Generation of an Industry-specific Physico-chemical Allocation Matrix. Application in the Dairy Industry and Implications for Systems Analysis (9 pp)

Background, Aims and Scope Allocation is required when quantifying environmental impacts of individual products from multi-product manufacturing plants. The International Organization for Standardization (ISO) recommends in ISO 14041 that allocation should reflect underlying physical relationships between inputs and outputs, or in the absence of such knowledge, allocation should reflect other relationships (e.g. economic value). Economic allocation is generally recommended if process specific information on the manufacturing process is lacking. In this paper, a physico-chemical allocation matrix, based on industry-specific data from the dairy industry, is developed and discussed as an alternative allocation method. Methods Operational data from 17 dairy manufacturing plants was used to develop an industry specific physico-chemical allocation matrix. Through an extensive process of substraction/substitution, it is possible to determine average resource use (e.g. electricity, thermal energy, water, etc) and wastewater emissions for individual dairy products within multi-product manufacturing plants. The average operational data for individual products were normalised to maintain industry confidentiality and then used as an industry specific allocation matrix. The quantity of raw milk required per product is based on the milk solids basis to account for dairy by-products that would otherwise be neglected. Results and Discussion Applying fixed type allocation methods (e.g. economic) for all input and outputs based on the quantity of product introduces order of magnitude sized deviations from physico-chemical allocation in some cases. The error associated with the quality of the whole of factory plant data or truncation error associated with setting system boundaries is insignificant in comparison. The profound effects of the results on systems analysis are discussed. The results raise concerns about using economic allocation as a default when allocating intra-industry sectoral flows (i.e. mass and process energy) in the absence of detailed technical information. It is recommended that economic allocation is better suited as a default for reflecting inter-industry sectoral flows. Conclusion The study highlights the importance of accurate causal allocation procedures that reflect industry-specific production methods. Generation of industry-specific allocation matrices is possible through a process of substitution/subtraction and optimisation. Allocation using such matrices overcomes the inherit bias of mass, process energy or price allocations for a multi-product manufacturing plant and gives a more realistic indication of resource use or emissions per product. The approach appears to be advantageous for resource use or emissions allocation if data is only available on a whole of factory basis for several plants with a similar level of technology. Recommendation and Perspective The industry specific allocation matrix approach will assist with allocation in multi-product LCAs where the level of technology in an industry is similar. The matrix will also benefit dairy manufacturing companies and help them more accurately allocate resources and impacts (i.e. costs) to different products within the one plant. It is recommended that similar physico-chemical allocation matrices be developed for other industry sectors with a view of ultimately coupling them with input-output analysis.     

Mass and energy allocation method analysis for an oil refinery characterization using multi-scale modeling

Purpose

The goal of this study is to characterize a single oil refinery using a mass and energy multi-scale allocation method for 13 different fossil fuel-based products. This method structures a rigorous modeling and data collection approach to fill the gap that exists in traditional methods of life cycle assessment (LCA).

Methods

The refinery system’s information is used to subdivide a main process at different sub-process levels and obtain multi-scale information using calculated factors for the main process system’s raw material inputs and outputs. The analysis was performed using derived sub-models from an overall technological model that individually simulate the production of each final refinery product. Based on the technological models of the individual product refineries, the same environmental indicators identified for mass and energy allocation were calculated.

Results and discussion

With this approach, it was possible to build a mass and energy LCA model of a fully parameterized refinery capable to accurately describe up to 98% the analyzed system. Incoming raw materials and effluents are weighted by specific factors calculated from the total of each principal process refining amount and the percentage outputs of the intermediate products. From a bottom-up approach, a primary data questionnaire was obtained with the total raw materials and waste quantities for each of the five key processes and their auxiliary processes. Information that previously appeared at the system (global refinery) and processes (main processes) level can now be accounted for in sub-process terms, thus allowing the monitoring information and environmental performance of the product and intermediate products to be found in a multiproduct system.

Conclusions

The refining sub-process’ environmental impact with regard to specific and intermediate products within the refinery processing steps was accurately estimated. This resulted not only in improvement of the property allocation but also in the identification of critical points in the processing steps, thus enabling optimization and innovation with regard to the processing lines and the refining process and producing a more efficient environmental impact analysis with great quality. The obtained formulations can be extended to more complex refining systems and other manufacturing processes.

     

Some Work and Some Play: Microscopic and Macroscopic Approaches to Labor and Leisure

Given the option, humans and other animals elect to distribute their time between work and leisure, rather than choosing all of one and none of the other. Traditional accounts of partial allocation have characterised behavior on a macroscopic timescale, reporting and studying the mean times spent in work or leisure. However, averaging over the more microscopic processes that govern choices is known to pose tricky theoretical problems, and also eschews any possibility of direct contact with the neural computations involved. We develop a microscopic framework, formalized as a semi-Markov decision process with possibly stochastic choices, in which subjects approximately maximise their expected returns by making momentary commitments to one or other activity. We show macroscopic utilities that arise from microscopic ones, and demonstrate how facets such as imperfect substitutability can arise in a more straightforward microscopic manner.     

Allocation of Process Gases Generated from Integrated Steelworks by an Improved System Expansion Method (7 pp)

Goal, Scope and Background Although a large number of life cycle inventory (LCI) analyses for steel-making processes or steel products have been conducted, the allocation of process gases generated from the steelworks has not yet been clearly solved. The most consistent settlement for avoiding the allocation problem has been generally known as a system expansion method. However, the existing subtracted operations for the process gases in that method are inconsistent to a system in which those gases are consumed at their unbalanced consumption ratio. The goal of this study is to suggest a more reasonable substitute for the process gases in the system expansion method and a modified system expansion method resettling the amount of process gases used. Methods To seek a more suitable one as a substituted operation of the process gas, a kind of by-product gas, in the system expansion method, it is necessary to analyze the composition of whole fuel consumed within a steelworks. Because the steelworks is supplied with a gap of electricity from the national grid electricity other than home power plants, we should also consider various carbon fossil fuels consumed in the external electric-power production. From this procedure, a composite fuel, which is composed of coal, heavy fuel oil and LNG, is derived as the alternative of the process gases such as BFG, COG, CFG, and LDG. In the sequential manufacturing line, IO(gas), which is the ratio of the quantity used to the quantity produced of each process gas, is increased as a functional unit proceeds to a following steel product of the next process. In the LCI system, where IO(gas) is higher than one, the IO(gas) is adjusted to nearly one. The adjustment of IO is conducted in the order of the amount of process gas used in the whole steelworks on the basis of the functional unit. Results and Discussion LCI analyses were carried out focusing on the alternative of the process gases for five steel products. As a functional unit goes down a lower stage, IO(gas) is increased due to the high consumption of those gases. We found a phenomenon that IO(gas) had a critical influence on the LCI results between no allocation and system expansion for the process gases through sensitivity analysis. To reduce this influence and adjust for the real situation of IO(gas), we applied an improved system expansion method to the process gases. That is, we partly substituted a process gas with LNG and rearranged the ratio between internal and external electricity (RIEE) as close as the values of IO(gas) to one. Conclusion As the alternative fuel for the process gases, a composite fuel was derived in the system expansion method. In addition to the composite fuel, which consisted of coal, HFO and LNG, an improved system expansion method was revised by adjusting a modified IO(gas) to nearly one, not the high value of IO(gas) for each process gas. Recommendation and Outlook This improved system expansion method can be applicable in the chemical industry as well as the steel industry, which have multi-function systems. Optimal LCI analysis may be achieved through the redistribution and optimization in the usage of process gases.     

Optimization of biopharmaceutical downstream processes supported by mechanistic models and artificial neural networks

Downstream process development is a major area of importance within the field of bioengineering. During the design of such a downstream process, important decisions have to be made regarding the type of unit operations as well as their sequence and their operating conditions. Current computational approaches addressing these issues either show a high level of simplification or struggle with computational speed. Therefore, this article presents a new approach that combines detailed mechanistic models and speed‐enhancing artificial neural networks. This approach was able to simultaneously optimize a process with three different chromatographic columns toward yield with a minimum purity of 99.9%. The addition of artificial neural networks greatly accelerated this optimization. Due to high computational speed, the approach is easily extendable to include more unit operations. Therefore, it can be of great help in the acceleration of downstream process development. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:696–707, 2017     

Economic allocation: Examples and derived decision tree

Goal, Scope and Background  In the recently published (Dutch) Handbook on LCA, economic allocation is advised as baseline method for most allocation situations in a detailed LCA. Although the Handbook on LCA aimed to provide a ‘cookbook’ with operational guidelines for conducting each step of an LCA, this was not completely achieved for the allocation step. The guidelines for allocation largely remained at the level of principles. This restricted elaboration of economic allocation may hamper application in practice. Therefore, this paper elaborates some examples applying economic allocation. Method  Two concepts are of particular importance when applying economic allocation: functional flow and multi-functional process. The definitions of these concepts are presented and discussed. The basic principle of economic allocation is that having determined the various functional flows of a multi-functional process, all other flows need to be allocated to these functional flows according to their shares in the total proceeds. Proceeds are based on prices and these are not always easy to determine for a process. A summary of possible solutions for different problems when determining prices is given. Results and Discussion  The examples presented focus on co-production and various recycling situations. All examples are hypothetical in order to avoid discussions on the data. The examples show that the prices of the functional flows determine the allocation results. It is of importance to have correct information on the relative prices of the functional flows at stake, especially whether they are negative or positive. Learning from these examples, we establish a decision tree for economic allocation. The decision tree is meant for identifying and handling multi-functionality situations starting from a defined (product) system. This decision tree is with minor adaptations also applicable to other allocation methods and has a more general value than for the economic allocation method only. Conclusions and perspective  The examples have helped us to establish a decision tree for handling the multi-functionality problem by economic allocation. The examples can be broadened to other materials and allocation situations. We would encourage others to provide other examples and experiences as we expect that these will help to further improve and refine the guidelines and decision tree for economic allocation in future.     

Imbalance and balancing: Development of ecological flow networks

In this paper we address balancing process of ecological flow networks. In existing approaches, macroscopic objectives to which systems organize are assumed. Flow balance provides only constraints for the optimization. Since flow balance and objectives are separated from each other, it is impossible to address how the appearance of objectives is related to flow balance. Therefore, we take an alternative approach, in which we directly describe a dynamics of balancing process. We propose a simple mathematical formula for local balancing dynamics and show that it can generate a self-organizing property, which could be seen as a primitive objective.     

Simulating electrostatic energies in proteins: perspectives and some recent studies of pKas, redox, and other crucial functional properties

Electrostatic energies provide what is arguably the most effective tool for structure-function correlation of biological molecules. Here, we provide an overview of the current state-of-the-art simulations of electrostatic energies in macromolecules, emphasizing the microscopic perspective but also relating it to macroscopic approaches. We comment on the convergence issue and other problems of the microscopic models and the ways of keeping the microscopic physics while moving to semi-macroscopic directions. We discuss the nature of the protein dielectric "constants" reiterating our long-standing point that the dielectric "constants" in semi-macroscopic models depend on the definition and the specific treatment. The advances and the challenges in the field are illustrated considering different functional properties including pK(a)'s, redox potentials, ion and proton channels, enzyme catalysis, ligand binding, and protein stability. We emphasize the microscopic overcharging approach for studying pK(a) 's of internal groups in proteins and give a demonstration of power of this approach. We also emphasize recent advances in coarse grained models with a physically based electrostatic treatment and provide some examples including further directions in treating voltage activated ion channels.     

A special view on the nature of the allocation problem

One of the remaining important problems of life cycle inventory analysis is the allocation problem. A proper solution of this problem calls for a proper understanding of the nature of the problem itself. This paper argues that the established definition of the allocation problem as the fact that one unit process produces more than one function, is not appropriate. That definition points to an important reason of the occurrence of the problem, but the situation of internal (closed-loop) recycling already indicates that there may be product systems which contain multifunction processes, but which nevertheless need not exhibit an allocation problem. The paper proceeds by examining a number of simple hypothetical cases, and proposes a precise and operational definition of the allocation problem. This enables a systematic categorization of approaches for dealing with the allocation problem.     

Allocation of energy use in petroleum refineries to petroleum products

Aim, Scope, and Background  Studies to evaluate the energy and emission impacts of vehicle/fuel systems have to address allocation of the energy use and emissions associated with petroleum refineries to various petroleum products because refineries produce multiple products. The allocation is needed in evaluating energy and emission effects of individual transportation fuels. Allocation methods used so far for petroleum-based fuels (e.g., gasoline, diesel, and liquefied petroleum gas [LPG]) are based primarily on mass, energy content, or market value shares of individual fuels from a given refinery. The aggregate approach at the refinery level is unable to account for the energy use and emission differences associated with producing individual fuels at the next sub-level: individual refining processes within a refinery. The approach ignores the fact that different refinery products go through different processes within a refinery. Allocation at the subprocess level (i.e., the refining process level) instead of at the aggregate process level (i.e., the refinery level) is advocated by the International Standard Organization. In this study, we seek a means of allocating total refinery energy use among various refinery products at the level of individual refinery processes. Main Features  We present a petroleum refinery-process-based approach to allocating energy use in a petroleum refinery to petroleum refinery products according to mass, energy content, and market value share of final and intermediate petroleum products as they flow through refining processes within a refinery. The approach is based on energy and mass balance among refining processes within a petroleum refinery. By using published energy and mass balance data for a simplified U.S. refinery, we developed a methodology and used it to allocate total energy use within a refinery to various petroleum products. The approach accounts for energy use during individual refining processes by tracking product stream mass and energy use within a refinery. The energy use associated with an individual refining process is then distributed to product streams by using the mass, energy content, or market value share of each product stream as the weighting factors. Results  The results from this study reveal that product-specific energy use based on the refinery process-level allocation differs considerably from that based on the refinery-level allocation. We calculated well-to-pump total energy use and greenhouse gas (GHG) emissions for gasoline, diesel, LPG, and naphtha with the refinery process-based allocation approach. For gasoline, the efficiency estimated from the refinery-level allocation underestimates gasoline energy use, relative to the process-level based gasoline efficiency. For diesel fuel, the well-to-pump energy use for the process-level allocations with the mass- and energy-content-based weighting factors is smaller than that predicted with the refinery-level allocations. However, the process-level allocation with the market-value-based weighting factors has results very close to those obtained by using the refinery-level allocations. For LPG, the refinery-level allocation significantly overestimates LPG energy use. For naphtha, the refinery-level allocation overestimates naphtha energy use. The GHG emission patterns for each of the fuels are similar to those of energy use. Conclusions  We presented a refining-process-level-based method that can be used to allocate energy use of individual refining processes to refinery products. The process-level-based method captures process-dependent characteristics of fuel production within a petroleum refinery. The method starts with the mass and energy flow chart of a refinery, tracks energy use by individual refining processes, and distributes energy use of a given refining process to products from the process. In allocating energy use to refinery products, the allocation method could rely on product mass, product energy contents, or product market values as weighting factors. While the mass- and energy-content-based allocation methods provide an engineering perspective of energy allocation within a refinery, the market-value-based allocation method provides an economic perspective. The results from this study show that energy allocations at the aggregate refinery level and at the refining process level could make a difference in evaluating the energy use and emissions associated with individual petroleum products. Furthermore, for the refining-process-level allocation method, use of mass — energy content- or market value share-based weighting factors could lead to different results for diesel fuels, LPG, and naphtha. We suggest that, when possible, energy use allocations should be made at the lowest subprocess level — a confirmation of the recommendation by the International Standard Organization for life cycle analyses. Outlook  The allocation of energy use in petroleum refineries at the refining process level in this study follows the recommendation of ISO 14041 that allocations should be accomplished at the subprocess level when possible. We developed a method in this study that can be readily adapted for refineries in which process-level energy and mass balance data are available. The process-level allocation helps reveal some additional energy and emission burdens associated with certain refinery products that are otherwise overlooked with the refinery-level allocation. When possible, process-level allocation should be used in life-cycle analyses.     

A Process Chaining Approach toward Product Design for Environment

This article presents an approach toward product design for environment (DfE) at the level that integrates environmental hazard analysis with models of transformation processes. As a complementary analysis tool to life-cycle assessment (LCA), this method would support detailed design decisions through modeling of a "process chain" for a subset of the product's life cycle. The building blocks for this approach are a set of unit process models that can convert process and design parameters into estimates for energy utilization, production scrap, and ancillary waste flows. These values for quantity of environmental releases can be integrated using a multicriiteria environmental hazard evaluation methodology that can estimate the "qualrty" of environmental releases. Finally, the waste information can be used to support a design model that can link design parameters to material, process, and operational parameter selection. A case study illustrating printed circuit board (PCB) assembly is presented to show process chain implementation in manufacturing applications.     

Case study and application of process analytical technology (PAT) towards bioprocessing: use of on-line high-performance liquid chromatography (HPLC) for making real-time pooling decisions for process chromatography

Process analytical technology (PAT) has been gaining a lot of momentum in the biopharmaceutical community due to the potential for continuous real time quality assurance resulting in improved operational control and compliance. This paper presents a PAT application for one of the most commonly used unit operation in bioprocessing, namely liquid chromatography. Feasibility of using a commercially available online-high performance liquid chromatography (HPLC) system for real-time pooling of process chromatography column is examined. Further, experimental data from the feasibility studies are modeled and predictions of the model are compared to actual experimental data. It is found that indeed for the application under consideration, the online-HPLC offers a feasible approach for analysis that can facilitate real-time decisions for column pooling based on product quality attributes. It is shown that implementing this analytical scheme allows us to meet two of the key goals that have been outlined for PAT, that is, "variability is managed by the process" and "product quality attributes can be accurately and reliably predicted over the design space established for materials used, process parameters, manufacturing, environmental, and other conditions." Finally, the implications of implementing such a PAT application in a manufacturing environment are discussed. The application presented here can be extended to other modes of process chromatography and/or HPLC analysis.     

Scenario Modelling in Prospective LCA of Transport Systems. Application of Formative Scenario Analysis (11 pp)

Background Tools and methods able to cope with uncertainties are essential for improving the credibility of Life Cycle Assessment (LCA) as a decision support tool. Previous approaches have focussed predominately upon data quality. Objective and Scope. An epistemological approach is presented conceptualising uncertainties in a comparative, prospective, attributional LCA. This is achieved by considering a set of cornerstone scenarios representing future developments of an entire Life Cycle Inventory (LCI) product system. We illustrate the method using a comparison of future transport systems. Method Scenario modelling is organized by means of Formative Scenario Analysis (FSA), which provides a set of possible and consistent scenarios of those unit processes of an LCI product system which are time dependent and of environmental importance. Scenarios are combinations of levels of socio-economic or technological impact variables. Two core elements of FSA are applied in LCI scenario modelling. So-called impact matrix analysis is applied to determine the relationship between unit process specific socio-economic variables and technology variables. Consistency Analysis is employed to integrate unit process scenarios, based on pair-wise ratings of the consistency of the levels of socio-economic impact variables of all unit processes. Two software applications are employed which are available from the authors. Results and Discussion The study reveals that each possible level or development of a technology variable is best conceived of as the impact of a specific socio-economic (sub-) scenario. This allows for linking possible future technology options within the socio-economic context of the future development of various background processes. In an illustrative case study, the climate change scores and nitrogen dioxide scores per seat kilometre for six technology options of regional rail transport are compared. Similar scores are calculated for a future bus alternative and an average Swiss car. The scenarios are deliberately chosen to maximise diversity. That is, they represent the entire range of future possible developments. Reference data and the unit process structure are taken from the Swiss LCA database 'ecoinvent 2000'. The results reveal that rail transport remains the best option for future regional transport in Switzerland. In all four assessed scenarios, four technology options of future rail transport perform considerably better than regional bus transport and car transport. Conclusions and Recommendations. The case study demonstrates the general feasibility of the developed approach for attributional prospective LCA. It allows for a focussed and in-depth analysis of the future development of each single unit process, while still accounting for the requirements of the final scenario integration. Due to its high transparency, the procedure supports the validation of LCI results. Furthermore, it is well-suited for incorporation into participatory methods so as to increase their credibility. Outlook and Future Work. Thus far, the proposed approach is only applied on a vehicle level not taking into account alterations in demand and use of different transport modes. Future projects will enhance the approach by tackling uncertainties in technology assessment of future transport systems. For instance, environmental interventions involving future maglev technology will be assessed so as to account for induced traffic generated by the introduction of a new transport system.     

Evaluation of Life Cycle Assessment Recycling Allocation Methods

Life cycle assessment practitioners struggle to accurately allocate environmental burdens of metals recycling, including the temporal dimension of environmental impacts. We analyze four approaches for calculating aluminum greenhouse gas emissions: the recycled content (RC) or cut‐off approach, which assumes that demand for recycled content displaces primary production; end‐of‐life recycling (EOLR), which assumes that postuse recycling displaces primary production; market‐based (MB) approaches, which estimate changes in supply and demand using price elasticities; and value‐corrected substitution (VCS), which allocates impact based on price differences between primary and recycled material. Our analysis suggests that applications of the VCS approach do not adequately account for the changing scrap to virgin material price ratio over time, whereas MB approaches do not address stock accumulation and depletion. The EOLR and RC approaches were analyzed using two case studies: U.S. aluminum beverage cans and vehicle engine blocks. These approaches produced similar results for beverage cans, which have a closed material loop system and a short product life. With longer product lifetimes, as noted with the engine blocks, the magnitude and timing of the emissions differs greatly between the RC and EOLR approaches. The EOLR approach indicates increased impacts at the time of production, offset by negative impacts in future years, whereas the RC approach assumes benefits to increased recycled content at the time of production. For vehicle engine blocks, emissions using EOLR are 140% higher than with RC. Results are highly sensitive to recycled content and future recycling rates, and the choice of allocation methods can have significant implications for life cycle studies.     

Allocation in recycling of composites - the case of life cycle assessment of products from carbon fiber composites

Purpose

Composites consist of at least two merged materials. Separation of these components for recycling is typically an energy-intensive process with potentially significant impacts on the components’ quality. The purpose of this article is to suggest how allocation for recycling of products manufactured from composites can be handled in life cycle assessment to accommodate for the recycling process and associated quality degradations of the different composite components, as well as to describe the challenges involved.

Method

Three prominent recycling allocation approaches were selected from the literature: the cut-off approach, the end-of-life recycling approach with quality-adjusted substitution, and the circular footprint formula. The allocation approaches were adapted to accommodate for allocation of impacts by conceptualizing the composite material recycling as a separation process with subsequent recycling of the recovered components, allowing for separate modeling of the quality changes in each individual component. The adapted allocation approaches were then applied in a case study assessing the cradle-to-grave climate impact and energy use of a fictitious product made from a composite material that in the end of life is recycled through grinding, pyrolysis, or by means of supercritical water treatment. Finally, the experiences and results from applying the allocation approaches were analyzed with regard to what incentives they provide and what challenges they come with.

Results and discussion

Using the approach of modeling the composite as at least two separate materials rather than one helped to clarify the incentives provided by each allocation approach. When the product is produced using primary materials, the cut-off approach gives no incentive to recycle, and the end-of-life recycling approach and the circular footprint formula give incentives to recycle and recover materials of high quality. Each of the allocation approaches come with inherent challenges, especially when knowledge is limited regarding future systems as in prospective studies. This challenge is most evident for the circular footprint formula, for example, with regard to the supply and demand balance.

Conclusions

We recommend modeling the composite materials in products as separate, individual materials. This proved useful for capturing changes in quality, trade-offs between recovering high quality materials and the environmental impact of the recycling system, and the incentives the different approaches provide. The cut-off and end-of-life approaches can both be used in prospective studies, whereas the circular footprint formula should be avoided as a third approach when no market for secondary material is established.

     

Evaluation of the effect of accounting method,IPCC v. LCA,on grass-based and confinement dairy systems’ greenhouse gas emissions

Life cycle assessment (LCA) and the Intergovernmental Panel on Climate Change (IPCC) guideline methodology, which are the principal greenhouse gas (GHG) quantification methods, were evaluated in this study using a dairy farm GHG model. The model was applied to estimate GHG emissions from two contrasting dairy systems: a seasonal calving pasture-based dairy farm and a total confinement dairy system. Data used to quantify emissions from these systems originated from a research study carried out over a 1-year period in Ireland. The genetic merit of cows modelled was similar for both systems. Total mixed ration was fed in the Confinement system, whereas grazed grass was mainly fed in the grass-based system. GHG emissions from these systems were quantified per unit of product and area. The results of both methods showed that the dairy system that emitted the lowest GHG emissions per unit area did not necessarily emit the lowest GHG emissions possible for a given level of product. Consequently, a recommendation from this study is that GHG emissions be evaluated per unit of product given the growing affluent human population and increasing demand for dairy products. The IPCC and LCA methods ranked dairy systems’ GHG emissions differently. For instance, the IPCC method quantified that the Confinement system reduced GHG emissions per unit of product by 8% compared with the grass-based system, but the LCA approach calculated that the Confinement system increased emissions by 16% when off-farm emissions associated with primary dairy production were included. Thus, GHG emissions should be quantified using approaches that quantify the total GHG emissions associated with the production system, so as to determine whether the dairy system was causing emissions displacement. The IPCC and LCA methods were also used in this study to simulate, through a dairy farm GHG model, what effect management changes within both production systems have on GHG emissions. The findings suggest that single changes have a small mitigating effect on GHG emissions (<5%), except for strategies used to control emissions from manure storage in the Confinement system (14% to 24%). However, when several management strategies were combined, GHG emissions per unit of product could be reduced significantly (15% to 30%). The LCA method was identified as the preferred approach to assess the effect of management changes on GHG emissions, but the analysis indicated that further standardisation of the approach is needed given the sensitivity of the approach to allocation decisions regarding milk and meat.     

Long range intermolecular forces between macroscopic bodies: macroscopic and microscopic approaches.

Van der Waals energies of interaction are calculated by two methods, the macroscopic method of Lifshitz and the microscopic method of London-Casimir and Polder-Hamaker for the case of two semi-infinite slabs separated by a thin film. When retardation effects may be neglected, the London-Hamaker approach yields values of dispersion interactions which almost coincide with those of the Lifshitz approach, the magnitude of the former values being larger by approximately 10–25%, which is attributed to the effect of the molecular environment in condensed media. At 50–100 Å film thicknesses where retardation effects are small, dispersion terms are generally the major part of van der Waals forces in the Lifshitz formulation. Hence, for 50–100 Å film thicknesses the Hamaker approach, which only includes dispersion interactions is generally adequate. By accounting for retardation effects, which significantly reduce the magnitude of dispersion interactions at several hundred Å, there is a reasonable agreement between the values obtained by the macroscopic and microscopic approaches. When polar substances are present and for film thicknesses of several hundred Å, where dispersion interactions are significantly reduced, the major contribution to van der Waals forces may arise from orientation and induction terms. For such cases the Hamaker approach may lead to critical underestimates of the calculated magnitude of van der Waals forces. An ad hoc way to overcome this difficulty which is applicable to any geometry is proposed. This study presents a simple procedure for the determination of free energies of interaction between macroscopic bodies of various shapes. The procedure, which is applicable when the molecules of bodies and surrounding medium are isotropic, yields results which closely approximate those obtained with the Lifshitz theory.     

Calculating the influence of alternative allocation scenarios in fossil fuel chains

Goal, Scope and Background

As part of an LCA study comparing an average Dutch passenger car running on petrol with a similar car running on bio-ethanol and comparing an average Dutch passenger car running on diesel with a similar car running on biodiesel, the question raised to get more insight into the allocations made in fossil fuel chains in existing data bases such as ecoinvent. Both biofuel and fossil fuel chains contain various allocation situations that have been approached differently by various authors leading to differing and incomparable results. For biofuel chains, stakeholders had obtained insight into the allocations in earlier studies, but for the allocations made for the fossil chains, this was not the case. Therefore, one part of the study, which is reported in this paper, focused on a quick scan of different allocation scenarios for fossil fuels chains using the Swiss ecoinvent v1.1 database.

Methods

The quick scan focused on three different allocation scenarios for fossil fuel chains: economic allocation, physical allocation and the ecoinvent default allocation. There appeared to be 54 multi-output (MO) processes linked to both the passenger car and the diesel system in the ecoinvent v1.1 database. Based on contribution analyses identifying which multi-output processes contribute most to one of the environmental impact categories of the characterisation, seven multi-output processes were selected that have been further analysed with the three allocation scenarios mentioned.

Results

The results show that although at the process level allocation factors may differ significantly (up to almost 250), the total results only differ modestly (1–1.5), at least for the present case.

Discussion

There is no general rule between these two. They depend on the scaling factor and the environmental impact related to the resource extractions and emissions of a particular multi-output process and its upstream processes in the total system analysed.

Conclusions

The results of this quick scan are mainly intended for illustrating and learning purposes focusing on the possible influence of different allocation scenarios for fossil fuel chains. Bearing these limitations in mind, it can be concluded that different allocation methods can generate large differences in allocation factors and thus also at the level of environmental impacts allocated to the derived single-output processes. Nevertheless, the aggregated results for the present case only differ modestly.