Biofuel mandates can impact the environment in multiple ways that may be positive or negative, including affecting life-cycle greenhouse gas (GHG) emissions by displacing fossil fuels, affecting soil carbon stocks due to accompanying land use change, and water quality due to changes in fertilizer requirements and the mix of crops used as feedstocks. To achieve desired environmental outcomes in the presence of a biofuel mandate, additional policy instruments must be adopted to supplement the mandate. We develop an integrated and spatially explicit ecosystem-economic modeling framework to analyze the cost-effectiveness of alternative policies to achieve desired targets for GHG emissions reduction from the agricultural and fuel sectors in the USA and nitrate leaching reduction in the Gulf of Mexico below the levels that would be achieved by a corn ethanol and/or a cellulosic ethanol mandate in the USA. We find that while a corn ethanol mandate lowers GHG emissions, it increases nitrate leaching due to the expansion of corn production; a cellulosic ethanol mandate lowers both GHG emissions and nitrate leaching relative to a corn ethanol mandate, but the additional carbon and nitrate prices are needed to achieve anticipated GHG reduction and nitrate reduction targets. We also find that accompanying a biofuel mandate with a GHG reduction target alone leads to substantial nitrate reduction co-benefits, but a nitrate reduction target alone is less effective in reducing GHG emissions. Combining a GHG standard with a nitrate standard can achieve GHG and nitrate reduction targets at lower carbon and nitrate prices as compared to implementing each of these policies independently. Our findings show that disregarding policy co-benefits can overestimate the GHG and nitrate prices needed to achieve policy targets and higher policy costs.

We investigate the impact of industrial facility relocation on the distribution of pollution incidence across different socioeconomic groups in the US. While facilities that relocated reduced their emissions by 68

Abstract Declining costs of photovoltaic (PV) technology and rising market and policy incentives are leading to the growing deployment of PV on cropland in the US Midwest, leading to concerns about the displacement of food and feed crop production. Agrivoltaic (AV) technology enables the dual use of land by co-locating PV energy and crop production, potentially reducing land-use competition with crop production. We develop a benefit-cost analysis framework to compare the net economic returns from AV to those with stand-alone PV and crop production on a representative field and show conditions under which AV can be more profitable for both a solar developer and a farmer. We integrate it with a crop and solar energy model to simulate the performance of various field designs and space and height configurations in AV systems to accommodate soybean production with conventional farm equipment under representative conditions in the US Midwest. We find that an AV system with soybean production is less profitable than PV alone for a solar developer due to the high capital costs of raising panel height, and less profitable for a farmer than leasing land for PV due to its adverse effects of shading on crop yield. We discuss the changes in technology and market prices of solar energy and soybeans that are necessary to make the AV system profitable for solar developers and farmers. We show that AV can worsen rather than mitigate the conflict between food crops and solar energy production in the Midwest.

Perennial bioenergy crops, such as miscanthus and switchgrass, and crop residues have the potential to scale up sustainable aviation fuel (SAF) production and mitigate carbon emissions. However, high establishment costs, delayed returns, and risk-return profiles that diverge from those of conventional crops can hinder incentives to adopt bioenergy crops. We develop an economic model that incorporates spatially varying joint yield and price distributions for the multiple crop choices a farmer faces and apply it to examine the incentives for risk-averse, present-biased, and credit-constrained farmers to produce cellulosic feedstocks under various biomass prices. We link this model to a biogeochemical model to quantify the spatially varying carbon mitigation benefits from these feedstocks in the rainfed region of the United States. We also analyze the cost-effectiveness of two carbon payment policies: annual and upfront. We find that risk-averse, present-biased, or credit-constrained farmers prefer to grow the lower-yielding but less risky switchgrass and harvest corn stover instead of the lower carbon, higher-yielding but riskier feedstock miscanthus, resulting in lower SAF production. Upfront carbon payments incentivize higher quantities of less carbon-intensive SAF production by risk-averse, credit-constrained, and present-biased farmers because they offset a part of the establishment costs of miscanthus. We also find that when farmers are credit-constrained, upfront payments are more cost-effective in terms of carbon mitigation per dollar spent. In contrast, annual payments are more cost-effective when farmers can access credit.

Achieving aerospace industry net-zero emissions by 2050 requires rapid scaling of sustainable aviation fuel (SAF) production. Leveraging existing infrastructure, proven technologies like Alcohol-to-Jet (ATJ), and low carbon intensity (CI) feedstocks (e.g., switchgrass and miscanthus) can support this transition and help achieve near-term emissions reduction targets. This study evaluates the implications of lignocellulosic ethanol biorefinery siting and integration with petroleum refineries to produce SAF across 1000 sites randomly sampled from areas suitable for perennial grasses in the U.S. rainfed region. To better understand the logistics of material transport and handoffs, we integrated models of biomass harvest, transport, ethanol, and ATJ production in a stochastic framework based on Monte Carlo simulations to characterize SAF minimum selling price (MSP) and carbon intensity (CI), considering site-specific parameters (e.g., feedstock production, transportation, taxes, incentives). The results indicate trade-offs between MSP and CI across locations, with median MSP ranging from 7.9 to 12.8 USD·gal-1 and CI from -9.7 to 39.4 gCO2e·MJ-1. Despite high estimated decarbonization costs (580 USD·tonCO2e-1), our results indicate that site-specific deployment of ATJ with low-CI feedstocks can improve sustainability outcomes. The framework provides a systematic approach to assess cost and sustainability trade-offs across locations, considering the end-to-end supply chain and supporting an informed investment in SAF production.

Agricultural land holds tremendous potential to contribute to net zero greenhouse gas emission goals by providing low carbon renewable energy to displace fossil fuels and by serving as a sink for sequestering carbon in the soil with climate-smart practices. This potential is, however, far from being realized. This paper examines the economic incentives and barriers to implementing land-based carbon mitigation strategies and discusses the specific features of land-based carbon mitigation practices on carbon emissions that need to be considered in designing policy incentives to induce adoption. Although a carbon price-based policy is socially efficient, the more commonly observed policies to promote land-based carbon mitigation include practice-based conservation programs, technology mandates, and sector-specific standards. The paper discusses the rationale for these alternative policy approaches and concludes with a discussion of emerging opportunities for designing policy and market-based approaches for promoting land-based carbon-mitigation and future directions for economics research.

Agrivoltaics, combining agriculture with photovoltaic systems, offers a promising solution to address land-use conflict between food and energy production. However, the complexities of agrivoltaics and its effects on the water-energy-carbon interactions remain poorly understood. In this study, we developed a process-based agrivoltaic model within the Community Land model 5 to assess the impacts of agrivoltaics on water, energy, and carbon cycles. The model was validated using data from agrivoltaic sites in Illinois and Colorado, generally capturing spatiotemporal variations in light conditions, soil moisture, and biomass carbon. Simulation results suggest that agrivoltaics significantly impact water, energy, and carbon budgets at the patch and system levels for maize and soybean in Illinois and grass in Colorado (2000-2014). Our findings show that the impacts of agrivoltaics vary by climate conditions and plant types. In dry climates, rainfall redistribution and shading from agrivoltaics conserve soil moisture and enhance evapotranspiration, promoting greater carbon assimilation and soil carbon storage for C3 grass. Conversely, in wetter regions, reduced solar radiation from shading becomes the dominant factor, lowering carbon assimilation and sequestration for maize and soybean. These results suggest that agrivoltaics can help mitigate drought impacts in arid environments. Our analysis of land equivalent ratios across different photovoltaic ground coverage ratios (PV GCR) shows that a medium PV GCR (60%) under "AgPV" deployment, where PV and plants share the same land, maximizes land-use efficiency at the study sites. Our modeling study supports informed decision-making to promote sustainable management of water, energy, and food resources amid environmental change.

Increasing global demands for food and energy necessitate innovative land-use solutions. Agrivoltaics, colocating solar photovoltaics with agriculture, shows promise, but its widespread adoption faces complex biophysical and economic trade-offs in a changing climate. Here, we develop an integrated biophysical-economic modeling framework to quantify how agrivoltaics affect biophysical and economic impacts across the Midwestern United States under both current and project climate conditions. We find strong regional divergences driven by climate gradients. In the humid eastern Midwest, solar panel shading limits photosynthesis, leading to reduced yields (maize-24%; soybean-16%) and lower farmers' profitability (maize-16%; soybean-2%) compared to conventional agriculture. Conversely, in the semiarid western region, shading alleviates heat and water stress, moderating yield reductions for maize (-12%) and even boosting soybean yields (+6%), resulting in improved economic returns (-6% for maize; +9% for soybean), for a scenario with 33% photovoltaic ground coverage ratio. Although agrivoltaics generate substantial electrical energy across all regions, high upfront installation costs challenge solar developers compared to standalone solar photovoltaics. However, our analysis identifies "win-win" opportunities where soybean-based agrivoltaics in the semiarid region produce economic benefits for both farmers and solar developers, highlighting the necessity for region-specific designs tailored to local climate conditions. Critically, future climate projections indicate eastward expansion of semiarid conditions, broadening areas where agrivoltaics can mitigate crop yield penalties (even boosting yield) and improve overall profitability, especially under high-emission scenarios. The results provide a mechanistic and economically integrated understanding essential for developing evidence-based and region-specific strategies to scale agrivoltaics in a changing climate.

Abstract The discovery of the DNA and the enhanced capacity to manage living organisms, combined with the challenges of climate change, food security, and rural development, led to the emergence of the concept of the bioeconomy. A broad definition of the bioeconomy is a sector of the economy that relies on living organisms and biological processes and services. The definition of the bioeconomy varies among nations reflecting different emphases and capabilities. The bioeconomy consists of multiple sectors and is evolving over time. This book is divided into three main segments. The first provides diverse perspectives on the bioeconomy, reflecting different geographic and disciplinary points of view. The second focuses on specific sectors and approaches within the bioeconomy and their development. The third presents several studies on emerging bioeconomy sectors in Latin America and other continents.

We quantify the impact of soybean oil-based biodiesel production on US cropland, using a method that accounts for the intermediate effect of soybean crushing facilities. Based on U.S. Environmental Protection Agency data for biodiesel production and proprietary data for soybean crushing facilities over 2011-2020, we find that the elasticities of soybean acreage and total cropland acreage with respect to soybean oil-based biodiesel production are 0.011 and 0.002, respectively. The direct land-use effect of soybean oil-based biodiesel is about 0.96 million acres of cropland expansion per billion gallons, about twice as high as some estimates for corn ethanol from previous studies.

The rapid expansion of solar energy on US farmland faces land-use conflicts and community opposition. Agrivoltaics is proposed as a middle-ground solution, but which factors will influence a solar developer’s decision to adopt agrivoltaics remain under-explored. We examined factors that will influence a developer’s decision to adopt agrivoltaics as well as the current status and future interests in agrivoltaics development in the US. Our findings show that reducing community opposition to large scale solar energy projects is the strongest driver of agrivoltaics adoption, carrying more than a 25% weight in their decisions. At the same time, developers face challenges such as coordination difficulties, a lack of regulatory assurances, and perceived risks associated with novel dual-use technology. Despite such challenges, we found that solar developers are building several agrivoltaics projects across the country and are interested in developing more in the future. Addressing barriers related to limited policy incentives, regulatory uncertainty, coordination challenges, and perceived risks will be critical to accelerating agrivoltaics development in the US.

Ecosystem models are increasingly central to the decision-making for environmental policy, conservation planning, and climate-related investments. Yet, the growing reliance on Multi-Model Ensembles (MMEs) of ecosystem models by practitioners and policymakers, sometimes under tight timelines and imperfect information, has frequently outpaced the scientific rigor required to ensure ensemble reliability. Here, MMEs refer to approaches that combine targeted predictions from multiple models with the expectation of improving robustness and quantifying predictive uncertainty. Poorly designed MMEs may create a false sense of confidence and lead to suboptimal policy and market decisions. This perspective argues that robust decision-making-relevant MMEs must be grounded on two pillars: (1) rigorous Model Intercomparison Projects (MIPs), which identify inter-model agreement and disagreement, characterize model uncertainties, and evaluate robustness with observationally based benchmarks-MIPs' diagnostic evaluation is so critical that it must be needed to drive MME's decision in model selection and weighting, especially when only a limited number of models available; and (2) co-design by both stakeholders and scientists to ensure that scenarios, metrics and uncertainty requirements provide decision-relevant information. Building upon the past success and lessons from the existing MIPs-MMEs efforts (e.g., climate/Earth system/crop), we derived the theoretical basis for MMEs, addressed their specific challenges in ecosystem modeling, and highlighted proper consideration of model numbers and diversity, risk of model inter-dependence, effective calibration of model parameters, possible overdue of some ecosystem model development, critical roles of open benchmark data across a wide range of conditions, and suggested use of Artificial Intelligence to support MIPs-MMEs. We highlighted the under-recognized opportunity for MIPs and MMEs to drive scientific progress and innovation through identifying better performing models, systematic benchmarking, feedback loops, and targeted model improvement. By following actionable best practice guidelines, MMEs can evolve from ad hoc aggregation of models into a trusted backbone of environmental policy and decision-making.

ABSTRACT The capacity to produce switchgrass efficiently and cost‐effectively across diverse environments can be pivotal in achieving the short‐ and medium‐term Sustainable Aviation Fuel targets set by the U.S. Department of Energy. This study evaluated the economic performance of forage‐ and bioenergy‐type switchgrass cultivars and their response to N fertilization under diverse marginal environments across the US Midwest that included Illinois (IL), Iowa (IA), Nebraska (NE), and South Dakota (SD). Data Envelopment Analysis (DEA) was used to evaluate the efficiency of 23 Decision‐Making Units (DMUs)—cultivar types and N fertilization rate combinations—while a cost–benefit analysis calculated their profitability over 5 years. Results showed that two energy‐type cultivars—“Independence” and “Liberty”—were superior economically to the forage cultivars. Independence performed best with the highest profit margin when fertilized at 56 kg N ha−1, particularly in the US hardiness zone 6a (Urbana, IL). Liberty exhibited the highest profit margins in hardiness zone 5b (Madrid, IA, and Ithaca, NE) at 56 kg N ha−1 and showed exceptional profitability with 28 kg N ha−1 in hardiness zone 6b (Brighton, IL). Switchgrass cultivar “Carthage” showed better efficiency score and profitability results in hardiness zone 4b (South Shore, SD) at 56 kg N ha−1. The profit trends observed in current study sites may indicate broader patterns across similar US hardiness zones. This study provides valuable insights for decision‐makers to optimize input strategies for biomass production of bioenergy switchgrass to meet renewable energy demands.

Energy crops will be critical for scaling up production of Sustainable Aviation Fuel in the United States and reducing greenhouse gas emissions. Here we examine the economic incentives for the extent and type of land conversion needed to scale up fuel production from a mix of cellulosic feedstocks and quantify its greenhouse gas intensity. We show that even with the availability of marginal non-cropland, there will be incentives for converting cropland to produce energy crops as the price of sustainable aviation fuel increases. But contrary to expectations, we find that scaling up fuel production by converting more cropland and more non-cropland from existing uses to energy crops lowers its net greenhouse gas intensity, due to high soil carbon sequestration rate of energy crops, even after considering land use change emissions.The potential savings in emissions are larger than the foregone soil carbon accumulation benefits from keeping that land in current uses.

Low carbon fuel policies such as the U.S. Renewable Fuel Standard (RFS), Canada Clean Fuel Regulations (CFR), and California Low Carbon Fuel Standard (LCFS) as well as the 45Z tax credit are intended to reduce greenhouse gas (GHG) emissions from transportation. Cellulosic feedstocks, optimized biorefineries, and favorable farming locations can significantly reduce biofuel carbon intensity (CI). Despite advances in field-to-fuel GHG monitoring and flexibility in resource allocation within biorefineries (e.g., governing net electricity production), rigid CI accounting procedures in current policies may limit CI responsiveness across candidate sites and processing facilities. This work examines a hypothetical biomass-to-sustainable aviation fuel (SAF) pathway using miscanthus and alcohol-to-jet (i) to demonstrate how GHG accounting requirements drive estimates of biofuel CIs and (ii) to explore potential CI and financial implications of scenario-specific life cycle assessment (LCA). Results demonstrate GHG accounting using the CFR/LCFS can reasonably account for distinct levels of net electricity production by a biorefinery, but only the CFR yields similar CI sensitivity to spatially explicit factors (feedstock CI, grid electricity CI) as scenario-specific LCA: most GHG accounting frameworks do not capture CI variation across candidate sites in the United States. Ultimately, this work demonstrates the importance of LCA methodological specifications in low carbon fuel policies and tax credits.

A policy that relies on farm-specific carbon-intensity scores can promote climate-smart agricultural practices.

Agrivoltaics (AV) systems create consistent shading throughout the crop growth cycle, making it essential to understand how these patterns affect crop growth and development. If shading leads to a yield penalty, identifying the specific yield components involved is key to optimizing management practices for sustainable co-production of electricity and crops. This study investigated how sorghum and soybean respond to shading within AV systems, with a specific focus on identifying the key yield components and grain yield affected by shading. Canopy biomass, grain yield, and yield components (grain number and weight) of sorghum (Sorghum bicolor) and soybean (Glycine max) grown under full sun conditions and photovoltaic (PV) panel shadings were compared. Source-sink manipulation was achieved through defoliation and de-graining in both sorghum and soybean to determine the key grain yield components affected by shading in the AV system. The yield penalty from PV shading was pronounced in soybean, whereas it was minor in sorghum. Enhancing sink size (i.e., grain number) in both crops was the key factor for minimizing the yield penalty caused by shading. Management practices after anthesis would be different for sorghum and soybean in AV systems. Increasing assimilates in sorghum during grain filling may help offset yield penalties by boosting grain weight. For soybeans, the focus should be on avoiding resource limitations during post-anthesis, as increased assimilates had a limited impact on grain weight. Both crops integrated with PV electricity generation increased the Land Equivalent Ratio (LER: 1.54 for sorghum and 1.23 for soybean) in AV systems. AV systems improved land use efficiency despite reduced crop yields due to shading, demonstrating their potential for sustainable food and energy co-production.

Cellulosic biomass-based sustainable aviation fuels (SAFs) can be produced from various feedstocks. The breakeven price and carbon intensity of these feedstock-to-SAF pathways are likely to differ across feedstocks and across spatial locations due to differences in feedstock attributes, productivity, opportunity costs of land for feedstock production, soil carbon effects, and feedstock composition. We integrate feedstock to fuel supply chain economics and life-cycle carbon accounting using the same system boundary to quantify and compare the spatially varying greenhouse gas (GHG) intensities and costs of GHG abatement with SAFs derived from four feedstocks (switchgrass, miscanthus, energy sorghum, and corn stover) at 4 km resolution across the U.S. rainfed region. We show that the optimal feedstock for each location differs depending on whether the incentive is to lower breakeven price, carbon intensity, or cost of carbon abatement with biomass or to have high biomass production per unit land. The cost of abating GHG emissions with SAF ranges from $181 Mg-1 CO(2)e to more than $444 Mg-1 CO(2)e and is lowest with miscanthus in the Midwest, switchgrass in the south, and energy sorghum in a relatively small region in the Great Plains. While corn stover-based SAF has the lowest breakeven price per gallon, it has the highest cost of abatement due to its relatively high GHG intensity. Our findings imply that different types of policies, such as volumetric targets, tax credits, and low carbon fuel standards, will differ in the mix of feedstocks they incentivize and locations where they are produced in the U.S. rainfed region.

Bird biodiversity in the United States is declining at alarming rates. Despite concerns about the link between climate change and the decline in bird biodiversity, there is limited understanding of the heterogeneous effects of climate change across species and regions and the extent to which these effects persist over time. Using a long-term dataset of the North American bird population from 1980 to 2015, we find statistically significant and robust evidence that an unconditional one-standard-deviation increase in the days above 25 °C (currently 7.8 days in a year but projected to exceed 28 days by the century’s end) decreases bird abundance and species richness by 2.5% and 1.7%, respectively; these effects are more pronounced for specialist birds (4.9% and 2.9%), long-distance migrant specialist species (5.2% and 3.2%), and bird populations in the drier areas, such as the West (7.0% and 2.5%). Additionally, we find no evidence of a diminishing impact of high temperatures on bird biodiversity over this period. Projecting forward to the end of this century, our models suggest that, depending on the extent of warming, the abundance and species richness of specialist birds could decline by 7%-16% and 4%-9%, respectively, relative to current levels. Though less pronounced, a statistically significant decline of 1–3% is also projected for generalist bird populations.