Allocation of environmental burdens in co-product systems: Process and product-related burdens (Part 2)

ISO 14041 requires that allocation by physical causality must reflect the quantitative changes in product outputs or functions and will not necessarily be in proportion to simple physical measure such as mass. This paper examines the instances where physical causality can be represented by mass. However, it also goes further than ISO to demonstrate that the type of causality in the system is not necessarily always the same and can change depending on the way the system is operated. Whole system modelling and the marginal allocation approach are used to identify the correct type of causality for different operating states of the system and the corresponding changes in the environmenta burdens. This is generally not possible with the other allocation methods, also examined in this paper. Both process- and product-related burdens are considered and the approach is illustrated by a reference to an existing system producing five boron co-products.     

Allocation of environmental burdens in co-product systems: Process and product-related burdens (part 2)

ISO 14041 requires that allocation by physical causality must reflect the quantitative changes in product outputs or functions and will not necessarily be in proportion to simple physical measure such as mass. This paper examines the instances where physical causality can be represented by mass. However, it also goes further than ISO to demonstrate that the type of causality in the system is not necessarily always the same and can change depending on the way the system is operated. Whole system modelling and the marginal allocation approach are used to identify the correct type of causality for different operating states of the system and the corresponding changes in the environmental burdens. This is generally not possible with the other allocation methods, also examined in this paper. Both process- and product-related burdens are considered and the approach is illustrated by a reference to an existing system producing five boron co-products     

System expansion and allocation in life cycle assessment of milk and beef production

Background, Goal and Scope  System expansion is a method used to avoid co-product allocation. Up to this point in time it has seldom been used in LCA studies of food products, although food production systems often are characterised by closely interlinked sub-systems. One of the most important allocation problems that occurs in LCAs of agricultural products is the question of how to handle the co-product beef from milk production since almost half of the beef production in the EU is derived from co-products from the dairy sector. The purpose of this paper is to compare different methods of handling co-products when dividing the environmental burden of the milk production system between milk and the co-products meat and surplus calves. Main Features  This article presents results from an LCA of organic milk production in which different methods of handling the co-products are examined. The comparison of different methods of co-product handling is based on a Swedish LCA case study of milk production where economic allocation between milk and meat was initially used. Allocation of the co-products meat and surplus calves was avoided by expanding the milk system. LCA data were collected from another case study where the alternative way of producing meat was analysed, i.e. using a beef cow that produces one calf per annum to be raised for one and a half year. The LCA of beef production was included in the milk system. A discussion is conducted focussing on the importance of modelling and analysing milk and beef production in an integrated way when foreseeing and planning the environmental consequences of manipulating milk and beef production systems. Results  This study shows that economic allocation between milk and beef favours the product beef. When system expansion is performed, the environmental benefits of milk production due to its co-products of surplus calves and meat become obvious. This is especially connected to the impact categories that describe the potential environmental burden of biogenic emissions such as methane and ammonia and nitrogen losses due to land use and its fertilising. The reason for this is that beef production in combination with milk can be carried out with fewer animals than in sole beef production systems. Conclusion, Recommendation and Perspective  Milk and beef production systems are closely connected. Changes in milk production systems will cause alterations in beef production systems. It is concluded that in prospective LCA studies, system expansion should be performed to obtain adequate information of the environmental consequences of manipulating production systems that are interlinked to each other.     

The need for co-product allocation in the life cycle assessment of agricultural systems—is “biophysical” allocation progress?

Purpose

Several new “biophysical” co-product allocation methodologies have been developed for LCA studies of agricultural systems based on proposed physical or causal relationships between inputs and outputs (i.e. co-products). These methodologies are thus meant to be preferable to established allocation methodologies such as economic allocation under the ISO 14044 standard. The aim here was to examine whether these methodologies really represent underlying physical relationships between the material and energy flows and the co-products in such systems, and hence are of value.

Methods

Two key components of agricultural LCAs which involve co-product allocation were used to provide examples of the methodological challenges which arise from adopting biophysical allocation in agricultural LCA: (1) the crop production chain and (2) the multiple co-products produced by animals. The actual “causal” relationships in these two systems were illustrated, the energy flows within them detailed, and the existing biophysical allocation methods, as found in literature, were critically evaluated in the context of such relationships.

Results and discussion

The premise of many biophysical allocation methodologies has been to define relationships which describe how the energy input to agricultural systems is partitioned between co-products. However, we described why none of the functional outputs from animal or crop production can be considered independently from the rest on the basis of the inputs to the system. Using the example of manure in livestock systems, we also showed why biophysical allocation methodologies are still sensitive to whether a system output has economic value or not. This sensitivity is a longstanding criticism of economic allocation which is not resolved by adopting a biophysical approach.

Conclusions

The biophysical allocation methodologies for various aspects of agricultural systems proposed to date have not adequately explained how the physical parameters chosen in each case represent causal physical mechanisms in these systems. Allocation methodologies which are based on shared (but not causal) physical properties between co-products are not preferable to allocation based on non-physical properties within the ISO hierarchy on allocation methodologies and should not be presented as such.

     

A new method of biophysical allocation in LCA of livestock co-products: modeling metabolic energy requirements of body-tissue growth

Purpose

In agricultural life cycle assessment (LCA), the allocation method chosen to divide impacts among co-products is an important issue, since it may change conclusions about a product’s impacts. We developed a biophysical allocation method to assign upstream environmental burdens and the use of raw materials at farm gate to the livestock co-products at the slaughterhouse based on their metabolic energy requirements.

Methods

Biophysical allocation is designed to build a relationship between co-products of a meat-production system and their associated net metabolic energy requirements. A metabolic growth model (Gompertz function) was combined with an energy calculation model to estimate metabolic energy requirements for the growth of an animal from birth to slaughter age. Allocation factors were calculated based on the energy required to maintain and produce body tissues (excluding waste), as a function of their chemical (protein and lipid) and physiological properties. This method was applied for an average beef cow and then compared to other allocation methods (e.g., mass, dry matter, protein, and economic).

Results and discussion

At slaughter age, carcass tissues required the most energy (44 %) due to their high quantity of protein; the gastrointestinal tract and liver required about 28 and 5 %, respectively, of total metabolic energy requirements due to their roles in body metabolism. Biophysical allocation considers the energy cost of building and maintaining the tissues, regardless of their final uses. It reflects physical relationships among co-products as well as other allocation methods do. It also reveals the cause-effect relationship between tissues according to the energy required to maintain physiological functions. Once the growing time until slaughter is set, biophysical allocation factors are not influenced over time, unlike those of economic allocation, which is highly influenced by price variability.

Conclusions

This study provides a generic and robust biophysical allocation method for estimating environmental burdens of co-products, in accordance with ISO allocation rules. The method can be considered an original contribution to international debates on allocation methods applied to livestock products in LCA. In this paper, it is applied to cattle-related product, but it is generic and the principles can be adapted to any kind of livestock species. It should be considered and discussed by stakeholders in livestock production industries.

     

Sensitivity analysis of methodological choices in road pavement LCA

Purpose

There are methodological questions concerning life cycle assessment (LCA) and carbon footprint evaluation of road pavements, including allocation among co-products or at end-of-life (EOL) recycling. While the development and adoption of a standard methodology for road pavement LCA would assist in transparency and decision making, the impact of the chosen method on the results has not yet been fully explored.

Methods

This paper examines the methodological choices made in UK PAS 2050 and asphalt Pavement Embodied Carbon Tool (asPECT), and reviews the allocation methods available to conduct road pavement LCA. A case study of a UK inter-urban road construction (cradle-to-laid) is presented to indicate the impact of allocation amongst co-products (bitumen and blast furnace slag); a typical UK asphalt production (cradle-to-gate) is modelled to show the influence of allocation at EOL recycling.

Results and discussion

Allocation based on mass is found to consistently lead to the highest figures in all impact categories, believed to be typical for construction materials. Changing from industry chosen allocation methods (Eurobitume, asPECT) to 100 % mass or economic allocation leads to changes in results, which vary across impact categories. This study illustrates how the allocation methods for EOL recycling affect the inventory of a unit process (asphalt production).

Conclusions and recommendations

Sensitivity analysis helps to understand the impact of chosen allocation method and boundary setting on LCA results. This initial work suggests that economic allocation to co-products used as secondary pavement materials may be more appropriate than mass allocation. Allocation at EOL recycling by a substitution method may remain most appropriate, even where the balance of credits between producers and users may be hampered by an inability to confidently predict future recycling rates and methods. In developing sector-specific guidelines, further sensitivity checks are recommended, such as for alternative materials and traffic management during maintenance.     

Life cycle assessment of biofuels: Energy and greenhouse gas balances

The promotion of biofuels as energy for transportation in the industrialized countries is mainly driven by the perspective of oil depletion, the concerns about energy security and global warming. However due to sustainability constraints, biofuels will replace only 10 to 15% of fossil liquid fuels in the transport sector. Several governments have defined a minimum target of GHG emissions reduction for those biofuels that will be eligible to public incentives, for example a 35% emissions reduction in case of biofuels in Members States of the European Union. This article points out the significant biases in estimating GHG balances of biofuels stemming from modelling choices about system definition and boundaries, functional unit, reference systems and allocation methods. The extent to which these choices influence the results is investigated. After performing a comparison and constructive criticism of various modelling choices, the LCA of wheat-to-bioethanol is used as an illustrative case where bioethanol is blended with gasoline at various percentages (E5, E10 and E85). The performance of these substitution options is evaluated as well. The results show a large difference in the reduction of the GHG emissions with a high sensitivity to the following factors: the method used to allocate the impacts between the co-products, the type of reference systems, the choice of the functional unit and the type of blend. The authors come out with some recommendations for basing the estimation of energy and GHG balances of biofuels on principles such as transparency, consistency and accuracy.     

Inventory for energy production in Canada

Publicly available databases are analysed to demonstrate their relevance to life cycle inventory for energy production in the Canadian context. Site specific emissions along with sectoral emissions data are combined with production data to construct an energy production model, which has been applied to air emissions. The allocation procedure leads to reasonable results for coal, natural gas and electricity. The detailed allocation of the inventory among petroleum co-products is outside the scope of this study as it requires incorporating knowledge of physical relationship (unit process) or using economic data.     

ECOBAS — A tool to develop ecosystem models exemplified by the shallow lake model EMMO

The modelling and simulation tool ECOBAS was extended in order to include special features supporting the development of ecological models. The «Graphical Model Editor» allows the connection of at least 2 modules in order to build a whole model to run simulations. With the ECOBAS simulation system the model can be tested extensively in order to find appropriate parameter sets («Parameter analysis» and «Parameter estimation») and to identify critical parameters («Sensitivity analysis»). The «Interaction Analysis» shows the internal dependencies of a model. ECOBAS integrates the steps of ecological modelling and creates well readable and complete documentations within one working step, supports modularization of models and the user is rid of the technical and numerical aspects of modelling. Hence ECOBAS is setting up complete, consistent and syntactical correct models.All new features of the ECOBAS-system will be introduced by applying it on the existing ecosystem model EMMO.     

How does co-product handling affect the carbon footprint of milk? Case study of milk production in New Zealand and Sweden

Purpose  

This paper investigates different methodologies of handling co-products in life cycle assessment (LCA) or carbon footprint (CF) studies. Co-product handling can have a significant effect on final LCA/CF results, and although there are guidelines on the preferred order for different methods for handling co-products, no agreed understanding on applicable methods is available. In the present study, the greenhouse gases (GHG) associated with the production of 1 kg of energy-corrected milk (ECM) at farm gate is investigated considering co-product handling.     

Economic Allocation in Life Cycle Assessment

This article examines methods for analyzing allocation in life cycle assessment (LCA); it focuses on comparisons of economic allocation with other feasible alternatives. The International Organization for Standardization's (ISO) guideline 14044 indicates that economic allocation should only be used as a last resort, when other methods are not suitable. However, the LCA literature reports several examples of the use of economic allocation. This is due partly to its simplicity and partly to its ability to illustrate the properties of complex systems. Sometimes a price summarizes complex attributes of product or service quality that cannot be easily measured by physical criteria. On the other hand, economic allocation does have limitations arising, for example, from the variability of prices and the low correlation between prices and physical flows. This article presents the state of the debate on the topic and some hypothetical examples for illustration. A general conclusion is that it is not possible to determine one “best” allocation method. The allocation procedure has to be selected on a case‐by‐case basis and no single approach is suitable for every situation. Despite its limitations, economic allocation has certain qualities that make it flexible and potentially suitable for different contexts. In some situations, economic allocation should not be the last methodological resort. The option of economic allocation should be considered, for example, whenever the prices of coproducts and coservices differ widely.     

On the development of protein pKa calculation algorithms

Protein pK(a) calculation methods are developed partly to provide fast non-experimental estimates of the ionization constants of protein side chains. However, the most significant reason for developing such methods is that a good pK(a) calculation method is presumed to provide an accurate physical model of protein electrostatics, which can be applied in methods for drug design, protein design, and other structure-based energy calculation methods. We explore the validity of this presumption by simulating the development of a pK(a) calculation method using artificial experimental data derived from a human-defined physical reality. We examine the ability of an RMSD-guided development protocol to retrieve the correct (artificial) physical reality and find that a rugged optimization landscape and a huge parameter space prevent the identification of the correct physical reality. We examine the importance of the training set in developing pK(a) calculation methods and investigate the effect of experimental noise on our ability to identify the correct physical reality, and find that both effects have a significant and detrimental impact on the physical reality of the optimal model identified. Our findings are of relevance to all structure-based methods for protein energy calculations and simulation, and have large implications for all types of current pK(a) calculation methods. Our analysis furthermore suggests that careful and extensive validation on many types of experimental data can go some way in making current models more realistic.     

Allocation of greenhouse gas production between wool and meat in the life cycle assessment of Australian sheep production

Purpose

Australia is the largest supplier of high-quality wool in the world. The environmental burden of sheep production must be shared between wool and meat. We examine different methods to handle these co-products and focus on proportional protein content as a basis for allocation, that is, protein mass allocation (PMA). This is the first comprehensive investigation applying PMA for calculating greenhouse gas (GHG) emissions for Australian sheep production, evaluating the variation in PMA across a large number of farms and locations over 20 years.

Materials and methods

Inventory data for two superfine wool Merino farms were obtained from farmer records, interviews and site visits in study 1. Livestock GHG emissions were modelled using Australian National GHG Inventory methods. A comparison was made of mass, protein mass and economic allocation and system expansion methods for handling co-production of wool and sheep meat. In study 2, typical crossbred ewe, Merino ewe and Merino wether flocks in each of the 28 locations in eight climate zones were modelled using the GrassGro/GRAZPLAN simulation model and historical climatic data to examine the variation in PMA values for different enterprise types.

Results and discussion

Different methods for handling co-products in study 1 changed allocated GHG emissions more than fourfold, highlighting the sensitivity to method choice. In study 2, enterprise, climate zone and year and their interactions had significant effects on PMA between wool and liveweight (LW) sold. The wool PMA (wool protein as proportion of total protein sold) least square means (LSM) were 0.61?±?0.003 for wethers, 0.43?±?0.003 for Merino ewes and 0.27?±?0.003 for crossbred ewe enterprises. The wool PMA LSM for the main effect of Köppen climate zone varied from 0.39 to 0.46. Two zones (no dry season/warm summer and distinctively dry and hot) had significantly lower wool PMA LSM, of 0.39 and 0.41, respectively, than the four other climate zones.

Conclusions

Effects of superfine wool production on GHG emissions differed between regions in response to differences in climate and productivity. Regarding methods for handling co-production, system expansion showed the greatest contrast between the two studied flocks and highlighted the importance of meat from wool production systems. However, we also propose PMA as a simple, easily applied allocation approach for use when attributional life cycle assessment (LCA) is undertaken.

     

Assessing environmental consequences of using co-products in animal feed

Purpose

The livestock sector has a major impact on the environment. This environmental impact may be reduced by feeding agricultural co-products (e.g. beet tails) to livestock, as this transforms inedible products for humans into edible products, e.g. pork or beef. Nevertheless, co-products have different applications such as bioenergy production. Based on a framework we developed, we assessed environmental consequences of using co-products in diets of livestock, including the alternative application of that co-product.

Methods

We performed a consequential life cycle assessment, regarding greenhouse gas emissions (including emissions related to land use change) and land use, for two case studies. Case 1 includes increasing the use of wheat middlings in diets of dairy cattle at the expense of using it in diets of pigs. The decreased use of wheat middlings in diets of pigs was substituted with barley, the marginal product. Case 2 includes increasing the use of beet tails in diets of dairy cattle at the expense of using it to produce bioenergy. During the production of biogas, electricity, heat and digestate (that is used as organic fertilizer) were produced. The decrease of electricity and heat was substituted with fossil fuel, and digestate was substituted with artificial fertilizer.

Results and discussion

Using wheat middlings in diets of dairy cattle instead of using it in diets of pigs resulted in a reduction of 329 kg CO₂ eq per ton wheat middlings and a decrease of 169 m² land. Using beet tails in diets of dairy cattle instead of using it as a substrate for anaerobic digestion resulted in a decrease of 239 kg CO₂ eq per ton beet tails and a decrease of 154 m² land. Emissions regarding land use change contributed significantly in both cases but had a high uncertainty factor, ±170 ton CO₂ ha^?1. Excluding emissions from land use change resulted in a decrease of 9 kg CO₂ eq for case 1 ‘wheat middlings’ and an increase of 50 kg CO₂ eq for case 2 ‘beet tails’.

Conclusions

Assessing the use of co-products in the livestock sector is of importance because shifting its application can reduce the environmental impact of the livestock sector. A correct assessment of the environmental consequences of using co-products in animal feed should also include potential changes in impacts outside the livestock sector, such as the impact in the bioenergy sector.     

Estimating brain functional connectivity with sparse multivariate autoregression

There is much current interest in identifying the anatomical and functional circuits that are the basis of the brain's computations, with hope that functional neuroimaging techniques will allow the in vivo study of these neural processes through the statistical analysis of the time-series they produce. Ideally, the use of techniques such as multivariate autoregressive (MAR) modelling should allow the identification of effective connectivity by combining graphical modelling methods with the concept of Granger causality. Unfortunately, current time-series methods perform well only for the case that the length of the time-series Nt is much larger than p, the number of brain sites studied, which is exactly the reverse of the situation in neuroimaging for which relatively short time-series are measured over thousands of voxels. Methods are introduced for dealing with this situation by using sparse MAR models. These can be estimated in a two-stage process involving (i) penalized regression and (ii) pruning of unlikely connections by means of the local false discovery rate developed by Efron. Extensive simulations were performed with idealized cortical networks having small world topologies and stable dynamics. These show that the detection efficiency of connections of the proposed procedure is quite high. Application of the method to real data was illustrated by the identification of neural circuitry related to emotional processing as measured by BOLD.     

Evaluation of accuracy and applicability of protein models: retrospective analysis of biological and biomedical predictions

In order to study protein function and activity structural data is required. Since experimental structures are available for just a small fraction of all known protein sequences, computational methods such as protein modelling can provide useful information. Over the last few decades we have predicted, with homology modelling methods, the structures for numerous proteins. In this study we assess the structural quality and validity of the biological and medical interpretations and predictions made based on the models. All the models had correct scaffolding and were ranked at least as correct or good by numerical evaluators even though the sequence identity with the template was as low as 8%. The biological explanations made based on models were well in line with experimental structures and other experimental studies. Retrospective analysis of homology models indicates the power of protein modelling when made carefully from sequence alignment to model building and refinement. Modelling can be applied to studying and predicting different kinds of biological phenomena and according to our results it can be done so with success.     

Dynamic modelling and analysis of biochemical networks: mechanism-based models and model-based experiments

Systems biology applies quantitative, mechanistic modelling to study genetic networks, signal transduction pathways and metabolic networks. Mathematical models of biochemical networks can look very different. An important reason is that the purpose and application of a model are essential for the selection of the best mathematical framework. Fundamental aspects of selecting an appropriate modelling framework and a strategy for model building are discussed. Concepts and methods from system and control theory provide a sound basis for the further development of improved and dedicated computational tools for systems biology. Identification of the network components and rate constants that are most critical to the output behaviour of the system is one of the major problems raised in systems biology. Current approaches and methods of parameter sensitivity analysis and parameter estimation are reviewed. It is shown how these methods can be applied in the design of model-based experiments which iteratively yield models that are decreasingly wrong and increasingly gain predictive power.