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Sub-Saharan Africa faces unique barriers to electricity development due to the large proportion of the population that is un-electrified and the prevalence of rural populations. Typically, power system expansion planning models assume all potential consumers can be immediately electrified. This assumption is unrealistic in sub-Saharan Africa, where electrification will likely be a gradual process over a number of years. Furthermore, since a large proportion of the population in sub-Saharan Africa is located in rural regions, the prioritization of these regions may impact how the grid develops. In this research, we develop a multi-period optimization model for power generation and transmission system expansion planning in sub-Saharan Africa. In contrast to existing models, which assume full electrification, we consider a variety of electrification policies and analyze the impact of varying the electrification rate and policy on the cost and resources selected for power system expansion. We test our model on a case study of Rwanda. We find that varying the year in which full electrification is reached has a larger impact on cost and generation capacity than varying the electrification policy does, although, when urban and rural regions are considered equitably, more rooftop solar is built. Varying the electrification policies has a larger impact on transmission expansion than on generation expansion and this impact is amplified when starting from zero initial system capacity rather than the original Rwanda system. Additionally, a sensitivity analysis shows that tightening the bounds on CO2eq emissions has a large impact on the generation portfolio and cost.

This paper addresses the long-term planning of electric power infrastructures considering high renewable penetration. To capture the intermittency of these sources, we propose a deterministic multi-scale Mixed-Integer Linear Programming (MILP) formulation that simultaneously considers annual generation investment decisions and hourly operational decisions. We adopt judicious approximations and aggregations to improve its tractability. Moreover, to overcome the computational challenges of treating hourly operational decisions within a monolithic multi-year planning horizon, we propose a decomposition algorithm based on Nested Benders Decomposition for multi-period MILP problems to allow the solution of larger instances. Our decomposition adapts previous nested Benders methods by handling integer and continuous state variables, although at the expense of losing its finite convergence property due to potential duality gap. We apply the proposed modeling framework to a case study in the Electric Reliability Council of Texas (ERCOT) region, and demonstrate massive computational savings from our decomposition.

Power sector capacity expansion models (CEMs) explore least-cost infrastructure trends under alternate techno-economic and policy scenarios. To maintain computational tractability when considering investment decisions over multiple decades, CEMs typically rely on a compact representation of annual hourly grid operations. Even as CEMs are being used to explore the role of variable renewable energy (RE) sources in the transition to a low-carbon grid, the required temporal resolution and operational detail in a CEM to adequately capture the fundamental economics of RE sources remains an open question. Here, we investigate the impact of embedding additional operational and temporal details in a CEM framework on the resulting projections for generation capacity additions and their utilization. Our approach is based on systematically comparing the outputs from a “chronological” CEM (C-CEM), which models annual grid operations using up to 12 representative days (288 h), with outputs from a commonly used “time slice” CEM (TS-CEM), using seasonally-averaged time blocks. The CEMs mainly differ in their representation of operational flexibility of thermal generators as well as the temporal resolution of load and RE generation. Studying the Texas grid over a range of hypothetical RE penetration scenarios, we find that, more often than not, the TS-CEM estimates higher solar capacity and lesser wind and natural gas capacity relative to the C-CEM, with 35% higher solar capacity projected by the TS-CEM in one scenario. We also test capacity projections of both CEMs through an hourly grid operations model to explore operational metrics, such as the ability to meet demand subject to intra and inter-annual variations in load and RE generation. This experiment reveals that C-CEM projections consistently lead to lower unmet demand compared to the TS-CEM capacity projections. These findings imply the need for sufficient temporal resolution and chronology or validated parameterizations that yield similar behavior to be included in power sector CEMs and multi-sector energy-economic models using a time slice representation.

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.

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.

The authors are founding team members of a new effort to develop an Open Energy Outlook for the United States. The effort aims to apply best practices of policy-focused energy system modeling, ensure transparency, build a networked community, and work toward a common purpose: examining possible US energy system futures to inform energy and climate policy efforts. Individual author biographies can be found on the project website: https://openenergyoutlook.org/. DeCarolis et al. articulate the benefits of forming collaborative teams with a wide array of disciplinary and domain expertise to conduct analysis with macro-energy system models. Open-source models, tools, and datasets underpin such efforts by enabling transparency, accessibility, and replicability among team members and with the broader modeling community.

Recent literature suggests that particle toxicity increases with decreasing particle diameter and increasing total particle surface area. Most inexpensive particle monitors are based upon light scattering and tend to lose sensitivity for particles with diameters less than about 0.3–0.35 µm. This raises the question of whether the measurement of PM2.5 “misses” the potential impact of very small particles (e.g., below 0.3 µm) due to lack of sensitivity and/or the low mass concentrations that these particles contribute to the total PM2.5. On the other hand, measuring only ultrafine particles (e.g., below 0.1 µm) would exclude significant numbers of still very small particles. The focus of simulating a novel particle monitor in this study, is to address limitations in current inexpensive particle monitors, and to realize a particle monitor that may be more relevant to adverse health outcomes by measuring both PM0.3 and PM2.5. The monitor uses optical scattering techniques, measuring light scattering by the particles at two forward angles, to determine PM0.3 and PM2.5. Experimental data from particle monitor prototypes that were developed show good agreement with simulation results. Such a monitor, that is low-cost and easy to use, can provide information directly to the users so that they can be driven to action. In particular, low-income communities that are often impacted by poor air quality will be able to more affordably determine real-time ambient conditions and drive positive change by helping to identify pollution sources and appropriate mitigation measures.

Summary Life cycle greenhouse gas (LC-GHG) emissions from electricity generated by a specific resource, such as gas and oil, are commonly reported on a country-by-country basis. Estimation of variability in LC-GHG emissions of individual power plants can, however, be particularly useful to evaluate or identify appropriate environmental policy measures. Here, we developed a regression model to predict LC-GHG emissions per kilowatt-hour (kWh) of electricity produced by individual gas- and oil-fired power plants across the world. The regression model uses power plant characteristics as predictors, including capacity, age, fuel type (fuel oil or natural gas), and technology type (single or combined cycle) of the plant. The predictive power of the model was relatively high (R2 = 81% for predictions). Fuel and technology type were identified as the most important predictors. Estimated emission factors ranged from 0.45 to 1.16 kilograms carbon dioxide equivalents per kilowatt-hour (kg CO2-eq/kWh) and were clearly different between natural gas combined cycle (0.45 to 0.57 kg CO2-eq/kWh), natural gas single cycle (0.66 to 0.85 kg CO2-eq/kWh), oil combined cycle power plants (0.63 to 0.79 kg CO2-eq/kWh), and oil single cycle (0.94 to 1.16 kg CO2-eq/kWh). Our results thus indicate that emission data averaged by fuel and technology type can be profitably used to estimate the emissions of individual plants.

Most thermoelectric power plants in the United States (U.S.) rely on fresh water for cooling, resulting in significant water consumption. An understanding of the regional and seasonal water stress impact of such water consumption is needed. In this study, we used a U.S. county-level water stress index, Available Water Remaining for the United States (AWARE-US), with facility-level thermoelectric power generation water consumption data, to quantify the regional and seasonal water stress impact of U.S. thermoelectric power plants. Water stress impact was evaluated as water-scarcity footprint (WSF) using monthly AWARE-US data. Results show that most thermoelectric power plants in the United States that use fresh water are in water-abundant regions. Our findings also show that a small fraction of the U.S. thermoelectric generation facilities (13% by power) located in water stressed regions contribute the most water stress impact (88% of the total WSF) caused by thermoelectric power generation in the U.S. Even if fresh water is abundant on an annual basis, many power plants are in counties with seasonal water shortages. Of the 401 counties with thermoelectric power plants using fresh water for cooling, 80 counties in February and 160 counties in August had fresh water withdrawal requirements that exceeded the sustainable fresh water available (availability minus demand or AMD) in that region. This means that 27% and 46% of power generation facilities have difficulty securing sustainable fresh water in February and August, respectively. This study is intended to support water consumption management in existing power plants and guide the deployment of future power plants to mitigate water stress impact.

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.

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.

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.

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.

Policies formulated to reduce greenhouse gas (GHG) emissions, such as a low-carbon fuel standard, frequently rely on life-cycle assessment (LCA) to estimate emissions, but LCA results are often highly uncertain. This study develops life-cycle models that quantitatively and qualitatively describe the uncertainty and variability in GHG emissions for both fossil fuels and ethanol and examines mechanisms to reduce those uncertainties in the policy process. Uncertainty regarding emissions from gasoline is non-negligible, with an estimated 90% confidence interval ranging from 84 to 100 g CO 2 e/MJ. Emissions from biofuels have greater uncertainty. The widths of the 90% confidence intervals for corn and switchgrass ethanol are estimated to be on the order of 100 g CO 2 e/MJ, and removing emissions from indirect land use change still leaves significant remaining uncertainty. Though an opt-in policy mechanism can reduce some uncertainty by incentivizing producers to self-report fuel production parameters, some important parameters, such as land use change emissions and nitrogen volatilization, cannot be accurately measured and self-reported. Low-carbon fuel policies should explicitly acknowledge, quantify, and incorporate uncertainty in life cycle emissions in order to more effectively achieve emissions reductions. Two complementary ways to incorporate this uncertainty in low carbon fuel policy design are presented.

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.

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.

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.

Petroleum fuels make up essentially all of the transportation fuels used today. But fossil fuel use has many economic and environmental downsides, including a weakening of our energy security due to reliance on imported energy sources, air pollution that impacts health, and greenhouse gas (GHG) emissions that contribute to climate change. To reduce fossil fuel use and GHG emissions in the transportation sector and improve energy security requires a coordinated effort to reduce travel demand, improve vehicle efficiency, and switch to cleaner, lower-carbon fuels. Here we focus on switching to new fuels and examine the potential role a national low carbon fuel standard (LCFS) can play in bringing this about.This report analyzes the costs and benefits of a national LCFS policy, together with or in place of the existing national Renewable Fuel Standard (RFS2). The companion report, National Low Carbon Fuel Standard: Policy Design Recommendations (PDR), suggests how best to design an LCFS. Both consider the possibility of an LCFS replacing or being adopted alongside RFS2.

Projections of increased domestic natural gas supply and low prices have encouraged increased natural gas utilization in the United States electricity sector. Natural gas can offset coal, likely decreasing overall greenhouse gas (GHG) emissions and other air emissions such as SO2 and NOX. Previous life cycle assessment (LCA) studies using limited system boundaries have attempted to quantify the benefit of offsetting coal use. However, these studies do not consider that relative regional fuel prices may contribute most to the choice of coal over natural gas. External incentives such as low natural gas prices compared to coal are required if natural gas is to displace coal. In this study, simplified economic dispatch models are used to determine how natural gas utilization will increase in the short-term in response to changes in natural gas prices in three US grid regions—ERCOT, MISO and PJM. The results indicate that the change in air emissions is lower than suggested by LCAs, since LCAs generally do not include the complexity of regional electricity grids. For instance, this study estimates that life cycle GHG emissions may, at best, decrease by 7‐15% due to low natural gas prices, compared to almost 50% reductions estimated by previous LCAs.

This study estimates the life cycle greenhouse gas (GHG) emissions from the production of Marcellus shale natural gas and compares its emissions with national average US natural gas emissions produced in the year 2008, prior to any significant Marcellus shale development. We estimate that the development and completion of a typical Marcellus shale well results in roughly 5500 t of carbon dioxide equivalent emissions or about 1.8 g CO2e/MJ of gas produced, assuming conservative estimates of the production lifetime of a typical well. This represents an 11% increase in GHG emissions relative to average domestic gas (excluding combustion) and a 3% increase relative to the life cycle emissions when combustion is included. The life cycle GHG emissions of Marcellus shale natural gas are estimated to be 63‐75 g CO2e/MJ of gas produced with an average of 68 g CO2e/MJ of gas produced. Marcellus shale natural gas GHG emissions are comparable to those of imported liquefied natural gas. Natural gas from the Marcellus shale has generally lower life cycle GHG emissions than coal for production of electricity in the absence of any effective carbon capture and storage processes, by 20‐50% depending upon plant efficiencies and natural gas emissions variability. There is significant uncertainty in our Marcellus shale GHG emission estimates due to eventual production volumes and variability in flaring, construction and transportation.