Challenge clusters facing LCA in environmental decision-making—what we can learn from biofuels

Purpose Bioenergy is increasingly used to help meet greenhouse gas (GHG) and renewable energy targets. However, bioenergy’s sustainability has been questioned, resulting in increasing use of life cycle assessment (LCA). Bioenergy systems are global and complex, and market forces can result in significant changes, relevant to LCA and policy. The goal of this paper is to illustrate the complexities associated with LCA, with particular focus on bioenergy and associated policy development, so that its use can more effectively inform policymakers. Methods The review is based on the results from a series of workshops focused on bioenergy life cycle assessment. Expert submissions were compiled and categorized within the first two workshops. Over 100 issues emerged. Accounting for redundancies and close similarities in the list, this reduced to around 60 challenges, many of which are deeply interrelated. Some of these issues were then explored further at a policyfacing workshop in London, UK. The authors applied a rigorous approach to categorize the challenges identified to be at the intersection of biofuels/bioenergy LCA and policy. Results and discussion The credibility of LCA is core to its use in policy. Even LCAs that comply with ISO standards and policy and regulatory instruments leave a great deal of scope for interpretation and flexibility. Within the bioenergy sector, this has led to frustration and at times a lack of obvious direction. This paper identifies the main challenge clusters: overarching issues, application and practice and value and ethical judgments. Many of these are reflective of the transition from application of LCA to assess individual products or systems to the wider approach that is becoming more common. Uncertainty in impact assessment strongly influences planning and compliance due to challenges in assigning accountability, and communicating the inherent complexity and uncertainty within bioenergy is becoming of greater importance. Conclusions The emergence of LCA in bioenergy governance is particularly significant because other sectors are likely to transition to similar governance models. LCA is being stretched to accommodate complex and broad policy-relevant questions, seeking to incorporate externalities that have major implications for long-term sustainability. As policy increasingly relies on LCA, the strains placed on the methodology are becoming both clearer and impedimentary. The implications for energy policy, and in particular bioenergy, are large

Bioaccumulation and ecotoxicity of carbon nanotubes

Carbon nanotubes (CNT) have numerous industrial applications and may be released to the environment. In the aquatic environment, pristine or functionalized CNT have different dispersion behavior, potentially leading to different risks of exposure along the water column. Data included in this review indicate that CNT do not cross biological barriers readily. When internalized, only a minimal fraction of CNT translocate into organism body compartments. The reported CNT toxicity depends on exposure conditions, model organism, CNT-type, dispersion state and concentration. In the ecotoxicological tests, the aquatic organisms were generally found to be more sensitive than terrestrial organisms. Invertebrates were more sensitive than vertebrates. Single-walled CNT were found to be more toxic than double-/multi-walled CNT. Generally, the effect concentrations documented in literature were above current modeled average environmental concentrations. Measurement data are needed for estimation of environmental no-effect concentrations. Future studies with benchmark materials are needed to generate comparable results. Studies have to include better characterization of the starting materials, of the dispersions and of the biological fate, to obtain better knowledge of the exposure/effect relationships

Life-Cycle Assessment Considerations for Batteries and Battery Materials

Rechargeable batteries are necessary for the decarbonization of the energy systems, but life-cycle environmental impact assessments have not achieved consensus on the environmental impacts of producing these batteries. Nonetheless, life cycle assessment (LCA) is a powerful tool to inform the development of better-performing batteries with reduced environmental burden. This review explores common practices in lithium-ion battery LCAs and makes recommendations for how future studies can be more interpretable, representative, and impactful. First, LCAs should focus analyses of resource depletion on long-term trends toward more energy and resource-intensive material extraction and processing rather than treating known reserves as a fixed quantity being depleted. Second, future studies should account for extraction and processing operations that deviate from industry best-practices and may be responsible for an outsized share of sector-wide impacts, such as artisanal cobalt mining. Third, LCAs should explore at least 2–3 battery manufacturing facility scales to capture size- and throughput-dependent impacts such as dry room conditioning and solvent recovery. Finally, future LCAs must transition away from kg of battery mass as a functional unit and instead make use of kWh of storage capacity and kWh of lifetime energy throughput

Life-Cycle Assessment Considerations for Batteries and Battery Materials

Rechargeable batteries are necessary for the decarbonization of the energy systems, but life-cycle environmental impact assessments have not achieved consensus on the environmental impacts of producing these batteries. Nonetheless, life cycle assessment (LCA) is a powerful tool to inform the development of better-performing batteries with reduced environmental burden. This review explores common practices in lithium-ion battery LCAs and makes recommendations for how future studies can be more interpretable, representative, and impactful. First, LCAs should focus analyses of resource depletion on long-term trends toward more energy and resource-intensive material extraction and processing rather than treating known reserves as a fixed quantity being depleted. Second, future studies should account for extraction and processing operations that deviate from industry best-practices and may be responsible for an outsized share of sector-wide impacts, such as artisanal cobalt mining. Third, LCAs should explore at least 2–3 battery manufacturing facility scales to capture size- and throughput-dependent impacts such as dry room conditioning and solvent recovery. Finally, future LCAs must transition away from kg of battery mass as a functional unit and instead make use of kWh of storage capacity and kWh of lifetime energy throughput

Tree-Based Automated Machine Learning to Predict Biogas Production for Anaerobic Co-digestion of Organic Waste

The dynamics of microbial communities involved in anaerobic digestion of mixed organic waste are notoriously complex and difficult to model, yet successful operation of anaerobic digestion is critical to the goals of diverting high-moisture organic waste from landfills. Machine learning (ML) is ideally suited to capturing complex and nonlinear behavior that cannot be modeled mechanistically. This study uses 8 years of data collected from an industrial-scale anaerobic co-digestion (AcoD) operation at a municipal wastewater treatment plant in Oakland, California, combined with a powerful automated ML method, Tree-based Pipeline Optimization Tool, to develop an improved understanding of how different waste inputs and operating conditions impact biogas yield. The model inputs included daily input volumes of 31 waste streams and 5 operating parameters. Because different wastes are broken down at varying rates, the model explored a range of time lags ascribed to each waste input ranging from 0 to 30 days. The results suggest that the waste types (including rendering waste, lactose, poultry waste, and fats, oils, and greases) differ considerably in their impact on biogas yield on both a per-gallon basis and a mass of volatile solids basis, while operating parameters were not good predictors of yield at this facility

Strategies for near-term scale-up of cellulosic biofuel production using sorghum and crop residues in the US

The Renewable Fuel Standard (RFS) initially set ambitious goals for US cellulosic biofuel production and, although the total renewable fuel volume reached 80% of the established target for 2017, the cellulosic fuel volume reached just 5% of the original goal. This shortfall has, in part, been ascribed to the hesitance of farmers to plant the high-yielding, low-input perennial biomass crops identified as otherwise ideal feedstocks. Policy and market uncertainty also hinder investment in capital-intensive new cellulosic biorefineries. This study combines remote sensing land use data, yield predictions, a fine-resolution geospatial modeling framework, and a novel facility siting algorithm to evaluate the potential for near-term scale-up of cellulosic fuel production using a combination of lower-risk annual feedstocks more familiar to US farmers: corn stover and biomass sorghum. Potential strategies include expansion or retrofitting of existing corn ethanol facilities and targeted construction of new facilities in resource-rich areas. The results indicate that, with a maximum 10% conversion of pastureland and cropland to sorghum in suitable regions, more than 80 of the 214 existing corn ethanol biorefineries could be retrofitted or expanded to accept cellulosic feedstocks and an additional 71 new biorefineries could be built. The resulting land conversion for bioenergy sorghum totals to 4.5% of US cropland and 3.7% of pastureland. If this biomass is converted to ethanol, the total increase in annual production could be 17 billion gallons, just over the original RFS 2022 cellulosic biofuel production target and equivalent to 12% of US gasoline consumption

Sorghum biomass production in the continental United States and its potential impacts on soil organic carbon and nitrous oxide emissions

National scale projections of bioenergy crop yields and their environmental impacts are essential to identify appropriate locations to place bioenergy crops and ensure sustainable land use strategies. In this study, we used the process-based Daily Century (DAYCENT) model with site-specific environmental data to simulate sorghum (Sorghum bicolor L. Moench) biomass yield, soil organic carbon (SOC) change, and nitrous oxide emissions across cultivated lands in the continental United States. The simulated rainfed dry biomass productivity ranged from 0.8 to 19.2 Mg ha−1 year−1, with a spatiotemporal average of (Formula presented.) Mg ha−1 year−1, and a coefficient of variation of 35%. The average SOC sequestration and direct nitrous oxide emission rates were simulated as (Formula presented.) Mg CO2e ha−1 year−1 and (Formula presented.) Mg CO2e ha−1 year−1, respectively. Compared to field-observed biomass yield data at multiple locations, model predictions of biomass productivity showed a root mean square error (RMSE) of 5.6 Mg ha−1 year−1. In comparison to the multi State (n = 21) NASS database, our results showed RMSE of 5.5 Mg ha−1 year−1. Model projections of baseline SOC showed RMSE of 1.9 kg/m2 in comparison to a recently available continental SOC stock dataset. The model-predicted N2O emissions are close to 1.25% of N input. Our results suggest 10.2 million ha of cultivated lands in the Southern and Lower Midwestern United States will produce >10 Mg ha−1 year−1 with net carbon sequestration under rainfed conditions. Cultivated lands in Upper Midwestern states including Iowa, Minnesota, Montana, Michigan, and North Dakota showed lower sorghum biomass productivity (average: 6.9 Mg ha−1 year−1) with net sequestration (average: 0.13 Mg CO2e ha−1 year−1). Our national-scale spatially explicit results are critical inputs for robust life cycle assessment of bioenergy production systems and land use-based climate change mitigation strategies

Drop-in biofuels offer strategies for meeting California's 2030 climate mandate

In 2015, California established a mandate that requires on-road greenhouse gas (GHG) emissions to be reduced by 40% below 1990 levels by 2030. We explore the feasibility of meeting this goal by large-scale commercialization of drop-in biofuels. Drop-in biofuels, although not clearly defined, are a class of fuels that can be produced from biomass and blended with either crude oil or finished fuels without requiring equipment retrofits. This article focuses on thermochemical routes at or near commercialization. We provide a bottom-up, spatially explicit cost analysis to evaluate whether California can meet its 2030 GHG reduction target with drop-in fuels alone. A takeaway from our analysis is that drop-in fuels, if their performance is consistent with small-scale and simulated results, can be viable low-carbon substitutes for gasoline and diesel. We find that California can meet, and even exceed, its 2030 GHG emissions target for on-road vehicles with drop-in biofuels alone, but this requires use of biomass resources located outside the state. Meeting the 40% reduction target in a cost-effective manner requires pyrolysis of herbaceous agricultural residues (96% of total fuel output) and the conversion of woody residues via methanol-to-gasoline (4%). This scale of production would require 58 million metric tons of biomass feedstock, or 20% of total available biomass residues in the United States. For comparison, California is responsible for 11% of transportation-related petroleum consumption in the US. The approximately 5 billion gallons (19 billion liters) per year of drop-in fuel would displace 30% of gasoline and 60% of diesel demand in California. If electricity offset credits are eliminated, the target can be met with a similar scale of production, but methanol-to-gasoline becomes the dominant route (>99%), biomass requirements increase by 33%, and average production costs increase by 20%. Following this policy pathway would increase national biofuel production by 30% relative to 2015 production levels

Drop-in biofuels offer strategies for meeting California's 2030 climate mandate

In 2015, California established a mandate that requires on-road greenhouse gas (GHG) emissions to be reduced by 40% below 1990 levels by 2030. We explore the feasibility of meeting this goal by large-scale commercialization of drop-in biofuels. Drop-in biofuels, although not clearly defined, are a class of fuels that can be produced from biomass and blended with either crude oil or finished fuels without requiring equipment retrofits. This article focuses on thermochemical routes at or near commercialization. We provide a bottom-up, spatially explicit cost analysis to evaluate whether California can meet its 2030 GHG reduction target with drop-in fuels alone. A takeaway from our analysis is that drop-in fuels, if their performance is consistent with small-scale and simulated results, can be viable low-carbon substitutes for gasoline and diesel. We find that California can meet, and even exceed, its 2030 GHG emissions target for on-road vehicles with drop-in biofuels alone, but this requires use of biomass resources located outside the state. Meeting the 40% reduction target in a cost-effective manner requires pyrolysis of herbaceous agricultural residues (96% of total fuel output) and the conversion of woody residues via methanol-to-gasoline (4%). This scale of production would require 58 million metric tons of biomass feedstock, or 20% of total available biomass residues in the United States. For comparison, California is responsible for 11% of transportation-related petroleum consumption in the US. The approximately 5 billion gallons (19 billion liters) per year of drop-in fuel would displace 30% of gasoline and 60% of diesel demand in California. If electricity offset credits are eliminated, the target can be met with a similar scale of production, but methanol-to-gasoline becomes the dominant route (>99%), biomass requirements increase by 33%, and average production costs increase by 20%. Following this policy pathway would increase national biofuel production by 30% relative to 2015 production levels

Sorghum biomass production in the continental United States and its potential impacts on soil organic carbon and nitrous oxide emissions

National scale projections of bioenergy crop yields and their environmental impacts are essential to identify appropriate locations to place bioenergy crops and ensure sustainable land use strategies. In this study, we used the process-based Daily Century (DAYCENT) model with site-specific environmental data to simulate sorghum (Sorghum bicolor L. Moench) biomass yield, soil organic carbon (SOC) change, and nitrous oxide emissions across cultivated lands in the continental United States. The simulated rainfed dry biomass productivity ranged from 0.8 to 19.2 Mg ha−1 year−1, with a spatiotemporal average of (Formula presented.) Mg ha−1 year−1, and a coefficient of variation of 35%. The average SOC sequestration and direct nitrous oxide emission rates were simulated as (Formula presented.) Mg CO2e ha−1 year−1 and (Formula presented.) Mg CO2e ha−1 year−1, respectively. Compared to field-observed biomass yield data at multiple locations, model predictions of biomass productivity showed a root mean square error (RMSE) of 5.6 Mg ha−1 year−1. In comparison to the multi State (n = 21) NASS database, our results showed RMSE of 5.5 Mg ha−1 year−1. Model projections of baseline SOC showed RMSE of 1.9 kg/m2 in comparison to a recently available continental SOC stock dataset. The model-predicted N2O emissions are close to 1.25% of N input. Our results suggest 10.2 million ha of cultivated lands in the Southern and Lower Midwestern United States will produce >10 Mg ha−1 year−1 with net carbon sequestration under rainfed conditions. Cultivated lands in Upper Midwestern states including Iowa, Minnesota, Montana, Michigan, and North Dakota showed lower sorghum biomass productivity (average: 6.9 Mg ha−1 year−1) with net sequestration (average: 0.13 Mg CO2e ha−1 year−1). Our national-scale spatially explicit results are critical inputs for robust life cycle assessment of bioenergy production systems and land use-based climate change mitigation strategies