Review on Suitability of Available LCIA Methodologies for Assessing Environmental Impact of the Food Sector

Production, processing, distribution, and consumption of a wide variety of products in the food sector have different ranges of environmental impacts. Methodologies used in environmental impact assessment differ in which set of impact categories is covered and which models are used to assess them. In the food sector, life cycle assessment results are mostly presented without any clear distinction of the principles applied to selecting the relevant methodology. In this paper, the most relevant life cycle impact assessment methodologies are determined from the list of recommended methodologies published recently in the international reference life cycle data system (ILCD) handbook. The range of the relevant impacts covered is considered as the main indicator decisive in selecting a methodology. The selection of the relevant set of impact categories is performed through an overview of more than 50 recent LCA case studies of different products in the sector. The result of the research is a short list of three LCIA methodologies recommended to be used for environmental impact assessment of products in the food sector

Review on Suitability of Available LCIA Methodologies for Assessing Environmental Impact of the Food Sector

Production, processing, distribution, and consumption of a wide variety of products in the food sector have different ranges of environmental impacts. Methodologies used in environmental impact assessment differ in which set of impact categories is covered and which models are used to assess them. In the food sector, life cycle assessment results are mostly presented without any clear distinction of the principles applied to selecting the relevant methodology. In this paper, the most relevant life cycle impact assessment methodologies are determined from the list of recommended methodologies published recently in the international reference life cycle data system (ILCD) handbook. The range of the relevant impacts covered is considered as the main indicator decisive in selecting a methodology. The selection of the relevant set of impact categories is performed through an overview of more than 50 recent LCA case studies of different products in the sector. The result of the research is a short list of three LCIA methodologies recommended to be used for environmental impact assessment of products in the food sector

Data Availability for Carbon Calculators in Measuring GHG Emissions Produced by the Food Sector

 The continuing increase in burning fossil fuels over recent decades along with the changing land use have resulted in a considerable increase in the amount of greenhouse gases (GHGs) which can potentially lead to climate change. Adaptation processes will become necessary in order to cope with these challenges in the future. Despite individuals’ and institutions’ willingness to reduce the amount of GHG emissions caused by their actions or their "carbon footprints", they may lack the knowledge to make effective choices. Carbon calculators have been developed to address these knowledge gaps by measuring and communicating the overall magnitude of the impacts and also the extent to which different behavior patterns contribute to GHG emissions. LCA databases, as providers of inventory data for carbon calculators, have an important role in helping to develop more complete and accurate tools to measure and report produced GHG emissions. For emissions-intensive behavior patterns, the food life cycle is a significant contributor to emissions resulting from activities including agriculture, processing, transport, storage, retail, consumption, and waste handling. This research seeks to classify and characterize these calculators and the agricultural activities or practices they cover, to provide the reader with an idea on the differences between these calculators, and why some of them could be more applicable to the food sector. The intent is to bring clarity to the discussion which could be a step forward in paving the way for the development of more reliable and comprehensive carbon calculators for measuring the GHG emissions of the food secto

Shelf life extension and food waste reduction

Waste is a significant problem in food supply chains. There is potential for spoilage of food products at any stage of the supply chain when the products reach their “best before” or “salable date”. As a key to the food waste problem, there is a trend towards developing shelf life extension solutions that are intended to allow products not only to last longer but also to improve their quality and nutritional benefits.The objective of this study is to explore whether shelf life extension actually results in the expected reductions of food waste. This issue is motivated by potential problems related to complexity in supply chains and consumer behavior.The study is based on a comprehensive literature review and empirical findings from several studies of the structure and functioning of food supply chains undertaken by a food research institute.This work concluded that the relation between shelf life extension and food waste reduction does not appear to be straightforward. Complex consumption behavior (e.g. shopping in larger volume results in longer storage periods at households), in combination with long supply chains and several storage points, implies that shelf life extension may not guarantee consumption before products have reached the “best before date”. Another important factor is the increasing demand for so-called “fresh products”, which may lead to the perception that products with longer shelf life are considered less fresh.This study has shown the need to more closely investigate the effects of various measures (such as shelf life extension) that are applied to reduce food waste. To that end, it would be beneficial to develop a method to investigate and monitor the effectiveness of proposed shelf life extension solutions for the purpose of food waste reduction with a holistic system perspective. This would also help policymakers in their decision-making process as well as solution providers to improve the effectiveness of such solutions. With this perspective in place, the effectiveness of such solutions could be improved. This would also help policymakers in their decision-making process

Green-Lean Synergy - Root-Cause Analysis in Food Waste Prevention

Purpose_The goal of this paper is to explore the possible synergetic effects between lean philosophy and green endeavors in improving resource efficiency in the food sector. To that end, it is investigated how a proper and tailor-made adaptation of the lean six sigma root cause analysis method could help in overcoming the complexities of increased resource efficiency in food production.Design/methodology/approach_The case study concerned reduction of waste at an industrial production line of a dough-based product, through the implementation of the lean six sigma tool.Findings_An achievement of a 50% reduction of waste on the studied process line was reached, thus exceeding the initial improvement goal.Research limitations/implications (if applicable)_While the explicit findings on the specific root causes of waste on this actual production line are not immediately transferrable to other cases, they show that applying this method to identifying and eliminating root causes of waste for other products and processes in the food sector could not only reduce costs but also contribute to more resource-efficient and sustainable industrial food production.Practical implications (if applicable)_ Political and public high interest in environmental and social sustainability associated with food waste render this an important development.Originality/value_ While the potential of linking green and lean efforts has been acknowledged, the application of the lean six sigma methodology for more sustainable food production has not yet been explored. This paper contributes to this researc

Wetland Mapping in Great Lakes Using Sentinel-1/2 Time-Series Imagery and DEM Data in Google Earth Engine

The Great Lakes (GL) wetlands support a variety of rare and endangered animal and plant species. Thus, wetlands in this region should be mapped and monitored using advanced and reliable techniques. In this study, a wetland map of the GL was produced using Sentinel-1/2 datasets within the Google Earth Engine (GEE) cloud computing platform. To this end, an object-based supervised machine learning (ML) classification workflow is proposed. The proposed method contains two main classification steps. In the first step, several non-wetland classes (e.g., Barren, Cropland, and Open Water), which are more distinguishable using radar and optical Remote Sensing (RS) observations, were identified and masked using a trained Random Forest (RF) model. In the second step, wetland classes, including Fen, Bog, Swamp, and Marsh, along with two non-wetland classes of Forest and Grassland/Shrubland were identified. Using the proposed method, the GL were classified with an overall accuracy of 93.6% and a Kappa coefficient of 0.90. Additionally, the results showed that the proposed method was able to classify the wetland classes with an overall accuracy of 87% and a Kappa coefficient of 0.91. Non-wetland classes were also identified more accurately than wetlands (overall accuracy = 96.62% and Kappa coefficient = 0.95)

Wetland Mapping in Great Lakes Using Sentinel-1/2 Time-Series Imagery and DEM Data in Google Earth Engine

The Great Lakes (GL) wetlands support a variety of rare and endangered animal and plant species. Thus, wetlands in this region should be mapped and monitored using advanced and reliable techniques. In this study, a wetland map of the GL was produced using Sentinel-1/2 datasets within the Google Earth Engine (GEE) cloud computing platform. To this end, an object-based supervised machine learning (ML) classification workflow is proposed. The proposed method contains two main classification steps. In the first step, several non-wetland classes (e.g., Barren, Cropland, and Open Water), which are more distinguishable using radar and optical Remote Sensing (RS) observations, were identified and masked using a trained Random Forest (RF) model. In the second step, wetland classes, including Fen, Bog, Swamp, and Marsh, along with two non-wetland classes of Forest and Grassland/Shrubland were identified. Using the proposed method, the GL were classified with an overall accuracy of 93.6% and a Kappa coefficient of 0.90. Additionally, the results showed that the proposed method was able to classify the wetland classes with an overall accuracy of 87% and a Kappa coefficient of 0.91. Non-wetland classes were also identified more accurately than wetlands (overall accuracy = 96.62% and Kappa coefficient = 0.95)

Quality-Based Clustering of Food Products for Customized Food Logistics

Perishability and quality deterioration over time makes food differ from industrial products. The objective of this research is to describe the food quality from a logistics perspective and specify which characteristics of food products are necessary to consider in designing food logistics. These characteristics include food sensory factors, food safety factors, packaging, and the interaction of all these factors with the surrounding environment which can occur related to the logistics processes.According to the results, food products are suggested to be divided into 6 major groups based on their similarity in the interaction with surrounding environment factors affecting the products during logistics activities. These groups are Vegetables, Fruits, Biennial vegetables, Chilled & Super-Chilled, Frozen, and Ambient. Subsequently, the sensitivity of each group was analyzed with regard to the environmental factors they are exposed to during logistics, such as e.g. temperature variations, humidity, or pressure and vibration.The results of this study can increase the knowledge and know-how for more efficient and safer food transports while minimizing the risk of damage to the food products during transport

Food waste drivers in Europe, from identification to possible interventions

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Data Availability for Carbon Calculators in Measuring GHG Emissions Produced by the Food Sector

The continuing increase in burning fossil fuels over recent decades along with the changing land use have resulted in a considerable increase in the amount of greenhouse gases (GHGs) which can potentially lead to climate change. Adaptation processes will become necessary in order to cope with these challenges in the future. Despite individuals’ and institutions’ willingness to reduce the amount of GHG emissions caused by their actions or their “carbon footprints”, they may lack the knowledge to make effective choices. Carbon calculators have been developed to address these knowledge gaps by measuring and communicating the overall magnitude of the impacts and also the extent to which different behavior patterns contribute to GHG emissions. LCA databases, as providers of inventory data for carbon calculators, have an important role in helping to develop more complete and accurate tools to measure and report produced GHG emissions. For emissions-intensive behavior patterns, the food life cycle is a significant contributor to emissions resulting from activities including agriculture, processing, transport, storage, retail, consumption, and waste handling. This research seeks to classify and characterize these calculators and the agricultural activities or practices they cover, to provide the reader with an idea on the differences between these calculators, and why some of them could be more applicable to the food sector. The intent is to bring clarity to the discussion which could be a step forward in paving the way for the development of more reliable and comprehensive carbon calculators for measuring the GHG emissions of the food secto