Sulfur dioxide (SO2) emissions and government spending on environmental protection in China - Evidence from spatial econometric analysis

Accepted Manuscript Sulfur dioxide (SO2) emissions and government spending on environmental protection in China - Evidence from spatial econometric analysis Jr-Tsung Huang PII:

S0959-6526(17)32931-1

DOI:

10.1016/j.jclepro.2017.12.001

Reference:

JCLP 11396

To appear in:

Journal of Cleaner Production

Received Date: 25 July 2017 Revised Date:

31 October 2017

Accepted Date: 1 December 2017

Please cite this article as: Huang J-T, Sulfur dioxide (SO2) emissions and government spending on environmental protection in China - Evidence from spatial econometric analysis, Journal of Cleaner Production (2018), doi: 10.1016/j.jclepro.2017.12.001. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Sulfur Dioxide (SO2) Emissions and Government Spending on Environmental Protection in China

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Sulfur Dioxide (SO2) Emissions and Government Spending on Environmental Protection in China－Evidence from Spatial Econometric Analysis

Abstract

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Jr-Tsung Huang National Chengchi University Taipei, 116, Taiwan

This study pays attention to a rarely discussed but important issue regarding the influence of the government’s environmental protection expenditure on Sulfur Dioxide (SO2) emissions in China under consideration of the potentially spatial

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dependence of SO2 emissions. A panel dataset of 30 provinces from 2008 to 2013 is utilized to estimate the panel Spatial Durbin models. The main findings of this study are that SO2 emissions can be effectively reduced by government spending on environmental protection and that the relationship between SO2 emissions and gross regional product (GRP) per capita in China is cubic polynomial (N-shaped). In addition, as direct investment from foreign countries toward China increases, SO2 emissions will be reduced. The trade factor also plays an important and negative role in SO2 emissions. Provinces with higher shares of secondary industries to total GRP

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will have more SO2 emissions. The investment completed in the treatment of industrial pollution in the private sector can effectively reduce SO2 emissions, and a higher population density causes a reduction in SO2 emissions. The coefficient of spatial autocorrelation ρ is statistically positive, confirming a positive spatial

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correlation (also meaning a spatial competition) of SO2 emissions among provinces in China. All these conclusions are consistent with those in previous works and with our expectations, and remain the same while a two-stage procedure is applied to cope with the potential endogeneity problem of GRP, with the exception of the effect of population density. JEL Classifications: C21; H72; Q53; Q58; R58 Keywords: China, Environmental Protection, Government Spending, Panel Spatial Durbin Model, Sulfur Dioxide (SO2) Corresponding Author: Dr. Jr-Tsung Huang Distinguished Professor of Public Finance, National Chengchi University, No. 64, ZhiNan Road Sec.2, Taipei 116, Taiwan. Tel: +886-2-22349884; Fax: +886-2-29387574; Email: [email protected]. 1

Sulfur Dioxide (SO2) Emissions and Government Spending on Environmental Protection in China

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I. INTRODUCTION Air pollution has been a serious problem worldwide for the past several decades

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and has been even more serious in China. China’s remarkable economic performance created by its open-door policy seems to have been accompanied by an obvious problem of environmental pollution, especially air pollution, during the last 25 years. In fact, air quality in China’s urban areas started to quickly deteriorate following the first few years of its economic reform. Since 2012, the hazy weather has severely affected many people in China. People living in those affected areas are not able to go out of their houses without wearing masks.

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The Chinese government has tried to mitigate this problem for a long time. In 1987, the “Law of the People’s Republic of China on the Prevention and Control of Atmospheric Pollution (中华人民共和国大气污染防治法)” was passed, and a

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method for controlling pollution emissions proposed, especially for a cap on the total SO2 volume resulting from coal consumption. This law has been amended many times since then. However, passing a law is one thing, and implementing it is another. The situation whereby the air is seriously polluted has not been effectively improved since that time. Several studies, such as Edmonds (1999) and He et al. (2002), have indicated that a number of China’s cities rank amongst the world’s worst polluted

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cities. The Ministry of Environmental Protection of the People’s Republic of China (PRC, hereafter) indicated that the Beijing-Tianjin-Hebei region, the Yangtze River delta, and the Pearl River delta are regions suffering from more serious air pollution than other regions. For example, in the Beijing-Tianjin-Hebei region, 11 out of 13

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prefecture-level or above cities are ranked among the top 20 cities with the most serious air pollution in China. According to He (2006), although some improvements in terms of reducing the pollution resulted during the 1990s owing to the reinforcement of pollution control policies, two-thirds of Chinese cities still fail to

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meet the air quality standards established by China’s Environmental Protection Agency (EPA), which signifies that more than three-fourths of people living in urban areas are exposed to severely polluted air. Due to the continuous hazy weather causing a collective panic in China, in 2012

the 18th National Congress of the Communist Party of China decided to introduce an important policy of “making great efforts to promote ecological progress” and thus reconsidered amending the law. Since then, many measures have been adopted. In October 2012, on behalf of the Ministry of Environmental Protection, the National Development and Reform Commission and the Ministry of Finance, the State Council released the Twelfth Five Year Plan on Air Pollution Prevention and Control in Key Regions, which targeted air quality improvement in 13 key regions, including the 1

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Beijing-Tianjin-Hebei region, Yangtze River delta, and Pearl River delta with 117 prefectures located in 19 provinces. However, a report entitled “Environmental Assessment in the PRC” conducted by the Asian Development Bank and scholars at Tsinghua University in 2013 indicated that 7 of the top 10 cities with the worst air in the world were located in China. Out of a total of 500 Chinese cities, less than 5 met the standard of air quality established by the World Health Organization (WHO). It is

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thus expected that China’s air pollution problems are likely to persist into the future as public concerns over the environment are tempered by a desire for economic growth. In fact, in the transportation sector, alternative fuels and the latest technological advances can contribute to environmental protection. Streets and Waldhoff (2000)

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concluded that emissions of carbon monoxide are projected to decline from 115 metric tons (mt, hereafter) in 1995 to 96.8 mt in 2020, due to more efficient combustion techniques, especially in the transportation sector. However, if these measures are not realized, carbon monoxide emissions could increase to 130 mt by 2020 in China. As indicated by Annamalai (2016), Dhinesh et al. (2016), and Dhinesh et al. (2017), Cymbopogon flexuosus biofuel could reduce smoke and NOX emissions and be a promising alternative fuel. However, the discovery of more efficient combustion techniques and alternative fuels might have to be financially supported by

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the government. Therefore, in addition to formulating policies for protecting the environment, the Ministry of Finance of the PRC has officially included spending on environmental protection as an item, referred to as “the 211th category”, that has been included in the government budget since 2006. This 211th category includes 31 items

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in 5 divisions, covering all aspects of environmental monitoring, pollution prevention, and ecological protection. Doing this has made environmental protection become an official item of expenditure in the government budget and guarantees government spending on environmental protection. However, this has given rise to an important issue that may be summed up by asking: “Does the government expenditure on environmental protection really mitigate the serious problem of air pollution in China?”

In order to explore this issue, the choice of air pollutant is important. Since air pollution is comprised of primary pollutants,1 both sulfur dioxide (SO2, hereafter) and particulate matter (PM) are air pollutants known to be closely associated with health damage. SO2 is one of a group of gases referred to as sulfur oxides (SOX). While all of these gases are harmful to human health and the environment, 2 SO2 is the 1

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These include particulates, sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon dioxide (CO2), carbon monoxide (CO), and hydrocarbons as well as other secondary pollutants such as nitrates, sulfates and ozone. For example, short-term exposure to SO2 can harm the human respiratory system and make breathing 2

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component of greatest concern and is used as the indicator for the larger group of gaseous sulfur oxides (SOX). While PM comprises a mixture of different particulate components, most SO2 results from electricity generation and industrial processes and, as noted by Poon et al. (2006),3 SO2 is a major source of air pollution in China. Moreover, many related articles have adopted SO2 as the dependent variable in their empirical models (for example, Heil and Selden, 2000; He, 2006; Maddison, 2006;

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Poon et al., 2006; He, 2008a; Papineau et al., 2009; Brajer et al, 2011; Lopez et al., 2011; Halkos and Paizanos, 2013). It is for this reason that this paper uses SO2 emissions as the proxy for air pollution. In addition, as Bernauer and Koubi (2013) noted that SO2 emissions could be controlled by way of government spending on

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improving production technology, this study analyzes SO2 emissions as opposed to ambient air quality. Table 1 presents the SO2 emissions for each province in selected years. It is shown that the rankings of SO2 emissions among provinces do not change over time. The top 5 provinces with the largest SO2 emissions are Shandong, Henan, Hebei, Shanxi, and Inner Mongolia in these selected years. In addition, the bottom 5 provinces also remain the same in the years selected. They are Tibet, Qinghai, Hainan, Beijing, and Tianjin.

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Whereas some efforts have been made to address criticisms regarding the possibility of spatial autocorrelation and heterogeneity problems in the economics literature, only a few studies related to SO2 emissions have devoted attention to such issues, namely, de Groot et al. (2004), Poon et al, (2006), and Maddison (2006). The goal of this paper is to use a panel dataset comprising 30 provinces in China from

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2008-2013 to explore the following three issues while considering the spatial dependence of SO2 emissions. First, is the government’s environmental protection spending really effective in reducing emissions of the air pollutant SO2? Second, what is the relationship between SO2 emissions and per capita gross regional product (GRP, hereafter)? Finally, if GRP is considered to be endogenous, will the results for the first two issues be robust? The remainder of this paper is organized as follows. In the next section, previous related studies are reviewed. Then, the spatial empirical model and the sources and descriptive statistics of the data are introduced. After analyzing the empirical results, the concluding remarks are presented. II. LITERATURE REVIEW

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difficult. Children, the elderly, and those who suffer from asthma are particularly sensitive to the effects of SO2. Smaller sources of SO2 emissions include: industrial processes such as extracting metal from ore, natural sources such as volcanoes, and locomotives, ships and other vehicles and heavy equipment that burn fuel with a high sulfur content. 3

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TABLE 1: Provincial SO2 Emissions in China (selected years, tons) Provinces 2008 2010 2013 Beijing 123,214 (29) 118,794 (29) 97,838 (29) Tianjin 240,100 (27) 236,700 (27) 216,832 (27) Hebei 1,345,100 (4) 1,253,463 (5) 1,411,999 (2) Shanxi 1,308,442 (5) 1,268,428 (4) 1,399,015 (4) Inner Mongolia 1,431,104 (3) 1,398,803 (2) 1,409,400 (3) Liaoning 1,130,696 (9) 1,051,419 (10) 1,126,122 (6) Jilin 377,513 (25) 363,005 (25) 413,190 (23) Heilongjiang 506,342 (20) 490,383 (22) 521,888 (21) Shanghai 446,102 (23) 378,908 (24) 239,951 (26) Jiangsu 1,130,273 (10) 1,074,155 (8) 1,053,555 (8) Zhejiang 740,559 (15) 701,330 (15) 661,575 (16) Anhui 555,694 (19) 538,424 (19) 529,069 (20) Fujian 428,897 (24) 419,656 (23) 389,060 (25) Jiangxi 583,125 (18) 564,222 (18) 584,046 (19) Shandong 1,691,881 (1) 1,590,301 (1) 1,827,235 (1) Henan 1,452,001 (2) 1,355,001 (3) 1,370,461 (5) Hubei 669,787 (16) 643,761 (16) 665,633 (15) Hunan 840,076 (13) 811,502 (12) 685,506 (14) Guangdong 1,135,915 (8) 1,070,488 (9) 847,278 (11) Guangxi 924,584 (11) 903,826 (11) 520,995 (22) Hainan 21,745 (30) 22,031 (30) 32,570 (30) Chongqing 782,400 (14) 746,092 (14) 586,925 (18) Sichuan 1,147,800 (7) 1,135,299 (7) 901,764 (10) Guizhou 1,235,695 (6) 1,175,494 (6) 1,104,284 (7) Yunnan 501,741 (21) 499,307 (21) 691,219 (13) Tibet 2,000 (31) 4,000 (31) 4,192 (31) Shaanxi 889,378 (12) 804,408 (13) 916,836 (9) Gansu 501,536 (22) 500,306 (20) 623,729 (17) Qinghai 134,807 (28) 135,698 (28) 156,601 (28) Ningxia 348,334 (26) 314,245 (26) 410,385 (24) Xinjiang 585,447 (17) 589,900 (17) 763,052 (12)

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Source: China Statistical Yearbook on Environment (2008-2013). Note: Numbers in parentheses are rankings.

The empirical literature on the determinants of pollution has been dominated by analyses focusing on the “Environmental Kuznets Curve (EKC, hereafter)” that posits that pollution will initially increase with economic development but then decrease after incomes reach a certain level.4 However, the EKC is not the primary issue in this study, but the role played by China’s provincial government in reducing air pollution through its environmental protection expenditure is. Therefore, this study 4

Since the two pioneering works on this topic, namely, Grossman and Krueger (1991, 1995), an abundance of studies have explored this idea from a number of different angles, such as, does pollution follow an EKC? At what income level does the turnaround occur? Do all pollutants follow the same trajectory? Is pollution reduction in developed economies due primarily to structural change, technological advances or regulation? 4

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reviews three strands of the literature, namely, China, the government’s environmental protection expenditure, and the spatial dependence of SO2 emissions. In fact, environmental protection can be treated as a public good. For studies related to the government’s expenditure on public goods, Lopez and Palacios (2010) concluded that total government expenditure is a negative and significant determinant of air pollution in Europe, even after controlling for the composition of public

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expenditure.5 Lopez et al. (2011) further developed theoretical and empirical models to conclude that the reallocation of government spending towards social and public goods reduces SO2 pollution and that any increases in total government spending have an ambiguous effect on SO2 pollution. 6 Recently, Halkos and Paizanos (2013)

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examined the impact of government spending on the environment using a panel of 77 countries for the period 1980-2000. For SO2, according to their panel and generalized method of moments (GMM) estimations, government spending was estimated to have a negative direct impact on per capita emissions. The indirect effect on SO2 was negative for low income levels and became positive as income increased. The total effect on SO2 was largely determined by the more dominant indirect effect. With respect to China’s SO2 pollution, Hao et al. (2015) used China’s city-level panel data between 2002 and 2012 to investigate the existence of convergence in per

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capita SO2 emissions across Chinese cities. Dynamic panel data estimators were utilized to avoid the endogeneity problem and the static estimation results were used to conduct a robustness check. The empirical results indicate that per capita GRP and the ratio of secondary industry to GRP are both positively correlated with per capita

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SO2 emissions. In addition, Lyu et al. (2016) applied the Logarithmic Mean Divisia Index method to explore the key contributors driving changes in air pollution emissions in China from 1997 to 2012. The decomposition results identified the economic growth effect as the primary factor in driving the growth of primary PM2.5,

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SO2 and NOX emissions in China, especially since 2002. In addition, economic activities from the secondary sector have contributed the most to the growth of all air pollutant emissions and the population effect has also played an essential role. The recent explosive expansion of the transportation sector might also have increased the air pollution, in particular NOX emissions. In order to examine the impacts of total government expenditure on the emissions of three typical pollutants, namely, SO2, soot, and chemical oxygen demand (COD), Zhang et al. (2017) further adopted the 5

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Lopez and Palacios (2010) used data for 21 countries for the period 1995-2006. However, in the case of SO2, the total number of observations was 14,078 for the 1995-2006 period distributed in 2,759 stations across 21 countries. Lopez et al. (2011) used data for 38 countries from 1986-1999 (including 1,900 observations distributed in 120 cities) obtained from the WHO Automated Meteorological Information System dataset for the case of SO2 pollution and several specifications of the empirical model. 5

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city-level panel data of 106 Chinese cities over the 2002-2014 period and the GMM method for controlling potential endogeneity to find that the total effects of government expenditure on these three pollutants are very different: the total effects for SO2, soot, and COD are decreasing, inverted U-shaped, and U-shaped, respectively.

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Whereas criticisms of the possibility of spatial autocorrelation and heterogeneity problems have been addressed in the EKC-related literature, relatively few studies have devoted any attention to these problems. de Groot et al. (2004) attempted to model EKC from a spatial perspective to test for the existence of the EKC in China. Using a panel dataset for 30 provinces from 1982 to 1997, as industrial waste gas

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emissions were measured in per capita terms, a result consistent with the typical Kuznets hypothesis was obtained. However, the level-results were actually consistent with an N-shaped income/emission relationship.7 Poon et al. (2006) utilized Chinese provincial-level panel data from 1998 to 2004 and a spatial econometric model that took into account potential regional spillover effects from high-polluting neighbors to find an EKC type (i.e., inverted-U) relationship for SO2, but not for soot particles. Even when using a panel dataset for 135 countries in 1990 and 1995 and considering the spatial dependence of air pollution, Maddison (2006) found that per capita

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national emissions of SO2 and NOX were shown to be strongly affected by the emissions per capita of neighboring countries. That is to say, there is no support for the suggestion from the EKC that environmental problems are resolved automatically by economic growth. There is instead a mechanism by which changes in emissions are transmitted to neighboring countries.8

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In order to investigate the primary issue, according to de Groot et al. (2004), Poon et al. (2006), and Maddison (2006), this study applies a spatial econometric approach as introduced in the next section. The advantages of this approach are that it

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considers the potential presence of the spatial dependency of SO2 emissions and avoids misleading and illusory results due to the assumption of the independence of provincial SO2 emissions. III. METHODOLOGY AND DATA This study is concerned more with the total volume of SO2 emissions that is really the cause of air pollution rather than per capita SO2 emissions. Certainly, some 7

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However, when the pollutant is measured per unit of output, a U-shaped pattern appears that is inconsistent with the expected inverted-U shape. Apparently, the way in which the pollution variable is measured has some influence on the nature of the income/pollution pattern obtained. National emissions per capita of nitrogen oxide are found to decrease by proximity to high per capita income countries which is inconsistent with nations achieving higher environmental quality at the expense of their neighbors. 6

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population factors will be considered in the empirical analysis of this study. Prior to using spatial analysis, the spatial distributions of SO2 emissions among provinces from 2008 to 2013 are depicted in Figure 1 to examine the spatial dependence of regional SO2 emissions. The darker the shade, the higher the level of the SO2 emissions is. As shown in Figure 1, during these 6 years, provinces with a higher (lower) level of SO2 emissions were clustered in northern and north-eastern (western

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and south-western) China. It seems that provincial SO2 emissions exhibit a pattern of spatial dependence, but this finding needs to be further confirmed statistically by Moran’s I, a spatial correlation index proposed by Moran (1950).9

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FIGURE 1: Spatial Distribution of Provincial SO2 Emissions in China (2008~2013) Sources: China Statistical Yearbook (2009-2014) and China Statistical Yearbook on Environment (2009-2014).

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Moran’s I is widely used to test for the presence of spatial dependence in observations taken on a lattice. As suggested by Anselin (1995), a disaggregated investigation of the nature of spatial autocorrelation may be performed using the local Moran’s I. Mobley et al. (2009) also asserted that Moran’s I is a reliable statistic and useful for testing for spatial correlation and spatial heterogeneity. The formula for Moran’s I can be described as follows: I=

n n

n

i =1

j =1

∑ ∑

wi , j

×

n

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i =1

j =1

∑ ∑

wi , j ( y i − y )( y j − y )

∑

th

n i =1

( yi − y ) 2

where yi refers to the SO2 emissions of the i province, i≠j, and wi,j is an element in the i-th row and j-th column of a spatial weight matrix W that equals 1 if regions i and j are adjacent, and equals 0 if regions i and j are non-adjacent. The numbers in the spatial weight matrix W are row-standardized, that is, the sum of the elements in each row is equal to 1. If the test results reject the null hypothesis of no spatial correlation, they imply that the observations feature spatial correlation. 7

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The estimated values of Moran’s I for SO2 emissions for each year from 2008 to 2013 are 0.048, 0.040, 0.035, 0.014, 0.005, and -0.011, respectively. According to the p-values, the null hypothesis of no spatial correlation is rejected at the 5% significance level in 2008-2010, and at the 10% level in 2011. Since the null hypothesis is statistically rejected in 4 out of 6 years, provincial SO2 emissions in China might be characterized by positive spatial correlation. Ignoring this feature in

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econometric models could give rise to inefficient or even biased estimates (LeSage and Pace, 2009). The commonly-used spatial econometric models for continuous dependent variables are the Spatial Durbin Model (SDM), Spatial Autoregressive model (SAR),

SEM.

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and Spatial Error Model (SEM). The SDM, extended from the SAR, was created by LeSage and Pace (2009), and includes spatial-lag terms for both the dependent and independent variables. The advantage of the SDM is that it overcomes the problems of omitted variables and spatial heterogeneity that might be ignored in the SAR and

Using a simple model with a single explanatory variable x, the panel SDM can be described as follows: N

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SO2i ,t = µi + ρ∑ wi , j SO2 j ,t + α + βxi ,t + θ ∑ wi , j x j ,t + ε i ,t , i ≠ j j =1

(1)

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where SO2i,t is the dependent variable of SO2 emissions for province i in year t, wi,j is an element in the i-th row and j-th column of a spatial weight matrix W, wi,jSO2j,t stands for the effect of adjacent dependent variables SO2j,t on SO2i,t, ρ is a coefficient

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of spatial autocorrelation, θ and β are coefficients of explanatory variables, α is the constant term, ε is the error term that is distributed normally with zero mean and variance σ2, and µi represents the spatial-specific effect in a panel data model. LeSage and Pace (2009) further considered an average total effect as the sum of the average

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direct effect and the average indirect effect.10 However, for simplicity, this study analyses only the average total effect of each explanatory variable on SO2 emissions. The SAR is the most common spatial econometric model in the literature because it can investigate the direct impact of SO2j on its neighboring SO2i and vice versa, and it is only concerned with the spatial lag effect of the dependent variable 10

The average direct effect stands for the average effect of changes in xi,t on SO2i,t, meaning the average of ∂SO2i/∂xi,r (which is n-1Σni=1∂SO2i/∂xi,r), and n ∂SO 2 i = β r [1 + ρwi ,i + ρ 2 ( w 2 ) i ,i + ......] + ∑ θ r wa ,i [ ρwi ,a + ρ 2 ( w 2 ) i ,a + ......], a ≠ i ∂x i , r a =1

In addition, the average indirect effect refers to the average effect of changes in xj,t on SO2i,t, meaning the average of ∂SO2i/∂xj,r (which is (n2-n)-1Σni=1Σnj=1,i≠j∂SO2i/∂xj,r), and n ∂SO 2i = β r [ ρwij + ρ 2 ( w 2 ) ij + ......] + θ r wij + ∑θ r wbj [ ρwib + ρ 2 ( w 2 ) ib + ......], i ≠ j, b ≠ j. ∂x j ,r b =1

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(Lee and Chen, 2010). According to Elhorst (2010), the SAR eliminates Σwi,jxi,j,tθ from Equation (1). In addition, the SEM aims to fix the spatial autocorrelation among the error terms. The SEM eliminates ρΣwi,jSO2j,t and θΣwi,jxj,t from Equation (1), and defines the error term as ϕi,t=ρΣwi,jϕj,t+εi,t, where ρ represents the coefficient of the spatial error. When ρ is statistically not equal to 0, it shows the existence of spatial correlation among the error terms, and the error term is no longer a white noise but is

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auto-correlated.

This study uses the Wald test to test two null hypotheses H01: θ=0 and H02=ρβ+θ=0 proposed by Elhorst (2010) to determine the appropriate spatial econometric model among the SDM, SAR, and SEM. If both H01: θ=0 and

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H02=ρβ+θ=0 are rejected, the SDM is chosen. However, if H01: θ=0 can not be rejected, then the SAR best describes the data and the (robust) LM tests point to the SAR, but if H02=ρβ+θ=0 can not be rejected, then the SEM best describes the data and the (robust) LM tests point to the SEM. Then, the Hausman test proposed by Hausman (1978) is adopted to determine which of the fixed-effects or the random-effects model is more appropriate.

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Since a panel dataset of 30 provinces from 2008 to 2013 is used in this paper, the issue regarding whether variables are stationary or not is important. Prior to estimating the empirical models, this study adopts the panel unit root test proposed by Levin, Lin and Chu (2002) to test the null hypothesis of a unit root for each variable.

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In addition, two tests for collinearity, namely, the pairwise correlation coefficient and the R2 of the auxiliary regression, are also conducted to determine whether collinearity exists between two or among all variables. The flow chart of the work progress is presented in Figure 2.

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According to the literature, several works provide evidence of an N-shaped relationship between SO2 and income (He, 2008a; Brajer et al., 2008; de Groot et al.,

FIGURE 2: Flow Chart of the Work Progress

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2004). Therefore, this article proposes a conventional cubic formulation by including per capita GRP and its squared and cubic terms. All economic and environmental variables adopted in the study are based on the past literature. Therefore, the SDM adopted in this study can be expressed as follows. N

SO2i ,t = µi + ρ∑ wi , j SO2 j ,t + α + β1 log(EXPEi ,t −1 ) + β 2GRPi ,t + β3GRPi ,2t + β 4GRPi ,3t + j =1

N

N

j =1

j =1

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β5 log(FDIi ,t −1 ) + β6 POPi ,t + β7 XM i ,t + β8 HEATi ,t + β9 log(INVIi ,t −1 ) + N

β10SECi ,t + θ1 ∑ wi , j log(EXPEj ,t −1 ) + θ2 ∑ wi , j GDPj ,t + θ3 ∑ wi , j GRP2 + N

N

N

j =1

j =1

(2)

j ,t

j =1

N

θ4 ∑ wi , j GRPj3,t + θ5 ∑ wi , j log(FDI j ,t −1 ) + θ6 ∑ wi , j POPj ,t + θ7 ∑ wi , j XM j ,t + N

N

j =1

j =1

j =1

j =1

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θ8 ∑ wi , j HEATj ,t +θ9 ∑ wi , j log(INVIi ,t −1 ) + θ10SECi ,t + ε i ,t , i ≠ j.

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The explanatory variables in Equation (2) are described as follows. The government spending on environmental protection (denoted as EXPE), the primary explanatory variable, is defined as provincial real total spending on the 211th category of environmental protection. As a matter of fact, government spending on environmental protection can be regarded as a public good. Both Lopez and Palacios

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(2010) and Lopez et al. (2011) concluded that a reallocation of the composition of government spending towards social and public goods reduces SO2 pollution. It is thus expected that EXPE has a negative impact on SO2 in this study. The real per capita GRP (denoted as GRP) is adopted to examine the relationship

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between SO2 emissions and GRP in China after controlling for other explanatory variables and the spatial dependence of SO2. As mentioned earlier, GRP and its squared and cubic terms are included in the empirical model. The relationship between GRP and SO2 is not clear in the literature. However, if δ1, δ2, and δ3 are the

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average total effects of the GRP, GRP squared, and GRP cubic terms on SO2, respectively, based on Dinda (2004), the scenario whereby δ1>0, δ2<0 and δ3=0 implies an inverted U-shaped relationship between GRP and SO2, i.e., an EKC, and δ1>0, δ2<0, and δ3>0 implies a cubic polynomial or N-shaped figure. Since He (2008a), Brajer et al. (2008), and de Groot et al. (2004) found an N-shaped relationship between income and SO2, this study expects to find an N-shaped figure as described in the scenario where δ1>0, δ2<0, and δ3>0. Foreign direct investment (denoted as FDI) is defined as real realized foreign direct investment. As regards the possible relationship between the rapid inflow of

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FDI and the worsening air pollution, He (2006) showed that,11 even after considering its influence through different channels, the total impact of FDI on industrial SO2 emissions is very small.12 According to Dinda (2004), a less developed economy may rely on technology transfer through FDI that may reduce pollution. He (2008b) also proposed that it is possible through FDI-led economic growth to reduce pollution due to the reinforcement of the technical effect.13 As concluded by He (2008b), FDI

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becomes a pollution reducing factor in the case of SO2 pollution. This study thus expects a negative influence of FDI on SO2 in China. In addition, population density (denoted as POP) is defined as people per square kilometer. Brajer et al. (2011) indicated that, by holding everything else equal, more

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densely populated cities are expected to have worse air quality (that is, there is a crowd effect). However, a higher population density implies a higher level of urbanization, which further improves air quality (that is, a civilization effect). The direction of effect of POP on SO2 depends on the relative magnitude of the crowd effect and civilization effect. This study thus expects there to be an ambiguous influence of POP on SO2. In the literature, the degree of openness (denoted as XM) is often measured as the trade intensity, and is equal to the ratio of the sum of exports and imports to total GDP. According to Poon et al. (2006), if increased trade changes the country’s comparative advantage in favor of cleaner manufacturing and production, a decline in SO2 may be expected. However, if trade liberalization causes

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a country to become more capital-intensive rather than labor-intensive, then capital-intensive activities are likely to be more pollution-intensive. Since China’s economy is still dominated by labor-intensive sectors such as electronics, it is thus expected that XM will have a negative impact on SO2.

11

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The impacts of the quantity of heat supplied (denoted as HEAT), investment completed in the treatment of industrial pollution in the private sector (denoted as INVI), and the share of secondary industry products in GRP (denoted as SEC) on SO2 emissions are very obvious. The supply of more heat will cause more serious SO2 emissions. It is thus expected that HEAT will have a positive impact on SO2. In He (2006) constructed a simultaneous model to study the FDI-emission nexus in China by exploring both the dynamic recursive FDI entry decision and the linkage from FDI entry to the final emission results under the intermediation of the scale, composition, and technique effects. The model was then estimated using a panel dataset of China’s 29 provinces’ industrial SO2 emissions during 1994-2001. 12 With an 1% increase in the FDI capital stock, industrial SO2 emissions will increase by 0.098%, in which case the increase in emissions caused by the impact of FDI on economic growth and the composition transformation will cancel out the reduction in emissions due to FDI’s impact in reinforcing environmental regulation. 13 He (2008b) used two air pollution cases in China, namely, the annual average concentration of SO2 emissions and total suspended particles (TSP), together with a panel database of 80 cities covering the period from 1993 to 2001, to estimate the emissions using the GMM, fixed-effects model, and the method of Anderson and Hsiao (1982). 11

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addition, similar to EXPE, it is expected that INVI will have a negative impact on SO2. Finally, the majority of SO2 emissions come from electricity generation and industrial processes in China, and this study thus expects there to be a positive influence of SEC on SO2.

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It is worth noting that the effect of EXEP, INVI and FDI on SO2 may not occur instantaneously and may also give rise to an endogeneity problem. Therefore, these three variables are lagged one-year, in natural logarithms, and measured at year 2000 constant prices to eliminate the effect of price changes over time. In addition, in view of a potential endogeneity problem associated with GRP, this study also follows Shen (2006) in that it uses a two-stage method in the spatial empirical model. The first

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stage involves regressing GRP on several variables proposed by previous work (Mankiw et al., 1992; Cole, 2007; Halkos and Paizanos, 2013; Welsch, 2004).14 Therefore, this study estimates empirical models with GRP treated as exogenous (Case 1) and endogenous (Case 2), respectively. Definitions, descriptive statistics, and the expected signs of impact of all variables are presented in Table 2. The sample used in this study consists of a panel dataset for 30 provinces

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covering the period from 2008 to 2013, which results in a total of 180 observations. The study excludes Tibet because the data for several variables were not available. The period starts in 2008 in this study because the one-year lagged variable of government spending on environmental protection has been available since 2007 and

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the newest data is available for the year 2013. It is for this reason that a 30×30 spatial adjacency matrix is employed. All variables are officially released by publications and the website of the central government of the People’s Republic of China and are stationary as a result of the finding that, according to the LLC t-statistic proposed by

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Levin, Lin and Chu (2002) shown in Table 2, the panel unit root test rejects the null hypothesis of a unit root for all variables used in this study.15 Moreover, all pairwise correlation coefficients between two explanatory variables are less than 0.75 and the R2 of the auxiliary regression is less 0.75 as well, indicating that there is no multicollinearity among the explanatory variables. IV. EMPIRICAL RESULTS For comparison purposes, the classical models and panel data models both with and without the spatial dependence of provincial SO2 emissions are estimated. In 14

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These explanatory variables are the logarithm of the real capital-labor ratio (denoted as KL), the literacy rate of population aged 15 or above (denoted as LIT), the growth of population (denoted as POPG), the logarithm of real R&D expenditure (denoted as RDEXP), and XM. Data sources are the China Statistical Yearbook (2009-2014), China Statistical Yearbook on the Environment (2009-2014), and CEIC Data’s China Premium Database. 12

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TABLE 2: Definition, Statistics, and Panel Unit Root Tests of Variables

Volume of SO2 Emissions, an index of air pollution. (unit: ton)

log(EXPEt-1)

Logarithm of provincial real total spending on the 211th category of environmental protection in the previous year.

GRPt GRPt2

Real per capita gross regional product of province. (unit: Chinese currency, Renminbi, RMB) Square of GRPt

GRPt3

Cube of GRPt

log(FDIt-1)

Logarithm of real realized foreign direct investment in the previous year. Population density (people per square kilometer).

Exp. Sign

22.20 (0.05)

-30.49 ***

－

2.88×104 (1.17×103)

-3.13 ***

＋

1.07×109 (1.23×109) 4.95×1013 (8.53×1013)

-30.89 ***

－

-54.56 ***

＋

4.98 (1.59)

-53.85 ***

？

423.51 (556.34)

-4.78 ***

？

0.30 (0.34)

-70.86 ***

－

9483.398 (949.162)

-18.86 ***

＋

Share of sum of exports and imports in GRP.

HEATt

Quantity of heat supplied in city, sum of steam and hot water. (unit: gigajoules)

log(INVI t-1)

Logarithm of real investment completed in the treatment of industrial pollution in the private sector in the previous year.

2.19 (0.94)

-10.87 ***

－

SECt

0.49 (0.08)

-13.43 ***

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XMt

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POPt

LLC t-statistic -6.36 ***

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SO2t

Mean (S.D.) 7.28×105 (4.19×105)

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Definition

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Variables

Share of product of secondary industry to GRP.

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Notes: 1. *, **, *** indicate that the LLC t-statistic with individual intercept and trend proposed by Levin, Lin and Chu (2002) rejects the null hypothesis of a unit root at the 10%, 5%, and 1% significance levels, respectively. 2. All the variables measured by monetary values are converted into year 2000 constant prices denominated in RMB.

addition, in seeking to determine whether there exists a potential endogeneity problem with GRP, this study estimates the above three empirical models while GRP is treated as either exogenous (Case 1) or endogenous (Case 2), respectively. This study analyzes the estimation results based on models without spatial dependence first, and then discusses the results from the spatial panel data models. 4.1 Models without Spatial Dependence The empirical results of the classical models and panel data models are presented 13

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in Table 3. It is shown that the estimation results for classical models and for panel data models are quite different. The symbol GRPH in Table 3 represents the predicted value of GRP based on the logarithm of the GRP regression and replaces GRP in the regression while GRP is considered to be endogenous.16 According to Table 3, the LM test suggests that either the fixed-effects or random-effects model is more appropriate than the classical model in both cases. Furthermore, the LR test concludes

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that the two-way fixed-effects model is better than the one-way fixed-effects model in both cases. Finally, the Hausman test concludes that the two-way fixed-effects model is more appropriate than the random-effects model in both Case 1 and Case 2. In Table 3, estimations using the two-way fixed-effects regressions show that

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EXEP does not have a statistically significant impact on SO2 in either case. GRP shows a monotonic increasing relationship with SO2 only in Case 2. HEAT has a statistically positive impact on SO2 only in Case 1. SEC has a statistically negative impact on SO2 in both cases, which is inconsistent with our expectations. XM also has a statistically negative impact on SO2 in both cases. Some of these findings are quite strange in that they ignore the spatial dependence of SO2 emissions in China. Thus, the panel SDM, which is a more suitable model and takes into consideration the spatial dependence of SO2 emissions in China, is estimated. 4.2 Fixed-Effects SDM

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Since both null hypotheses H01：θ=0 and H02=ρβ+θ=0 are rejected at the 1%

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significance level in both cases as shown in Table 4, the panel SDM is chosen. Furthermore, the results of the LR test suggest that the one-way fixed-effects SDM should be better than the two-way fixed-effects SDM and the Hausman test indicates that the fixed-effects SDM is better than the random-effects SDM in both cases. Therefore, the fixed-effects SDMs for both cases are estimated. The advantage of the fixed-effects SDM is that it considers both the spatial dependence of SO2 emissions and the spatial specific-effect µi as shown in Equation (2) that may control for time-invariant omitted variables based on Brajer et al. (2011).17 As mentioned before, for simplicity, this study analyses the estimation results of the fixed-effects SDM only 16

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The estimated logarithm of the GRP regression is as follows: ln(GRPi,t) = 6.793*** + ηi + τt + 0.242 × ln(KLi,t)*** + 0.945 × (LITi,t)*** + 1.332 × (POPGi,t)*** - 0.002 × ln(RDEXPi,t) + 0.204 × (XMi,t)***, where R2 = 0.996, F-statistic=880.33***. The F-statistic for the two-way vs. one-way fixed-effects test is 56.072***, implying rejection of the one-way fixed-effects model. The Lagrange Multiplier Test vs. the Classical Model (3) yields 187.65***, implying rejection of the classical model. The Hausman test with a resulting p-value of <0.0001 shows that the two-way fixed-effects model is better than the random-effects model. For example, since China’s heavy industry is still concentrated in the northern part of the country and coal remains the predominant energy source for industrial production, the northern regions tend to have more severe pollution problems. On the other hand, regions along the coastline are more likely to have cleaner air despite having larger populations and more developed economies. 14

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log(INVIt-1)

204655.28 *** (25797.05) 648030.99 ** (304325.47)

Constant

-320013.83 (207692.80) 180 -2478.27

21067.91 (15548.97)

18264.56 (15870.96)

530513.74 * (314570.97)

-808596.11 * (419674.82)

-623095.16 * (354711.58)

118056.55 (289516.72) 180 -2481.117

1133150.00 *** (325156.90) 180 -2247.47 230.05 *** 22.59 *** 75.79 ***

661143.95 (477112.69) 180 -2247.864 202.66 *** 3.505 *** 76.79 ***

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Sample Log likelihood LM Test LR Test Hausman Test

216061.71 *** (26239.01)

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SECt

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TABLE 3: Results of Empirical Models of SO2 without Spatial Dependence Classical Model Two-way Fixed-effects Model Case 1: Case 2: Case 1: Case 2: Variables GRP=GRPt GRP=GRPHt GRP=GRPt GRP=GRPHt (GRP is (GRP is (GRP is (GRP is exogenous) endogenous) exogenous) endogenous) log(EXPEt-1) 167376.08 *** 105144.59 *** -35487.32 -43299.72 (32615.94) (33116.12) (34540.90) (34404.60) GRP -44.29 ** -66.06 ** 23.16 47.65 * (18.16) (26.35) (19.44) (27.70) GRP2 9.85×10-4 * 1.79×10-3 ** -4.99×10-4 -9.84×10-4 -4 -4 -4 (5.04×10 ) (7.73×10 ) (3.36×10 ) (6.59×10-4) GRP3 -8.77×10-9 ** -1.72×10-8 ** 3.32×10-9 7.16×10-9 -9 -9 -9 (4.23×10 ) (6.88×10 ) (2.34×10 ) (5.46×10-9) log(FDIt-1) 39553.43 ** 36852.81 ** -16506.23 -12496.07 (17176.70) (18361.79) (19678.76) (19483.27) POPt 127.77 * 170.31 ** -134.35 -82.38 (66.31) (73.00) (84.38) (91.29) XMt -75759.84 -102047.15 -452773.08 *** -432700.20 ** (103303.30) (110824.21) (170213.62) (167194.63) HEATt 8.06 *** 8.47 *** 6.10 * 5.35 (1.68) (1.82) (3.73) (3.81)

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Notes: 1. *, **, *** indicate that the null hypothesis is rejected at the 10%, 5%, and 1% significance levels, respectively. 2. Numbers in parentheses are standard errors.

for the average total effect, and those of both the average direct and indirect effects are presented in the Appendix. By comparing Tables 3 with 4, it appears that factors insignificantly affecting SO2 in Table 3 become significant after considering the spatial dependence of SO2 emissions in Table 4, possibly due to including too many parameters for both spatial- and time-specific effects and omitting the variable for the spatial dependence of SO2 emissions that causes inconsistent estimation results for some of the explanatory variables in Table 3. According to Table 4, the empirical results of the fixed-effects SDM model are

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TABLE 4: Fixed-Effects SDM Model of SO2: Average Total Effect Case 1: Case 2: Variables GRP=GRPt GRP=GRPHt (GRP is exogenous) (GRP is endogenous) Ln(EXPEt-1) -2.02×105 *** -2.88×105 *** 4 (4.75×10 ) (5.71×104) GRP 25.90 * 58.74 * (13.57) (33.14) GRP2 -6.47×10-4 * -1.14×10-3 * -4 (3.60×10 ) (9.01×10-2) GRP3 6.74×10-9 ** 1.84×10-8 * -9 (3.03×10 ) (9.73×10-9) ln(FDIt-1) -1.61×105 *** -1.16×105 ** 4 (4.63×10 ) (4.58×104) POPt -503.73 * 38.82 (267.71) (212.25) XMt -8.42×105 *** -1.42×106 *** 5 (2.54×10 (3.12×105) HEATt 8.40 -4.27 (6.04) (7.95) ln(INVIt-1) -8.15×104 ** -1.16×105 *** 4 (3.72×10 ) (3.61×104) SECt 2.16×106 *** 2.36×106 *** 5 (6.94×10 ) (6.79×105) ρ 0.14* 0.15 * (8.60×10-2) 0.09 2 σe 3.27×109*** 3.16×109 *** 8 (3.45×10 ) 3.34×108 Sample 180.00 180.00 Wald Test-SAR 27.21 *** 29.98 *** Wald Test-SEM 25.89 *** 30.31 *** Log Likelihood Spatial Fixed Effects -2227.47 -2224.55 Spatial & Time Fixed Effects -2222.22 -2220.59 LR Test for One-way Fixed 10.49 7.92 Hausman Test 21.28 *** 10.66 *

Notes: 1. *, **, *** indicate that the null hypothesis is rejected at the 10%, 5%, and 1% significance levels, respectively. 2. Numbers in parentheses are standard errors.

very robust because the statistically significant factors are the same in both Case 1 and Case 2, except for POP. The primary explanatory variable EXPE has a statistically negative average total effect on SO2 at the 1% significance level in both cases. This implies that government increases in spending on environmental protection can mitigate SO2 emissions effectively by way of monitoring, strict law enforcement, research and development, and so on, in China. It is also noted that government spending on environmental protection is an effective way of coping with the 16

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increasingly serious problem of SO2 pollution in China. This conclusion is reasonable and similar to the findings in both Lopez and Palacios (2010) and Lopez et al. (2011), while also being consistent with our expectations.

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With regard to the relationship between SO2 emissions and GRP, in Table 4, it is shown that the average total effects of GRP, GRP squared, and GRP cubed on SO2 are statistically positive, negative, and positive, respectively, in all cases. This result implies that the relationship between SO2 and GRP is not of the EKC type, but is a cubic polynomial or N-shaped relationship in the case of China. This implies that SO2 will initially increase as GRP increases, but will then start to decrease after a certain level of GRP is reached. Finally, SO2 will increase as GRP increases again after

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reaching another certain level of GRP. This conclusion is consistent with that in He (2008a), Brajer et al. (2008), and de Groot et al. (2004), and is also consistent with our expectations. The average total effect of FDI is statistically negative at the 1% significance level in Case 1 but at the 5% level in Case 2. This implies that as there is more direct investment from foreign countries toward China, SO2 emissions will be reduced. The explanation is that China may rely on technology transfer through FDI from which it is possible to reduce pollution due to the technical effect reinforcement. This

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conclusion is consistent with that in Dinda (2004) and He (2008b) and also consistent with our expectations. Nevertheless, the average total effect of POP is statistically negative at the 10%

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significance level only in Case 1, implying that the negative impact of population density on SO2 (the civilization effect) dominates the positive impact (the crowd effect). Therefore, a higher population density results in better air quality by reducing SO2 emissions. This conclusion is consistent with our expectations.

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The average total effects of XM and SEC are statistically negative at the 1% significance level in both cases. However, the former is negative and the latter is positive. The trade factor plays an important role with a negative effect on SO2 emissions. Any province with a higher degree of openness will have a lower level of SO2 emissions due to changes in the province’s comparative advantage in favor of cleaner manufacturing and production. This conclusion is consistent with that in Poon et al. (2006) and is also consistent with our expectations. In addition, SEC (equivalent to industrialization which is defined as the share of the product of secondary industry to total GRP) also plays an important role in China’s SO2 emissions. Any province with a higher level of SEC will have more SO2. This result is reasonable because the majority of SO2 emissions arise from electricity generation and industrial processes in 17

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China. Not surprisingly, similar to EXPE, the average total effect of INVI is statistically

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negative at the 5% significance level in Case 1 but at the 1% level in Case 2. This result is consistent with our expectations and implies that encouraging the private sector to invest in the treatment of industrial pollution is an effective way of reducing SO2 emissions. However, the magnitude of this average total effect (in absolute value terms) of INVI is lower than that of EXEP in both Case 1 and Case 2. That is to say, the public sector is more important than the private sector in terms of reducing SO2 emissions in China.

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Finally, regarding the spatial dependence of SO2, it is found that the coefficient of spatial autocorrelation ρ in Equation (2) is statistically positive at the 10% significance levels in both Case 1 and Case 2. This result confirms that there exists a positive spatial correlation (also meaning a spatial competition) of SO2 emissions among provinces in China, indicating that a province’s SO2 emissions is affected by its neighboring provinces’ SO2 emissions. This result is also consistent with the result from Moran’s I test. V. CONCLUDING REMARKS

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Unlike previous works that primarily focus on exploring the existence of an EKC, this study pays attention to a rarely discussed issue regarding the influence of government spending on environmental protection to combat air pollution while taking into consideration the potentially spatial dependence of SO2 emissions. This

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study adopts a panel dataset for 30 provinces from 2008 to 2013 to estimate panel SDM models. The main findings of this study are that SO2 emissions can be effectively reduced by government spending on environmental protection and that the relationship between SO2 emissions and per capita GRP is cubic polynomial

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(N-shaped) in China. Other factors, such as foreign direct investment, investment completed in the treatment of industrial pollution in the private sector, and the share of the sum of exports and imports in GRP have a statistically negative average total effect on SO2 emissions. However, the share of the product of secondary industry in GRP has a statistically positive average total effect on SO2 emissions. All conclusions remain the same as when considering the potential endogeneity problem of GRP and a two-stage procedure is applied in this study. Under the circumstances where China’s air pollution problems are likely to persist into the future, how to resolve these problems will become increasingly important to the Chinese government. This study not only provides empirical evidence to support the government policy of spending money on environmental 18

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protection, but also suggests several solutions to mitigate SO2 emissions in China. Indeed, government spending on environmental protection is able to effectively decrease SO2 emissions in China. Thus, to cope with the severe problem of air pollution, the Chinese government may consider allocating a larger budget to environmental protection.

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In addition, the current slowdown in economic growth in China might not be a bad thing. According to the conclusions reached in this paper, there is a cubic polynomial or N-shaped relationship between SO2 and GRP in China. This implies that SO2 emissions will initially increase with GRP, but will then decrease after GRP attains a certain level, and then increase again after GRP reaches another certain level.

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If China’s economy still grows at a high rate, China’s per capita GRP might pass that certain level soon and SO2 emissions will increase with GRP. This might further produce even worse air pollution which is not what China would like to have. It is thus possible that the current slowdown in economic growth in China might result in SO2 emissions remaining at the stage where they actually decrease with GRP. Finally, this study suggests that attracting more FDI, promoting international trade, encouraging more private sector investment in the treatment of industrial pollution, and stimulating the development of the tertiary sector at the expense of

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reducing the share of the secondary industry in the economy are all effective ways of mitigating the problem of SO2 emissions in China. If the Chinese government can implement any of the above suggestions, it is believed that the air quality will improve and people in China and even in the rest of the world will live in a more comfortable environment in the future.

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ACKNOWLEDGEMENTS

The author acknowledges the financial support provided for this research by the

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Ministry of Science and Technology, Taiwan (MOST 105-2410-H-004-014). The author is grateful for all the comments and suggestions provided by participants in seminars held in both the School of Public Finance and Management at the Hubei University of Economics, Wuhan, on April 25, 2017, and the School of Economics and Management at Fuzhou University, Fuzhou, China on May 31, 2017. The author would also like to thank Ms. Wei-Ya Lin and Ms. Pong-Ju Chen for their assistance in data collection and computer processing during the production of this paper. All views and errors are solely those of the author. REFERENCES Anderson, T.W., Hsiao, C., 1982. Formulation and estimation of dynamic models 19

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Sulfur Dioxide (SO2) Emissions and Government Spending on Environmental Protection in China

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ACCEPTED MANUSCRIPT Sulfur Dioxide (SO2) Emissions and Government Spending on Environmental Protection in China

18.21 (15.42)

4.75 (11.18)

21.15 (16.22)

GRP2

-1.89×10-5 (2.34×10-4)

-5.38×10-4 (3.68×10-4)

-3.62×10-5 (2.29×10-4)

GRP3

-8.48×10-10 (1.86×10-9)

6.76×10-9 ** (3.20×10-9)

ln(FDIt-1)

-1.60×104 (1.57×104)

-1.26×105 *** (3.62×104)

XMt HEATt

-108.40 (73.72) -3.49×105 * (1.40×105) 2.84 (3.10)

-348.40 * (223.60) -3.75×105 (2.53×105)

23.39 (32.53)

27.73 (21.24)

31.00 (34.56)

-6.10×10-4 (4.02×10-4)

-5.96×10-4 (5.45×10-4)

-3.74×10-4 (8.96×10-4)

-6.07×10-4 (5.35×10-4)

-5.32×10-4 (9.79×10-4)

-6.60×10-10 (1.81×10-9)

7.40×10-9 ** (3.52×10-9)

6.41×10-9 (4.52×10-9)

9.62×10-9 (7.91×10-9)

6.68×10-9 (4.47×10-9)

1.17×10-8 (8.99×10-9)

-2.04×104 (1.62×104)

-1.41×105 *** (3.90×104)

-8.47×103 (1.53×104)

-9.31×104 *** (3.56×104)

-1.18×104 (1.58×104)

-1.04×105 *** (3.86×104)

-116.32 * (73.82)

-3.64×105 ** (1.44×105)

4.61 (5.41)

-387.41 * (241.59)

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POPt

26.68 (21.71)

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3.99 (11.37)

3.09 (3.00)

-4.78×105 * (2.56×105)

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GRP

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ln(EXPEt-1)

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Variables

APPENDIX: Spatial Fixed-Effects SDM Model of SO2: Average Direct and Indirect Effects Case 1: GRP=GRPt (GRP is exogenous) Case 2: GRP=GRPHt (GRP is endogenous) β Wx Direct Effect Indirect Effect β Wx Direct Effect Indirect Effect 4 5 4 5 4 5 4 -1.78×10 -1.59×10 *** -2.40×10 -1.78×10 *** -3.13×10 -2.21×10 *** -3.98×10 * -2.49×105 *** 4 4 4 4 4 4 4 (2.84×10 ) (4.51×10 ) (2.71×10 ) (4.97×10 ) (2.56×10 ) (4.74×10 ) (2.47×10 ) (5.60×104)

5.31 (5.68)

-31.59 (65.62)

66.38 (188.00)

-28.11 (63.00)

66.93 (195.92)

-4.56×105 *** (1.38×105)

-7.76×105 *** (2.61×105)

-4.86×105 *** (1.40×105)

-9.36×105 *** (2.89×105)

-0.77 (3.14)

-2.89 (6.16)

-0.81 (3.03)

-3.46 (7.35)

1.63×104 (1.24×104)

-8.78×104 *** (2.90×104)

1.28×104 (1.24×104)

-9.43×104 *** (3.29×104)

1.02×104 (1.24×104)

-1.13×105 *** (2.84×104)

5.82×103 (1.22×104)

-1.22×105 *** (3.25×104)

SECt

-7.82×104 (3.03×105)

2.00×106 *** (5.96×105)

-1.78×104 (2.81×105)

2.17×106 *** (6.09×105)

7.27×103 (2.71×105)

2.07×106 *** (5.48×105)

7.74×104 (2.51×105)

2.29×106 *** (6.00×105)

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ln(INVIt-1)

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ACCEPTED MANUSCRIPT Highlights 1. SO2 emissions can be effectively reduced by government spending on environmental protection in China.

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2. In China, the relationship between SO2 emissions and gross regional product (GRP) per capita in China is cubic polynomial (N-shaped). 3. As direct investment from foreign countries toward China increases, SO2 emissions will be reduced. 4. The trade factor also plays an important and negative role in SO2 emissions.

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5. Provinces with higher shares of secondary industries to total GRP will have more SO2 emissions.

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6. Both the investment completed in the treatment of industrial pollution in the private sector and population density can effectively reduce SO2 emissions. 7. There is a positive spatial correlation (also meaning a spatial competition) of SO2 emissions among provinces in China.

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8. All these conclusions remain the same while a two-stage procedure is applied to cope with the potential endogeneity problem of GRP, with the exception of the effect of population density.