Shift in the marginal supply of vegetable oil

Background, Aims and Scope

The consequential approach to system delimitation in LCA requires that consideration of the technologies and suppliers included are ‘marginal’, i.e. that they are actually affected by a change in demand. Furthermore, coproduct allocation must be avoided by system expansion. Vegetable oils constitute a significant product group included in many LCAs that are intended for use in decision support. This article argues that the vegetable oil market has faced major changes around the turn of the century. The aim of this study is to study the marginal supply of vegetable oil as it has shifted to palm oil and describe the product system of the new supply.

Methods

The methods for identification of marginal technologies and suppliers and for avoiding co-product allocation are based on the work of Weidema (2003). The marginal vegetable oil is identified on the basis of agricultural statistics on production volumes and prices. A co-product from palm oil production is palm kernel meal, which is used for fodder purposes where it has two main properties: protein and energy. When carrying out system expansion, these properties are taken into account.

Results

The major vegetable oils are soy oil, palm oil, rapeseed oil and sun oil. These oils are substitutable within the most common applications. Based on market trends, a shift from rapeseed oil to palm oil as the marginal vegetable oil is identified around the year 2000, when palm oil turns out to be the most competitive oil. It is recommended to regard palm oil and its dependent co-product palm kernel oil as the marginal vegetable oil. The analysis of the product system shows that the demand for 1 kg palm oil requires 4.49 kg FFB (oil palm fruit) and the displacement of 0.035 kg soybeans (marginal source of fodder protein) and 0.066 kg barley (marginal source of fodder energy).

Discussion

The identification of the marginal vegetable oil and the avoidance of co-product allocation by system expansion showed that several commodities may be affected when using the consequential approach. Hence, the product system for vegetable oils is relatively complex compared to traditional LCAs in which average technologies and suppliers are applied and in which co-product allocation is carried out by applying an allocation factor.

Conclusions

This article presents how the marginal vegetable oil can be identified and that co-product allocation between oils and meal can be avoided by system expansion, by considering the energy and protein content in the meal, which displaces a mix of the marginal sources of energy and protein for animal fodder (barley and soy meal, respectively).

Recommendations and Perspectives

The implication of a shift in the marginal vegetable oil is significant. Many LCAs on rapeseed oil have been conducted and are being used as decision support in the bio energy field. Thus, based on consequential LCA methodology, it is argued that these LCAs need to be revised, since they no longer focus on the oil actually affected.

     

Consequences of Future Data Center Deployment in Canada on Electricity Generation and Environmental Impacts: A 2015–2030 Prospective Study

The environmental impacts of data centers that provide information and communication technologies (ICTs) services are strongly related to electricity generation. With the increasing use of ICT, many data centers are expected to be built, causing more absolute impacts on the environment. Given that electricity distribution networks are very complex and dynamic systems, an environmental evaluation of future data centers is uncertain. This study proposes a new approach to investigate the consequences of future data center deployment in Canada and optimize this deployment based on the Energy 2020 technoeconomic model in combination with life cycle assessment methodology. The method determines specific electricity sources that will power the future Canadian data centers and computes related environmental impacts based on several indicators. In case‐study scenarios, the largest deployment of data centers leads to the smallest impact per megawatt of data centers for all of the environmental indicators. It is found that an increase in power demand by data centers would lead to a reduction in electricity exports to the United States, driving the United States to generate more electricity to meet its energy demand. Given that electricity generation in the United States is more polluting than in Canada, the deployment of data centers in Canada is indirectly linked to an increase in overall environmental impacts. However, though an optimal solution should be found to mitigate global greenhouse gas emissions, it is not clear whether the environmental burden related to U.S. electricity generation should be attributed to the Canadian data centers.     

When Comparing Alternative Fuel‐Vehicle Systems,Life Cycle Assessment Studies Should Consider Trends in Oil Production

Petroleum from unconventional reserves is making an increasingly important contribution to the transportation fuel supply, but is generally more expensive and has greater environmental burdens than petroleum from conventional sources. Life cycle assessments (LCAs) of alternative fuel‐vehicle technologies typically consider conventional internal combustion engine vehicles fueled by gasoline produced from the average petroleum slate used in refineries as a baseline. Large‐scale deployment of alternative fuel‐vehicle technologies will decrease petroleum demand and lead to decreased production at the economic margin (unconventional oil), but this is not considered in most current LCAs. If marginal petroleum resources have larger impacts than average petroleum resources, the environmental benefits of petroleum demand reduction are underestimated by the current modeling approaches. Often, models include some consequential‐based impacts (such as indirect land‐use change for biofuels), but exclude others (such as avoided unconventional oil production). This approach is inconsistent and does not provide a robust basis for public policy and private investment strategy decisions. We provide an example to illustrate the potential scale of these impacts, but further research is needed to establish and quantify these marginal effects and incorporate them into LCAs of both conventional and alternative fuel‐vehicle technologies.     

LCA of soybean meal

Background, Aim and Scope  Soybean meal is an important protein input to the European livestock production, with Argentina being an important supplier. The area cultivated with soybeans is still increasing globally, and so are the number of LCAs where the production of soybean meal forms part of the product chain. In recent years there has been increasing focus on how soybean production affects the environment. The purpose of the study was to estimate the environmental consequences of soybean meal consumption using a consequential LCA approach. The functional unit is ‘one kg of soybean meal produced in Argentina and delivered to Rotterdam Harbor’. Materials and Methods  Soybean meal has the co-product soybean oil. In this study, the consequential LCA method was applied, and co-product allocation was thereby avoided through system expansion. In this context, system expansion implies that the inputs and outputs are entirely ascribed to soybean meal, and the product system is subsequently expanded to include the avoided production of palm oil. Presently, the marginal vegetable oil on the world market is palm oil but, to be prepared for fluctuations in market demands, an alternative product system with rapeseed oil as the marginal vegetable oil has been established. EDIP97 (updated version 2.3) was used for LCIA and the following impact categories were included: Global warming, eutrophication, acidification, ozone depletion and photochemical smog. Results  Two soybean loops were established to demonstrate how an increased demand for soybean meal affects the palm oil and rapeseed oil production, respectively. The characterized results from LCA on soybean meal (with palm oil as marginal oil) were 721 gCO₂ eq. for global warming potential, 0.3 mg CFC11 eq. for ozone depletion potential, 3.1 g SO₂ eq. for acidification potential, −2 g NO₃ eq. for eutrophication potential and 0.4 g ethene eq. for photochemical smog potential per kg soybean meal. The average area per kg soybean meal consumed was 3.6 m²year. Attributional results, calculated by economic and mass allocation, are also presented. Normalised results show that the most dominating impact categories were: global warming, eutrophication and acidification. The ‘hot spot’ in relation to global warming, was ‘soybean cultivation’, dominated by N₂O emissions from degradation of crop residues (e.g., straw) and during biological nitrogen fixation. In relation to eutrophication and acidification, the transport of soybeans by truck is important, and sensitivity analyses showed that the acidification potential is very sensitive to the increased transport distance by truck. Discussion  The potential environmental impacts (except photochemical smog) were lower when using rapeseed oil as the marginal vegetable oil, because the avoided production of rapeseed contributes more negatively compared with the avoided production of palm oil. Identification of the marginal vegetable oil (palm oil or rapeseed oil) turned out to be important for the result, and this shows how crucial it is in consequential LCA to identify the right marginal product system (e.g., marginal vegetable oil). Conclusions  Consequential LCAs were successfully performed on soybean meal and LCA data on soybean meal are now available for consequential (or attributional) LCAs on livestock products. The study clearly shows that consequential LCAs are quite easy to handle, even though it has been necessary to include production of palm oil, rapeseed and spring barley, as these production systems are affected by the soybean oil co-product. Recommendations and Perspectives  We would appreciate it if the International Journal of Life Cycle Assessment had articles on the developments on, for example, marginal protein, marginal vegetable oil, marginal electricity (related to relevant markets), marginal heat, marginal cereals and, likewise, on metals and other basic commodities. This will not only facilitate the work with consequential LCAs, but will also increase the quality of LCAs.     

Global warming intensity of biofuel derived from switchgrass grown on marginal land in Michigan

Energy crops for biofuel production, especially switchgrass (Panicum virgatum), are of interest from a climate change perspective. Here, we use outputs from a crop growth model and life cycle assessment (LCA) to examine the global warming intensity (GWI; g CO₂ MJ⁻¹) and greenhouse gas (GHG) mitigation potential (Mg CO₂ year⁻¹) of biofuel systems based on a spatially explicit analysis of switchgrass grown on marginal land (abandoned former cropland) in Michigan, USA. We find that marginal lands in Michigan can annually produce over 0.57 hm³ of liquid biofuel derived from nitrogen-fertilized switchgrass, mitigating 1.2–1.5 Tg of CO₂ year⁻¹. About 96% of these biofuels can meet the Renewable Fuel Standard (60% reduction in lifecycle GHG emissions compared with conventional gasoline; GWI ≤37.2 g CO₂ MJ⁻¹). Furthermore, 73%–75% of these biofuels are carbon-negative (GWI less than zero) due to enhanced soil organic carbon (SOC) sequestration. However, simulations indicate that SOC levels would fail to increase and even decrease on the 11% of lands where SOC stocks >>200 Mg C ha⁻¹, leading to carbon intensities greater than gasoline. Results highlight the strong climate mitigation potential of switchgrass grown on marginal lands as well as the needs to avoid carbon rich soils such as histosols and wetlands and to ensure that productivity will be sufficient to provide net mitigation.     

Rebound effects of price differences

Goal, Scope and Background  Traditionally, comparative life cycle assessments (LCA) have not considered rebound effects, for instance in case of significant price differences among the compared products. No justifications have been made for this delimitation in scope. This article shows that price differences and the consequent effects of marginal consumer expenditure may influence the conclusions of comparative LCA significantly. We also show that considerations about rebound effects of price differences can be included in LCAs. Methods  The direct rebound effect of a price difference is marginal consumption. Based on statistical data on private consumption in different income groups (Statistics Denmark 2005a, 2005b), the present article provides an estimate of how an average Danish household will spend an additional 1 DKK for further consumer goods, when the household has gained money from choosing a cheaper product alternative. The approach is to use marginal income changes and the following changes in consumption patterns as an expression for marginal consumption. Secondly, the environmental impact potentials related to this marginal consumption are estimated by the use of environmental impact intensity data from an IO-LCA database (Weidema et al. 2005). Finally, it is discussed whether, and in which ways the conclusions of comparative LCAs can be affected by including the price difference between product alternatives. This is elucidated in a case study of a comparative LCA screening of two different kinds of Danish cheese products (Fricke et al. 2004). Results  Car purchase and driving, use and maintenance of dwelling, clothing purchase and insurance constitutes the largest percentages of the marginal consumption. In a case study of two cheeses, the including the impact potentials related to the price difference results in significant changes in the total impact potentials. Considering the relatively small price difference of the two products, it is likely also to have a significant influence on the results of comparative LCAs more generally. Discussion  The influence of marginal consumption in comparative LCAs is relevant to consider in situations with large differences in the price of the product alternatives being compared, and in situations with minor differences in the impact potentials related to the alternatives. However, different uncertainties are linked to determining the pattern for marginal consumption and the environmental impact potential related to this. These are first of all related to the method used, but also include inaccurate data of consumption in households, aggregation and weighting of income groups, aggregation of product groups, estimation and size of the price difference, and the general applicability of the results. Conclusion  Incorporating marginal consumption in consequential LCAs is possible in practice. In the case study used, including the rebound effects of the price difference has a significant influence on the result of the comparative LCA, as the result for the impact categories acidification and nutrient enrichment changes in favour of the expensive product. Recommendations and Perspectives  It is recommended that the rebound effects of price differences should be included more frequently in LCAs. In order to ensure this, further research in marginal consumption and investment patterns and IO data for different countries or regions is required. Furthermore, this study does not consider the economic distributional consequences of buying an expensive product instead of a cheaper product (e.g. related to how the profit is spent by those who provided the product). It should also be noted, that more expensive products not necessarily result in less consumption, as those who provided the product also will spend the money they have earned from the sale. Ideally, these consequences should also be further investigated. Likewise, the development of databases to include marginal consumption in PC-tools is needed. In general, considerations of marginal consumption would favour expensive product alternatives, depending, however, on the type of consumer. ESS-Submission Editor: Dr. David Hunkeler (david.hunkeler@aquaplustech.ch)     

LiSET: A Framework for Early‐Stage Life Cycle Screening of Emerging Technologies

While life cycle assessment (LCA) is a tool often used to evaluate the environmental impacts of products and technologies, the amount of data required to perform such studies make the evaluation of emerging technologies using the conventional LCA approach challenging. The development paradox is such that the inputs from a comprehensive environmental assessment has the greatest effect early in the development phase, and yet the data required to perform such an assessment are generally lacking until it is too late. Previous attempts to formalize strategies for performing streamlined or screening LCAs were made in the late 1990s and early 2000s, mostly to rapidly compare the environmental performance of product design candidates. These strategies lack the transparency and consistency required for the environmental screening of large numbers of early‐development candidates, for which data are even sparser. We propose the Lifecycle Screening of Emerging Technologies method (LiSET). LiSET is an adaptable screening‐to‐LCA method that uses the available data to systematically and transparently evaluate the environmental performance of technologies at low readiness levels. Iterations follow technological development and allow a progression to a full LCA if desired. In early iterations, LiSET presents results in a matrix structure combined with a “traffic light” color grading system. This format inherently communicates the high uncertainty of analysis at this stage and presents numerous environmental aspects assessed. LiSET takes advantage of a decomposition analysis and data not traditionally used in LCAs to gain insight to the life cycle impacts and ensure that the most environmentally sustainable technologies are adopted.     

Life cycle assessment of emerging technologies: A review

In recent literature, prospective application of life cycle assessment (LCA) at low technology readiness levels (TRL) has gained immense interest for its potential to enable development of emerging technologies with improved environmental performances. However, limited data, uncertain functionality, scale up issues and uncertainties make it very challenging for the standard LCA guidelines to evaluate emerging technologies and requires methodological advances in the current LCA framework. In this paper, we review published literature to identify major methodological challenges and key research efforts to resolve these issues with a focus on recent developments in five major areas: cross‐study comparability, data availability and quality, scale‐up issues, uncertainty and uncertainty communication, and assessment time. We also provide a number of recommendations for future research to support the evaluation of emerging technologies at low technology readiness levels: (a) the development of a consistent framework and reporting methods for LCA of emerging technologies; (b) the integration of other tools with LCA, such as multicriteria decision analysis, risk analysis, technoeconomic analysis; and (c) the development of a data repository for emerging materials, processes, and technologies.     

Generalized estimating equations: A pragmatic and flexible approach to the marginal GLM modelling of correlated data in the behavioural sciences

Within behavioural research, non‐normally distributed data with a complicated structure are common. For instance, data can represent repeated observations of quantities on the same individual. The regression analysis of such data is complicated both by the interdependency of the observations (response variables) and by their non‐normal distribution. Over the last decade, such data have been more and more frequently analysed using generalized mixed‐effect models. Some researchers invoke the heavy machinery of mixed‐effect modelling to obtain the desired population‐level (marginal) inference, which can be achieved by using simpler tools—namely by marginal models. This paper highlights marginal modelling (using generalized estimating equations [GEE]) as an alternative method. In various situations, GEE can be based on fewer assumptions and directly generate estimates (population‐level parameters) which are of immediate interest to the behavioural researcher (such as population means). Using four examples from behavioural research, we demonstrate the use, advantages, and limits of the GEE approach as implemented within the functions of the ‘geepack’ package in R.     

Method for assessing the potential of miscanthus on marginal lands for high temperature heat demand: The case studies of France and Belgium

Energy crops on marginal lands are seen as an interesting option to increase biomass contribution to the primary energy mix. However, in the literature there is currently a lack of integrated assessments of margin land availability, energy crop production potential and supply chain optimisation. Assessing the potential and the cost of these resources in a given region is therefore a difficult task. This work also emphasises the importance on a clear definition and discussion about marginal lands and the related ethical issues embedded in the concept to ensure positive societal impacts of the results. This study proposes a methodology to estimate and analyse, in terms of economic costs, the potential of miscanthus grown on marginal lands from the production to the final point of use. Different datasets are assembled and a supply chain optimisation model is developed to minimize the total cost of the system. Miscanthus is used as a representative energy crop for the Belgian and French case studies. High temperature heat demand is considered as final use. The miscanthus can be traded by truck either in the form of chips or pellets. The results show that the miscanthus on marginal lands could supply high temperature heat up to 38 TWh in France and 1.4 TWh in Belgium with an average cost of around 50 €/t. The different sensitivity analyses showed that the yield variation has the strongest influence on the final cost, together with the distances and the cost of production of miscanthus. The main pattern observed is the local consumption of miscanthus chips and export of the surplus (if any) to the neighbouring regions. Pellets are only of marginal interest for France and are never observed for Belgium. Distances and availability of sufficient feedstocks are the two main parameters impacting the production of pellets.     

Spatial auto-correlation and auto-regressive models estimation from sample survey data

Maximum likelihood estimation of the model parameters for a spatial population based on data collected from a survey sample is usually straightforward when sampling and non-response are both non-informative, since the model can then usually be fitted using the available sample data, and no allowance is necessary for the fact that only a part of the population has been observed. Although for many regression models this naive strategy yields consistent estimates, this is not the case for some models, such as spatial auto-regressive models. In this paper, we show that for a broad class of such models, a maximum marginal likelihood approach that uses both sample and population data leads to more efficient estimates since it uses spatial information from sampled as well as non-sampled units. Extensive simulation experiments based on two well-known data sets are used to assess the impact of the spatial sampling design, the auto-correlation parameter and the sample size on the performance of this approach. When compared to some widely used methods that use only sample data, the results from these experiments show that the maximum marginal likelihood approach is much more precise.     

Allocation of environmental burdens in co-product systems: Product-related burdens (Part 1)

This paper uses an industrial case study of a boron system producing five co-products to examine different allocation methods recommended by ISO 14041 and compare them with the allocation methods most commonly used by LCA practitioners. In particular, allocation by physical causality is discussed. The paper illustrates how the use of whole system modelling can help to identify the correct type of causality for allocation. The case examined here concerns marginal changes of product-related parameters in the system, in this case represented by the output of boron co-products. The analysis shows that in some cases it can he correct to allocate the burdens on the basis of a simple physical quantity, such as mass, as long as the allocation parameter is based on physical causation and is not chosen arbitrarily. In whole system modelling, the correct causality is identified by the model itself, so that the possibility of allocation by an arbitrary parameter is avoided. However, as for system disaggregation and expansion, allocation through mathematical modelling may only be possible if detailed data for the system are available.     

Attributional and consequential LCA of milk production

Background, aim and scope  Different ways of performing a life cycle assessment (LCA) are used to assess the environmental burden of milk production. A strong connection exists between the choice between attributional LCA (ALCA) and consequential LCA (CLCA) and the choice of how to handle co-products. Insight is needed in the effect of choice on results of environmental analyses of agricultural products, such as milk. The main goal of this study was to demonstrate and compare ALCA and CLCA of an average conventional milk production system in The Netherlands. Materials and methods  ALCA describes the pollution and resource flows within a chosen system attributed to the delivery of a specified amount of the functional unit. CLCA estimates how pollution and resource flows within a system change in response to a change in output of the functional unit. For an average Dutch conventional milk production system, an ALCA (mass and economic allocation) and a CLCA (system expansion) were performed. Impact categories included in the analyses were: land use, energy use, climate change, acidification and eutrophication. The comparison was based on four criteria: hotspot identification, comprehensibility, quality and availability of data. Results  Total environmental burdens were lower when using CLCA compared with ALCA. Major hotspots for the different impact categories when using CLCA and ALCA were similar, but other hotspots differed in contributions, order and type. As experienced by the authors, ALCA and use of co-product allocation are difficult to comprehend for a consequential practitioner, while CLCA and system expansion are difficult to comprehend for an attributional practitioner. Literature shows concentrates used within ALCA will be more understandable for a feeding expert than the feed used within CLCA. Outcomes of CLCA are more sensitive to uncertainties compared with ALCA, due to the inclusion of market prospects. The amount of data required within CLCA is similar compared with ALCA. Discussion  The main cause of these differences between ALCA and CLCA is the fact that different systems are modelled. The goal of the study or the research question to be answered defines the system under study. In general, the goal of CLCA is to assess environmental consequences of a change in demand, whereas the goal of ALCA is to assess the environmental burden of a product, assuming a status-quo situation. Nowadays, however, most LCA practitioners chose one methodology independent of their research question. Conclusions  This study showed it is possible to perform both ALCA (mass and economic allocation) and CLCA (system expansion) of milk. Choices of methodology, however, resulted in differences in: total quantitative outcomes, hotspots, degree of understanding and quality. Recommendations and perspectives  We recommend LCA practitioners to better distinguish between ALCA and CLCA in applied studies to reach a higher degree of transparency. Furthermore, we recommend LCA practitioners of different research areas to perform similar case studies to address differences between ALCA and CLCA of the specific products as the outcomes might differ from our study.     

Food purchases: Impacts from the consumers’ point of view investigated with a modular LCA

The goal of this research work was to assist consumers in considering environmental aspects of food consumption. A simplified, modular LCA approach has been used to evaluate the impacts from the consumers’ point of view. Comparative LCA’s have been calculated for five single aspects of decisions: type of agricultural practice, origin, packaging material, type of preservation, and consumption. The inventory for one module includes the environmental impacts related to one particular product characteristic. The modular LCA allows one to investigate the trade-offs among different decision parameters. It could be shown that most of the decision parameters might have an influence on the overall impact of a vegetable product. Greenhouse production and vegetables transported by air cause the highest surplus environmental impact. For meat products, the agricultural production determines the overall environmental impact. The total impact for vegetable or meat purchases may vary by a factor of eight or two-and-a-half. Different suggestions for consumers have been ranked according to the variation of average impacts, due to a marginal change of behaviour. Avoiding air-transported food products leads to the highest decrease of environmental impacts.     

Morphological and physiological traits for higher biomass production in perennial rhizomatous grasses grown on marginal land

A widespread criticism of growing energy crops is that they displace much needed food crops and cause upward pressure on food prices. One solution is the use of marginal land that is unfavourable for food production and is currently underutilized. However, the yield of crops growing on marginal land is reduced because they are subjected to a range of abiotic stresses such as extremes of temperature and rainfall and edaphic factors such as increased soil salinity. Therefore, to achieve acceptable economic returns on the use of this land it will be necessary to, on the one hand improve management practices and on the other to select for plant genotypes which are able to tolerate and potentially overcome the stressful conditions they are exposed to. Here, we review the morphological and physiological traits of perennial rhizomatous grasses that could be modified to overcome these stresses and to maximize biomass production on marginal land. The traits include aspects of crop phenology, canopy and leaf photosynthesis, biomass partitioning, nutrient and water use efficiency and heat, cold and salt tolerance. It is proposed that newly developed biotechnological methods combined with high‐throughput plant phenotyping offer opportunities to rapidly select new genotypes that could achieve economic yields on large areas of marginal land.     

Multiple two-sample testing under arbitrary covariance dependency with an application in imaging mass spectrometry

Large-scale hypothesis testing has become a ubiquitous problem in high-dimensional statistical inference, with broad applications in various scientific disciplines. One relevant application is constituted by imaging mass spectrometry (IMS) association studies, where a large number of tests are performed simultaneously in order to identify molecular masses that are associated with a particular phenotype, for example, a cancer subtype. Mass spectra obtained from matrix-assisted laser desorption/ionization (MALDI) experiments are dependent, when considered as statistical quantities. False discovery proportion (FDP) estimation and  control under arbitrary dependency structure among test statistics is an active topic in modern multiple testing research. In this context, we are concerned with the evaluation of associations between the binary outcome variable (describing the phenotype) and multiple predictors derived from MALDI measurements. We propose an inference procedure in which the correlation matrix of the test statistics is utilized. The approach is based on multiple marginal models. Specifically, we fit a marginal logistic regression model for each predictor individually. Asymptotic joint normality of the stacked vector of the marginal regression coefficients is established under standard regularity assumptions, and their (limiting) correlation matrix is estimated. The proposed method extracts common factors from the resulting empirical correlation matrix. Finally, we estimate the realized FDP of a thresholding procedure for the marginal p-values. We demonstrate a practical application of the proposed workflow to MALDI IMS data in an oncological context.     

Understanding the future of lithium: Part 2, temporally and spatially resolved life‐cycle assessment modeling

An array of emerging technologies, from electric vehicles to renewable energy systems, relies on large‐format lithium ion batteries (LIBs). LIBs are a critical enabler of clean energy technologies commonly associated with air pollution and greenhouse gas mitigation strategies. However, LIBs require lithium, and expanding the supply of lithium requires new lithium production capacity, which, in turn, changes the environmental impacts associated with lithium production since different resource types and ore qualities will be exploited. A question of interest is whether this will lead to significant changes in the environmental impacts of primary lithium over time. Part one of this two‐part article series describes the development of a novel resource production model that predicts future lithium demand and production characteristics (e.g., timing, location, and ore type). In this article, part two, the forecast is coupled with anticipatory life‐cycle assessment (LCA) modeling to estimate the environmental impacts of producing battery‐grade lithium carbonate equivalent (LCE) each year between 2018 and 2100. The result is a normalized life‐cycle impact intensity for LCE that reflects the changing resource type, quantity, and region of production. Sustained growth in lithium demands through 2100 necessitates extraction of lower grade resources and mineral deposits, especially after 2050. Despite the reliance on lower grade resources and differences in impact intensity for LCE production from each deposit, the LCA results show only small to modest increases in impact, for example, carbon intensity increases from 3.2 kg CO₂e/kg LCE in 2020 to 3.3 kg CO₂e/kg LCE in 2100.     

Environmental assessment of yield improvements obtained by the use of the enzyme phospholipase in mozzarella cheese production

Background, aim and scope  Phospholipase is an enzyme which is able to increase the yield of cheese in, for instance, mozzarella production. Milk production is the most important source of environmental impacts in cheese production and it is obvious to assume that the milk saving that comes with the use of phospholipase reduces the overall environmental impacts of the final product. Production of industrial phospholipase is, however, also associated with environmental burdens and it is not known whether and to what extent the use of phospholipase is justified by overall environmental improvements. The aim of the present study is therefore to assess the environmental impacts that come with the use of industrial phospholipase in mozzarella production and compare with the savings that come with the avoided milk production. The study addresses mozzarella production in Denmark. Methods  LCA is used as analytical tool and environmental modelling is facilitated in SimaPro 7.1.8 LCA software. Yield improvements refer to full scale industrial application of phospholipase in cheese industry. The study is a comparative analysis and a marginal and market-oriented approach is taken. The study addresses contribution to global warming, acidification, nutrient enrichment, photochemical smog formation, energy consumption and use of agricultural land. Estimation of environmental impact potentials is based on Eco-indicator 95 v.2.1 equivalency factors. Toxicity is addressed by qualitative means. Results  The environmental impacts induced by phospholipase production are small compared with the savings obtained by reduced milk consumption for mozzarella production when all impact indicators are considered. Sensitivity analyses and data quality assessments indicate that this general outcome of the study is robust, although results at the more detailed level are the subject of much variation and uncertainty. Discussion  Transport of the enzyme from producer to mozzarella producer is insignificant and the general outcome of the study is considered applicable to other regions of the world where milk is produced in modern milk production systems. Conclusions  Use of phospholipase as a yield improvement factor is a means of reducing environmental impact of mozzarella production. Recommendations and perspectives  The total annual global warming mitigation potential of phospholipase used in production of mozzarella and other pasta filata products is in the order of 7 × 10⁸ kg CO₂ equivalents. The use of phospholipase is driven by overall cost savings and it is therefore recommended that the enzyme should be given attention as a cost-efficient means of reducing greenhouse gas emissions.     

西北干旱区利用间套作促进能源植物的高产高效

在全球性能源紧缺和我国能源植物大规模种植困难等大背景下,优质、充足的原料供应已成为制约生物质能源产业发展的主要限制因素。在确保能源植物高效生产和克服"与粮争地、与人争粮"现实的同时,挖掘我国边际土壤高产高效生产能源植物的土地优势和增产潜力。通过筛选评价适宜西北干旱地区高抗逆的新型能源植物种类,开发应用能源植物与粮经作物间套作栽培技术,实现新型能源植物对逆境资源的高效利用和可持续规模化种植,提高能源植物的生产力和优化能源物种的区域配置,增加土地产值和农民收入,缓解能源紧缺,达到经济、生态和社会效益多赢,为我国能源和粮食安全提供技术支撑。     

Environmental sustainability of orthopedic devices produced with powder bed fusion

Additive manufacturing consists in melting metallic powders to produce objects from 3D data, layer upon layer. Its industrial applications range from automotive, biomedical (e.g., prosthetic implants for dentistry and orthopedics), aeronautics and others. This study uses life cycle assessment to evaluate the possible improvement in environmental performance of laser‐based powder bed fusion additive manufacturing systems on prosthetic device production. Environmental impacts due to manufacturing, use, and end of life of the designed solution were assessed. In addition, two powder production technologies, gas atomization (GA) and plasma atomization (PA), were compared in order to establish the most sustainable one. Production via traditional subtractive technologies and the additive manufacturing production were also compared. 3D building was found to have a significant environmental advantage compared to the traditional technology. The powder production process considerably influences on a damage point of view the additive manufacturing process; however, its impact can be mitigated if GA powders are employed.