Sources of emission reductions: Market and policy-stringency effects

Accepted Manuscript Sources of emission reductions: Market and policy-stringency effects

Erik Hille, Muhammad Shahbaz PII: DOI: Reference:

S0140-9883(18)30446-8 https://doi.org/10.1016/j.eneco.2018.11.006 ENEECO 4215

To appear in:

Energy Economics

Received date: Revised date: Accepted date:

19 May 2018 3 November 2018 8 November 2018

Please cite this article as: Erik Hille, Muhammad Shahbaz , Sources of emission reductions: Market and policy-stringency effects. Eneeco (2018), https://doi.org/10.1016/ j.eneco.2018.11.006

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Sources of emission reductions: Market and policy-stringency effects

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Erik Hillea a HHL Leipzig Graduate School of Management Corresponding author Jahnallee 59, 04109 Leipzig, Germany E-mail: [email protected]

Muhammad Shahbazb Montpellier Business School 2300 Avenue des Moulins, 34080 Montpellier, France E-mail: [email protected]

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Abstract: International trade and economic development affect air emissions. Previous studies have decomposed their effects into scale, composition, and technique effects. While the scale and composition effects occur through market responses, the technique effect is a policy-stringency influence through the mix of environmental policies. This study analyzes whether the market or policy-stringency effects are more prominent. Previous studies have been unable to adequately separate the market and policy-stringency effects. To independently measure the technique effect, we use two indicators of policy stringency, i.e. shadow prices of energy and industrial energy prices. These policy stringency measures are treated as endogenous. The effects on six types of air emissions are estimated utilizing a sector-specific, international panel dataset that includes newly industrialized and former transition economies. The empirical results show that the major source of emissions reductions is the policy-stringency effect through carbon-related policies. Pollution offshoring to countries with weaker carbon-related regulation has a minor role in the reduction of air emissions. Keywords: Air Pollution; Policy Stringency; Pollution Offshoring; Energy Prices JEL Classification: F18, O44, Q48, Q53, Q58

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1 Introduction Emissions of greenhouse gases have continued to rise. For anthropogenic carbon dioxide (CO2), which accounts for roughly two-thirds of total greenhouse gas emissions, global emissions grew by 144 percent between 1970 and 2014 (World Bank 2018). Greenhouse gases affect the global environment and are (negative) global public goods. In contrast, the total emissions of anthropogenic sulfur dioxide (SO2), a pollutant that has, unlike the global effects of greenhouse gases, strong local environmental effects, increased by two percent between 1970 and 2010 (European Commission 2016). At the same time, there has been a global market integration.1 Global exports of goods and services as the share of gross domestic product increased from 13.3 percent in 1970 to 30.2 percent in 2014, and global foreign direct investments relative to the gross domestic product increased from 0.5 percent to 2.2 percent (World Bank 2018). These concurrent developments have attracted attention to the relationship between international market integration and environmental quality.

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Research on the trade-environment nexus began in the 1970s (Baumol 1971, Walter 1973, Markusen 1975). Concerns about climate change have shifted the discussion to the link between globalization and greenhouse gas emissions (Managi 2004, Peters and Hertwich 2008, Aldy and Pizer 2015). Recent literature reviews include Copeland (2011) and Cherniwchan et al. (2017).2 The political-economy links between trade policy and the environment were studied by Hillman and Ursprung (1992, 1994), who asked, with trade policy politically determined, whether environmentalists would support free trade or protectionism for a pollution-intensive product. The trade policy position of environmentalists depended on whether environmentalists cared about the local environment or the global environment. If only the local environment mattered, the incentive of environmentalists was to ensure that pollution-intensive production occurred abroad and domestic demand was catered through imports under free trade. If the global environment mattered, the incentive was to reduce overall consumption, which was facilitated by protectionism in the local market.

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There are different influences on air emissions. For example, Grossman and Krueger (1993) distinguished scale, composition, and technique effects. The scale effect is the change in emissions because of increased production or economic activity. The composition effect is due to changes in the pattern of production, which may positively or negatively affect emissions through the pollution intensity of the output produced by different sectors. The technique effect refers to the increased demand for environmental quality and regulation stringency due to the increased incomes that typically accompany economic growth and trade liberalization. While the first two effects occur through market responses, in deriving from how much is produced and what is produced, the third effect is a policy-stringency influence on emissions through the environmental policy mix.3 Strangely because of the policy perspective, political economy and rents are missing from the Grossman-Krueger taxonomy. Politically determined policies are a trade-off between 1

Trade barriers were substantially reduced by the nine General Agreement on Tariffs and Trade (GATT) and World Trade Organization (WTO) rounds as well as by the numerous bilateral and multilateral free trade agreements. At the same time, technological change lowered communication and transportation costs (Tamiotti et al. 2009). 2 For a summary of early research, see Siebert (1985). 3 Certainly, the policy stringency affects emissions not only directly but also indirectly through altered market responses. Stricter environmental regulation may indirectly reduce emissions by decreasing the output produced (scale effect) and fostering the specialization of firms in less pollution-intensive production (composition effect). Therefore, the policy-stringency effect refers to the direct effects and may be regarded as a lower boundary of the reductions in emissions.

ACCEPTED MANUSCRIPT outcomes sought by voters at large and by producer interests (see Hillman 2018). Policy determination can result in restrictions on emissions that are beneficial for society at large or environmental policies that allow rents for producers in polluting sectors (MacKenzie 2017). This paper examines the magnitude of the scale, composition, and technique effects in a context that recognizes the link between emissions, international trade, and economic development. The focus is on whether the market or the policy-stringency effects are more prominent.

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In a previous study, to estimate the effects on air pollution, Antweiler et al. (2001) used data on SO2 concentrations for 108 cities in 43 countries for the period 1971-1996. They used lagged per capita income as a proxy for policy stringency. While the scale effect and the direct composition effect (represented by the capital-labor ratio) increase air pollution, the technique effect decreases air pollution. The average trade-induced composition effect decreases SO2 concentrations. Specifically, trade openness tends to increase air pollution in high-income economies that were assumed to have more stringent environmental regulations and decrease air pollution in low-income economies that implicitly had less stringent environmental regulations. This empirical result reflects the comparative advantage of highincome capital-abundant countries in relatively pollution-intensive output. Overall, trade effects on the environment are relatively small.

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Antweiler et al.’s (2001) approach was, for instance, furthered by Cole and Elliot (2003). They analyzed country-level emissions and, because of identification issues, estimated joint scale and technique effects using lagged GDP per capita. While negative joint scale and technique effects were found for SO2 emissions, the estimated corresponding values for CO2 and nitrogen oxides (NOX) were positive. Therefore, environmental regulation was not as restrictive in reducing CO2 and NOX emissions. Using the same procedure, after controlling for the endogeneity of income and trade openness, Managi et al. (2009) found that trade reduces emissions in OECD (high-income) countries and increases SO2 and CO2 emissions in non-OECD (low-income) countries. Negative trade-induced composition effects were only found for both OECD and non-OECD countries for biochemical oxygen demand (BOD). Although the estimated short-term trade effects found by Managi et al. (2009) were also small, large long-term consequences were detected.

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Alternative estimation methods were used by Levinson (2009) and Brunel (2017), who relied on index and structural decomposition analyses. As both studies could not directly measure sector-specific changes in emissions intensities, the technique effect was estimated as the residual, i.e. the difference between the total effect and the scale and composition effects. Levinson (2009) found that the technique effect was the major source of the cleanup of emissions of four air pollutants in the U.S. manufacturing between 1987 and 2001. Trade only had a minor influence in contributing four percent to the overall emissions reductions. Brunel (2017) found that the reduction in three air pollutants emitted by EU manufacturing between 1995 and 2008 was mainly attributable to a strong technique effect. The major difference to the results of Levinson (2009) for the U.S. was a significantly positive composition effect, indicating that EU manufacturers increasingly specialized in the production of emissionintensive goods after 2001.4

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The taxonomy has also often been used to analyze the FDI-environment relationship (Bao et al. 2011, Yang et al. 2013, Hille et al. 2018). An alternative empirical approach to measure the impact of trade on the environment is to use Computable General Equilibrium Models (Cole et al. 1998, Böhringer et al. 2012).

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A challenge in the empirical studies is the measurement of the technique effect. Antweiler et al. (2001) observed that there is a potential identification problem because measures of the scale-of-output and technique effects both involve increases in income and tend to be highly correlated. Cherniwchan et al. (2017) regarded the flawed measurement of the technique effect as a potential explanation for the relatively small trade-induced composition effect estimates. Cole and Elliot (2003) proposed that different measures, i.e. contemporaneous income for the scale effect and the lagged income for the technique effect, are required. Nonetheless, their final specification included joint scale and technique effects through lagged GDP per capita terms, which was also the procedure adopted in later studies using international panel data (Cole 2006, Managi et al. 2009, Löschel et al. 2013). Consequently, previous analyses have been unable to adequately separate the market and policy stringency effects.

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This study separates the joint scale and technique effect into market and policy-stringency influences. The scale effect is still estimated using income. For the technique effect, two internationally comparable, sector-specific measures of policy stringency are used. In this regard, policy stringency is understood as the sum of politically determined policies to restrict emissions. Policy stringency is expected to be related to income. As incomes increase, voters in democracies become more attuned to the benefits of a clean environment and through political processes seek reduced emissions. Hence, compared to Antweiler et al. (2001), policy stringency is directly used instead of an income term to infer policy stringency from increased income.5 Compared to Cole and Elliot (2003) or Managi et al. (2009), the joint scale and technique effects are separated instead of estimated jointly.

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Specifically, this study considers the effects of carbon-related policies on six different air emissions. The shadow prices of emission-relevant energy of Althammer and Hille (2016) and sector-specific energy prices are used as the two measures of policy stringency. To address endogeneity as a possible source of the inconclusive results, a dynamic panel generalized method of moments (GMM) estimator is applied. In addition to income and trade openness, we treat policy stringency as endogenous. The data covers 14 manufacturing sectors across 28 OECD countries for the period 1996-2009 and includes newly industrialized countries and former transition economies.6 The empirical results show comparatively large direct technique effects. Thus, the policy-stringency effect through carbon-related policies is the major source of emissions reductions for almost all of the six air pollutants. Depending on the air pollutant, the reductions are, however, mostly offset by increased emissions due to strong scale or composition effects. In other words, the market effects through increased and more pollutionintensive production counteract the policy-stringency effect. With regard to trade, the pollution-offshoring motive to countries with weaker carbon-related regulation is a significant determinant of comparative advantage but, on average, does not dominate the other sources of comparative advantage. In general trade liberalization through the trade-induced composition effect is associated with a small increase in emissions for most of the air pollutants. The paper proceeds as follows: Section 2 describes the measures of policy stringency and emissions. Then, the augmented trade-environment model, the GMM estimator, and the data 5

Antweiler et al. (2001) address the identification issue by using contemporaneous GDP per km 2 as the proxy for the scale of economic activity and the one-period lagged, three-year moving average GNP per capita for the technique effect. In particular the latter variable, reflecting the location of ownership, is difficult to implement in a multi-country, sector-specific empirical analysis. 6 The panel dataset does not include the most recent years, because for those years data on the sectors’ use of different energy carriers, which is needed to determine precise sector-specific energy prices, are not yet available in the World Input-Output Database.

ACCEPTED MANUSCRIPT are introduced. In Section 3 the results for the different air emissions and policy stringency measures are presented and discussed, followed by the conclusion in Section 4.

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2 Methodology and data 2.1 Measures of policy stringency and development of air emissions We consider policy stringency variables to measure the technique effect on emissions. A detailed recent overview of the measures used for environmental policy stringency is provided by Brunel and Levinson (2016). They conclude that approaches applied in empirical research either encounter conceptual problems or have limited applicability. Typical disadvantages are a lack of international comparability, missing sectoral disaggregation, and limited representation of the multidimensionality of policy regulation. Approaches that address these challenges are compliance-cost estimates derived from a shadow price approach as well as sector-specific energy prices (van Soest et al. 2006, Sato and Dechezlepretre 2015). These two types of relative policy stringency measures are used for empirical analysis.7

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The first measure is the sector-specific shadow prices of emission-relevant energy of Althammer and Hille (2016). A number of studies have implemented a shadow price approach to indirectly estimate private sector abatement costs by including a pollutant either as an input or as an output in the technology (Pittmann 1981, Coggins and Swinton 1996, Huhtala and Marklund 2006, Hille 2018). For example, van Soest et al. (2006) consider energy as a polluting input and estimate shadow prices of energy for two manufacturing sectors in nine Western European countries for the period 1978-1996. As energy is certainly used in all industries, their method offers the possibility of application across a larger set of countries and sectors. This is done by Althammer and Hille (2016), who determine shadow prices for 33 primary, secondary, and tertiary sectors in 28 OECD countries. Specifically, they apply the aforementioned approach to carbon-related policies by estimating the shadow prices of emission-relevant energy. Their shadow prices address the multidimensionality of carbonrelated policies by reflecting all government policies that affect the price of emission-relevant energies. This includes, among others, command-and-control policies like emissions standards, technology restrictions, and market-based instruments like carbon-related taxes.

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Shadow prices are typically determined by estimating firm-level or sectoral cost functions. The approaches rely on microeconomic theory and the past decisions of market participants to reveal profit-maximizing or cost-minimizing behavior. In this regard, the shadow price of a polluting input is defined as the potential decrease in outlays on other variable inputs that can be realized by increasing the usage of the polluting input while keeping the level of output constant (van Soest et al. 2006). For instance, in the context of energy policy, if carbonrelated energies are weakly regulated, the prices of these inputs will be comparatively low and cost-minimizing firms will use relatively more carbon-related energy inputs. Hence, low shadow prices signal a relatively weak carbon-related policy and high shadow prices signal a stricter carbon-related policy. An alternative measure of policy stringency is sector-specific energy prices. Energy prices have been used as a proxy for carbon-related policies, particularly when competitiveness concerns are considered (Aldy and Pizer 2015, Sato and Dechezlepretre 2015). The rationale is that command-and-control instruments, such as emissions standards, and market-based policy instruments, such as cap- and trade schemes or emissions taxes, operate predominantly by increasing energy prices. Especially for the time period under consideration, most of the 7

Several studies regarded shadow prices as the preferred relative measure of environmental policy stringency (Jaffe et al. 2002, Kneller and Manderson 2012). However, due to data restrictions these studies could not use shadow prices as an explanatory variable in their estimations and had to use other proxies instead.

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cross-country variation in industrial energy prices is attributed to the tax component and not to wholesale price differences (Sato et al. 2015b). Thus, government policy may be regarded as a key driver of energy price differences, implying that a high energy price is a sign of a relatively stringent carbon-related regulation and a low energy price a sign of a weak carbonrelated regulation. Compared to the shadow prices of emission-relevant energy, energy prices are a more direct reflection of private-sector energy costs and encompass the policy-induced effects on all energy carriers. In other words, when using energy prices as a measure of policy stringency, emission-relevant energy carriers are not exclusively considered as the polluting input but also carbon-neutral energy carriers are included. Following the reasoning of Althammer and Hille (2016), sector-specific energy prices are estimated by calculating a weighted average based on energy prices of seven energy carriers, the respective sectors’ gross energy use of the energy carriers, and the overall energy price development.8

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The effect of the policy stringency measures is analyzed on six different air emissions: sulfur oxides (SOX), nitrogen oxides (NOX), carbon monoxide (CO), non-methane volatile organic compounds (NMVOC), carbon dioxide (CO2), and methane (CH4). The emissions are responsible for three different environmental impacts of high international relevance (Genty et al. 2012). First, acidifying substances, such as SOX and NOX, cause acid rain. Second, NOX, CO, and NMVOC are tropospheric ozone precursors, which are mainly emitted during the combustion of fossil fuels. Third, the greenhouse gases CO2 and CH4 may cause global warming. Thus, while the first two categories have rather regional effects with in many cases international relevance, greenhouse gases are air emissions with global effects.

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During the period under consideration, i.e. from 1996 to 2009, for a set of 14 manufacturing sectors in 28 OECD countries,9 the policy stringency measures and air emissions levels in parts moved in opposite directions. On the one hand, the lagged shadow and energy prices increased, on average, by 50 and 52 percent respectively between 1996 and 2009. However, when comparing the prices across countries or within the same country across different sectors, a heterogeneous development can be observed (Althammer and Hille 2016). For instance, while the mean shadow and energy prices in the Czech Republic grew by 185 and 239 percent respectively, the corresponding Korean values decreased by 16 and 25 percent. On the other hand, the average gross emissions of five of the six air pollutants decreased during the same period. In particular the gross air emissions of SOX and NOX fell by 39 and 31 percent respectively, followed by CO and CO2 with a 16 percent decrease and CH4 with 4 percent lower emissions. Only the gross emissions of NMVOC rose by 12 percent. Regarding the mean emissions per person engaged, the development is less positive. While the values for SOX and NOX still significantly decreased by 30 and 17 percent respectively, the average emissions per person engaged of the other four air pollutants increased and are again headed by NMVOC with a 59 percent rise. The apparently negative relationship between policy stringency and air emissions can also be observed in Figures A.1 and A.2 (in Appendix A). The figures show the lagged shadow prices with the sectoral emissions per person engaged for SOX, an often-analyzed air pollutant with mainly regional effects, and for CO2, the most important greenhouse gas. Except for three cases, the linear trends illustrate that higher values of policy stringency in 2008 tended to be

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The seven energy carriers used for the energy price estimation are electricity, coal, natural gas, diesel, gasoline, light fuel oil, and heavy fuel oil. 9 The analyzed dataset is introduced in Section 2.3. A detailed overview of the included countries and sectors can be found in Tables B.1 and B.2 (in Appendix B).

ACCEPTED MANUSCRIPT associated with lower SOX and CO2 emissions per person engaged in the subsequent year.10 While a similar relationship can be found for both air emissions, the strength of the negative relationship appears to be in parts less pronounced for CO2. Analogous relationships can be found if other years, air pollutants, or energy prices are considered.

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2.2 Model This paper employs a specification which is based on the trade-environment model of Cole and Elliot (2003). Their model allows the estimation of a joint scale and technique effect, a direct composition effect, and a trade-induced composition effect. The specification is altered primarily in two ways. First, the joint scale and technique effect is split into market and policy-stringency influences by using separate income and policy stringency measures for each effect. Second, the measures of policy stringency are used instead of the income terms to estimate the trade-induced composition effects that originate from differences in environmental regulation. Equation 1 shows the final specification:

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Eict   0  1INCict   2 INCict2  3 Pict 1   4 Pict2 1  5 K / Lict   6 K / Lict 2

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  7 K / L ict INCict   8 K / L ict Pict 1   9TOict  10TOict RPict 1  11TOict RP

2 ict 1

 12TOict RK / L ict  13TOict RK / L ict  14TOict RK / L ict RPict 1   ict 2

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with  ict   ic   ict

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Eict denotes emissions per person engaged of the six different air pollutants in sector i of country c for year t. INCict represents the value added per person engaged. The one period lagged policy stringency is given by Pict-1. As the measures of policy stringency, the shadow and energy prices are used. (K/L)ict refers to the capital-labor ratio and (K/L)ictINCict as well as (K/L)ictPict-1 are interaction effects of K/L and INC and of K/L and P respectively. Trade openness TOict is measured as the ratio of the sum of sectoral imports and exports to the sectoral output.11 Given the importance of intermediate goods trade for offshoring activities, the export and import volumes also include trade in intermediate inputs obtained from the national input-output databases. TOict(RP)ict-1 denotes the interaction of trade openness and the sector’s lagged relative policy stringency and TOict(RK/L)ict represents the interaction of trade openness and the sector’s relative capital-labor ratio. Consequently, TOict(RK/L)ict(RP)ict-1 stands for the interaction of trade openness, the relative capital-labor ratio, and the lagged relative policy stringency. Lastly, εict is the error term consisting of the unobserved countrysector fixed effects µic and the idiosyncratic shocks vict. In accordance with earlier research, the direct scale effect is represented by the income and squared income term, INC and INC2.12 In order to avoid identification problems and to individually measure the policy-induced direct technique effect, the one period lagged policy stringency P and its squared value P2 are included as separate terms. By addressing different sources of variation, this research design helps to avoid potential identification problems between policy-stringency and market effects in two ways. On the one hand, instead of using 10

The three exceptions are the transport equipment sector for SO X emissions as well as the leather and footwear and the coke, refined petroleum, and nuclear fuel sectors for CO 2 emissions. 11 Rose (2004) provides an overview of measures of trade policy and trade liberalization. Trade intensities are a measure of trade liberalization that is often used in research on both the country and sectoral level (Antweiler et al. 2001, Frankel and Rose 2005, Hille 2018). Alternatively, trade barriers, particularly in the form of tariff rates, may be regarded as a sound indicator. However, as the used dataset includes a large number of countries, which are part of a free trade area, this paper relies on an outcome-based measure. 12 An overview of the estimated effects and the corresponding terms in Equation 1 is provided in Table C.1 (in Appendix C).

ACCEPTED MANUSCRIPT two different income-related measures, the scale effect is reflected by a measure of economic activity, i.e. the sector-specific value added per person engaged, and the technique effect is reflected by independently estimated policy stringency measures. On the sectoral level, the correlation coefficients between the income and policy stringency measures in linear and squared terms are rather low. On the other hand, the scale of economic activity has a contemporaneous effect on air emissions, whereas the effect of regulation is likely to occur with a delay. Therefore, the income is measured in current terms and policy stringency is lagged.

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The direct composition effect is captured by the capital-labor ratio K/L as well as the squared capital-labor ratio (K/L)2. In addition, the cross products (K/L)INC and (K/L)P are included, because the effects of income and policy stringency on air emissions may depend on the current capital-labor ratio and vice versa. To estimate the trade-induced composition effect, the measure of trade openness is interacted with potential sources of comparative advantage. Prior studies commonly distinguished between two opposing sources of comparative advantage, namely differences in environmental regulation and capital-labor endowments (Antweiler et al. 2001, Cole 2006, Managi et al. 2009). The trade-induced regulation effect postulates that less strictly regulated countries have a comparative advantage in emissionintensive production, implying in the extreme case that these countries become pollution havens and the strictly regulated countries focus on the production of clean goods. This effect is captured by the interaction of trade openness and the lagged relative policy stringency TO(RP) as well as by the interaction with the squared lagged relative policy stringency TO(RP)2. As an increase in trade openness is expected to decrease the air emissions of sectors subject to high policy stringency ˆ10  0 and ˆ11  0 are predicted (Cole and Elliot 2003). In contrast, the capital-labor effect hypothesizes that, in the presence of trade liberalization, countries will increase their specialization in accordance with their existing endowments. In other words, capital-abundant countries will increase their specialization in the capitalintensive and, thus, emission-intensive production and labor-abundant countries will focus on less-dirty, labor-intensive production. The size of this effect is tested using the interaction of trade openness and the relative capital-labor ratio TO(RK/L) as well as the interaction with the squared relative capital-labor ratio TO(RK/L)2. Given that the capital-labor effect predicts that trade liberalization increases air emissions of capital-abundant countries, the estimated coefficients may for instance take the form ˆ12  0 and ˆ13  0 (Cole and Elliot 2003).

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Besides identification problems, another concern of the research on the trade-environment nexus is that several explanatory variables in Equation 1 are potentially endogenous. While Antweiler et al. (2001) and Cole and Elliot (2003) do not control for this issue, the more recent literature often treats income and trade openness as endogenous (Frankel and Rose 2005, Managi et al. 2009, McAusland and Millimet 2013). In addition, we consider policy stringency, which has in the prior trade-environment literature been mainly reflected by income terms, to be endogenously determined. The rationale is that while environmental regulation influences environmental pollution, the opposite may also be the case. For example, a high level of pollution may increase the demand of voters for the normal good environmental quality and for a stricter environmental policy. Disregarding simultaneity between these regressors and the environmental pollution variable may be another reason why the empirical literature has estimated mixed results and relatively small effects of trade on the environment.13

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Studies that address the potential endogeneity of trade and income find mixed results. While Frankel and Rose (2005) and McAusland and Millimet (2013) estimate relatively small trade effects, Managi et al. (2009) find that

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To estimate Equation 1 and address the potential endogeneity of the explanatory variables, the system GMM estimator developed by Arellano and Bover (1995) and Blundell and Bond (1998) is applied. The estimator is an augmented version of the Arellano and Bond (1991) difference GMM estimator and allows for two-step standard error correction. System GMM is particularly suited for the analyzed dataset. Namely, it is designed for panel data with many individuals and relatively few time periods, which may contain fixed effects, heteroscedasticity, and autocorrelation within individuals. To control for endogeneity the lagged values of the three potentially endogenous variables (Holtz-Eakin et al. 1988, Arellano and Bond 1991) and their first differences (Arellano and Bover 1995, Blundell and Bond 1998) are used as instruments in the transformed equation and levels equation respectively. Moreover, Windmeijer’s (2005) finite sample correction is utilized to improve the efficiency of the two-step robust estimation and avoid downward biased standard errors.

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2.3 Data The GMM estimations are based on a sector-specific panel dataset, including information on 14 manufacturing sectors for 28 OECD countries for the period 1996-2009. While the time period is limited by the availability of sectoral data on the gross energy use of the seven energy carriers, the selection of countries and manufacturing sectors is determined by the availability of the shadow prices of emission-relevant energy in Althammer and Hille (2016). An overview of the included countries and sectors is provided in Tables B.1 and B.2 (in Appendix B). Besides highly developed countries also former transition economies from Eastern Europe as well as newly industrialized economies such as Mexico and Turkey are included. To some extent the analyzed period contains the implementation efforts of the parties to the Kyoto Protocol and the environmental effects of the transformation process after the fall of the Iron Curtain.

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The data is mainly derived from the World Input-Output Database (WIOD 2012a, b, c). Complementary data on energy prices and shadow prices of emission-relevant energy is used from the International Energy Agency (2013) and Althammer and Hille (2016) respectively. The OECD (2017) provides exchange rates and country-specific price indices, which are utilized in addition to the sector-specific deflators from the WIOD to convert all monetary variables into 2005 U.S. dollars. The final variables and their units of measurement are summarized in Table C.2 (in Appendix C).

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3 Results and discussion 3.1 Results of the GMM estimations The results of the GMM estimations of Equation 1 are summarized in Tables 1 and 2 for the six different air pollutants. While Table 1 shows the estimates when shadow prices are used as the measure of policy stringency, Table 2 provides the estimates when industrial energy prices are implemented. Prior to the interpretation of the empirical results, the validity of the instruments needs to be assessed. First, the number of instruments is strictly smaller than the number of groups in the panel for all estimations. In other words, the model is not over-fitted and, therefore, the results are not biased. Second, the null hypothesis of the Hansen test of joint exogenous instruments cannot be rejected for all air pollutants. The same holds true for the validity of the individual instruments, which has been confirmed by separate difference-in-Hansen statistics that are available upon request. Third, apart from a marginally significant value for the second‐order the effects are small in the short term but large in the long term. However, only Managi et al. (2009) derive their estimations from a model like Grossman and Krueger (1993) and, hence, consider the decomposed effects.

ACCEPTED MANUSCRIPT serial correlation test for NOX, no serial correlation can be detected. Consequently, overall the instruments utilized in the GMM estimation are regarded as valid. Table 1: Regression results using shadow prices as the measure of policy stringency

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SOX NOX CO NMVOC CO2 CH4 INC 6.666** 1.708** 5.194*** 2.307 1.067* 0.186 (3.300) (0.810) (1.551) (1.783) (0.566) (0.358) INC2 -0.0007* -0.0002** -0.0001 -0.0002 -0.0001* -0.00002 (0.0003) (0.0001) (0.0002) (0.0002) (0.0001) (0.00004) Pt-1 -14.823** -3.465** -16.602** -14.904** -1.124** -2.703* (7.387) (1.368) (6.578) (7.269) (4.94) (1.531) 2 Pt-1 9.843* 2.285** 8.508** 9.651** 733** 1.557* (5.062) (9.49) (4.236) (4.635) (325) (911) K/L 2.857* 0.589* 0.507 1.920** 0.223 0.114 (1.634) (0.304) (1.048) (0.772) (0.160) (0.155) 2 (K/L) 0.00014*** 0.00003* -0.00001 0.00004 0.00001 0.00002** (0.00005) (0.00001) (0.00004) (0.00005) (0.00001) (0.00001) (K/L)INC -0.0003 -0.0001 -0.0003*** -0.0002* -0.00002 -0.0001** (0.0002) (0.0001) (0.0001) (0.0001) (0.00003) (0.0000) (K/L)Pt-1 -5.253** -1.075** -0.434 -2.553** -0.444** -0.247 (2.451) (0.423) (1.231) (1.121) (0.209) (0.393) TO -7.146* -1.562** -8.693** -8.454** -0.484* -1.700** (3.914) (0.671) (3.616) (3.919) (0.249) (0.852) TO relative Pt-1 12.134* 2.614 13.715** 14.093** 0.679 2.551* (6.809) (1.157) (6.225) (6.632) (0.449) (1.390) TO relative Pt-12 -5.044* -1.117** -4.921* -5.761** -0.304* -0.972* (2.821) (0.481) (2.696) (2.635) (0.181) (0.526) TO relative (K/L) -0.486 -0.058 -0.088 -0.214 0.032 0.039 (0.367) (0.093) (0.310) (0.280) (0.047) (0.056) TO relative (K/L)2 -0.016*** -0.004*** -0.003 -0.009 -0.002*** -0.003** (0.006) (0.002) (0.004) (0.006) (0.001) (0.001) TO rel (K/L) rel Pt-1 0.823** 0.164** 0.198 0.444** 0.051 0.049 (0.372) (0.073) (0.283) (0.202) (0.038) (0.062) Constant 5.081** 1.262*** 6.947*** 5.396** 433* 1,149** (2.534) (4.67) (2.654) (2.632) (176) (580) Observations 5,321 5,324 5,170 5,320 5,244 5,244 Wald test 326.05**** 504.26*** 3344.94*** 351.96*** 278.26*** 120.04*** Hansen test 387.45 385.82 377.03 383.25 386.83 383.78 AR(1) 0.40 1.07 -0.82 -1.50 0.56 -0.43 AR(2) -1.04 -1.89* 0.61 0.84 -1.32 -1.64 a Significance codes: * p< 0.10, ** p<0.05, *** p<0.01 b Heteroskedasticity-corrected standard errors are reported in parentheses. c The number of instrumented lags of the potentially endogenous regressors was restricted to 9 years. Regarding the estimates in Table 1, predominantly coefficients that are as expected can be found. Accordingly, the estimated signs are robust for the six different air emissions. The coefficients of the two direct income terms INC and INC2 imply that a growth of the value added entails a direct increase of air emissions at a decreasing marginal rate. The emissions per person engaged are further increased by rises in the capital-labor ratio in the form of the

ACCEPTED MANUSCRIPT coefficients of the direct composition effect variables K/L and K/L2. In contrast, rising shadow prices lead to a reduction of emissions per person engaged at a decreasing marginal rate. Given that all estimated direct coefficients of the regulation measure are at least significant at the 10 percent level, the policy-stringency influence through carbon-related policies can be regarded as an important source of emissions reductions for all six air pollutants. Table 2: Regression results using energy prices as the measure of policy stringency SOX NOX CO NMVOC CO2 CH4 10.239*** 2.793*** 3.111** 3.122 1.788*** 0.164 (2.972) (0.768) (1.397) (2.528) (0.483) (0.358) 2 INC -0.0017*** -0.0005*** 0.0005* -0.0005 -0.0003*** -0.00001 (0.0005) (0.0001) (0.0002) (0.0004) (0.0001) (0.00004) Pt-1 -9,167 -2,236** -18,115** -11,394 -456 -2,241 (5.907) (1,022) (8,727) (7,894) (381) (1,910) Pt-12 6.324 1,467* 9,144* 7,928 307 1,377 (4.000) (682) (5,472) (5,200) (245) (1,056) K/L 2.618* 0.342 0.744 2.279** 0.129 0.153 (1.455) (0.254) (1.213) (1.066) (0.140) (0.108) (K/L)2 0.00015** 0.00004*** -0.00003 0.00003 0.00001** 0.00002** (0.00006) (0.00001) (0.00005) (0.00005) (0.00001) (0.00001) (K/L)INC -0.0004** -0.0001*** -0.0003*** -0.0002* -0.00003 -0.0001** (0.0002) (0.0000) (0.0001) (0.0001) (0.00002) (0.0000) (K/L)Pt-1 -5.228** -0.874** -0.436 -3.256*** -0.363** -0.293 (2.041) (0.341) (1.078) (1.169) (0.177) (0.290) TO -3.494 -0.820* -8.552** -6.633 -0.064 -1.408 (3.033) (0.480) (4.319) (4.361) (0.198) (0.875) TO relative Pt-1 5.870 1.180 13.894* 11.498 -0.065 2.155 (5.292) (0.809) (7.880) (7.993) (0.368) (1.362) TO relative Pt-12 -2.662 -0.546 -4.859 -4.965 -0.016 -0.872* (2.206) (0.336) (3.262) (3.331) (0.152) (0.480) TO relative (K/L) -0.469 0.003 -0.161 -0.377 0.046 0.025 (0.423) (0.098) (0.319) (0.382) (0.049) (0.053) TO relative (K/L)2 -0.015** -0.004*** -0.003 -0.008 -0.002*** -0.003** (0.007) (0.002) (0.004) (0.007) (0.001) (0.001) TO rel (K/L) rel Pt-1 0.785** 0.121* 0.229 0.587** 0.035 0.057 (0.328) (0.065) (0.284) (0.256) (0.037) (0.046) Constant 2,994 867** 7,385** 3,916 186 942 (2,014) (358) (3,129) (2,668) (136) (736) Observations 5,335 5,338 5,184 5,334 5,258 5,258 Wald test 443.63*** 514.08*** 2874.49*** 348.63*** 290.98*** 158.62*** Hansen test 384.99 384.77 375.09 378.97 386.64 381.46 AR(1) 1.06 1.00 -0.69 -1.47 0.94 -0.32 AR(2) -1.54 -1.77* 0.47 0.84 -1.47 -1.64 a Significance codes: * p< 0.10, ** p<0.05, *** p<0.01 b Heteroskedasticity-corrected standard errors are reported in parentheses. c The number of instrumented lags of the potentially endogenous regressors was restricted to 9 years.

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The coefficient estimates of trade openness terms are, in parts, different from those predicted by theory. This concerns the significant direct impact of trade openness TO and the signs of the capital-labor effect coefficients. Such theoretically counterintuitive signs along with

ACCEPTED MANUSCRIPT significant direct trade liberalization effects are often found in studies estimating similar specifications (Cole and Elliot 2003, Löschel et al. 2013, Managi et al. 2009). The estimates may be an indication that other than the two tested sources of comparative advantage are relevant for the changes in air emissions. Nevertheless, the coefficients of the trade-induced regulation effect are mostly significant and as expected. This suggests that pollution offshoring to countries with less strict carbon-related regulation is a significant motive and leads to a reduction of air emissions from domestic manufacturing.

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To ensure the robustness of the results, the estimates are compared to those using industrial energy prices as an alternative measure of policy stringency. The estimates in Table 2 generally verify the prior finding. The coefficients are fairly similar to the ones utilizing the shadow prices and the estimated signs are again robust for the six different air pollutants. However, while increasing the significance levels of e.g. all income terms, in particular the significance levels of the estimated coefficients of the trade-induced regulation effect are lower. In other words, compared to the relative energy prices, the relative shadow prices better capture changes in the air emissions, resulting in more pronounced estimates of the respective regulation-induced offshoring effects.

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3.2 Elasticity estimates To interpret the magnitude of the effects, elasticities are computed for the direct scale (value added per person engaged INC), direct composition (capital-labor ratio K/L), direct technique (lagged policy stringency P), and trade-induced composition effect (trade openness TO). Based on the results in Tables 1 and 2, Table 3 reports the elasticities for the estimations using either the shadow prices or the energy prices as the measure of policy stringency. In Table 4 the elasticity estimates of the two air emissions SOX and CO2, which are often analyzed as representatives of local and global air pollutants, are compared to those of Antweiler et al. (2001), Cole and Elliot (2003), and Managi et al. (2009). Specifically, the three prior studies focused on SO2, the most important compound of the group of SOX, and, in parts, on CO2.14 Table 5 provides the elasticities for SOX and CO2 for examples of groups of countries, i.e. the EU 1995 members, the three NAFTA members, the Eastern European countries, and the remaining nations from Asia and Oceania.

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SOX NOX Shadow prices as the measure of policy stringency Direct scale 1.132 0.731 Direct technique -3.714 -2.254 Direct composition 3.682 1.903 Trade-induced composition 0.738 0.413 Energy prices as the measure of policy stringency Direct scale 1.721 1.183 Direct technique -1.918 -1.492 Direct composition 3.397 1.177 Trade-induced composition 0.335 0.059

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0.624 -5.829 0.421 0.261

0.445 0.857 0.167 -4.710 -1.460 -5.691 2.723 1.361 0.893 0.435 0.199 0.150

0.384 -6.533 0.610 0.581

0.598 1.421 0.147 -2.590 -0.522 -3.979 3.217 0.836 1.130 0.418 -0.082 0.053

When comparing the elasticities, one needs to keep in mind that the estimates are based on different datasets and estimation techniques. Antweiler et al. (2001) analyze SO 2 concentrations only and estimate separate direct scale and technique effects. In Cole and Elliot (2003) and Managi et al. (2009) joint scale and technique effects are estimated.

ACCEPTED MANUSCRIPT Table 4: Comparison of elasticities for two air pollutants

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This study Antweiler et Cole and Managi et al. (2009) al. (2001) Elliot Shadow Energy OECD OECD (2003) short term long term prices prices Elasticities for SOX (this study) and SO2 (other studies) Direct scale 1.132 1.721 0.112 to 0.398 Direct technique -3.714 -1.918 -0.905 to -1.577 Direct scale & technique -1.700 -0.176 -10.908 Direct composition 3.682 3.397 0.583 to 1.006 2.300 0.146 9.012 Trade-induced composition 0.738 0.335 -0.388 to -0.882 0.300 -0.117 -0.333

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Elasticities for CO2 Direct scale 0.857 1.421 Direct technique -1.460 -0.522 Direct scale & technique 0.460 -0.058 -2.388 Direct composition 1.361 0.836 0.450 0.046 2.301 Trade-induced composition 0.199 -0.082 0.049 -0.043 -0.099 a Self-prepared using Antweiler et al. (2001), Cole and Elliot (2003), Managi et al. (2009), and own estimates

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In Table 3 comparatively large direct technique effects can be found. In terms of absolute values, the technique effect is the most important driver of emissions changes for all air emissions when shadow prices are used for the empirical analysis and for three of six air emissions when energy prices are utilized. Consequently, given that the other effects are primarily positive, increases in policy stringency are the major source of cleanup of air pollution emitted by manufacturers. This confirms the results of earlier research on the technique effect, particularly the sector-specific decomposition analyses (Levinson 2009, Brunel 2017). However, these sectoral studies, relying on a different methodology, cannot make a judgement on causality or on the policy-stringency influence.

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While the technique effects of CO2 emissions are the smallest for the shadow and energy price estimations, larger effects of policy stringency can be observed for pollutants with strong regional environmental impacts. Only the technique effects of CH4 do not fit into this pattern. This finding verifies the prior observations from the descriptive analysis based on Figures A.1 and A.2 (in Appendix A), which showed a partly weaker relationship between the lagged policy stringency measures and CO2 emissions per person engaged compared to the respective relationship of SOX. The empirical results remain the same if the technique effect and the scale effect are considered together, although varying and only partly strong scale effects are estimated. Similarly, the joint scale and technique effect elasticities of Cole and Elliot (2003) and Managi et al. (2009) reported in Table 4 imply better environmental outcomes for SO2 than for CO2 emissions. An explanation for this pattern may be that the perception of future damages of acidifying substances and ozone precursors is more imminent given the strong local effects and, therefore, they have been subject to a stricter regulation during recent decades. In comparison to the technique effect and the joint scale and technique effect estimates of SO2 and CO2 of earlier research, both similar magnitudes and more negative values are detected. In particular for the estimations using shadow prices, the absolute values of the effects lie above those of Antweiler et al. (2001) and Cole and Elliot (2003), who do not control for

ACCEPTED MANUSCRIPT endogeneity, and between the short-term and long-term effects of Managi et al. (2009), who treat income and trade openness as endogenous.

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Depending on the air pollutant several large positive direct composition effect elasticities are estimated, implying that a rise in the capital-labor ratio directly increases the emissions per person engaged. Thus, the market influence through either the direct composition or the direct scale effect is the strongest driver to mostly offset the emissions reductions achieved by the technique effect. Similar to the technique effect and the joint scale and technique effect, the direct composition effect is, except for CO, stronger for air emissions with mainly regional effects than for those with a global effect. This is supported by the direct composition effects of earlier studies reported in Table 4. Interestingly, this paper’s direct composition effect elasticities of SOX and CO2 are again larger than the corresponding estimates of Antweiler et al. (2001) and Cole and Elliot (2003) and lie between the short- and long-term elasticities of Managi et al. (2009).

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The trade-induced composition effect elasticities reported in Table 3 are, except for one case, all positive and range between -0.08 and 0.74. In other words, for an average manufacturing sector a one percent increase in trade openness coincides with mainly small rises in the emissions per person engaged of up to a maximum of 0.74 percent. The comparably small impacts of trade on air emissions coincide with the results of earlier studies and reflect that the different determinants of comparative advantage partly cancel out for an average country. However, as can be observed in Figures D.1 and D.2 (in Appendix D), the trade-induced composition effects differ significantly across countries in terms of both magnitude and sign. Apart from the national level elasticities for NOX and CO2 where the elasticities mostly range between -1 and 1, several larger elasticities are estimated for SOX, CO, NMVOC, and CH4 indicating that the countries’ trade effects are driven by varying sources of comparative advantage.

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Given that, on average, trade liberalization increases the emissions of most air pollutants, the significant trade-induced regulation effect tends to not dominate the other sources of comparative advantage. Figures D.1 to D.4 (in Appendix D), which plot the trade elasticities against the relative capital-labor ratios and the two measures of policy stringency, support this observation. While no clear relationship can be detected between the trade elasticities and the relative capital-labor ratio, the relationship between the trade elasticities and the relative policy stringencies is predominantly positive. If the trade pattern was mainly determined by the trade-induced regulation effect, a negative relationship should be detected. Hence, pollution offshoring to countries with a weaker carbon-related regulation has a role in domestic emission reductions, but other determinants that increase air pollution, on average, outweigh the effect. This finding is in line with the results of earlier studies on pollution offshoring and the pollution haven hypothesis that do not find evidence for a strong tradeinduced regulation effect (Cole and Elliot 2003, Levinson 2009, Brunel 2017). Interestingly, Table 3 also shows that the environmental impacts of trade liberalization are less harmful for the global pollutants CO2 and CH4 than for the local pollutants. Thus, while the direct impact of policy stringency reflected by the technique effect reduces local emissions more than the global ones, trade-induced regulation effects reflecting comparative advantage motives appear to have a larger effect on the reduction of global pollutants. Other studies do not yet present a clear picture on this issue (Cole and Elliot 2003, Managi et al. 2009). The findings remain generally unchanged when the regional elasticities in Table 5 are analyzed. Nonetheless, several interesting differences in the magnitude of the elasticities can be observed. First, the direct technique effects tends to be smaller in absolute terms in

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countries with stricter regulations, such as the EU 1995 member states, and larger in countries with less stringent policies.15 In particular in the Eastern European countries SOX and CO2 emissions were more responsive to increases in policy stringency. This indicates that the marginal effect on emission reductions tends to decrease with increasing policy stringency. In countries with stricter regulation firms seem to have already implemented more energy efficient machinery, reorganized production processes, installed end-of-pipe technologies, or done other pollution abatement activities, making it more costly and difficult to further reduce pollution. Second, while the direct composition effect is fairly similar across the groups of countries for CO2, the largest corresponding effect for SO2 can be found for the Eastern European countries. This may be explained by the structural change after the fall of the Iron Curtain and the associated improvements of the air quality for emissions with strong regional effects. Third, the only group of countries with small but persistently negative trade-induced composition effects is the NAFTA member states. Hence, while pollution offshoring is less of an issue for the highly developed countries in Western Europe, Asia, and Oceania, it seems to be more relevant for the Northern American countries. Table 5: Elasticity estimates for groups of countries NAFTA

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SOX CO2 SOX Shadow prices as the measure of policy stringency Direct scale 1.149 0.795 0.664 Direct technique -2.668 -1.015 -3.166 Direct composition 4.430 1.495 1.185 Trade-induced composition 1.418 0.479 -0.278 Energy prices as the measure of policy stringency Direct scale 1.747 1.319 1.009 Direct technique -1.208 -0.335 -1.799 Direct composition 4.095 0.930 1.090 Trade-induced composition 0.779 0.081 -0.200

CO2

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Asia and Oceania SOX CO2

0.687 1.181 0.567 1.582 1.493 -1.610 -13.280 -3.201 -2.944 -1.421 0.598 5.949 1.385 3.599 1.658 -0.255 0.221 -0.697 0.338 0.202 1.140 -0.612 0.359 -0.234

1.810 -7.310 5.478 -0.572

0.951 2.385 2.457 -1.198 -1.497 -0.499 0.834 3.328 1.034 -1.129 0.190 0.064

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Overall, the magnitudes of the direct effect elasticities are often larger than those of prior studies. The trade-induced composition effects are comparatively small, on average, but the estimates show significant variation on the national level. Other main characteristics are comparable to earlier research, e.g., the differences between the elasticities of air pollutants with mainly local effects vs. the ones with global effects. Two possible explanations for the differences in the magnitudes are the control for endogeneity biases and the data coverage. On the one hand, it is rather obvious that the elasticity estimates for SOX and CO2 are primarily larger than the corresponding estimates of studies that do not address the potential endogeneity of regressors. Instead, this paper is one of the few studies with larger effects that lie between the short-term and long-term values of Managi et al. (2009). They consider the results of studies that neglect potential endogeneity and do not apply a dynamic model as short-term effects and the controlled values after an adjustment process as long-term effects. A similar reasoning may also help explaining the differences observed here, as we treat income, trade openness, and policy stringency as 15

See e.g. Althammer and Hille (2016) and OECD (2017) for a detailed ranking of the country’s policy stringency based on shadow prices and the EPS index respectively.

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endogenous and utilize a dynamic panel GMM estimator. On the other hand, the use of a sector-specific international dataset can be beneficial to determine more precise estimates of the different drivers of the trade-induced composition effect. Cherniwchan et al. (2017) identify the missing reflection of within industry as well as within firm adjustments in national data as a source of downward biased trade-induced composition effects. The few studies that analyze non-highly developed countries also find larger estimates, suggesting that extended datasets may result in higher national-level trade effects on the environment driven by comparative advantage (Managi et al. 2009, Barrows and Ollivier 2016, Bombardini and Li 2016). Both cases apply to this analysis as the results are based on a sector-specific, multicountry dataset that also includes non-highly developed economies.

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4 Conclusion and policy implications Public concerns over well-reported environmental problems such as global warming and local particulate matter pollution pressure governments to take actions to reduce air emissions with local and global environmental effects. In the past, governments in the developed world solved environmental problems through politically determined policies.16

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This study shows that, if the link between air emissions, international trade, and economic development is jointly considered, policy-stringency rather than market effects reduce the emissions of manufacturers. Hence, the key policy implication is that government policies through command-and-control regulations and market-based instruments improve the environmental quality. The results also reveal that carbon-related policies reduce both local and global air pollutants, adding cross-country evidence for manufacturing sectors to the emerging body of research on spillover effects of climate policies and local pollution regulation.17 Specifically, increases in policy stringency tend to have a stronger direct effect on emissions with predominantly local environmental effects. The intuition is that improvements of the local air quality are directly beneficial for society at large and, therefore, governments have a strong interest in implementing effective regulations. In contrast, the reduction of greenhouse gas emissions allows free-riding. Although fears about massive relocations of industries are not supported, significant trade-induced pollution offshoring effects to countries with weaker carbon-related policies are found in general and for global emissions in particular. Consequently, earnest multilateral commitments on the reduction of global emissions or the implementation of international market-based instruments appear to be inevitable complements of national regulations to resolve the externality problem and limit global warming.

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When interpreting the policy implications, it is equally important to recognize three constraints. First, the estimated effects on emissions across countries are average terms. This implies that concerns about stricter regulations leading to the relocation of plants and significant pollution offshoring effects may be legitimate for a subset of producers, 16

An example is the U.S. Acid Rain Program that, since its inception in 1995, has drastically reduced SO2 and NOX emissions, resulting in a recovery from decades of acid rain and producing far-ranging human health benefits (Napolitano et al. 2007). 17 Brunel and Johnson (2017) provide an overview of the literature on the co-benefits of pollution regulation. While a significant number of studies have analyzed how climate policy influences the local air quality (Burtraw et al. 2003, Boyce and Pastor 2013, Nemet et al. 2010, Parry et al. 2015), only few studies have investigated how local pollution regulation affects greenhouse gas emissions (Brunel and Johnson 2017, Holland 2012). In general, the prior research on climate policies finds that spillover effects are heterogeneous across countries and within countries across sectors. The literature on the local pollution regulation tends to find that spillovers do not significantly change greenhouse gas emissions. Moreover, many sector-specific studies only focus on the electricity sector (e.g. Burtraw et al. 2003, Holland 2012). An interesting exception is Brunel and Johnson (2017).

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particularly in pollution-intensive sectors (Sato et al. 2015a). While firms in these sectors often receive special treatment, e.g. under the EU emissions trading system, governments may, nonetheless, introduce regulatory incentives for the producers to reward pollution abatement efforts. The estimated heterogeneous trade-induced composition effects at the country level also indicate that the determinants of comparative advantage vary significantly across countries. Therefore, depending on the country’s pattern of production, for some countries stricter regulations may entail more severe problems of pollution offshoring. In these countries the implementation of supportive measures, that e.g. facilitate the development and transfer of energy-efficient technologies, seems to be of particular importance. Second, the relevance of the market and policy-stringency effects for emissions reductions may change over time and with the level of development. For example, Barrows and Ollivier (2016) found in a firm-level study of CO2 emissions in India that the direct composition effect was responsible for more than half of the emission intensity reductions and, hence, was stronger than the direct technique effect. Therefore, different policy responses are required in low-income countries compared to high-income countries to improve the environmental quality. Third, while the analysis can certainly provide advice regarding the sources of emission reductions, the decision on the implementation of environmental regulations is not made in isolation. Governments trade off environmental concerns against economic and political concerns. For instance, despite the fact that pollution offshoring has a role in the reduction of greenhouse gas emissions, the potential effect of more stringent policies on (short-run) job losses may be regarded as more relevant because voters are directly affected. Similarly, producer interests in the form of business performance and productivity may be impaired by environmental regulations (Lanoie et al. 2011, Hille and Möbius 2018). A well-designed policy mix would balance these partially diverging interests and adhere to the country-specific patterns of production, but may not be politically feasible.

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This political impossibility may provide a rationale for future research. We regard additional country-level research as highly important to refine our estimates and design a comprehensive environmental policy mix. For instance, future studies may analyze which mix of specific policy instruments is most effective to both achieve emission reductions and secure the country’s industrial competitiveness. Our study can also be extended by including other factors of production and environmental quality. For example, financial development may impact the environmental quality through consumer, business, and wealth effects (Shahbaz et al. 2018). Financial development helps reaping the fruits of globalization. At the same time, globalization can facilitate technology transfer (Saggi 2002). In other words, it is a way to import energy-efficient technologies in order to enhance the domestic output while lowering the product-specific ecological footprint. Hence, energy innovations and their relationship with trade openness are further potential determinants that may be considered when estimating the regulatory effects on emissions (Balsalobre-Lorente et al. 2018).

ACCEPTED MANUSCRIPT Acknowledgments We would like to thank Arye L. Hillman. This research strongly benefited from his comments and feedback. Moreover, we are thankful for the valuable suggestions of two anonymous referees as well as the comments received at the 23rd Annual Conference of the European Association of Environmental and Resource Economists 2017, the 2017 Ariel Conference on the Political Economy of Public Policy, and the 2017 International Conference on Energy, Finance and the Macroeconomy in Montpellier.

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ACCEPTED MANUSCRIPT Appendix A: Scatter plots of sectoral emissions and lagged shadow prices

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Figure A.1: Sectoral emissions of SOX plotted against the lagged shadow prices of emissionrelevant energy for a set of 28 countries in 2009 (including a linear trend) a, b a While the SOX emissions per person engaged (in kilograms) are displayed on the y-axis, the x-axis shows the one period lagged shadow prices of emission-relevant energy (in thousands of 2005 U.S. dollars per ton of oil equivalent); b Self-prepared using Althammer and Hille (2016) and WIOD (2012a, c).

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Figure A.2: Sectoral emissions of CO2 plotted against the lagged shadow prices of emissionrelevant energy for a set of 28 countries in 2009 (including a linear trend) a, b a While the CO2 emissions per person engaged (in kilograms) are displayed on the y-axis, the x-axis shows the one period lagged shadow prices of emission-relevant energy (in thousands of 2005 U.S. dollars per ton of oil equivalent); b Self-prepared using Althammer and Hille (2016) and WIOD (2012a, c).

ACCEPTED MANUSCRIPT Appendix B: Overview of the included countries and sectors Table B.1: List of countries (in total 28 countries) Asia and Oceania (4 countries) Australia, Japan, Korea, Turkey Americas (3 countries) Canada, Mexico, United States

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Western Europe (14 countries) Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, United Kingdom

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East and Southeast Europe (7 countries) Czech Republic, Estonia, Greece, Hungary, Poland, Slovak Republic, Slovenia

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Sector Food, beverages, and tobacco Textiles and textile products Leather and footwear Wood and products of wood and cork Pulp, paper, printing, and publishing Coke, refined petroleum, and nuclear fuel Chemicals and chemical products Rubber and plastics Other nonmetallic mineral Basic metals and fabricated metal Machinery, nec Electrical and optical equipment Transport equipment Manufacturing, nec; recycling

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Table B.2: Included manufacturing sectors and the respective division-level ISIC Rev. 3.1 (in total 14 sectors)

ACCEPTED MANUSCRIPT Appendix C: Overview of variables and estimated effects Table C.1: Overview of estimated effects Terms included in the estimation INC, INC2

Direct technique effect

P, P2

Direct composition effect

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Trade-induced composition effect Trade-induced regulation effect

TO, TO(RP), TO(RP)2, TO(RK/L), TO(RK/L)2, TO(RK/L)RP TO(RP), TO(RP)2

Trade-induced capital-labor effect

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Table C.2: Variables and their units of measurement

Description Emissions of sulfur oxides per person engaged Emissions of nitrogen oxides per person engaged Emissions of carbon monoxide per person engaged Emissions of non-methane volatile organic compounds per person engaged Emissions of carbon dioxide per person engaged Emissions of methane per person engaged Gross value added per person engaged One period lagged shadow price of emissionrelevant energy One period lagged energy price Capital-labor ratio

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Effect Direct scale effect

Trade openness Relative one period lagged measure of policy stringency expressed relative to the corresponding sample's average policy stringency Relative capital-labor ratio expressed relative to the sample's total capital-labor ratio

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Unit of measurement kg per person kg per person kg per person kg per person t per person kg per person 2005 $K per person 2005 $K per toe 2005 $K per toe 2005 $K per man years worked 2005 $ per 2005 $K Percentage

Percentage

ACCEPTED MANUSCRIPT Appendix D: Scatter plots of country-specific trade-induced composition elasticities

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Figure D.1: Trade elasticities plotted against the relative capital-labor ratios based on the results in Table 1 a a While the estimated trade-induced composition elasticities estimated at the sample means are displayed on the y-axis, the x-axis shows the country-specific relative capital-labor ratios.

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Figure D.2: Trade elasticities plotted against the relative capital-labor ratios based on the results in Table 2 a a While the estimated trade-induced composition elasticities estimated at the sample means are displayed on the y-axis, the x-axis shows the country-specific relative capital-labor ratios.

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Figure D.3: Trade elasticities plotted against the lagged relative shadow prices based on the results in Table 1 a a While the estimated trade-induced composition elasticities estimated at the sample means are displayed on the y-axis, the x-axis shows the relative one period lagged shadow prices of emission relevant energy.

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Figure D.4: Trade elasticities plotted against the lagged relative energy prices based on the results in Table 2 a a While the estimated trade-induced composition elasticities estimated at the sample means are displayed on the y-axis, the x-axis shows the relative one period lagged energy prices.

ACCEPTED MANUSCRIPT Highlights

We decompose the effects of trade and economic development on air emissions

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Sector-specific panel data on six air emissions in 28 OECD countries is analyzed

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We use shadow prices of energy and energy prices to measure policy stringency

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The major source of emissions reductions is the policy-stringency effect

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Pollution offshoring has a minor role in the reduction of air emissions

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