Assessing Air Pollutant-Induced, Health-Related External Costs in the

Critical Review pubs.acs.org/est

Assessing Air Pollutant-Induced, Health-Related External Costs in the Context of Nonmarginal System Changes: A Review Till M. Bachmann* European Institute for Energy Research (EIFER), Emmy-Noether-Str. 11, 76131 Karlsruhe, Germany S Supporting Information *

ABSTRACT: Marginal analysis is the usual approach to environmental economic assessment, for instance, of health-related external costs due to energy-associated air pollutant emissions. However, nonlinearity exists in all steps of their assessment, i.e., atmospheric dispersion, impact assessment, and monetary valuation. Dedicated assessments thus appear necessary when evaluating large systems or their changes such as in green accounting or the implications of economy-wide energy transitions. Corresponding approaches are reviewed. Tools already exist that allow assessing a marginal change (e.g., one power plant’s emissions) for different background emission scenarios that merely need to be defined and implemented. When assessing nonmarginal changes, the top-down approach is considered obsolete, and four variants of the bottom-up approach with different application domains were identified. Variants 1 and 2 use precalculated external cost factors with different levels of sophistication, suitable for energy systems modeling, optimizing for social (i.e., private and external) costs. Providing more reliable results due to more detailed modeling, emission sources are assessed individually or jointly in variants 3 and 4, respectively. Aiming at considering nonlinearity more fully and simultaneously following marginal analysis principles, I propose a variant 3-based approach, subdividing an aggregate (i.e., a nonmarginal change) into several smaller changes. Its strengths and drawbacks, notably the associated effort, are discussed.

1. INTRODUCTION Environmental economics is concerned with valuing a marginal (i.e., small) utility change caused by variations in environmental assets1 due to changes in human activities and their associated environmental pressures, such as emissions or land use changes. However, what if human activities to be assessed occur in a context that involves changes that can no longer be considered small? Two situations can be distinguished: (1) when whole sectors or even economies are analyzed all else staying the same (a prominent recent example being Muller2) or (2) when in the assessment of a small activity, such as a single power plant, considerable changes in the background system occur, for instance, due to political decisions taken. A prominent example is Germany’s exit from nuclear power with its implications on other means of power production (so-called “Energiewende”, energy transition or energy reforms). In the two cases mentioned, taking a marginal analysis approach is inappropriate if changes in the foreground (i.e., a whole sector or economy) or background system (i.e., weather conditions or overall emission level) affect the assessment of socalled external costs in a strongly nonlinear way. In the following, external costs are defined, the standard approaches to their assessment are presented, and prominent examples of nonlinearity are given. 1.1. External Costs. External costs are monetized externalities. According to European Commission,3 “an external cost arises, when the social or economic activities of one group of © 2015 American Chemical Society

persons have an impact on another group and when that impact is not fully accounted, or compensated for, by the first group” (p 9). Thus, external costs arise if the costs of the environmental impacts, for instance, of a power plant are not covered or compensated for by the plant operator (i.e., reflected in the price of electricity). In general, two different kinds of externalities exist.4 Technological (or true) externalities are due to changes in the technological coefficients of production5 or, expressed differently, shifts in the production function.6 This means that the amount of inputs (labor, energy, or material) per unit output and/or the production process as such (machinery) is changed due to this externality. One example is fishermen’s output being affected by water pollution. Beside production functions, utility functions of consumers (e.g., human health impacts due to air pollution) can also be affected. In general, all emission-related externalities belong to this category. So-called pecuniary (or pseudo or market/price-induced) externalities result from a change in the prices of some inputs or outputs in the economy.6,7 As long as changes in surplus of producers or consumers are offset by changes in the surplus of other consumers or producers, these effects can be disregarded Received: Revised: Accepted: Published: 9503

January 28, 2015 July 20, 2015 July 20, 2015 August 3, 2015 DOI: 10.1021/acs.est.5b01623 Environ. Sci. Technol. 2015, 49, 9503−9517

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Environmental Science & Technology in (marginal) external cost assessments.7,8 When there are distortions such as taxes, subsidies, or technological externalities in the indirectly affected markets, however, welfare changes occur that ought to be considered in cost−benefit analyses (CBAs), requiring general equilibrium approaches.7,8 While in the remainder mainly technological externalities are considered, pecuniary externalities are regularly assessed in the context of nonmarginal system changes (cf. Sections 1.3 and 2). 1.2. Standard Approach To Assess External Costs: Marginal Damage Costs. External costs constitute a market failure that environmental policy making seeks to eliminate by integrating them into market prices (so-called internalization). The goal is to internalize external costs up to the optimal point where marginal abatement costs equal marginal damage costs,1 based on a partial equilibrium framework (ceteris paribus or “all else being equal”). Accordingly, the assessed activity and its consequences are sufficiently small to be characterized as marginal.9,10 For practicality reasons, only quasi, i.e., not truly marginal damage costs are often assessed that result, for instance, from the operation of a power plant during a given year.10−13 According to economic theory, external costs refer only to the noninternalized fraction of the total environmental damage costs.14 In practice defining the degree of internalization of external effects is often difficult, depending on national policies and also on the methods for their quantification. For simplicity, the term external costs is used henceforth alongside with marginal damage costs without considering the degree of internalization. In the air pollution context, individual point or line sources are analyzed, typically consisting of a stationary combustion plant or a trip with an internal combustion engine vehicle, respectively.3 Aggregates of several individual sources constitute a so-called area source.15 Depending on their extent and their emission intensity, emissions from area sources regularly do no longer qualify as marginal. Traditionally environmental (or welfare) economics, concerned with environmental externalities, follows microeconomic principles.1,4 As a result, external costs shall be assessed in a comprehensive as well as in a spatially and temporally explicit way, referred to as the marginal damage cost approach.1,16 In its assessment chain, two main steps are generally distinguished, the first one having several substeps. Impacts of a specific activity on goods and services are usually first assessed in physical terms (e.g., additional cases of morbidity, fraction of potentially disappearing plant or animal species, area of corroded building materials, and reduced acid buffering capacity). When following the Impact Pathway Approach, the assessment distinguishes substeps corresponding to the Driver-Pressure-State-Impact-Response (DPSIR) scheme without including responses.17 For polluting activities (drivers) and similar to any environmental risk assessment, the assessment follows a bottom-up approach by establishing a causal link between emissions (pressure or burden), environmental concentrations (state), and finally receptors that, upon exposure, show some effect (impact, mostly negative). Once physical impacts are assessed in a spatially and temporally differentiated way, they are valued in monetary terms in a second step, resulting in what is usually referred to as damage. Valuation should be based on the preferences of the general public.18,19 Depending on the good or service in question, different ways to monetization exist,1,11,20 usually distinguishing between valuing marketed goods through market

prices and valuing nonmarketed goods through revealed preference techniques (observed behavior on surrogate markets) or stated preference techniques (surveys on hypothetical markets). If study-specific data are not available, monetary values determined at a so-called study site (one context) are regularly applied to the target or policy site (another context), thus transferring values across space and time (so-called benefit transfer or value transfer).1,21−23 The specific method used and the (dis)similarity of contexts determine the associated uncertainties. Frequently, the notions “marginal damage cost approach”, “Impact Pathway Approach”, and “bottom-up (or micro) approach” are used synonymously. In a strict sense, however, the two latter approaches establish a causal link between any kind of activity and associated impacts with subsequent valuation.10,16 In contrast to the marginal damage cost approach, they are also able to assess nonmarginal (or inframarginal) activities. In order to assess external costs, computer tools are regularly employed. For each of the above-mentioned (sub)steps, specific models are used. In case of emissions into air, for instance, different models are needed to assess ambient air pollution, human exposure, and associated impacts. As outlined next, nonlinearity exists in all of these steps. 1.3. Nonlinearity in the External Cost Assessment of Air Pollutants. Due to their important weight in quantified external costs, nonlinearity is reviewed with a focus on assessments of classical air pollutants. These are SO2, NOx, NH3, NMVOCs (nonmethane volatile organic compounds), primary and secondary particles, and ozone (pressures). Thereby the largest share of human health-related external costs is regularly due to primary particulate matter (PM) and the secondary pollutants ozone and secondary PM.24,25 Nonmarginal assessments are confronted with marginal assessments. When assessing air quality-related changes, the important secondary pollutants ozone and secondary fine particles are formed in a nonlinear way due to competition between reaction partners.16,26 These implications are even more pronounced with more considerable changes of, for instance, combustion processes27−29 or agricultural practices, being the source of an important precursor for secondary particle formation, i.e., ammonia.30,31 When assessing related impacts, nonlinearity exists also in marginal analyses such as the beneficial fertilizing effect of SO2 on crop yield,3,32 the threshold effect for ozone,33 and stepfunctions for impacts from acidification on ecosystems with differing sensitivities/resiliences.34 In nonmarginal contexts, there is even less evidence that the cause−effect mechanism can be approximated by (constant) linear relationships at substantially different ambient air quality levels.9,35−40 It is noted, however, that contrary evidence exists for PM-related mortality41 and for SO2-related mortality due to short-term exposure in Europe,42 the latter end point being less recognized.43 Economic studies dealing with nonmarginal (or large) system changes are usually concerned with impacts on relative prices or effects of projects on consumption.22,44−46 These impacts invalidate the ceteris paribus assumption of marginal analysis. Being less frequently investigated, this is also true for valuing environmental impacts: large system changes are likely to result in nonmarginal changes in utility per person. Related valuation increases (or decreases) nonlinearly with the utility 9504

DOI: 10.1021/acs.est.5b01623 Environ. Sci. Technol. 2015, 49, 9503−9517

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Environmental Science & Technology

this case, relative output or price changes occur in markets other than the primarily targeted market (e.g., the power sector), involving so-called “spillovers” (including feedback effects) or indirect impacts, requiring to extend the analysis from partial equilibrium to general equilibrium.1,7,8,22,56,61,62 Formally, the linear, first-order Taylor approximation of the utility/welfare function does no longer hold.45,58 The second kind of criterion refers to the (relative) size of an affected “good”, including emission amounts,63 the number of households affected,64 or an (administrative or other) area (socalled “next unit”,65 “situation”,66 “spatial dimension”, or “geographical extent”64). The third type of criterion concerns the size of the state change in the environment, particularly when thresholds are surpassed.67 Not surprisingly, most of the explicit, though not quantitative definitions found for nonmarginality rely on purely economic criteria (cf. Supporting Information, Table S1). Specific thresholds to decide upon marginality exist neither for the (absolute or relative) size of the human activity, nor of its pressures, (physical and/or price-related) impacts or damages. There is no “rule of thumb”.56 Rather, nonmarginality depends on context. Still, underlying all of these considerations is that using the same linear approximations to describe the considered system is no longer valid in situations of nonmarginality: prices, outputs, consumption, or ecosystem function patterns considerably change relative to the status quo or some other reference situation. In the following section, various studies on external cost assessments of larger systems are discussed as examples. For the purpose of the current study and despite the difficulties in defining marginality, any study is included that evaluates an activity that comes close to or exceeds the size of one country’s economic sector. An indication of the size of the investigated change will be discussed in Section 3.2.5 for those studies providing sufficient information.

loss (or gain) for several reasons, including budget constraints, the dead-anyway effect (i.e., willingness-to-pay for small reductions in mortality risks increasing with the initial risk level), a positive income elasticity of willingness-to-pay for the environment, and differing demands when supply changes.1,23,47−54 While also true for marginal analyses,55 using the “simple multiplication method” by which a quantity change is multiplied by a quantity change-independent (i.e., constant) marginal value is no longer valid.22,50,54,56,57 Further, nonconvexities in the analysis of large projects (such as economies of scale) and also of smaller projects impacting ecosystems require reconsidering simple marginal analysis.58,59 1.4. Objective and Approach Taken. When projects (such as policies) lead to considerable changes in an economy or when assessing external costs of aggregates, the studied object and/or its consequences can no longer be considered marginal. An aggregate could be a whole economy, a sector within a country or a certain type of activity (including the implementation of a policy) in a larger geographical area like a federal state or province. Note that in economics the analysis of aggregates generally no longer falls into the domain of microeconomics but of macroeconomics.4 While reviews of marginal external cost assessment approaches exist (e.g., Kim60 and the literature cited therein), the assessment of nonmarginal systems or their changes have not specifically been reviewed. Given the prominent example of the energy transition and without denying the importance of other impacts and activities, this study, therefore, seeks to provide an overview on how environmental, notably classical air pollutant-related consequences of nonmarginal changes in the energy sector have been analyzed in the past decade, partly also drawing on studies on the related transport sector. Three questions are sought to be answered. In Section 2, I explore what characterizes a nonmarginal change. The second question is how have environmental externalities related to air pollution been assessed in a nonmarginal context? I distinguish two cases: (1) assessing nonmarginal changes due to policies and/or concerning aggregates (e.g., economic sectors; cf. Sections 3.1 and 3.2 and the Supporting Information, Table S2 for exemplary studies) and (2) assessing marginal activities (e.g., a power plant) in a considerably different context (e.g., substantially reduced emission levels; cf. Section 3.3). Third, the final question is: up to which size of a change does a marginal analysis of environmental externalities yield sufficiently reliable results? I address the latter question in Section 4, together with four other issues. First, I discuss to what extent current marginal analyses of external costs are indeed marginal. Second, I provide examples of nonlinearity in the assessment of pressures other than classical air pollutant emissions. Third and based on their evaluation, I identify primary applications of the approaches assessing external costs from aggregates. Finally, I propose a new approach that seeks to more appropriately assess the utility changes of the individuals affected by an economic aggregate (i.e., a nonmarginal system change) in view of the different kinds of nonlinearity present in the assessment.

3. EXISTING APPROACHES FOR ASSESSING ENVIRONMENTAL EXTERNALITIES IN THE CONTEXT OF NONMARGINAL CHANGES Generally, two approaches exist to assess environmental externalities in the context of nonmarginal changes: top-down and bottom-up, presented in Sections 3.1 and 3.2, respectively. Studies assessing marginal activities in view of a substantially changed background situation are reviewed in Section 3.3. 3.1. External Costs of Aggregates: The Top-Down Approach. The top-down approach had first been proposed and applied in the prominent study by Hohmeyer68 on the electricity sector’s external costs. The approach was later termed top-down to delimit it from the bottom-up approach described in Section 1.2.16,69−72 Hohmeyer68 quantified air pollution-related external costs in a five-step procedure. First, national and power sector-specific air pollutant emissions are determined. Second, emissions of different air pollutants are aggregated by using so-called toxicity factors, derived from regulatory maximum permissible pollutant concentration values at the workplace, normalized to carbon monoxide. Third, and based on the toxicity-weighted, aggregated emissions, the contribution of the power sector to air pollution is estimated (28% for the investigated case of Western Germany in 1982). Fourth, estimates of air pollution-induced upper and lower bound damages to plants, animals, human health, materials, and climate are identified, largely taken from the literature. These

2. DEFINITION OF A NONMARGINAL CHANGE With the aim being to characterize a nonmarginal change, the criteria to decide on marginality found in the consulted literature refer to changes either of prices or of quantities of different kinds (cf. Supporting Information, Table S1). The first criterion revolves around purely economic considerations. In 9505


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Figure 1. Distinguishing features of the four variants of the bottom-up approach to quantify external costs from aggregates.

required for local impacts, such as from using land or water, from hotspot pollution such as spills, from pollutant emissions at low altitudes, or due to noise and/or visual impacts. The evaluation of related local assessments is, however, beyond the scope of this study. I have identified the studies assessing external costs of aggregates to belong to one of four variants of the bottom-up approach (cf. Figure 1). The distinction not always being clearcut (as further discussed in Section 4.3.2), the variants are distinguished according to (a) the stage of the impact pathway at which aggregation occurs (at the level of burden, of costs or of both), (b) the level of sophistication (e.g., none, settingdependent, or site-specific), and (c) whether or not emission sources are considered jointly when assessing the related impact pathways. The exemplary studies are characterized more fully in the Supporting Information, Table S2. When characterizing the studies, I further distinguish three application domains: (i) determining external costs (potentially) avoided by a project (e.g., a policy), (ii) determining external costs associated with an aggregate, and (iii) optimizing for social (i.e., private and external) costs. 3.2.1. Bottom-Up Variant 1: Monetization of Aggregated Burden. The idea of the “monetization of aggregated burden” (or short “aggregated burden”) approach (also termed “simple aggregation methodology”16 or “simple multiplication method”56) is to multiply representative external cost values associated with a specific activity or related burden (so-called “unit external costs”, “damage factors”, or “damage values”) with an aggregated quantity of the corresponding activity or burden (such as emissions) occurring in a given geographic area (usually a country). For the European electricity sector, the damage factors had originally been determined by using the external costs (in €/kWh) of so-called reference power plants, corresponding to the country-specific state-of-the-art in terms of efficiency and

are either related to a given year or a unit of power generated. In the final fifth step, combining the results of steps three and four yields upper and lower bound power-related external costs, ultimately related to a unit of power generated (e.g., kWh). More recent examples of the top-down approach (e.g., those mentioned in Kim60 and Sundqvist73) deviate from this procedure on specific points (e.g., how emissions are aggregated). Still, they share the general principle of using aggregated receptor-level estimates (i.e., at exposure, impact, or damage level) that are allocated to the specific sector according to certain criteria (e.g., share of pollutant emissions), without establishing a causal link between emissions and resulting ambient air pollutant concentrations. Still other studies do not even try to link drivers to pressures, thereby evaluating whole economies or rather overall pollution in a given area without separating geographically or sectorally distinct sources (e.g., for the USA74 and Europe75). 3.2. External Costs of Aggregates: The Bottom-Up Approach. The bottom-up approach for assessing environmental economic costs or benefits of aggregates proceeds as follows: total as opposed to marginal external costs are quantified following the impact pathway approach that can then be related to a quantitative indication of the activity or reference unit (such as total amount of kWh produced) to obtain so-called average external costs.9,10,72 When assessing external costs in general, environmental burdens can be distinguished according to the area impacted, ranging from a specific site to the globe. While global impacts such as global warming can be evaluated independently from the site of emission, assessing regional and local impacts following welfare economic principles requires site-dependent emission information. When assessing aggregates, the main issue is how to deal with regional impacts associated with pollution from area sources, as reflected in the approaches presented below. By contrast, more specific analyses are 9506


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Figure 2. Largest total (top, logarithmic scale) and area-specific emission changes (bottom) of classical air pollutants derived for those bottom-up studies on aggregates that provide sufficient emission data; also stated are the variants the studies belong to, their geographic domain, and for which kind of comparison the largest emission change was identified.

emission abatement.16 Relating the external costs to the quantities of pollutants emitted by the corresponding reference power plant yielded values in € per ton of pollutant emitted. To obtain aggregate external costs, these damage factors were multiplied by the corresponding sector or economy-wide activities or emissions. Using emission factors, in unit kg/ kWh, of state-of-the-art reference power plants risks, however, underestimating overall emissions and thus sector-wide external costs.16 More recently, an alternative procedure to quantify damage factors has been followed: rather than generalizing external cost estimates from individual (reference) sources, classical air pollutant emissions from different geographic zones and released at different heights (e.g., below and above 100 m) were varied (generally by 15%), and external costs per ton of pollutant emitted were determined for the emissions occurring in each of those zones.76,77 Country or EU27-specific averages

were obtained through emission-weighting and then aggregating the zone-specific external costs76 (see also Section 3.2.2). Like the top-down approach (cf. Section 3.1), this variant also starts from nationally (or regionally) aggregated emissions but then applies damage factors, derived according to a bottomup procedure, establishing a causal link between sources and impacts (cf. Section 1.2). The damage factors may have been calculated for the specific study or taken from other studies. In contrast to variant 2 (cf. Section 3.2.2), the factors are rather generic in that only one factor for one country is used without distinguishing for instance between emission heights or surrounding population densities or in that the assessment relies on factors for reference plants that are not necessarily representative. Applied examples of this variant beyond those already mentioned include Becker et al.,78 Alves and Uturbey,79 and Akhtar et al.,80 optimizing for social costs of the Israeli electricity sector, determining external costs associated with the 9507


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resolved emission inventory, the impacts of overall emission scenarios with and without the contribution of the sector are compared. This variant has been used, for instance, to determine external costs associated with individual or all sectors of a national economy28,29,63,93 and to quantify (potentially) avoided external costs of the activities affected by air qualityrelated policies in the USA94,95 or Europe.96,97 Except for Holland et al.,96 local impacts are not specifically assessed in contrast to the approach for point sources (see Sections 1.2 and 3.2.3) where air quality models can be nested down to the local scale.33 3.2.5. Size of the Change. Defining an indicative size of a system change is not straightforward. For the power sector’s activity, usually the amount of electricity produced is used. Due to fuel shifting or the installation of emission mitigation measures, small changes in the amount of electricity produced may still lead to important changes in emissions and thus impacts. In turn, whether or not absolute emission changes are substantial depends on the corresponding area (dilution). With the aim to compare the size of the change as investigated by the studies, total emissions are used that are shown with and without being normalized to the area of the administrative unit in which emissions occur (cf. Figure 2 and Supporting Information, Table S2). This area only serves as a proxy given that the affected area will usually be larger due to potential long-range transport, further reducing the normalized emission values. Figure 2 compares the largest total and area-specific emissions reported in those studies providing sufficient information. Several studies do not only assess two situations but investigate different scenarios and/or different points in time. Figure 2 only depicts the largest emission change by also stating how it is obtained: when comparing between scenarios for a given base year (“present”), when comparing a current to a future state (“across time”) or when comparing between future states (“future”). Not surprisingly, global studies investigate the largest absolute emission changes,85,86 while the largest area-specific emission changes are observed for a study on whole economies in Europe.29 The two USA studies rank next for both emission metrics.93,95 In the end, information provided by the studies is not sufficient to judge whether or not linearity can be assumed. For this at least concentration changes, if not exposure changes would be needed, not provided by the studies. 3.3. External Costs of Single Sources in a Context of Nonmarginal Changes. The issue of appropriately assessing external costs of a marginal activity in a changing context has hardly been addressed so far. Bachmann and van der Kamp98 analyzed an emission mitigation measure for the same coal-fired power plant for varying hypothetical environmental contexts, using site-dependent calculations. A similar study analyzed waste incinerators located at sites with differing population densities.99 While notably atmospheric background pollution and receptor densities vary between different environments, the impacts of policy changes on the context have not been assessed. Krewitt100 and more recently van der Kamp and Bachmann25 assessed the impact of methodological choices on the external costs of a given power plant. New scientific evidence, re-evaluations, and general updates, including of background emissions, were largely responsible for the changes

Brazilian power sector and determining (potentially) avoided external costs of the U.S. energy system due to air quality and climate change related policies, respectively. 3.2.2. Bottom-Up Variant 2: Weighted Marginal Damage Costs. The idea of the “weighted marginal damage costs” (or short “weighting”) approach (also termed “a mixture of bottom-up and top-down approaches”)72 is to calculate marginal damage cost factors for individual (marginal) activities grouped according to specific characteristics (also termed “clusters”72 or “site classification”10). These are then scaled with the occurrence of these characteristics in a given geographical area. The activity could be a certain way of producing power or a journey in a specific mode of transport (e.g., car, train or bus). For the transport sector and when assessing air pollutionrelated external costs, Korzhenevych et al.81 characterize activities according to site-specific criteria regarding the surrounding population density (e.g., rural, urban and metropolitan areas), thus, partly reflecting variability in more localized impacts. For the energy sector and no just distinguishing between countries (see Section 3.2.1), Preiss et al.76 and Holland et al.77 further differentiate releases according to emission heights or situation (i.e., rural vs urban), respectively. By employing marginal external cost factors corresponding to these further differentiations, average or weighted marginal costs are obtained. This variant has been used, for instance, to optimize for social costs of the energy or electricity sector at the national, continental, or global level,82−87 to determine external costs associated with the chemical or metal industry in the EU27,63 and to quantify (potentially) avoided external costs of the Chinese electricity and household sector subject to CO2 reduction policies.88 Using this variant has also been suggested in documents for or by public bodies. The German Federal Environment Agency’s position is ambiguous: It recommends using the top-down approach for total national external costs while apparently suggesting a variant 2-type approach when it comes to assessing for instance all power plants in a given federal land.10,89 Commissioned by the European Commission, Maibach et al.72 and its update81 by contrast are unequivocal in suggesting to use this variant to derive nationally representative external costs. 3.2.3. Bottom-Up Variant 3: Summing Marginal External Costs from Individual Sources. From a marginal analysis point of view, the “summing marginal external costs from individual sources” (or short “summing single sources”) approach is presumably the most straightforward. First, it calculates the marginal external costs of all individual sources of an aggregate separately, potentially involving many calculations. Afterward all results are aggregated by simple summing. Exemplary studies following this variant assess the operation of all individual power plants of a close to monopolist utility in South Africa,90 about 13 000 individual emission sources in Europe77,91 or all individual anthropogenic emission sources in the USA.2,13,92 3.2.4. Bottom-Up Variant 4: Simultaneous Assessment of External Costs from Multiple Sources. The idea of the “simultaneous assessment of external costs from multiple sources” (or short “multi-source”) approach (also termed “Multi-source EcoSense Aggregation”)16 is to assess in a dedicated calculation the external costs of the total burden of a specific sector (or other aggregate) in a given geographic area. Based on a spatially (e.g., administrative units) and sectorally 9508


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quality models. The most detailed external cost assessments rely on Eulerian chemistry transport models (CTMs) either directly (at least mentioning the possibility to analyze specific emission sources)101 or indirectly when using, for instance, the EcoSenseWeb model98,102,103 or the RAINS model.96,104 The EcoSenseWeb and RAINS models use source−receptor matrices, derived from the European Monitoring and Evaluation Programme’s (EMEP) Eulerian model: to obtain clear signals regarding concentration changes, emissions of all source areas are subsequently varied by (nonmarginal) 15%.33,91,105−107 Obtaining robust values with Eulerian CTMs is also hampered by imprecisions in the numerical solution that can be similar or even larger than the assessed atmospheric concentration changes (so-called Gibbs phenomenon).28,108,109 Using a so-called tagging method, Brandt et al.28 are able to follow marginal analysis principles, on the expense of increased computational, memory, and storage requirements. Other marginal analyses rely on less sophisticated air quality models. Gaussian plume models are limited to the local scale, thereby not considering chemistry (nonlinear or not) and disregarding damages likely to occur beyond that scale.79,110 While assessing concentration changes beyond the local scale and considering nonlinear chemistry,12,16,111−113 the uncertainty in the results of Lagrange CTMs increase beyond the local scale. Other regional scale studies only approximate nonlinear chemistry in their air quality models. This concerns, for instance, the source−receptor matrices derived for the Climatological Regional Dispersion Model (CRDM)114 as used by Muller and co-workers2,13,92 and the so-called uniform world model,115,116 also accounting for spatial variations to a more limited extent. Except for Brandt et al.,101 marginal analyses either do not appropriately capture the relevant air quality-related external costs (e.g., when concentrating only on the local scale), do not duly account for nonlinearities or air parcels’ movements, or rely on source−receptor matrices of nonmarginal emission changes. Once established, such source−receptor matrices disregard nonlinearities associated with specific emission profiles including the issue of appropriately dealing with intermittency (e.g., frequent ramp up and down times of peakload power plants) or seasonal variations. The use of air quality models fully taking into account nonlinear atmospheric chemistry is therefore strongly advisible, as is done for instance for scenario results relevant for EU air quality policies while during their preparation simplified models are used.106 4.2. Nonlinearity beyond Classical Air Pollutants. Nonlinearity also concerns the assessment of pressures other than classical air pollutants emitted by sources other than (more or less steady) electricity production. Several or all of these pressures become relevant when assessing sectors or whole economies. Already at the level of emissions, nonsteady operation modes induce nonlinearity. While this also concerns power plants, road transport-related emission levels in particular are a nonlinear function of vehicles’ speed, being influenced by regulation and traffic volume.57 Depending on the journey length, cold start emissions contribute more or less substantially. Also at the activity level, external congestion costs are a nonlinear function of speed−flow relationships.71 Classical air pollutants can be classified as fund pollutants, i.e., pollutants for which the environment has some absorptive capacity.117 Other types of pollutants can be distinguished:

analyzed. The impacts of differing background emissions have, however, not specifically been analyzed. Tools that assess site-dependent external costs, following the impact pathway approach (such as those used for the variants “summing single sources” or “multi-source”, cf. sections 3.2.3 and 3.2.4), generally allow integrating different background emission scenarios. EcoSenseWeb, for instance, already provides two different background emission scenarios, corresponding to the years 2010 and 2020.33 Similarly, the Economic Valuation of Air pollution (EVA) model system is capable of analyzing a scenario consisting of four different emission situations, each corresponding to one year (i.e., 2000, 2007, 2011, and 2020).101 However, merely smooth trends rather than considerable changes in the system are currently provided by both models. In the applications found, the Air Pollution Emissions Experiments and Policy analysis model (APEEP) was usually used to assess marginal damage costs only for one emission situation in the USA.9,13,92 The Community Multiscale Air Quality (CMAQ) modeling system in turn includes several emission scenarios.95 The corresponding conclusions are discussed next.

4. DISCUSSION AND CONCLUSION This study reviewed approaches to assess classical air pollutantrelated external costs of the energy sector in the context of nonmarginal system changes. Marginal activities (e.g., a power plant) have not frequently been assessed in a nonmarginal change context (e.g., substantially lower background emissions). However, it requires merely implementing a more distinct or alternative future emission scenario into the models such as those mentioned in Section 3.3. Allowing notably for ambient air quality level-dependent dose−response functions, however, will require more effort, but this is conceivable. Two approaches exist to assess external costs in the context of nonmarginal changes: top-down and bottom-up. Not establishing a causal link between emissions and resulting ambient air pollutant concentrations, I consider the top-down approach obsolete (cf. Section 4.3.1). Regarding the bottom-up approach, I distinguish four variants that are currently used to analyze nonmarginal changes. Except for the analyzed object, however, the same models and data are used as for marginal analyses. Beyond general equilibrium effects, concerns about nonlinearity in the quantification of nonmarginal damage costs are only rarely expressed and evaluated.28,29 Before proposing a new way of assessing external costs of an aggregate (i.e., a nonmarginal changes, cf. Section 4.4), I explore the relation between marginal analysis and infinitesimal calculus with a focus on air quality modeling (Section 4.1). Further nonlinearities exist when assessing external costs from burdens other than classical air pollutants caused by electricity production, summarized in Section 4.2. Finally and based on a critical evaluation of the top-down approach and of the four bottom-up variants, I suggest their main application domains (Section 4.3). Section 4.5 concludes. 4.1. Marginal Analysis, Infinitesimal Calculus, and Air Quality Modeling. This study focuses on external costs in the context of nonmarginal changes. Another question is to what extent (quasi) marginal damage costs are indeed quantified following infinitesimal calculus, suggested by the attribute “marginal”. Deviations from marginal analysis principles mainly occur for studies assessing external costs of marginal human activities using parametrized models derived from more complex air 9509


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Table 1. Evaluation of the Four Variants of the Bottom-Up Approach To Quantify External Costs from Aggregates and Suggested Application variant 1: “aggregated burden”

variant 2: “weighting”

variant 3: “summing single sources”

variant 4: “multi-source”

strength(s)

•Simple calculation

•Simple calculation •External cost factors more specific than for variant 1

•Site-dependent assessment of external costs •Assessment can be adapted to context (when having full access to the model) •Nonlinearity beyond air quality modeling can potentially be taken into account for each subsequent addition of the sector’s sources (cf. Section 4.4)

•Site-dependent assessment of external costs •Assessment can be adapted to context (when having full access to the model) •Just one model run per sector/ scenario needed •Nonlinearity in the air quality assessment of sector-wide emissions are taken into account

weakness(es)

•External cost factors not necessarily context-specific (less specific than for variant 2) •External cost factors can only be adapted to a new context when having full access to the model •none

•External cost factors not necessarily context-specific •External cost factors can only be adapted to a new context when having full access to the model

•Many model runs are needed •Nonlinearity in the air quality assessment of sector-wide emissions are usually not taken into account

•Nonlinearity beyond air quality modeling can only be taken into account for the situation when all of a sector’s sources are emitting

•Energy system models optimizing for social (= private and external) costs •Obtaining rough order-ofmagnitude values

•Adequate when following a sequential marginal analysis (cf. Section 4.4)

•Adequate when evaluating an aggregate as a whole at once

suggested application

- Noise is an example of a flow pollutant, being short-lived and thus not accumulating.118 In its impact assessment, strong nonlinearity prevails, leading to marginal external costs being lower than average external costs.3,71,81 - Heavy metals and persistent organic pollutants are examples of stock pollutants, i.e., pollutants accumulating in the environment resulting from only a small or no absorptive capacity.117,118 When there are nonlinearities and/or thresholds in the cause−effect relationships, marginal damages considerably change with increasing stocks. Mercury and lead are examples of stock pollutants with potential effect thresholds.119 - Certain trace elements have nonlinear, hockey-stickshaped dose−response functions, i.e., being beneficial at low doses and toxic at higher doses,120 a phenomenon also termed hormesis. - Being classified either as a fund or stock pollutant, radiative forcing of CO2 increases with increasing stocks.121 Note that different discounting schemes are discussed when assessing nonmarginal climate-related changes.122,123 While radiation-related hormesis is contentious,124−127 further examples of hormesis beyond the beneficial fertilizing effect of SO2 on crop yield stated above are temperature-related mortality128 and amenities of agricultural land or recreational sites as a function of proximity or visitor density, respectively.129 Ecosystem-related impacts are not only due to classical air pollutants but even more so due to other pressures such as land use, water use, or direct releases of for instance pesticides into soil and water. It is well-established that impacts on ecosystems involve nonlinearities and thresholds67 that may also need dedicated monetary valuation approaches.130 Benefit transfers risk involving nonlinearity if initial levels of ecosystem quality are not sufficiently close at the study and policy site.21 Given these nonlinearities, it is difficult (or even impossible) to decide in general and a priori when a human activity change can no longer be expected to lead to marginal impacts,

proportional to the change. This is particularly the case for threshold effects for which even a marginal human activity change can lead to a crossing of a tipping point and, thus, altering a system entirely (e.g., collapse of the Gulf Stream, change in the organization and functioning of an ecosystem). For nonmarginal changes, the relevance of fully taking account of nonlinearity is likely to be more important than for smaller changes. 4.3. Evaluation and Application of the Approaches To Assess Aggregates. In the following, first the appropriateness of the top-down approach is discussed. Before evaluating the different bottom-up variants, I state potential overlaps between them. 4.3.1. Evaluation and Application of the Top-Down Approach. While deserving recognition for being a pioneering approach, the top-down approach has several limitations. Merely aiming at order of magnitude values, already Hohmeyer68 acknowledged the methodological shortcomings of this approach, largely attributable to a lack of knowledge then. Specific limitations concern the appropriate treatment of secondary pollutants, consideration of cause-effect relationships, reliance on previous estimates and approximations, and the lack of temporal and/or spatial detail, including the assumption that imports equal exports and the aggregated treatment of different life cycle stages.16,29,32,69,131 Later studies only partly resolved these limitations, e.g., by considering spatial resolution of exposure combined with dose−response functions while external costs are allocated to the sector of interest as a function of emission shares.132,133 Despite these shortcomings, the top-down approach is still used. When relying on aggregated damage estimates, the German Environmental Protection Agency10,89 considers it suitable for computing total and average costs and recommends it when assessing an entire sector. Given the advances in science, data availability, and computer power, however, the approach appears obsolete or, at least, does not correspond to the state-of-the-art, discussed below. This is mainly because it does not establish a link between the 9510


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whose emissions are already considered in the baseline. While the issue is presumably negligible for the USA study (Muller2 merely adds one ton per source to the baseline at a time), there is no related information available for the South African case. If the emitting source contributes only little (i.e., marginally) to the overall emission situation, this may be tolerable as only the change at the margin is of interest. Based on the above considerations, I suggest the following main areas of application. The “weighting” variant may be justified to obtain rough order-of-magnitude values, not apt for a sound decision-support in general. For computational reasons, however, optimization studies will continue to rely on precalculated external cost factors with potentially an increased context specificity in terms of emission heights (e.g., ground-level, low-stack, high-stack), spatial reference (e.g., by country) including population densities (e.g., metropolitan, urban, peri-urban, rural) and/or source type (e.g., point or area source), as already partly available.76,77,81,91 A drawback is that, if values derived in one context are applied to another, precalculated external costs have limitations similar to any other kind of benefit transfer, discussed elsewhere.21,22,135 Not relying on precalculated damage factors, the “summing single sources” and “multi-source” variants are more apt to incorporate nonlinearities and can usually more flexibly be adapted to the question at hand. In contrast to the “summing single sources” variant, nonlinear atmospheric chemistry of a nonmarginal change is duly taken into account by the “multisource” variant. As discussed next, this is not necessarily also true for other nonlinearities. In addition, specifically assessing local air pollution is not regularly carried out, Holland et al.96 being an exception regarding the urban environment. 4.4. Accounting More Fully for Nonlinearities When Assessing Aggregates. Due account of nonlinearity is not only needed for atmospheric chemistry but also when assessing impacts in order to more correctly assess associated utility changes. While extreme ambient air pollution mainly occurs in specific countries such as India and China,36 substantially reduced pollutant levels occur when assessing external costs with and without air pollution, for instance, of whole (developed or developing) economies. Without related anthropogenic emissions, not only the atmospheric chemistry changes considerably but also the slope of the dose−response function at low exposure levels applies,35 depending on the strength of natural emissions or other anthropogenic sources upwind. While nonadditivity has already been demonstrated when external costs of a whole economy are compared with the sum of external costs from parts of its emissions,28,29 considering nonlinearity particularly in the impact assessment has not yet been attempted.14 A related proposal is made in the following. 4.4.1. Proposed Approach. Following the “summing single sources” variant and provided that a nonmarginal project can be split into smaller projects, it is conceivable that different sequences of these smaller projects (such as the operation of individual power plants) are iteratively evaluated according to marginal analysis principles,58 taking account of nonlinear behavior in each step of the assessment chain (cf. Section 1.2). The starting point is an emission scenario without the economic activity in question in a given area. Beyond the additional data needs, there are two issues: the effort involved and the question which sequence to choose. Instead of calculating all possible and distinct combinations of n individual activities (i.e., ∑nk=0( kn ) = ∑nk=0(n!/(k!(n − k)!))

source(s), the induced state changes (ambient concentrations), and the final damages. 4.3.2. Delimitation of the Bottom-Up Variants. The four bottom-up variants described in Section 3.2 are not necessarily clearly distinct. For instance, if the “weighting” variant does not rely on precalculated generic external cost factors (e.g., of selected sources or a mix thereof) but assesses each single emission source in a given area, the result is the same as for the “summing single sources” variant. This is the reason why, for instance, the study by Muller2 is classified to pertain to the latter variant. As can be seen from the effort made by Holland et al.77 and Preiss et al.,76 the delimitation between the “aggregated burden” and “weighting” variants is vague: if only a distinction between countries had been made, the studies would have belonged to the former variant. As they additionally distinguished between emission heights, more specific damage factors have been obtained, corresponding to the latter variant. The similarity of the variants from a methodological point of view translates into a certain that they also give similar results. Given that the external cost factors of variants 1 and 2 are generally derived with help of the assessment frameworks used in variants 3 and 4, the results will be close provided the assumptions, the goal and the scope of the studies are sufficiently similar. Depending on the degree to which nonlinear relationships exist (e.g., in atmospheric modeling), also the results obtained by the “multi-source” variant may or may not be close to the “summing single source” variant. The strengths and weaknesses of the different variants are further discussed next. 4.3.3. Evaluation and Application of the Bottom-up Variants. When evaluating approaches, their purpose needs to be considered. As shown in Section 3.2, the different bottom-up variants predominantly serve different purposes. By using precalculated external cost factors, optimization studies rely on the “aggregated burden” or “weighting” variants.78,82−87 The more advanced variants have been used for green accounting purposes.2,29,134 The “multi-source” variant was used in policy evaluation studies commissioned by public institutions.94−97 Table 1 contrasts the different variants in terms of ease of use, their context specificity, and their ability to consider nonlinearity in the assessment. The variants relying on precalculated external cost factors are easy to use. As the investigated questions and their contexts are subject to change, however, variants 3 and 4 provide more flexibility and are more specific than variants 1 and 2. Due to nonlinear atmospheric chemistry, there is an issue concerning the first three variants when adding external costs from different sources. In the presence of nonlinearity, DrosteFranke et al.29 and Brandt et al.28 showed that the sum of external costs from individual changes is not equal to the external costs from a joint consideration of the same changes. As a result, the three variants may easily over or underestimate an aggregate’s external costs by between 10% and 20%. There is a risk of double-counting emissions. Depending on the way in which the precalculated external cost factors are derived, this issue not only concerns the “summing single sources” variant but also the “aggregated burden” and “weighting” variants. There is no issue when emission sources are evaluated based on reduced baseline emissions (as used when deriving source−receptor matrices).91 There is a certain degree of double-counting, however, when assessing a source 9511


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Figure 3. Illustration of the calculation needs for an aggregate composed of different elements (here: 5) according to the default “multi-source” (left), Starrett’s sequential marginal analysis (middle) and the newly proposed “analysis of aggregates at their margins” approach (right).

with k representing the count of the elements jointly considered, ranging from 0 to n), I suggest to reduce the effort involved by obtaining an indication of the variability in the marginal damage costs by means of determining external costs associated with the first and last unit of emission. To this end, two reference situations are taken as the point of departure, i.e., one reference situation with and one without the nonmarginal project (e.g., a sector). The impact of an individual activity (e.g., a power plant) are analyzed by subsequently excluding (adding) each individual activity from (to) the reference situation with (without) the nonmarginal project. For ease of calculation and provided the individual activity is indeed marginal, separately adding the individual activities for both reference situations (rather than excluding them at the more polluted reference situation) is an option. While still being laborious, only two times n calculations plus the scenarios with and without the aggregate are needed, i.e., twice as many as for the standard “summing single sources” variant. For any n > 3, this is more efficient than evaluating all distinct combinations (i.e., ∑nk=0( kn ) > 2(n + 1)). Figure 3 depicts the computational requirements when assessing an aggregate, consisting of five elements for illustrative purposes, according to the default “multi-source” (left), Starrett’s sequential marginal analysis (middle), and the proposed “analysis of aggregates at their margins” approach (right). Each “slot” in Figure 3 represents one dedicated calculation while noting that for Starrett’s sequential marginal analysis only distinct combinations need to be assessed. Note that the right half of the “analysis of aggregates at their margins” approach in Figure 3 (“aggregate without (w/o) one element”) reflects the idea of the standard “summing single sources” variant, switching each individual element off (or on) at the reference situation (“1”) where all sources emit. In case more than two reference situations shall be analyzed, the computational effort increases linearly with the number of reference situations investigated. For the European91 and US American studies,2,13,92 for instance, this means to calculate the external costs twice for about 13000 and 10000 individual emission sources in Europe and in the USA, respectively, including also the need to derive new source-receptor matrices or new damage per ton factors for each reference situation. In addition, effort is needed in defining the reference situations’

emissions and postprocessing of results (cf. section 4.4.2). Overall, the effort needed to follow this approach is substantial. While emphasizing stepwise marginal analysis here, note that the proposed procedure can also be used for stepwise nonmarginal analyses: the nonmarginal project then corresponds to a whole economy and what is termed individual activity or element constitutes the economy’s sectors, probably still implying nonmarginal consequences. While natural emissions form the natural baseline, the interplay particularly of emissions from the energy and/or transport sectors with the agricultural sector are worth exploring due to the importance of agricultural ammonia emissions in the creation of secondary particle formation (agriculture was responsible for more than 90% and 80% of ammonia emissions in the EU in 2010 and in the USA in 2006, respectively).30,31 4.4.2. Postprocessing Individual Results. Different ways are conceivable how to process the external costs obtained from the “analysis of aggregates at their margins” approach. First, I explore whether following the inverse idea of Starrett58 is an option for aggregation by implementing the measure with the largest positive welfare effect first. In that case, the lowest (highest) normalized external costs of the activity included (excluded) for the reference situation without (with) the nonmarginal project provide the bounding values. Because larger activities induce larger impacts while also providing larger benefits ceteris paribus, normalization is performed, i.e., relating external costs to output, preferably expressed in physical terms (e.g., kWh of electricity produced or MJ of energy supplied for the power sector). When more than one sector is concerned, monetary outputs will presumably provide the best option for normalization. While providing bounding estimates including a ranking between individual activities, determining aggregate external costs based on just one lower and one upper boundary value is not obvious. Still, the ranking may provide valuable information. Second and strictly following the “summing single sources” variant, aggregate external costs for the reference situations with and without the nonmarginal change are obtained by respectively summing the external costs obtained for each of the two reference situations analyzed. This provides bounding estimates, however, without truly accounting for the nonlinear developments occurring between the two reference situations. To obtain central estimates (i.e., total external costs of the 9512


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increase.45,62 These latter aspects have, however, not been addressed here.

aggregate), the bounding estimates (aggregated or not) need to be interpolated, linearly or nonlinearly. Without associated results being available as yet, averaging total external costs at both reference situations is considered a straightforward solution. Once results are available, further solutions may be identified. 4.4.3. Nonlinear Monetary Valuation? Considering nonlinear valuation is perhaps less evident in economic appraisal than taking nonlinear dose−response functions into account. Despite following marginal analysis principles, the overall welfare effects, for instance, of life expectancy reductions over national economies may not be marginal, with associated implications in their valuation (cf. section 1.3). While regression models from meta-analyses allow accounting for valuing impacts depending on their size (such as diminishing marginal willingness-to-pay for improved health),136,137 the question remains which value to use for the first and the last unit of emission of a nonmarginal change. In the end, it needs to be seen to what extent the less steep (steeper) slope of the dose−response function at low (high) ambient air concentrations compensates the corresponding higher (lower) marginal willingness-to-pay, additionally influenced by nonlinear atmospheric chemistry. In this respect, even more research is needed. 4.5. Concluding Remarks. The ultimate question from an analyst’s point of view cannot be resolved: there is no explicit threshold for the size of a change beyond which marginal analysis of externalities is no longer valid. Put another way, as long as the nonlinearities do not lead to considerable deviations, marginal analysis is valid. When exceeding an effect threshold, however, even small changes induce nonmarginal consequences (e.g., for ecosystem impacts).67 In line with Brandt et al.27 and according to current practice in EU air quality policy analysis,106 the discussed implications of nonlinearity call for not relying on simplified approaches when assessing external costs of nonmarginal (and of marginal) system changes, despite contrary policy makers’ expectations14 and suggestions found in the scientific literature. The insights gained from this study will provide a better guidance regarding the optimal and operational instruments for internalization, for instance, on a European level. This notably concerns new results from following more adequate approaches to assess external costs from nonmarginal changes (i.e., economic sectors) in view of nonlinearities, mutual influences of emissions from different sectors and also the consideration of more local effects. At the same time, it is noted that the effort behind carrying out a corresponding assessment is quite substantial and further issues may be identified when implementing it. Relying on many data and models, external cost estimates are inherently uncertain.99,138,139 While parameter or model uncertainty will not be changed by the proposed approach, it helps checking the adequacy of current practice when estimating external costs of aggregates, for instance, for green accounting purposes. In the end, it needs to be seen to what extent external costs from individual sources or aggregates will effectively change. Finally, feedback effects exist between the nonseparable elements pollution and economic activity,7,18,140,141 calling for extending partial equilibrium modeling to general equilibrium modeling and also for integrating macroeconomics with microeconomics. When also assessing future nonmarginal scenarios, the demand on the analysts will even further

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ASSOCIATED CONTENT

S Supporting Information *

Complementary information on definitions of nonmarginality in studies explicitly addressing nonmarginal changes (cf. Section 2) and an overview on the examples how external costs of aggregates have been assessed following a bottom-up approach (cf. Section 3.2). The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.5b01623.

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AUTHOR INFORMATION

Corresponding Author

*Email: [email protected]. Telephone: +49 721 6105 1361. Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS The valuable exchanges with my colleague Jonathan van der Kamp during the preparation of the document are acknowledged.

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REFERENCES

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