Drivers and implications of alternative routes to fuels decarbonization in net-zero energy systems.

Energy transition scenarios are characterized by increasing electrification and improving efficiency of energy end uses, rapid decarbonization of the electric power sector, and deployment of carbon dioxide removal (CDR) technologies to offset remaining emissions. Although hydrocarbon fuels typically decline in such scenarios, significant volumes remain in many scenarios even at the time of net-zero emissions. While scenarios rely on different approaches for decarbonizing remaining fuels, the underlying drivers for these differences are unclear. Here we develop several illustrative net-zero systems in a simple structural energy model and show that, for a given set of final energy demands, assumptions about the use of biomass and CO2 sequestration drive key differences in how emissions from remaining fuels are mitigated. Limiting one resource may increase reliance on another, implying that decisions about using or restricting resources in pursuit of net-zero objectives could have significant tradeoffs that will need to be evaluated and managed.

Environmental Aspects of Biotechnology.

A key motivation behind the development and adoption of industrial biotechnology is the reduction of negative environmental impacts. However, accurately assessing these impacts remains a formidable task. Environmental impacts of industrial biotechnology may be significant across a number of categories that include, but may not be limited to, nonrenewable resource depletion, water withdrawals and consumption, climate change, and natural land transformation/occupation. In this chapter, we highlight some key environmental issues across two broad areas: (a) processes that use biobased feedstocks and (b) industrial activity that is supported by biological processes. We also address further issues in accounting for related environmental impacts such as geographic and temporal scope, co-product management, and uncertainty and variability in impacts. Case studies relating to (a) lignocellulosic ethanol, (b) biobased plastics, and (c) enzyme use in the detergent industry are then presented, which illustrate more specific applications. Finally, emerging trends in the area of environmental impacts of biotechnology are discussed.

Discrepancies and Uncertainties in Bottom-up Gridded Inventories of Livestock Methane Emissions for the Contiguous United States.

In this analysis we used a spatially explicit, simplified bottom-up approach, based on animal inventories, feed dry matter intake, and feed intake-based emission factors to estimate county-level enteric methane emissions for cattle and manure methane emissions for cattle, swine, and poultry for the contiguous United States. Overall, this analysis yielded total livestock methane emissions (8916 Gg/yr; lower and upper 95% confidence bounds of ±19.3%) for 2012 (last census of agriculture) that are comparable to the current USEPA estimates for 2012 and to estimates from the global gridded Emission Database for Global Atmospheric Research (EDGAR) inventory. However, the spatial distribution of emissions developed in this analysis differed significantly from that of EDGAR and a recent gridded inventory based on USEPA. Combined enteric and manure methane emissions from livestock in Texas and California (highest contributors to the national total) in this study were 36% lesser and 100% greater, respectively, than estimates by EDGAR. The spatial distribution of emissions in gridded inventories (e.g., EDGAR) likely strongly impacts the conclusions of top-down approaches that use them, especially in the source attribution of resulting (posterior) emissions, and hence conclusions from such studies should be interpreted with caution.

Large-scale hydrological modeling for calculating water stress indices: implications of improved spatiotemporal resolution, surface-groundwater differentiation, and uncertainty characterization.

Physical water scarcities can be described by water stress indices. These are often determined at an annual scale and a watershed level; however, such scales mask seasonal fluctuations and spatial heterogeneity within a watershed. In order to account for this level of detail, first and foremost, water availability estimates must be improved and refined. State-of-the-art global hydrological models such as WaterGAP and UNH/GRDC have previously been unable to reliably reflect water availability at the subbasin scale. In this study, the Soil and Water Assessment Tool (SWAT) was tested as an alternative to global models, using the case study of the Mississippi watershed. While SWAT clearly outperformed the global models at the scale of a large watershed, it was judged to be unsuitable for global scale simulations due to the high calibration efforts required. The results obtained in this study show that global assessments miss out on key aspects related to upstream/downstream relations and monthly fluctuations, which are important both for the characterization of water scarcity in the Mississippi watershed and for water footprints. Especially in arid regions, where scarcity is high, these models provide unsatisfying results.

How to address data gaps in life cycle inventories: a case study on estimating CO2 emissions from coal-fired electricity plants on a global scale.

One of the major challenges in life cycle assessment (LCA) is the availability and quality of data used to develop models and to make appropriate recommendations. Approximations and assumptions are often made if appropriate data are not readily available. However, these proxies may introduce uncertainty into the results. A regression model framework may be employed to assess missing data in LCAs of products and processes. In this study, we develop such a regression-based framework to estimate CO2 emission factors associated with coal power plants in the absence of reported data. Our framework hypothesizes that emissions from coal power plants can be explained by plant-specific factors (predictors) that include steam pressure, total capacity, plant age, fuel type, and gross domestic product (GDP) per capita of the resident nations of those plants. Using reported emission data for 444 plants worldwide, plant level CO2 emission factors were fitted to the selected predictors by a multiple linear regression model and a local linear regression model. The validated models were then applied to 764 coal power plants worldwide, for which no reported data were available. Cumulatively, available reported data and our predictions together account for 74% of the total world's coal-fired power generation capacity.

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.

Implications of near-term coal power plant retirement for SO2 and NOX and life cycle GHG emissions.

Regulations monitoring SO(2), NO(X), mercury, and other metal emissions in the U.S. will likely result in coal plant retirement in the near-term. Life cycle assessment studies have previously estimated the environmental benefits of displacing coal with natural gas for electricity generation, by comparing systems that consist of individual natural gas and coal power plants. However, such system comparisons may not be appropriate to analyze impacts of coal plant retirement in existing power fleets. To meet this limitation, simplified economic dispatch models for PJM, MISO, and ERCOT regions are developed in this study to examine changes in regional power plant dispatch that occur when coal power plants are retired. These models estimate the order in which existing power plants are dispatched to meet electricity demand based on short-run marginal costs, with cheaper plants being dispatched first. Five scenarios of coal plant retirement are considered: retiring top CO(2) emitters, top NO(X) emitters, top SO(2) emitters, small and inefficient plants, and old and inefficient plants. Changes in fuel use, life cycle greenhouse gas emissions (including uncertainty), and SO(2) and NO(X) emissions are estimated. Life cycle GHG emissions were found to decrease by less than 4% in almost all scenarios modeled. In addition, changes in marginal damage costs due to SO(2), and NO(X) emissions are estimated using the county level marginal damage costs reported in the Air Pollution Emissions Experiments and Policy (APEEP) model, which are a proxy for measuring regional impacts of SO(2) and NO(X) emissions. Results suggest that location specific parameters should be considered within environmental policy frameworks targeting coal plant retirement, to account for regional variability in the benefits of reducing the impact of SO(2) and NO(X) emissions.

Uncertainty in life cycle greenhouse gas emissions from United States natural gas end-uses and its effects on policy.

Increasing concerns about greenhouse gas (GHG) emissions in the United States have spurred interest in alternate low carbon fuel sources, such as natural gas. Life cycle assessment (LCA) methods can be used to estimate potential emissions reductions through the use of such fuels. Some recent policies have used the results of LCAs to encourage the use of low carbon fuels to meet future energy demands in the U.S., without, however, acknowledging and addressing the uncertainty and variability prevalent in LCA. Natural gas is a particularly interesting fuel since it can be used to meet various energy demands, for example, as a transportation fuel or in power generation. Estimating the magnitudes and likelihoods of achieving emissions reductions from competing end-uses of natural gas using LCA offers one way to examine optimal strategies of natural gas resource allocation, given that its availability is likely to be limited in the future. In this study, the uncertainty in life cycle GHG emissions of natural gas (domestic and imported) consumed in the U.S. was estimated using probabilistic modeling methods. Monte Carlo simulations are performed to obtain sample distributions representing life cycle GHG emissions from the use of 1 MJ of domestic natural gas and imported LNG. Life cycle GHG emissions per energy unit of average natural gas consumed in the U.S were found to range between -8 and 9% of the mean value of 66 g CO(2)e/MJ. The probabilities of achieving emissions reductions by using natural gas for transportation and power generation, as a substitute for incumbent fuels such as gasoline, diesel, and coal were estimated. The use of natural gas for power generation instead of coal was found to have the highest and most likely emissions reductions (almost a 100% probability of achieving reductions of 60 g CO(2)e/MJ of natural gas used), while there is a 10-35% probability of the emissions from natural gas being higher than the incumbent if it were used as a transportation fuel. This likelihood of an increase in GHG emissions is indicative of the potential failure of a climate policy targeting reductions in GHG emissions.

Uncertainty analysis of life cycle greenhouse gas emissions from petroleum-based fuels and impacts on low carbon fuel policies.

The climate change impacts of U.S. petroleum-based fuels consumption have contributed to the development of legislation supporting the introduction of low carbon alternatives, such as biofuels. However, the potential greenhouse gas (GHG) emissions reductions estimated for these policies using life cycle assessment methods are predominantly based on deterministic approaches that do not account for any uncertainty in outcomes. This may lead to unreliable and expensive decision making. In this study, the uncertainty in life cycle GHG emissions associated with petroleum-based fuels consumed in the U.S. is determined using a process-based framework and statistical modeling methods. Probability distributions fitted to available data were used to represent uncertain parameters in the life cycle model. Where data were not readily available, a partial least-squares (PLS) regression model based on existing data was developed. This was used in conjunction with probability mixture models to select appropriate distributions for specific life cycle stages. Finally, a Monte Carlo simulation was performed to generate sample output distributions. As an example of results from using these methods, the uncertainty range in life cycle GHG emissions from gasoline was shown to be 13%-higher than the typical 10% minimum emissions reductions targets specified by low carbon fuel policies.