Effects of Carbon Mitigation on Co-pollutants at Industrial Facilities in Europe.

• Author: Zwickl, Klara

1. INTRODUCTION

   Carbon combustion simultaneously releases carbon dioxide (C[O.sub.2]) and air pollutants such as sulfur oxides ([SO.sub.X]), nitrogen oxides ([NO.sub.X]), and particulate matter (PM). More stringent climate policies therefore may generate air quality and public health co-benefits. Omitting these co-benefits may lead to substantial underestimation of the economic benefits from carbon mitigation. To estimate the full social cost of carbon, or what Shindell (2015) terms the "social cost of atmospheric release," air quality co-benefits need to be incorporated along with climate benefits.

   A crucial difference between C[O.sub.2] and co-emitted air pollutants--also termed co-pollutants--is that C[O.sub.2] is a uniformly mixed pollutant: a ton of emissions has the same climate impact independent of the location of release, and hence abatement is most efficient wherever its marginal costs are lowest, again independent of the location. Co-emitted air pollutants, by contrast, are non-uniformly mixed: the environmental and health damages are proximate to the location of release, and hence the total health damages depend on the number of people exposed (see, e.g., Muller and Mendelsohn 2007). For pollutants of the latter type, spatially differentiated policies have been recommended that take into account variations in damages, and hence abatement benefits, as well as in abatement costs (Tietenberg 1995; Muller and Mendelsohn 2009; Muller 2012; Boyce and Pastor 2013).

   Air quality co-benefits of carbon mitigation policies in the form of positive public health externalities are important for two reasons. First, they can be sufficiently large that carbon mitigation policies are "in countries' own interests," helping to surmount collective action problems at the international level (Parry et al. 2014, 2015). If national compliance with international climate agreements were driven primarily by non-climate benefits of mitigation, and therefore would be undertaken even without the climate rationale, the additionality of international agreements may be limited (Zhang and Wang 2011). Second, variations across polluters in the extent of co-benefits per ton of carbon abatement imply that "one-size-fts-all" carbon mitigation policies may not be optimal (Muller 2012; Parry et al. 2014, 2015).

   Despite the importance of air quality co-benefits from economic, public health, and environmental perspectives, there has been little empirical research on the relationship between [CO.sub.2] emissions and co-pollutants at the level of individual pollution sources. Most previous analyses are either simulation studies relying on ad hoc parameters to calculate the impact of carbon mitigation on co-pollutant emissions and their regional distribution, or are based on aggregate data that can return misleading results if the two types of pollutants are partially an outcome of different economic activities (i.e. caused by different sources).

   Exceptions are Muller (2012) and Boyce and Pastor (2013), who calculate ratios of co-pollutant emissions and C[O.sub.2] at the level of pollution sources. These intensity ratios, however, implicitly assume a unit elasticity between carbon release and co-pollutant emissions rather than empirically estimating this relationship. The fact that C[O.sub.2] and co-pollutants are emitted by the same sources does not necessarily imply a unit elasticity relationship at the margin, whereby a one percent change in C[O.sub.2] emissions is accompanied by a one percent change in the same direction in co-pollutant emissions.

   Variations in emissions of both greenhouse gases and air pollutants can be explained by scale effects, composition effects, and technology effects (Grossman and Krueger 1991; Copeland and Taylor 2004; Bollen and Brink 2014). Scale effects are due to changes in economic output, and thereby emissions, that affect neither the economy-wide nor the point source-level relationship between greenhouse gases and co-pollutants. For example, in the electricity sector, a recession might be expected to reduce output, greenhouse gases, and co-pollutants rather proportionally, with a co-pollutant elasticity close to one. Composition effects reflect changes in the sectoral composition of the economy that change emissions at the aggregate level due to different co-pollution ratios of the various economic sectors. For example, an economy-wide recession might affect some sectors more than others. Thus, while point source-level co-pollutant ratios are unaffected, composition effects alter economy-wide ratios between greenhouse gases and co-pollutants.

   Finally, technology effects refer to substitutions across inputs, new emissions control technologies, or energy savings, and can alter the point-source level relationship between greenhouse gases and co-pollutants substantially (Holland 2010; Brunel and Johnson 2019). For example, endof-pipe controls such as scrubbers can significantly reduce co-pollutants, while at the same time these devices need electricity to operate and therefore increase C[O.sub.2] emissions. (1) An increase in the combustion temperature in natural gas-fired power plants reduces C[O.sub.2] per kilowatt but increases [NO.sub.X] emissions. Co-pollutant and C[O.sub.2] emissions can also be complements; e.g. fuel switching from coal or oil to natural gas reduces both C[O.sub.2] and S[O.sub.2] emissions, since natural gas has lower sulfur content (Holladay and Soloway 2016; Gillingham and Huang 2019). For these reasons, the relationship between C[O.sub.2] and co-pollutants is likely to vary across facilities and an empirical estimate of its size at the source level is warranted.

   A practical impediment to such an analysis has been the fact that in many countries, [CO.sub.2] and co-pollutant emissions are reported in separate databases that cover overlapping but different sets of facilities, lacking common codes for facility identification. This separation reflects the fact that regulatory policies for C[O.sub.2] and conventional pollutants often were formulated independently of each other. In this study, we take advantage of a novel European dataset, the European Pollutant Release and Transfer Register (E-PRTR), which provides annual facility-level data on C[O.sub.2] as well as co-pollutants starting in the year 2007. These data allow us to estimate the elasticities of co-pollutant emissions with respect to C[O.sub.2] emissions.

   An analysis of European industrial facilities is of particular interest against the background of the implementation of the world's first international emissions trading scheme for carbon (EU ETS) in 2005, which sets an overall cap for carbon emissions in the participating European countries (28 EU countries plus Iceland, Liechtenstein, and Norway), but allows carbon trading across countries and sectors. At the same time, the European Union is continuously attempting to improve local air quality through taxes and total emissions caps on co-pollutants (Cole et al. 2005). Despite continuous regulatory eforts over the last decades, air pollution is still high. Lelieveld et al. (2019) find a per capita mortality rate from air pollution exposure in Europe of 129 deaths per 100,000 inhabitants in the EU-28 and an average reduction in life expectancy by 2.2 years, due to a combination of low air quality and high population density. These excess pollution damages can be lowered through second-best carbon prices, which not only address climate, but also co-pollutant damages. The second-best carbon price would deviate from its Pigouvian level depending on the level of co-pollutant regulation. If marginal damages from co-pollutants are sub-optimally high, the carbon price should be set above its Pigouvian rate. Spatial or sectoral heterogeneity in air quality co-pollution elasticities would further imply that differentiated policies provide strong efficiency as well as equity improvements over a uniform carbon price.

   To the best of our knowledge, this study is the first to estimate co-pollutant elasticities from panel data at the point-source level. This type of analysis is needed not only for a precise assessment of the overall magnitude of air quality co-benefits of climate mitigation, but also for the efficient design of differentiated policies. We provide estimates of co-pollutant elasticities, based on all [CO.sub.2] variations in the data, and also based specifically on climate policy-induced variations, where the latter is most relevant for the assessment of air quality co-benefits.

   We find evidence of substantial and statistically significant co-pollutant elasticities of around 1.0 for sulfur oxides ([SO.sub.X]), 0.9 for nitrogen oxides ([NO.sub.X]), and 0.7 for particulate matter ([PM.sub.10]) for the average facility in the full sample. We find considerable variation in the magnitude of co-pollutant elasticities across economic sectors. The energy sector is characterized by relatively high co-pollutant elasticities of 1.6 for [SO.sub.X], and 1.0 for [NO.sub.X] and [PM.sub.10]. Identifying climate policy-induced changes in C[O.sub.2] emissions based on changes in regulatory stringency, we estimate co-pollutant elasticities in the electricity sector of 1.2 to 1.8 for [SO.sub.X], 1.1 to 1.5 for [NO.sub.X], and 0.8 for [PM.sub.10]. Using these estimates to calculate monetary co-benefits suggests that conventional European Environmental Agency estimates that omit air quality co-benefits significantly underestimate the benefits of carbon mitigation.

   The remainder of the paper is organized as follows. Section 2 reviews the literature on co-pollutants of carbon emissions and air quality co-benefits of carbon mitigation. Section 3 describes the data. Section 4 presents the identification strategies. Section 5 reports the results of the empirical analysis. Section 6 monetizes the co-pollutant damage estimates and compares them to European damage cost estimates for...