ESP v1.0: methodology for exploring emission impacts of future scenarios in the United States

Loughlin, Daniel H.; Benjey, William; Nolte, Christopher G.

doi:10.5194/gmd-4-287-2011

Cited by 47 publications

(30 citation statements)

References 7 publications

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“…In previous work, we investigated the sensitivity of benefit results to baseline emissions, finding that benefits decline sublinearly with declining baseline emissions totals (i.e., if baseline emissions are reduced by 25%, the benefits will decline by less than 25%; Thompson et al, 2014a). Other studies have examined the impacts of carbon policy on criteria pollution and associated cobenefits while incorporating air quality policy, but full chemical transport modeling was either used in reduced form as per ton benefits estimates (Akhtar et al, 2013) or not conducted (Loughlin et al, 2011).…”

Section: United States Regional Energy Policy Modelmentioning

confidence: 99%

Air quality co-benefits of subnational carbon policies

Thompson

Rausch

Saari

et al. 2016

Journal of the Air & Waste Management Association

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To mitigate climate change, governments ranging from city to multi-national have adopted greenhouse gas (GHG) emissions reduction targets. While the location of GHG reductions does not affect their climate benefits, it can impact human health benefits associated with co-emitted pollutants. Here, an advanced modeling framework is used to explore how subnational level GHG targets influence air pollutant co-benefits from ground level ozone and fine particulate matter. Two carbon policy scenarios are analyzed, each reducing the same total amount of GHG emissions in the Northeast US: an economy-wide Cap and Trade (CAT) program reducing emissions from all sectors of the economy, and a Clean Energy Standard (CES) reducing emissions from the electricity sector only. Results suggest that a regional CES policy will cost about 10 times more than a CAT policy. Despite having the same regional targets in the Northeast, carbon leakage to noncapped regions varies between policies. Consequently, a regional CAT policy will result in national carbon reductions that are over six times greater than the carbon reduced by the CES in 2030. Monetized regional human health benefits of the CAT and CES policies are 844% and 185% of the costs of each policy, respectively. Benefits for both policies are thus estimated to exceed their costs in the Northeast US. The estimated value of human health co-benefits associated with air pollution reductions for the CES scenario is two times that of the CAT scenario.Implications: In this research, an advanced modeling framework is used to determine the potential impacts of regional carbon policies on air pollution co-benefits associated with ground level ozone and fine particulate matter. Study results show that spatially heterogeneous GHG policies have the potential to create areas of air pollution dis-benefit. It is also shown that monetized human health benefits within the area covered by policy may be larger than the model estimated cost of the policy. These findings are of particular interest both as U.S. states work to develop plans to meet state-level carbon emissions reduction targets set by the EPA through the Clean Power Plan, and in the absence of comprehensive national carbon policy.PAPER HISTORY

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Section: United States Regional Energy Policy Modelmentioning

confidence: 99%

Air quality co-benefits of subnational carbon policies

Thompson

Rausch

Saari

et al. 2016

Journal of the Air & Waste Management Association

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“…The database includes representations of the Clean Air Interstate Rule (CAIR), the Mercury and Air Toxics (MATs) rule, Tier III on-road vehicle emission standards, industrial New Source Performance Standards (NSPSs) and state-level Renewable Portfolio Standards (RPSs). The database and its earlier versions have been used in a variety of applications related to air pollution as well as climate change (Akhtar et al, 2013;Loughlin, 2013;Loughlin et al, 2013;Loughlin et al, 2011).…”

Section: Resultsmentioning

confidence: 99%

“…Tools such as NE-MARKAL provide state-level resolution. Alternatively, approaches such as the Emission Scenario Projection method translate regional growth factors into county-level, pollutant-specific and source category-specific factors (Loughlin et al, 2011;Ran et al, 2015) that can be used in detailed air quality modeling exercises. In estimating impacts, it may also be advantageous to integrate models and methods that site new power sector and industrial facilities based upon heuristics, consistent with other scenario assumptions (e.g., Hobbs et al, 2010;Kraucunas et al, 2015).…”

Section: Future Directionsmentioning

confidence: 99%

Role of future scenarios in understanding deep uncertainty in long-term air quality management

Gamas

Dodder

Loughlin

et al. 2015

Journal of the Air & Waste Management Association

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The environment and its interactions with human systems, whether economic, social, or political, are complex. Relevant drivers may disrupt system dynamics in unforeseen ways, making it difficult to predict future conditions. This kind of "deep uncertainty" presents a challenge to organizations faced with making decisions about the future, including those involved in air quality management. Scenario Planning is a structured process that involves the development of narratives describing alternative future states of the world, designed to differ with respect to the most critical and uncertain drivers. The resulting scenarios are then used to understand the consequences of those futures and to prepare for them with robust management strategies. We demonstrate a novel air quality management application of Scenario Planning. Through a series of workshops, important air quality drivers were identified. The most critical and uncertain drivers were found to be "technological development" and "change in societal paradigms." These drivers were used as a basis to develop four distinct scenario storylines. The energy and emissions implications of each storyline were then modeled using the MARKAL energy system model. NO x emissions were found to decrease for all scenarios, largely a response to existing air quality regulations, whereas SO 2 emissions ranged from 12% greater to 7% lower than 2015 emissions levels. Future-year emissions differed considerably from one scenario to another, however, with key differentiating factors being transition to cleaner fuels and energy demand reductions.Implications: Application of scenarios in air quality management provides a structured means of sifting through and understanding the dynamics of the many complex driving forces affecting future air quality. Further, scenarios provide a means to identify opportunities and challenges for future air quality management, as well as a platform for testing the efficacy and robustness of particular management options across wide-ranging conditions. IntroductionIn the United States, air quality managers at the federal and state levels develop emission reduction strategies to meet air quality standards. Emission inventories are projected into the future, both with and without each candidate strategy. Air quality models are then used to evaluate the resulting changes in air quality. At the federal level, illustrative emission reduction strategies are a key component of Regulatory Impact Analyses (RIAs) (e.g., U.S. Environmental Protection Agency [EPA], 2009[EPA], , 2012a[EPA], 2012b[EPA], , 2014). These strategies demonstrate how an air quality standard can be met and are used in estimating the resulting costs and health and environmental benefits. For states, emission reduction strategies often support State Implementation Plans (SIPs), which specify how the state will comply with air quality standards into the future (e.g., California Air Resources Board [CARB], 2007; Texas Commission on Environmental Quality [TCEQ], 2012). In RIA and SIP applicatio...

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“…Model outputs include demands, technologies, fuel use and emissions of pollutants to 2055. MARKAL's regional-, technology-, and pollutant-specific emission projections are then used to develop growth and control factors to grow the base year emission inventory to 2055 [39]. Following expected BAU trends, in the Reference Case used for this work the LDV sector is predominantly comprised of gasoline combustion engine technologies (a moderate to minor amount of LDV demand is assumed to be met with alternative technologies and fuels including electricity).…”

Section: Methodsmentioning

confidence: 99%

“…First, the comprehensive accounting of regional emissions evolution under business-as-usual (BAU) conditions to provide a Reference Case for comparison with FCEV and HDV Cases. The approach for the developed 2055 Reference Case follows the methodology described by Loughlin et al, 2011 [39]. Energy system progression and the evolution of emissions in major economic sectors is estimated using output from the Market Allocation (MARKAL) model.…”

Section: Methodsmentioning

confidence: 99%