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Uncertainty in energy planning: Estimating the health impacts of air pollution from fossil fuel electricity generation
Energy Research & Social Science 6 (2015) 74–77
Contents lists available at ScienceDirect
Energy Research & Social Science journal homepage: www.elsevier.com/locate/erss
Short communication
Uncertainty in energy planning: Estimating the health impacts of air pollution from fossil fuel electricity generation Allison Bridges, Frank A. Felder ∗ , Kathryn McKelvey, Ishanie Niyogi Edward J. Bloustein School of Planning and Public Policy, Rutgers University, 33 Livingston Avenue, New Brunswick, NJ 08901, United States
a r t i c l e
i n f o
Article history: Received 24 July 2014 Received in revised form 4 December 2014 Accepted 5 December 2014 Keywords: Energy planning Health impacts Fossil fuel Electricity
a b s t r a c t Costs of external effects such as health impacts of energy production are not reflected in market prices and as a result are often not taken into account during strategic energy planning. Computationally efficient, reduced-form models that estimate the health impact of air pollution from fossil fuel fired electricity generation can reduce the time and resources needed to analyze policy alternatives. Such models are currently being used for preliminary screening, retrospective studies, as well as in comprehensive multipollutant economy-wide approaches. One challenge faced by energy planners concerned with lowering emissions, particularly those using integrated multi-model frameworks for analysis, lies in the trade-off between the uncertainty associated with reduced-form air quality models and the need for sophisticated photochemical modeling that can be prohibitively time and resource intensive. © 2014 Published by Elsevier Ltd.
1. Introduction Among the external effects of electrical generation, this paper is concerned specifically with air-pollution emissions from coal and natural gas fired generation, which account for nearly 70 percent of electricity generated in the United States and constitute a significant portion of the downstream impacts associated with electric power generation [1]. Electric utilities are regulated at both the state and federal levels under the Clean Air Act. The United States Environmental Protection Agency (EPA) established ambient air quality standards for six “criteria pollutants”: particulate matter, ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide, and lead. The Clean Air Act requires states to formulate state implementation plans to pursue achievement of the National Ambient Air Quality Standards (NAAQS) of these criteria pollutants. State implementation plans assign emission limits for electricity-generating units (EGUs), usually as performance standards, for particulate matter (PM2.5 ), sulfur dioxide, and nitrogen oxide. Although power plant point sources are one of the primary emitters of criteria pollutants, cost-benefit analyses conducted by State Energy Offices often do not include both market and nonmarket impacts that take into account the health impacts of air pollution [1]. Environmental regulations for criteria pollutants do
∗ Corresponding author. Tel.: +1 848 932 2750. E-mail address: [email protected] (F.A. Felder). http://dx.doi.org/10.1016/j.erss.2014.12.002 2214-6296/© 2014 Published by Elsevier Ltd.
not internalize the entire costs of the adverse health impacts and environmental damage that these pollutants cause. These additional costs borne by society, therefore, need to be accounted for in addition to those costs internalized in electricity markets. Fig. 1 is a diagram of the high-level analytical capability that is needed to integrate market and non-market costs and benefits into strategic energy planning. The focus of this review is on nearterm human health effects resulting directly from emissions (rather than sources of energy) and does not include other strategic planning objectives such as non-human related environmental impacts, energy security and reliability, and public process considerations such as public participation and input [2]. External costs associated with fossil fuel fired EGU emissions of atmospheric greenhouse gases (GHGs) that cause climate change are also associated with long term health impacts, but are beyond the scope of this paper. The Clean Air Act sets standards specifically for small particles, less than 2.5 m in aerodynamic diameter (PM2.5 ), as evidence suggests these particles are able to enter human airways leading to both respiratory and cardiovascular morbidity and mortality [3–6]. Particulate matter, a microscopic liquid or solid, can be either directly emitted by burning fuel or formed through secondary processes in the atmosphere. Emissions of PM2.5 from coal-fired power plants alone were estimated to cause over 13,000 deaths, 9700 hospitalizations, and 20,000 heart attacks in 2010 with a total monetized value of more than $100 billion [7]. As changes in air emissions are a quantifiable effect of electrical generation, this type of valuation of air quality health impacts
A. Bridges et al. / Energy Research & Social Science 6 (2015) 74–77
75
Fig. 1. High-level integration of economic and human health-effect modeling in strategic energy planning.
requires (1) an estimation of air pollutants (and their precursors), (2) estimation of air pollution concentrations, and (3) estimations of health outcomes.
2. Methods for determining air quality and quantifying the health impact Photochemical modeling of air pollution that captures the non-linear chemistry that gives rise to certain pollutants is unfortunately expensive and time consuming. While there are examples of U.S. state energy plans that consider health effects [8], practical challenges remain in the integration of advanced air quality modeling with health impact assessments during strategic energy planning. Computationally robust photochemical models used in the evaluation of air pollution include the Community Multi-Scale Air Quality model (CMAQ), the Comprehensive Air Quality Model with Extensions (CAMX), and the CALPUFF dispersion model. Models such as these are capable of characterizing a full scale assessment of air quality although they vary in their assumptions regarding spatial and temporal domains, background pollutant concentrations, atmospheric chemistry, meteorology, and deposition [9]. Other models, such as the California TIMES model (CA-TIMES), are more comprehensive for analyses that span the sectors of energy, economics, environment, and engineering [10]. The time and resource requirements of these models often preclude their widespread use in strategic energy planning. Alternatively, a more computationally efficient reduced form technique can be used. A reduced form model simplifies either the prediction of air quality changes or the quantification of the impact on human health. Reduced-form models are increasingly being used by state and national-level agencies, as they are less resource intensive and more flexible [11]. The EPA developed reduced-form models, sometimes referred to as screening models, which can be applied before the refined air quality model to determine if refined modeling is needed [12]. These models provide estimates of the impact of air pollution emission changes on ambient particulate matter concentrations, translate these estimates into health effect impacts, and then monetize the impacts. Reduced-form techniques determine health impacts by either (1) using a benefit per ton estimate based on full-scale photochemical modeling, or (2) using a simplified air quality model that is based on the responsiveness of pollutant levels to changing emissions (a source–receptor relationship) [11]. Examples of computational tools such as these include the Geographic Information System (GIS) based BenMAP tool as well as the Powerplant Impact Estimator (PIE) and the Co-Benefits Risk Assessment (COBRA) Screening Model. These models allow energy planners to evaluate energy policy and planning alternatives in order to determine which scenarios are most likely to meet
climate and energy goals and therefore warrant more in-depth analysis. Such preliminary analyses can provide rough estimates of costs and are practical, but often lack the accuracy and credibility of sophisticated air quality models. Fann [11] suggests that reducedform methods, such as the source-receptor relationship, may not capture the full scope of changes in ambient air pollution because the treatment of secondary formation, transport, and deposition is simplified. Other factors that can lessen the reliability of reducedform analysis include: spatial distribution of the population and the relevance of the concentration-response functions [9] and [11]; differences between the benefit per ton of reducing directly emitted PM2.5 and reductions in PM2.5 precursor emissions [10]; and recognition that air quality monitoring data only indirectly indicate potential health effects [12]. A model such as COBRA requires only a short amount of time to compare different policy scenarios, but the tool limits the user’s ability to change baseline emissions or conduct analysis for various time horizons. As a screening model, COBRA is inappropriate for use in analyses that involve large volumes of emission changes over a wide geographic area, determination of attainment, or in estimating rebound effects or reductions that take place within capped regions [13]. Despite such drawbacks, reduced-form models have been used in scoping studies to assess the potential of a particular technology or policy to improve health outcomes [14] as well as in studies to evaluate how much of a negative health impact a current policy has on the population in comparison to a selected alternative [15]. Often, the inclusion of health externalities in the evaluation of EGU alternatives may significantly alter the way decision makers weigh competing costs and benefits. For example, McCubbin and Sovacool used COBRA to compare the health and environmental benefits of wind power versus natural gas. Findings of their report suggested that for the period 2012–2031, existing wind turbines in one of the study areas resulted in the avoidance of $560 million and $4.38 billion in human health and climate related externalities in comparison to the likely externalities that would have resulted from the same installed capacity from natural gas fired EGUs [16]. Screening models also can be integrated with other methods of analysis and models in order to take into account a greater breadth of policy and market conditions [17–23]. This integration can increase uncertainty, particularly when the reduced-form model, such as COBRA, has not been validated [24]. Given the complex nature of PM2.5 formation and transport, energy planners may be able to decrease uncertainty by basing reduced-form benefits assessments on photochemical models. A tool such as BenMAP is designed so that the user can enter previously obtained data from models such as CMAQ. This type of analysis allows for increased accuracy in the health impact assessment because the evaluation is based on a specific point source and for a specific population (taking into account size and distribution relative to the point source).
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A. Bridges et al. / Energy Research & Social Science 6 (2015) 74–77
3. Conclusions and future research There is a need to quantify the health impact of fossil fuel EGU air pollution. Many sophisticated models exist for determining air quality, but they are often impractical for use as tools to initially assess the health impact of numerous possible alternatives in the course of developing climate and energy policies that work to reduce air pollution. The development of tools such as reducedform screening models is helpful in filling this void. The extent to which energy planners should make use of reduce-form tools largely depends on the purpose of the analysis, the questions being asked, the resources available, and the tolerance for uncertainty. Reduced-form approaches increase uncertainty. As such, there is a need to develop decision-making frameworks capable of managing uncertainty in the development of climate and energy policy making. Past analyses of air-quality health impacts from EGUs indicate that the inclusion of such non-market costs in energy planning can save lives and resources. A review conducted by the National Research Council, that analyzed the hidden costs of energy production and use, collected data on particulate matter, sulfur dioxide, and oxides of nitrogen from 406 coal-fired power plants in the United States. For the study year 2005, aggregate damages were estimated to be approximately $62 billion, or 3.2 cents per kilowatt hour (kWh) [1]. However, study authors noted damages per plant varied widely. The variability in results indicates that an approach that lacks precision may lead to poor decision making regarding the determination of optimal strategies to meet climate and energy goals while reducing health impacts of EGUs. A study on health-related damages associated with emissions from coal-fired power plants conducted by Levy, Baxter, and Schwartz evaluated what factors contributed to variations in the estimated damages [25]. The authors monetized damages associated with 407 coal-fired power plants in the United States, focusing specifically on premature mortality from fine particulate matter. Their analysis found variability in damages per ton of emissions was almost entirely explained by population exposure per unit emissions and was highly correlated with sulfur dioxide emissions, related to fuel and control technologies. These results suggest more efficient outcomes can result from control strategies that are applied with a level of precision with regard to population concentration around the point source and local atmospheric conditions. In addition to careful consideration of uncertainty, the use of reduced-form models in cost-benefit analysis, that is comprehensive in scope and not likely to be followed by more in-depth analysis, should be transparent in the presentation of results and underlying assumptions. For example, the Northeast States Coordinated Air Use Management (NESCAUM) association developed the Multi-Pollutant Policy Analysis Framework. This framework integrates various methodologies in order to conduct a cost-benefit analysis that comprehensively addresses the complex nature of air quality management. An application of this methodology can be seen in NESCAUM’s analysis of Maryland’s Climate Action Plan [19]. Different scenarios and methods of calculating costs and benefits related to Maryland’s Renewable Portfolio Standards (RPS), the Regional Greenhouse Gas Initiative (RGGI), and the Maryland Clean Cars Act were considered. The assessment examined air quality control in terms of the energy, economic, and environmental impact in addition to the health impact using the EPA COBRA tool. While NESCAUM’s framework indicates there is a desire on the part of decision-makers engaged in strategic energy planning to use an integrated platform of models that is better able to capture both market and non-market effects, the authors of the NESCAUM study recommend that the second phase of analysis employ CMAQ to conduct photochemical modeling (in combination with BenMAP) [19].
There is a need for the integration of climate, energy, and air quality planning. However, the integration of models that assess health effects related to air pollution with other economic models is an iterative process. Models should be appropriately linked and account for the uncertainties within each step of the analysis, as well as throughout the entire modeling platform. Such comprehensive analysis of energy, air quality, economic and health impacts is necessary in the evaluation of Climate Action Plans, but depends heavily on the availability of data and the refinement of power sector assumptions. Model development should therefore follow two tracks: (1) verify and validate existing screening models such as COBRA and (2) develop tools that are practical as well as transparent about inherent uncertainties and assumptions. These steps will further bolster the ability of the larger energy, health, and climate planning community to optimally target strategies for reducing air pollution and the resultant health impacts of EGUs. Acknowledgements The authors gratefully acknowledge the information and feedback provided by Denise Mulholland of the U.S. Environmental Protection Agency, Anna Belova of Abt Associates, and Amelia Greiner of the Johns Hopkins School of Medicine, Center for Child and Community Health Research. The authors also appreciate helpful comments provided by anonymous reviewers. References [1] National Resource Council. Hidden costs of energy: unpriced consequences of energy production and use; 2010. p. 506 http://www. nap.edu/openbook.php?record id=12794&page=1 [2] Felder F, Andrews C, Hulkower S. Global energy futures and their economic and environmental implications. In: Sioshansi FP, editor. Energy sustainability and the environment: technology, incentives, behavior. Elsevier Press; 2011. p. 30–61. [3] Krewski D, Jerret M, Burnett RT, Ma R, Hughes E, Shi Y, et al. Extended follow-up and spatial analysis of the American Cancer Society study linking particulate air pollution and mortality, HEI Research Report. Boston, MA: Health Effects Institute; 2009 http://pubs.healtheffects.org/view.php?id=315 [4] Laden F, Schwartz J, Speizer FE, Dockery DW. Reduction in fine particulate air pollution and mortality: extended follow-up of the Harvard Six Cities Study. Am J Respir Crit Care Med 2006;173(6):667–72. [5] Samet JM, Dominici F, Curriero FC, Coursac I, Zeger SL. Fine particulate air pollution and mortality in 20 U.S. cities, 1987–1994. N Engl J Med 2000;343(24):1742–9. [6] Woodruff TJ, Darrow LA, Parker JD. Air pollution and post neonatal infant mortality in the United States, 1999–2002. Environ Health Perspect 2008;116(1):110–5. [7] Clean Air Task Force. The toll from coal: an updated assessment of death and disease from America’s Dirtiest Energy Source; 2010, September http://www.catf.us/resources/publications/files/The Toll from Coal.pdf [8] New York State Energy Plan. Health, energy production and energy use issue brief. New York State Energy Plan 2009; 2009 http://energyplan. ny.gov/Plans/2009.aspx [9] Levy JI, Wilson AM, Evans JS, Spengler JD. Estimation of primary and secondary particulate matter intake fractions for power plants in Georgia. Environ Sci Technol 2003;37:5528–36. [10] McCollum D, Yang C, Yeh S, Ogden J. Deep greenhouse gas reduction scenarios for California e Strategic implications from the CA-TIMES energy-economic systems model. Energy Strategy Rev 2012;1:19–32. [11] Fann N, Baker KR, Fulcher CM. Characterizing the PM2.5-related health benefits of emission reductions for 17 industrial, area and mobile emission sectors across the U.S. Environ Int 2012;49:141–51. [12] United States Environmental Protection Agency (EPA). Co-Benefits Risk Assessment (COBRA) Screening Model. http://www.epa.gov/ statelocalclimate/resources/cobra.html [13] United States Environmental Protection Agency (EPA). User’s manual for the cobenefits risk assessment (COBRA) screening model version: 2.4; 2012, October http://www.epa.gov/statelocalclimate/resources/cobra.html [14] Latimer D. Economic benefits of Colorado BART controls [PowerPoint slides]. US EPA Region 8; 2006 climatewest.files.wordpress.com/ 2011/07/barteconomics.pdf [15] McCue M, Deaton P, Nost E, Rachow J. Iowa coal & health: a preliminary mapping study. Iowa Chapter of Physicians for Social Responsibility; 2010 http://www.psr.org/resources/iowa-coal-and-health.html
A. Bridges et al. / Energy Research & Social Science 6 (2015) 74–77 [16] McCubbin D, Sovacool BK. Quantifying the health and environmental benefits of wind power to natural gas. Energy Policy 2013;53:429–41. [17] Abt Associates Inc. Assessing the economic impact of dominion Virginia power’s. Coal-fired power plant in Wise County, Virginia, Compared to investments in energy efficiency. Prepared under contract for the Wise Energy for Virginia Coalition; 2009 http://www.abtassociates.com/reports/ Economic Analysis of Wise County Coal Plant 1-22-09.pdf [18] Jensen V, Lounsbury E. Assessment of energy efficiency potential in Georgia, ICF Consulting. Georgia Environmental Facilities Authority; 2005 http:// cleanefficientenergy.org/resource/georgia-assessment-energy-efficiencypotential [19] Northeast States Coordinated Air Use Management (NESCAUM). Integrating climate and air quality planning in Maryland: phase I. NESCAUM; 2009 www.nescaum.org/documents/md multip final 20mar09.pdf [20] Regional Economic Models Inc. (REMI). Economic impact of oil and natural gas conservation policies. Prepared for U.S. Environmental Protection Agency and the State of Connecticut; 2004 http://www.remi.com/ uploads/File/Articles/Economic Impact of Oil and Natural Gas Conservation Policies.pdf
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[21] Gallaher M, Rogozhin A, Petrusa J. Retrospective benefit-cost evaluation of U.S. DOE Geothermal Technologies R&D Program investments: impacts of a cluster of energy technologies. RTI International, U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy; 2010 http://energy. gov/eere/geothermal/downloads/retrospective-benefit-cost-evaluation-usdoe-geothermal-technologies-rd [22] Pelsoci TM. Retrospective benefit-cost evaluation of U.S. DOE wind energy R&D program. Delta Research Co.; 2010 http://www1.eere.energy.gov/ analysis/pdfs/wind bc report10-14-10.pdf [23] Hoppock D, Adair SK, Murray B, Tarr J. Benefits of early state action in environmental regulation of electric utilities: North Carolina’s clean smokestacks act. Duke University; 2012 http://nicholasinstitute.duke. edu/climate/policydesign/benefits-of-early-state-action-in-environmentalregulation-of-electric-utilities [24] D. Mulholland, Senior Analyst, USEPA State Climate and Energy Program, personal communication, April 4, 2013. [25] Levy JI, Baxter LK, Schwartz J. Uncertainty and variability in health-related damages from coal-fired power plants in the United States. Risk Anal 2009;29(7):1000–14 www.ncbi.nlm.nih.gov/pubmed/19392676
Contents lists available at ScienceDirect
Energy Research & Social Science journal homepage: www.elsevier.com/locate/erss
Short communication
Uncertainty in energy planning: Estimating the health impacts of air pollution from fossil fuel electricity generation Allison Bridges, Frank A. Felder ∗ , Kathryn McKelvey, Ishanie Niyogi Edward J. Bloustein School of Planning and Public Policy, Rutgers University, 33 Livingston Avenue, New Brunswick, NJ 08901, United States
a r t i c l e
i n f o
Article history: Received 24 July 2014 Received in revised form 4 December 2014 Accepted 5 December 2014 Keywords: Energy planning Health impacts Fossil fuel Electricity
a b s t r a c t Costs of external effects such as health impacts of energy production are not reflected in market prices and as a result are often not taken into account during strategic energy planning. Computationally efficient, reduced-form models that estimate the health impact of air pollution from fossil fuel fired electricity generation can reduce the time and resources needed to analyze policy alternatives. Such models are currently being used for preliminary screening, retrospective studies, as well as in comprehensive multipollutant economy-wide approaches. One challenge faced by energy planners concerned with lowering emissions, particularly those using integrated multi-model frameworks for analysis, lies in the trade-off between the uncertainty associated with reduced-form air quality models and the need for sophisticated photochemical modeling that can be prohibitively time and resource intensive. © 2014 Published by Elsevier Ltd.
1. Introduction Among the external effects of electrical generation, this paper is concerned specifically with air-pollution emissions from coal and natural gas fired generation, which account for nearly 70 percent of electricity generated in the United States and constitute a significant portion of the downstream impacts associated with electric power generation [1]. Electric utilities are regulated at both the state and federal levels under the Clean Air Act. The United States Environmental Protection Agency (EPA) established ambient air quality standards for six “criteria pollutants”: particulate matter, ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide, and lead. The Clean Air Act requires states to formulate state implementation plans to pursue achievement of the National Ambient Air Quality Standards (NAAQS) of these criteria pollutants. State implementation plans assign emission limits for electricity-generating units (EGUs), usually as performance standards, for particulate matter (PM2.5 ), sulfur dioxide, and nitrogen oxide. Although power plant point sources are one of the primary emitters of criteria pollutants, cost-benefit analyses conducted by State Energy Offices often do not include both market and nonmarket impacts that take into account the health impacts of air pollution [1]. Environmental regulations for criteria pollutants do
∗ Corresponding author. Tel.: +1 848 932 2750. E-mail address: [email protected] (F.A. Felder). http://dx.doi.org/10.1016/j.erss.2014.12.002 2214-6296/© 2014 Published by Elsevier Ltd.
not internalize the entire costs of the adverse health impacts and environmental damage that these pollutants cause. These additional costs borne by society, therefore, need to be accounted for in addition to those costs internalized in electricity markets. Fig. 1 is a diagram of the high-level analytical capability that is needed to integrate market and non-market costs and benefits into strategic energy planning. The focus of this review is on nearterm human health effects resulting directly from emissions (rather than sources of energy) and does not include other strategic planning objectives such as non-human related environmental impacts, energy security and reliability, and public process considerations such as public participation and input [2]. External costs associated with fossil fuel fired EGU emissions of atmospheric greenhouse gases (GHGs) that cause climate change are also associated with long term health impacts, but are beyond the scope of this paper. The Clean Air Act sets standards specifically for small particles, less than 2.5 m in aerodynamic diameter (PM2.5 ), as evidence suggests these particles are able to enter human airways leading to both respiratory and cardiovascular morbidity and mortality [3–6]. Particulate matter, a microscopic liquid or solid, can be either directly emitted by burning fuel or formed through secondary processes in the atmosphere. Emissions of PM2.5 from coal-fired power plants alone were estimated to cause over 13,000 deaths, 9700 hospitalizations, and 20,000 heart attacks in 2010 with a total monetized value of more than $100 billion [7]. As changes in air emissions are a quantifiable effect of electrical generation, this type of valuation of air quality health impacts
A. Bridges et al. / Energy Research & Social Science 6 (2015) 74–77
75
Fig. 1. High-level integration of economic and human health-effect modeling in strategic energy planning.
requires (1) an estimation of air pollutants (and their precursors), (2) estimation of air pollution concentrations, and (3) estimations of health outcomes.
2. Methods for determining air quality and quantifying the health impact Photochemical modeling of air pollution that captures the non-linear chemistry that gives rise to certain pollutants is unfortunately expensive and time consuming. While there are examples of U.S. state energy plans that consider health effects [8], practical challenges remain in the integration of advanced air quality modeling with health impact assessments during strategic energy planning. Computationally robust photochemical models used in the evaluation of air pollution include the Community Multi-Scale Air Quality model (CMAQ), the Comprehensive Air Quality Model with Extensions (CAMX), and the CALPUFF dispersion model. Models such as these are capable of characterizing a full scale assessment of air quality although they vary in their assumptions regarding spatial and temporal domains, background pollutant concentrations, atmospheric chemistry, meteorology, and deposition [9]. Other models, such as the California TIMES model (CA-TIMES), are more comprehensive for analyses that span the sectors of energy, economics, environment, and engineering [10]. The time and resource requirements of these models often preclude their widespread use in strategic energy planning. Alternatively, a more computationally efficient reduced form technique can be used. A reduced form model simplifies either the prediction of air quality changes or the quantification of the impact on human health. Reduced-form models are increasingly being used by state and national-level agencies, as they are less resource intensive and more flexible [11]. The EPA developed reduced-form models, sometimes referred to as screening models, which can be applied before the refined air quality model to determine if refined modeling is needed [12]. These models provide estimates of the impact of air pollution emission changes on ambient particulate matter concentrations, translate these estimates into health effect impacts, and then monetize the impacts. Reduced-form techniques determine health impacts by either (1) using a benefit per ton estimate based on full-scale photochemical modeling, or (2) using a simplified air quality model that is based on the responsiveness of pollutant levels to changing emissions (a source–receptor relationship) [11]. Examples of computational tools such as these include the Geographic Information System (GIS) based BenMAP tool as well as the Powerplant Impact Estimator (PIE) and the Co-Benefits Risk Assessment (COBRA) Screening Model. These models allow energy planners to evaluate energy policy and planning alternatives in order to determine which scenarios are most likely to meet
climate and energy goals and therefore warrant more in-depth analysis. Such preliminary analyses can provide rough estimates of costs and are practical, but often lack the accuracy and credibility of sophisticated air quality models. Fann [11] suggests that reducedform methods, such as the source-receptor relationship, may not capture the full scope of changes in ambient air pollution because the treatment of secondary formation, transport, and deposition is simplified. Other factors that can lessen the reliability of reducedform analysis include: spatial distribution of the population and the relevance of the concentration-response functions [9] and [11]; differences between the benefit per ton of reducing directly emitted PM2.5 and reductions in PM2.5 precursor emissions [10]; and recognition that air quality monitoring data only indirectly indicate potential health effects [12]. A model such as COBRA requires only a short amount of time to compare different policy scenarios, but the tool limits the user’s ability to change baseline emissions or conduct analysis for various time horizons. As a screening model, COBRA is inappropriate for use in analyses that involve large volumes of emission changes over a wide geographic area, determination of attainment, or in estimating rebound effects or reductions that take place within capped regions [13]. Despite such drawbacks, reduced-form models have been used in scoping studies to assess the potential of a particular technology or policy to improve health outcomes [14] as well as in studies to evaluate how much of a negative health impact a current policy has on the population in comparison to a selected alternative [15]. Often, the inclusion of health externalities in the evaluation of EGU alternatives may significantly alter the way decision makers weigh competing costs and benefits. For example, McCubbin and Sovacool used COBRA to compare the health and environmental benefits of wind power versus natural gas. Findings of their report suggested that for the period 2012–2031, existing wind turbines in one of the study areas resulted in the avoidance of $560 million and $4.38 billion in human health and climate related externalities in comparison to the likely externalities that would have resulted from the same installed capacity from natural gas fired EGUs [16]. Screening models also can be integrated with other methods of analysis and models in order to take into account a greater breadth of policy and market conditions [17–23]. This integration can increase uncertainty, particularly when the reduced-form model, such as COBRA, has not been validated [24]. Given the complex nature of PM2.5 formation and transport, energy planners may be able to decrease uncertainty by basing reduced-form benefits assessments on photochemical models. A tool such as BenMAP is designed so that the user can enter previously obtained data from models such as CMAQ. This type of analysis allows for increased accuracy in the health impact assessment because the evaluation is based on a specific point source and for a specific population (taking into account size and distribution relative to the point source).
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3. Conclusions and future research There is a need to quantify the health impact of fossil fuel EGU air pollution. Many sophisticated models exist for determining air quality, but they are often impractical for use as tools to initially assess the health impact of numerous possible alternatives in the course of developing climate and energy policies that work to reduce air pollution. The development of tools such as reducedform screening models is helpful in filling this void. The extent to which energy planners should make use of reduce-form tools largely depends on the purpose of the analysis, the questions being asked, the resources available, and the tolerance for uncertainty. Reduced-form approaches increase uncertainty. As such, there is a need to develop decision-making frameworks capable of managing uncertainty in the development of climate and energy policy making. Past analyses of air-quality health impacts from EGUs indicate that the inclusion of such non-market costs in energy planning can save lives and resources. A review conducted by the National Research Council, that analyzed the hidden costs of energy production and use, collected data on particulate matter, sulfur dioxide, and oxides of nitrogen from 406 coal-fired power plants in the United States. For the study year 2005, aggregate damages were estimated to be approximately $62 billion, or 3.2 cents per kilowatt hour (kWh) [1]. However, study authors noted damages per plant varied widely. The variability in results indicates that an approach that lacks precision may lead to poor decision making regarding the determination of optimal strategies to meet climate and energy goals while reducing health impacts of EGUs. A study on health-related damages associated with emissions from coal-fired power plants conducted by Levy, Baxter, and Schwartz evaluated what factors contributed to variations in the estimated damages [25]. The authors monetized damages associated with 407 coal-fired power plants in the United States, focusing specifically on premature mortality from fine particulate matter. Their analysis found variability in damages per ton of emissions was almost entirely explained by population exposure per unit emissions and was highly correlated with sulfur dioxide emissions, related to fuel and control technologies. These results suggest more efficient outcomes can result from control strategies that are applied with a level of precision with regard to population concentration around the point source and local atmospheric conditions. In addition to careful consideration of uncertainty, the use of reduced-form models in cost-benefit analysis, that is comprehensive in scope and not likely to be followed by more in-depth analysis, should be transparent in the presentation of results and underlying assumptions. For example, the Northeast States Coordinated Air Use Management (NESCAUM) association developed the Multi-Pollutant Policy Analysis Framework. This framework integrates various methodologies in order to conduct a cost-benefit analysis that comprehensively addresses the complex nature of air quality management. An application of this methodology can be seen in NESCAUM’s analysis of Maryland’s Climate Action Plan [19]. Different scenarios and methods of calculating costs and benefits related to Maryland’s Renewable Portfolio Standards (RPS), the Regional Greenhouse Gas Initiative (RGGI), and the Maryland Clean Cars Act were considered. The assessment examined air quality control in terms of the energy, economic, and environmental impact in addition to the health impact using the EPA COBRA tool. While NESCAUM’s framework indicates there is a desire on the part of decision-makers engaged in strategic energy planning to use an integrated platform of models that is better able to capture both market and non-market effects, the authors of the NESCAUM study recommend that the second phase of analysis employ CMAQ to conduct photochemical modeling (in combination with BenMAP) [19].
There is a need for the integration of climate, energy, and air quality planning. However, the integration of models that assess health effects related to air pollution with other economic models is an iterative process. Models should be appropriately linked and account for the uncertainties within each step of the analysis, as well as throughout the entire modeling platform. Such comprehensive analysis of energy, air quality, economic and health impacts is necessary in the evaluation of Climate Action Plans, but depends heavily on the availability of data and the refinement of power sector assumptions. Model development should therefore follow two tracks: (1) verify and validate existing screening models such as COBRA and (2) develop tools that are practical as well as transparent about inherent uncertainties and assumptions. These steps will further bolster the ability of the larger energy, health, and climate planning community to optimally target strategies for reducing air pollution and the resultant health impacts of EGUs. Acknowledgements The authors gratefully acknowledge the information and feedback provided by Denise Mulholland of the U.S. Environmental Protection Agency, Anna Belova of Abt Associates, and Amelia Greiner of the Johns Hopkins School of Medicine, Center for Child and Community Health Research. The authors also appreciate helpful comments provided by anonymous reviewers. References [1] National Resource Council. Hidden costs of energy: unpriced consequences of energy production and use; 2010. p. 506 http://www. nap.edu/openbook.php?record id=12794&page=1 [2] Felder F, Andrews C, Hulkower S. Global energy futures and their economic and environmental implications. In: Sioshansi FP, editor. Energy sustainability and the environment: technology, incentives, behavior. Elsevier Press; 2011. p. 30–61. [3] Krewski D, Jerret M, Burnett RT, Ma R, Hughes E, Shi Y, et al. Extended follow-up and spatial analysis of the American Cancer Society study linking particulate air pollution and mortality, HEI Research Report. Boston, MA: Health Effects Institute; 2009 http://pubs.healtheffects.org/view.php?id=315 [4] Laden F, Schwartz J, Speizer FE, Dockery DW. Reduction in fine particulate air pollution and mortality: extended follow-up of the Harvard Six Cities Study. Am J Respir Crit Care Med 2006;173(6):667–72. [5] Samet JM, Dominici F, Curriero FC, Coursac I, Zeger SL. Fine particulate air pollution and mortality in 20 U.S. cities, 1987–1994. N Engl J Med 2000;343(24):1742–9. [6] Woodruff TJ, Darrow LA, Parker JD. Air pollution and post neonatal infant mortality in the United States, 1999–2002. Environ Health Perspect 2008;116(1):110–5. [7] Clean Air Task Force. The toll from coal: an updated assessment of death and disease from America’s Dirtiest Energy Source; 2010, September http://www.catf.us/resources/publications/files/The Toll from Coal.pdf [8] New York State Energy Plan. Health, energy production and energy use issue brief. New York State Energy Plan 2009; 2009 http://energyplan. ny.gov/Plans/2009.aspx [9] Levy JI, Wilson AM, Evans JS, Spengler JD. Estimation of primary and secondary particulate matter intake fractions for power plants in Georgia. Environ Sci Technol 2003;37:5528–36. [10] McCollum D, Yang C, Yeh S, Ogden J. Deep greenhouse gas reduction scenarios for California e Strategic implications from the CA-TIMES energy-economic systems model. Energy Strategy Rev 2012;1:19–32. [11] Fann N, Baker KR, Fulcher CM. Characterizing the PM2.5-related health benefits of emission reductions for 17 industrial, area and mobile emission sectors across the U.S. Environ Int 2012;49:141–51. [12] United States Environmental Protection Agency (EPA). Co-Benefits Risk Assessment (COBRA) Screening Model. http://www.epa.gov/ statelocalclimate/resources/cobra.html [13] United States Environmental Protection Agency (EPA). User’s manual for the cobenefits risk assessment (COBRA) screening model version: 2.4; 2012, October http://www.epa.gov/statelocalclimate/resources/cobra.html [14] Latimer D. Economic benefits of Colorado BART controls [PowerPoint slides]. US EPA Region 8; 2006 climatewest.files.wordpress.com/ 2011/07/barteconomics.pdf [15] McCue M, Deaton P, Nost E, Rachow J. Iowa coal & health: a preliminary mapping study. Iowa Chapter of Physicians for Social Responsibility; 2010 http://www.psr.org/resources/iowa-coal-and-health.html
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