Life cycle analysis of biochemical cellulosic ethanol under multiple scenarios

Cellulosic ethanol is widely believed to offer substantial environmental advantages over petroleum fuels and grain‐based ethanol, particularly in reducing greenhouse gas emissions from transportation. The environmental impacts of biofuels are largely caused by precombustion activities, feedstock production and conversion facility operations. Life cycle analysis (LCA) is required to understand these impacts. This article describes a field‐to‐blending terminal LCA of cellulosic ethanol produced by biochemical conversion (hydrolysis and fermentation) using corn stover or switchgrass as feedstock. This LCA develops unique models for most elements of the biofuel production process and assigns environmental impact to different phases of production. More than 30 scenarios are evaluated, reflecting a range of feedstock, technology and scale options for near‐term and future facilities. Cellulosic ethanol, as modeled here, has the potential to significantly reduce greenhouse gas (GHG) emissions compared to petroleum‐based liquid transportation fuels, though substantial uncertainty exists. Most of the conservative scenarios estimate GHG emissions of approximately 45–60 g carbon dioxide equivalent per MJ of delivered fuel (g CO₂e MJ^?1) without credit for coproducts, and 20–30 g CO₂e MJ^?1 when coproducts are considered. Under most scenarios, feedstock production, grinding and transport dominate the total GHG footprint. The most optimistic scenarios include sequestration of carbon in soil and have GHG emissions below zero g CO₂e MJ^?1, while the most pessimistic have life‐cycle GHG emissions higher than petroleum gasoline. Soil carbon changes are the greatest source of uncertainty, dominating all other sources of GHG emissions at the upper bound of their uncertainty. Many LCAs of biofuels are narrowly constrained to GHG emissions and energy; however, these narrow assessments may miss important environmental impacts. To ensure a more holistic assessment of environmental performance, a complete life cycle inventory, with over 1100 tracked material and energy flows for each scenario is provided in the online supplementary material for this article.     

Influence of spatially dependent,modeled soil carbon emission factors on life‐cycle greenhouse gas emissions of corn and cellulosic ethanol

Converting land to biofuel feedstock production incurs changes in soil organic carbon (SOC) that can influence biofuel life‐cycle greenhouse gas (GHG) emissions. Estimates of these land use change (LUC) and life‐cycle GHG emissions affect biofuels' attractiveness and eligibility under a number of renewable fuel policies in the USA and abroad. Modeling was used to refine the spatial resolution and depth extent of domestic estimates of SOC change for land (cropland, cropland pasture, grassland, and forest) conversion scenarios to biofuel crops (corn, corn stover, switchgrass, Miscanthus, poplar, and willow) at the county level in the USA. Results show that in most regions, conversions from cropland and cropland pasture to biofuel crops led to neutral or small levels of SOC sequestration, while conversion of grassland and forest generally caused net SOC loss. SOC change results were incorporated into the Greenhouse Gases, Regulated Emissions, and Energy use in Transportation (GREET) model to assess their influence on life‐cycle GHG emissions of corn and cellulosic ethanol. Total LUC GHG emissions (g CO₂eq MJ^?1) were 2.1–9.3 for corn‐, ?0.7 for corn stover‐, ?3.4 to 12.9 for switchgrass‐, and ?20.1 to ?6.2 for Miscanthus ethanol; these varied with SOC modeling assumptions applied. Extending the soil depth from 30 to 100 cm affected spatially explicit SOC change and overall LUC GHG emissions; however, the influence on LUC GHG emission estimates was less significant in corn and corn stover than cellulosic feedstocks. Total life‐cycle GHG emissions (g CO₂eq MJ^?1, 100 cm) were estimated to be 59–66 for corn ethanol, 14 for stover ethanol, 18–26 for switchgrass ethanol, and ?7 to ?0.6 for Miscanthus ethanol. The LUC GHG emissions associated with poplar‐ and willow‐derived ethanol may be higher than that for switchgrass ethanol due to lower biomass yield.     

Life cycle assessment of renewable diesel production from lignocellulosic biomass

Purpose

Governments around the world encourage the use of biofuels through fuel standard policies that require the addition of renewable diesel in diesel fuel from fossil fuels. Environmental impact studies of the conversion of biomass to renewable diesel have been conducted, and life cycle assessments (LCA) of the conversion of lignocellulosic biomass to hydrogenation-derived renewable diesel (HDRD) are limited, especially for countries with cold climates like Canada.

Methods

In this study, an LCA was conducted on converting lignocellulosic biomass to HDRD by estimating the well-to-wheel greenhouse gas (GHG) emissions and fossil fuel energy input of the production of biomass and its conversion to HDRD. The approach to conduct this LCA includes defining the goal and scope, compiling a life cycle inventory, conducting a life cycle impact assessment, and executing a life cycle interpretation. All GHG emissions and fossil fuel energy inputs were based on a fast pyrolysis plant capacity of 2000 dry tonnes biomass/day. A functional unit of 1 MJ of HDRD produced was adopted as a common unit for data inputs of the life cycle inventory. To interpret the results, a sensitivity analysis was performed to measure the impact of variables involved, and an uncertainty analysis was performed to assess the confidence of the results.

Results and discussion

The GHG emissions of three feedstocks studied—whole tree (i.e., chips from cutting the whole tree), forest residues (i.e., chips from branches and tops generated from logging operations), and agricultural residues (i.e., straw from wheat and barley)—range from 35.4 to 42.3 g CO_2,eq/MJ of HDRD (i.e., lowest for agricultural residue- and highest for forest residue-based HDRD); this is 53.4–61.1 % lower than fossil-based diesel. The net energy ratios range from 1.55 to 1.90 MJ/MJ (i.e., lowest for forest residue- and highest for agricultural residue-based HDRD) for HDRD production. The difference in results among feedstocks is due to differing energy requirements to harvest and pretreat biomass. The energy-intensive hydroprocessing stage is responsible for most of the GHG emissions produced for the entire conversion pathway.

Conclusions

Comparing feedstocks showed the significance of the efficiency in the equipment used and the physical properties of biomass in the production of HDRD. The overall results show the importance of efficiency at the hydroprocessing stage. These findings indicate significant GHG mitigation benefits for the oil refining industry using available lignocellulosic biomass to produce HDRD for transportation fuel.

     

Life Cycle–based Assessment of Energy Use and Greenhouse Gas Emissions in Almond Production,Part I: Analytical Framework and Baseline Results

This first article of a two‐article series describes a framework and life cycle–based model for typical almond orchard production systems for California, where more than 80% of commercial almonds on the world market are produced. The comprehensive, multiyear, life cycle–based model includes orchard establishment and removal; field operations and inputs; emissions from orchard soils; and transport and utilization of co‐products. These processes are analyzed to yield a life cycle inventory of energy use, greenhouse gas (GHG) emissions, criteria air pollutants, and direct water use from field to factory gate. Results show that 1 kilogram (kg) of raw almonds and associated co‐products of hulls, shells, and woody biomass require 35 megajoules (MJ) of energy and result in 1.6 kg carbon dioxide equivalent (CO₂‐eq) of GHG emissions. Nitrogen fertilizer and irrigation water are the dominant causes of both energy use and GHG emissions. Co‐product credits play an important role in estimating the life cycle environmental impacts attributable to almonds alone; using displacement methods results in net energy and emissions of 29 MJ and 0.9 kg CO₂‐eq/kg. The largest sources of credits are from orchard biomass and shells used in electricity generation, which are modeled as displacing average California electricity. Using economic allocation methods produces significantly different results; 1 kg of almonds is responsible for 33 MJ of energy and 1.5 kg CO₂‐eq emissions. Uncertainty analysis of important parameters and assumptions, as well as temporary carbon storage in orchard trees and soils, are explored in the second article of this two‐part article series.     

Life Cycle Performance of Cellulosic Ethanol and Corn Ethanol from a Retrofitted Dry Mill Corn Ethanol Plant

This study conducts a life cycle assessment of a simulated dry mill corn ethanol facility in California’s Central Valley retrofitted to also produce ethanol from corn stover, a cellulosic feedstock. The assessment examines three facility designs, all producing corn ethanol and wet distiller’s grains and solubles as a co-product: a baseline facility with no cellulosic retrofit, a facility retrofitted with a small capacity for stover feedstock, and a facility retrofitted for a large capacity of stover feedstock. Corn grain is supplied by rail from the Midwest, while stover is sourced from in-state farms and delivered by truck. Two stover feedstock supply scenarios are considered, testing harvest rates at 25 or 40 % of stover mass. Allocation is required to separate impacts attributable to co-products. Additional scenarios are explored to assess the effect of co-product allocation methods on life cycle assessment results for the two fuel products, corn ethanol and stover ethanol. The assessment tracks greenhouse gas (GHG) emissions, energy consumption, criteria air pollutants, and direct water consumption. The GHG intensity of corn ethanol produced from the three facility designs range between 61.3 and 68.9 g CO₂e/MJ, which includes 19.8 g CO₂e/MJ from indirect land use change for Midwestern corn grain. The GHG intensity of cellulosic ethanol varies from 44.1 to 109.2 g CO₂e/MJ, and 14.6 to 32.1 g CO₂e/MJ in the low and high stover capacity cases, respectively. Total energy input ranges between 0.60 and 0.71 MJ/MJ for corn ethanol and 0.13 to 2.29 MJ/MJ for stover ethanol. This variability is the result of the stover supply scenarios (a function of harvest rate) and co-product allocation decisions.     

Including albedo in time‐dependent LCA of bioenergy

Albedo change during feedstock production can substantially alter the life cycle climate impact of bioenergy. Life cycle assessment (LCA) studies have compared the effects of albedo and greenhouse gases (GHGs) based on global warming potential (GWP). However, using GWP leads to unequal weighting of climate forcers that act on different timescales. In this study, albedo was included in the time‐dependent LCA, which accounts for the timing of emissions and their impacts. We employed field‐measured albedo and life cycle emissions data along with time‐dependent models of radiative transfer, biogenic carbon fluxes and nitrous oxide emissions from soil. Climate impacts were expressed as global mean surface temperature change over time (?T) and as GWP. The bioenergy system analysed was heat and power production from short‐rotation willow grown on former fallow land in Sweden. We found a net cooling effect in terms of ?T per hectare (?3.8 × 10^–11 K in year 100) and GWP₁₀₀ per MJ fuel (?12.2 g CO₂e), as a result of soil carbon sequestration via high inputs of carbon from willow roots and litter. Albedo was higher under willow than fallow, contributing to the cooling effect and accounting for 34% of GWP₁₀₀, 36% of ?T in year 50 and 6% of ?T in year 100. Albedo dominated the short‐term temperature response (10–20 years) but became, in relative terms, less important over time, owing to accumulation of soil carbon under sustained production and the longer perturbation lifetime of GHGs. The timing of impacts was explicit with ?T, which improves the relevance of LCA results to climate targets. Our method can be used to quantify the first‐order radiative effect of albedo change on the global climate and relate it to the climate impact of GHG emissions in LCA of bioenergy, alternative energy sources or land uses.     

Land management change greatly impacts biofuels’ greenhouse gas emissions

Harvesting corn stover for biofuel production may decrease soil organic carbon (SOC) and increase greenhouse gas (GHG) emissions. Adding additional organic matter into soil or reducing tillage intensity, however, could potentially offset this SOC loss. Here, using SOC and life cycle analysis (LCA) models, we evaluated the impacts of land management change (LMC), that is, stover removal, organic matter addition, and tillage on spatially explicit SOC level and biofuels’ overall life cycle GHG emissions in US corn–soybean production systems. Results indicate that under conventional tillage (CT), 30% stover removal (dry weight) may reduce baseline SOC by 0.04 t C ha^?1 yr^?1 over a 30‐year simulation period. Growing a cover crop during the fallow season or applying manure, on the other hand, could add to SOC and further reduce biofuels’ life cycle GHG emissions. With 30% stover removal in a CT system, cover crop and manure application can increase SOC at the national level by about 0.06 and 0.02 t C ha^?1 yr^?1, respectively, compared to baseline cases without such measures. With contributions from this SOC increase, the life cycle GHG emissions for stover ethanol are more than 80% lower than those of gasoline, exceeding the US Renewable Fuel Standard mandate of 60% emissions reduction in cellulosic biofuels. Reducing tillage intensity while removing stover could also limit SOC loss or lead to SOC gain, which would lower stover ethanol life cycle GHG emissions to near or under the mandated 60% reduction. Without these organic matter inputs or reduced tillage intensity, however, the emissions will not meet this mandate. More efforts are still required to further identify key practical LMCs, improve SOC modeling, and accounting for LMCs in biofuel LCAs that incorporate stover removal.     

Energy consumption and greenhouse gas emissions from enzyme and yeast manufacture for corn and cellulosic ethanol production

Enzymes and yeast are important ingredients in the production of ethanol, yet the energy consumption and emissions associated with their production are often excluded from life-cycle analyses of ethanol. We provide new estimates for the energy consumed and greenhouse gases (GHGs) emitted during enzyme and yeast manufacture, including contributions from key ingredients such as starch, glucose, and molasses. We incorporated these data into Argonne National Laboratory’s Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation model and observed that enzymes and yeast together contribute 1.4 and 27?% of farm-to-pump GHG emissions for corn and cellulosic ethanol, respectively. Over the course of the entire corn ethanol life cycle, yeast and enzymes contribute a negligible amount of GHG emissions, but increase GHG emissions from the cellulosic ethanol life cycle by 5.6?g CO₂e/MJ.     

Life Cycle–based Assessment of Energy Use and Greenhouse Gas Emissions in Almond Production,Part II: Uncertainty Analysis through Sensitivity Analysis and Scenario Testing

This is the second part of a two‐article series examining California almond production. The part I article describes development of the analytical framework and life cycle–based model and presents typical energy use and greenhouse gas (GHG) emissions for California almonds. This part II article builds on this by exploring uncertainty in the life cycle model through sensitivity and scenario analysis, and by examining temporary carbon storage in the orchard. Sensitivity analysis shows life cycle GHG emissions are most affected by biomass fate and utilization, followed by nitrous oxide emissions rates from orchard soils. Model sensitivity for net energy consumption is highest for irrigation system parameters, followed by biomass fate and utilization. Scenario analysis shows utilization of orchard biomass for electricity production has the greatest potential effect, assuming displacement methods are used for co‐product allocation. Results of the scenario analysis show that 1 kilogram (kg) of almond kernel and associated co‐products are estimated to cause between ?3.12 to 2.67 kg carbon dioxide equivalent (CO₂‐eq) emissions and consume between 27.6 to 52.5 megajoules (MJ) of energy. Co‐product displacement credits lead to avoided emissions of between ?1.33 to 2.45 kg CO₂‐eq and between ?0.08 to 13.7 MJ of avoided energy use, leading to net results of ?1.39 to 3.99 kg CO₂‐eq and 15.3 to 52.6 MJ per kg kernel (net results are calculated by subtracting co‐product credits from the results for almonds and co‐products). Temporary carbon storage in orchard biomass and soils is accounted for by using alternative global warming characterization factors and leads to a 14% to 18% reduction in CO₂‐eq emissions. Future studies of orchards and other perennial cropping systems should likely consider temporary carbon storage.     

Policy Implications of Allocation Methods in the Life Cycle Analysis of Integrated Corn and Corn Stover Ethanol Production

A biorefinery may produce multiple fuels from more than one feedstock. The ability of these fuels to qualify as one of the four types of biofuels under the US Renewable Fuel Standard and to achieve a low carbon intensity score under California’s Low Carbon Fuel Standard can be strongly influenced by the approach taken to their life cycle analysis (LCA). For example, in facilities that may co-produce corn grain and corn stover ethanol, the ethanol production processes can share the combined heat and power (CHP) that is produced from the lignin and liquid residues from stover ethanol production. We examine different LCA approaches to corn grain and stover ethanol production considering different approaches to CHP treatment. In the baseline scenario, CHP meets the energy demands of stover ethanol production first, with additional heat and electricity generated sent to grain ethanol production. The resulting greenhouse gas (GHG) emissions for grain and stover ethanol are 57 and 25 g-CO₂eq/MJ, respectively, corresponding to a 40 and 74 % reduction compared to the GHG emissions of gasoline. We illustrate that emissions depend on allocation of burdens of CHP production and corn farming, along with the facility capacities. Co-product handling techniques can strongly influence LCA results and should therefore be transparently documented.     

Fuel Economy and Greenhouse Gas Emissions Labeling for Plug‐In Hybrid Vehicles from a Life Cycle Perspective

Fuel economy has been an effective indicator of vehicle greenhouse gas (GHG) emissions for conventional gasoline‐powered vehicles due to the strong relationship between fuel economy and vehicle life cycle emissions. However, fuel economy is not as accurate an indicator of vehicle GHG emissions for plug‐in hybrid (PHEVs) and pure battery electric vehicles (EVs). Current vehicle labeling efforts by the U.S. Environmental Protection Agency (EPA) and Department of Transportation have been focused on providing energy and environmental information to consumers based on U.S. national average data. This article explores the effects of variations in regional grids and regional daily vehicle miles traveled (VMT) on the total vehicle life cycle energy and GHG emissions of electrified vehicles and compare these results with information reported on the label and on the EPA's fuel economy Web site. The model results suggest that only 25% of the life cycle emissions from a representative PHEV are reflected on current vehicle labeling. The results show great variation in total vehicle life cycle emissions due to regional grid differences, including an approximately 100 gram per mile life cycle GHG emissions difference between the lowest and highest electric grid regions and up to a 100% difference between the state‐specific emission values within the same electric grid regions. Unexpectedly, for two regional grids the life cycle GHG emissions were higher in electric mode than in gasoline mode. We recommend that labels include stronger language on their deficiencies and provide ranges for GHG emissions from vehicle charging in regional electricity grids to better inform consumers.     

Implications of land class and environmental factors on life cycle GHG emissions of Miscanthus as a bioenergy feedstock

Replacement of fossil fuels with sustainably produced biomass crops for energy purposes has the potential to make progress in addressing climate change concerns, nonrenewable resource use, and energy security. The perennial grass Miscanthus is a dedicated energy crop candidate being field tested in Ontario, Canada, and elsewhere. Miscanthus could potentially be grown in areas of the province that differ substantially in terms of agricultural land class, environmental factors and current land use. These differences could significantly affect Miscanthus yields, input requirements, production practices, and the types of crops being displaced by Miscanthus establishment. This study assesses implications on life cycle greenhouse gas (GHG) emissions of these differences through evaluating five Miscanthus production scenarios within the Ontario context. Emissions associated with electricity generation with Miscanthus pellets in a hypothetically retrofitted coal generating station are examined. Indirect land use change impacts are not quantified but are discussed. The net life cycle emissions for Miscanthus production varied greatly among scenarios (?90–170 kg CO₂eq per oven dry tonne of Miscanthus bales at the farm gate). In some cases, the carbon stock dynamics of the agricultural system offset the combined emissions of all other life cycle stages (i.e., production, harvest, transport, and processing of biomass). Yield and soil C of the displaced agricultural systems are key parameters affecting emissions. The systems with the highest potential to provide reductions in GHG emissions are those with high yields, or systems established on land with low soil carbon. All scenarios have substantially lower life cycle emissions (?20–190 g CO₂eq kWh^?1) compared with coal‐generated electricity (1130 g CO₂eq kWh^?1). Policy development should consider the implication of land class, environmental factors, and current land use on Miscanthus production.     

Energy Requirements and Greenhouse Gas Emissions of Maize Production in the USA

This meta-study quantitatively and qualitatively compares 21 published life cycle assessment (LCA)-type studies for energy consumption and greenhouse gas (GHG) emissions of maize production in the USA. Differences between the methodologies and numerical results obtained are described. Nonrenewable energy consumption in maize production (from cradle-to-farm gate) ranges from 1.44 to 3.50 MJ/kg of maize, and GHG emissions associated with maize production range from ?27 to 436 g CO₂ equivalent/kg of maize. Large variations between studies exist within the input data for lime application, fuels purchased, and life cycle inventory data for fertilizer and agrochemical production. Although most studies use similar methodological approaches, major differences between studies include the following: (1) impacts associated with human labor and farm machinery production, (2) changes in carbon dioxide emissions resulting from soil organic carbon levels, and (3) indirect N₂O emissions.     

Life cycle emissions and energy study of biodiesel derived from waste cooking oil and diesel in Singapore

Background, aim and scope  

Biodiesel derived from Waste Cooking Oil (WCO) is considered highly environmentally sustainable since WCO is a waste product from domestic and commercial cooking processes and then recycled to a transportation fuel in Singapore. In addition, it avoids the conversion of land use for crop production. This is a strong advantage for Singapore which has relatively smaller land space than other countries. The import of virgin oil as feedstock into Singapore is also avoided. Therefore, the more appropriate feedstock to produce biodiesel in Singapore context is WCO. According to the National Environment Agency, diesel vehicles in Singapore contribute 50% of the total particulate matter smaller than 2.5 μm (PM_0.25) emissions to air ambient. Hence, the aim of this life cycle assessment study was to compare the environmental performances of biodiesel derived from WCO and low sulphur diesel in terms of global warming potential, life cycle energy efficiency (LCEE) and fossil energy ratio (FER) using the life cycle inventory. The results of this study would serve as a reference for energy policy makers and environmental agencies.     

Modeling Corn Ethanol and Climate

New fuel regulations based on life cycle greenhouse gas (GHG) emissions have focused renewed attention on life cycle models of biofuels. The BESS model estimates 25% lower life cycle GHG emissions for corn ethanol than does the well-known GREET model, which raises questions about which model is more accurate. I develop a life cycle metamodel to compare the GREET and BESS models in detail and to explain why the results from these models diverge. I find two main reasons for the divergence: (1) BESS models a more efficient biorefinery than is modeled in the cases to which its results have been compared, and (2) in several instances BESS fails to properly count upstream emissions. Adjustments to BESS to account for these differences raise the estimated global warming intensity (not including land use change) of the corn ethanol pathway considered in that model from 45 to 61 g CO₂e MJ⁻¹. Adjusting GREET to use BESS's biorefinery performance and coproduct credit assumptions reduces the GREET estimate from 64 to 61 g CO₂e MJ⁻¹. Although this analysis explains the gap between the two models, both models would be improved with better data on corn production practices and by better treatment of agricultural inputs.     

Greenhouse gas emissions from forestry in East Norway

Purpose

So far no calculations have been made for greenhouse gas (GHG) emissions from forestry in East Norway. This region stands for 80 % of the Norwegian timber production. The aim of this study was to assess the annual GHG emissions of Norwegian forestry in the eastern parts of the country from seed production to final felling and transport of timber to sawmill and wood processing industry (cradle-to-gate inventory), based on specific Norwegian data.

Methods

The life cycle inventory was conducted with SimaPro applying primary and secondary data from Norwegian forestry. GHG emissions of fossil-related inputs from the technosphere were calculated for the functional unit of 1 m³ timber extracted and delivered to industry gate in East Norway in 2010. The analysis includes seed and seedling production, silvicultural operations, forest road construction and upgrading, thinning, final felling, timber forwarding and timber transport on road and rail from the forest to the industry. Norwegian time studies of forestry machines and operations were used to calculate efficiency, fuel consumption and transport distances. Due to the lack of specific Norwegian data in Ecoinvent, we designed and constructed unit processes based on primary and secondary data from forestry in East Norway.

Results and discussion

GHG emissions from forestry in East Norway amounted to 17.893 kg CO₂-equivalents per m³ of timber delivered to industry gate in 2010. Road transport of timber accounted for almost half of the total GHG emissions, final felling and forwarding for nearly one third of the GHG emissions. Due to longer road transport distances, pulpwood had higher impact on the climate change category than saw timber. The construction of forest roads had the highest impact on the natural land transformation category. The net CO₂ emissions of fossil CO₂ corresponded to 2.3 % of the CO₂ sequestered by 1 m³ of growing forest trees and were compared to a calculation of biogenic CO₂ release from the forest floor as a direct consequence of harvesting.

Conclusions

Shorter forwarding and road transport distances, increased logging truck size and higher proportion of railway transport may result in lower emissions per volume of transported timber. A life cycle assessment of forestry may also consider impacts on environmental categories other than climate change. Biogenic CO₂ emissions from the soil may be up to 10 times higher than the fossil-related emissions, at least in a short-term perspective, and are highly dependent on stand rotation length.     

Climate change impacts on life cycle greenhouse gas (GHG) emissions savings of biomethanol from corn and soybean

Purpose

The purpose of this study is to assess and calculate the potential impacts of climate change on the greenhouse gas (GHG) emissions reduction potentials of combined production of whole corn bioethanol and stover biomethanol, and whole soybean biodiesel and stalk biomethanol. Both fuels are used as substitutes to conventional fossil-based fuels. The product system includes energy crop (feedstock) production and transportation, biofuels processing, and biofuels distribution to service station.

Methods

The methodology is underpinned by life cycle thinking. Crop system model and life cycle assessment (LCA) model are linked in the analysis. The Decision Support System for Agrotechnology Transfer – crop system model (DSSAT-CSM) is used to simulate biomass and grain yield under different future climate scenarios generated using a combination of temperature, precipitation, and atmospheric CO₂. Historical weather data for Gainesville, Florida, are obtained for the baseline period (1981–1990). Daily minimum and maximum air temperatures are projected to increase by +2.0, +3.0, +4.0, and +5.0 °C, precipitation is projected to change by ±20, 10, and 5 %, and atmospheric CO₂ concentration is projected to increase by +70, +210, and +350 ppm. All projections are made throughout the growing season. GaBi 4.4 is used as primary LCA modelling software using crop yield data inputs from the DSSAT-CSM software. The models representation of the physical processes inventory (background unit processes) is constructed using the ecoinvent life cycle inventory database v2.0.

Results and discussion

Under current baseline climate condition, net greenhouse gas (GHG) emissions savings per hectare from corn-integrated biomethanol synthesis (CIBM) and soybean-integrated biomethanol synthesis (SIBM) were calculated as ?8,573.31 and ?3,441 kg CO₂-eq. ha^?1 yr^?1, respectively. However, models predictions suggest that these potential GHG emissions savings would be impacted by changing climate ranging from negative to positive depending on the crop and biofuel type, and climate scenario. Increased atmospheric level of CO₂ tends to minimise the negative impacts of increased temperature.

Conclusions

While policy measures are being put in place for the use of renewable biofuels driven by the desire to reduce GHG emissions from the use of conventional fossil fuels, climate change would also have impacts on the potential GHG emissions reductions resulting from the use of these renewable biofuels. However, the magnitude of the impact largely depends on the biofuel processing technology and the energy crop (feedstock) type.     

Life cycle assessment of eucalyptus short rotation coppices for bioenergy production in southern France

Short rotation coppices (SRC) are considered prime candidates for biomass production, yielding good‐quality feedstock that is easy to harvest. Besides technical, social and economical aspects, environmental issues are important to be taken into account when developing SRC. Here, we evaluated the environmental impacts of delivering 1 GJ of heat from eucalyptus SRC using life cycle assessment (LCA), based on management scenarios involving different rotations lengths, fertilizer input rates, stem densities and harvest methods. Compared to equivalent fossil chains, all eucalyptus scenarios achieved savings of fossil energy and greenhouse gas (GHG) emissions in the 80–90% range, and had generally lower impacts, except for eutrophication. The 3 year rotation scenario was the most energy and GHG‐intensive, whereas manual felling for the longer rotations resulted in twofold larger photochemical ozone impacts compared to the other scenarios. Transportation of wood chips and fertilization were the top two contributors to the impacts, the latter being more important with the shorter rotation lengths due to the evergreen character of eucalyptus. The possibility of including ecosystem carbon dynamics was also investigated, by translating the temporary sequestration of atmospheric CO₂ in the above and belowground biomass of eucalyptus as CO₂ savings using various published equivalence factors. This offset the life cycle GHG emissions of heat provision from eucalyptus SRC by 70–400%.     

Contribution of N2O to the greenhouse gas balance of first-generation biofuels

In this study, we analyze the impact of fertilizer‐ and manure‐induced N₂O emissions due to energy crop production on the reduction of greenhouse gas (GHG) emissions when conventional transportation fuels are replaced by first‐generation biofuels (also taking account of other GHG emissions during the entire life cycle). We calculate the nitrous oxide (N₂O) emissions by applying a statistical model that uses spatial data on climate and soil. For the land use that is assumed to be replaced by energy crop production (the ‘reference land‐use system’), we explore a variety of options, the most important of which are cropland for food production, grassland, and natural vegetation. Calculations are also done in the case that emissions due to energy crop production are fully additional and thus no reference is considered. The results are combined with data on other emissions due to biofuels production that are derived from existing studies, resulting in total GHG emission reduction potentials for major biofuels compared with conventional fuels. The results show that N₂O emissions can have an important impact on the overall GHG balance of biofuels, though there are large uncertainties. The most important ones are those in the statistical model and the GHG emissions not related to land use. Ethanol produced from sugar cane and sugar beet are relatively robust GHG savers: these biofuels change the GHG emissions by −103% to −60% (sugar cane) and −58% to −17% (sugar beet), compared with conventional transportation fuels and depending on the reference land‐use system that is considered. The use of diesel from palm fruit also results in a relatively constant and substantial change of the GHG emissions by −75% to −39%. For corn and wheat ethanol, the figures are −38% to 11% and −107% to 53%, respectively. Rapeseed diesel changes the GHG emissions by −81% to 72% and soybean diesel by −111% to 44%. Optimized crop management, which involves the use of state‐of‐the‐art agricultural technologies combined with an optimized fertilization regime and the use of nitrification inhibitors, can reduce N₂O emissions substantially and change the GHG emissions by up to −135 percent points (pp) compared with conventional management. However, the uncertainties in the statistical N₂O emission model and in the data on non‐land‐use GHG emissions due to biofuels production are large; they can change the GHG emission reduction by between −152 and 87 pp.     

Life cycle assessment of energy and GHG emissions during ethanol production from grass straws using various pretreatment processes

Purpose  

The aim of this study was to perform a well-to-pump life cycle assessment (LCA) to investigate the overall net energy balance and environmental impact of bioethanol production using Tall Fescue grass straw as feedstock. The energy requirements and greenhouse gas (GHG) emissions were compared to those of gasoline to explore the potential of bioethanol as sustainable fuel.