries, pattern of growth and mode of consumption. It is reasonable to conclude that the continued growth in industrial GDP scale accelerated COD and NH4-N discharges. The slower the GDP grows the smaller the contribution it makes to COD and NH4-N discharges. Therefore, China will still have to face the pressure from GDP growth and industrial pollutions discharges in the foreseeable future.

 3.1.2. Structure Effect

Unlike the production effect (ΔEpdn), the structure effect (ΔEstr) showed no significant regularity. The structure effect was relatively stable, as shown in Table 2 and Table 3. The change values of COD was ranging from −30.31 × 104 t (−6.08%) to 17.78 × 104 t (3.57%), while in the majority of cases it was negative and the average annual value was −6.93 × 104 t (−1.39%); while in the case of NH4-N, the change values were between −4.41 × 104 t (−12.20%) and 3.61 × 104 t (9.98%), whereas in most instances it was negative and the average annual value was −1.04 × 104 t (−2.88%). Consequently, changes of economic structure in China’s industry played a certain role in reducing COD and NH4-N discharges, but to a small extent. Through the analysis of atmospheric pollutants emissions in the American Midwest during the period of 1970–2000, Tao et al. also concluded that the decrease of emission level could be contributed to by 80% by the intensity effect and 20% by the structure effect [23]. The reason may lie in that the industrial structural change over the past nine years is insufficient because of slow change effect. Therefore, industrial water pollutant reduction by structure adjustment should be a long-term policy goal. In respect of industrial pollutants reduction, China’s economic structure optimization has a long way to go.

 3.1.3. Intensity Effect

As shown in Table 2, in most cases the intensity effect (ΔEity) showed the maximum negative values among the influencing factors for both COD and NH4-N, indicating that the industrial pollutants reduction was mainly due to the technological progress in the industrial sectors. Particularly, the average total change (ΔEtot) of industrial COD discharges during 2001 and 2009 was −14.89 × 104 t, while the change brought by production effect, intensity effect and structure effects were 72.97 × 104 t, −6.93 × 104 t and −80.94 × 104 t, respectively. Obviously, it was the intensity effect that accounted for 543% of the average total change of industrial COD discharge that pushed industrial COD discharges downward. It was the same situation in NH4-N discharges. The intensity effect is the overall reflection of various environment laws and regulations, tax policies, and other measures, taking direct investment in pollution control as an example. A dominant intensity effect manifests the effectiveness of the various government measures used to reduce pollutant discharges.

As shown in Table 2, the average intensity effect on the changes of COD discharges was −80.94 × 104 t, the cleaner production effect (ΔEtec) and pollution abatement effect (ΔEaba) were −53.37 × 104 t and −27.57 × 104 t, respectively, indicating that both the cleaner production effect and the pollution abatement effect had a decremental effect on industrial COD discharges. Furthermore, the cleaner production effect and pollution abatement effect accounted for 66% and 34% of the intensity effect, respectively, while in the case of NH4-N the ratios were 55% and 45%, respectively, indicating that the cleaner production effect played a major role.

The average changes of industrial pollutant discharges for all sub-sectors between 2001 and 2009 (a) COD; (b) NH4-N.

 3.2. Sector Comparisons

As the industrial wastewater pollutant discharges are the sum of all sub-sectors, it is essential to decompose the contribution of each sector to the production effect, structure effect, intensity effect, cleaner production effect and pollution abatement effect. The results are given below.

3.2.1. COD Case

Figure 3a showed that there was an increase in COD discharge from fifteen industrial sectors (including textile manufacture, processing of food from agricultural products, mining and processing of non-ferrous metal ores and so on), and the total increment was 3.17 × 104 t, accounting for −21.32% of the average total change; the discharges of twenty-five industrial sectors (including manufacture of paper and paper products, manufacture of beverages, manufacture of foods and so on) decreased, and the total decrease was −18.07 × 104 t, accounting for 121.32% of the total change.

The main contributors to the production effect of COD emissions were manufacture of paper and paper products, processing of food from agricultural products, manufacture of raw chemical materials and chemical products, manufacture of textiles and manufacture of beverages (see Table 4). These five sectors increased by 69.63% the total production effect over the period of 2001–2009. Therefore, these five sectors should be the top sectors to reduce industrial COD emissions. One potential option to reduce industrial COD emission, or at least limit its growth rate would be policies to slow down the expansion of these sectors’ economic scale or encourage restructuring to less COD discharge sectors.

ijerph-09-02226-t004_Table 4

The top five contributing sub-sectors to the average production effect of industrial COD discharges (104 t).

Industry sectors Production effect
Manufacture of paper and paper products 27.74
Processing of food from agricultural products 9.72
Manufacture of raw chemical materials and chemical products 7.96
Manufacture of textile 4.78
Manufacture of beverages 3.60
Subtotal of the five sub-sectors 50.81
Total sum of all industrial sub-sectors 72.97

The top six contributors to the accumulated structure effect increased 2.361 × 104 t due to an increase in output shares (see Table 5). However, over the same period there was a decrease in industrial COD emission, thanks to other sectors like manufacture of raw chemical materials and chemical products and processing of food from agricultural products (see Figure 3a).

ijerph-09-02226-t005_Table 5

The top six contributing sub-sectors related to the structure effectand share rate of COD changes between 2001 and 2009.

Industry sectors Share rate (%) ΔEstr (104 t)
2001 2009
Manufacture of paper and paper products 2.25 2.83 0.693
Smelting and pressing of ferrous metals 7.78 10.22 0.516
Production and distribution of water 0.10 0.05 0.430
Manufacture of communication equipment, computers and Other electronic equipment 4.04 11.35 0.313
Manufacture of electrical machinery and equipment 0.51 2.27 0.234
Manufacture of transport equipment 7.26 10.46 0.175

The total amount of COD decrement in the structure effects in these three sectors was 5.30 × 104 t, and this offset accounted for 224.36% of the average total structure effects of the top six sectors (presented in Table 5) between 2001 and 2009. Furthermore, the clean-technology effect of other sectors accounted for 84% of the intensity effect and had an incremental effect on COD discharge, and this might indicate that the other sectors include some sectors with low clean-technology; while the pollution abatement effect for mining of other ores played a major role in the increase of COD discharges. So these two industrial sectors should be the top sectors to boost their intensity effect.

 3.2.2. NH<sub>4</sub>-N Case

Overall, during the period of 2001–2009, the average total change of China’s industrial NH4-N discharge in wastewater was −1.46 × 104 t, and the effects brought by economic scale, economic structure and technological level of the sector were 5.85 × 104 t, −1.04 × 104 t, and −6.27 × 104 t, respectively. Figure 3b showed that there was an increase in NH4-N discharge from twenty-two industrial sectors (including mining and washing of coal, other sectors, manufacture of textile and so on), and the total increment was 0.39 × 104 t, accounting for −26.74% of the average total change; the discharges of eighteen industrial sectors (including manufacture of general purpose machinery, mining and processing of nonmetal ores, mining of other ores and so on) decreased, and the total decrement was −1.85 × 104 t, accounting for 126.74% of the average total change. The main contributors to the average production effect of industrial NH4-N discharges were manufacture of raw chemical materials and chemical products, manufacture of paper and paper products, processing of food from agricultural products, manufacture of foods, manufacture of textile and smelting and pressing of ferrous metals (see Table 6). These six sub-sectors increased 76.08% of the average total production effect over the period of 2001–2009. Therefore, these six sectors should be the top sectors to reduce industrial NH4-N discharge.

ijerph-09-02226-t006_Table 6

The top six contributing sub-sectors related to the average production effect of industrial NH4-N discharges (104 t).

Industry sectors Production effect
Manufacture of raw chemical materials and chemical products 2.72
Manufacture of paper and paper products 0.52
Processing of food from agricultural products 0.48
Manufacture of foods 0.27
Manufacture of textile 0.23
Smelting and pressing of ferrous metals 0.23
Subtotal of the five sub-sectors 4.45
Total sum of all industrial sub-sectors 5.85

The top six contributors to the accumulated structure effect increased 0.117 × 104 t/year due to an increase in output shares (see Table 7). Over the same period, the changes of NH4-N emission in output shares for manufacture of raw chemical materials and chemical products and other sectors decreased (see Figure 3b). The total amount of decrement of NH4-N emission from the structure effects in these two sectors was 0.87 × 104 t, and this offsets accounted for 745.38% of the average total structure effects of the top six sectors during 2001 and 2009.

ijerph-09-02226-t007_Table 7

The top six contributing sub-sectors with related to the structure effect and share rate of NH4-N changes during 2001–2009.

Industry sectors Share rate (%) ∆Estr(104 t)
2001 2009
Smelting and pressing of ferrous metals 7.78 10.09 0.053
Manufacture of communication equipment, computers and other electronic equipment 4.04 13.33 0.021
Production and distribution of water 0.10 0.60 0.017
Manufacture of transport equipment 7.26 11.42 0.016
Manufacture of electrical machinery and equipment 0.51 3.52 0.006
Manufacture of paper and paper products 2.25 2.21 0.004

Particularly, the effect brought by technological level of other sectors, mining and washing of coal was positive, with the value of 0.262 × 104 t and 0.014 × 104 t, respectively; while the structure effect were −0.339 × 104 t and −0.005 × 104 t, respectively. Thus, the intensity effect of the other sectors, mining and washing of coal didn’t play its due role and the technological level should be improved. Additionally, the effect brought by technological level of other sectors, mining and washing of coal was positive with the cleaner production effect accounting for 98% of the intensity effect.

 4. Conclusions

Over period of 2001–2009, China’s industrial COD discharges decreased 14.89 million tons, and the contribution by production scale, structural scale, intensity scale, cleaner production scale and pollution abatement scale were 72.97 × 104 t, −6.93 × 104 t, −80.94 × 104 t, −53.37 × 104 t and −27.57 × 104 t, respectively. Likewise, the average changes of industrial NH4-N discharges were −1.46 × 104 t, and the ΔEpdn,, ΔEstr,, ΔEity,, ΔEtec, and ΔEaba were 5.85 × 104 t, −1.04 × 104 t, −6.27 × 104 t, −3.43 × 104 t and −2.84 × 104 t, respectively. The results also indicate that:

(1) Production effect was the major contributor to the increment of COD and NH4-N discharges in industrial wastewater, while the intensity effect played a crucial role in COD and NH4-N reduction.

(2) As one of the two parts in the intensity effect, the clean-technology effect played a major role in pollution reduction.

(3) Compared with the production effect and intensity effect, the structure effect played a minor role. However, it did decrease industrial pollution discharges between 2001 and 2009, so industrial structure adjustment should be a long-term policy goal in pollution reduction.

(4) The main contributors to the average production effect of the industrial COD discharges were manufacture of paper and paper products, processing of food from agricultural products, manufacture of raw chemical materials and chemical products, manufacture of textile and manufacture of beverages, while the main contributing sub-sectors to the average production effect of the industrial NH4-N discharges were manufacture of raw chemical materials and chemical products, manufacture of paper and paper products, processing of food from agricultural products, manufacture of foods, manufacture of textile and smelting and pressing of ferrous metals. Therefore, these sectors should be the top sectors to offsetthe growth of industrial COD or NH4-N discharges.

 5. Policy Implications

China’s 12th Five-Year Plan (2011–2015) is aiming for average annual GDP growth of 7% and environment protection by cutting the release of COD and ammonia nitrogen (NH4-N) by 8% and 10%, respectively. In order to achieve harmonized development of both economic growth and environmental sustainability, China must strengthen its environmental regulations. Our research results suggest the following pollution reduction strategies:

(1) Manufacture of paper and paper products, processing of food from agricultural products, manufacture of raw chemical materials and chemical products, manufacture of textile and manufacture of beverages should be the top sectors for industrial COD reduction. Manufacture of raw chemical materials and chemical products, manufacture of paper and paper products, processing of food from agricultural products, manufacture of foods, manufacture of textile and smelting and pressing of ferrous metals should be the top sectors for industrial NH4-N reduction. One potential option to reduce industrial COD and NH4-N discharged in wastewater, or at least to limit its growth rate would be policies that slow down the expansion of these sectors’ economic scaleor encourage their restructuring and adjustment to less pollution sectors. Policies should be implemented to encourage the growth of low pollution sectors, and to limit the expansion of pollutants-intensive sectors through the reductionof their exports or the expansion of their imports.

(2) Mining of other ores and mining and washing of coal should be the top sectorstoimportor upgrade advanced technologies.

(3) Economic growth should be integrated with harmonized industrial development and industrial pollutants (both COD and NH4-N) reduction.

 Acknowledgments

The authors are grateful for financial support by National Major Science and Technology Program for Water Pollution Control and Treatment (Nos. 2009ZX07106-001 and 2009ZX07106-03-01), and Ministry of Environmental Protection’s Special Funds for Scientific Research in the Public Interest (No. 201109010-04) of China.

 References Zhang K.M. Wen Z.G. Review and challenges of policies of environmental protection and sustainable development in China J. Environ. Manag. 2008 88 1249 1261 10.1016/j.jenvman.2007.06.019 China Passes Germany in Economic Rankings. (accessed on 15 January 2009) Available online:http://edition.cnn.com/2009/WORLD/asiapcf/01/15/china.economy/index.html?iref=topnews Backer E. Heggelund G. China and UN environmental policy: Institutional growth, learning and implementation Int. Environ. Agreem. Polit. La. E. 2007 7 415 438 10.1007/s10784-007-9053-3 Ministry of Environmental Protection of the People’s Republic of China Annual Statistic Report on Environment in China China Environmental Science Press Beijing, China 2001-2009 Grossman G.M. Krueger A.B. Environmental Impacts of a North American Free Trade Agreement Working paper No. 3914 National Bureau of Economic Research Cambridge, MA, USA 1991 Douglas H.E. Thomas M.S. Stoking the fires? CO2 emissions and economic growth J. Public Econ. 1995 57 85 101 10.1016/0047-2727(94)01449-X Friedl B. Getzner M. Determinants of CO2 emissions in a small open economy Ecol. Econ. 2003 45 133 148 10.1016/S0921-8009(03)00008-9 Ang B.W. Decomposition Methodology in Energy Demand and Environmental Analysis Handbook of Environmental and Resource Economics van den Bergh J.C.J.M. Edward Elgar Publishers Cheltenham, UK 1999 Ma C.B. Stern D.I. China’s changing energy intensity trend: A decomposition analysis Energy Econ. 2008 30 1037 1053 10.1016/j.eneco.2007.05.005 Sinton J.E. Levine M.D. Changing energy intensity in Chinese industry Energy Policy 1994 22 239 255 10.1016/0301-4215(94)90162-7 Huang J.P. Industrial energy use and structural change: A case study of the People’s Republic of China Energy Econ. 1993 15 131 136 10.1016/0140-9883(93)90032-M He J. What is the role of openness for China’s aggregate industrial SO2 emission? A structural analysis based on the Divisia decomposition method Ecol. Econ. 2010 69 868 886 10.1016/j.ecolecon.2009.10.012 Ang B.W. Choi K.H. Decomposition of aggregate energy and gas emission intensities for industry: A refined Divisia index method Energy J. 1997 18 59 73 Ang B.W. Liu F.L. A new energy decomposition method: Perfect in decomposition and consistent in aggregation Energy 2001 26 537 548 10.1016/S0360-5442(01)00022-6 Wang W.C. Mu H.L. Kang X.D. Song R.C. Ning Y.D. Changes in industrial electricity consumption in china from 1998 to 2007 Energy Policy 2010 38 3684 3690 10.1016/j.enpol.2010.02.046 Hu C.Z. Huang X.J. Characteristics of carbon emission in china and analysis on its cause China Popul. Resour. Environ. 2008 18 38 42 Ang B.W. Decomposition analysis for policymaking in energy: What is preferred method? Energy Policy 2004 32 1131 1139 10.1016/S0301-4215(03)00076-4 Sun J.W. Changes in energy consumption and energy intensity: A complete decomposition model Energy Econ. 1998 20 85 100 Grossman G.M. Krueger A.B. Economic growth and the environment Quart. J. Econ. 1995 110 353 377 10.2307/2118443 De Bruyn S.M. Explaining the environmental Kuznets curve: The case of sulphur emissions Environ. Dev. Econ. 1997 2 485 503 10.1017/S1355770X97000260 Ang B.W. Zhang F.Q. Choi K.H. Factorizing changes in energy and environmental indicators through decomposition Energy 1998 23 489 495 10.1016/S0360-5442(98)00016-4 National Bureau of Statistics of China China Statistical Yearbook China Statistics Press Beijing, China 2002-2010 Tao Z.N. Hewings G. Donaghy K. An economic analysis of Midwestern US criteria pollutant emissions trends from 1970 to 2000 Ecol. Econ. 2010 69 1666 1674 10.1016/j.ecolecon.2010.03.016 Appendix ijerph-09-02226-t008_Table 8

Industrial sectors and labels.

Industrial sectors ID Industrial sectors ID
Mining and Washing of Coal I1 Manufacture of Medicines I21
Extraction of Petroleum and Natural Gas I2 Manufacture of Chemical Fibers I22
Mining and Processing of Ferrous Metal Ores I3 Manufacture of Rubber I23
Mining and Processing of Non-ferrous Metal Ores I4 Manufacture of Plastics I24
Mining and Processing of Nonmetal Ores I5 Manufacture of Non-metallic Mineral Products I25
Mining of Other Ores I6 Smelting and Pressing of Ferrous Metals I26
Processing of Food from Agricultural Products I7 Smelting and Pressing of Non-Ferrous Metals I27
Manufacture of Foods I8 Manufacture of Metal Products I28
Manufacture of Beverages I9 Manufacture of General Purpose Machinery I29
Manufacture of Tobacco I10 Manufacture of Special Purpose Machinery I30
Manufacture of Textile I11 Manufacture of Transport Equipment I31
Manufacture of Texile Wearing Apparel, Footware, and Caps I12 Manufacture of Electrical Machinery and Equipment I32
Manufacture of Leather, Fur, Feather and Related Products I13 Manufacture of Communication Equipment, Computers and Other Electronic Equipment I33
Processing of Timber, Manufacture of Wood, Bamboo, Rattan, Plam, and Straw Products I14 Manufacture of Measuring, Instruments and Machinery for Cultural Activity and Office Work I34
Manufacture of Furniture I15 Manufacture of Artwork and Other Manufacturing I35
Manufacture of Paper and Paper Products I16 Recycling and Disposal of Water I36
Printing, Reproduction of Recording Media I17 Production and Distribution of Electric Power and Hear Power I37
Manufacture of Articles for Culture, Education and Sport Activity I18 Production and Distribution of Gas I38
Processing of Petroleum, Coking, Processing of Nuclear Fuel I19 Production and Distribution of Water I39
Manufacture of Raw Chemical Materials and Chemical Products I20 Other Sectors I40