Search

Abstract Transforming the agri-food system from a “take-make-waste”, or linear production system, to a circular bioeconomy that reduces, recycles, recovers, reuses, and regenerates wastes and transitions from fossil to biobased fuels and products is being hailed as critical for meeting a growing population’s food and fuel needs in environmentally sustainable ways. While a transformation towards a circular bioeconomy is an appealing strategy to achieve multiple environmental goals, we argue that this strategy needs to go beyond a techno-centric focus and adopt an economic value-based lens to balance the desire for circularity with its costs, benefits, and distributional effects on society. This perspective analyzes the mechanisms that sustain the existing linear economy and proposes a novel social cost-benefit framework to determine the optimal level and path to circularity. We present five critical pathways to achieve a sustainable circular bioeconomy in a market economy consisting of decentralized decision-makers.

Societal Impact Statement Transformative agricultural strategies like agrivoltaics (AV) are essential for addressing the pressing global issues of sustainable energy and food production in a changing climate. Conservation‐agrivoltaics (Conservation‐AV) provides the potential to meet these needs while reinforcing natural resources and protecting the environment. It could enhance the ecological benefits of AV by improving soil health and biodiversity. It could create economic opportunities for farmers and increase the resilience and diversity of food crops under changing climate conditions. Furthermore, it could inform stakeholders about the benefits and challenges of implementing conservation agriculture management practices (CAMP) in AV and encourage further exploration and adoption of this innovative approach. Summary Transformative strategies in agriculture are needed to address urgent global challenges related to energy and food production while reinforcing natural resources and the environment. Agrivoltaics (AV) has emerged in the past decade as one solution to this fundamental challenge of improving energy and food security. AV is defined as the co‐location of solar photovoltaic (PV) panels and crops on the same land to optimize food and energy production simultaneously and sustainably. Here, we propose that AV, together with conservation agriculture management practices (CAMP) strategies can help to intensify food security and energy production while reinforcing natural resources and the environment. Our main assertions in this opinion article are that: (1) AV systems need to overcome several agronomical, environmental, and ecological challenges to intensify food and energy production sustainably; (2) CAMP applied to AV systems can preserve the environment and ensure climate‐resilient food production; (3) implementation of CAMP in AV can lead to long‐term carbon sequestration, lower greenhouse gas emissions, and maintain or increase crop yields while preserving soil health and biodiversity; and (4) adoption of CAMP in AV can bring economic benefits, although challenges need to be overcome. This opinion article proposes a new ecosystem approach to integrate renewable energy and sustainable food production and encourages research on the effects of CAMP on AV systems.

Abstract Determining optimal management practices for the profitable production of perennial energy crops is critical for scaling up production beyond experimental levels. Although many experimental field studies have examined the effects of management practices on the performance of miscanthus and switchgrass, there are no recommendations for economically optimal nitrogen (N) application rates and how they should vary spatially and with the age of the energy crop as well as on optimal rotation age of the energy crop to maximize profits. We develop a modeling framework to determine economically optimal crop management decisions and simulate the variability under various scenarios for miscanthus and switchgrass production across 2287 counties in the rainfed United States. We find that profit‐maximizing N recommendations for these crops vary across maturity stages and regions and can increase the landowner's profits compared with a uniform N rate across ages and regions. We also find that the optimal rotation for these crops is shorter than the productive physical lifespan (15–20 and 10 years for miscanthus and switchgrass, respectively). Specifically, the N rate that maximizes the economic returns is negligible for miscanthus and 111 kg ha−1 for switchgrass production at age 2. The mean profit‐maximizing N rate increases with age for miscanthus, peaking at 151 kg ha−1 at age 11 before declining to 114 kg ha−1 at the optimal rotation age of 13 years while that for switchgrass is 150 kg ha−1 for middle‐aged stands and declines to 114 kg ha−1 at the optimal rotation of 8–9 years. We find that miscanthus is the most profitable energy crop in the northern region of the rainfed United States while switchgrass is most profitable in the south of the rainfed United States. Our findings are useful for improving assessments of the profitability of energy crops and guiding future management decisions by landowners.

Dengue virus (DENV) infection continues to be a public health challenge, lacking a specific cure. Vaccination remains the primary strategy against dengue; however, existing live-attenuated vaccines display variable efficacy across four serotypes, influenced by host serostatus and age, and predominantly inducing humoral responses. To address this limitation, this study investigates a multiepitope-based immunogen designed to induce robust cellular immunity across all DENV serotypes. The chimeric immunogen integrates H-2d specific MHC-I binding T-cell epitopes derived from conserved domains within the DENV envelope protein. Immuno-informatics analyses supported its stability, non-allergenic nature, and strong MHC-I binding affinity as an antigen. To assess the immunogenicity of the multiepitope, it was expressed in murine bone-marrow-derived dendritic cells (BMDCs) that were used to prime mice. In this experimental model, simultaneous exposure to T-cell epitopes from all four DENV serotypes initiated distinct IFNγ-CD8 T-cell responses for different serotypes. These results supported the potential of the multiepitope construct as a vaccine candidate. While the optimization of the immunogen design remains a continuous pursuit, this proof-of-concept study provides a starting point for evaluating its protective efficacy against dengue infection in vivo. Moreover, our results support the development of a multiepitope vaccine that could trigger a pan-serotype anti-dengue CD8 response.

Abstract Optimal management of the perennial bioenergy crops, miscanthus and switchgrass, requires an understanding of their responsiveness to nitrogen (N) fertilizer at different maturity stages across locations and growing conditions. Earlier studies that have examined the yield response of these crops to N and stand age using field experiments or meta‐analysis techniques provide mixed evidence. We extend earlier studies by applying a multi‐level mixed‐effects (MLME) meta‐regression model to conduct a more extensive multivariate regression of yield response of these crops to N and stand age, while controlling for climate and location conditions and unobserved factors related to study design. Our findings are based on 1403 and 2811 yield observations for miscanthus and switchgrass, respectively, from experiments conducted between 2002 and 2019 across the rainfed region in the United States. We find statistically significant evidence that an additional year of maturity increases miscanthus and switchgrass yields but at a decreasing rate; yields peak at the 7th and 6th year respectively, for the observed range of applied N rates and stands. We also find that an increase in N application increases yield by a statistically significant level, but at a declining rate; the magnitude of the yield response to N is, however, small and varies with the age of the crop. The impact of N is larger on older compared to younger and middle‐aged stands of miscanthus. In contrast, the impact of N on switchgrass is larger on middle‐aged compared to younger and older stands of switchgrass. We do not find a statistically significant effect of soil productivity on yield for either crop. This analysis provides a basis for developing N application recommendations and optimal rotation age for miscanthus and switchgrass and shows that these energy crops can grow just as productively on low productivity land as on high productivity land.

Abstract Marginal land has received wide attention for its potential to produce bioenergy feedstocks while minimizing diversion of productive agricultural land from food crop production. However, there has been no consensus in the literature on how to define or identify land that is marginal for food crops and beneficial for bioenergy crops. Studies have used different definitions to quantify the amount of such land available; these have largely been based on assumed biophysical thresholds for soil quality and productivity that are unchanging over space and time. We discuss the limitations of these definitions and the rationale for considering economic returns and environmental outcomes in classifying land as marginal. We then propose the concept of “socially” marginal which is defined as land that is earning close to zero returns after accounting for the monetized costs of environmental externalities generated. We discuss a broad set of criteria for classifying land as socially marginal for food crops and suitable for bioenergy crops; with these criteria, this classification depends on spatially varying and time‐varying factors, such as climate and market conditions and policy incentives. While there are challenges related to identifying this marginal land, satellite and other large‐scale datasets increasingly enable such analysis at a fine spatial resolution. We also discuss reasons why landowners might choose not to convert bioenergy‐suitable land to bioenergy crops, and thus the need for policy incentives to support conversion of land that is socially beneficial for bioenergy crop production.

Abstract We quantify long-run adaptation of U.S. corn and soybean yields to changes in temperature and precipitation over 1951–2017. Results show that although the two crops became more heat- and drought-tolerant, their productivity under normal temperature and precipitation conditions decreased. Over 1951–2017, heat- and drought-tolerance increased corn and soybean yields by 33% and 20%, whereas maladaptation to normal conditions reduced yields by 41% and 87%, respectively, with large spatial variations in effects. Changes in climate are projected to reduce average corn and soybean yields by 39–68% and 86–92%, respectively, by 2050 relative to 2013–2017 depending on the warming scenario. After incorporating estimated effects of climate-neutral technological advances, the net change in yield ranges from (−)13 to 62% for corn and (−)57 to (−)26% for soybeans in 2050 relative to 2013–2017. Our analysis uncovers the inherent trade-offs and limitations of existing approaches to crop adaptation.

Abstract Planting bioenergy crops on marginal land is critical for avoiding competition with food crop production. While many studies have estimated marginal land availability using various methods, only a few studies have considered the role of socioeconomic factors in affecting perceptions about the availability of marginal land. This study analyzes land‐use survey data to examine the determinants of farmers' perceptions of marginal land availability on their farms. We find that farmers' perceptions are affected by a combination of unfavorable biophysical (e.g., soil water capacity, temperature variability, and slope) and socioeconomic factors, of which farm size appears to be significant. Interestingly, we identify different determinants of perceptions among farmers that claim to have marginal land and those that do not; the former are determined mainly by unfavorable biophysical factors, while the latter are mainly explained by small farm size. We further apply a prediction model that is trained by a machine learning algorithm to Midwestern states, and derive maps of marginal land likelihood and associated dominant influencing factors. The results suggest that marginal land is primarily under pastureland and grassland cover and in the Dakotas and Nebraska; there is also some marginal land under crop production in the Corn Belt. Our findings contribute to improving understanding of the complex determinants of heterogeneous perceptions of marginal land and can inform the design of more targeted policies for bioenergy crop adoption.

Biodiesel production from soybean has been growing in the United States and although its amount is small by comparison with corn ethanol, its addition to existing demands on land can have nonlinear effects on land use, due to an upward sloping and increasingly inelastic supply of land. It is critical to quantify these effects to inform future policies that may expand production of soy biodiesel. Here we apply a multi-period, partial equilibrium economic model (BEPAM) to determine land use under a validated counterfactual scenario with no biofuel policy or with corn ethanol mandate alone to isolate the extent to which expansion of biodiesel production in the US led to the conversion of Conservation Reserve Program (CRP) acres and other noncropland to crop production, over the 2007–2018 period. We find that the land use change intensity of biodiesel ranged from 0.78 to 1.5 million acres per billion gallons in 2018 which is substantially higher than that of corn ethanol, that ranged from 0.57 to 0.75; estimates at the lower end of these ranges are obtained under the assumption that there is no conversion of permanent pastureland to cropland and better supported by model validation than the upper end of these ranges. The land use change elasticity with respect to changes in land rent was more inelastic for biodiesel than for corn ethanol. The largest levels of expansion in cropland were in Iowa, Minnesota, North Dakota, Kansas, Michigan and Mississippi.

An estimated 3.9 billion individuals in 128 nations (about 40% of global population) are at risk of acquiring dengue virus infection. About 390 million cases of dengue are reported each year with higher prevalence in the developing world. A recent modeling-based report suggested that half of the population across the globe is at risk of dengue virus infection. In any given dengue outbreak, a percentage of infected population develops severe clinical manifestations, and this remains one of the “unsolved conundrums in dengue pathogenesis”. Although, host immunity and virus serotypes are known to modulate the infection, there are still certain underlying factors that play important roles in modulating dengue pathogenesis. Advanced genomics-based technologies have led to identification of regulatory roles of non-coding RNAs. Accumulating evidence strongly suggests that viruses and their hosts employ non-coding RNAs to modulate the outcome of infection in their own favor. The foremost ones seem to be the cellular microRNAs (miRNAs). Being the post-transcriptional regulators, miRNAs can be regarded as direct switches capable of turning “on” or “off” the viral replication process. Recently, role of long non-coding RNAs (lncRNAs) in modulating viral infections via interferon dependent or independent signaling has been recognized. Hence, we attempt to identify the “under-dog”, the non-coding RNA regulators of dengue virus infection. Such essential knowledge will enhance the understanding of dengue virus infection in holistic manner, by exposing the specific molecular targets for development of novel prophylactic, therapeutic or diagnostic strategies.

The pandemic of COVID-19 has emerged as a serious health crisis globally and India too has been extensively affected with 604,641 active cases reported, till date. The present study focuses on the demographic, clinical and laboratory profile of such patients from a tertiary level non-COVID respiratory care hospital. This is a retrospective observational study. Seventy-seven sick patients fulfilling COVID suspect criteria were admitted to the isolation area. Their RT-PCR test was done from the designated laboratory and 35 of them were confirmed to be COVID-19 patients. The detailed demographic, clinical and laboratory profile of these COVID-19 patients was studied. The mean age was 46±17 years with male predominance (57%). Majority of the cases (83%) were symptomatic. The most common symptom was cough (66%) followed by breathlessness and fever. Nineteen (54.3%) patients had one or the other co-morbidity and 16 (45.7%) had chronic lung diseases as one of the comorbidities. Nearly half of the patients (51%) required supplementary oxygen on presentation. Two patients were put on invasive mechanical ventilation while 4 patients required non-invasive ventilation before being shifted to the COVID hospital. Hence, it can be concluded that COVID-19 in patients of chronic respiratory diseases manifests with higher prevalence of symptoms and also higher severity of disease. Further, the symptomatology of COVID-19 closely mimics the acute exacerbation of chronic lung diseases, so cautious screening and testing should be done, especially at the pulmonary department.

The processing of polyprotein(s) to form structural and non-structural components remains an enigma due to the non-existence of an efficient and robust Hepatitis E Virus (HEV) culture system. We used the BacMam approach to construct an HEV replication model in which the HEV genome was cloned in the BacMam vector under the CMV promoter. The recombinant BacMam was used to infect Huh7 cells to transfer the HEV genome. HEV replication was authenticated by the presence of RNAs of both the polarity (+) and (−) and formation of hybrid RNA, a replication intermediate. The presence of genes for Papain-like Cysteine Protease (PCP), methyltransferase (MeT), RNA dependent RNA polymerase (RdRp), and ORF2 was confirmed by PCR amplification. Further, the infectious nature of the culture system was established as evidenced by the cross-infection of uninfected cells using the cell lysate from the infected cells. The HEV replication model was validated by detection of the ORF1 (Open Reading Frame1) encoded proteins, identified by Western blotting and Immunofluorescence by using epitope-specific antibodies against each protein. Consequently, discrete bands of 18, 35, 37, and 56 kDa corresponding to PCP, MeT, RdRp, and ORF2, respectively, were seen. Besides demonstrating the presence of non-structural enzymes of HEV along with ORF2, activity of a key enzyme, HEV-methyltransferase has also been observed. A 20% decrease in the replicative forms of RNA could be seen in presence of 100 μM Ribavirin after 48 h of treatment. The inhibition gradually increased from 0 to 24 to 48 h post-treatment. Summarily, infectious HEV culture system has been established, which could demonstrate the presence of HEV replicative RNA forms, the structural and non-structural proteins and the methyltransferase in its active form. The system may also be used to study the mechanism of action of Ribavirin in inhibiting HEV replication and develop a therapy.

A Low Carbon Fuel Standard seeks to regulate indirect land use change by including its related carbon emissions in the carbon intensity of biofuels. Khannaet al. show the economic cost of abatement achieved by including this factor is much larger than the social cost of carbon.

This data represents the effect of miR-155 on the expression of commonly used housekeeping genes, GAPDH, Beta Actin, RPL13A, and U6. The human miR-155 and control RNA were transfected to A549 cells by electroporation. Expression of these genes was compared in both groups by real-time PCR. The significant up-regulation in the expression of GAPDH was observed in the miR-155 transfected samples as compared to control while no major change was observed in the expression of the other three genes. Keywords: MiR-155, Glucose metabolism, Housekeeping genes, microRNA electroporation

Mandates, like the renewable fuel standard (RFS), for biofuels from corn and cellulosic feedstocks, impact the environment in multiple ways by affecting land use, nitrogen (N)-leakage, and greenhouse gas (GHG) emissions. We analyze the differing trade-offs these different types of biofuels offer among these multi-dimensional environmental effects and convert them to a monetized value of environmental damages (or benefits) that can be compared with the economic costs of extending these mandates over the 2016–2030 period. The discounted values of cumulative net benefits (or costs) are then compared to those with a counterfactual level of biofuels that would have been produced in the absence of the RFS over this period. We find that maintaining the corn ethanol mandate at 56 billion l till 2030 will lead to a discounted cumulative value of an economic cost of $199 billion over the 2016–2030 period compared to the counterfactual scenario; this includes $109 billion of economic costs and $85 billion of net monetized environmental damages. The additional implementation of a cellulosic biofuel mandate for 60 billion l by 2030 will increase this economic cost by $69 billion which will be partly offset by the net discounted monetized value of environmental benefits of $20 billion, resulting in a net cost of $49 billion over the 2016–2030 period. We explore the sensitivity of these net (economic and environmental) costs to alternative values of the social costs of carbon and nitrogen and other technological and market parameters. We find that, unlike corn ethanol, cellulosic biofuels can result in positive net benefits if the monetary benefits of GHG mitigation are valued high and those of N-damages are not very high.

Irrigation is critical to sustain agricultural productivity in dry or semi-dry environments, and center pivots, due to their versatility and ruggedness, are the most widely used irrigation systems. To effectively use center pivot irrigation systems, producers require tools to support their decision-making on when and how much water to irrigate. However, currently producers make these decisions primarily based on experience and/or limited information of weather. Ineffective use of irrigation systems can lead to overuse of water resources, compromise crop productivity, and directly reduce producers’ economic return as well as bring negative impacts on environmental sustainability. In this paper, we surveyed existing precision irrigation research and tools from peer-reviewed literature, land-grant university extension and industry products, and U.S. patents. We focused on four challenge areas related to precision irrigation decision-support systems: (a) data availability and scalability, (b) quantification of plant water stress, (c) model uncertainties and constraints, and (d) producers’ participation and motivation. We then identified opportunities to address the above four challenge areas: (a) increase the use of high spatial-temporal-resolution satellite fusion products and inexpensive sensor networks to scale up the adoption of precision irrigation decision-support systems; (b) use mechanistic quantification of ‘plant water stress’ as triggers to improve irrigation decision, by explicitly considering the interaction between soil water supply, atmospheric water demand, and plant physiological regulation; (c) constrain the process-based and statistical/machine learning models at each individual field using data-model fusion methods for scalable solutions; and (d) develop easy-to-use tools with flexibility, and increase governments’ financial incentives and support. We conclude this review by laying out our vision for precision irrigation decision-support systems for center pivots that can achieve scalable, economical, reliable, and easy-to-use irrigation management for producers.

Biofuels policies induce land use changes (LUC), including cropland expansion and crop switching, and this in turn alters water and soil management practices. Policies differ in the extent and type of land use changes they induce and therefore in their impact on water resources. We quantify and compare the spatially varying water impacts of biofuel crops stemming from LUC induced by two different biofuels policies by coupling a biophysical model with an economic model to simulate the economically viable mix of crops, land uses, and crop management choices under alternative policy scenarios. We assess the outputs of an economic model with a high-resolution crop-water model for major agricultural crops and potential cellulosic feedstocks in the US to analyze the impacts of three alternative policy scenarios on water balances: a counterfactual 'no-biofuels policy' (BAU) scenario, a volumetric mandate (Mandate) scenario, and a clean fuel-intensity standard (CFS) scenario incentivizing fuels based on their carbon intensities. While both biofuel policies incentivize more biofuels than in the counterfactual, they differ in the mix of corn ethanol and advanced biofuels from miscanthus and switchgrass (more corn ethanol in Mandate and more cellulosic biofuels in CFS). The two policies differ in their impact on irrigated acreage, irrigation demand, groundwater use and runoff. Net irrigation requirements increase 0.7% in Mandate and decrease 3.8% in CFS, but in both scenarios increases are concentrated in regions of Kansas and Nebraska that rely upon the Ogallala aquifer for irrigation water. Our study illustrates the importance of accounting for the overall LUC and shifts in agricultural production and management practices in response to policies when assessing the water impacts of biofuels.

A micro-level model of farmer decision making is developed to examine the extent to which uncertainty about potential yields influences the value of site-specific technologies. The economic and environmental benefits of these technologies arise from two sources: information gathering and variable-rate nitrogen application is higher on fields with low average potential yields, high spatial variability, positively skewed potential yield distributions, responsive yield to nitrogen, and low uncertainty. Variable-rate application decreases nitrogen use by reducing the extent of overapplication. However, in the presence of uncertainty about potential yields, the incentives to overapply nitrogen irrespective of the method of application, uniform of variable rate, can reduce the economic and environmental benefits of site-specific technologies.

Woody feedstocks will play a critical role in meeting the demand for biomass-based energy products in the US. We developed an integrated model using comparable system boundaries and common set of assumptions to ascertain unit cost and greenhouse gas (GHG) intensity of electricity and ethanol derived from slash pine (Pinus elliottii) at the production and consumption levels by considering existing automobile technologies. We also calculated abatement cost of greenhouse gas (GHG) emissions with respect to comparable energy products derived from fossil fuels. The production cost of electricity derived using wood chips was at least cheaper by 1 ¢ MJ-1 over electricity derived from wood pellets. The production cost of ethanol without any income from cogenerated electricity was costlier by about 0.7 ¢ MJ-1 than ethanol with income from cogenerated electricity. The production cost of electricity derived from wood chips was cheaper by at least 0.7 ¢ MJ-1 than the energy equivalent cost of ethanol produced in presence of cogenerated electricity. The cost of using ethanol as a fuel in a flex-fuel vehicle was at least higher by 6 ¢ km-1 than a comparable electric vehicle. The GHG intensity of per km distance traveled in a flex-fuel vehicle was greater or lower than an electric vehicle running on electricity derived from wood chips depending on presence and absence of GHG credits related with co-generated electricity. A carbon tax of at least $7 Mg CO2e-1 and $30 Mg CO2e-1 is needed to promote wood-based electricity and ethanol production in the US, respectively. The range of abatement cost of GHG emissions is significantly dependent on the harvest age and selected baseline especially for electricity generation.

An option-value model is developed to analyze the impacts of output price uncertainty, high sunk costs of adoption, and site-specific conditions on the optimal timing of adoption of two interrelated site-specific technologies, soil testing and variable rate technology (VRT). The model incorporates the potential for adopting these two technologies jointly or sequentially. The implications of the pattern of adoption for nitrogen pollution and for the design of a cost-share subsidy policy to accelerate the adoption of these technologies to reduce nitrogen pollution are also analyzed. Ignoring the potential for sequential adoption would tend to underpredict the adoption so soil testing and overpredict the adoption of VRT. Cost-share subsidies to induce accelerated adoption of VRT would be most effective at reducing nitrogen pollution if targeted toward fields with relatively high spatial variability in soil quality or soil fertility, and either low average soil quality or low average soil fertility.

Exports of pellets from the United States (US) are growing significantly to meet the demand for renewable energy in the European Union. This transatlantic trade in pellets has raised questions about the greenhouse gas (GHG) intensity of these pellets and their effects on conventional forest product markets in the US. This paper examines the GHG intensity of pellets exported from the US using either forest biomass only or forest and agricultural biomass combined. We develop an integrated dynamic, price-endogenous, partial equilibrium model of the forestry, agricultural, and transportation sectors in the US to investigate not only the direct life-cycle GHG intensity of pellets but also the accompanying indirect market and land use effects induced by changes in prices of forest and agricultural products over the 2007–2032 period. Across different scenarios of high and low pellet demand that can be met with either forest biomass only or with forest and agricultural biomass, we find that the GHG intensity of pellet based electricity is 74% to 85% lower than that of coal-based electricity. We also find that the GHG intensity of pellets produced using agricultural and forest biomass is 28% to 34% lower than that of pellets produced using forest biomass only. GHG effects due to induced direct and indirect changes in forest carbon stock caused by changes in harvest rotations, changes in land use and in conventional wood production account for 11% to 26% of the overall GHG intensity of pellets produced from forest biomass only; these effects are negative with the use of forest and agricultural biomass.

Power utility companies in the United Kingdom are using imported wood pellets from the southern region of the United States for electricity generation to meet the legally binding mandate of sourcing 15% of the nation’s total energy consumption from renewable sources by 2020. This study ascertains relative savings in greenhouse gas (GHG) emissions for a unit of electricity generated using imported wood pellet in the United Kingdom under 930 different scenarios: three woody feedstocks (logging residues, pulpwood, and logging residues and pulpwood combined), two forest management choices (intensive and non-intensive), 31 plantation rotation ages (year 10 to year 40 in steps of 1 year), and five power plant capacities (20–100 MW in steps of 20 MW). Relative savings in GHG emissions with respect to a unit of electricity derived from fossil fuels in the United Kingdom range between 50% and 68% depending upon the capacity of power plant and rotation age. Relative savings in GHG emissions increase with higher power plant capacity. GHG emissions related to wood pellet production and transatlantic shipment of wood pellets typically contribute about 48% and 31% of total GHG emissions, respectively. Overall, use of imported wood pellets for electricity generation could help in reducing the United Kingdom’s GHG emissions. We suggest that future research be directed to evaluation of the impacts of additional forest management practices, changing climate, and soil carbon on the overall savings in GHG emissions related to transatlantic wood pellet trade.

This letter examines the effectiveness of various biofuel and climate policies in reducing future processing costs of cellulosic biofuels due to learning-by-doing. These policies include a biofuel production mandate alone and supplementing the biofuel mandate with other policies, namely a national low carbon fuel standard, a cellulosic biofuel production tax credit or a carbon price policy. We find that the binding biofuel targets considered here can reduce the unit processing cost of cellulosic ethanol by about 30% to 70% between 2015 and 2035 depending on the assumptions about learning rates and initial costs of biofuel production. The cost in 2035 is more sensitive to the speed with which learning occurs and less sensitive to uncertainty in the initial production cost. With learning rates of 5–10%, cellulosic biofuels will still be at least 40% more expensive than liquid fossil fuels in 2035. The addition of supplementary low carbon/tax credit policies to the mandate that enhance incentives for cellulosic biofuels can achieve similar reductions in these costs several years earlier than the mandate alone; the extent of these incentives differs across policies and different kinds of cellulosic biofuels.