The Effect of Economic Growth, Urbanization, and Industrialization on

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The effect of economic growth, urbanization, and industrialization on fine particulate matter (PM2.5) concentrations in China Guangdong Li, Chuanglin Fang, Shaojian Wang, and Siao Sun Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b02562 • Publication Date (Web): 06 Oct 2016 Downloaded from http://pubs.acs.org on October 7, 2016

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Environmental Science & Technology

The effect of economic growth, urbanization, and industrialization on fine particulate matter (PM2.5) concentrations in China Guangdong Li1, Chuanglin Fang1*, Shaojian Wang2*, Siao Sun1 1. Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences (CAS), 11A Datun Road, Chaoyang District, Beijing 100101, China 2. Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, China

Corresponding author: Chuanglin Fang, Shaojian Wang E-mail: [email protected]; [email protected]. TEL: +86-01064889101

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ACS Paragon Plus Environment


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The effect of economic growth, urbanization, and industrialization on fine particulate matter (PM2.5) concentrations in China

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Abstract: Rapid economic growth, industrialization, and urbanization in China has led to extremely severe air

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pollution that causes increasing negative effects on human health, visibility, and climate change. However, the

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influence mechanisms of these anthropogenic factors on fine particulate matter (PM2.5) concentrations are poorly

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understood. In this study, we combined panel data and econometric methods to investigate the main anthropogenic

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factors that contribute to increasing PM2.5 concentrations in China at the prefecture level from 1999 to 2011. The

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results showed that PM2.5 concentrations and three anthropogenic factors were cointegrated. The panel Fully

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Modified Least Squares and panel Granger causality test results indicated economic growth, industrialization, and

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urbanization increases PM2.5 concentrations in the long run. The results implied that if China persists in its current

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development pattern, economic growth, industrialization and urbanization will inevitably lead to increased PM2.5

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emissions in the long term. Industrialization was the principal factor that affected PM2.5 concentrations for the total

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panel, the industry-oriented panel and the service-oriented panel. PM2.5 concentrations can be reduced at the cost of

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short-term economic growth and industrialization. However, reducing the urbanization level is not an efficient way

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to decrease PM2.5 pollutions in the short term. The findings also suggest that a rapid reduction of PM2.5

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concentrations relying solely on adjusting these anthropogenic factors is difficult in a short term for the heavily

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PM2.5-polluted panel. Moreover, the Chinese government will have to seek much broader policies that favor a

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decoupling of these coupling relationships.

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INTRODUCTION

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China’s rapid economic growth and urbanization since its reform and opening up has not only increased

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comprehensive national power and residents’ living standards but also triggered severe ecological problems and

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environmental pollution,1, 2 especially atmospheric pollution, which has recently attracted broad attention.3, 4 Fine

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particulate matter (PM2.5 – particles with an aerodynamic diameter that is not larger than 2.5 µm) has been reported

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as a major pollutant that threatens human health, decreases visibility, and affects the regional and global climate.5, 6

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In response to severe and persistent PM2.5 pollution, the Chinese State Council declared a goal to reduce PM2.5

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concentrations by 25% by 2017, relative to the 2012 level.7 To achieve this ambitious goal, an exploration of the 2


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impact of anthropogenic factors (particularly rapid economic growth, urbanization and industrialization) on PM2.5

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is a prerequisite.8, 9 However, few studies have quantitatively investigated the influence mechanisms of economic

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growth, urbanization, and industrialization on fine particulate matter (PM2.5) concentrations, which means that the

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dynamic relationship between these variables in China are poorly understood.10 This information is particularly

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important for China’s decision-makers to make policies to control air pollution and to make decisions on the

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tradeoff between development and conservation.

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A shortage of long-term and large-scale PM2.5 pollution data (monitoring networks have not been consistently

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established in most developing countries) has also resulted in a lack of a full understanding of these complex

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relationships. Estimating long-term PM2.5 concentrations through remote sensing is a feasible method to address the

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problem.11 By combining satellite derived PM2.5 concentrations data and socioeconomic data, we developed a

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long-term panel data from 1999 to 2011. To address heterogeneity, we divided this panel into five sub-panels based

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on the industrial development level and PM2.5 pollution level of all Chinese prefecture-level cities. A set of

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complete econometrics methods including the panel unit root test, the Pedroni cointegration test, the panel Fully

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Modified Least Squares (FMOLS) test, the panel Granger causality test, variance decomposition and impulse

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response were utilized to quantitatively examine the impact of economic growth, urbanization, and industrialization

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on PM2.5 concentrations over the short and long term and trends and magnitudes for the five panels.

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DATA AND METHODS

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China has 337 relatively stable prefecture-level cities (a scale smaller than province but larger than county) and

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was used in this paper as the unit of analysis to identify the impact of GDP (gross domestic product) per capita,

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industrialization, and urbanization on PM2.5 concentrations. These cities can be used to characterize different

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conditions and the regional variation of the PM2.5 concentrations in China at the prefecture level, and sufficient

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socioeconomic data exists to support our study at the prefecture level (see Supporting Information Figure S1 for the

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spatial units in our analysis; Taiwan, Hong Kong, and Macao are not included in the analyses). Based on this

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spatial unit we empirically developed a panel data (panel data are data where multiple cases were observed at two

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or more time periods) at the prefecture-level over the period of 1999 to 2011.

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The ground-level PM2.5 concentrations used in our study were estimated by combining the data that were

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retrieved from the Aerosol Optical Depth (AOD) of the Moderate Resolution Imaging Spectroradiometer (MODIS)

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products of the National Aeronautics and Space Administration (NASA) and Multi-angle Imaging

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SpectroRadiometer (MISR) instruments with an aerosol vertical profile and scattering properties that were

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simulated by the GEOS-Chem chemical transport model.11, 12 The global PM2.5 concentrations dataset contains data

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for the medium- to long-term average PM2.5 concentrations (with three-year moving averages from 1999 to 2011)

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with an approximate 10 km resolution.11 The validation results indicated that the satellite-derived estimates were

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generally consistent to the ground-based measurements in China (r = 0.798; slope = 0.849; n = 121, see Supporting

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Information Figure S2).11 Thus, this dataset can be applied to the analysis of the dynamics of PM2.5 concentrations

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in large regional studies.11 This dataset can be download from the Atmospheric Composition Analysis Group

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(http://fizz.phys.dal.ca/~atmos/martin/?page_id=140). In this study, we utilized a subset of the global PM2.5 3



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concentrations dataset that contained China’s prefecture-level cities from 1999 to 2011 (see Supporting Information

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Figure S3).

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The panel data of economic growth, urbanization and industrialization were derived from the China City

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Statistical Yearbook and Chinese Regional Economy Statistical Yearbook for 337 prefecture-level cities.13, 14 We

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used the change in GDP per capita as an indicator to quantify economic growth. All GDP per capita data were

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converted to constant prices. Urbanization was measured by the proportion of the permanent population of an urban

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area to the total population. We used the share of industry value-added of GDP to characterize industrialization.

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The panel data were logarithmically transformed (natural logarithms) to reduce the heteroscedasticity to obtain a

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stationary panel before applied to empirical tests.

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Because the pace of development and the characteristics of various spatial units across China were different, the

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impact of economic growth, urbanization, and industrialization on PM2.5 concentrations across China may be

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heterogeneous. Therefore, it can be helpful to group the prefecture-level cities into several categories and analyze

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them separately to provide greater insight. We therefore grouped our total panel (panel C) into four sub-panels

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including agriculture-oriented (panel I1), industry-oriented (panel I2), service-oriented (panel I3) and heavily

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PM2.5-polluted (panel H) panels (see Supporting Information Table S1-4 for more detailed information).

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The main objective of this study was to investigate the effect of economic growth, urbanization, and

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industrialization on PM2.5 concentrations, in China. To realize this goal, a panel data model was used. The

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long-term relationship between PM2.5 concentrations, GDP per capita, industrialization and urbanization was

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formulated as follows:

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LNPM 2.5it =α + β1 LNGDPPCit + β 2 LN URBAN it + β 3 LN INDit + ε it

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where LNPPM2.5 is the natural logarithm of PM2.5 concentrations, LNGDPPC is the natural logarithm of GDP per

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capita, LNURBAN is the natural logarithm of urbanization and LNIND is the natural logarithm of industrialization,

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i=1, …, N for each prefecture-level city in the panel, t=1, …, T refers to the time period, α is constant, β1, β2, and β3

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are long-term elasticity estimates of GDP per capita, urbanization and industrialization, respectively, and εit is error

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terms.

(1)

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Here we used panel Granger causality to examine relationships between PM2.5 concentrations and other factors.

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Variable X is said to be the Granger cause of Y if at time t, Yt+l is better predicted by using past values of X than by

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not doing so (or X should help improving the prediction of Y).15 The estimation procedure included five steps (see

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Supporting Information Text S1 for the detailed description):

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(1) Two types of panel unit root tests from Levin, Lin & Chu t (LLC)16 and Im, Pesaran and Shin W-stat (IPS)17 were used to test whether our variables were stationary at the level or at the first difference.

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(2) The Pedroni cointegration test was applied to explore whether there was any long-term relationship between

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PM2.5 concentrations, economic growth, urbanization and industrialization. Pedroni conducted a series of tests for

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panel cointegration and included two alternative hypotheses types, namely, the homogeneous alternative and the

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heterogeneous alternative.18

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(3) For the Panel Fully Modified Least Squares (FMOLS) estimates, if the four variables were cointegrated, the

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Panel FMOLS regression19 can be utilized to estimate whether LNGDPPC, LNIND and LNURBAN (independent

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variables) had a positive or negative long-term relationship with LNPM2.5 (dependent variable).

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(4) For the Panel Granger causality test, if the variables were cointegrated, the Vector Error-Correction Model

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(VECM) was employed to investigate the short- and long-term bidirectional or unidirectional Granger causality

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between the variables.20 According to the general VECM model, short-term Granger causality, also referred as

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“weak Granger causality”, “short-term fluctuations” or “short-term disequilibrium”, usually concerns a period of

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1-2 years according to relevant lags selected in the test, whereas long-term Granger causality, also referred as

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“long-term equilibrium relationship”, concerns the whole study period from 1999-2011 in this test. However, if the

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variables were not cointegrated, the Vector Autoregressive Model (VAR) was utilized to identify the short-term

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Granger causality.

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(5) As the panel Granger causality tests indicated only the causality among the variables, we employed the

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variance decomposition method to capture the amount of information that each variable contributed to the other

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variables in the autoregression.21 We also used an impulse response function to examine the response of one

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variable to an impulse in the other variables.22

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RESULTS

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Panel unit root test results. The panel unit root test was applied to the natural logarithm of the four variables,

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PM2.5 concentrations, GDP per capita, industrialization and urbanization, in two models (intercept alone and

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intercept and trend) to test the stationarity of the variables. Table 1 reports the Levin, Lin & Chu t (LLC) and Im,

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Pesaran and Shin W-stat (IPS) test results, which indicate that at levels not all of the variables were stationary.

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However, four variables were stationary at the first difference. Thus, the null hypothesis can be rejected at the 1%

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level of significance, which indicates that the four variables contained a panel unit root. Supporting Information

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Table S5 shows the results for the four sub-panels, all of which contain a panel unit root.

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Table 1. Panel unit root test results of variables for panel C. Level First difference Intercept Intercept and trend Intercept Intercept and trend Levin, Lin & Chu t LNPM2.5 -36.8901*** -12.8776*** -40.1577*** -43.3925*** LNGDPPC 7.9962 -34.4917*** -34.2228*** -24.3503*** LNURBAN -2434.59*** -2566.27*** -2580.75*** -47.5252*** *** *** *** LNIND -24.6527 -20.835 -17.8525 -18.1678*** Im, Pesaran and Shin W-stat LNPM2.5 -18.9786*** 9.853 -19.3127*** -23.9887*** *** *** LNGDPPC 33.5389 -14.4999 -18.014 -3.4653*** LNURBAN -162.689*** -75.4164*** -195.06*** -16.289*** *** *** LNIND -0.1877 -5.6757 -10.743 -1.7616** Notes: The panel unit root tests with intercept and trend are carried independently; the optimal lag lengths are obtained automatically with the Schwarz information criteria (SIC). *** Indicates significance at 1% level. Variable

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Pedroni cointegration test results. Because the variables were stationary at the first difference, the Pedroni

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cointegration test was used. The results of the Pedroni cointegration test for the panel C data are given in Table 2.

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The results reveal that ten statistics were significant for rejecting the null hypothesis of no cointegration, which

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shows the existence of a long-term relationship between the independent variables, economic growth,

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industrialization and urbanization, and the dependent variable PM2.5 concentrations, in China. Supporting

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Information Table S6 also indicates the same result for the four sub-panels.

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Table 2. Pedroni cointegration test results for panel C.

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Alternative hypothesis: common AR coefs. (within-dimension) Statistic Panel v-Statistic -0.3908 Panel rho-Statistic 8.3586 Panel PP-Statistic -11.0098 Panel ADF-Statistic -20.6232 Panel v-Statistic (Weighted statistic) -6.4642 Panel rho-Statistic (Weighted statistic) 9.5352 Panel PP-Statistic (Weighted statistic) -16.6866 Panel ADF-Statistic (Weighted statistic) -28.8429 Alternative hypothesis: individual AR coefs. (between-dimension) Group rho-Statistic 16.9664 Group PP-Statistic -24.4278 Group ADF-Statistic -32.2108 Note: we use the automatic selection based on Schwarz to choose the optimal lag length.

Prob. 0.6520 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

The Panel Fully Modified Least Squares (FMOLS) test results. Giving that the variables were cointegrated,

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the panel FMOLS was employed based on equation (1), and the results are presented in Table 3. All of the panels

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showed that economic growth, industrialization and urbanization had a positive long-term relationship with PM2.5

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concentrations. This result is the most important finding in this test. It suggests that an increase in economic growth,

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industrialization and urbanization will increase PM2.5 concentrations in the long term in China for these

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prefecture-level cities. A 1% increase in the GDP per capita, industrialization and urbanization will increase PM2.5

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concentrations by 0.12%, 0.12% and 0.20% respectively, for panel C; 0.11%, 0.15% and 0.17% for panel I1; 0.16%,

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0.26% and 0.21% for panel I2; 0.09%, 0.12% and 0.55% for panel I3; and 0.15%, 0.49% and 0.27% for panel H. A

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trend was evident for the effect of LNIND on LNPM2.5; specifically, the heavily PM2.5-polluted panel had the

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strongest effect, which was closely followed by panels I2, I1 and I3. For panels I2 and H, the effects of economic

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growth on LNPM2.5 were stronger. Moreover, urbanization had the greatest impact on LNPM2.5 for panel I3.

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Table 3. The Panel Fully Modified Least Squares (FMOLS) test results with LNPM2.5 as the dependent variable. Panel

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LNGDPPC LNIND LNURBAN Coefficients t-statistics Coefficients t-statistics Coefficients t-statistics 0.1212*** 13.2792 0.1186*** 4.1601 0.1993*** 5.1120 C 0.1111*** 9.4880 0.1521*** 4.4084 0.1745*** 3.8037 I1 0.1551*** 7.7861 0.2550*** 2.4208 0.2068** 2.3129 I2 0.0913*** 4.2139 0.1249** 2.0429 0.5463*** 3.8644 I3 0.1482*** 6.3403 0.4947*** 8.6743 0.2714*** 3.0225 H ** Indicates significance at 5% level. *** Indicates significance at 1% level. Note that the coefficients value is the coefficient estimates of equation (1) for the log- transformed variables.

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Panel Granger causality test results. The panel FMOLS tests provided a relatively reliable result that identified

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the effect of four independent variables on LNPM2.5; however, the directions (bidirectional or unidirectional) of

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possible causal relationships between variables remained untested. Because the variables were cointegrated, we

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used the Vector Error-Correction Model (VECM) to test the panel Granger causality, and the results are presented

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in Table 4. Two types of tests were considered for panel Granger causality. The first type was long-term causality,

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which depended on the significance of the error-correction terms ECT (-1). The second type was short-term

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causality, which was determined by the joint significance of the coefficients of the lagged terms of each

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independent variable. If the variables, LNPM2.5, LNGDPPC, LNURBAN and LNIND are cointegrated, then it is

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expected that at least one or all the ECTs have negative coefficient and should be significantly non-zero. Short run

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Granger causality is determined by implementing a Wald test of the significance of the lags of each of the

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explanatory variables.

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Table 4 indicates that the coefficient of ECT (-1) is completely negative and significant at the 1% level for panels

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C and I1, which indicates bi-directional long-term causality (i.e., a feedback effect) among PM2.5 concentrations,

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economic growth, industrialization and urbanization for these two panels. Moreover, a bi-directional long-term

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causality was found between PM2.5 concentrations, urbanization and industrialization for panels I2 and I3, and a

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bi-directional long-term causality was found between PM2.5 concentrations, economic growth and industrialization

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for panel H. In addition, a one-way causal relationship was found running from economic growth to the other

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variables for panels I2 and I3 and running from urbanization to the other variables for panel H.

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The short-causal relationship indicates a bi-directional positive causal relationship between PM2.5 concentrations

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and other factors including economic growth, industrialization, and urbanization for panels C and I1. The

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short-causality also identifies a bi-directional positive causal relationship between LNPM2.5, LNURBAN, and

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LNIND for panel I2. Moreover, a unidirectional short-run causal relationship was concluded running from

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economic growth to PM2.5 concentrations for panel I2, and from industrialization to PM2.5 concentrations for panel

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I3. Figure 1 shows the long- and short-term causal relationships among the four variables for the five panels. It is

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worth noting that all the links built between PM2.5 concentrations and social-economic factors are based on the

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statistical test (i.e., the Panel Granger Causality test). These links represent Granger causality showing statistical

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causal relationships between variables, which may not be “true causality”.

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Table 4. Panel Granger causality test results. Panel C

I1

I2

Dependent variable ∆LNPM2.5 ∆LNGDPPC ∆LNIND ∆LNURBAN ∆LNPM2.5 ∆LNGDPPC ∆LNIND ∆LNURBAN ∆LNPM2.5 ∆LNGDPPC ∆LNIND

Independent variables Short run causality (χ2-Wald statistics) ∆LNPM2.5 102.2109*** 22.2378*** 5.9045** 148.7444*** 8.2102*** 9.1660*** 1.2085 4.7504*

∆LNGDPPC 130.0746*** 5.3372* 0.9662 105.5832*** 0.5290 7.1769** 11.8464*** 0.0480

∆LNIND 144.7306*** 16.6776*** 1.5789 49.9758*** 34.6976*** 4.9426* 54.6176*** 0.1202 -

Long run causality ∆LNURBAN 20.5743*** 20.1023*** 3.2116 29.1685*** 2.9033 1.6523 4.8906* 18.8542*** 11.5163***

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ECT (-1) -0.7266*** -0.1325*** -0.1250*** -0.0022** -0.6648*** -0.2456*** -0.0462*** -0.0053*** -0.3865*** -0.0160 -0.7321***

t-statistics -32.2161 -12.4810 -11.4274 -2.2359 -26.0327 -16.0701 -4.5380 -3.0820 -9.7787 -1.4186 -13.6546


∆LNURBAN 9.6533*** 8.6323*** 35.1574*** -0.0078** -2.1015 ∆LNPM2.5 3.0907 27.9483*** 0.6320 -0.9489*** -12.4027 ∆LNGDPPC 2.3047 0.9927 2.8455 -0.0146 -1.1418 ∆LNIND 1.0513 3.3080 0.1709 -0.1456*** -4.4493 -1.9100 ∆LNURBAN 3.1847 0.4452 1.5146 -0.0122* ∆LNPM2.5 1.8454 0.2696 3.6301 -1.2241*** -13.2509 H ∆LNGDPPC 19.9780*** 4.9678* 2.2613 -0.0501*** -6.0574 ∆LNIND 13.3850*** 1.3965 0.2137 -0.0160*** -3.3911 ∆LNURBAN 0.4424 4.7330* 1.8989 -0.0111 -1.1034 Note: The null hypothesis is that there is no causal relationship between variables, and ∆ represents the difference operator. ECT (-1) is the error correction term lagged one period. * Indicates significance at 10% level. ** Indicates significance at 5% level. *** Indicates significance at 1% level. I3

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One of the most important findings in the panel Granger causality results is that industrialization significantly

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increased PM2.5 concentrations more than economic growth and urbanization for panels C, I2 and I3 in the

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short-term. For panel I1, however, economic growth had a greater effect on PM2.5 concentrations than the other

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variables. The lack of causal relationship from economic growth and urbanization to PM2.5 concentrations for panel

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I3 indicates that slowing economy and reducing urbanization in short term will not necessarily decrease PM2.5

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concentrations in these prefecture-level cities. In panel H, economic growth, industrialization and urbanization did

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not have a short-term effect on PM2.5 concentrations. However, a bi-directional long-term causal relationship was

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found among PM2.5 concentrations, industrialization and economic growth, which implies that the increasing PM2.5

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concentrations was a long-term process driven by an increase in both industrialization and economic growth. This

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also suggest that a rapid reduction of PM2.5 concentrations relying solely on adjusting these anthropogenic factors is

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difficult in a short term for the heavily PM2.5-polluted panel.

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Figure 1. Long-run causality and short-run causality between PM2.5 concentrations (PM2.5), economic growth (GDPPC), industrialization (IND) and urbanization (URBAN).

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The results of variance decomposition and impulse response. Figure 2 shows the results of the variance

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decomposition analysis for panel C, which indicates that PM2.5 concentrations were mostly explained by its own

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innovative shocks (60.12%), whereas the contributions of economic growth, industrialization and urbanization to

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PM2.5 concentrations were equal to 16.13%, 23.67% and 0.08%, respectively (see Supporting Information Table

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S7). Thus, the contribution of industrialization to PM2.5 concentrations was important and was closely followed by

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economic growth and urbanization. Additionally, economic growth was principally self-explanatory (88.21%).

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Moreover, 22.42% of industrialization was explained by one standard deviation shock in economic growth. Most of

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the effects on industrialization were explained by its own standard shocks (69.01%), but the contribution of

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urbanization was negligible (0.004%). The results also reveal that 99.72% of urbanization can be explained by its

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own innovative shocks, whereas the contributions of economic growth and industrialization to urbanization were

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equal to 0.02% and 0.008%, respectively. Supporting Information Table S7-11 and Figure S4-7 show detailed

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results for the variance decomposition of the four sub-panels. In general, the variance decomposition results

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indicate that the contribution of industrialization in explaining PM2.5 concentrations was the most important for

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panels I2, I3 and H, whereas economic growth was crucial for panel I1, and urbanization had a negligible effect for

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all panels.

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Figure 2. Variance decomposition of four variables for panel C.

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The impulse response analysis results for panel C are shown in Figure 3. This impulse response analysis revealed

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the responses in one variable due to shocks that originate from other variables. PM2.5 concentrations first decreased

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then stabilized because of shocks from decreasing economic growth and urbanization, whereas the response in

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PM2.5 concentrations to shocks originated in industrialization increased in the earlier four years. The response of 9



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economic growth fluctuated because of shocks from increasing industrialization and urbanization and decreasing

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PM2.5 concentrations in the previous four years. The response in industrialization first fluctuated then stagnated

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because of shocks from increasing economic growth and PM2.5 concentrations in the earlier four years. The

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response of urbanization increased in the previous two years because of shocks of increasing PM2.5 concentrations

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and economic growth. Supporting Information Figure S8-11 provides the detailed results for the impulse response

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analysis of the four sub-panels.

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Figure 3. Responses of variables to one S.D. innovations for panel C.

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DISCUSSION

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Previous research has shown that an increase in PM2.5 concentrations has been affected synthetically by natural

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and socio-economic variables.8 However, to simplify these variables, we have chosen the three variables, economic

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growth, industrialization and urbanization. We assumed that these three variables are the most important

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socio-economic factors that affect PM2.5 concentrations and that these variables can indicate the overall trend and

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condition of China's socio-economic development because socio-economic development is a complex interactive

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system. The results of our analysis confirmed our hypothesis. These three variables had strong explanatory power

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for the increase in PM2.5 concentrations (for example, the results of the FMOLS analysis indicated that all R2 of the

240

regressions were greater than 0.95).

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All of the long-term causal relationships that we identified were consistent with our expectations and accorded to

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the economic and environmental reality in China. We deduced that if the development mode in Chinese cities keep

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unchanged, controlling PM2.5 concentrations has an adverse effect on economic growth, urbanization and

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industrialization or at least on two of these variables for most of the panels based on the long-term causality results. 10


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Conversely, under current mode of development, increases in economic growth, urbanization and industrialization

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will inevitably lead to increased PM2.5 emissions. This implies that the feedback effect exists between PM2.5

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emissions and economic growth, urbanization and industrialization in the current setup. Thus, when establishing

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policies to control PM2.5 emissions, policymakers should carefully make trade-offs between reduction of PM2.5

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concentrations and increased economic growth, urbanization and industrialization under the current economic

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development mode. Even more important, the Chinese government will have to seek much broader policies

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(including transforming economic growth pattern, upgrading industrial structure and adjusting urbanization process)

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that favor a decoupling of these coupling relationships. Our findings also indicate that the relationships between

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PM2.5 concentrations and economic growth, urbanization, and industrialization across China were heterogeneous,

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which was exemplified by the differences in the agriculture-oriented panel (I1), industry-oriented panel (I2),

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service-oriented panel (I3), and the heavily PM2.5-polluted panel (H). We found that bi-directional long-term

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causality exists between PM2.5 concentrations, economic growth, urbanization, and industrialization for panels C

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and I1. Moreover, bidirectional long-term causal relationships between PM2.5 concentrations, urbanization, and

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industrialization were found for panels I2 and I3, and bi-directional long-term causality was found among PM2.5

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concentrations, economic growth and industrialization for panel H. Therefore, different regional policies are

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necessary to establish corresponding strategies to reduce PM2.5 emissions.

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The short-term Granger causality tests indicate that most of the results were consistent with the long-term results

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for panels C, I1 and I2. Moreover, we found that industrialization was more significant than economic growth and

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urbanization in increasing PM2.5 concentrations in panels C, I2 and I3 in short-term causality, which suggests that

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reducing industrialization is probably one of the most effective countermeasures to reduce PM2.5 concentrations in

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the short term for these panels. However, for panel I1, economic growth has a greater effect on PM2.5

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concentrations followed by industrialization and urbanization, which implies that a stronger coupling relationship

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exhibits between economic growth and PM2.5 concentrations in agriculture-oriented cities. In panel H, economic

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growth, industrialization and urbanization did not have short-term effects on PM2.5 concentrations, which indicates

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adjusting these three anthropogenic variables in short term will not significantly reduce PM2.5 concentrations. In

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addition, compared with economic growth and industrialization, reducing the urbanization level will not effectively

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reduce PM2.5 pollutions in the short term for all panels. This finding was further validated by the results of the

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variance decomposition for LNPM2.5. For example, a one standard deviation shock in urbanization explained only

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0.08% and 0.003% of the PM2.5 concentrations for panels C and H, respectively.

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The impulse response analysis results do not show a decreasing linear relationship between PM2.5 concentrations

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and economic growth. However, the variance decomposition results demonstrate that the contribution of economic

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growth in explaining PM2.5 concentrations was higher than urbanization. Even for panel I1, economic growth has a

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greater effect on PM2.5 concentrations than the other variables in the short term. This result again indicates that

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there was a tight relationship between PM2.5 emissions and economic growth in China. In contemporary China,

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economic growth is generally the most important target of the central and local governments. The 13th Five Year

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Plan of China is a vital program document that sets goals and guidelines covering many social, economic and

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environmental issues and informs Chinese policymaking from 2016 to 2020. The plan proposes that an annual 11



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growth rate of approximately 6.5% is still China's minimum requirement to maintain economic growth and improve

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peoples’ livelihood.23 Thus, the tight relationship between PM2.5 emissions and economic growth indicates that

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China needs to urgently explore a new pathway that uncouples the effects of economic growth on PM2.5 emissions

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in order to avoid air pollution aggravation due to economic growth. In this context, for central and local

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governments of China, the transformation of the economic growth pattern is necessary to mitigate PM2.5 emissions.

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Hence, one of the priorities is to accelerate the transformation of the economic development pattern changing from

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the extensive pattern relying on large amounts of resources and energy consumptions (with inefficient growth and

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increased PM2.5 emissions) to the intensive pattern mainly based on technical progresses and efficiency

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improvement (with efficient growth and reduced PM2.5 emissions). The transition from the extensive economic

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growth pattern towards intensive pattern helps reduce PM2.5 emissions. Although this transformation is not likely to

292

happen in the short term, such a change is necessary and urgent, because it is an indispensable and fundamental

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step in reducing the PM2.5 pollutions.

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As expected, industrialization caused PM2.5 emissions in both the short and long terms as indicated by the

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Granger causality test. The close linkage between PM2.5 emissions and industrialization indicates that the current

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industrial development pattern was highly dependent on the energy use and resource consumption, leading to high

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pollutions. In recent years, China has experienced a period when economic development has been driven

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dominantly by heavy industry, especially the heavy-chemical industry contributing more than 70% of the total

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industrial value since 2006.24 However, we conclude that China’s economic growth will not continue to rely on

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heavy industries in the foreseeable future. On the one hand, China has severe overcapacity problem after rapid

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development of heavy-industrialization in recent years, especially steel, aluminum and cement industries. This

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problem will get worse if China still relies on heavy industries as an economic driver. On the other hand, both the

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central and local governments in China have realized that economic development relying on heavy industries is

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unsustainable and it needs to be altered. While progress has been made, there is still a long way to go before

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industrial development transformation can be reached. The following measures may help speed up the

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transformation which benefits the mitigation of PM2.5 emissions: 1) to transform the main industrial development

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driver from traditional resources and energy consumptions to innovation and technical progresses; 2) to upgrade

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China’s industrial structure by encouraging the tertiary industry development; 3) to limit and eliminate heavy

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pollution industries. In addition, to raise industrial PM2.5 emissions standards and to improve energy use

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efficiencies are among the more direct control measures to reduce PM2.5 pollutions.

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The results of the Granger causality test confirm that urbanization caused PM2.5 emissions for most of the panels

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with the exception of panels I3 and H in the short term, although the effect was weaker than other variables.

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Therefore, a gradual slowdown of urbanization may actually help reduce PM2.5 pollutions for these panels.

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However, this deceleration of urbanization is not likely to occur, because a high target for increasing urbanization

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to 60% in 2020 was established in the New-type Urbanization Plan (2014-2020)25 that was recently released by

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China’s central government. Moreover, Chinese cities usually have more densely populated urban areas in

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comparison to cities in developed countries. As the result shows, a city with a high urbanization level tends to have

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318

high PM2.5 concentrations in current China. Hence, limiting population influx into densely populated urban areas is

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probably a practical strategy to reduce PM2.5 emissions.

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It is worth noting that future scenarios may be totally different from the current state. Inevitably, China will, like

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other developed countries, enter a different economic state. For example, people will no longer find it desirable to

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further crowd into large cities. And heavy industry will no longer add enough value to the economy to fuel further

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growth, which will further result in a move to lighter industries and finally services. A changing economic state

324

will certainly change causality between PM2.5 concentrations and other variables. Hence a simple extrapolation of

325

the future trend based on current relations may deviate from the reality. This, again, requires Chinese government

326

to seek much broader policies (including transforming economic growth pattern, upgrading industrial structure and

327

adjusting urbanization process) to decouple relationships between PM2.5 concentrations and other socio-economic

328

factors towards a more sustainable development mode.

329

One limitation of this study is that the spatial unit of this analysis is the prefecture-level city, a relative large

330

spatial scale in China. Although several different panels were utilized to address heterogeneity in this study, we

331

should investigate this heterogeneity and the differences more deeply. Moreover, different areas of a

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prefecture-level city have different PM2.5 concentrations, especially in urban and rural areas. However, It would be

333

difficult to obtain long-term data of PM2.5 concentrations and social economic variables for a panel of a more

334

refined spatial unit. Therefore, if these data can be obtained (from a PM2.5 observation station, for example), this

335

research would be helpful for future study. Second, this study found that economic growth was more important in

336

explaining PM2.5 concentrations than urbanization. Future studies that employ GDP per capita as a proxy for

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economic growth and PM2.5 concentrations as a proxy for environmental impact may provide a deeper insight into

338

the interactive relationship between environmental impact and economic growth and Kuznets’ environmental curve

339

can be tested. Third, exploring the causal relationships between PM2.5 emissions and other potentially related

340

variables, such as heavy industry development, automobile use, exports and dust pollution, may be an important

341

direction for future studies. Much evidence exists that the increased development of the construction, metal and

342

machine production sectors, exports and urban expansion has exacerbated PM2.5 emissions.8, 9

343 344

ASSOCIATED CONTENT

345 346 347 348 349 350 351 352 353 354

Supporting Information Supporting Information Available: Prefecture-level divisions in China for period of 1999-2011 (Figure S1). The validation result of satellite-derived estimates and ground-based measurements in China for PM2.5 (Figure S2). Spatial distribution of mean PM2.5 concentrations in China from 1999 to 2011 (Figure S3). Descriptive statistics for five panels used in this study for the period 1999-2011 (Table S1). The mean changes of PM2.5 concentrations, per capita GDP, urbanization and industrialization for five panels in 1999 and 2011 (Table S2). The list of prefecture-level divisions for four sub-panels (Table S3). A list of baseline characteristics for the major prefecture-level divisions in China (Table S4). The detailed description for the estimation procedure (Text S1). The detailed estimation results of the four sub-panels (Tables S5-S11, Figures S4-S11). This material is available free of charge via the Internet at http://pubs.acs.org. 13



355

AUTHOR INFORMATION

356 357 358

*E-mail: [email protected]. Tel.: +86-01064889301. Fax: +86-01064889301 (C.F.).

359 360

Notes The authors declare no competing financial interest.

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ACKNOWLEDGEMENTS

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This work was supported by the Natural Science Foundation of China (Grant nos. 41590842, 41501175 and 71433008).

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Corresponding Author *E-mail: [email protected]. Tel.: +86-02064111475. Fax: +86-02064111475 (S.W.).

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Figure 1. Long-run causality and short-run causality between PM2.5 concentrations (PM2.5), economic growth (GDPPC), industrialization (IND) and urbanization (URBAN). 339x222mm (300 x 300 DPI)


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Figure 2. Variance decomposition of four variables for panel C Figure 2. Variance decompositi 190x154mm (300 x 300 DPI)



Figure 3. Responses of variables to one S.D. innovations for panel C Figure 3. Responses of variabl 190x162mm (300 x 300 DPI)


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The Effect of Economic Growth, Urbanization, and Industrialization on

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