Inequity in consumption of goods and services adds to racial-ethnic disparities in air pollution exposure.

Fine particulate matter (PM2.5) air pollution exposure is the largest environmental health risk factor in the United States. Here, we link PM2.5 exposure to the human activities responsible for PM2.5 pollution. We use these results to explore "pollution inequity": the difference between the environmental health damage caused by a racial-ethnic group and the damage that group experiences. We show that, in the United States, PM2.5 exposure is disproportionately caused by consumption of goods and services mainly by the non-Hispanic white majority, but disproportionately inhaled by black and Hispanic minorities. On average, non-Hispanic whites experience a "pollution advantage": They experience ∼17% less air pollution exposure than is caused by their consumption. Blacks and Hispanics on average bear a "pollution burden" of 56% and 63% excess exposure, respectively, relative to the exposure caused by their consumption. The total disparity is caused as much by how much people consume as by how much pollution they breathe. Differences in the types of goods and services consumed by each group are less important. PM2.5 exposures declined ∼50% during 2002-2015 for all three racial-ethnic groups, but pollution inequity has remained high.

Regional allocation of biomass to U.S. energy demands under a portfolio of policy scenarios.

The potential for widespread use of domestically available energy resources, in conjunction with climate change concerns, suggest that biomass may be an essential component of U.S. energy systems in the near future. Cellulosic biomass in particular is anticipated to be used in increasing quantities because of policy efforts, such as federal renewable fuel standards and state renewable portfolio standards. Unfortunately, these independently designed biomass policies do not account for the fact that cellulosic biomass can equally be used for different, competing energy demands. An integrated assessment of multiple feedstocks, energy demands, and system costs is critical for making optimal decisions about a unified biomass energy strategy. This study develops a spatially explicit, best-use framework to optimally allocate cellulosic biomass feedstocks to energy demands in transportation, electricity, and residential heating sectors, while minimizing total system costs and tracking greenhouse gas emissions. Comparing biomass usage across three climate policy scenarios suggests that biomass used for space heating is a low cost emissions reduction option, while biomass for liquid fuel or for electricity becomes attractive only as emissions reduction targets or carbon prices increase. Regardless of the policy approach, study results make a strong case for national and regional coordination in policy design and compliance pathways.

Impacts of variability in cellulosic biomass yields on energy security.

The practice of modeling biomass yields on the basis of deterministic point values aggregated over space and time obscures important risks associated with large-scale biofuel use, particularly risks related to drought-induced yield reductions that may become increasingly frequent under a changing climate. Using switchgrass as a case study, this work quantifies the variability in expected yields over time and space through switchgrass growth modeling under historical and simulated future weather. The predicted switchgrass yields across the United States range from about 12 to 19 Mg/ha, and the 80% confidence intervals range from 20 to 60% of the mean. Average yields are predicted to decrease with increased temperatures and weather variability induced by climate change. Feedstock yield variability needs to be a central part of modeling to ensure that policy makers acknowledge risks to energy supplies and develop strategies or contingency plans that mitigate those risks.

Life cycle environmental impacts of wastewater-based algal biofuels.

Recent research has proposed integrating wastewater treatment with algae cultivation as a way of producing algal biofuels at a commercial scale more sustainably. This study evaluates the environmental performance of wastewater-based algal biofuels with a well-to-wheel life cycle assessment (LCA). Production pathways examined include different nutrient sources (municipal wastewater influent to the activated sludge process, centrate from the sludge drying process, swine manure, and freshwater with synthetic fertilizers) combined with emerging biomass conversion technologies (microwave pyrolysis, combustion, wet lipid extraction, and hydrothermal liquefaction). Results show that the environmental performance of wastewater-based algal biofuels is generally better than freshwater-based algal biofuels, but depends on the characteristics of the wastewater and the conversion technologies. Of 16 pathways compared, only the centrate cultivation with wet lipid extraction pathway and the centrate cultivation with combustion pathway have lower impacts than petroleum diesel in all environmental categories examined (fossil fuel use, greenhouse gas emissions, eutrophication potential, and consumptive water use). The potential for large-scale implementation of centrate-based algal biofuel, however, is limited by availability of centrate. Thus, it is unlikely that algal biofuels can provide a large-scale and environmentally preferable alternative to petroleum transportation fuels without considerable improvement in current production technologies. Additionally, the cobenefit of wastewater-based algal biofuel production as an alternate means of treating various wastewaters should be further explored.

Policy implications of uncertainty in modeled life-cycle greenhouse gas emissions of biofuels.

Biofuels have received legislative support recently in California's Low-Carbon Fuel Standard and the Federal Energy Independence and Security Act. Both present new fuel types, but neither provides methodological guidelines for dealing with the inherent uncertainty in evaluating their potential life-cycle greenhouse gas emissions. Emissions reductions are based on point estimates only. This work demonstrates the use of Monte Carlo simulation to estimate life-cycle emissions distributions from ethanol and butanol from corn or switchgrass. Life-cycle emissions distributions for each feedstock and fuel pairing modeled span an order of magnitude or more. Using a streamlined life-cycle assessment, corn ethanol emissions range from 50 to 250 g CO(2)e/MJ, for example, and each feedstock-fuel pathway studied shows some probability of greater emissions than a distribution for gasoline. Potential GHG emissions reductions from displacing fossil fuels with biofuels are difficult to forecast given this high degree of uncertainty in life-cycle emissions. This uncertainty is driven by the importance and uncertainty of indirect land use change emissions. Incorporating uncertainty in the decision making process can illuminate the risks of policy failure (e.g., increased emissions), and a calculated risk of failure due to uncertainty can be used to inform more appropriate reduction targets in future biofuel policies.

Life cycle assessment of lignocellulosic ethanol: a review of key factors and methods affecting calculated GHG emissions and energy use.

Lignocellulosic ethanol has potential for lower life cycle greenhouse gas emissions compared to gasoline and conventional grain-based ethanol. Ethanol production 'pathways' need to meet economic and environmental goals. Numerous life cycle assessments of lignocellulosic ethanol have been published over the last 15 years, but gaps remain in understanding life cycle performance due to insufficient data, and model and methodological issues. We highlight key aspects of these issues, drawing on literature and a case study of corn stover ethanol. Challenges include the complexity of feedstock/ecosystems and market-mediated aspects and the short history of commercial lignocellulosic ethanol facilities, which collectively have led to uncertainty in GHG emissions estimates, and to debates on LCA methods and the role of uncertainty in decision making.