The heterogeneous effect of democracy, political globalization, and urbanization on PM2.5 concentrations in G20 countries: Evidence from panel quantile regression

Accepted Manuscript The heterogeneous effect of democracy, political globalization, and urbanization on PM2.5 concentrations in G20 countries: Evidence from panel quantile regression Ningli Wang, Huiming Zhu, Yawei Guo, Cheng Peng PII:

S0959-6526(18)31428-8

DOI:

10.1016/j.jclepro.2018.05.092

Reference:

JCLP 12949

To appear in:

Journal of Cleaner Production

Received Date: 8 January 2018 Revised Date:

10 May 2018

Accepted Date: 11 May 2018

Please cite this article as: Wang N, Zhu H, Guo Y, Peng C, The heterogeneous effect of democracy, political globalization, and urbanization on PM2.5 concentrations in G20 countries: Evidence from panel quantile regression, Journal of Cleaner Production (2018), doi: 10.1016/j.jclepro.2018.05.092. 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.

ACCEPTED MANUSCRIPT Title Page Title The heterogeneous effect of democracy, political globalization, and urbanization on PM2.5 concentrations in G20 countries: Evidence from panel quantile regression The affiliation and full address of all authors

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Ningli Wang1, Huiming Zhu1*, Yawei Guo1, Cheng Peng1 1 College of Business Administration, Hunan University, Changsha 410082, China *

Corresponding author. E-mail: [email protected] Telephone number: 13517311705

Abstract

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PM2.5 emissions have serious adverse impacts on health and impede transport activities, especially air and highway. Consequently, policymakers and economists have focused on this issue. The primary

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objective of this study is to investigate the effect of democracy, political globalization, and urbanization on PM2.5 concentrations within G20 countries. Ecological modernization theory is used to gain the best understanding of the impact of these driving forces on PM2.5 concentrations and obtains an analytical framework. The method utilized is the panel quantile regression, which takes into account the unobserved individual and distributional heterogeneity. The results demonstrate that, first, the direct

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effect of democracy on PM2.5 concentrations is significantly positive in countries with higher emissions, and has no impact on lower-emission countries. Second, the direct effect of political globalization on PM2.5 concentrations is significantly positive and especially greater in extremely low- and

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high-emission countries. Third, persuasive evidence proves the existence of an environmental Kuznets curve between urbanization and PM2.5 concentrations. Additionally, this paper further assesses the

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direct and indirect influence mechanisms of democracy and political globalization on PM2.5 concentrations across pollution levels. Both have a positive (negative) indirect effect on PM2.5 concentrations in countries with higher (lower) emissions, through its effect on GDP per capita. The total effect appears positive, suggesting that the increase in democracy and political globalization degrade environmental quality. These results provide policymakers with critical policy recommendations that contribute to the reduction of PM2.5 concentrations and ensure sustainable economic development in the G20 countries. Keywords: PM2.5 concentration; Democracy; Political globalization; Urbanization; G20 countries; Panel quantile regression

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Graphical Abstract

ACCEPTED MANUSCRIPT Word count: 11912 words

The heterogeneous effect of democracy, political globalization, and urbanization on

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PM2.5 concentrations in G20 countries: Evidence from panel quantile regression

Abstract:

PM2.5 emissions have serious adverse impacts on health and impede transport activities, especially air and highway. Consequently, policymakers and economists have focused on this issue. The primary

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objective of this study is to investigate the effect of democracy, political globalization, and urbanization on PM2.5 concentrations within G20 countries. Ecological modernization theory is used

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to gain the best understanding of the impact of these driving forces on PM2.5 concentrations and obtains an analytical framework. The method utilized is the panel quantile regression, which takes into account the unobserved individual and distributional heterogeneity. The results demonstrate that, first, the direct effect of democracy on PM2.5 concentrations is significantly positive in countries with higher emissions, and has no impact on lower-emission countries. Second, the direct effect of political globalization on PM2.5 concentrations is significantly positive and especially greater in extremely

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low- and high-emission countries. Third, persuasive evidence proves the existence of an environmental Kuznets curve between urbanization and PM2.5 concentrations. Additionally, this paper further assesses the direct and indirect influence mechanisms of democracy and political globalization

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on PM2.5 concentrations across pollution levels. Both have a positive (negative) indirect effect on PM2.5 concentrations in countries with higher (lower) emissions, through its effect on GDP per capita.

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The total effect appears positive, suggesting that the increase in democracy and political globalization degrade environmental quality. These results provide policymakers with critical policy recommendations that contribute to the reduction of PM2.5 concentrations and ensure sustainable economic development in the G20 countries. Keywords: PM2.5 concentration; Political globalization; Urbanization; Democracy; G20 countries; Panel quantile regression

ACCEPTED MANUSCRIPT 1. Introduction Environmental quality has received close attention from academics and industry as a critical topic. Fine particles are a major component of air pollution that reduce visibility and are toxic to public health in many countries. PM2.5 is a particulate matter (PM) in air with an aerodynamic diameter of

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less than 2.5 um, and a main pollutant that influences human health and radiation balance (Cliff et al., 2005). With its small size, strong adsorption and other features, PM2.5 may carry heavy metals and sulfates into the respiratory tract and lungs. Developed countries started research on PM2.5 earlier than their less-developed counterparts, with the United States developing PM2.5 emission standards in

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1997, that is, the National Ambient Air Quality Standards. As the severity of air pollution continues to increasingly cause fog and haze, the PM2.5 index has been incorporated into the air quality monitoring

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systems of many countries and international organization. For instance, on October 6, 2006, the World Health Organization released its latest Air Quality Guidelines for the world. In May 2008, the European Union (EU) issued the “About the European air quality and cleaner air instructions”, which sets the target concentration limits for PM2.5. Decreasing PM2.5 concentration is the main objective of policies to improve air quality. Rd and Dockery (2006) confirmed that high PM2.5 concentrations are associated with an increased incidence of cardiovascular and respiratory disease and lead to

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premature death from cancer. Therefore, how to control PM2.5 concentrations to decrease these adverse effects has become an urgent topic for researchers. In the 1980s, increasing researchers have investigated the relations between environmental

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deterioration and social, political, and institutional changes; based on some of these work, the concepts of Ecological Modernization theory (EMT) were developed (Mol, 2000). EMT is concerned with the

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way in which economic, political, and social forces interact with one other in providing environmental goods and services (Lemprière, 2016). EMT requires going beyond merely displaying that societies modify their institutions in response to environmental problems and demonstrates that such modifications result in ecological improvements (York and Rosa, 2003). The EMT also claims that the implementation of environmental management practices can reduce the impact on the environment while achieving economic benefits (Gunderson and Yun, 2017). Under these circumstances, the aim of this study is to explore the impact of democracy, political globalization, and urbanization on the entire conditional distribution of PM2.5 concentrations. These three main variables are driving forces behind modernization and the effects of these three factors on the environment quality are within the

ACCEPTED MANUSCRIPT theoretical framework of the EMT. Recently, there have been some concerns about the relationship between institutional quality (such as democracy) and environment pollution. Romuald (2011) investigated the relationship between institutional quality and pollution and they found that numerous environmental problems may

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be due to institutional failure and poor government management. Similarly, the political globalization is also plays a significant role in reducing environmental pollution. Environmental pollution is not a national or regional issue, but a global one. If a country has high PM2.5 emissions, neighbouring countries are affected. Therefore, the policies aiming to reduce PM2.5 concentrations must be taken on

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collectively to be effective. The political cooperation among countries is crucial on a regional and global level to fight against such PM2.5 emissions. Along with globalization, the whole world is

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participating in the state of urban development. According to estimates from the United Nations Department of Economic and Social Affairs, the world's average urbanization rate increased from 29.6% in 1950 to 54% in 2014, and by 2050, the percentage of urban dwellers worldwide is expected to increase to 66%. Because most economic activities are concentrated in urban areas, environmental pollution also has increased. PM2.5 concentration is a typical indicator of urban air quality and

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influenced by rapid urbanization progress. Niu et al. (2013) considered that rapid urbanization leads to an increase in PM2.5 emissions. Generally, a considerable amount of practical work has been performed on the issues of PM2.5 emissions. However, most of the literature about PM2.5 has focused on its physicochemical characteristics, chemical composition, sources and adverse effects. Few studies

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have analysed the fundamental influencing factors of PM2.5 concentrations. In this context of the literatures, incorporating the two political variables (i.e., democracy and political globalization) and

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urbanization into the analysis of the driving forces of PM2.5 concentrations is required. Few studies have investigated the determinants of PM2.5 concentration from multiple national levels or an international organization perspective. The majority of the relevant studies analyse the fundamental influencing factors of PM2.5 concentrations in China (e.g., Guan et al., 2014; Meng et al., 2015; Xu and Lin, 2018). And these literatures have failed to consider the broader sample of developed and developing countries. To address this problem, our study extends the sample to the G20 countries—generally recognized as the world's leading economic groups. The G20 has a sufficient capacity to reflect the global trends in economic development and environmental conditions. By 2014, the G20 comprised 4 billion people (approximately 60% of the world's population); the GDP of the

ACCEPTED MANUSCRIPT G20 countries accounted for 85% of the worldwide economy and its trade volumes accounted for 80% of the world's total. Numerous politicians and scholars have asserted that the G20 should have undertaken a greater number of obligations to reduce air pollutants emissions to improve the global environment sustainably. Considering the crucial global role played by the G20 countries, exploring

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the influencing factors that contribute to reduce PM2.5 emissions in G20 countries is worthwhile. Additionally, most previous studies have used the standard regression approach, such as ordinary least squares (OLS) estimation, to describe only the average relationship of PM2.5 concentrations with its influencing factors. This approach requires random error terms to strictly satisfy classical econometric

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assumptions, that is, zero mean, homoscedasticity, and normal distribution. Nevertheless, these assumptions are hardly satisfied in reality because most socioeconomic variables exhibit a high degree

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of non-normality, fat tails, excess kurtosis and skewness. In the presence of these characteristics the conditional mean approach may not capture the heterogeneous effect of influencing factors to the entire distribution of PM2.5 concentrations and may provide estimations which are not robust. Unlike existing research, this study utilizes a panel quantile regression model to investigate the impact of the driving forces on PM2.5 emissions due to the possible distributional heterogeneity among G20

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countries. Compared with the traditional OLS estimation, quantile regression not only improves the robustness of the model, but also reveals the important information on the tail of the data distribution and obtains a more complete picture of the sample data, particularly for the non-normal distribution data. Xu and Lin (2018) argued that the heterogeneous effects of the driving forces on the different

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quantile provinces should be considered while analysing the mitigation of PM2.5 pollution. Based on the various aforementioned reasons, this study applies a panel quantile regression model to explore the

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effect of democracy, political globalization, and urbanization on PM2.5 concentrations in G20 countries.

This paper contributes to the existing literature in four aspects. First, this study employs panel quantile regression to explore the effect of democracy, political globalization, and urbanization on PM2.5 concentrations through combining these three key elements of modernization within the EMT analytical frame. Compared with the commonly used OLS regression approach, panel quantile regression can provide more detailed results and demonstrate the possible heterogeneity. Second, the study focuses on the G20 countries, generally recognised as the world's leading economic groups, and a broader sample of developed and developing countries, which is essential because very few studies

ACCEPTED MANUSCRIPT address this issue from international organisation perspective. Third, our model contains some relevant control variables, which may resolve the neglect of variable bias problems the in literature (Han et al., 2014); concretely, the GDP per capita, trade openness, foreign direct investment, fossil fuel energy consumption, and renewable energy consumption are chosen. Fourth, this article incorporates two

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political variables into the analysis of environmental pollution and quantitatively evaluates the direct and indirect effects of democracy and political globalisation on PM2.5 concentrations. According to our review of the literature, no research has examined the direct and indirect mechanisms of these two political variables on PM2.5 concentrations. Additionally, this study examines the validity of the

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environmental Kuznets curve (EKC) hypothesis between urbanisation and PM2.5 concentrations within G20 for the first time.

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The remainder of the paper is organized as follows. Section 2 briefly reviews the related literature. Section 3 describes the data and methodology. Section 4 provides the empirical results and discussions. Section 5 presents the conclusion and policy recommendations. 2. Literature review 2.1. Democracy and environment quality

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The literatures has documented that the connection between environmental pollution and economic growth is not formed in isolation from political institutions related to the process of creating environmental policy. Dasgupta et al. (2002) argued that institutional development is a time-consuming process but strengthening supervision is crucial to reducing pollution. A considerable

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portion of the literature has incorporated the related political variables into the income–pollution nexus. Some have documented that democracy can improve a country’s environmental quality (e.g., Farzin

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and Bond, 2006; Bernauer and Koubi, 2009). Farzin and Bond (2006) argued that democratization improves the citizens understanding of the situation, their organization, and the resulting protests. Citizens can express their preferences and put pressure on their governments, enhancing the awareness of states and political entrepreneurs about environmental protection requirements. Bernauer and Koubi (2009) documented that democracies and especially presidential form of government have a positive effect on environmental quality. Several scholars have asserted the reverse: democracy may cause environmental deterioration. For instance, Heilbronner (1974) argued that authoritarian nations can limit population dynamics, whereas democratic nations must respect the freedom of the people, and global population growth threatens environmental performance. There are still some other researches

ACCEPTED MANUSCRIPT revealed that democracy had no significant impact on environmental quality. For example, Scruggs (1998) discovered little correlation between democracy and three environmental indicators (particulate emissions, dissolved oxygen demand, foecal coliform). More recently, You et al. (2015) pointed out that the major drawback of these studies is that the

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results might be biased because the heterogeneity of the dependent variable distribution was ignored; their findings suggested that the impact of democracy on environmental quality is heterogeneous across quantiles. The empirical results so far have been limited and conflicting. To the best of our knowledge, no paper has expressly emphasized the role of GDP per capita as a key factor in explaining

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the indirect influence of democracy on PM2.5 concentrations. Our study takes another step towards investigating the ambiguous nexus between democracy and environmental quality, which present the

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direct and indirect influence mechanisms of democracy on PM2.5 concentrations. Our analysis extends the importance of GDP per capita in explaining the relationship between democracy and environmental quality, which can prevent over- or under-estimating its real effect. 2.2. Political globalization and environment quality

According to Dreher et al. (2008), a country's political globalization comprises the number of

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embassies it hosts, its membership in major international organizations, its participation in U.N. Security Council missions, and its signatures on international treaties. Political globalization contributes to building a strong association between developed and developing nations and affects the environmental quality. On the one hand, political globalization provides a platform for diffusing

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government policies, which may include energy conservation or technical efficiency for environmental protection. Fredriksson and Mani (2004) suggested that the combination of political stability and trade

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integration produce rigorous environmental regulations. Paramati et al. (2017) confirmed the role of political globalization in mitigating CO2 emissions across the EU, G20 and OECD economies. On the other hand, political globalization has reinforced exchanges and cooperation among countries, which facilitates trade liberalization and trade-related activities. This trade influences the environment when all the goods and services produced in the economy are directly and indirectly related to the uses of power and energy, resulting in a corresponding level of emissions increases. The studies by Halicioglu (2009) and Shahbaz et al. (2012) also argued that the international trade depletes natural resources and increases the consumption of natural resources, leading to a decline in environmental quality. Shahbaz et al. (2015) obtained some notable findings that economic globalization has allowed for the effective

ACCEPTED MANUSCRIPT control of CO2 emissions, whereas social and political globalization led to an increase in pollution emissions. In addition, Wen et al. (2016) provided empirical evidence of bidirectional causality between GDP and globalization and indicated that higher political globalization was found to impede the development of GDP in the 92 sample countries. Given this background, this study aims to

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investigate the ambiguous nexus between political globalization and environmental quality by considering the role of GDP as a critical factor in explaining the indirect effect of political globalization on PM2.5 emissions, which can prevent over- or under-estimating its real effect. 2.3. Urbanization and environment quality

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The attention to climate change and the need for global mitigation action have received increasing awareness, and the long-standing debate on the environmental Kuznets curve (EKC) has heated (e.g.,

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Selden and Song, 1994; Stern, 2004; Kaika and Zarvas, 2013a and 2013b). Selden and Song (1994) found that per capita emissions of all four important air pollutants exhibit inverted-U relationships with per capita GDP. Stern (2004) presented the evidence that the statistical analysis of their study on which the EKC is based is not robust. Specifically, the EKC assumes a relationship between emissions and output: lower economic development growth increases emissions, whereas the relationship is

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inversed at higher levels of output. It graphically means that emissions are an inverted U shaped function of output. Kaika and Zarvas (2013a) reviewed the evolution of the EKC-concept and the possible causes of an EKC-pattern. And they further analyzed scale, composition and technique effect on EKC. Kaika and Zarvas (2013b) concluded that the EKC can apply to certain countries and types of

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pollutants but not as a suitable policy for every country or every pollutant. For this reason, defining the boundaries in which the EKC can be valid is essential. Many studies have already tested for the EKC

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hypothesis of economic growth and environmental pollution (such as CO2 emissions) nexus (e.g., Itkonen, 2012). However, a few have considered the existence of EKC in terms of the relationship between pollution and urbanization (e.g., Ehrhardt-Martinez et al., 2002; York et al., 2003; Maruotti, 2011). Ehrhardt-Martinez et al. (2002) and York et al. (2003) considered that urbanization is a suitable proxy for modernization and thus environmental impact should be reduced with a higher proportion of urban population. Maruotti (2011) obtained evidence for confirming EMT, predicting that environmental impact may follow the Kuznets curve associated with urbanization. As a major urban air pollutant, PM2.5 concentrations are directly affected by human activities and the surrounding environment (Chan and Yao, 2008). Ma et al. (2016) noted that accelerating urbanization has promoted social and economic

ACCEPTED MANUSCRIPT development, producing various sources of pollution and demonstrating that higher urbanisation increases PM2.5 concentrations. Li et al. (2016) documented that urbanization, economic growth, and industrialization increased PM2.5 concentrations in the long run, whereas reducing the urbanization level was not an efficient way to decrease PM2.5 emissions in the short term. In summary, the effect of

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urbanization on the environment is controversial and divergent, but no study has examined the existence of EKC for PM2.5 concentrations and urbanization within the G20 countries. Another major drawback of the discussed research is the results could be biased owing to the unobserved heterogeneity originating from countries’ regulations or behaviours that have differing influences on

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the environment. Based on the above analysis, this study further investigates the existence of EKC between urbanization and PM2.5 emissions within the G20 countries by using a panel quantile

3. Data and methodologies 3.1. Data descriptions and analysis

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regression model.

This paper focuses on the effect of democracy, political globalization, and urbanization on PM2.5 emissions for the G20 countries from 2000 to 2014. The G20 countries in the sample are all the

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member countries minus the EU: Argentina, Australia, Brazil, Canada, China, France, Germany, India, Indonesia, Italy, Japan, Korea, Mexico, Russia, Saudi Arabia, South Africa, Turkey, the United Kingdom, and the United States. The EU is excluded because the determinants of PM2.5 concentrations are examined based on country-level panel data and the EU comprises 28 countries;

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thus their participation in the analysis might be problematic. Another reason is that France, Germany, Italy, and the UK belong to the EU and G20; including the EU may cause duplication of the data. The

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choice of sample selected for this paper was primarily dictated by the availability of reliable data. In this empirical analysis, the dependent variable is PM2.5 concentration. Our source of PM2.5 concentration data was the Environmental Performance Index. The main variables are democracy, political globalization, and urbanization. For the primary measures of the dimensions of democracy, the study use the indicators taken directly from the Freedom House (FH) Political Rights Index (including electoral process, political pluralism and participation, and functioning of government) and Civil Liberties Index(including four subcategories: personal autonomy and individual rights, associational and organizational rights, freedom of expression and belief and the rule of law), which assigns value to each country on a scale of 1-7,

ACCEPTED MANUSCRIPT where 1 represents the highest degree of freedom and 7 the lowest. The FH measure can be used as a primary measure and robust check for any other measures taken as a major measure in most empirical studies of democracy (e.g., You et al., 2015; Lv et al., 2017). To simplify the interpretation and understanding, the FH Political Rights Index is reversed (by subtracting each value from 8); the higher

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(lower) scores represent the more (less) democratic countries. The simple total of these two indices’ scores are used as a proxy for the overall democracy level. Political globalization is extracted from the KOF Index of Globalization. GDP, TRADE, FDI, FOSSI, RENEW, URB, GLOB, ENC, GROS, INFL, and POPgr (see Table 1 for the definitions) are obtained from the World Development Indicators

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(WDI) database of the World Bank1. Considering the variables are in different units, normalizing the data series and transforming them into a uniform measurement, by taking the natural logarithms for all

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variables except for FDI, DEMOC, INFL and POPgr to avoid the problem related to the distribution of data attributes, is required. The log conversion of data series is a preferred method for estimating coefficients because they can be interpreted as elasticities. For variables that do not take a logarithm, their corresponding coefficients are finally converted into elasticities. The variables’ descriptions, data definitions, and data sources are presented in Table 1.

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[Insert Tables 1 and 2 about here]

Table 2 reports the descriptive statistics of the variables and pairwise correlations between our main variables of interest. The distributions of all the variables are skewed, and the kurtosis values

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show that the data series distributions with longer tails are more concentrated than the normal distribution. The Jarque–Bera test significantly rejects the null hypothesis of normality, demonstrating

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the non-normality of the unconditional distribution of all the variables. To justify the use of quantile regression, the following discuss the distribution of PM2.5 concentrations in greater detail. It is obvious that the distribution of PM2.5 is skewed. And the preliminary evidence of the heterogeneous distribution of PM2.5 concentrations across countries can be provided by presenting a simple unconditional distribution plot of PM2.5 concentration. Fig.1 shows an overview of the geographical distribution of the average annual PM2.5 concentrations of the G20 countries using the quantile maps over seven intervals: the maximum value is 45.133 (China) and the minimum is 2.660 (Australia). It shows that the levels of PM2.5 concentration across countries vary widely. Because of the high variability in the data series and all the variables are skewed, the conclusions drawn from the OLS regression are questionable. This result provides further motivation for using the quantile regression

ACCEPTED MANUSCRIPT approach in the PM2.5 concentrations equation. [Insert Fig.1 about here] 3.2. Methodologies 3.2.1. Panel quantile regression model

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To explore the impact of democracy, political globalization, and urbanization on PM2.5 concentrations within the G20 countries, this study utilizes the quantile regression model with fixed effects, which provides a more detailed description. From a policy point of view, making clear what occurs in the extreme situation of distribution would be more notable and meaningful. It is significant

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to understand the behaviour of high pollution emissions to assist government policymakers with developing more effective environmental protection policies. However, traditional regression

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techniques concentrate on the mean effects, which may cause under- or over-estimation of the correlative effect or even fail to test significant relationships (Binder and Coad, 2011). The panel quantile regression method with fixed effects makes an evaluation of the conditional heterogeneous covariance effects of PM2.5 concentration influencing factors possible, which has control over the unobserved individual heterogeneity. Studies by Koenker (2004), Lamarche (2010), and Canay (2011)

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have concentrated on the econometric theory of utilizing quantile regressions to panel data. Conceiving the fixed effect panel quantile regression model as follows: Q y (τ k | α i , xit ) = α i + xit' γ (τ k )

(1)

it

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The primary problems for fixed effect panel quantile regression are it contains a large quantity of

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fixed effects ( α i ) and is vulnerable to the incidental parameters problem (Lancaster, 2000). The estimator is inconsistent when the quantity of individuals increases to infinity, whereas the quantity of observations for each cross-sectional is fixed. In quantile regression, it is impracticable to use the standard methods to remove the unobserved fixed effects; because these methods depend on expectations being linear operators, which cannot be satisfied for conditional quantiles (Canay, 2011). Koenker (2004) proposes a proper method called the shrinkage method to address these problems. The introduction of a penalty term in the minimization to solve the problem of estimating a mass of parameters makes this approach unique. Concretely, the parameters are estimated as follows: (γˆ (τ k , λ ), {α i (λ )}iN=1 ) = min

K

T

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∑∑∑ w ρτ ( y k

k =1 t =1 i =1

k

it

− α i − xit' γ (τ k )) + λ

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∑| α i =1

i

|

(2)

ACCEPTED MANUSCRIPT where i is the index for countries ( N ), T is the index for the number of observations per country, K is the index for the quantiles, x is the matrix of the explanatory variables, and ρτ k is the

quantile loss function. In addition, wk is the relative weight given to the k-th quantile, which controls for the contribution of the k-th quantile in the estimation of the fixed effects. In our study, we utilized

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equally weighted quantiles wk = 1 / K (Lamarche, 2011). λ is the tuning parameter that decreases the individual effects to zero to improve the performance of the estimate of β . If the coefficient of the penalty term λ equals zero, the penalty term disappears and the model could obtain the usual fixed

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effects estimator. And if the λ increases to infinity, it could acquire an estimate of the model without individual effects. In the empirical analysis, our study set λ = 0.65 , following Lv et al. (2017). To

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check the robustness of our results, the study also implemented a sensitivity analysis with a different value of λ .

This paper investigates the effect of democracy, political globalization and urbanization on PM2.5 concentrations throughout the concentrations distribution. The equation (2) is modified as follows:

Q yit = α i + ξ t + γ 1τ GDPit + γ 2τ GDPit2 + γ 3τ TRADEit + γ 4τ FDI it + γ 5τ FOSSI it

(3)

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+γ 6τ RENEWit + γ 7τ URBit + γ 8τ URBit2 + γ 9τ DEMOCit + γ 10τ GLOBit

where the countries are indexed by i , and time by time t . yit is the pollution indicator. The GDP per capita, trade openness, foreign direct investment, fossil fuel energy consumption and renewable energy

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consumption are chosen as control variables. These variables have a certain impact on the environment. For example, Shafiei and Salim (2014) claimed that examining the relationship between renewable

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energy consumption and pollutant emission is a worthwhile academic exploration and they showed that renewable energy consumption reduces CO2 emissions. Al-Mulali and Ozturk (2015) suggested that countries can implement trade-related measures and strategies to increase environmental protection from trade. Zhu et al. (2016) examined the validity of the pollution haven hypothesis, the halo effect hypothesis and the EKC hypothesis in ASEAN countries. Zhou and Feng (2017) argued that fossil energy consumption is a major source of environmental pollution emissions and found that implementation of environmental regulation is an effective measure to prevent environmental deterioration. In Eq. (3), if γ 1τ > 0 and γ 2τ < 0 , the EKC between GDP and PM2.5 concentrations can be proved to exist at the τ-th quantile; and if γ 7τ > 0 and γ 8τ < 0 , the EKC between urbanization

ACCEPTED MANUSCRIPT and PM2.5 concentrations can be confirmed to exist at the τ-th quantile. Considering the data have already been made a logarithmic transformation, the levels of income and urbanization at the turning point of EKC are computed as following: (4)

PeakURB (τ ) = e −γ 7τ / 2γ 8τ

(5)

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PeakGDP (τ ) = e −γ 1τ / 2γ 2τ

Information can be obtained from the Eqs. (4) and (5) about which levels of income and urbanisation countries surpass when PM2.5 concentrations begin to decrease.

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3.2.2. The direct and indirect influence mechanisms of democracy and political globalization on PM2.5 concentrations To capture both the direct and indirect effects of democracy and political globalization on PM2.5

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concentrations in the G20 countries, the method of this paper is based on the joint estimation of two equations. One defines pollution as a function of income, democracy, political globalization, and other factors. The second defines income as a function of democracy, political globalization, and other explanatory variables. Defining the pollution Eq. (6) and income Eq. (7) are as following (Cole, 2007; Zhang et al., 2016): PM 2.5it = α i + γ 1GDPit + γ 2 GDPit2 + γ 3TRADEit + γ 4 FDI it + γ 5 FOSSI it

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+γ 6 RENEWit + γ 7URBit + γ 8URBit2 + γ 9 DEMOCit + γ 10 GLOBit + uit

GDPit = ϕ i + β1DEMOCit + β 2GLOBit + β 3 ENCit + β 4GROSit + β 5 INFLit + β 6 POPgrit + ε it

(6)

(7)

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where subscripts i and t represent the country and year, respectively. u it and ε it represent the error terms. Eq. (6) is constructed on the basis of Eq. (3). The choice of their variables is the same, but

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the method of estimation is different. Our study employs the two-stage least squares approach (Kelejian, 1971) to evaluate Eqs. (6) and (7). The result of the Hausman test (Hausman, 1978) showed that both satisfied the fixed effect estimation. Considering a potential endogenous problem existed in Eq. (7), this study applied the instrument variables approach, which chooses the quadratic of the fitted values of per capita GDP as the instrument variable (Wooldridge, 2001; Zhang et.al, 2016). Following Cole (2007) and Leitão (2010), the total effect of democracy on PM2.5 concentrations can be expressed as follows:

ACCEPTED MANUSCRIPT dPM 2.5 ∂PM 2.5 ∂PM 2.5 ∂GDP = + × dDEMOC ∂DEMOC ∂GDP ∂DEMOC ∂GDP ∂GDP = γ 9τ + (γ 1τ × + 2γ 2τ × GDP × ) ∂DEMOC ∂DEMOC = γ 9τ + (γ 1τ + 2γ 2τ × GDP) × β 1

(8)

where ∂PM 2.5 / ∂DEMOC , (∂PM 2.5 / ∂GDP) × (∂GDP / ∂DEMOC ) and dPM 2.5 / dDEMOC denote

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the direct, indirect, and total effect of democracy on the environment, respectively. It can discover that the total and indirect effects may change depending on the different income levels. Similarly, the total effect of political globalization on PM2.5 concentrations can also be expressed as Eq. (9):

(9)

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dPM 2.5 ∂PM 2.5 ∂PM 2.5 ∂GDP = + × dGLOB ∂GLOB ∂GDP ∂GLOB ∂GDP ∂GDP = γ 10τ + (γ 1τ × + 2γ 2τ × GDP × ) ∂GLOB ∂GLOB = γ 10τ + (γ 1τ + 2γ 2τ × GDP) × β 2

where dPM 2.5 / dGLOB , ∂PM 2.5 / ∂GLOB and (∂PM 2.5 / ∂GDP) × (∂GDP / ∂GLOB) denote the total, direct, and indirect effects of political globalization on the environment, respectively. It can also find that the total and indirect effects may also change depending on different income levels. In general, applying the direct and indirect influence mechanisms of democracy and political

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globalization to PM2.5 concentrations can prevent over- or under-estimating its real effect.

4. Empirical results and discussions

Section 4.1 shows the panel unit root and panel cointegration of the independent variables and

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dependent variable. Section 4.2 presents and discusses the quantile regression results. Section 4.3 depicts the direct and indirect effects of democracy and political globalization on PM2.5

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concentrations. Section 4.4 exhibits the elasticities comparison and comprehensive analysis. Section 4.5 provides the robustness analysis. In the process of analyzing the influence paths, the study decomposes the environmental impact into four main influence effects: scale, composition, technique, and environmental regulation effects. The scale effects arise from the fact that economies with higher per capita incomes increase production and extraction of natural resources and services. Keeping output mix and production techniques unchanged, the expansion of the scale inevitably leads to a corresponding increase in environmental pollution. The composition effects reflect the shifts in production patterns,

from the more

energy-intensive

and

material manufacturing sector towards the

more

environmentally-friendly and information-based industries or services sector, which are less polluting.

ACCEPTED MANUSCRIPT The technique effects indicate improvements in technologies that allow the use of less input per unit of output or the adoption of cleaner production technologies. So the pollution per unit of output could decrease in the case of keeping the output mix unchanged. The environmental regulation effects show that government or related agencies design policies with stringent environmental standards and

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innovations to create cleaner energy utilisation, which contributes to decreasing emissions. The willingness of the governments to implement environmental regulation is a key factor affecting environmental quality. 4.1. Panel unit root test and panel cointegration results

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Before estimating the panel quantile regression models, this study check the stationary properties for all the variables concerned. Based on panel data, the Fisher-ADF, Fisher-PP, and IPS tests are

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chosen to conduct the panel unit root test: the results are represented in Table 3. It can conclude that the level series of FDI, INFL, URB, and POPgr are stationary, so are their first-order differenced series. Other variables are the I(1) series at the 1% significance level during the sample period. According to Pedroni (2000), the study examines whether there is a long-run equilibrium relationship exists among these variables using a panel co-integration test, which is called the Kao Residual

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Co-integration Test; the result is represented in the last line in Table 3.The statistic value is statistically significant at the 1% level, which denotes that there is a long-run equilibrium relationship among considered variables during the sample period.

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[Insert Table 3 about here]

4.2. Panel quantile regression results

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To facilitate a comparison, the model is first estimated by the pooled and fixed effects of the OLS regression. Columns 1 and 2 in Table 4 present the pooled and one-way individual fixed effects of the OLS regression estimates, respectively. Most variables tend to increase and decrease together in different regions over time. As noted by Baltagi (2008), time period fixed effects control for all time-specific, spatial-invariant variables whose omission could bias the estimates in a typical time-series analysis. To control for such an effect, our study mainly focus on the results estimated with a two-way fixed effect, which are demonstrated in Column 3. And it is found that only the effect of RENEW is consistent across specifications. Finally, the fully modified OLS (FMOLS) technique proposed by Pedroni (2000) is employed to estimate long-run elasticities. It notes that common time dummies are

ACCEPTED MANUSCRIPT intended to capture certain types of cross-sectional dependency. Column 4 reports the results of the FMOLS. [Insert Table 4 about here] Considering the distributional heterogeneity, a panel quantile regression with fixed effects is

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employed. Table 5 presents the results of the panel quantile regression estimation, which report the 5th, 10th, 20th, 30th, 40th, 50th, 60th, 70th, 80th, 90th and 95th percentiles of the conditional concentrations distribution. The lower quantiles, such as the 10th, 20th, 30th, 40th, and 50th, refer to the countries with lower PM2.5 concentrations, for example, Australia, Brazil, Argentina and South

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Africa. The higher quantiles, such as the 60th, 70th, 80th, 90th, and 95th refer to the countries with higher PM2.5 concentrations, for instance, China, India, Korea and Italy. Generally, the empirical

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results indicate that the impacts of influencing factors on PM2.5 concentrations are clearly heterogeneous.

[Insert Table 5 about here]

The impact of democracy on PM2.5 concentrations is significantly positive in the higher quantiles.

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This result indicates that democracy exacerbates environmental degradation in higher-emissions countries, but has no significant impact on the environmental quality of the countries with lower emissions. Our results can be explained in that economic growth rates in nations with democratic institutions are higher than those with less liberal political arrangements, other things being equal. If

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direct regulation was ignored, democratic regimes would tend to exhibit higher growth rates of pollutants than less liberal regimes (Grier and Tullock, 1989) (scale effect). In countries with higher

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emissions, authoritarian states can limit population dynamics, whereas a democratic state must respect the freedoms granted to its residents. Global population growth and energy consumption threatens environmental performance (scale effect). But from another perspective, democratization may improve the citizens’ understanding of the situation, their organization and the resulting protests. They are more likely to express their preferences and put pressure on their governments, enhancing the awareness of states and political entrepreneurs about environmental protection requirements (environmental regulation effect). Scale effect surpasses the environmental regulation effect in general due to democracy eventually shows exacerbating environmental degradation. Additionally, two primary situations that may contribute to democracy have an insignificant impact on the environmental quality

ACCEPTED MANUSCRIPT in countries with lower emissions. First, those countries may have fewer sources of pollution or fewer conflicts between economic growth and environmental quality. Second, the citizens of those countries have a high awareness of environmental protection or enjoyed relatively complete national environmental protection policy systems. Even so, a small amount of unavoidable particles are still

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produced to meet the energy consumption requirements of everyday life. Regarding political globalization, it can observe that the effect of political globalization on PM2.5 concentrations is significantly positive at all quantiles. It also indicates that the impact decreases in magnitude as track it from the lower to higher quantiles of the distribution, roughly. Specifically, the

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impact of political globalization on environmental quality is greater for extremely low- and high-emission countries because the estimated coefficient is the largest at the 5th, 10th, and 95th

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quantile and smallest at the 90th quantile. One possible reason is that globalization may increase the speed of structural change by altering the industrial structure of countries due to industries oriented towards fulfilling foreign demand for their products and thus leading to an increase in the use of resources and atmospheric pollution levels (scale effect). Another reason may be that the marginal cost of pollution is relatively high in extremely low-emission countries, and the cumulative effects of

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environmental pollution are relatively more significant in extremely high-emission countries. Additionally, political cooperation among countries may contribute to a decrease PM2.5 emissions by providing a platform for diffusing government policies, which may include energy conservation or technical efficiency for environmental protection (environmental regulation effect) and offering

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necessary technical assistance (technological effect). This is also in line with the view of EMT, which indicated that environmental degradation could be relieved by technical innovation driven

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environmental regulation (Zhu et al., 2011). Generally, scale effect surpasses the sum of environmental regulation effect and technological effect due to political globalization exacerbating environmental pollution.

With respect to urbanization, the results provide evidence that support the inverted U-shaped EKC hypothesis for the relationship between urbanization and PM2.5 concentrations, because the coefficient of URB is significantly positive and URB2 is significantly negative. The turning points of EKC are calculated at various quantile levels using Eq. (5) (see the last line in Table 5). This result demonstrates that the levels of urbanization at the turning point of the EKC may increase when PM2.5 concentrations increase among the G20 countries. And it can also find that the urbanization levels in

ACCEPTED MANUSCRIPT most

of

the

G20

countries

have

been

reached,

which

indicates

that

the

global

concentrations–urbanization relationship is monotonic except for China, Indonesia, and India. The result implies that PM2.5 concentrations increase in the initial stage of urbanization, while suddenly decrease as the urbanization level surpasses a certain point. And this result is in line with the view of

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EMT. The usual explanation for this situation is that in the early stages of development, rapid urbanization led to a dramatic increase in urban population (Li et al., 2015), resulting in an increased demand for housing and the rapid development of urban real estate. Housing construction and real estate activities generate a considerable amount of dust, and dust is the main source in the formation of

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PM2.5 (Meng et al., 2015). This situation is called the scale effect of production on environment. It creates an upward trend in the EKC when production shifts from primary production to industrial

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production. However, in later stages, rapid urbanisation results in remarkable improvements in urban infrastructure and software facilities (Xu et al., 2016). These attract a large number of talents gathered in urban areas, which contributes to human capital accumulation. Human capital accumulation enhances environmental awareness and promotes the R&D (research and development) of energy-saving and abatement technologies (Lin and Zhao, 2016). Accordingly, PM2.5 pollution intensity gradually decreases as the levels of urbanization increase. At this stage, urbanization

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development provides opportunity for investing in information-based industries and services. And it also improves production techniques or adopts cleaner technology. These impacts are the called the composition and technique effects and they can surpass the scale effect and produce a downward trend

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in the EKC curve.

Finally, the results for the model’s other control variables are also informative. First, the impact of

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GDP per capita on PM2.5 emissions is observed. The coefficients of GDP are negative and GDP2 are positive, but insignificant at all quantiles except the 80th percentile. This result does not support the EKC hypothesis with respect to income and pollution. Unlike the result of the urbanisation–PM2.5 concentrations nexus, the result for the income–PM2.5 concentrations nexus contradicts the anticipation of the modernisation perspective. This finding of no evidence satisfying the EKC hypothesis is in accordance with the results of Richmond and Kaufmann (2006) and Iwata et al. (2011). Second, the coefficients of Trade and FDI are only significantly positive at lower quantiles (the 5th, 10th and 20th quantiles). This information implies that a higher level of trade openness and foreign direct investment exacerbate the environmental pollution in lower-emission countries; but the impact

ACCEPTED MANUSCRIPT is insignificant in higher-emission countries. This result also indicates that there is evidence of pollution haven effects in Australia, Brazil, Argentina and South Africa. And this finding is similar to Merican et al. (2007). Third, the coefficients of the FOSSI demonstrate that the consumption of fossil fuel energy has a positive impact on PM2.5 concentrations but is only significant in higher-emissions

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countries. Accordingly, regulating the consumption of fossil energy is critical to reaching a beneficial environmental protection condition and sustainable resource utilization. Finally, It can observe that the coefficients of RENEW are significantly negative throughout the distribution, implying that increasing the use of renewable energy reduces PM2.5 emissions for the G20 countries. To achieve stable and

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sustainable growth in renewable energy consumption, relevant government agencies should formulate and implement efficient supporting policies to facilitate investment in new renewable energy

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technologies. The corresponding panel quantile regression diagrams are provided in Fig. 2. [Insert Fig.2 about here]

4.3. The direct and indirect effect of democracy and political globalization on PM2.5 concentrations

Table 6 presents the regression results of Eqs. (6) and (7). From regression result of the PM2.5

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equation, it can conclude that the statistically significant coefficients of GDP and GDP2 do not support the existence of an inverted U-shaped EKC between income and pollution, while significantly positive coefficient of URB and significantly negative coefficient of URB confirm the existence of EKC

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between urbanization and PM2.5 concentrations. The similar result is supported by Shafiei and Salim (2014). From the regression result of the income equation, it can obtain that GDP per capita might

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increase by 0.2718% with a one-percentage increase in democracy and might increase by 1.5605% with a one-percentage increase in political globalization. In other words, democracy and political globalization may have a positive impact on economic growth. Then, the study chooses representative quantiles of GDP per capita as the different income levels and estimates the direct, indirect and total effects of democracy and political globalization on PM2.5 concentrations within the G20 countries during 2000–2014. The results are shown in Tables 7 and 8. [Insert Tables 6, 7 and 8 about here] The results demonstrate that, first, both democracy and political globalization may directly affect PM2.5 concentrations positively at different levels of concentrations, and the magnitude of the direct

ACCEPTED MANUSCRIPT effect of democracy on PM2.5 concentrations increased along with the GDP per capita levels, roughly. For example (Table 7), regarding the GDP per capita at the 10th quantile, PM2.5 concentrations will increase by 1.7640% with a one-percentage increase in the degree of democracy; and regarding 95th quantile of GDP per capita, concentrations will increase by 2.5250%, which is greater than the

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increased margin of PM2.5 emissions when the GDP per capita remains at the 10th percentile (1.7640%). Second, democracy and political globalization may indirectly affect PM2.5 concentrations negatively in lower-emissions countries (the 5th, 10th, 20th, 30th, and 40th quantiles) but positively in countries with higher emissions (the 50th, 60th, 70th, 80th, 90th, and 95th quantiles). And the

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magnitude of the positive indirect effect will increase along with the increased GDP per capita levels, roughly. For instance (Table 8), when GDP per capita remains at the 50th quantile, PM2.5

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concentrations may increase by 0.0201% with every 1% increase in the degree political globalization; and when considering the 95th quantile of GDP per capita, concentrations may increase by 0.3075% with every 1% increase in the degree of political globalization.

Compared with the influencing mechanisms of these two driving forces from Fig. 3, the effect of democracy on PM2.5 concentrations in the G20 countries does not largely depend on the levels of

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GDP per capita. As for the reasons, it can argue that the role of democracy through the GDP per capita on the environment can be divided into two opposite parts. On one side, in the earlier stage of economic development, a country’s economic base is often relatively weak and the normal market competition order has not yet been well established. In this case, the prevalence of democracy may

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gradually lead to an appropriate management of the immature economic system, which may cause a reduction in PM2.5 emissions (environmental regulation effect). On the other side, when an economic

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situation gradually develops, the contradiction between economic growth and environmental quality may gradually escalate. And the rapid growth causes increased PM2.5 emissions (scale effect). These two opposing impacts offset each other; ultimately, the indirect effect of democracy on the concentrations, to a large extent, does not depend on the levels of GDP per capita. Unlike democracy, the total effects of political globalization on PM2.5 concentrations depend on the levels of GDP per capita, to a certain extent, particularly in countries with higher emissions. One possible explanation is that political globalization has reinforced exchanges and cooperation among countries. These interactions facilitate trade liberalization and economic activities that influence the environment when all the goods and services produced in the economy are indirectly related to the uses of power and

ACCEPTED MANUSCRIPT energy (e.g., natural gas, oil products), resulting in a corresponding level of increased emissions(scale effect). Moreover, economic growth may strengthen environmental activities, and public authorities have authorized the laws on collecting information on pollution that enable local communities to set higher environmental standards (environmental regulation effect). In summary, the direct and indirect

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influence mechanisms of democracy and political globalization on PM2.5 concentrations, by considering the role of GDP per capita as a critical factor in the explanation, can avoid overestimating their real effects in countries with lower emissions and underestimating their real effects in countries with higher emissions.

4.4. Elasticities comparison and comprehensive analysis

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[Insert Fig.3 about here]

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This study demonstrates the elasticities comparison of the factors that influence PM2.5 concentrations (including main and control variables) across quantiles from two perspectives in Fig.4. One perspective is based on variables, which involves setting all the main variables and control variables as the backbone and comparing the elasticities of these influencing factors. The other perspective is based on quantile levels, which means setting the quantile levels as the backbone and

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comparing the elasticities of influencing factors to PM2.5 concentrations. An interesting phenomenon can be observed from the results: PM2.5 has the greatest positive reaction to the changes of democracy, followed by political globalization, whereas the negative reaction to the changes of urbanization level is the greatest. In comparison, other control variables have a relatively small influence on PM2.5

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concentrations. As the driving forces of PM2.5 concentrations, the impact of democracy, political globalization and urbanization on concentrations are also heterogeneous in the G20 countries with

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different pollution levels.

Based on the above analysis, the effects of democracy, political globalization, and urbanization on PM2.5 concentrations are within the theoretical framework of the EMT. Thus, the study unites the effect of democracy, political globalization, and urbanization on PM2.5 concentrations in a common theoretical body. Fig.5 graphically displays the relationships and influence paths of these three driving forces on PM2.5 concentrations under the same theoretical and analytical framework. It explains the influence paths by decomposing the environmental impact of these variables into four main channels, namely, scale, composition, technique, and environmental regulation effects, which are marked by highlighting with different colors and also mentioned in the analysis section above. With respect to the

ACCEPTED MANUSCRIPT influence path of urbanization on PM2.5 concentrations, urbanization directly affects PM2.5 concentrations through scale, composition and technique effects at different levels of urbanization, and the magnitudes of the three effects are different. Scale effect plays a dominant role in the early stages of urbanization. As the urbanization level surpasses a certain point, the composition and technique

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effects dominant. Similarly, the influence paths of direct and indirect impacts of democracy and political globalization on PM2.5 concentrations can be analogous to understanding. And each of these influence paths always has one type of effect that plays a dominant role, which specific analysis can be seen in sections 4.2 and 4.3. Besides, it is worth noting that the magnitude of each effect is different

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depending on different market conditions. [Insert Fig.4 and Fig.5 about here]

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4.5. Robustness analysis

To test the reliability of our results, this study implements two robustness tests that considered different values of λ and an alternative model specification. First, it investigates whether our results are robust to different λ and experiments with different values of λ , that is, λ = 0.2 , 0.4, 0.6, 0.8, 1.0. To save space, the study will present the main variables of interest (see the results in Table 9). The

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findings are almost consistent with those from the panel quantile regression results with λ = 0.65 . Second, Chaisse and Gugler (2009) argued that FDI plays a crucial role in facilitating trade. Dash and Sharma (2010) indicated that FDI and TRADE have a complementary relationship. Considering the relationship between these two variables, this study implements two additional model specifications.

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Specification I contains only TRADE. Specification II contains only FDI. To save space, the results of the latter robust checks are not reported because the results are almost the same as the model

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specification that included both variables (the results are available from the authors). The results from these two robustness checks largely support previous results. [Insert Table 9 about here]

5. Conclusions and policy recommendations The objective of this paper is to investigate the effect of democracy, political globalization, and urbanization on PM2.5 concentrations at different pollution levels within the G20 countries. This study utilizes the panel quantile regression method to achieve our objectives, which considered unobserved individual and distributional heterogeneity. Compared with the OLS regression, quantile regression

ACCEPTED MANUSCRIPT analysis obtains a more detailed picture of the influencing factors that affect PM2.5 concentrations at different pollution levels. Moreover, to avoid an omitted-variable bias and obtain more comprehensive and tenable results, certain related control variables are included in our model. The empirical results suggest that the effects of influencing factors on PM2.5 concentrations are heterogeneous across

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quantiles. Furthermore, the study unites the effect of democracy, political globalization, and urbanization on PM2.5 concentrations in a common theoretical body through combining these three key elements of modernization within the EMT analytical frame. It explains the influence paths by decomposing the environmental impact of these variables into four main channels, namely, scale,

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composition, technique, and environmental regulation effects, which can help us obtain a more comprehensive understanding about the effect of the influencing factors on PM2.5 concentrations.

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This study implies that the PM2.5 concentrations are strongly associated with democracy, political globalization and urbanization. First, the direct effect of democracy on PM2.5 concentrations is significantly positive in countries with higher emissions and has no impact on concentrations in lower-emission countries. Democracy, through its effect on GDP per capita, may have a positive (negative) indirect effect on PM2.5 concentrations in higher-emissions (lower-emissions) countries,

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but both are relatively small. In sum, the total effect point out to be positive, meaning democracy may make the environmental quality worse in higher-emission countries. Second, political globalisation may also have a positive (negative) indirect effect on concentrations in higher-emissions (lower-emissions) countries, through its effect on GDP per capita. Unlike democracy, the effect of

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political globalization on PM2.5 concentrations depends on the levels of per capita GDP, to a certain extent, especially in countries with higher emissions. The direct effect of political globalization on

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concentrations is more obvious in countries with extremely low- and high-emissions. The total effect is positive, which also refers to environmental degradation. Third, this study also verifies the validity of the EKC hypothesis for urbanization and PM2.5 concentrations within the G20 countries. The results also indicate that most G20 countries have already reached their peak of PM2.5 concentrations, implying that the concentrations–urbanization relationship is essentially monotonically decreasing, except for China, Indonesia, and India. Fourth, no evidence supported the EKC hypothesis about the relationship between GDP per capita and PM2.5 concentrations. Besides, the empirical analysis confirmed that renewable energy consumption plays a critical role in reducing PM2.5 concentrations, whereas the consumption of fossil fuel energy contributes to higher PM2.5 concentrations in countries

ACCEPTED MANUSCRIPT with higher emissions. Based on the aforementioned findings, the following policy implications could be made to improve the environmental quality in the G20 countries. First, according to the direct and indirect influence mechanisms of democracy on PM2.5 concentrations, emissions control initiatives should be

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tailored discriminatively across the countries with the most and least emissions. Policy formulation should consider increasing the absolute value of the negative indirect effects by adjusting the environmental regulation effect. Second, considering that economic growth caused by political globalization will reduce environmental pollution, the government should adjust income per capita to

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increase the environmental regulation effect. Moreover, countries should simultaneously strengthen political cooperation and provide a platform to spread environmental protection awareness. Each

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country, through cooperation, would provide the other with the essential technical and financial assistance to affect environmental protection. Third, in terms of the existence of the EKC relationship between urbanization and PM2.5 concentration, governments should be made aware of this as it relates to their country’s situation; then, they can develop appropriate plans to reduce PM2.5 emissions according to the different stages of the urbanization process. They should take effective measures to

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reduce building activities and fixed asset investment-related dust in the early stage of the urbanization process. These actions could include closing construction sites, supporting car wash-on-site systems, and construction site watering. Moreover, the transport sector in urban areas is also a major source of PM2.5 emissions. Governments should strongly invest in infrastructure expansion such as creating

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policies to expand public transport systems and improve energy-saving technology, by establishing national R&D centres and encouraging enterprises to participate through favoured policies. Fourth,

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considering that the consumption of fossil fuel energy exacerbates environmental degradation and renewable energy consumption plays a crucial role in reducing PM2.5 concentrations, transforming the traditional energy structure’s reliance on fossil fuels into renewable energy consumption is urgent. To achieve steady, sustainable growth in the renewable energy sector (e.g., clean hydropower, solar, and nuclear energy), governments should develop and implement effective support policies that promote investment in new renewable energy technologies. These smart investments may increase the share of renewable energy sources.

ACCEPTED MANUSCRIPT Acknowledgements Our deepest gratitude goes to the Co-Editor-in-Chief, Dr. Jiri Jaromir Klemeš, Associate Editor, Dr. Charbel Jose Chiappetta Jabbour, and the anonymous reviewers for their helpful comments and constructive suggestions that greatly improved the quality of this paper. This research is partly supported

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Zhang, Y.J., Jin, Y.L., Chevallier, J., Shen, B., 2016. The effect of corruption on carbon dioxide emissions in APEC countries: a panel quantile regression analysis. Technological Forecasting & Social Change 112, 220-227.

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Zhou, X., Feng, C., 2017. The impact of environmental regulation on fossil energy consumption in China: Direct and indirect effects. Journal of Cleaner Production 142, 3174-3183.

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1

For our data set in Excel format, please contact with the corresponding author.

ACCEPTED MANUSCRIPT Table 1 Variables definitions and data sources.

a

Data Source

PM2.5

Average Exposure to PM2.5

Environment Performance Indexa

GDP

GDP per capita [constant US$(2010)]

World Development Indicatorsb

TRADE

Ratio of imports plus exports to GDP

World Development Indicators

FDI

Foreign direct investment, net inflows (% of GDP)

FOSSI

Fossil fuel energy consumption (% of total)

RENEW

Renewable energy consumption (% of total final energy consumption)

URB

Urban population (% of total)

DEMOC

Sum of the Freedom House Political Rights and Civil Liberties Indices

Freedom Housec

GLOB

Political globalization

KOF index of globalizationd

ENC

Energy consumption (kg of oil equivalent per capita)

World Development Indicators

GROS

Gross fixed capital formation (% of GDP)

World Development Indicators

INFL

Inflation, GDP deflator (annual %)

World Development Indicators

POPgr

Population growth (annual %)

RI PT

Definition

World Development Indicators World Development Indicators

SC

M AN U

AC C

EP

http://sedac.ciesin.columbia.edu/data/set/epi-environmental-performance-index-2016. http://data. worldbank.org/indicator. c www.freedomhouse.org/ratings/index.htm. d http://globalization.kof.ethz.ch/. b

World Development Indicators World Development Indicators

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Variables

1

World Development Indicators

ACCEPTED MANUSCRIPT Table 2 Summary of descriptive statistics. Mean

SD

Min

Q1

Median

Q3

Max

Skewness

Kurtosis

JB

PM2.5

2.344

0.629

0.693

2.015

2.342

2.565

3.918

0.218

3.572

6.154**

GDP

9.611

1.103

6.646

8.980

9.708

10.627

10.901

-0.724

2.700

24.956***

TRADE

3.895

0.369

2.986

3.712

3.958

4.121

4.700

-0.534

2.826

13.905***

FDI

2.260

1.942

-3.621

1.003

2.025

3.107

12.718

FOSSI

4.384

0.177

3.833

4.321

4.451

4.494

4.605

RENEW

1.931

1.887

-5.115

2.142

2.265

2.826

3.947

URB

4.250

0.277

3.320

4.216

4.361

4.402

4.533

DEMOC

10.691

3.931

2.000

9.000

12.000

14.000

GLOB

4.462

0.119

4.019

4.443

4.502

4.530

ENC

7.910

0.757

6.034

7.338

8.144

8.445

GROS

3.106

0.243

2.482

2.952

3.058

INFL

5.920

7.697

-18.932

1.678

POPgr

0.937

0.690

-1.854

0.484

RI PT

Variables

8.265

442.280***

-1.363

3.969

99.408***

-2.369

9.169

718.602***

-1.848

5.758

252.510***

SC

1.543

-1.078

2.713

56.156***

4.589

-2.058

7.079

398.843***

9.041

-0.584

2.495

19.208***

3.200

3.818

0.837

3.542

36.758***

3.354

7.779

52.851

2.605

13.655

1670.489***

0.970

1.345

2.998

0.180

4.137

16.897***

M AN U

14.000

Note: (1) All the variables are in natural log form except FDI, DEMOC, INFL, POPgr. (2) JB denotes the empirical

AC C

EP

respectively.

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statistic of the Jarque–Bera test for normality. (3) *** and ** denote Statistical significance at the 1% and 5% levels,

2

ACCEPTED MANUSCRIPT Table 3 Results of panel unit root tests and co-integration test of variables. ADF

PP

IPS

Series Constant

TC

Constant

TC

Constant

TC

72.4991***

44.1199

61.0455**

42.2580

-1.4045*

-0.8308

GDP

22.1833

50.1951

35.2851

50.6129

2.5972

-0.8394

TRADE

44.2358

47.9901

42.0250

66.4466***

-1.2208

-0.8983

126.3760***

103.6870***

141.6630***

129.1660***

-7.4149***

-5.8781***

FOSSI

32.1890

26.0034

32.3298

26.4492

2.6912

1.9285

RENEW

34.6702

59.2621***

32.4351

81.9728***

2.201

-0.3321

144.9830***

32.6092

270.1510***

85.3007***

-10.1279***

10.3804

DEMOC

17.7409

15.7930

30.5872

10.3263

-0.2442

0.8068

GLOB

43.8813

47.8217

58.3888**

43.5341

-0.9530

-0.6945

ENC

16.5119

51.3024*

12.6046

57.8278**

4.3218

-0.9030

GROS

41.3058

40.1956

44.2176

35.4627

-0.3466

-0.3355

INFL

103.3090***

82.7102***

106.6940***

133.7930***

-5.6868***

-3.4108***

POPgr

121.5150***

109.9430***

55.0534**

60.8303**

1.7888

-3.9874***

△PM2.5

97.9740***

93.0561***

97.0336***

111.741***

-5.5081***

-5.4031***

△GDP

121.6920***

83.4707***

127.0380***

121.2420***

-7.4664***

-4.6492***

△TRADE

171.3030***

126.4990***

225.6160***

220.6140***

-10.9551***

-8.1866***

△FDI

182.4650***

161.0290***

269.4750***

229.0650***

-11.6846***

-11.0117***

△FOSSI

153.3870***

145.6120***

168.5930***

192.2880***

-9.3788***

-9.4820***

△RENEW

155.6920***

140.8650***

196.8330***

227.0300***

-9.1579***

-9.0791***

URB

SC

FDI

M AN U

PM2.5

RI PT

Level

EP

TE D

First difference

72.3632***

153.5310***

114.3480***

168.1320***

1.8421

-12.2970***

27.8626***

80.3564***

38.5738***

94.3884***

-3.0275***

-6.9776***

△GLOB

146.0870***

103.9450***

157.3830***

136.3210***

-8.8045***

-6.2598***

△ENC

135.3810***

140.8240***

217.3410***

220.3140***

-8.2223***

-8.9285***

△GROS

112.8850***

87.9808***

126.7150***

129.8840***

-6.8658***

-5.0340***

△INFL

228.5480***

172.7160***

321.6650***

267.3670***

-14.7098***

-11.5679***

△POPgr

146.8810***

170.0880***

148.4360***

118.5430***

-3.6429***

-211.7410***

△URB

AC C

△DEMOC

Kao Residual Co-integration Test: −3.7584 (0.0001). Note: (1) TC represents trend and constant. (2)***, ** and * denote statistical significance at the 1%, 5% and 10% levels, respectively.

3

ACCEPTED MANUSCRIPT Table 4 OLS regression estimation results. OLS one-way fixed effect

OLS two-way fixed effect

FMOLS

GDP

8.4274***

- 2.6943***

-0.8143

-1.5690**

GDP2

-0.4162***

0.1462***

0.0574

0.0806**

0.0129

0.1050***

0.0944**

0.1032

FDI

-1.2449

0.2375

0.4182

0.3744

FOSSI

-0.0990

0.3345*

0.1849

-0.0328

RENEW

-0.0869***

-0.1866***

-0.1061 ***

-0.2315***

URB

-16.6953***

19.7757***

URB2

1.4922***

-2.4469***

DEMOC

-2.7373**

GLOB

1.1317***

SC

TRADE

RI PT

OLS pooled

9.2224***

10.1078*

-1.1105***

-1.2804*

M AN U

Variables

1.7155**

-0.7951

4.4517*

0.6107*

0.5273*

0.7120

Note: ***, ** and *denote statistical significance at the 1%, 5% and 10% levels, respectively.

AC C

EP

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.

4

ACCEPTED MANUSCRIPT

Quantile Levels 10th

20th

30th

40th

50th

60th

70th

80th

90th

95th

GDP

-0.3514

-0.2887

-0.7616

-1.2594

-1.4464

-1.4296

-1.4774

-1.5320

-1.7882*

-1.5632

-2.0776

GDP2

0.0178

0.0158

0.0402

0.0658

0.0755

0.0743

0.0766

0.0798

0.0927*

0.0808

0.1051

TRADE

0.1051**

0.0953**

0.0786

0.0784

0.0582

0.0597

0.0712

0.0800

0.0884

0.0830

0.0866

FDI

1.0460**

0.8360*

0.8040**

0.4550

0.1160

0.0190

0.0460

-0.2870

-0.3400

-0.459

-0.2850

FOSSI

0.2815

0.1649

0.2153

0.2371

0.2960

0.3192*

0.3465**

0.3731**

0.4000**

0.3690**

0.3581*

RENEW

-0.1922***

-0.1906***

-0.1813***

-0.1817***

-0.1804***

-0.1802***

-0.1798***

-0.1762***

-0.1724***

-0.1713***

-0.1801***

URB

16.4690***

16.3020***

16.6502***

18.1040***

18.9328***

19.1348***

18.8655***

18.8137***

19.4851***

18.6141**

20.6737***

URB2

-2.1818***

-2.1637***

-2.1836***

-2.3468***

-2.4447***

-2.4693***

-2.4334***

-2.4244***

-2.4947***

-2.3873***

-2.6125***

2.1840

1.7640

1.9380

1.8710

2.1420

2.4380**

2.4690**

2.4820**

2.3640**

2.4790**

2.5250**

1.8580***

1.8250***

1.5751***

1.5906***

1.5391***

1.5181***

1.5541***

1.5373***

1.5573***

1.4874***

1.7237***

43.562

43.257

45.266

47.331

48.161

48.249

48.428

49.665

49.332

52.284

Peak-URB

M AN U

TE D

GLOB

48.049

AC C

DEMOC

SC

5th

EP

Variables

RI PT

Table 5 Panel quantile regression estimation results.

Note: (1) This table shows the results of the panel quantile regression model with the different PM2.5 concentrations as dependent variables. Democracy, political globalization, urbanization, and other control variables are the independent variables. (2) ***, **, and * denote the statistical significance at the 1%, 5% and 10% levels, respectively.

5

ACCEPTED MANUSCRIPT Table 6 The regression results of PM2.5 equation and income equation. Variable

Coefficient

Std. Error

t-Statistic

P-Value

-2.6943***

0.7748

-3.4776

0.0006

GDP2

0.0840

***

0.0397

3.6796

0.0003

TRADE

0.1063***

0.0350

2.9988

0.0030

0.1628

0.0028

0.8579

0.3917

*

0.1908

1.7531

0.0808

GDP

FDI

RI PT

The regression result of the PM2.5 equation

0.2501

RENEW

-0.2205***

0.0237

-7.8845

0.0000

URB

15.3407***

2.9826

6.6303

0.0000

URB2

-1.9284***

0.3520

DEMOC

3.5959**

0.0077

GLOB

0.8263*

0.3516

DEMOC

0.2718***

GLOB

1.5605***

ENC

0.8173*** ***

-6.9505

0.0000

2.2214

0.0272

1.7366

0.0837

2.18E-06

1246.725

0.0000

5.89E-06

264753.6

0.0000

1.46 E-06

560247.2

0.0000

2.64 E-06

60620.57

0.0000

M AN U

The regression result of the income equation

SC

FOSSI

0.1603

INFL

-0.0830***

2.96 E-08

-28054.91

0.0000

POPgr

3.0465***

1.27 E-06

23933.92

0.0000

TE D

GROS

AC C

EP

Note: ***, ** and *denote the statistical significance at the 1%, 5% and 10% levels, respectively.

6

ACCEPTED MANUSCRIPT

Indirect effect

Total effect

5th

2.1840

-0.0248

2.1592

10th

1.7640

-0.0099

1.7541

20th

1.9380

-0.0120

1.9260

25th

1.9130

-0.0160

1.8970

30th

1.8710

-0.0173

1.8537

40th

2.1420

-0.0155

2.1265

50th

2.4380

0.0035

2.4415

60th

2.4690

0.0338

2.5028

70th

2.4820

0.0436

2.5256

75th

2.4760

0.0487

2.5247

80th

2.3640

0.0511

2.4151

90th

2.4790

0.0479

2.5269

95th

2.5250

0.0536

2.5786

SC

Direct effect

M AN U

Quantile

RI PT

Table 7 Direct, indirect and total effect of democracy on PM2.5 concentrations.

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EP

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Note: Total effect is the sum of direct effect and indirect effect.

7

ACCEPTED MANUSCRIPT

Table 8 Direct, indirect and total effect of political globalization on PM2.5 concentrations. Indirect effect

Total effect

5th

1.8580

-0.1424

1.7156

10th

1.8250

-0.0566

1.7683

20th

1.5751

-0.0690

1.5061

25th

1.5027

-0.0916

1.4111

30th

1.5906

-0.0992

1.4914

40th

1.5391

-0.0891

1.4501

50th

1.5181

0.0201

1.5383

60th

1.5541

0.1941

70th

1.5373

0.2501

75th

1.4653

80th

1.5573

90th

1.4874

95th

1.7237

SC

RI PT

Direct effect

1.7482

1.7874

M AN U

Quantile

0.2795

1.7449

0.2932

1.8505

0.2751

1.7625

0.3075

2.0313

AC C

EP

TE D

Note: Total effect is the sum of direct effect and indirect effect.

8

ACCEPTED MANUSCRIPT

Table 9 Robustness analysis: Alternative values of λ . Variables

Quantile Levels 5th

λ = 1.0

-2.2531***

-2.2705**

-2.2243***

-2.3342***

-2.3682***

-2.5482***

1.7130

1.6800

1.8080

1.7800

2.1260*

2.2990**

2.3260**

2.2520**

2.1720**

2.2870**

2.3700**

GLOB

1.3005***

1.0650***

0.9347***

0.9011***

0.8623***

0.8785***

0.9217***

0.8840***

0.9247***

0.8768**

1.1685***

URB

15.9004**

16.7868**

16.3768**

17.4734***

18.0470***

17.9063***

18.1732***

17.8012***

18.5865***

19.2079***

20.2439***

URB2

-2.0938***

-2.1916***

-2.1245***

-2.2503***

-2.3127***

-2.2965***

-2.3223***

-2.2757***

-2.3648***

-2.4295**

-2.5318***

1.7300

1.5780

1.7720*

1.7410*

2.0030**

2.2450**

2.3000**

2.3180**

2.3100**

2.3070**

2.3810**

GLOB

1.5288***

1.3105***

1.1604***

1.1595***

URB

16.2168***

16.4749***

16.3778***

17.7931***

URB2

-2.1487***

-2.1804***

-2.1476***

-2.3081***

2.1410

1.6870

1.8680

1.8270

GLOB

1.8023***

1.7413***

1.5076***

1.5228***

URB

17.1508**

URB2

-2.2818**

-2.2124**

-2.1960**

2.2600

2.0880

2.2150*

GLOB

2.1996***

1.7980***

URB

16.7644**

URB2

SC

RI PT

-2.2637**

GLOB

18.8386

20.5492***

-2.2332***

DEMOC

18.4984

95th ***

-2.1224***

DEMOC

17.5065

90th ***

-2.1428***

16.5904**

17.8579

80th ***

-2.1240***

16.5817**

17.6733

70th ***

URB2

DEMOC

λ = 0.8

60th ***

M AN U

λ = 0.6

17.7379

50th ***

16.1937

DEMOC

17.4514

40th ***

1.1041***

1.0970***

1.1418***

1.1249***

1.1826***

1.0866**

1.3560***

18.5920***

18.9116***

18.5323***

18.5321***

19.2225***

18.6098**

20.4523***

-2.4006***

-2.4389***

-2.3897***

-2.3868***

-2.4622***

-2.3824***

2.0790*

2.3620**

2.4150**

2.4250**

2.3190**

2.4540**

2.4780**

1.4700***

1.4403***

1.4769***

1.4600***

1.4864***

1.4347***

1.6633***

TE D

λ = 0.4

16.4894

30th ***

URB

DEMOC

16.4770

20th ***

-2.5825***

18.7597***

19.8248***

19.5620***

19.8528***

20.1743***

19.7428***

21.2920***

20.6394**

-2.4411***

-2.5680***

-2.5432***

-2.5724***

-2.6061***

-2.5499***

-2.7215***

-2.6354***

2.1230*

2.3360*

2.5480**

2.6350**

2.5700**

2.6120**

2.6810**

2.6340**

1.9176***

1.9409***

1.8812***

1.8399***

1.8908***

1.9086***

1.8890***

1.8924***

2.0252***

16.1236**

16.5975**

18.5885**

19.5600***

19.4084***

19.9308**

19.6254***

19.4717***

20.3494**

20.1566**

-2.2509**

-2.1626**

-2.2036**

-2.4293***

-2.5460***

-2.5323***

-2.5950***

-2.5527***

-2.5311***

-2.6242***

-2.5939**

2.3730

2.1990

2.1810*

2.1270*

2.2760*

2.4020**

2.5090**

2.5530**

2.3480**

2.5860**

2.4610*

2.3464***

2.2679***

2.0479***

2.0674***

2.0054***

1.9549***

1.9855***

2.0392***

1.9834***

2.0085***

2.1477***

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λ = 0.2

10th **

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Lambda

Note: ***, ** and *denote the statistical significance at the 1%, 5% and 10% levels, respectively.

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Fig.1. Geographical distribution of average annual PM2.5 concentrations of the G20 countries.

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Fig.2. Quantile regression estimates with 95% confidence intervals for the impact of influential factors on PM2.5 concentrations. The vertical axes show the coefficient estimates of the variables over the PM2.5 concentrations’ distribution. The horizontal axes depict the quantile levels. The red horizontal solid lines represent the corresponding

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OLS estimations with their 95% confidence interval (red dashed line).

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(b) The effect of political globalization on PM2.5 concentrations

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(a) The effect of democracy on PM2.5 concentrations

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Fig.3. The direct, indirect, and total effect of democracy and political globalization on PM2.5 concentrations.

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(a) Elasticities comparison among variables

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(b) Elasticities comparison across quantiles

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Fig.4. Elasticities comparisons of the factors that influence PM2.5 from variables’ and quantile levels’ perspectives.

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Fig.5. The influence paths of democracy, political globalization, and urbanization on PM2.5 concentrations within the ecological modernization theory framework. The influence effects are marked by highlighting them

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with different colors, including positive and negative effects.

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ACCEPTED MANUSCRIPT The heterogeneous effect of democracy, political globalization and urbanization on PM2.5 concentrations in G20 countries: Evidence from panel quantile regression

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● This article uses panel data to investigate the driving forces of PM2.5 concentrations in G20 countries. ● We examine the direct and indirect influence mechanisms of democracy and political globalization on PM2.5 concentrations across quantiles.

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● Democracy significantly exacerbates environmental degradation in higher-emissions countries.

● Political globalization significantly increases the PM2.5 emissions, especially in extremely low- and

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high-emission countries.

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● An environmental Kuznets curve relationship exists between urbanisation and PM2.5 concentrations.