CABOT-O3: An Optimization Model for Air Quality Benefit-Cost and Distributional Impacts Analysis.

Macpherson et al. (2017) presented a mathematical programming model that identifies minimum-cost control strategies that reduce emissions regionally to meet ambient air quality targets. This project introduces the Cost And Benefit Optimization Tool for Ozone (CABOT-O3), which extends the previous model by updating emissions and air quality relationships, adding a health impacts module, and quantifying distributional impacts. The tool draws upon source apportionment photochemical air quality modeling to characterize the contribution of emissions reductions to ambient ozone concentrations across the contiguous United States. The health impacts analysis module estimates the change in the number and economic value of premature deaths using modeled changes in ozone levels resulting from the application of emission control strategies. These extensions allow us to evaluate strategies to attain ozone air quality standards at minimum cost or to maximize net benefit, while assessing the change in the distribution of health impacts. In a case study applied to stationary pollution sources, we find that, when compared to minimizing costs to meet a uniform ozone standard, maximizing net benefits results in greater emissions and ozone concentration reductions in some parts of the country and fewer in others. Our results highlight potential equity-efficiency trade-offs in designing air quality policies.

Marginal abatement cost curve for nitrogen oxides incorporating controls, renewable electricity, energy efficiency, and fuel switching.

A marginal abatement cost curve (MACC) traces out the relationship between the quantity of pollution abated and the marginal cost of abating each additional unit. In the context of air quality management, MACCs are typically developed by sorting control technologies by their relative cost-effectiveness. Other potentially important abatement measures such as renewable electricity, energy efficiency, and fuel switching (RE/EE/FS) are often not incorporated into MACCs, as it is difficult to quantify their costs and abatement potential. In this paper, a U.S. energy system model is used to develop a MACC for nitrogen oxides (NOx) that incorporates both traditional controls and these additional measures. The MACC is decomposed by sector, and the relative cost-effectiveness of RE/EE/FS and traditional controls are compared. RE/EE/FS are shown to have the potential to increase emission reductions beyond what is possible when applying traditional controls alone. Furthermore, a portion of RE/EE/FS appear to be cost-competitive with traditional controls. IMPLICATIONS: Renewable electricity, energy efficiency, and fuel switching can be cost-competitive with traditional air pollutant controls for abating air pollutant emissions. The application of renewable electricity, energy efficiency, and fuel switching is also shown to have the potential to increase emission reductions beyond what is possible when applying traditional controls alone.

Controlling for exogenous environmental variables when using data envelopment analysis for regional environmental assessments.

Researchers are increasingly using data envelopment analysis (DEA) to examine the efficiency of environmental policies and resource allocations. An assumption of the basic DEA model is that decisionmakers operate within homogeneous environments. But, this assumption is not valid when environmental performance is influenced by variables beyond managerial control. Understanding the influence of these variables is important to distinguish between characterizing environmental conditions and identifying opportunities to improve environmental performance. While environmental assessments often focus on characterizing conditions, the point of using DEA is to identify opportunities to improve environmental performance and thereby prevent (or rectify) an inefficient allocation of resources. We examine the role of exogenous variables such as climate, hydrology, and topography in producing environmental impacts such as deposition, runoff, invasive species, and forest fragmentation within the United States Mid-Atlantic region. We apply a four-stage procedure to adjust environmental impacts in a DEA model that seeks to minimize environmental impacts while obtaining given levels of socioeconomic outcomes. The approach creates a performance index that bundles multiple indicators while adjusting for variables that are outside management control, offering numerous advantages for environmental assessment.

Assessing sediment yield for selected watersheds in the Laurentian Great Lakes Basin under future agricultural scenarios.

In the Laurentian Great Lakes Basin (GLB), corn acreage has been expanding since 2005 in response to high demand for corn as an ethanol feedstock. This study integrated remote sensing-derived products and the Soil and Water Assessment Tool (SWAT) within a geographic information system (GIS) modeling environment to assess the impacts of cropland change on sediment yield within four selected watersheds in the GLB. The SWAT models were calibrated during a 6 year period (2000-2005), and predicted stream flows were validated. The R(2) values were 0.76, 0.80, 0.72, and 0.81 for the St. Joseph River, the St. Mary River, the Peshtigo River, and the Cattaraugus Creek watersheds, respectively. The corresponding E (Nash and Sutcliffe model efficiency coefficient) values ranged from 0.24 to 0.79. The average annual sediment yields (tons/ha/year) ranged from 0.12 to 4.44 for the baseline (2000 to 2008) condition. Sediment yields were predicted to increase for possible future cropland change scenarios. The first scenario was to convert all "other" agricultural row crop types (i.e., sorghum) to corn fields and switch the current/baseline crop rotation into continuous corn. The average annual sediment yields increased 7-42 % for different watersheds. The second scenario was to further expand the corn planting to hay/pasture fields. The average annual sediment yields increased 33-127 % compared with baseline conditions.