Energy Technologies

Biofuels have been criticized for raising food prices and reducing food production. While biofuels have rightly been blamed for contributing to reduced food security at a time of record-high food prices in 2008, they have not been credited with reducing the cost of gasoline, also at a time of record-high prices. We discuss the food-versus-biofuel trade-off associated with biofuel production and model the effects of biofuel production in markets for key crops and gasoline, showing that food consumers lose from biofuels but gasoline consumers enjoy substantial benefits. We also suggest ways to address the food-versus-biofuel debate.

The United States Energy Information Administration publishes annual forecasts of nationally aggregated energy consumption, production, prices, intensity and GDP. These government issued forecasts often serve as reference cases in the calibration of simulation and econometric models, which climate and energy policy are based on. This study tests for rationality of published EIA forecasts under symmetric and asymmetric loss. We find strong empirical evidence of asymmetric loss for oil, coal and gas prices as well as natural gas consumption, GDP and energy intensity.

This paper provides comparisons of a variety of time series methods for short run forecasts of the main greenhouse gas, carbon dioxide, for the United States, using a recently released state level data set from 1960-2001. We test the out-of-sample performance of univariate and multivariate forecasting models by aggregating state level forecasts versus forecasting the aggregate directly. We find evidence that forecasting the disaggregate series and accounting for spatial effects drastically improves forecasting performance under Root Mean Squared Forecast Error Loss. Based on the in-sample observations we attempt to explain the emergence of voluntary efforts by states to reduce greenhouse gas emissions. We find evidence that states with decreasing per capita emissions and a "greener" median voter are more likely to push towards voluntary cutbacks in emissions.

Concern about the possible affects of biofuels on deforestation have led to assigning biofuel producers with the responsibility for greenhouse gas (GHG) emissions of the indirect land use changes (ILUC) associated with their activities when assessing their compliance with biofuel policies. We show that the computation of the ILUC is shrouded with uncertainty; they vary frequently, and are strongly affected by policy choices. It seems that its overall impact on GHGs is relatively minor. Once the ILUCs are introduced other indirect effects of biofuel may need to be considered which will increase the cost of biofuel regulations. Concentrating on direct regulation of biofuel and on efforts to reduce deforestation, wherever it occurs, may be more effective than debating and refining the ILUC.

California is the destination for over one-quarter of immigrants to the United States, and immigrants now make up over one-quarter of the state's population. To ensure that transportation systems and services adequately meet the needs of recent immigrants, planners need a firm understanding of the travel behavior of immigrant groups. This paper reports on key findings from a three-phased study:(1)analysisofdataoncommutetravelofCaliforniaimmigrants from the 1980, 1990, and 2000 Censuses; (2) focus groups with recent Mexican immigrants on their transportation experiences and needs in six California regions; and (3) interviews with community-based organizations in nine California regions on the transportation needs and wants of Mexican immigrants. These findings point to a long list of potential strategies for agencies and organizations to consider in efforts to more effectively meet the transportation needs of Mexican and other immigrants in California.

The vast capabilities of autonomous underwater vehicles (AUVs)—such as in assisting scientific research, conducting military tasks, and repairing oil pipelines—are limited by high operating costs and the relative inaccessibility of power in the open ocean. Wave powered AUV charging stations may address these issues. With projected increases in usage of AUVs globally in the next five years, AUV charging stations can enable less expensive and longer AUV missions. This paper summarizes the design process and investigates the feasibility of a wave powered, mobile AUV charging station, including the choice of a wave energy converter and AUV docking station as well as the ability to integrate the charging station with an autonomous surface vehicle. The charging station proposed in this paper meets many different commercial, scientific, and defense needs, including continuous power availability, data transmission capabilities, and mobility. It will be positioned as a hub for AUV operations, enabling missions to run autonomously with no support ship. The potential market for this design is very promising, with an estimated $1.64 million market size just for AUV technologies by 2025.

Effective policymaking to achieve net zero greenhouse gas emissions demands an understanding of the complex drivers of, and barriers to, consumer adoption behavior via behaviorally realistic energy system models. Existing models tend to oversimplify by focusing on homogenized financial factors while neglecting consumer heterogeneity and non-monetary influences. This study develops and applies a comprehensive framework for evaluating the behavioral realism of consumer adoption models, informed by the adoption literature. It introduces a typology for factors influencing low-carbon technology adoption decisions: monetary and nonmonetary factors relating to household characteristics, psychology, technological attributes, and contextual conditions. Next, reviews of the consumer adoption and decision-making literature identify the most influential adoption factor categories for distributed solar photovoltaics, electric vehicles, and air-source heat pumps. Finally, the extent to which a selection of energy system models accounts for these adoption factors is assessed. Existing models predominantly emphasize the economic aspects of technology, which are generally identified as the most important factors. Where the models fall short — in considering moderately important factor categories — sector-specific and agent-based models can offer more behaviorally realistic insights. This study sheds light on which types of factors are most important for consumer adoption decisions and investigates how well current models rise to the challenge of behavioral realism. The end-to-end analysis presented enables internally consistent comparisons across models and energy technologies. This research advances timely conversations on consumer adoption. It could inform more behaviorally realistic energy system modeling, and thereby more effective decarbonization policymaking.

Hydrogen is pivotal in the transition to sustainable energy systems, playing major roles in power generation and industrial applications. Metal–organic frameworks (MOFs) have emerged as promising mediums for efficient hydrogen storage. However, identifying potential candidates for deployment is challenging due to the vast number of currently available synthesized MOFs. This study integrates molecular simulations, machine learning, and techno-economic analysis to evaluate the performance of MOFs across broad operation conditions for hydrogen storage applications. While previous screenings of MOF databases have predominantly emphasized high hydrogen capacities under cryogenic conditions, this study reveals that optimal temperatures and pressures for cost minimization depend on the raw price of the MOF. Specifically, when MOFs are priced at $15/kg, among the 9720 MOFs tested, 9692 MOFs achieve the lowest cost at temperatures between 170 K and 250 K and a pressure of 150 bar. Under these optimal conditions, 362 MOFs deliver a lower levelized cost of storage than 350 bar compressed gas hydrogen storage. Furthermore, this study reveals key material properties that result in low system cost, such as high surface areas (>3000 m2/g), large void fractions (>0.78), and large pore volumes (>1.1 cm3/g).

Growing the bioeconomy requires products and pathways that are cost-competitive. Technoeconomic analyses (TEAs) aim to predict the long-term economic viability and often use what are known as n^th plant cost and performance parameters. However, as TEA is more widely adopted to inform everything from early-stage research to company and investor decision-making, the n^th plant approach is inadequate and risks being misused to inform the early stages of scale-up. Some methods exist for conducting first-of-a-kind/pioneer plant cost analyses, but these receive less attention and have not been critically evaluated. This article explores TEA methods for early-stage scale-up, critically evaluates their applicability to biofuels and bioproducts, and recommends strategies for producing TEA results better suited to guiding prioritization and successful scale-up of bioprocesses.

Increasing wind energy generation is central to grid decarbonization, yet methods to estimate wind energy potential are not standardized, leading to inconsistencies and even skewed results. This study aims to improve the fidelity of wind energy potential estimates through an approach that integrates geospatial analysis and machine learning (i.e., Gaussian process regression). We demonstrate this approach to assess the spatial distribution of wind energy capacity potential in the Contiguous United States (CONUS). We find that the capacity-based power density ranges from 1.70 MW/km2 (25th percentile) to 3.88 MW/km2 (75th percentile) for existing wind farms in the CONUS. The value is lower in agricultural areas (2.73 ± 0.02 MW/km2, mean ± 95 % confidence interval) and higher in other land cover types (3.30± 0.03 MW/km2). Notably, advancements in turbine manufacturing could reduce power density in areas with lower wind speeds by adopting low specific-power turbines, but improve power density in areas with higher wind speeds (>8.35 m/s at 120m above the ground), highlighting opportunities for repowering existing wind farms. Wind energy potential is shaped by wind resource quality and is regionally characterized by land cover and physical conditions, revealing significant capacity potential in the Great Plains and Upper Texas. The results indicate that areas previously identified as hot spots using existing approaches (e.g., the west of the Rocky Mountains) may have a limited capacity potential due to low wind resource quality. Improvements in methodology and capacity potential estimates in this study could serve as a new basis for future energy systems analysis and planning.