Regional variations in greenhouse gas emissions of biobased products in the United States—corn-based ethanol and soybean oil

Intensive cultivation and post-harvest vegetable oil production stages are major sources of greenhouse gas (GHG) emissions. Variation between production systems and reporting disparity have resulted in discordance in previous emissions estimates. The aim of this study was to assess global systems-wide variation in GHG emissions resulting from palm, soybean, rapeseed and sunflower oil production. Such an analysis is critical to understand the implications of meeting increasing edible oil demand. To achieve this, we performed a unified re-analysis of life cycle input data from diverse palm, soybean, rapeseed, and sunflower oil production systems, from a saturating search of published literature. The resulting dataset reflects almost 6000 producers in 38 countries, and is representative of over 71% of global vegetable oil production. Across all oil crop systems, median GHG emissions were 3.81 kg CO2e per kg refined oil. Crop specific median emissions ranged from 2.49 kg CO2e for rapeseed oil to 4.25 kg CO2e for soybean oil per kg refined oil. Determination of the carbon cost of agricultural land occupation revealed that carbon storage potential in native compared to agricultural land cover drives variation in production GHG emissions, and indicates that expansion of production in low carbon storage potential land, whilst reforesting areas of high carbon storage potential, could reduce net GHG emissions whilst boosting productivity. Nevertheless, there remains considerable scope to improve sustainability within current production systems, including through increasing yields whilst limiting application of inputs with high carbon footprints, and in the case of palm oil through more widespread adoption of methane capture technologies in processing stages.

Greenhouse gas release from land use change (the so-called "carbon debt") has been identified as a potentially significant contributor to the environmental profile of biofuels. The time required for biofuels to overcome this carbon debt due to land use change and begin providing cumulative greenhouse gas benefits is referred to as the "payback period" and has been estimated to be 100-1000 years depending on the specific ecosystem involved in the land use change event. Two mechanisms for land use change exist: "direct" land use change, in which the land use change occurs as part of a specific supply chain for a specific biofuel production facility, and "indirect" land use change, in which market forces act to produce land use change in land that is not part of a specific biofuel supply chain, including, for example, hypothetical land use change on another continent. Existing land use change studies did not consider many of the potentially important variables that might affect the greenhouse gas emissions of biofuels. We examine here several variables that have not yet been addressed in land use change studies. Our analysis shows that cropping management is a key factor in estimating greenhouse gas emissions associated with land use change. Sustainable cropping management practices (no-till and no-till plus cover crops) reduce the payback period to 3 years for the grassland conversion case and to 14 years for the forest conversion case. It is significant that no-till and cover crop practices also yield higher soil organic carbon (SOC) levels in corn fields derived from former grasslands or forests than the SOC levels that result if these grasslands or forests are allowed to continue undisturbed. The United States currently does not hold any of its domestic industries responsible for its greenhouse gas emissions. Thus the greenhouse gas standards established for renewable fuels such as corn ethanol in the Energy Independence and Security Act (EISA) of 2007 set a higher standard for that industry than for any other domestic industry. Holding domestic industries responsible for the environmental performance of their own supply chain, over which they may exert some control, is perhaps desirable (direct land use change in this case). However, holding domestic industries responsible for greenhouse gas emissions by their competitors worldwide through market forces (via indirect land use change in this case) is fraught with a host of ethical and pragmatic difficulties. Greenhouse gas emissions associated with indirect land use change depend strongly on assumptions regarding social and environmental responsibilities for actions taken, cropping management approaches, and time frames involved, among other issues.

Life cycle greenhouse gas emissions impacts of the adoption of the EU Directive on biofuels in Spain. Effect of the import of raw materials and land use changes

Biofuels, Bioenergy and the Bioeconomy in North and South.

Soybean products play an important role in Argentina's bioeconomy. Greenhouse gas (GHG) emissions from soybean byproducts have been widely assessed to meet sustainability requirements for soybean oil biodiesel, especially by decision makers in the private and public sectors, in response to growing EU and USA market demands. Previous studies have focused primarily on GHG emissions from soybean cultivation and biodiesel production but not on the main byproducts like soy oil and meal. Over the past 15 years, we have participated in these calculations, with methods certified by independent verification bodies. Using real field data, this study presents the total GHG emissions of Argentina's main soybean products taking into account agriculture, biorefinery, and distribution stages and following EU Renewable Energy Directives I and II (EU RED I and II). The aim of the study was to assess the GHG emissions of Argentina's soybean‐producing chain through an integrated life cycle approach, applying mass and energy allocation methods. The results indicate that GHG emissions from soybean cultivation ranged from 186 to 266 kgCO2eq per ton of dry soybean, and from 9 to 13 gCO2eq per MJ of biodiesel. The highest emissions were associated with crop residues, agrochemical production, and fuel use. Over 50% of emissions in soybean farming were attributed to soil N2O, mainly from crop residues, according to the Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies (GREET) model. Emissions from soybean oil production were estimated at 149.72 kgCO2eq per ton of oil, consistent with previous studies. For soybean meal production, emissions resulted in 73.57 kgCO2eq per ton of meal, with 66.1% attributed to natural gas consumption. This study provides a comprehensive evaluation of GHG emissions across the soybean production chain. Its results can support decision making for emission reductions in key stages of the process.

This study updates the life cycle greenhouse gas (GHG) emissions for soybean biodiesel with revised system boundaries and the inclusion of indirect land use change using the most current set of agricultural data. The updated results showed that life cycle GHG emission from biodiesel use was reduced by 81.2% compared to 2005 baseline diesel. When the impacts of lime application and soil N2O emissions were excluded for more direct comparison with prior results published by the National Renewable Energy Laboratory (NREL), the reduction was 85.4%. This is a significant improvement over the 78.5% GHG reduction reported in the NREL study. Agricultural lime accounted for 50.6% of GHG from all agricultural inputs. Soil N2O accounted for 18.0% of total agricultural emissions. The improvement in overall GHG reduction was primarily due to lower agricultural energy usage and improved soybean crushing facilities. This study found that soybean meal and oil price data from the past ten years had a significant positive correlation (R2 = 0.73); hence, it is argued that soybean meal and oil are both responsible for indirect land use change from increased soybean demand. It is concluded that when there is a strong price correlation among co-products, system boundary expansion without a proper co-product allocation for indirect land use change produces erroneous results. When the emissions associated with predicted indirect land use change were allocated and incorporated using U.S. EPA model data, the GHG reduction for biodiesel was 76.4% lower than 2005 baseline diesel.

PurposeVariability in consumer behaviour can significantly influence the environmental performance of products and their associated impacts and this is typically not quantified in life cycle assessments. The goal of this paper is to demonstrate how consumer behaviour data can be used to understand and quantify the variability in the greenhouse gas emissions from domestic laundry washing across Europe.MethodsData from a pan-European consumer survey of product usage and washing habits was combined with internal company data on product format greenhouse gas (GHG) footprints and in-home measurement of energy consumption of laundry washing as well as literature data to determine the GHG footprint of laundry washing. The variability associated with four laundry detergent product formats and four wash temperature settings in washing machines were quantified on a per wash cycle basis across 23 European countries. The variability in GHG emissions associated with country electricity grid mixes was also taken into account. Monte Carlo methods were used to convert the variability in the input parameters into variability of the life cycle GHG emissions. Rank correlation analysis was used to quantify the importance of the different sources of variability.Results and discussionBoth inter-country differences in background electricity mix as well as intra-country variation in consumer behaviour are important for determining the variability in life cycle GHG emissions of laundry detergents. The average GHG emissions related to the laundry washing process in the 23 European countries in 2014 was estimated to be 5 × 102 g CO2−eq/wash cycle, but varied by a factor of 6.5 between countries. Intra-country variability is between a factor of 3.5 and 5.0 (90% interval). For countries with a mainly fossil-based electricity system, the dominant source of variability in GHG emissions results from consumer choices in the use of washing machines. For countries with a relatively low-carbon electricity mix, variability in life cycle GHG emissions is mainly determined by laundry product-related parameters.ConclusionsThe combination of rich data sources enabled the quantification of the variability in the life cycle GHG emissions of laundry washing which is driven by a variety of consumer choices, manufacturer choices and infrastructural differences of countries. The improved understanding of the variability needs to be balanced against the cost and challenges of assessing of consumer habits.

In recent years, many public policies and regulations have supported the biofuel expansion in many countries. One of the main reasons for this support was concern about the environment, since biofuels, when used in place of traditional fossil fuel-based transportation fuels, lead to lower greenhouse gas (GHG) emissions. However, this argument was contradicted with the discussion on indirect land use change (ILUC) from biofuel expansion, leading to higher GHG emissions. Acknowledging this debate, we present a literature review focusing on the recent studies on ILUC and GHG emissions related to the biomass production and processing. We provide a summary of the main issues in this debate that focus on the impact of model characteristics on the GHG emissions computation, specifically those triggered by land use change.

Environmental aspects of ethanol derived from no-tilled corn grain: nonrenewable energy consumption and greenhouse gas emissions

A dominance analysis of greenhouse gas emissions, beef output and land use of German dairy farms

Previous studies of greenhouse gas emissions (GHGE) from beef production systems in northern Australia have been based on models of ‘steady-state’ herd structures that do not take into account the considerable inter-annual variation in liveweight gain, reproduction and mortality rates that occurs due to seasonal conditions. Nor do they consider the implications of flexible stocking strategies designed to adapt these production systems to the highly variable climate. The aim of the present study was to quantify the variation in total GHGE (t CO2e) and GHGE intensity (t CO2e/t liveweight sold) for the beef industry in northern Australia when variability in these factors was considered. A combined GRASP–Enterprise modelling platform was used to simulate a breeding–finishing beef cattle property in the Burdekin River region of northern Queensland, using historical climate data from 1982–2011. GHGE was calculated using the method of Australian National Greenhouse Gas Inventory. Five different stocking-rate strategies were simulated with fixed stocking strategies at moderate and high rates, and three flexible stocking strategies where the stocking rate was adjusted annually by up to 5%, 10% or 20%, according to pasture available at the end of the growing season. Variation in total annual GHGE was lowest in the ‘fixed moderate’ (~9.5 ha/adult equivalent (AE)) stocking strategy, ranging from 3799 to 4471 t CO2e, and highest in the ‘fixed high’ strategy (~5.9 ha/AE), which ranged from 3771 to 7636 t CO2e. The ‘fixed moderate’ strategy had the least variation in GHGE intensity (15.7–19.4 t CO2e/t liveweight sold), while the ‘flexible 20’ strategy (up to 20% annual change in AE) had the largest range (10.5–40.8 t CO2e/t liveweight sold). Across the five stocking strategies, the ‘fixed moderate’ stocking-rate strategy had the highest simulated perennial grass percentage and pasture growth, highest average rate of liveweight gain (121 kg/steer), highest average branding percentage (74%) and lowest average breeding-cow mortality rate (3.9%), resulting in the lowest average GHGE intensity (16.9 t CO2e/t liveweight sold). The ‘fixed high’ stocking rate strategy (~5.9 ha/AE) performed the poorest in each of these measures, while the three flexible stocking strategies were intermediate. The ‘fixed moderate’ stocking strategy also yielded the highest average gross margin per AE carried and per hectare. These results highlight the importance of considering the influence of climate variability on stocking-rate management strategies and herd performance when estimating GHGE. The results also support a body of previous work that has recommended the adoption of moderate stocking strategies to enhance the profitability and ecological stability of beef production systems in northern Australia.

Southland has witnessed a pronounced change in its agricultural landscape in recent years. Greater profitability of dairy relative to sheep farming has led to a large number of dairy conversions over the last 20 years, with the scope for further substantive conversions into the future. The economic and social benefits have been extensively reported, but less is understood about the environmental impacts associated with this land use change. To investigate the potential effect of land use change from sheep and beef to dairy on economic and environmental outcomes in the Southland region of New Zealand, farm-scale enterprise simulation models were linked with spatially explicit land resource information. By overlaying individual farm parcels with land resource information, land area and topography data for each farm were attained. Estimated pasture production (PP) for each land use capability (LUC) Class provided indicative data for the modelling exercise on the productive use of the land across the region. The approach provided a method for the expansion of farm scale modelling to a regional scale. A representative DairyNZ Production System 3 was used to investigate the influence of increasing dairy cow numbers and associated inputs at the farm level. A representative sheep and beef farm was also modelled. To account for a dairy support area, used to carry dry cows during the winter, a second step involved the modelling of a larger System 3 dairy farm that included a milking platform area and an adjacent support area. This farm system was considered for regional up-scaling to allow for a more comprehensive capture of nutrient losses and financial outcomes. Estimates of annual nitrogen (N) leaching values from dairy farms ranged from 21 to 44 kg N/ha, and were higher for farms with greater pasture production potential, due to the greater amount of N cycling and increased number of urine patches from the higher number of livestock numbers carried. Annual N leaching from the sheep and beef farms ranged from 8 to 17 kg N/ha. Annual greenhouse gas (GHG) emissions were also higher from farms with greater productive potential, ranging from 7.1 to 15.4 t CO2-e/ha for dairy and from 2.1 to 6.9 t CO2-e/ha for sheep and beef farms. In contrast to leaching, GHG emissions were higher from poorly-drained soils compared with well-drained soils; annual nitrous oxide (N2O) emissions accounted for 22% and 35% of total GHG emissions from dairy farms on well- and poorly-drained soils, respectively, and up to 40% from sheep and beef farms on poorly-drained soils. The new dairy farms resulting from conversion would largely fall in an N leaching range of 25 to 31 kg N/ha and have GHG emissions of 7.0 to 10.5 t CO2-e/ha. Depending on future regional regulations that may be implemented, a large number of potential dairy farms might leach more N than the allowable limit, and mitigation techniques will need to be implemented. A shift in land use from the current 15% of land area under dairying to a potential 46% led to a large increase in regional profit (76%). The environmental impact from this land use change, however, became substantial, with regional nitrate leaching increasing by 34% and GHG emissions by 24%. Conversion of more farms into dairying increased farm profit, N leaching and GHG emissions in the region compared with the current situation. It must be noted, however, that the up-scaling of potential dairy conversion was based on land resources defined by the productive potential of the landscapes found in Southland and that the actual level of conversion could differ substantially if additional or different farming scenarios were tested.

Biofuels have gained increasing attention as an alternative to fossil fuels for several reasons, one of which is their potential to reduce the greenhouse gas (GHG) emissions from the transportation sector. Recent studies have questioned the validity of claims about the potential for biofuels to reduce GHG emissions relative to the liquid fossil fuels they are replacing when emissions due to direct and indirect land use changes (ILUCs) that accompany biofuels are included in the life-cycle GHG intensity of biofuels. Studies estimate that the GHG emissions released from ILUC could more than offset the direct GHG savings by producing biofuels and replacing liquid fossil fuels and create a “carbon debt” with a long payback period. The estimates of this payback period, however, vary widely across biofuels from different feedstocks and even for a single biofuel across different modeling assumptions. In the case of corn ethanol, this payback period is found to range from 15 to 200 years. We discuss the challenges in estimating the ILUC effect of a biofuel and differences across biofuels, and its sensitivity to the assumptions and policy scenarios considered by different economic models. We also discuss the implications of ILUC for designing policies that promote biofuels and seek to reduce GHG emissions. In a first best setting, a global carbon tax is needed to set both direct and indirect land use change emissions to their optimal levels. However, it is unclear whether unilateral GHG mitigation policies, even if they penalize the ILUC related emissions, would increase social welfare and lead to optimal emission levels. In the absence of a global carbon tax, incentivizing sustainable land use practices through certification standards, government regulations and market-based pressures may be a viable option for reducing ILUC.

Assessment of the potential of digestibility-improving enzymes to reduce greenhouse gas emissions from broiler production

Few life cycle assessments (LCAs) on willow biomass production have investigated the effects of key geographically specific parameters. This study uses a spatial LCA model for willow biomass production to determine spatially explicit greenhouse gas (GHG) emissions and energy return on investment (EROI), including land use conversion from pasture and cropland or grassland. There were negative GHG emissions on 92% of the land identified as suitable for willow biomass production, indicating this system’s potential for climate change mitigation. For willow planted on cropland or pasture, life cycle GHG emissions ranged from −53.2 to −176.9 kg CO2eq Mg-1. When willow was grown on grassland the projected decrease in soil organic carbon resulted in a slightly positive GHG balance. Changes in soil organic carbon (SOC) associated with land use change, transportation distance, and willow yield had the greatest impacts on GHG emissions. Results from the uncertainty analysis exhibited large variations in GHG emissions between counties arising from differences in these parameters. The average EROI across the entire region was 19.2. Willow biomass can be a carbon negative or low-carbon energy source with a high EROI in regions with similar infrastructure, transportation distances, and growing conditions such as soil characteristics, land cover types, and climate.