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# Development of life cycle assessment methodology in food supply chain

Amin Nikkhah

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Amin Nikkhah. Development of life cycle assessment methodology in food supply chain. Environmental Sciences. Ghent University, 2021. English. NNT: tel-03974599

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GHENT
UNIVERSITY

# Development of Life Cycle Assessment
in Food Supply Chain

Dissertation submitted in fulfillment of the requirement for the degree of Doctor of
Bioscience Engineering

By

Amin Nikkhah

Department of Food Technology, Safety and Health

Faculty of Bioscience Engineering

Ghent University

Defense date : 01/10/2021
# Development of life cycle assessment in food supply chain

Dissertatie ingediend om te voldoen aan de vereiste voor het behalen van het doctoraatsdiploma van Doctor in de Bio-ingenieurswetenschappen : levensmiddelenwetenschappen en voeding

door

Amin Nikkhah

Faculteit Bio-ingenieurswetenschappen

Universiteit Gent

Datum Verdediging : 01/10/2021
THE PAGE IS INHERENTLY BLANK
PROMOTERS AND ACADEMIC MEMBERS IN GHENT UNIVERSITY

**Promoter:** Prof. Dr. ing. Sam Van Haute

Department of Food Technology, Safety and Health, Faculty of Bioscience
Engineering, Ghent University

Department of Environmental Technology, Food Technology and Molecular
Biotechnology, Ghent University Global Campus, Incheon, South Korea

**Co-Promoters:** Prof. Dr. ir. Jo Dewulf

Research Group Sustainable Systems Engineering (STEN), Ghent University,
Coupure Links 653, 9000 Ghent, Belgium

Prof. Dr. ir. Sami Ghnimi

Laboratory of Automatic Control, Process and Pharmaceutical Engineering
(LAGEPP), University Claude Bernard Lyon 1, CNRS, UMR 5007, 43
Boulevard 11 Novembre 1918, 69622 Villeurbanne, France

ISARA, 23 Rue Jean Baldissini, 69007, Lyon, France

**Dean of Faculty:** Prof. Dr. ir. Marc Van Meirvenne

**Rector:** Prof. Dr. ir. Rik Van de Walle
EXAMINATION COMMITTEE

**Prof. Dr. ir. Filip Tack** (Chairman)

Department of Green Chemistry and Technology, Faculty of Bioscience
Engineering, Ghent University, Coupure links 653, 9000 Ghent, Belgium

**Prof. Dr. ing. Imca Sampers** (Secretary)

Department of Food Technology, Safety & Health, Faculty of Bioscience
Engineering, Ghent University, Campus Coupure, Coupure Links 653,
9000 Ghent, Belgium

**Prof. Dr. ir. Kurt A. Rosentrater**

Department of Agricultural and Biosystems Engineering, Iowa State
University, Ames, IA 50011, USA

**Prof. Dr. ir. Heekwan Lee**

Department of Environmental Engineering, Incheon National University,
South Korea

**Prof. Dr. Tanja Cirkovic Velickovic**

Department of Food Technology, Safety & Health, Faculty of Bioscience
Engineering, Ghent University, Campus Coupure, Coupure Links 653,
9000 Ghent, Belgium

**Prof. Dr. ir. Mieke Uyttendaele**

Department of Food Technology, Safety & Health, Faculty of Bioscience
Engineering, Ghent University, Campus Coupure, Coupure Links 653,
9000 Ghent, Belgium
All rights reserved. No part of the publication may be reproduced in any form by print, photoprint, microfilm, electronic or any other means without written permission from the publisher.

Faculty of Bioscience Engineering
Coupure Links 653, 9000 Gent, Gent Belgium.

ISSN XXX-XXX

Ghent, Belgium 2021
# Preface

I, Amin Nikkhah, conceived and developed the material presented in this dissertation under the supervision of Prof. Sam Van Haute, Prof. Jo Dewulf, and Prof. Sami Ghnimi. The promoter and co-promoters have reviewed the manuscripts and the dissertation, and they have provided critical and valuable feedback. In addition, Prof. Filip Tack, Prof. Kurt A. Rosentrater, Prof. Heekwan Leee, Prof. Tanja Cirkovic Velickovic, Prof. Imca Sampers, and Prof. Mieke Uyttendaele, academic members of the dissertation committee, reviewed the thesis and provided constructive feedback. Chapters 1–7 of this dissertation have been prepared as individual manuscripts that are currently published, and under review in peer-reviewed journals for publication. The details of each chapter are described below.

## Chapter 1

A version of this chapter was published in a Springer book series:

Nikkhah, A., Van Haute, S. 2021. Hybrid Application of LCA for Analyzing Global Warming Potential of Food Supply Chain, *Handbook of Climate Change Across the Food Supply Chain*, Springer (In press).

This chapter provides relevant literature regarding the application of life cycle assessment (LCA) in food supply chain, the studied impact categories by LCA, hybrid LCA, and the potential contribution of LCA in the agri-food sector.

## Chapter 2

A version of this chapter has been written and will be submitted to a journal:

Food safety and nutritional value, the neglected aspects in life cycle assessment of food production, (ready to submit).

The aim of this chapter is to develop a scheme to consider the environmental impacts, human health, and nutritional properties of food production systems simultaneously. A food safety-based impact category (microbial exposure of cooked-chilled foods) was developed for the LCA methodology. Moreover, the inclusion of multiple functional units (with the focus of nutritional content) in LCA is discussed in this chapter.
## Chapter 3

A version of this chapter was published in the Journal of *Science of the Total Environment*:

Nikkhah, A., Firouzi, S., El Haj Assad, M., Ghnimi, S. (2019). Application of analytical hierarchy process to develop a weighting scheme for life cycle assessment of agricultural production, *Science of the Total Environment*. 665, 538–545.

The objective of this chapter was to develop a weighting scheme for the agricultural production LCA using an analytic hierarchy process.

## Chapter 4

A version of this chapter has been published in the Journal of *Foods*:

Nikkhah, A., Firouzi, S., Dadaei, K., Van Haute, S. 2021. Measuring circularity in food supply chain using life cycle assessment; refining oil from olive kernel. *Foods*. 10(3), 590.

The aim of this chapter is to develop LCA methodology as a powerful tool for measuring the circularity of the food supply chain. This chapter investigated the circularity of a food waste valorization system (refining oil from olive kernel), and an inventory of an industrial-based olive kernel oil production system is also provided in this chapter.

## Chapter 5

A version of this chapter has been published in *Scientific Reports-Nature*:

Ghnimi, S., Nikkhah, A., Dewulf, J., Van Haute, S. 2021. Life cycle assessment and energy comparison of aseptic ohmic heating and appertization of chopped tomatoes with juice. *Scientific Reports-Nature*, 11(1), 1-12.

The aim of this chapter is to develop LCA by extending its application in an innovative food technology (ohmic heating). In this regard, the life cycle environmental impact assessment and energy consumption of chopped tomatoes with juice (CTwJ) processing and packaging upon appertization and aseptic ohmic heating systems were investigated.
## Chapter 6

A version of this chapter has been published in *Scientific Reports-Nature*:

Nikkhah, A., Van Haute, S., Jovanovic, V., Jung, H., Dewulf, J., Cirkovic Velickovic, T., Ghnimi, S. 2021. Life Cycle Assessment of Edible Insects (*Protaetia brevitarsis seulensis* larvae) as a future protein and fat source. *Scientific Reports*, 4(9), 1653-1674.

The aim of this chapter is to develop LCA as a decision support tool for moving toward a sustainable future food system, considering edible insects as a future protein and fat source. Moreover, the protein, fat content, and fatty acid profile of the investigated insect are provided in this chapter to determine its potential food application and use these nutritional properties as functional units for LCA analysis.

## Chapter 7

A version of this chapter has been written and will be submitted to the *Journal of Cleaner Production*:

Optimization of resource allocation in agri-food sector using coupled life cycle assessment, genetic algorithm and Delphi methodology.

The objective of this chapter is to use LCA for the optimization of FSC, not only evaluation.

For this purpose, this chapter provides a framework for optimization of resource allocation in the agri-food sector based on a hybrid LCA, genetic algorithm, and Delphi.

## Chapter 8

Summarizes the outcomes of the presented dissertation.
<figure><img src="image_3.png" /></figure>

Outline of this PhD dissertation, entitled: "Development of life cycle assessment in food supply chain"
# Summary – Samenvatting
# Summary

Life cycle assessment (LCA) is the core methodology used by the European Union to evaluate the carbon footprint of products. LCA is one of the best, most practical, and most standardized approaches for evaluating the environmental consequences of a food supply chain (FSC). It covers a comprehensive range of impact and damage categories. The aim of this dissertation is to develop LCA methodology throughout FSC by (i) food safety and nutrition content inclusion in LCA, (ii) a weighting scheme development for LCA, and (iii) measuring circularity using LCA. This PhD thesis also extends the application of LCA to emerging food technology and future food systems. In addition, this PhD provides a novel framework for hybrid LCA and artificial intelligence (AILCA) for FSC optimization. **Chapter 1** focuses on the relevant literature regarding the application of LCA in the FSC, hybrid LCA, and the potential contribution of LCA in the agri-food sector. In **Chapter 2**, a food safety-based impact category (microbiological safety of cooked-chilled foods) for LCA was developed. Moreover, the potential inclusion of multiple functional units (focusing on nutritional content) in the LCA was considered. As a case study, a Ghent University developed microbial risk model was applied to quantify the damage by microbial hazards (Bacillus cereus as case) in cooked-chilled foods, and then it was added to the human health damage assessment. **Chapter 3** provides a weighting scheme for LCA based on a multi-criteria decision-making tool. Many agricultural systems could benefit from using the developed weighting factors for LCA studies. **Chapter 4** focused on developing the LCA as a tool to measure the circularity of the FSC. For this purpose, the circularity of a food waste valorization system (refining oil from olive kernel) was investigated using LCA. An inventory of an industrial olive kernel oil production system is also provided in this study. The results indicated that natural gas consumption had the highest impact of the investigated impact categories. The global warming potential of 1 kg of produced oil from olive kernel was 0.32 kg CO2 eq. Moreover, the damages of 1 kg of oil production from olive kernel to human health, ecosystem quality, and resource depletion were 5.13 × 10⁻⁷ DALY, 7.73 × 10⁻² PDF·m²·yr., and 24.26 MJ, respectively. **Chapter 5** extends the LCA methodology to emerging food technologies. As a case study, chopped tomatoes with juice (CTwJ) processing and packaging upon
appertization and aseptic ohmic heating systems were evaluated. The results clearly
showed that the environmental impacts of tomato juice processing and packaging upon
appertization were higher than those of ohmic heating systems. The uncertainty analysis
results indicated that the global warming potential for producing one kg of packaged CTwJ
with appertization ranges from 4.13 to 4.44 kg CO2eq, while for the ohmic heating system
it ranges from 2.54 to 2.78 kg CO2eq. Likewise, the application of LCA was extended to
a future food system (edible insects as a future protein and fat source) in **Chapter 6**. The
protein, fat content, and fatty acid profile of the investigated insect (*Protaetia brevitarsis
seulensis* larvae) were analyzed to determine their potential food applications and to be
used as nutritional functional units for LCA analysis. The results revealed that the studied
edible insect production system has beneficial environmental effects on various impact
categories (ICs). This food production system can mitigate the negative environmental
effects of these ICs but has a negative environmental impact on some other ICs, such as
global warming potential. In **Chapter 7**, a framework was developed to optimize resource
allocation in the agri-food sector based on a hybrid LCA, genetic algorithm, and Delphi in
order to apply LCA for optimization, not only modeling. **Chapter 8** discusses the general
conclusions of this PhD thesis and perspectives for future research.
Samenvatting

Levenscyclusanalyse (LCA) is de kern-methodologie die gebruikt wordt door de Europese Unie om de carbon footprint van producten te evalueren. LCA is een van de beste, meest praktische, en meest gestandardiseerde benaderingen voor het evalueren van de impact van een food supply chain (FSC) op het milieu. LCA omvat een waaier van impact- en schade-categorieën. Het doel van deze dissertatie is het ontwikkelen van een LCA methodology voor FSCs door (i) inclusie van voedselveiligheid en nutritionele factoren in LCA, (ii) ontwikkelen van weighting scheme voor LCA, (iii) het meten van circulariteit met LCA. Deze dissertatie breidt de toepassing van LCA uit tot opkomende voedingstechnologieën en future food systemen. Tevens wordt in deze dissertatie een nieuw raamwerk voor hybrid LCA en artificiële intelligentie (AILCA) voor FSC optimalisatie. Hoofdstuk 1 focust op de relevante literatuur betreffende de toepassing van LCA in FSC, hybrid LCA, en de mogelijke bijdrage van LCA in de agri-food sector. In Hoofdstuk 2 werd een LCA impact categorie geïntroduceerd betreffende voedselveiligheid (microbiële veiligheid van gekookte-gekoelde levensmiddelen). Tevens werd de mogelijke inclusie van meerdere nutritionele functionele eenheden behandeld. Als case-study werd een door de UGent ontwikkeld microbieel-risico model toegepast om de schade door een microbieel gevaar (Bacillus cereus in deze casus) in gekookte-gekoelde levensmiddelen te kwantificeren en dit werd toegevoegd aan de bestaande beoordeling van schade aan menselijke gezondheid. Hoofdstuk 3 behandelt een weighting-scheme voor LCA gebaseerd op een multi-criteria decision-making tool. Het zou voordelig kunnen zijn voor vele landbouwsystemen om weighting-factors voor LCA te gebruiken. Hoofdstuk 4 focust op het ontwikkelen van LCA als een tool om de circulariteit van FSCs te meten. De circulariteit van een voedingsafval valorisatie-systeem (raffineren van olijfpitolie) werd onderzocht met LCA. Tevens werd een inventaris van een industrieel olijfpitolie productiesysteem weergegeven. De resultaten suggereren dat aardgas consumptie de grootste impact had van de onderzochte impact categorieën. Het global warming potentieel van 1 kg geproduceerde olijfpitolie was 0.32 kg CO2eq. De productie van 1 kg olie leidde tot een berekende schade aan de menselijke gezondheid, ecosysteem kwaliteit, en depletie van grondstoffen van 5.13 × 10⁻⁷ DALY, 7.73 × 10⁻²
PDF·m²·yr., and 24.26 MJ, respectievelijk. In **Hoofdstuk 5** werd de LCA methodologie toegepast op *emerging* voedingstechnologieën. Als case-study werden versneden tomaten met tomatensap (CTwJ) verwerkt en verpakt en vervolgens werden appertiseren en *ohmic heating* geëvalueerd. De resultaten tonen aan dat de milieu-impact van appertiseren hoger was dan deze van *ohmic heating*. Onzekerheids-analyse gaf aan dat het *global warming potential* van 1 kg CTwJ met appertiseren tussen de 4.13 en 4.44 kg CO₂eq varieerde, terwijl dit voor het *ohmic heating* systeem tussen 2.54 to 2.78 kg CO₂eq varieerde. In **Hoodstuk 6** werd LCA toegepast op een *future food* systeem (eetbare insecten als toekomstige eiwit en vet bron). Het eiwit en vetgehalte en de vetzuursamenstelling van het onderzochte insect (*Protaetia brevitarsis seulensis* larvae) werden geanalyseerd om mogelijke voedingstoepassingen te bepalen en om te gebruiken als nutritionele functionele eenheid voor de LCA analyse. De resultaten toonden aan dat het bestudeerde eetbare insectenproductiesysteem gunstige effecten heeft op bepaalde impact categorieën (ICs). Dit voedselproductiesysteem kan de negatieve milieu-impact van deze ICs verminderen maar heeft een negatieve milieu-impact op sommige andere ICs, zoals *global warming potential*. In **Hoofdstuk 7** werd een raamwerk ontwikkeld voor het optimaliseren van grondstof-allocatie in de *agri-food* sector, door middel van *hybrid* LCA, *genetic algorithm*, en de Delphi methode en dit om LCA toe te passen voor optimalisatie en niet enkel voor modeleren. In **Hoofdstuk 8** tenslotte worden de algemene conclusies van de dissertatie weergegeven en perspectieven voor toekomstig onderzoek toegelicht.
# Acknowledgements

I would like to express my sincere gratitude to my promoter, Prof. Sam Van Haute, for all of his supports and guidelines given to me throughout my PhD and it was a great opportunity for me to have his advices as a supervisor and a also a friend.

I would like to express my highest level of appreciation to my co-promotors Prof. Sami Ghnimi and Prof. Jo Dewulf for their guidance, expertise, valuable comments, and constructive suggestions.

Furthermore, I am genuinely grateful to all members of Department of Food Technology, Safety and Health, Faculty of Bioscience Engineering, Ghent University, specifically Prof Liesbeth Jacxsens, and Prof. Frank Devlieghere, for providing me with the training courses.

I am also grateful for the positive and constructive environment in the Food research Centre at Ghent University Global Campus. To all my friends and colleagues, thank you for your friendship, guidance, encouragement, and laughter: these great times have helped me to keep a good work/life balance.

Last and most importantly, I would like to express my deepest appreciation to my family for their endless supports, understanding and their love dedicated to me during my Ph.D. studies.

October, 2021

Amin Nikkhah
# Table of Content

## 1. Introduction

1.1. Introduction .................................................................................................................. 2

1.2. Decision support tools for analyzing sustainability .................................................. 3

1.3. Life cycle assessment ........................................................................................................ 3

1.3.1. Goal and scope definition .......................................................................................... 3

1.3.2. Inventory Analysis ........................................................................................................ 4

1.3.3. Impact assessment ........................................................................................................ 5

1.3.4. Interpretation of results ............................................................................................ 5

1.4. Impact categories in LCA .................................................................................................... 5

1.5. Food supply chain ................................................................................................................ 8

1.6. LCA contribution toward sustainable food production ............................................ 9

1.7. Application of LCA by farmers .......................................................................................... 10

1.8. Uses of LCA in the food processing and packaging .................................................... 11

1.9. LCA results for consumers .................................................................................................. 12

1.10. LCA methodology development .................................................................................. 13

1.11. LCA of emerging food technologies ............................................................................ 13

1.12. LCA of future protein sources ........................................................................................ 16

1.13. Hybrid LCA in FSC ............................................................................................................ 18

1.14. Hybrid LCA for climate change mitigation of FSC .................................................... 21

1.15. Conclusions ........................................................................................................................ 23

## 2. Methodology development; food safety and nutritional indices for LCA

2.1. Introduction .................................................................................................................... 34

2.2. LCA of food systems ........................................................................................................ 36

2.3. Food safety .......................................................................................................................... 37

2.4. Microbiological risk inclusion in LCA ............................................................................ 40

2.5. DALY inclusion in IMPACT 2002+ ............................................................................ 42

2.6. Inclusion of nutritional value in LCA .................................................................................. 45

2.7. Conclusions ........................................................................................................................ 50

## 3. Methodology development; a weighting scheme for LCA

3.1. Introduction .................................................................................................................... 58

3.2. Materials and method ........................................................................................................ 60

3.2.1. LCA application ............................................................................................................ 61

3.2.2. Weighting using analytic hierarchy process ................................................................ 50

3.2.3. The case study ................................................................................................................ 63

3.3. Results and discussion ........................................................................................................ 63

3.3.1. Interpretation of LCA results .................................................................................... 63
3.3.2. LCA + AHP results ................................................................................................................ 67

3.4. Conclusion .................................................................................................................................................. 70

## 4. Methodology development; measuring circularity using LCA

4.1. Introduction .................................................................................................................................................. 81

4.2. Materials and methods ................................................................................................................................ 85

4.2.1. Refining oil from olive kernel ........................................................................................................ 85

4-2-2. Measuring circularity using LCA ........................................................................................................ 87

4.3. Results and discussion ................................................................................................................................ 91

4.3.1. Interpretation of mid-point Ica results .................................................................................... 91

4.3.2. Interpretation of end-point damage assessment .................................................................... 94

4.3.3. Uncertainty of GWP's results ........................................................................................................ 97

4.4. Conclusions .................................................................................................................................................. 99

## 5. LCA of innovative food technologies; ohmic heating

5.1. Introduction .................................................................................................................................................. 106

5.2. Materials and Methods ................................................................................................................................ 110

5.2.1. Tomato processing ................................................................................................................................ 110

5.2.2. Appertization line ................................................................................................................................ 110

5.2.3. Aseptic ohmic heating line ........................................................................................................ 113

5.2.4. Energy auditing ................................................................................................................................ 113

5.2.5. Software .................................................................................................................................................. 115

5.2.6. LCA application ................................................................................................................................ 115

5.2.7. Uncertainty analysis ................................................................................................................................ 116

5.3. Results and discussion ................................................................................................................................ 117

5.3.1. Energy auditing ................................................................................................................................ 117

5.3.2. Interpretation of LCA results ........................................................................................................ 118

5.3.3. Uncertainty analysis ................................................................................................................................ 124

5.3.4. Mitigation strategies ................................................................................................................................ 125

5.4. Conclusions .................................................................................................................................................. 127

## 6. LCA of novel food; edible insects

6.1. Introduction .................................................................................................................................................. 135

6.1.1. Current status of edible insect production .................................................................................. 136

6.1.2. Literature review ................................................................................................................................ 137

6.2. Materials and methods ................................................................................................................................ 140

6.2.1. Insect production system ................................................................................................................ 140

6.2.2. Sample preparation ................................................................................................................................ 141

6.2.3. Determination of protein and fat content.................................................................................. 142

6.2.4. Fatty acid methyl esters preparation .................................................................................... 142
<table><tr><td>6.2.5. Analysis and identification of fatty acids using GC/EI-MS</td><td>143</td></tr><tr><td>6.2.6. Objective</td><td>143</td></tr><tr><td>6.2.7. Inventory analysis</td><td>144</td></tr><tr><td>6.2.8. Impact assessment and sensitivity analysis</td><td>145</td></tr><tr><td>6.3. Results and discussion</td><td>146</td></tr><tr><td>6.3.1. Protein and fat content of Protaetia brevittarsis seulensis</td><td>146</td></tr><tr><td>6.3.2. LCA results</td><td>149</td></tr><tr><td>6.3.3. Uncertainty analysis</td><td>155</td></tr><tr><td>6.4. Conclusions</td><td>156</td></tr></table>

## 7. Modeling and optimization

<table><tr><td>7.2. Introduction</td><td>163</td></tr><tr><td>7.2. Materials and Methods</td><td>165</td></tr><tr><td>7.2.1. Life cycle assessment</td><td>165</td></tr><tr><td>7.2.2. Modeling</td><td>168</td></tr><tr><td>7.2.3. K-Fold cross validation</td><td>168</td></tr><tr><td>7.2.4. Model performance evaluation</td><td>169</td></tr><tr><td>7.2.5. Genetic algorithm</td><td>170</td></tr><tr><td>7.3. Results and discussions</td><td>173</td></tr><tr><td>7.3.1. LCA of current condition</td><td>173</td></tr><tr><td>7.3.2. Modeling</td><td>178</td></tr><tr><td>7.3.3. Optimization</td><td>181</td></tr><tr><td>7.3.4. LCA of optimized scenarios</td><td>181</td></tr><tr><td>7.4. Conclusions</td><td>183</td></tr></table>

## 8. Conclusions

<table><tr><td>1.1. LCA, past, trend</td><td>189</td></tr><tr><td>1.2. LCA methodology development</td><td>190</td></tr><tr><td>1.3. LCA as a powerful tool for emerging and novel food systems</td><td>191</td></tr><tr><td>1.4. AI-LCA</td><td>192</td></tr><tr><td>1.5. Uncertainty in LCA results</td><td>193</td></tr><tr><td>1.6. Future direction for LCA of food systems</td><td>194</td></tr><tr><td>Appendix</td><td>196</td></tr><tr><td>List of scientific publications</td><td>203</td></tr></table>
List of Abbreviations

<table><tr><td>AC</td><td>acidification</td><td>kg</td><td>kilogram</td></tr><tr><td>AD</td><td>abiotic depletion</td><td>kWh</td><td>kilowatt hour</td></tr><tr><td>ADF</td><td>abiotic depletion of fossil fuels</td><td>LCA</td><td>life cycle assessment</td></tr><tr><td>AET</td><td>aquatic ecotoxicity</td><td>LCI</td><td>life cycle inventory</td></tr><tr><td>AEU</td><td>aquatic eutrophication</td><td>LO</td><td>land occupation</td></tr><tr><td>AHP</td><td>analytic hierarchy process</td><td>LU</td><td>land use</td></tr><tr><td>C</td><td>carcinogens</td><td>m³</td><td>cubic meter</td></tr><tr><td>CC</td><td>climate change</td><td>ME</td><td>marine aquatic ecotoxicity</td></tr><tr><td>CED</td><td>cumulative non-renewable energy demand</td><td>MEx</td><td>mineral extraction</td></tr><tr><td>CFC</td><td>chlorofluorocarbon</td><td>MJ</td><td>mega joule</td></tr><tr><td>CFU</td><td>colony-forming unit</td><td>WF</td><td>weighted index</td></tr><tr><td>CH₄</td><td>methane</td><td>MUFA</td><td>monounsaturated fatty acid</td></tr><tr><td>CML</td><td>institute of environmental sciences</td><td>N₂O</td><td>nitrous oxide</td></tr><tr><td>CO₂</td><td>carbon dioxide</td><td>NC</td><td>non-carcinogens</td></tr><tr><td>CR</td><td>consistency ratio</td><td>NI</td><td>normalization index</td></tr><tr><td>CTwJ</td><td>chopped tomatoes with juice</td><td>NIST</td><td>institute of standards and technology</td></tr><tr><td>DALY</td><td>disability-adjusted life year</td><td>NOx</td><td>nitrogen oxides</td></tr><tr><td>DANI</td><td>DALY nutritional index</td><td>NRE</td><td>non-renewable energy</td></tr><tr><td>EF</td><td>ecological footprint</td><td>OLD</td><td>ozone layer depletion</td></tr><tr><td>EP</td><td>eutrophication</td><td>PBS</td><td>protaetia brevitaris seulensis</td></tr><tr><td>Eq</td><td>equivalent</td><td>PD</td><td>potash depletion</td></tr><tr><td>FA</td><td>fatty acids</td><td>PDF</td><td>potentially disappeared fraction</td></tr><tr><td>FAME</td><td>fatty acid methyl esters</td><td>Tₚ₀</td><td>outlet temperature of the product</td></tr><tr><td>FD</td><td>fossil resources depletion</td><td>PEF</td><td>pulsed electric fields</td></tr><tr><td>FE</td><td>fresh water aquatic ecotoxicity</td><td>PhD</td><td>phosphate depletion</td></tr><tr><td>FSC</td><td>food supply chain</td><td>CI</td><td>consistency index</td></tr><tr><td>FU</td><td>functional unit</td><td>PO</td><td>photochemical oxidation</td></tr><tr><td>GA</td><td>genetic algorithm</td><td></td><td></td></tr></table>
<table><tr><td>GC</td><td>gas chromatography</td><td>RI</td><td>respiratory inorganics</td></tr><tr><td>GHG</td><td>greenhouse gas</td><td>RO</td><td>respiratory organics</td></tr><tr><td>GWP</td><td>global warming potential</td><td>SD</td><td>standard deviation</td></tr><tr><td>HPP</td><td>high pressure processing</td><td>SFA</td><td>saturated fatty acid</td></tr><tr><td>HT</td><td>human toxicity</td><td>SO<sub>2</sub></td><td>sulfur dioxide</td></tr><tr><td>IA</td><td>impact assessment</td><td>t</td><td>ton</td></tr><tr><td>IC</td><td>impact category</td><td>TE</td><td>terrestrial ecotoxicity</td></tr><tr><td>ILCD</td><td>international reference life cycle data system</td><td>WD</td><td>water depletion</td></tr><tr><td>IPCC</td><td>intergovernmental panel on climate change</td><td>Wh</td><td>watt-hour</td></tr><tr><td>IR</td><td>ionizing radiation</td><td>YLDs</td><td>years lived with disability</td></tr><tr><td>ISO</td><td>international standardization organization</td><td>YLL</td><td>years of life lost due to premature mortality</td></tr><tr><td>PUFA</td><td>polyunsaturated fatty acid</td><td>ANN</td><td>artificial neural network</td></tr><tr><td>REPFED</td><td>refrigerated and processed foods of extended durability</td><td>RI</td><td>random consistency index</td></tr><tr><td>WFi</td><td>weighted factor</td><td>T<sub>pi</sub></td><td>inlet temperature of the product</td></tr></table>
List of Figures

<table><tr><td>Figure 1.1. Four phases of an LCA study within the food supply chain</td><td>10</td></tr><tr><td>Figure 1.2. Frequency of coupling other models with LCA</td><td>20</td></tr><tr><td>Figure 1.3. Frequency of hybrid LCA application with different purposes</td><td>20</td></tr><tr><td>Figure 1.4. Hybrid LCA for analyzing GWP of food supply chains</td><td>23</td></tr><tr><td>Figure 2.1. Impact categories considered by different impact assessment methodologies</td><td>35</td></tr><tr><td>Figure 2.2. The effects of various impact categories on the damage assessment</td><td>36</td></tr><tr><td>Figure 2.3. Hybrid application of food safety indicator with LCA</td><td>44</td></tr><tr><td>Figure 2.4. A framework for hybrid application of multiple functional units in LCA of FSC</td><td>50</td></tr><tr><td>Figure 3.1. Life Cycle Assessment (ISO Framework)</td><td>60</td></tr><tr><td>Figure 3.2. The hybrid approach of LCA and AHP</td><td>62</td></tr><tr><td>Figure 3.3. Off-farm and on-farm practices in the life cycle assessment</td><td>63</td></tr><tr><td>Figure 3.4. Contribution of each pollutant to different phases of cured tobacco</td><td>65</td></tr><tr><td>Figure 3.5. Normalized amounts of various impact categories, calculated by AHP</td><td>68</td></tr><tr><td>Figure 4.1. Characterization and application of olive kernel</td><td>84</td></tr><tr><td>Figure 4.2. Different parts of an olive fruit</td><td>86</td></tr><tr><td>Figure 4.3. Olive oil production line with a daily production capacity of 100 kg</td><td>87</td></tr><tr><td>Figure 4.4. Measuring the circularity of olive kernel oil production using LCA</td><td>91</td></tr><tr><td>Figure 4.5. Contribution of consumed inputs to the environmental impact</td><td>94</td></tr><tr><td>Figure 4.6. Damage assessment of olive kernel oil production (per 1 kg produced oil)</td><td>95</td></tr><tr><td>Figure 4.7. Single scores of damage categories in the olive kernel oil production chain</td><td>97</td></tr><tr><td>Figure 4.8. Effect of impact assessment methodology selection on the global warming</td><td>98</td></tr></table>
<table><tr><td>Figure 5.1. Appertization line with 6 t/h capacity</td><td>111</td></tr><tr><td>Figure 5.2. Aseptic Ohmic heating line with 4 t/h capacity</td><td>112</td></tr><tr><td>Figure 5.3. Inputs contribution to the environmental impact upon appertization</td><td>121</td></tr><tr><td>Figure 5.4. Inputs contribution to the environmental impact upon ohmic heating</td><td>121</td></tr><tr><td>Figure 5.5. Normalized values of input damages in CTwJ processing and packaging</td><td>122</td></tr><tr><td>Figure 5.6. Normalized values of inputs damages</td><td>123</td></tr><tr><td>Figure 5.7. Normalized damage assessment of tomato juice processing and packaging</td><td>124</td></tr><tr><td>Figure 6.1. Life cycle assessment of Protaetia brevitaris seulensis production</td><td>140</td></tr><tr><td>Figure 6.2. Proportion of consumed inputs in environmental impact</td><td>152</td></tr><tr><td>Figure 6.3. Normalized damage assessment of production system</td><td>153</td></tr><tr><td>Figure 6.4. Normalized values of different edible insect damage categories</td><td>153</td></tr><tr><td>Figure 7.1. The primary inputs consumption for pomegranate production</td><td>167</td></tr><tr><td>Figure 7.2. A hybrid life cycle assessment, genetic algorithm and Delphi framework</td><td>172</td></tr><tr><td>Figure 7.3. Percentage of environmental impacts of consumed inputs</td><td>176</td></tr><tr><td>Figure 7.4. Damage assessment of one ton pomegranate production</td><td>177</td></tr><tr><td>Figure 7.5. Environmental impacts of inputs within measured and optimized scenario</td><td>183</td></tr></table>
# List of Tables

<table><tr><td>Table 1.1. Impact categories considered by different IA methodology</td><td>6</td></tr><tr><td>Table 1.2. Recently published studies on the LCA of emerging food technologies</td><td>15</td></tr><tr><td>Table 1.3. Recently published studies on the LCA of future protein sources</td><td>17</td></tr><tr><td>Table 1.4. Recently published studies on the use of hybrid LCA in FSC</td><td>18</td></tr><tr><td>Table 2.1. Damage factors characterization of various impact categories</td><td>44</td></tr><tr><td>Table 2.2. Normalization factors of the studied damage categories for Western Europe</td><td>45</td></tr><tr><td>Table 2.3. Application of nutritional-LCA in food supply chain</td><td>46</td></tr><tr><td>Table 3.1. Inputs consumption for tobacco production</td><td>64</td></tr><tr><td>Table 3.2. The characterization indices of tobacco production</td><td>67</td></tr><tr><td>Table 3.3. Normalization and weighted indices of tobacco production</td><td>70</td></tr><tr><td>Table 4.1. Main primary inventory data for the olive kernel oil production system</td><td>89</td></tr><tr><td>Table 4.2. Environmental consequence of the olive kernel oil production system</td><td>93</td></tr><tr><td>Table 4.3. Environmental impacts based on different damage categories</td><td>95</td></tr><tr><td>Table 5.1. Summary of the literature on the LCA of tomato processing</td><td>109</td></tr><tr><td>Table 5.2. Overview of energy and water consumption for producing 1 kg of CTwJ</td><td>117</td></tr><tr><td>Table 5.3. Characterization indices for tomato juice production</td><td>119</td></tr><tr><td>Table 5.4. Total characterized indicators of CTwJ within the selected IA methodologies</td><td>125</td></tr><tr><td>Table 6.1. The relevant literature on LCA of edible insect-based food</td><td>138</td></tr><tr><td>Table 6.2. Main primary inventory data</td><td>145</td></tr><tr><td>Table 6.3. Fatty acid profile of Protaetia brevitarsis seulensis larvae</td><td>148</td></tr><tr><td>Table 6.4. Characterization indices of Protaetia brevitarsis seulensis production</td><td>151</td></tr><tr><td>Table 6.5. Single score of damage categories in edible insect production</td><td>155</td></tr></table>
<table><tr><td>Table 6.6. Characterization indices of edible insects determined</td><td>156</td></tr><tr><td>Table 7.1. Recently published papers on the use of LCA based AI-based optimization</td><td>173</td></tr><tr><td>Table 7.2. On-orchard emissions in pomegranate production</td><td>175</td></tr><tr><td>Table 7.3. Environmental impacts of pomegranate production according to different IC</td><td>164</td></tr><tr><td>Table 7.4. Environmental impacts of the production system under current condition</td><td>178</td></tr><tr><td>Table 7.5. Average and standard deviation of RMSE for the developed models</td><td>179</td></tr><tr><td>Table 7.6. The model performance of the best developed models extracted from K-fold</td><td>179</td></tr><tr><td>Table 7.7. The optimized amounts of variables calculated using ANN+GA</td><td>181</td></tr><tr><td>Table 7.8. Measured and optimized values of environmental impacts of the production</td><td>182</td></tr></table>
Author's Curriculum Vitae

Amin Nikkhah is a PhD student at the faculty of Bioscience Engineering, Department of Food technology, Safety and Health, and at the same time, he is also a teaching/research assistant in the Department of Environmental Technology, Food Technology and Molecular Biotechnology at Ghent University. He has published more than 50 scientific papers in some high impact journals, such as *Renewable and Sustainable Energy Reviews* (Impact factor=15), *Journal of Cleaner Production* (Impact factor=9), *Science of the Total Environment* (Impact factor=8), and *Energy* (Impact factor=7). As a young scientist, his research has been cited over 555 times and has an h-index of 16 and i10-index of 19. Cumulative impact factor of Amin's publications stands at over 100. Cumulative impact factor of Amin's publications during his PhD stands at over 55. In 2017, he was selected as an elite student by the BMN Elites Foundation. His current research interest is development of Life Cycle Assessment (LCA) methodology.
# Chapter 1

# Introduction and literature review

Partially redrafted after:

Nikkhah, A., Van Haute, S. 2021. Hybrid application of LCA for analyzing global warming potential of food supply chain, *Handbook of Climate Change Across the Food Supply Chain*, Springer. In press.
## Summary

Life cycle assessment (LCA) is one of the best, most practical, and most standardized approaches for evaluating the environmental consequences of a food supply chain (FSC). It covers a relatively comprehensive range of impact categories including global warming potential, acidification, eutrophication, and ecotoxicity. LCA has been applied to investigate the environmental impacts of various FSCs worldwide. This chapter provides the relevant literature regarding the LCA methodology and its evolvement. The literature review highlighted that there is still room for improvement in the LCA methodology, extension of LCA application in novel foods and innovative technologies, and application of LCA as an optimization tool, not only evaluation. In the latter case, recent studies show that the hybrid application of LCA is more powerful than single LCA. LCA can be combined with other methodologies to model, and optimize processes and decision-making purposes as well. In this regard, LCA has been coupled with mathematical approaches (regression, Cobb-Douglas, and data envelopment analysis), artificial-based intelligence (artificial neural networks, adaptive neuro-fuzzy based interface, particle swarm optimization, and genetic algorithms), and multi-criteria decision making (analytic hierarchy process, and fuzzy analytic hierarchy process).

### 1.1. Introduction

In recent years, because of the population growth and increasing demand for food, some keywords, such as “achieving food security” and “sustainable agriculture” have become trends for policymakers, authorities, producers, and consumers (Ala and Ridwan, 2020; Ulian et al., 2020). Additionally, the second aim of the United Nations (UN) is to “end hunger, achieve food security, improve nutrition and promote sustainable agriculture.” One solution to achieve the second goal of the UN is resource conservation and management. In this regard, decision support tools play a key role in resource management. So far, this has been applied by policymakers and producers to achieve a
more sustainable production system. They can be used for modeling, prediction, or
optimization of different production systems.

**1.2. Decision support tools for analyzing sustainability**

Decision support tools can contribute to the identification, evaluation, and optimization
of multiple control variables potentially leading to improvements in the performance of
supply chains in general and sustainability of the system in particular (Taticchi et al.,
2015). To date, various decision support tools have been proposed and developed in the
agri-food sector to improve the sustainability of food production systems. The sustainable
development of new alternative technologies in agri-food systems needs to focus
thorough attention to resource management within production systems and achieve a
better understanding of its multiple impacts (Nikkhah et al., 2016). In this respect, life
cycle assessment (LCA) is a well known standardized methodology for examining the
sustainability of the food production systems due to its cradle-to-grave perspective.

**1.3. Life cycle assessment**

The procedures of LCA are standardized by ISO 14040-44 (Huang et al., 2018).
LCA is a methodology to comprehensively evaluate the environmental consequences of
human activities from the extraction of raw materials, through processing/manufacturing,
transportation, consumption, and to waste (cradle to grave) (Saba et al., 2020). The LCA
study comprises four phases, which are described in the subsequent subsections.

**1.3.1. Goal and scope definition**

Goal and scope definition are considered the first phase of each LCA application. It is
also needed to determine the functional units (FUs) considered by the system and system
boundaries in the first phase of an LCA study (Elhami et al., 2017). Various FUs are considered to be evaluated by LCA, including mass-based (Bacenetti et al., 2020), land-based (Pishgar-Komleh et al., 2020a), energy-based, nutritional-based (McAuliffe et al., 2020), and economic-based (Van der Werf and Salou, 2015) FUs. The choice of FU can significantly affect the process of decision-making toward determining a sustainable food system (Masset et al., 2015). However, mass-based FUs are usually one of the common FUs in LCA studies in the food sector. However, nutritional FUs are more appropriate in some food systems and thus, certain studies have applied nutritional properties as FUs, such as protein quality for human diets (Sonesson et al., 2017) and the contribution of foods to the nutrient composition of a weekly diet (Tyszler et al., 2014).

After the selection of appropriate FUs, the system boundary should be specified. A system boundary may be chosen as the cradle of the process, for instance, from the production of raw materials to the grave (the treatment of the waste). Some LCA studies have specified the system boundary from cradle to gate, gate to gate, or gate to grave.

### 1.3.2. Inventory Analysis

The aim of the life cycle inventory analysis is to determine the inputs and outputs of the investigated system. The emissions of the system (to air/soil/water) are divided into the background (off-site) and foreground (on-site). Background emissions consist of the pollutants from the production of inputs (usually off-site emissions); while foreground emissions consist of the direct emissions from the consumption of inputs, such as on-site CO₂ emissions from the burning of diesel fuel in a food industry engine. Several LCA databases have been developed since the release of environmental management standards (ISO14040 and ISO14044) (ISO, 2066a,b), including Agri-Footprint, Ecoinvent,
ELCD, and the Swiss input-output database (Durlinger et al., 2014; Martínez-Rocamora et al., 2016; Wernet et al., 2016; Famiglietti et al., 2019). SimaPro, Gabi, Umberto, and OpenLCA software can also be used for LCA analysis (Emami et al., 2019; Silva et al., 2019).

#### 1.3.3. Impact assessment

The selection of impact categories and calculation of the characterization factors of the selected impact categories are mandatory in the impact assessment of LCA studies. On the other hand, the normalization of the investigated impact categories and weighting of the damage categories are optional in LCA analysis. Several impact assessment methodologies have been proposed for LCA analysis, such as CML-IA baseline, Ecological Scarcity 2013, EDIP 2003, EPD (2018), EF Method, IMPACT 2002+, and ILCD 2011.

#### 1.3.4. Interpretation of results

The last LCA phase is the interpretation of the obtained results. In this phase, the results are explained and discussed in comparison with other relevant studies.

### 1.4. Impact categories in LCA

The different impact categories that are considered in various impact assessment methodologies are shown in Table 1.1. LCA covers a wide variety of impact categories in which global warming potential (GWP) is the main impact category included in almost all LCA's impact assessment methodologies. Therefore, LCA has been applied to investigate the GWP of various FSCs worldwide. Some other impact categories are (directly/indirectly) connected to GWP, such as ozone depletion and formation. GWP
characterization factors of food products are expressed as kg CO₂-eq, usually calculated according to IPCC guidelines (IPCC, 2013) in most LCA impact assessment methodologies (Huijbregts et al., 2016). The obtained GWP characterization factor can be normalized and weighted. Several methods have been applied for weighting LCA results, including panel-based, monetization, and distance-to-target (Castellani et al., 2016).

<table><caption>**Table 1.1: Impact categories considered by different IA methodology**</caption><thead><tr><th>CML-IA baseline</th><th>Ecological Scarcity 2013</th><th>IMPACT 2002+</th><th>EDIP 2003</th><th>ILCD 2011</th><th>EPD (2018)</th><th>EF Method</th></tr></thead><tbody><tr><td>Abiotic depletion</td><td>Water resources</td><td>Carcinogens</td><td>Global warming</td><td>Freshwater ecotoxicity</td><td>Ozone layer depletion</td><td>Cancer human health effects</td></tr><tr><td>Abiotic depletion (fossil fuels)</td><td>Energy resources</td><td>Aquatic eutrophication</td><td>Ozone depletion</td><td>Ozone depletion</td><td>Eutrophication</td><td>Ozone depletion</td></tr><tr><td>Acidification</td><td>Mineral resources</td><td>Respiratory organics</td><td>Resources (all)</td><td>Ionizing radiation</td><td>Abiotic depletion, fossil fuels</td><td>Ionising radiation</td></tr><tr><td>Ozone layer depletion</td><td>Radioactive waste to deposit</td><td>Non-carcinogens</td><td>Ozone formation (Human)</td><td>Human toxicity, cancer effects</td><td>Photochemical oxidation</td><td>Photochemical ozone formation</td></tr><tr><td>Photochemical oxidation</td><td>Heavy metals into soil</td><td>Ionizing radiation</td><td>Ecotoxicity water chronic</td><td>Particulate matter</td><td>Abiotic depletion, elements</td><td>Climate change - fossil</td></tr><tr><td>Fresh water aquatic ecotox.</td><td>Ozone layer depletion</td><td>Land occupation</td><td>Terrestrial eutrophication</td><td>Climate change</td><td>Acidification</td><td>Non-cancer human health effects</td></tr><tr><td>Global warming</td><td>Main air pollutants and PM</td><td>Aquatic ecotoxicity</td><td>Aquatic eutrophication</td><td>Marine eutrophication</td><td>Water scarcity</td><td>Eutrophication freshwater</td></tr><tr><td>Eutrophication</td><td>Carcinogenic substances into air</td><td>Global warming</td><td>Aquatic eutrophication</td><td>Photochemical ozone formation</td><td>Global warming</td><td>Acidification terrestrial and freshwater</td></tr><tr><td>Marine aquatic ecotoxicity</td><td>Heavy metals into air</td><td>Terrestrial acid/nutri</td><td>Human toxicity air</td><td>Acidification</td><td></td><td>Climate change</td></tr><tr><td>Terrestrial ecotoxicity</td><td>Water pollutants</td><td>Non-renewable energy</td><td>Human toxicity water</td><td>Terrestrial eutrophication</td><td></td><td>Water scarcity</td></tr><tr><td rowspan="3">Human toxicity</td><td>POP into water</td><td>Aquatic acidification</td><td>Human toxicity soil</td><td>Freshwater eutrophication</td><td></td><td>Climate change - biogenic</td></tr><tr><td>Heavy metals into water</td><td>Mineral extraction</td><td>Acidification</td><td>Ionizing radiation</td><td></td><td>Ecotoxicity freshwater</td></tr><tr><td>Pesticides into soil</td><td>Ozone layer depletion</td><td>Ecotoxicity water acute</td><td>Human toxicity, non-cancer effects</td><td></td><td>Land use</td></tr></tbody></table>

Continued on next page
<table><caption>**Table 1.1- continued from previous page**</caption><thead><tr><th>CML-IA baseline</th><th>Ecological Scarcity 2013</th><th>IMPACT 2002+</th><th>EDIP 2003</th><th>ILCD 2011</th><th>EPD (2018)</th><th>EF Method</th></tr></thead><tbody><tr><td></td><td>Global warming</td><td>Terrestrial ecotoxicity</td><td>Ecotoxicity soil chronic</td><td>Land use</td><td></td><td>Eutrophication marine</td></tr><tr><td></td><td>Land use</td><td>Respiratory inorganics</td><td>Hazardous waste</td><td>Water resource depletion</td><td></td><td>Resource use, energy carriers</td></tr><tr><td></td><td>Radioactive substances into water</td><td></td><td>Slags/ashes</td><td>Mineral, fossil &amp; ren resource depletion</td><td></td><td>Resource use, mineral and metals</td></tr><tr><td></td><td>Radioactive substances into air</td><td></td><td>Bulk waste</td><td></td><td></td><td>Respiratory inorganics</td></tr><tr><td></td><td>Non radioactive waste to deposit</td><td></td><td>Radioactive waste</td><td></td><td></td><td>Eutrophication terrestrial</td></tr><tr><td></td><td>Noise</td><td></td><td>Ozone formation (Vegetation)</td><td></td><td></td><td>Climate change - land use and transform.</td></tr></tbody></table>

The impact categories considered by LCA methodology is explained as follows:

Aromatic acidification, terrestrial acid/nutria, terrestrial ecotoxicity, and land occupation are the impact categories considered by the ecosystem quality damage category. Land occupation: the damage take into account the result of either conversion of land or occupation of land (Restrepo et al., 2015), Aquatic acidification: the impacts to the water ecosystem affected by the acid rain due to some acidic gases such as nitrogen oxides (NOx) and sulfur dioxide (SO2) (Yang et al., 2020), Terrestrial ecotoxicity: the impacts of toxic substances on the terrestrial ecosystems (Van der Werf, 2004), Terrestrial eutrophication: the deposition onto the terrestrial environment of compounds that promote biomass formation (Huijbregts et al., 200), and Aquatic ecotoxicity: the impact on water ecosystems due to the emissions to air, water, and soil (Yoshanti and Dowaki, 2017).

Carcinogens, non-carcinogens, respiratory organics, respiratory inorganics, ionizing radiation, and ozone layer depletion are the impact categories considered under the human health damage category. Carcinogens: the emissions of carcinogenic substances

<footnote>* Source: SimaPro 9.0 Software (PRe, 2010).</footnote>
to air, soil and water (Restrepo et al., 2015), *Non-carcinogens*: the chemicals that do not cause cancer, but they are still considered harmful (Alsaleh, 2017), *Respiratory organics*: the smog caused by emissions of dust, sulfur and nitrogen oxides to air (Morsali, 2018), *Respiratory inorganics*: the winter smog caused by emissions dust, sulphur and nitrogen oxides to the atmosphere (Russo et al., 2016), *Ionizing radiation*: the particles that have a high enough energy to interact with the atoms of a target (Neira and Van Deventer, 2010), *Ozone layer depletion*: the increased UV radiation as a result of emission of ozone depleting substances to air (Rai and Rastogi, 2020).

Global warming is the impact category under the climate change damage category.

*Global warming:* the increase in average temperatures of planet earth due to the greenhouse effect of greenhouse gases released by human activities (Yilmaz and Can, 2020).

Non-renewable energy and mineral extraction are the two impact categories which are categorized under the resoursec damage category. *Non-renewable energy:* the energy content of the consumed inputs (Lopes et al., 2003), and *Mineral extraction:* the excavation and recuperation of mineralization and from the crust of the Earth (Revuelta, 2018).

**1.5. Food supply chain**

Food supply chains (FSC) are networks in which food products move from farm to fork/waste, including the production/agricultural phase, food processing, packaging, consumption, retail, and waste (Nikkhah et al., 2020). Several factors influence FSCs, including sustainability, which refers to the quality of life improvement, for the current as well as subsequent generations (Tümenbatur and Tanyaş, 2016). FSCs are one of the
main contributors to global warming; for instance, in Europe, FSCs are responsible for 31% of the global warming potential (Jonkman et al., 2019). However, LCA can be applied by policymakers, farmers, companies, and consumers to mitigate FSC greenhouse gas emissions. The following paragraphs explain how LCA can be applied to different phases of FSCs to contribute to sustainability.

**1.6. LCA contribution toward sustainable food production**

Different phases of an LCA study within the FSC are shown in Figure 1.1. LCA is the core methodology in the European Union used to evaluate the carbon footprint of products (Van der Werf et al., 2020). Lu and Halog, (2020) investigated the frequency of LCA application in different stages of FSC. They highlighted that the agricultural phase is the most frequently included, while the retail, preparation, consumer level, and waste phases are included fewer times.
<figure><img src="image_4.png" /><figcaption>**Figure 1.1:** Four phases of an LCA study within the food supply chain.</figcaption></figure>

**1.7. Application of LCA by farmers**

Over the last few years, several studies have been conducted to investigate the environmental impacts of the agricultural phase of food production. These results could be effectively used by farmers for making decisions regarding the mitigation of environmental impacts of agricultural processes. For example, many studies indicate that nitrogen-based chemical fertilizer has the largest environmental adverse effects in some agricultural production systems due to its contribution to impact categories, including
global warming and eutrophication (Bacenetti et al., 2020; Mostashari-Rad et al., 2020). Accordingly, this can encourage producers to mitigate the environmental impact of nitrogen-based fertilizers through (i) nutrient content monitoring of soil and (ii) the selection of an environment friendly fertilizer, such as bio-fertilizer or a chemical fertilizer with lower environmental consequences (Graham and Vance, 2000; Nikkhah et al., 2017). In this regard, using green manures, bio-fertilizers, and farmyards can be considered as alternatives to supply the nitrogen required by crops (Firouzi et al., 2017). Another issue with the agricultural phase of the FSC is the substantial fossil fuel footprint that has been reported as one the largest contributors to the environmental burdens of food production systems (Pishgar-Komleh et al., 2020a; Saber et al., 2020). In this case, the replacement of fossil-based fuels with biofuel could remarkably mitigate the environmental impacts of the entire system (Esmaeilpour-Troujeni et al., 2020).

**1.8. Uses of LCA in the food processing and packaging**

Although environmental data related to the agricultural phases of FSCs are increasingly becoming available, there is a gap regarding the LCA data on food processing (Sanjuán et al., 2014). However, LCA can be effectively used on a company scale to evaluate the overall environmental impacts of food production systems, identification of environmental hotspots, and development of various scenarios toward moving to a more environmentally-friendly food processing/packaging system. The methodology is also applicable to have a decent comparison of application of different technologies in a food processing and packaging system in order to find the most environmentally-efficient system. The comparison of LCA results obtained from a specific company with available
relevant LCA studies can contribute to have a better picture of overall environmental impacts of the production system. Moreover, the hybrid application of LCA methodology can also contribute to modeling and optimizing the food production systems.

Environmental legislation could also significantly contribute to reducing the environmental impacts of FSC (Nikkhah et al., 2017). For example, a law in France and also a recommendation from the Swiss Federal Office for the Environment encourage producers to label their food products with environmental indicators like greenhouse gas emissions (Cros et al., 2010; De Menna et al., 2015). There are already some food manufacturing companies that compute the environmental footprint of their products, mentioning these data on the food product packages. In this regard, LCA as a powerful tool can be used by the food processing industry to examine their environmental footprints.

## 1.9. LCA results for consumers

Consumers can play a key role in moving to sustainable food production systems. Gruber et al., (2016) studied the LCA of the customer stage of FSCs, i.e., shopping, storage, preparation, and disposal. The authors indicated that the shopping phase has the largest adverse effect on the selected impact categories and highlighted that if consumers act carelessly regarding environmental protection, then the customer stage will appear as an environmental hotspot for FSCs. However, information on the environmental life cycle of the food that customers eat is not always available to them (Jones et al., 2008). For instance, the consumer awareness of the environmental impacts of the meat industry has been studied in Belgium, Germany, Finland, Portugal, the Netherlands, and the United States, with percentages of aware participants ranging from 23% to 35% (Sanchez-Sabate and Sabaté, 2019). However, replacing meat with alternatives could mitigate the
environmental impacts of human diets. The following section explains the GWP of meat alternatives.

**1.10. LCA methodology development**

The first LCA study was done over 50 years ago. Since then, this methodology kept evolving and this development has led to standardization by the International Organization for Standardization (ISO) in the ISO-14040 series (ISO 14040:1997, ISO 14041:1999, ISO 14042:2000, ISO 14043:2000) (Finkbeiner, 2013). The above-mentioned standards have been updated, and the LCA is explained within ISO 14040 and 14044 standards (ISO, 2006a,b). However, the same LCA standards are being used to perform LCA studies in various sectors. In addition, there are still open questions and concerns regarding the application and extension of this methodology. The LCA standards are explained broadly and there is no specification regarding its application in food systems. Moreover, the extension of this methodology to emerging food technologies and future food systems can promote its role in mitigation of environmental impacts of food sector which is one of the main drivers of sustainability challenges, including climate change, and human health.

**1.11. LCA of emerging food technologies**

Scientific studies have shown that most FSCs are not sufficiently sustainable (Notarnicola et al., 2015). Thus, in recent years, various new technologies have been proposed and developed to address this issue. Sustainable development of new alternatives technologies in agri-food systems requires a thorough consideration of resource management in the production systems and a greater understanding of multiple impacts.
The term "clean technologies" is defined as "manufacturing processes or product technologies that reduce pollution or waste, energy use, or material use in comparison to the technologies that they replace". As it was mentioned in section 1.10, the extension of LCA methodology to emerging food technologies can promote its role as a decision support tool toward moving to a sustainable food system which can contribute to the mitigation of environmental impacts. However, emerging technologies pose unique concerns for the LCA community, due to the lack of available data, and inherent uncertainties regarding their development (Miller and Keoleian, 2015). The uncertainties of parameter are often higher for emerging technologies since the production system, inputs and outputs have not been completely established (Bartek et al., 2021). The examples of emerging food technologies are the high-pressure processing, infrared heating, microwave heating, cold plasma, power ultrasound, electrolysed water, and ohmic heating. Active, biodegradable, intelligent and nanomaterial-based packaging are the examples of emerging food packaging technologies (Fung et al. 2017). Moni et al. (2020) review on the published documents on LCA of emerging food technologies, and Silva and Sanjuan, (2019) review on alternative food processing technologies indicated that the development of a consistent framework for LCA analysis of emerging food technologies is crucial. A summary of the published studies on the LCA of emerging food technologies is provided in Table 1.2.
<table><caption>**Table 1.2: Recently published studies on the LCA of emerging food technologies**</caption><thead><tr><th>Technology</th><th>Studied food product</th><th>The studied region</th><th>Functional unit</th><th>Impact assessment methodology</th><th>Environmental hotspots</th><th>Reference</th></tr></thead><tbody><tr><td>Active packaging</td><td>Fresh beef</td><td>NA</td><td>1 kg fresh beef</td><td>CML</td><td>Cattle fattening</td><td>Zhang et al. (2015)</td></tr><tr><td>Thermal, high pressure processing, and pulsed electric fields technologies compression</td><td>Watermelon and tomato juice</td><td>German institute of food Technologies, Germany</td><td>One kg of packaged tomato juice</td><td>Not available</td><td>Packaging</td><td>Aganovic et al. (2017)</td></tr><tr><td>Deep-freezing, drying assisted by infrared radiation, andohmic aseptic treatment</td><td>Apricots</td><td>Italy</td><td>1 kg on dry basis of semi-finished apricots produced and packaged</td><td>ReCiPe</td><td>Processing phases and pack-aging materials</td><td>De Marco and Iannone, 2017</td></tr><tr><td>Ultra-high pressure homogenisation</td><td>Milk</td><td>NA</td><td>1000 l of raw milk</td><td>ReCiPe midpoint</td><td>Electricity consumption</td><td>Valsasina et al. (2017)</td></tr><tr><td>Pulsed electric fields technology at an industrial scale</td><td>Packaged peeled tomatoes</td><td>Italy</td><td>1 kg of processed tomato</td><td>ReCiPe.</td><td>Canning</td><td>Arnal et al. (2018)</td></tr><tr><td>Novel filtrating technologies</td><td>Fresh-cut produce</td><td>NA</td><td>1 ton of cut lettuce to be washed</td><td>ReCiPe</td><td>Energy consumption</td><td>Vigil et al. (2020)</td></tr><tr><td>High pressure processing</td><td>Orange juice and sliced Parma ham</td><td>Italy</td><td>1 kg of final product</td><td>ReCiPe Midpoint</td><td>Energy consumption</td><td>Cacace et al. (2020)</td></tr></tbody></table>
1.12. LCA of future protein sources

There is a need to discover alternative sources of protein due to the increasing demand for meat. In addition, many studies have shown that most common meat production systems, such as beef (Biswas and Naude, 2016; Perez-Martinez et al., 2018), chicken (Kalhor et al., 2016; Skunca et al., 2018; López-Andrés et al., 2018), pork (Reckmann et al., 2013; Winkler et al., 2016), and fish (Buchspies et al., 2011; Dekamin et al., 2015), are not sufficiently environment friendly. Hence, the development of new alternative sustainable food sources is essential. LCA has been applied to compare the environmental consequences of protein alternatives. For instance, Halloran et al. (2017) compared the environmental impact of cricket farms to that of chicken farms in Thailand. The results revealed that protein from insects is more environmentally efficient compared to that of chicken. Likewise, Smetana et al. (2019) reported that insect biomass is twice as environmentally efficient compared to chicken meat. One study investigated the LCA of cell-cultured chicken ovalbumin. The results showed lower GHG emissions compared to the traditionally produced dried egg white powder (Maljanen et al., 2020). Another study, investigated the LCA of milk production within the conventional dairy systems, and two plant-based alternatives, i.e., soy milk and almond milk. They indicated that the selection of FU has a strong influence on their LCA results, and they suggested that multiple FUs are necessary for LCA analysis in food systems (Grant and Hicks, 2018).
Table 1.3 summarizes the published studies on the LCA of future protein sources.
<table><caption>**Table 1.3: Recently published studies on the LCA of future protein sources**</caption><thead><tr><td>Future food</td><td>The studied region</td><td>Functional unit</td><td>Impact assessment methodology</td><td>Environmental hotspot</td><td>Reference</td></tr></thead><tbody><tr><td>Seaweed</td><td>NA</td><td>Dried seaweed with a protein content of one ton</td><td>CML 200</td><td>drying the harvested seaweed</td><td>Van Oirschot et al. (2017)</td></tr><tr><td>Edible insect (Black Soldier Fly)</td><td>Indonesia</td><td>One ton of bio-waste</td><td>IPCC 2013 100a and ReCiPe Midpoint Hierarchist (H)</td><td>Electricity consumption</td><td>Mertenat et al. (2019)</td></tr><tr><td>Edible insect (Black Soldier Fly)</td><td>Not available</td><td>One kg of dried and pelletized organic fertilizer, one kg of fresh insect used as pet food, one kg of protein, and one kg of insect fat used as feed</td><td>IMPACT2002+</td><td>Feed production and energy use</td><td>Smetana et al. (2019)</td></tr><tr><td>Edible insect (Black soldier fly and mealworm)</td><td>Poland</td><td>One kg</td><td>IMPACT2002+</td><td>Raw materials consumption</td><td>Smetana et al. (2020)</td></tr><tr><td>Microalgae</td><td>Germany</td><td>1 ton docosahexaenoic acid</td><td>ReCiPe 2016</td><td>Aquaculture</td><td>Bartek et al. (2021)</td></tr><tr><td>Plant-based burger patties</td><td>Beef from Irish and Brazilian origins</td><td>a single 4 oz burger patty and nutrient density unit</td><td>product environmental footprint</td><td>NA</td><td>Saget et al. (2021)</td></tr><tr><td>Seaweed (Saccharina latissima)</td><td>Netherlands</td><td>1 kg of edible product</td><td>ReCiPe Midpoint</td><td>Cultivation phase</td><td>Slegers et al. (2021)</td></tr><tr><td>Meat substitution in burgers</td><td>Germany</td><td>Raw burger patties (weight 113 g) and ready to eat cooked burger made of burger patty</td><td>IMPACT 2002+</td><td>NA</td><td>Smetana et al. (2021)</td></tr></tbody></table>
## 1.13. Hybrid LCA in FSC

Recent studies show that the hybrid application of LCA is more powerful than single LCA applications. LCA can be combined with other methodologies to model, optimize processes, and enhance decision-making purposes. In this regard, LCA has been coupled with mathematical approaches (multiple regression, and data envelopment analysis), artificial-based intelligence (artificial neural networks, adaptive neuro-fuzzy based interfaces, particle swarm optimization, and genetic algorithms), and multi-criteria decision making (analytic hierarchy process). Table 1.4 shows a few recently published papers on the use of hybrid LCA in FSC.

<table><caption>**Table 1.4:** Recently published studies on the use of hybrid LCA in FSC (from 2018 to now)</caption><thead><tr><td>Food/service</td><td>Number(s) of farms/sites</td><td>Selected impact categories</td><td>Decision making/modeling/optimization</td><td>Joint LCA with</td><td>Authors</td></tr></thead><tbody><tr><td>Chicken meat</td><td>One farm</td><td>GWP, OLD, TE, AC, MEx, NRE, AEU, LO, AET, RO, IR, RI, NC, and C</td><td>Optimization</td><td>Genetic algorithm</td><td>López-Andrés et al. (2018)</td></tr><tr><td>Arecanut</td><td>70 farms</td><td>GWP, C, AC, AET, EU, IR, LO, MEx, NC, NRE, OLD, RI, RO, and TE</td><td>Optimization</td><td>Data Envelopment Analysis</td><td>Paramesh et al. (2018)</td></tr><tr><td>Paddy rice</td><td>240 farms</td><td>GWP, AD, AC, EP, TE, HT, PO, OLD, and ME, and FE</td><td>Modeling</td><td>Artificial neural networks and adaptive neuro-fuzzy inference system</td><td>Nabavi-Pelesaraei et al. (2018)</td></tr></tbody></table>

Continued on next page
<table><caption>**Table 1.4** – continued from previous page</caption><thead><tr><th>Food/service</th><th>Number(s) of farms/sites</th><th>Selected impact categories</th><th>Decision making/modeling/optimization</th><th>Joint LCA with</th><th>Authors</th></tr></thead><tbody><tr><td>Grape</td><td>58 orchards</td><td>GWP, AC, HT, AD, EP, ME, OLD, FE, TE, and PO</td><td>Optimization</td><td>Data Envelopment Analysis</td><td>Mohseni et al. (2018)</td></tr><tr><td>Sugarcane</td><td>120 farms</td><td>GWP, AC, AD, TE, PO, EP, OLD, HT, ME, and FE</td><td>Modeling</td><td>Artificial neural networks and Adaptive neuro-fuzzy inference system</td><td>Kaab et al. (2019)</td></tr><tr><td>Rice milling factories</td><td>60 milling factories</td><td>GWP, AD, OLD, AC, PO, EP, HT, TE, ME, and FE</td><td>Modeling</td><td>Adaptive neuro-fuzzy inference system</td><td>Nabavi-Pelesaraei, et al. 2019</td></tr><tr><td>Rice</td><td>Multiple years and farm sizes</td><td>GWP and EP</td><td>Optimization</td><td>Data envelopment analysis</td><td>Masuda (2019)</td></tr><tr><td>Grocery stores</td><td>30 groceries</td><td>GWP and CEDnr</td><td>Optimization</td><td>Data envelopment analysis</td><td>Álvarez-Rodríguez et al. (2019)</td></tr><tr><td>Maize</td><td>Field experiment al design</td><td>GWP, ODP, POCP, AP, EP, and AD</td><td>Decision making</td><td>Fuzzy analytic hierarchy process</td><td>Król-Badziak et al. (2021)</td></tr></tbody></table>

*Abbreviations: global warming potential (GWP), abiotic depletion (AD), human toxicity (HT), acidification (AC), cumulative non-renewable energy demand (CEDnr), aquatic ecotoxicity (AET), aquatic eutrophication (AEU), carcinogens (C), climate change (CC), eutrophication (EP), fossil resources depletion (FD), respiratory inorganics (RI), fossil resources depletion (FRD), fresh water aquatic ecotoxicity (FE), land occupation (LO), terrestrial ecotoxicity (TE), ionizing radiation (IR), land use (LU), marine aquatic ecotoxicity (ME), mineral extraction (MEx), photochemical oxidation (PO), non-carcinogens (NC), non-renewable energy (NRE), ozone layer depletion (OLD), phosphate resources depletion (PhRD), Potash resources depletion (PRD), respiratory organics (RO), and water depletion (WD).

In this chapter, the frequency of hybrid LCA applications with various models and with different purposes in FSCs was investigated. Twenty recently published papers (from 2018 to now) on hybrid LCA in FSCs were investigated; the results are shown in Figure 1.2. The results indicate that the data envelopment analysis and adaptive neuro-fuzzy inference system are the most frequently applied approaches. The results shown in Figure 1.3 indicate that the hybrid applications of LCA with multi-criteria decision-making
models have been included fewer times in hybrid LCA studies in FSCs, compared to modeling and optimization.

<figure><img src="image_5.png" /><figcaption>**Figure 1.2:** Frequency of coupling other models with LCA.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Figure 1.3:** Frequency of hybrid LCA application with different purposes.</figcaption></figure>
1.14. Hybrid LCA for climate change mitigation of FSC

LCA has been coupled with mathematical and artificial-based intelligence approaches to model, optimize, and make decisions in various food production systems (Figure 1.4). In this regard, a hybrid application of LCA with regression, artificial neural networks, and/or adaptive neuro-fuzzy inference systems can be applied to model a FSC. The integration of data envelopment analysis, particle swarm optimization, and genetic algorithms with LCA can contribute to the optimization of a system in terms of its mitigation of the environmental impacts. Hybrid LCA and multi criteria decision making (analytic hierarchy and fuzzy analytic hierarchy processes) is a tool which can be used by policymakers toward designing/moving toward a more environment friendly system.

As an example of hybrid application of LCA, the LCA + DEA framework was applied for the environmental optimization of grocery stores. The proposed framework could decrease approximately 9% of the GWP of the investigated grocery stores (Álvarez-Rodríguez et al., 2019). In another study, Pishgar-Komleh et al., (2020b) applied a hybrid LCA+DEA model to optimize the wheat production system in Poland. They indicated that there is significant potential for the mitigation of global warming caused by the system. In another study, Pishgar-Komleh et al. (2020a) modeled the obtained LCA data using artificial neural networks and subsequently applied two optimization algorithms: namely, the multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO). The potential decreases of GWP according to MOGA and MOPSO were 43% and 39%, respectively. Elsoragaby et al. (2020) applied a hybrid LCA and genetic algorithm to optimize the overall environmental impacts of rice production.
They indicated that there is a potential to mitigate the GHG emissions by 20% and 46% within the transplanting and broadcast seeding methods, respectively.

Hybrid LCA shows potential in mitigating the environmental impacts of food production systems. However, three major issues need to be considered in hybrid LCA applications in FSCs. Few studies have been conducted on hybrid LCA + multi-criteria decision making. However, the combination of LCA + MCDM models could effectively be applied for decision-making in FSC, such as choosing an appropriate line/technology for food processing considering multiple criteria. Another issue with the hybrid application of LCA in FSCs is the data collection limitation from food processing and packaging industries.

Therefore, studies concerning hybrid LCA applications of food processing and packaging are highly valuable toward designing a sustainable FSC. Additionally, to the best knowledge of the authors, there is no published document on the practical application of the recommendations based on the optimization models/algorithm of hybrid LCA studies in the food sector. Thus, further research is needed focusing on implementing the recommendations of the developed models/algorithms and evaluating their accuracies in real-life conditions.
<figure><img src="image_7.png" /><figcaption>**Figure 1.4:** Hybrid LCA for analyzing GWP of food supply chains.</figcaption></figure>

**1.15. Conclusions**

Recent studies highlight that most food production systems are not sufficiently sustainable. In this regard, moving toward sustainable food production is essential to address the UN development goals. Several methodologies have been proposed and developed to contribute to moving towards sustainability. Among them, the LCA methodology has been widely applied to evaluate various production systems. It can also be used by policymakers as a decision support tool to select an environment-friendly production system. Hybrid LCA is a promising tool that contributes to designing an environment friendly FSC. Nevertheless, four issues need to be addressed in future hybrid LCA applications in FSC: i) LCA methodology development, ii) developing LCA in future foods and emerging food technologies systems, iii) investigating the further
potentials of the coupled implementation of LCA and multi-criteria decision making; iv)
focusing on the neglected phases of FSC, such as food processing; v) application of LCA
as an optimization tool, not only evaluation.

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Chapter 2

Methodology development;

Microbiological risk and nutritional value
inclusion in LCA

Partially redrafted after:

Food safety and nutritional value, the neglected aspects in life cycle assessment of food
production (ready ro submit).
## Summary

LCA covers a limited number of human health-related impact categories besides climate change, ecosystem quality, and resources damage categories. However, the food safety and nutritional value-as the essential aspects for selecting the appropriate food production system-have been neglected in the life cycle assessment of FSC. Therefore, in this chapter, the inclusion of these aspects in LCA methodology is discussed. More specifically, a framework for the inclusion of a food safety indicator as the disability-adjusted life year (DALY) value of the produced product to the human health damage category was developed. As a case study, a model called Perfect Model was applied to quantify the microbiological exposure of cooked-chilled meals to be included in human health damage category.

## 2.1. Introduction

The LCA objective is addressing all relevant environmental impacts occurring along with complete product life cycles, from raw material extraction (cradle), and manufacturing to end-of-life (grave), and determining the hotspots as well (Ögmundarson et al., 2020). LCA as a "cradle-to-grave" approach consists of four steps of goal definition, inventory analysis, impact assessment and interpretation (Agrawal, 2020). The impact categories which are considered by various LCA impact assessment methodologies are shown in Figure 2.1. Some impact assessment methodologies also cover the end-points damage assessment. For instance, IMPACT 2002+ impact assessment covers four damage categories of human health (disability-adjusted life year), ecosystem quality (PDF*m2*yr), global warming (kgCO₂eq), and resource depletion (MJ) (Figure 2.2).
<table><thead><tr><th>Impact category</th><th>CML-IA<br>baseline</th><th>Ecological<br>Scarcity 2013</th><th>EDIP 2003</th><th>EF Method</th><th>EPD (2018)</th><th>ILCD 2011</th><th>IMPACT 2002+</th></tr></thead><tbody><tr><td>Global warming</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Ozone layer depletion</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Acidification</td><td>✓</td><td>✗</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Eutrophication</td><td>✓</td><td>✗</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Photochemical oxidation</td><td>✓</td><td>✗</td><td>✗</td><td>✗</td><td>✓</td><td>✗</td><td>✗</td></tr><tr><td>Fresh water ecotox</td><td>✓</td><td>✗</td><td>✗</td><td>✓</td><td>✗</td><td>✓</td><td>✗</td></tr><tr><td>Water resources</td><td>✗</td><td>✓</td><td>✗</td><td>✓</td><td>✓</td><td>✓</td><td>✗</td></tr><tr><td>Terrestrial ecotoxicity</td><td>✓</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td><td>✓</td></tr><tr><td>Energy resources</td><td>✗</td><td>✓</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td></tr><tr><td>Human toxicity</td><td>✓</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td><td>✓</td><td>✗</td></tr><tr><td>Abiotic depletion</td><td>✓</td><td>✗</td><td>✗</td><td>✗</td><td>✓</td><td>✗</td><td>✗</td></tr><tr><td>Main air pollutants and PM</td><td>✗</td><td>✓</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td></tr><tr><td>Carcinogenic</td><td>✗</td><td>✓</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td><td>✓</td></tr><tr><td>Ecotoxicity</td><td>✓</td><td>✗</td><td>✓</td><td>✗</td><td>✗</td><td>✗</td><td>✓</td></tr><tr><td>Water pollutants</td><td>✗</td><td>✓</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td></tr><tr><td>Heavy metals</td><td>✓</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td></tr><tr><td>Radioactive waste deposit</td><td>✓</td><td>✗</td><td>✓</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td></tr><tr><td>Mineral resources</td><td>✗</td><td>✓</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td><td>✓</td></tr><tr><td>Land use</td><td>✗</td><td>✓</td><td>✗</td><td>✓</td><td>✗</td><td>✓</td><td>✓</td></tr><tr><td>Radioactive substances</td><td>✗</td><td>✓</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td></tr><tr><td>POP into water</td><td>✗</td><td>✓</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td></tr><tr><td>Non radioactive waste to deposit</td><td>✗</td><td>✓</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td></tr><tr><td>Respiratory organics</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td><td>✓</td></tr><tr><td>Pesticides into soil</td><td>✗</td><td>✓</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td></tr><tr><td>Non-carcinogens</td><td>✗</td><td>✗</td><td>✗</td><td>✓</td><td>✗</td><td>✗</td><td>✓</td></tr><tr><td>Noise</td><td>✗</td><td>✓</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td></tr><tr><td>Ionizing radiation</td><td>✗</td><td>✗</td><td>✗</td><td>✓</td><td>✗</td><td>✓</td><td>✓</td></tr><tr><td>Non-renewable energy</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td><td>✓</td></tr><tr><td>Respiratory inorganics</td><td>✗</td><td>✗</td><td>✗</td><td>✓</td><td>✗</td><td>✗</td><td>✓</td></tr><tr><td>Resources</td><td>✗</td><td>✗</td><td>✓</td><td>✓</td><td>✗</td><td>✓</td><td>✗</td></tr><tr><td>Ozone formation</td><td>✗</td><td>✗</td><td>✓</td><td>✓</td><td>✗</td><td>✓</td><td>✗</td></tr><tr><td>Particulate matter</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td><td>✓</td><td>✗</td></tr><tr><td>Ecotoxicity water acute</td><td>✗</td><td>✗</td><td>✓</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td></tr><tr><td>Hazardous waste</td><td>✗</td><td>✗</td><td>✓</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td></tr><tr><td>Slags/ashes</td><td>✗</td><td>✗</td><td>✓</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td></tr><tr><td>Bulk waste</td><td>✗</td><td>✗</td><td>✓</td><td>✗</td><td>✗</td><td>✗</td><td>✗</td></tr></tbody></table>

**Figure 2.1:** Impact categories considered by different impact assessment methodologies.

*The bold impact categories are the human health-based impact categories
<figure><img src="image_8.png" /><figcaption>**Figure 2.2:** The effects of various impact categories on the damage assessment categories (Jolliet et al., 2003)</figcaption></figure>

## 2.2. LCA of food systems

Food systems are a significant contributor to GHG emissions, with a share of around 30% of the global total (Clark et al., 2020). Food supply chain (FSC) refers to a network of food-based business enterprises where the food products transfer from farm to fork, involving pre-farm and on-farm operations, processing, packaging, food service, and consumption level (see Chapter 1). LCA is a powerful tool to select an environmental-efficient food system. It has been used for environmental impacts assessment of food
systems throughout its life cycle, from agriculture (Król-Badziak et al., 2021), food processing (Sieti et al., 2019), packaging (Humbert et al., 2009), distribution (Álvarez-Rodríguez et al., 2019), consumer-level (Parajuli et al., 2021), to waste management (Behrooznia et al., 2020). A study reviewed the frequency of LCA application in different phases of food systems and they concluded that the agricultural is the most frequently phase included in the LCA. In contrast, the retail, preparation, consumer-level, and waste phases have been included fewer times (Lu and Halog, 2020).

Climate change, ecosystem quality, human health and resources are the four pillars of an LCA study (See Figure 2.2). LCA is rapidly evolving and the recent environmental concerns have been included as the new impact categories in this methodology. For instance, land use is considered as an impact category in Ecological Scarcity 2013 (Frischknecht et al., 2013), ILCD 2011 (Chomkhamsri et al., 2011), EF Method (Huijbregts et al., 2008), and IMPACT 2002+ impact assessment methodologies. Another recent example is the framework suggested for the inclusion of microplastic, which may end in our food (Saling et al., 2020).

Human health is an essential endpoint impact category for LCA since a number of midpoint categories potentially cause impacts to the human health (Kobayashi et al., 2015). However, food safety is an essential aspect for selecting the appropriate food production system (López-Gálvez et al. (2021)) - have been neglected in the LCA of FSC.

## 2.3. Food safety

Food production and consumption are remarkable drivers of sustainability challenges, including climate change, freshwater scarcity, and human health. There is a vital need for a more robust method (LCA) and metrics (Disability-Adjusted Life Years) to quantitatively
examine the sustainability of the food supply chain in terms of nutritional-health-
environmental perspective (Green et al., 2020). In this regard, there are some impact
categories that are included in human health damage categories, such as carcinogens,
non-carcinogens, respiratory organics, respiratory inorganics, ionizing radiation, and
ozone layer depletion. However, microbial exposure of the final product is neglected in
this damage category. For example, several studies have reported the lower
environmental impacts of insect-based protein (Oonincx and De Boer, 2012; Smetana et
al., 2019,2020) compared to the conventional meat production systems. However, there
are some food safety concerns (such as microbiological risk) associated with the
consumption of certain insects (Imathiu, S., 2020), which need to be included in the LCA
analysis of meat and its alternatives. Another example can be the difference between the
bacterial contamination in greenhouse and open field of fresh produce-based products,
which is not considered by LCA studies addressing the comparison LCA of open-field,
and the greenhouse based agricultural production systems (Romero-Gámez et al., 2019;
Bartzas et al., 2015; Mujkic and Andakudi Kesavan, 2020). Nevertheless, food safety is
rarely considered in LCA and the human health-related impact categories in LCA of food
mainly express the exposure to chemicals, such pesticide residues, rather than microbial
exposure (Thoma et al., 2018). There is only one study addressing the microbiological
exposure in LCA of FSC in which a high-pressure treated ham production system
compared to the traditional one using LCA with the inclusion of potential human health
damage by *Listeria monocytogenes* (Villamonte, et al., 2014). However, there are few
studies addressing the inclusion of microbial exposure in other sectors, such as
wastewater treatment (Harder et al., 2015), and urban water and sanitation infrastructures
(Harder et al., 2017).

Human health-related impact categories are expressed as a single disability-adjusted life
years (DALY) based on human-health end-point damage category in LCA studies. DALY
was developed by the World Health Organization and comprises both the years of life lost
(YLL) and the years lived with disability (YLD) as a result of a given health problem (Al-
Hemoud et al., 2018). In other words, DALY is a measure of overall disease burden,
expressed as the number of years lost due to ill-health, disability or early death. The
human health related problems are expressed as DALY in LCA and quantitative microbial
risk assessment (Harder et al., 2014), which is an opportunity to include the DALY amount
caused by microbial risk in LCA of food systems.

In the case of food industry, ready-to-eat meals are interesting given the growing demand
for ready meals consumption in many high-income and middle-income countries (Hillier-
Brown et al., 2017). Cooked-chilled foods, ready meals, microwave dinners, and
refrigerated and processed foods of extended durability (REPFEDs), are all synonyms for
a growing group of complex food products. There are concerns regarding the three
bacteria of psychrotrophic *Clostridium botulinum*, *Listeria monocytogenes*, and *Bacillus
cereus* in the case of REPFEDs consumption (Daelman, 2013). It is noticeable that the
consumption of REPFEDs is also linked to higher fat and energy and lower micronutrient
intake (Hillier-Brown et al., 2017). The microbiological risk of the produced ready to eat
meals has not been included in the LCA analysis of those products.
## 2.4. Microbiological risk inclusion in LCA

*Bacillus cereus* are heat resistant endospore forming Gram-positive bacteria. It can also grow at low temperatures, and it is ubiquitous in the environment as well as in REPFEDs. However, the symptoms caused by this bacterium are usually mild (diarrhea or vomiting). This study suggested a framework for the inclusion of microbiological risk caused by *Bacillus cereus* associated with the consumption of REPFEDs in the LCA results. More specifically, an approach based on the application of microbiological risk as DALY value to the human health damage category was developed in this study. As a case study, a model called Perfect Model was applied to quantify the microbiological exposure of REPFEDs and then, it was added to the human health damage assessment. In other words, this study has developed LCA through adding a new and essential impact category to human health damage category. The percentage of packages with a too high *Bacillus cereus* concentration (i.e., $\ge 10^5$ CFU/g) was 0.43% or 4313 packages per million packages, according to the Perfect Model (Daelman, 2013). Moreover, an additional 0.04% of packages can contain the emetic toxin.

There were some assumptions in the Perfect Model (Daelman, 2013) that could lead to an overestimation, including the assumption that all the *B. cereus* strains existing in the products have both the ability to produce diarrhoeal and emetic toxins. However, not all strains have the necessary genes to produce the emetic or the diarrhoeal toxins. Another assumption was that all *B. cereus* entering the model are spores, which is a worst-case assumption since only spores will survive the thermal processing. It was also assumed that no spoilage takes place which is another worst-case assumption. Regarding to host differences, in this Perfect model, units (batch/portion/package) with a *B. cereus*
concentration of 10⁵ CFU/g or more were considered ‘risky’ units. It is still not easy to determine which concentration of B. cereus will cause emetic or diarrhoeal symptoms given the diversity in B. cereus strains and the two B. cereus syndromes, complex bacteria-host cell interactions, complex regulation of toxin gene expression, lack of an easy-to-use method for enterotoxin quantification, etc. Therefore, the model is actually an exposure assessment and not a risk assessment.

In the next step, the DALY value of the microbiological risk of one package of ready-to-eat meat was quantified. DALY is composed of years lived with disability (YLDs) and years of life lost due to premature mortality (YLL) and it is simply the sum of the YLD and YLL (Devleesschauwer et al., 2014):

$$Daly = YLD + YLL \qquad (1)$$

No *Bacillus cereus* cases attributed to industrially prepared REPFEDs have been reported. The death rate for *Bacillus cereus* is considered as zero (Kirk et al., 2015). Therefore, only the illness rate is needed to calculate the Daly value. The following equation shows how it can be calculated:

$$\text{The DALY value of one package} = (\text{The duration of disease} \times \text{disability weight}) \times \left(\frac{\text{Risky packages}}{\text{One million packages}}\right) \qquad (2-1)$$

The duration of the disease is 18 hours (Griffiths and Schraft, 2017). “A disability weight is a weight factor that reflects the severity of the disease on a scale from 0 (perfect health) to 1 (equivalent to death). This value was considered as 0.2 for diseases caused by *Bacillus cereus*, according to Haagsma et al. (2015). Years Lost due to Disability (YLD)
are calculated by multiplying the incident cases by duration and disability weight for the condition (Haagsma et al., 2015). Equation 2-3 was used to quantify the DALY value:

$$ \textit{The Daly value of one package} = \left( \frac{0.75}{365} \times 0.2 \right) \times \left( \frac{4750}{1000000} \right) \qquad (2-3) $$

Accordingly, the microbiological risk of one package of ready-to-eat meat was determined to be 1.95 × 10⁻⁶ DALY. Many ready to eat-based LCA studies could benefit from using the developed microbiological risk impact category in this study.

## 2.5 DALY inclusion in IMPACT2002+

The developed microbiological risk impact category was included in the IMPACT 2002+ impact assessment methodology since it has been widely used in the food sector for LCA analyzing. It covers 15 impact categories including some human-health related impact categories (see Figure 2.2). Most impact assessment methodologies cover only the mid-points impact categories. However, IMPACT 2002+ impact assessment besides 15 impact categories covers also four damage categories (human health, ecosystem quality global warming, and resource depletion). In other words, 15 mid-points impact categories are evaluated by IMPACT 2002+, and then those impact categories are expressed based on four damage categories. In addition, this impact assessment methodology is being updated regularly, and the new version of this impact assessment has been published in 2019 as IMPACT World+ (Bulle et al., 2019).

The above-mentioned quantified DALY-based microbiological risk can be added to the human health damage category. Figure 2.3 shows the inclusion of quantified DALY-based microbiological risk to LCA using IMPACT 2002+ impact assessment methodology. In general for food products, inclusion in LCA of other categories of food safety could be
done, including a number of chemical safety hazards of which the magnitude could
change due to the applied resources and food processing technologies. This includes
pesticide residuals, heavy metals, mycotoxins etc.

Nutritional risks is one of the aspects of human health risk which needs to be included in
the LCA analysis of food systems. One example of nutritional risk is the possible
connection between fat consumption and cardiovascular diseases (Haukenes, 2004).
The inclusion of the above-mentioned risks could be done in a similar fashion as for the
microbial safety, i.e. quantifying based on the DALY value.

Table 2.1 also shows the damage factors characterization of various impact categories, including human health related impacts. In the next step of LCA, the human health damage category which includes the microbiological risk can be also normalized using the normalization factors of the damage categories (Table 2.2).
<figure><img src="image_9.png" /><figcaption>**Figure 2.3:** Hybrid application of food safety indicator with LCA</figcaption></figure>

<table><caption>**Table 2.1:** Damage factors characterization of various impact categories (Jolliet et al., 2003)</caption><thead><tr><th>Impact category</th><th>Damage factor</th><th>Unit</th></tr></thead><tbody><tr><td>Carcinogens</td><td>1.45E-06</td><td>DALY/kg chloroethylene</td></tr><tr><td>Non-carcinogens</td><td>1.45E-06</td><td>DALY/kg chloroethylene</td></tr><tr><td>Respiratory organics</td><td>2.13E-06</td><td>DALY/kg ethylene</td></tr><tr><td>Respiratory inorganics</td><td>7.00E-04</td><td>DALY/kg PM2.5</td></tr><tr><td>Ionizing radiation</td><td>2.10E-10</td><td>DALY/Bq carbon-14</td></tr><tr><td>Ozone layer depletion</td><td>1.05E-03</td><td>DALY/kg CFC-11</td></tr><tr><td>Aquatic ecotoxicity</td><td>8.86E-05</td><td>PDF.m2.yr/kg.tdethylgelynceo I</td></tr><tr><td colspan="3">Continued on next page</td></tr></tbody></table>
<table><caption>**Table 2.1** – continued from previous page</caption><thead><tr><th>Impact category</th><th>Damage factor</th><th>Unit</th></tr></thead><tbody><tr><td>Terrestrial ecotoxicity</td><td>8.86E-05</td><td>PDF.m2.yr/kg.tdethylgelynceo I</td></tr><tr><td>Terrestrial acid/nutri</td><td>1.04</td><td>PDF.m<sup>2</sup>.yr/kg SO<sub>2</sub></td></tr><tr><td>Land occupation</td><td>1.09</td><td>PDF.m<sup>2</sup>.yr/m<sup>2</sup>.organic arable land.yr</td></tr><tr><td>Aquatic acidification</td><td></td><td></td></tr><tr><td>Aquatic eutrophication</td><td></td><td></td></tr><tr><td>Global warming</td><td>1</td><td>kg CO<sub>2</sub>/kg CO<sub>2</sub></td></tr><tr><td>Non-renewable energy</td><td>45.6</td><td>MJ/kg crude oil</td></tr><tr><td>Mineral extraction</td><td>5.10E-02</td><td>MJ/kg iron</td></tr></tbody></table>

<table><caption>**Table 2.2:** Normalization factors of the studied damage categories for Western Europe (Jolliet et al., 2003)</caption><thead><tr><th>Damage category</th><th>Unit</th><th>Normalization factor</th></tr></thead><tbody><tr><td>Human health</td><td>DALY/person/year</td><td>0.0077</td></tr><tr><td>Ecosystem Quality</td><td>PDF.m2.yr/person/year</td><td>4650</td></tr><tr><td>Climate Change</td><td>kg CO<sub>2</sub>/person/year</td><td>9950</td></tr><tr><td>Resources</td><td>MJ/person/year</td><td>152000</td></tr></tbody></table>

## 2.6. Inclusion of nutritional value in LCA

Functional unit provides a reference to which the input and output data are normalized, allowing the comparison with other LCA studies with the same functional unit (Del Borghi et al., 2020). The FU should be consistent with the goal and scope of the LCA study and it has to be well-defined and quantifiable, according to ISO 14044 (ISO, 2066a,b). Biomass has been widely used as a FU for LCA of agricultural production systems, such as maize (Król-Badziak et al., 2021), potato and tomato (Parajuli et al., 2020), walnut (Khanali et al., 2020), tobacco (Mirkarimi et al., 2020), eggs (Zargar Ershadi et al., 2020), and sugarcane (Naseri et al., 2020). Land-based FU is the second-largest FU considered by LCA studies in agricultural sectors (Tricase et al., 2018; Alishah et al., 2019; Tassielli
et al., 2019). Economic value-based FUs may also be considered as the integrated product quantity and quality single measure (Cerutti et al., 2014). The common FUs in LCA of FSC are biomass (mass, volume, number of final products), economic value, land usage, and energy generation/production. However, nutrition is the primary function of food (Saarinen et al., 2017). Therefore, more recently, nutrition is gaining attention as a FU, such as protein quantity and quality, fat (omega 3), and vitamin. Table 2.3 displays the application of nutritional-LCA in food supply chain. For instance, protein content is commonly used FU in LCA of alternative meat production systems.

Weidema and Stylianou, (2020) raised the question whether nutrition is a functional unit for LCA or if it is an impact? Given that a diet that is not nutritionally balanced may cause human health impacts, they proposed a hybrid nutrient balance indicator with the DALY Nutritional Index (DANI).

<table><caption>**Table 2.3:** Application of nutritional-LCA in food supply chain</caption><thead><tr><td>Product</td><td>Nutritional functional unit</td><td>Impact assessment</td><td>Focus of study</td><td>Reference</td></tr></thead><tbody><tr><td>Chicken, farmed salmon and wild cod</td><td>A normalized factor (nutrients gained from a kg of product are divided by daily required amount of nutrients)</td><td>Literature data</td><td>Comparing the environmental impacts of aquaculture and agri-food products</td><td>Mungkung et al. (2007)</td></tr><tr><td>Pork</td><td>protein</td><td>Simplified LCA</td><td>Comparison of organic and conventional systems</td><td>Teixeira et al. (2013)</td></tr><tr><td>Gryllusbimaculatus De Geer (field cricket) and Acheta domesticus (house cricket)</td><td>Protein</td><td>ILCD</td><td>Comparing environmental impacts of insect with chicken</td><td>Halloran et al. (2017)</td></tr></tbody></table>

Continued on next page
<table><caption>**Table 2.3** – continued from previous page</caption><thead><tr><td>Product</td><td>Nutritional functional unit</td><td>Impact assessment</td><td>Focus of study</td><td>Reference</td></tr></thead><tbody><tr><td>Whole-wheat bread, chicken fillet, minced pork, minced beef, drinking milk, and pea soup</td><td>Quality and quantity of proteins</td><td>IPCC for global warming</td><td>PQI-based, g protein, and g digestible protein nutritional FU development</td><td>Sonesson et al. (2017)</td></tr><tr><td>Hermetia illucens (Black Soldier Fly)</td><td>Protein and lipid</td><td>CML 2 baseline 2000, IPCC 2007, Cumulative energy demand method, and CML 2001</td><td>Food waste bioconversion by insect</td><td>Salomone et al. (2017)</td></tr><tr><td>29 food products</td><td>Mass and multiple recommended nutrients including vitamin</td><td>Global warming potential</td><td>Taking nutrition into account in LCA of food</td><td>Saarinen et al. (2017)</td></tr><tr><td>Dairy products</td><td>Fat and Protein Corrected Milk</td><td>ILCD 2011</td><td>Traditional food manufacturing</td><td>Bava et al. (2018)</td></tr><tr><td>Spanish agri-food system</td><td>The daily energy requirement of 2000 kcal of an adult</td><td>ILCD/PEF</td><td>Efficiency of Spanish agri-food system</td><td>Laso et al. (2018)</td></tr><tr><td>Vegetable multi cropping system</td><td>Combined nutritional equivalent unit</td><td>A combination of methods</td><td>Vegetable multi-cropping system environmental impacts</td><td>Li et al. (2018)</td></tr><tr><td>Beef, chicken, lamb, pork</td><td>100 g meat, 1 g omega-3 fatty acids, 1 g non-competing omega-3 fatty acids, 1% nutrient density score</td><td>Global warming potential</td><td>Nutritional functional units for meat production</td><td>McAuliffe et al. (2018)</td></tr><tr><td>Black soldier fly (Hermetia illucens) and mealworm (Tenebrio molitor)</td><td>One kg insect margarine</td><td>IMPACT2002+</td><td>Insect margarine</td><td>Smetana et al. (2020)</td></tr><tr><td>Regional-average diets in Spain</td><td>Nutritional and the socio-economic dimensions</td><td>Not available</td><td>Inclusion of food affordability and nutritional values in LCA</td><td>Battle-Bayer et al. (2020)</td></tr></tbody></table>
The selection of FU can sometimes significantly influence the results/decisions coming
from LCA analysis. For instance, the selection of FU can also affect the LCA results in
comparing environmental impacts associated with food systems. In this regard, Cerutti et
al. (2013) indicated that comparing the same fruit production systems with different
functional units might lead to completely different scenarios. A review on LCA studies
comparing agricultural products from conventional and organic farming indicated that
impacts per area farmed land (1 hectare or m²) are usually less in organic systems.
However, the environmental impacts per a unit of biomass production in organic systems
are often higher (Meier et al., 2015). The results of a case on cattle, sheep, pigs, and
poultry farming systems indicated that LCA final results associated with different systems
may be dramatically altered when the nutrient content of meat replaces the mass of meat
as the functional unit, due to differences of their nutritional properties (McAuliffe et al.,
2018). Consequently, consideration of a single FU based on biomass or land could lead
to results that may not be completely precise. Therefore, some LCA studies have
compared multiple FUs in order to better understand the environmental impacts of food
supply chain. However, the traditional single-FU or comparing different FUs are no longer
able to handle these complex problems in the food systems, and the establishment of a
framework for integrating multiple FUs in LCA of FSC remains mainly unexplored.
Integration of multiple FUs in LCA of FSC must be addressed in a multi-criteria context.
To overcome these constraints, this study suggests a framework for the hybrid application
of multiple FUs in LCA of FSC as shown in Fig. 2.4. In this respect, multi-criteria decision-
making (MCDM) as one of the advanced and practical methodologies can be applied to
integrate various FUs in LCA of food systems. FUs in a food system can be integrated
using an MCDM approach through a structured questionnaire and a panel of experts.

To date, several MCDM approaches have been developed. Analytical hierarchy
process (AHP), multi-criteria optimization and compromise solution (VIKOR), the
technique for order of preference by similarity to ideal solution (TOPSIS), elimination and
choice expressing reality (ELECTRE), and preference ranking organization method for
enrichment evaluation (PROMETHEE) are the common applied MCDM (Al Garni et al.,
2016). A limitation of the MCDM employment for integrating FUs may come from the
limited number of available experts on LCA of food systems and the results depend on
the selection of panel, and thus, an objective result is not 100 percent attainable. Another
limitation can be the limited number of criteria that can be considered by a food LCA study.
<figure><img src="image_10.png" /><figcaption>**Figure 2.4:** A framework for the hybrid application of multiple functional units in LCA of FSC</figcaption></figure>

## 2.7. Conclusions

To establish a sustainable food supply chain, it is essential to consider the environmental impacts, human health and nutritional properties simultaneously. This study developed a food safety-based impact category (microbiological exposure of cooked-chilled foods) for LCA. Moreover, the inclusion of multiple functional units (with the focus of nutritional content) in LCA is discussed in this study. Further research is
needed to include other aspects of food safety (chemical hazards, physical hazards) and
ultimately food quality and nutrition can be introduced as impact in LCA studies in FSC.

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# Chapter 3

## Methodology development; Developing a weighting scheme for LCA

Partially redrafted after:

Nikkhah, A., Firouzi, S., El Haj Assad, M., Ghnimi, S. (2019). Application of analytical hierarchy process to develop a weighting scheme for life cycle assessment of agricultural production, *Science of the Total Environment*. 665, 538–545. (Impact factor = 6.5).
## Summary

The impact assessment (IA) step in LCA studies is classified into three steps of characterization, normalization and weighting. In this chapter of the PhD dissertation, various impact categories were weighted using Analytic Hierarchy Process (AHP), as a multi criteria decision making tool. The tobacco production system was the example of agricultural system. The data for LCA analysis were collected from 225 farms. The data for AHP analysis were gathered by surveying 12 LCA experts. The results indicated that on-farm emissions of CO2, CH4, N2O, NH3, NOx and SO2 were accounted for 30, 93, 93, 99, 38 and 5% of the total emissions (cradle to farm gate), respectively. The LCA+AHP showed that the weighted factors of global warming, terrestrial eutrophication, acidification, fossil resources depletion, phosphate resources depletion and potash resources depletion were 1, 0.79, 0.52, 0.68, 0.42, and 0.26, respectively. Many agricultural systems could benefit from using the developed weighting factors for LCA studies in the agricultural sector. Based on the new developed weighted factors, the weighted indices of aforementioned impact categories for tobacco production were 0.24, 0.41, 0.19, 1.04, 0.23, and 0.20, respectively. This implies that fossil resources depletion impact category was attributed the highest negative environmental impacts of tobacco production followed by terrestrial eutrophication.

## 3.1. Introduction

Combination of LCA with other models is now an emerging topic in environmental impacts evaluation of agricultural production. For example, combination of LCA and artificial neural networks was employed to better predict the environmental burden of lentil production (Elhami et al., 2017). In a similar study, a coupled application of LCA and adaptive neuro-fuzzy inference system was applied to evaluate the environmental consequences of canola production supply chain (Mousavi-Avval et al., 2017a). Another study highlighted that integration of LCA and Cobb-Douglas modeling can contribute to
the resource management of the process of kiwifruit production (Nikkhah et al., 2016).
Mousavi-Aval et al. (2017b) integrated multi-objective genetic algorithm and LCA. They
emphasized that this integration could be used to optimize resource consumption of
canola production.

Based on LCA procedure, any LCA study involves four steps of goal definition, inventory analysis, impact assessment and interpretation (Tabatabaie et al., 2018). The impact assessment in an LCA study is classified into three steps of characterization, normalization and weighting (Nikkhah, 2018). There are some methods for weighting the impact categories in an LCA study; three main groups of weighting methods are monetization, distance to target and panel-based (Castellani et al., 2016). In this regard, multi criteria decision-making is known as one of the most advanced and effective approaches for comparison and analysis (Pourjavad et al., 2011). Thus, it can be employed to compare between various impact categories of agricultural production. Nevertheless, weighting and prioritizing of different impact categories of agricultural production using multi criteria decision-making has not been investigated yet. In this regard, Analytic Hierarchy Process (AHP) is one of the most common and widely used multi criteria decision-making tools (Amini et al., 2016). In this regard, the aim of this chapter of the PhD dissertation is to integrate the LCA and AHP for environmental evaluation of an agricultural system with tobacco production as a case study.
**3.2. Materials and method**

**3.2.1. LCA application**

As illustrated in Figure 3.1, and based on the ISO framework (ISO, 2006), the procedure of LCA comprises four major steps including goal and scope definition, inventory analysis, impact assessment, and interpretation.

<figure><img src="image_11.png" /><figcaption>**Figure 3.1: Life Cycle Assessment (ISO Framework)**</figcaption></figure>

The aim was to investigate the environmental burdens of tobacco production system. In this research, two mass and land based functional units (FUs) were considered. More specifically, one ton of tobacco and one hectare of tobacco farm were considered as FUs. This step compiles all resources consumption and emissions of pollutants by a system based on the specific FUs. The amounts of resource consumption for the case study were gathered using face to face interviews. Then, the amounts of each pollutant were
quantified.

As mentioned before, the impact assessment is classified into three steps including characterization, normalization and weighting. In this study, six impact categories were considered, namely acidification potential, terrestrial eutrophication potential, global warming potential, depletion of phosphate source, depletion of potash source, and depletion of fossil sources. For each impact category, the characterization index was computed through the coefficient of each pollutant on that impact category. Then, the indices were normalized. The normalization indices were calculated through dividing the characterization indices by normalization factors based on the following equation:

$$NI_i = \frac{CI_i}{NF_i} \qquad (3-1)$$

where $NI_i$ is the normalization index of the impact category i, $CI_i$ is the characterization index of the impact category i, and $NF_i$ is the normalization factor of the impact category i (Tayefeh et al., 2018).

### 3.2.2. Weighting using analytic hierarchy process (AHP)

In this study, the weighted factors for the studied impact categories were determined using AHP method. The data for AHP analysis were gathered by surveying 12 LCA experts around the world. The names of the experts who were contributed to the AHP section are listed in Appendix C3.1. An AHP research has three main steps including hierarchy construction, priority analysis, and consistency verification (Ho, 2008; Yaghoubi et al., 2019). The consistency ratio is employed to compute the degree of consistency index and it can be expressed according to Deng et al. (2014) as:

$$CR = \frac{CI}{RI} \qquad (3-2)$$

where CR is the consistency ratio, CI is the consistency index, and RI is the random
consistency index.

Then, the weighting indices were calculated through multiplying the normalization indices by weighted factors as:

$$WF_i = NI_i \times WF_i \qquad (3-3)$$

where WFᵢ is the weighted index of the impact category i, NIᵢ is the normalization index of the impact category i, WFᵢ is the weighted factor of the impact category i (determined by AHP). Figure 3.2 illustrates the hybrid approach of LCA and AHP. The primary data were entered into Excel 2013 and then analyzed by Matlab2014b software.

<figure><img src="image_12.png" /><figcaption>**Figure 3.2:** The hybrid approach of LCA and AHP.</figcaption></figure>
### 3.2.3. The case study

The research was conducted in Guilan province, northern Iran, covering an area of 509.5 km², of which arable land constitutes 333 km². The province is the main tobacco production region in the Northern parts of Iran. Virginia tobacco is produced in tobacco farms of Guilan Province, Iran (Zamani, 2010). Figure 3.3 illustrates the off-farm and on-farm operations in the life cycle assessment of tobacco leaf production system. The required data for this study were collected from 225 tobacco farmers using a face to face interview. Each farmer was asked for explaining in detail the agronomic operations and preliminary curing of leaves in tobacco production.

<figure><img src="image_13.png" /><figcaption>Figure 3.3: Off-farm and on-farm practices in the life cycle assessment of tobacco leaf production system</figcaption></figure>

## 3.3. Results and discussion

### 3.3.1. Interpretation of LCA results

Table 3.1 displays the inputs consumption of tobacco production systems. The gasoline, diesel and natural gas, and electricity consumption for one ton production of tobacco were calculated to be 65.01 L, 26.26 L, 1114.55 m³, and 1852.10 kWh, respectively. The aforementioned inputs consumption for producing tobacco in each
hectare were 125.08 L, 50.18 L, 2158.12 m³, and 3583.44 kWh, respectively. The quantities of chemical fertilizers including N, P₂O₅ and K₂O for one ton production of tobacco were 17.72, 16.78 and 58.51 kg, respectively. Moreover, the consumption of these inputs based on one hectare land were 34.20, 32.41, and 112.80 kg, respectively.

<table><caption>**Table 3.1:** Inputs consumption for tobacco production</caption><thead><tr><th rowspan="2">Inputs and output</th><th colspan="2">Consumption</th><th colspan="2">Standard deviation</th></tr><tr><th>(Unit per one ton tobacco)</th><th>(Unit per one hectare)</th><th>(Unit per one ton tobacco)</th><th>(Unit per one hectare)</th></tr></thead><tbody><tr><td>Gasoline (L)</td><td>65.01</td><td>125.08</td><td>8.66</td><td>15.14</td></tr><tr><td>Diesel fuel (L)</td><td>26.26</td><td>50.18</td><td>3.73</td><td>2.99</td></tr><tr><td>Natural gas (m³)</td><td>1114.55</td><td>2158.12</td><td>153.27</td><td>487.27</td></tr><tr><td>Electricity (kWh)</td><td>1852.10</td><td>3583.44</td><td>184.19</td><td>487.27</td></tr><tr><td>N (kg)</td><td>17.72</td><td>34.20</td><td>2.13</td><td>4.60</td></tr><tr><td>P₂O₅ (kg)</td><td>16.78</td><td>32.41</td><td>1.96</td><td>4.56</td></tr><tr><td>K₂O (kg)</td><td>58.51</td><td>112.80</td><td>6.40</td><td>13.20</td></tr></tbody></table>

Figure 3.4 illustrates the contribution of each pollutant to different phases of cured tobacco leaf production. The results indicated that off-farm contribution of CO₂, NOₓ and SO₂ were 75, 79 and 98%, respectively. The high amount of off-farm CO₂ emission is due to electricity consumption which is mostly generated from fossil fuels (Ministry of Energy of Iran, 2017). In addition, greenhouse gas emissions per capita in Iran were raised from 6.0 tons per person in 2006 to 7.5 tons per person in 2014 (Nikkhah, 2018). In this case, using alternative green resources for electricity generation can be considered as a GHG mitigation strategy for Iran. In this regard, Mohammadi-Maghanaki et al. (2013) claimed
that the potential of energy generation from agricultural and forest wastes, municipal
wastes, livestock wastes, sewage and industrial wastes in Iran is equal 132-million-ton oil
equivalent.

On the other hand, on-farm CH₄, N₂O and NH₃ were accounted for 30, 93 and 99% of total emissions, respectively. Several studies suggested that the high on-farm emission of NH₃ is due to the application of N-based fertilizer (Firouzi et al., 2017; Nikkhah et al., 2017; Tayefeh et al., 2018).

<figure><img src="image_14.png" /><figcaption>**Figure 3.4:** Contribution of each pollutant to different phases of cured tobacco leaf production chain</figcaption></figure>

Table 3.2 shows the characterization indices of tobacco production system. Global warming potential characterization index was determined to be 1884 kgCO2eq. This index for the production of peanut, greenhouse and open field strawberry and rice in traditional and consolidated rice farms in Guilan province were reported to be 311 (Nikkhah et al.,
2015), 695 and 585 (Khoshnevisan et al., 2013), 1312, and 762 kgCO₂ (Khoshnevisan et al., 2014b), respectively. Moreover, the characterization index of global warming potential was 3639 kgCO₂eq per hectare (Table 3.4). This index for the production of rice in traditional and consolidated rice farms were reported to be 4864 and 4040 kgCO₂eq per hectare, respectively (Khoshnevisan et al., 2014b). So, it can be concluded that the global warming potential of tobacco production was higher comparing to the other crop production systems in the region; however, the global warming potential of tobacco production was less than rice production based on the land-based FU. More specifically, given that the yield of tobacco production is less than other products, the environmental of its production is higher based on the mass-based FU and is less according to land-based FU.

Terrestrial eutrophication characterization index for one ton tobacco production was 20 kgNOₓeq. This index for the production of peanut was reported to be 16 kgNOₓeq (Nikkhah et al., 2015). In addition, the characterization index of terrestrial eutrophication was computed 16 kgNOₓeq per hectare (Table 3.4).

Characterization index of acidification potential was calculated to be 14 kgSO₂eq. This index for the production of peanut, greenhouse and open field strawberry and rice in traditional and consolidated rice farms in Guilan province were reported to be 6 (Nikkhah et al., 2015), 2 and 3 (Khoshnevisan et al., 2013), 3 and 6 kgSO₂eq, respectively. Moreover, the acidification potential characterization index was 38 kgSO₂eq per hectare (Table 3.2). This acidification potential for the production of rice in traditional and consolidated rice farms were reported 18 and 22 kgSO₂eq per hectare, respectively (Khoshnevisan et al., 2014b).
<table><caption>**Table 3.2:** The characterization indices of tobacco production</caption><thead><tr><th rowspan="2">Impact category</th><th colspan="2">Consumption</th><th colspan="2">Standard deviation</th></tr><tr><th>(Unit per one ton tobacco)</th><th>(Unit per one hectare)</th><th>(Unit per one ton tobacco)</th><th>(Unit per one hectare)</th></tr></thead><tbody><tr><td>Acidification (kg SO<sub>2</sub>eq)</td><td>14</td><td>38</td><td>1.26</td><td>4.70</td></tr><tr><td>Global warming (kg CO<sub>2</sub>eq)</td><td>1884</td><td>3639</td><td>167.87</td><td>417.879</td></tr><tr><td>Terrestrial eutrophication (kg NO<sub>x</sub>eq)</td><td>20</td><td>27</td><td>2.13</td><td>3.16</td></tr><tr><td>Fossil resources depletion (MJ)</td><td>59659</td><td>115444</td><td>7832.36</td><td>18477.53</td></tr><tr><td>Phosphate resources depletion (kg P<sub>2</sub>O<sub>5</sub>)</td><td>4</td><td>8</td><td>0.49</td><td>1.14</td></tr><tr><td>Potash resources depletion (kg K<sub>2</sub>O)</td><td>6</td><td>12</td><td>0.67</td><td>1.39</td></tr></tbody></table>

### 3.3.2. LCA + AHP results

Figure 3.5 displays the normalized amounts of various impact categories which were calculated by AHP. If CR ≤ 0.10, the level of inconsistency can be considered as acceptable (Dweiri et al., 2015). The CR in this study was 0.04, that is, within the acceptance range. The normalized values of different impact categories of global warming, terrestrial eutrophication, acidification, fossil resources depletion, phosphate resources depletion and potash resources depletion were 1, 0.79, 0.52, 0.68, 0.42, and 0.26, respectively. Several agricultural systems could benefit from using the developed weighting factors for LCA studies in the agri-food sector.
<figure><img src="image_15.png" /><figcaption>**Figure 3.5:** Normalized amounts of various impact categories, calculated by AHP</figcaption></figure>

In the next step, the developed weighted factors were used for determining the weighted indices for our case study. Table 3.3 shows the results of the hybrid application of LCA and AHP for environmental impact assessment of tobacco production. The results indicated that the weighted indices of acidification potential and terrestrial eutrophication potential, global warming potential, depletion of fossil sources, depletion of phosphate source and depletion of potash source, and for one ton production of tobacco were 0.19, 0.41, 0.24, 1.04, 0.23, and 0.20, respectively. In addition, the weighted indices of the aforementioned impact categories per one hectare of tobacco farm were 0.37, 0.79, 0.46, 2.01, 0.45, and 0.38, respectively. This implies that fossil resources depletion impact category had the largest negative environmental impacts of tobacco production followed by terrestrial eutrophication. In similar studies which have been conducted in the same region, the highest negative impacts on the environment during peanut production have
been reported to be related to the fossil resources depletion and terrestrial eutrophication impact categories (Nikkhah et al., 2015), and for kiwifruit production related to the phosphate resources depletion and terrestrial eutrophication (Nikkhah et al., 2016). Curing of fresh Virginia tobacco leaves constitutes the last process of tobacco production system in the studied case (Figure 3.3). A large amount of energy is required to perform this process (Cao et al., 2017). More than 90 percent of the total energy input in tobacco production system belongs to the leaves curing stage (Reed, 2008). Therefore, it is the main responsible for fossil fuel depletion in tobacco production system. The natural gas is the dominant fossil fuel used for warming up the tobacco leaf curing barns in northern Iran. Using a controlled temperature and humidity system to perform the process and utilization of the maximum curing barns capacity are among the applied approaches to mitigate the fuel consumption in tobacco leaves curing process and then lessen the total fossil resources depletion in tobacco production. More than 50 percent of tobacco farms of the study region are less than 1 ha in size. Therefore, aggregation of small tobacco farms to economize using the modern large tobacco curing barns equipped with the automatic control facilities and isolated walls and roofs also are among the useful ways to diminish the fossil energy consumption of the process. Research on the replacement of some of fossil fuel use with the renewable energy sources like the solar energy and biomass should be considered to cut down much more amount of fossil energy use.
<table><caption>**Table 3.3: Normalization and weighted indices of tobacco production**</caption><thead><tr><th rowspan="2">Impact category</th><th colspan="2">Normalization index</th><th colspan="2">Weighted index</th></tr><tr><th>(Unit per one ton tobacco)</th><th>(Unit per one hectare)</th><th>(Unit per one ton tobacco)</th><th>(Unit per one hectare)</th></tr></thead><tbody><tr><td>Acidification potential</td><td>0.37</td><td>0.71</td><td>0.19</td><td>0.37</td></tr><tr><td>Terrestrial eutrophication</td><td>0.52</td><td>1.00</td><td>0.41</td><td>0.79</td></tr><tr><td>Global warming</td><td>0.24</td><td>0.46</td><td>0.24</td><td>0.46</td></tr><tr><td>Fossil fuel depletion</td><td>1.52</td><td>2.95</td><td>1.04</td><td>2.01</td></tr><tr><td>Phosphate sources depletion</td><td>0.55</td><td>1.06</td><td>0.23</td><td>0.45</td></tr><tr><td>Potash sources depletion</td><td>0.75</td><td>1.45</td><td>0.20</td><td>0.38</td></tr></tbody></table>

**3.4. Conclusion**

Weighting is an optional step of the impact assessment phase in an LCA study, and it is not specified in the ISO 14040 and ISO 14044. However, there are different methodologies toward weighting of LCA results. This study applied Analytic Hierarchy Process (as a novel methodology) to develop a weighting scheme for LCA of agricultural production. The data related to AHP analysis were collected from experts in the field of LCA of agricultural production. The integration of LCA and AHP indicated that the weighted factors of impact categories of acidification potential, terrestrial eutrophication, global warming, fossil resources depletion, phosphate resources depletion and potash resources depletion were 0.52, 0.79, 1, 0.68, 0.42 and 0.26, respectively. Many
agricultural systems could benefit from using the developed weighting scheme for LCA studies in the agricultural sector.

In the second step of this study, the developed weighted factors were applied to a specific agricultural system. Iranian tobacco production system was considered as the agricultural case study. The results indicated that the weighted indices of acidification potential, terrestrial eutrophication potential, global warming potential, depletion of fossil sources, depletion of phosphate source, and depletion of potash source for one ton production of tobacco were 0.41, 0.19, 0.24, 1.04, 0.23 and 0.20, respectively. This implies that fossil resources depletion impact category had the largest negative environmental impacts for tobacco production followed by terrestrial eutrophication.

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Chapter 4

Methodology development; measuring
circularity using LCA

Partially redrafted after:

Nikkhah, A., Firouzi, S., Dadaei, K., Van Haute, S. 2021. Measuring circularity in food supply chain using life cycle assessment; refining oil from olive kernel. *Foods*. 10(3), 590.
## Summary

Valorization of food waste is a potential strategy toward a circular food supply chain. In this regard, measuring the circularity of food waste valorization systems is highly important to better understand multiple environmental impacts. Therefore, this study investigated the circularity of a food waste valorization system (refining oil from olive kernel) using a life cycle assessment methodology. An inventory of an industrial-based olive kernel oil production system is also provided in this study. The system boundary was the cradle to the factory gate of the production system. The results indicated that natural gas consumption was the highest contributor to most of the investigated impact categories. The global warming potential of one kg of oil produced from olive kernel was calculated to be 1.37 kg CO2eq. Moreover, the calculated damages of 1 kg oil production from olive kernel to human health, ecosystem quality, and resource depletion were $5.29 \times 10^{-7}$ DALY, 0.12 PDF·m²·yr., and 24.40 MJ, respectively.

## 4.1. Introduction

The circular economy concept is gaining growing attention as an alternative to the linear economy—"take, make, waste,"—which exists now (Haupt and Hellweg, 2019; Secondi, 2020). In a linear economy, natural resources are transformed into goods that provide economic value; however, they come with a limited life span, and are disposed of in the environment with minimum recovery of resources (Ruggieri et al., 2016; Babbitt et al., 2018). This system puts enormous stress on the carrying capacity of the planet (Scheepens et al., 2016). The circular economy describes a system with minimum loss of resources by reusing, recycling, and recovering materials and energy (Ellen MacArthur Foundation 2013; European Environment Agency 2014; Haupt and Zschokke, 2017; Patwa et al., 2021). Various strategies have been suggested for moving from a linear economy to a circular one, including R-based frameworks, such as the 3Rs strategy
(reduce-reuse-recycle), the 4Rs (introducing “recover” as the fourth R), the 6Rs, and even the 9Rs (Kirchherr et al., 2017; Bassi and Dias, 2019).

The measurement of circularity is the first step in moving toward a circular system, as quoted by Peter Ducker: “what gets measured gets managed” (Haupt and Hellweg, 2019).

There is not a unique approach for measuring a circular economy, since the understanding of a circular economy is still being explored (Korhonen et al., 2018). To date, some assessment indices have been applied to measure circularity, such as the material circularity indicator (Ellen MacArthur Foundation and Granta Design, 2015), the circular economy index (Di Maio et al., 2015), material flow analysis (Linder et al., 2017), food loss and waste (Vilariño et al., 2017), and life cycle assessment (LCA) methodology (ISO, 2006a,b). Corona et al. (2019) reviewed the applied approaches for measuring circularity and found three assessment frameworks, seven measurement indices, and nine assessment indicators. In this regard, LCA has been used for decades for the evaluation of the environmental impact of products and services but, more recently, it has shown to be a promising method to measure circularity. It is an appropriate method to investigate the environmental consequences of circular product designs and large-scale changes to move toward a more circular economy (Haupt and Zschokke, 2017). However, in recent years, LCA has been applied to measure circularity in various sectors, such as bio-based materials (Lokesh et al., 2020; Dahiya et al., 2020), tourism (Scheepens et al., 2016), and concrete production (Colangelo et al., 2020). There are also some published documents addressing the connection between LCA and the circular economy concept in the food supply chain (Pauer et al., 2019; Vilariño et al., 2017; Sadhukhan et al., 2020).
In the case of the food supply chain, approximately one-third of the total global food production, which is equal to 1.3 billion tons per year, is wasted in the food production/consumption chain (Dahiya et al., 2018). This includes food loss (such as losses and spoilage at the producer level before the market) or waste (such as losses at retailer and consumer levels) (Shafiee-Jood and Cai, 2016). In fact, food loss and waste (FLW) refer to a certain amount of food, nutrients, or calories that intentionally/unintentionally disappear from food systems (Wesana et al., 2019). A large part of FLW is avoidable, and could be decreased by implementing different strategies at each level of the life cycle of the production system (Strotmann et al., 2017; Principato et al., 2019). Although food waste has been understood as a critical global issue (Muhammad and Rosentrater, 2020), food waste has high potential for reuse or recovery in a circular economy prospective (Ingrao et al., 2018).

In this regard, the olive-based products industry is an interesting case, as it is an economically important industry (Negro et al., 2017). As a globally energy-intensive sector, the olive processing industry faces sustainability challenges (Khounani et al., 2021). Espadas-Aldana et al. (2019) studied 23 published papers on the LCA of olives and olive oil and concluded that the global warming potential (GWP) of one liter of olive oil production is equal to 1.6 kg CO2eq. The olive oil production supply chain also faces crucial challenges regarding waste management. For example, 80% of olive mass is composed of olive pulp and stones; therefore, waste production is four times higher than that produced within the extraction process (Espadas-Aldana et al., 2019). In this regard, the by-products and residues generated in olive processing are not commonly used and end up as waste (Khounani et al., 2021). Thus, valorization of food waste could be
considered as an effective strategy to make the supply chain of olive-based products more circular. In this regard, measuring the circularity of food waste valorization systems is highly important for improving understanding of multiple environmental impacts. One of these wastes is olive kernel (stone). Olive kernel is an important by-product generated in the pitted table olive industry (Nunes et al., 2016). The characterization and application of olive kernel are described in Figure 4.1. However, the current and main use of olive kernel is as direct solid feedstock for biofuel generation for domestic application (Gomez-Martin et al., 2018). However, this currently may not be a realistic option for an oil-rich country. In this regard, establishing an environmentally efficient approach for olive kernel utilization could actually improve the overall sustainability of olive-based product supply chains. This PhD dissertation is the first report on the LCA of industrial-scale refining oil from olive kernel (as an olive processing waste valorization approach) system.

<figure><img src="image_16.png" /><figcaption>**Figure 4.1:** Characterization and application of olive kernel (Rodríguez et al., 2008)</figcaption></figure>