Review and expert survey of allocation methods used in life cycle assessment of milk and beef

Purpose Beef and dairy production systems produce several by-products, such as fertilizers, bioenergy, hides, and pet foods, among which the environmental impacts arising from production should be allocated. The choice of allocation method therefore inevitably affects the results of life cycle assessment (LCA) for milk and beef. The aims of this study were to map out the different allocation methods used in dairy and beef LCA studies and to clarify the rationale for selecting a certain method. Methods A literature review was conducted to identify the different allocation methods used in LCA studies of milk and beef production and the products using beef by-products as a raw material. The justifications for the use of different methods in the studies were also collected. To map out the perspectives of LCA practitioners and further clarify the reasoning behind the use of certain allocation methods, a mixed method survey with quantitative questions and qualitative explanatory fields was sent to the authors included in the literature review. Results and discussion The literature review showed that the most commonly used allocation method between milk and meat was biophysical allocation, which is also the recommended method in LCA guidelines of milk production. Economic allocation was the second most common method, although the rationale for using economic allocation was weak. By-products, such as inedible body parts, were not considered in milk studies and were taken into account in only a small number of beef studies. This might be because most of the studies have cradle-to-farm gate system boundaries. According to the survey, a significantly higher share of LCA practitioners would allocate impacts also to these by-products. Conclusions The allocation is usually done between milk and meat, and other by-products are not taken into account. Since these materials are an unavoidable part of production and there are numerous uses for them, these outputs should be recognized as products and also taken into consideration in LCA studies.

Influence of Different Allocation Methods for Recycling and Dynamic Inventory on CO2 Savings and Payback Times of Light-Weighted Vehicles Computed under Product- and Fleet-Based Analyses: A Case of Internal Combustion Engine Vehicles

Light weighting by material substitution is a key to reducing GHG emissions during vehicle operation. The GHG benefits are a salient factor in selecting lightweight materials for vehicles. Although the literature has performed lightweight material selections using GHG benefits under product- and fleet-based life-cycle inventory (LCI) analyses, recycling effects have therein been accounted for by arbitrarily selecting allocation methods for recycling, as the consensus on their selection is absent. Furthermore, studies have mistreated the temporal variations of the LCI parameters (the dynamic inventory (DI)), though that could be an important factor affecting the overall LCI results when allocation methods for recycling are in place. Therefore, to investigate their influence on greenhouse gas (GHG) benefit evaluations, an LCI case study was conducted, centered on aluminum- and magnesium-substituted internal combustion engine vehicles (ICEVs) at the product- and fleet- levels. “CO2 savings” and the “CO2 payback time”, as well as four allocation methods for recycling, were considered to represent the GHG benefits and address the recycling effects, respectively. The dynamic inventory was based on the world average electricity grid mix change. The results indicate that changing the conditions of the DI and the allocation methods for recycling could alter the better performing material under fleet-based analyses. Therefore, we ascertained that the choice of the allocation method for recycling and conducting fleet-scale dynamic LCI analyses in the presence of the DI is pivotal for material selections.

Resources Allocation to Suppress the Spread of the Epidemic: A New Model with Consideration of Regional Influence (Preprint)

BACKGROUND During epidemics, how to allocate resources to suppress the spread of infectious disease is of great significance. Many researches focused on how to suppress the spread of infectious disease in the contact network. However, obtaining the contact network in a short period of time is difficult. OBJECTIVE When resources cannot meet the needs of multiple regions, it is necessary to consider how to allocate resources among multiple regions to limit the spread of the disease without details of the contact network. METHODS It proposed a resources allocation model to measure the cost of different allocation methods by the number of new infected individuals over a period of time. By calculating the probability of a susceptible individual being infected, it estimated the number of new infected individuals. In order to calculate the probability of a susceptible individual being infected by an infected individual in another region, conditional mutual information was introduced to estimate the strength of association relationship between regions. RESULTS Based on the proposed model, it compared the model costs of four different resources allocation methods and found three factors that affect the performance of each method, including the percentage of infected individuals, the resource coverage percentage, and the distribution of infected individuals in each region. CONCLUSIONS No method is better than other methods under any situations. When resources are allocated, the method with the least cost in a short period of time should be adopted according to the current infections, so as to control the epidemic as soon as possible.

Basic Bidding Formats: Characteristics and Differences

In standard auction theory, the ‘revenue equivalence theorem’ asserts that the outcomes of the elementary allocation methods coincide. However, bidding processes differ fundamentally with regard to the decision situation of the participants: Is it at all imperative to take into consideration the number of competitors (‘stochastic’ strategy) or not (‘deterministic’ course of action)? Furthermore, established auction theory neglects the operating modes of procurement alternatives under uncertainty. Apart from the lacking knowledge how many rivals have to be beaten, tenderers regularly are ignorant of the buyer’s reserve price. Then it is even more tentative to calculate an offer based on probability theory. Consequently, the suppliers’ propensity to collude increases.

A Comparison of IPCC Guidelines and Allocation Methods to Estimate the Environmental Impact of Barley Production in the Basque Country through Life Cycle Assessment (LCA)

This study aimed to estimate the environmental impact of barley production in the Basque Country, Northern Spain, using cradle-to-gate life cycle assessment (LCA) methodology, as well as to assess how methodological choices (i.e., the use of IPCC 2019 Guidelines versus allocation methods) can influence such estimation. The production of mineral fertiliser and the direct emissions of nitrous oxide (N2O) resulting from the application of nitrogen (N) fertiliser were identified as the two main contributors (40% and 30% of all greenhouse gas emissions, respectively) to the environmental impact of barley production. Pertaining to GHG emissions themselves, the use of calcium ammonium nitrate fertiliser was found to be the main contributor. Therefore, the optimization of N fertiliser application was established as a key process to reduce the environmental impact of barley production. The fertiliser-related release of N and phosphorous (P) to the environment was the main contributor to particulate matter formation, terrestrial acidification, and terrestrial and marine eutrophication. The incorporation of environmental data on NH3, NOx, NO3−, and PO43− to the LCA led to a more accurate estimation of barley production impact. A sensitivity analysis showed that the use of economic allocation, compared to mass allocation, increased the estimation of climate change-related impact by 80%. In turn, the application of the IPCC 2019 Refinement Guidelines increased this estimation by a factor of 1.12 and 0.86 in wet regions and decreased in dry regions, respectively. Our results emphasise the importance of the choice of methodology, adapted to the specific case under study, when estimating the environmental impact of food production systems.

An energy-aware approach for resources allocating in the internet of things using a forest optimization algorithm

Purpose Internet of things (IoT) is essential in technical, social and economic domains, but there are many challenges that researchers are continuously trying to solve. Traditional resource allocation methods in IoT focused on the optimal resource selection process, but the energy consumption for allocating resources is not considered sufficiently. This paper aims to propose a resource allocation technique aiming at energy and delay reduction in resource allocation. Because of the non-deterministic polynomial-time hard nature of the resource allocation issue and the forest optimization algorithm’s success in complex problems, the authors used this algorithm to allocate resources in IoT. Design/methodology/approach For the vast majority of IoT applications, energy-efficient communications, sustainable energy supply and reduction of latency have been major goals in resource allocation, making operating systems and applications more efficient. One of the most critical challenges in this field is efficient resource allocation. This paper has provided a new technique to solve the mentioned problem using the forest optimization algorithm. To simulate and analyze the proposed technique, the MATLAB software environment has been used. The results obtained from implementing the proposed method have been compared to the particle swarm optimization (PSO), genetic algorithm (GA) and distance-based algorithm. Findings Simulation results show that the proper performance of the proposed technique. The proposed method, in terms of “energy” and “delay,” is better than other ones (GA, PSO and distance-based algorithm). Practical implications The paper presents a useful method for improving resource allocation methods. The proposed method has higher efficiency compared to the previous methods. The MATLAB-based simulation results have indicated that energy consumption and delay have been improved compared to other algorithms, which causes the high application of this method in practical projects. In the future, the focus will be on resource failure and reducing the service level agreement violation rate concerning the number of resources. Originality/value The proposed technique can solve the mentioned problem in the IoT with the best resource utilization, low delay and reduced energy consumption. The proposed forest optimization-based algorithm is a promising technique to help enterprises participate in IoT initiatives and develop their business.