Scientific Challenges in Performing Life-Cycle Assessment in the Food Supply Chain
Abstract
:1. Introduction
2. Materials and Methods
3. Results
3.1. Scope and Goal
3.2. Inventory Analysis
3.3. Impact Assessment
3.4. Interpretation of the Results
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Djekic, I.; Sanjuán, N.; Clemente, G.; Jambrak, A.R.; Djukić-Vuković, A.; Brodnjak, U.V.; Pop, E.; Thomopoulos, R.; Tonda, A. Review on environmental models in the food chain - Current status and future perspectives. J. Clean. Prod. 2018, 176, 1012–1025. [Google Scholar] [CrossRef]
- Notarnicola, B.; Tassielli, G.; Renzulli, P.A. Modeling the agri-food industry with life cycle assessment. In Life Cycle Assessment Handbook: A Guide for Environmentally Sustainable Products; Wiley: Hoboken, NJ, USA, 2012; pp. 159–183. [Google Scholar]
- Roy, P.; Orikasa, T.; Thammawong, M.; Nakamura, N.; Xu, Q.; Shiina, T. Life cycle of meats: An opportunity to abate the greenhouse gas emission from meat industry in Japan. J. Environ. Manag. 2012, 93, 218–224. [Google Scholar] [CrossRef]
- Romdhana, H.; Bonazzi, C.; Esteban-Decloux, M. Computer-aided process engineering for environmental efficiency: Industrial drying of biomass. Dry. Technol. 2016, 34, 1253–1269. [Google Scholar] [CrossRef] [Green Version]
- Akkerman, R.; Farahani, P.; Grunow, M. Quality, safety and sustainability in food distribution: A review of quantitative operations management approaches and challenges. OR Spectr. 2010, 32, 863–904. [Google Scholar] [CrossRef]
- Battini, D.; Persona, A.; Sgarbossa, F. A sustainable EOQ model: Theoretical formulation and applications. Int. J. Prod. Econ. 2014, 149, 145–153. [Google Scholar] [CrossRef]
- Manzini, R.; Accorsi, R. The new conceptual framework for food supply chain assessment. J. Food Eng. 2013, 115, 251–263. [Google Scholar] [CrossRef]
- Djekic, I. Environmental Impact of Meat Industry—Current Status and Future Perspectives. Procedia Food Sci. 2015, 5, 61–64. [Google Scholar] [CrossRef]
- Gutierrez, M.M.; Meleddu, M.; Piga, A. Food losses, shelf life extension and environmental impact of a packaged cheesecake: A life cycle assessment. Food Res. Int. 2017, 91, 124–132. [Google Scholar] [CrossRef] [PubMed]
- Wernet, G.; Bauer, C.; Steubing, B.; Reinhard, J.; Moreno-Ruiz, E.; Weidema, B. The ecoinvent database version 3 (part I): Overview and methodology. Int. J. Life Cycle Assess. 2016, 21, 1218–1230. [Google Scholar] [CrossRef]
- ISO. ISO 14040:2006 Environmental Management—Life Cycle Assessment—Principles and Framework; International Organization for Standardization: Geneva, Switzerland, 2006. [Google Scholar]
- Djekic, I.; Tomasevic, I. Environmental impacts of the meat chain—Current status and future perspectives. Trends Food Sci. Technol. 2016, 54, 94–102. [Google Scholar] [CrossRef]
- Djekic, I.; Tomasevic, I. Chapter 3—Environmental Indicators in the Meat Chain In Quantification of Sustainability Indicators in the Food Sector, 1st ed.; Muthu, S.S., Ed.; Springer: Singapore, 2018; pp. 55–82. [Google Scholar] [CrossRef]
- Jolliet, O.; Müller-Wenk, R.; Bare, J.; Brent, A.; Goedkoop, M.; Heijungs, R.; Itsubo, N.; Peña, C.; Pennington, D.; Potting, J. The LCIA midpoint-damage framework of the UNEP/SETAC life cycle initiative. Int. J. Life Cycle Assess. 2004, 9, 394. [Google Scholar] [CrossRef] [Green Version]
- Herva, M.; Franco, A.; Carrasco, E.F.; Roca, E. Review of corporate environmental indicators. J. Clean. Prod. 2011, 19, 1687–1699. [Google Scholar] [CrossRef] [Green Version]
- Galli, A.; Wiedmann, T.; Ercin, E.; Knoblauch, D.; Ewing, B.; Giljum, S. Integrating ecological, carbon and water footprint into a “footprint family” of indicators: Definition and role in tracking human pressure on the planet. Ecol. Indic. 2012, 16, 100–112. [Google Scholar] [CrossRef]
- Galli, A.; Wackernagel, M.; Iha, K.; Lazarus, E. Ecological footprint: Implications for biodiversity. Biol. Conserv. 2014, 173, 121–132. [Google Scholar] [CrossRef]
- Đekić, I.; Tomašević, I. Environmental footprints in the meat chain. IOP Conf. Ser. Earth Environ. Sci. 2017, 85, 012015. [Google Scholar] [CrossRef]
- Djekic, I.; Miocinovic, J.; Tomasevic, I.; Smigic, N.; Tomic, N. Environmental life-cycle assessment of various dairy products. J. Clean. Prod. 2014, 68, 64–72. [Google Scholar] [CrossRef]
- Rebelato, M.G.; Rodrigues, A.M.; Thomaz, A.G.D.B.; Saran, L.M.; Madaleno, L.L.; Oliveira, O.J.D. Developing an index to assess human toxicity potential of sugarcane industry. J. Clean. Prod. 2019, 209, 1274–1284. [Google Scholar] [CrossRef]
- Hertwich, E.G.; Mateles, S.F.; Pease, W.S.; McKone, T.E. Human toxicity potentials for life-cycle assessment and toxics release inventory risk screening. Environ. Toxicol. Chem. 2001, 20, 928–939. [Google Scholar] [CrossRef]
- Singh, V.; Dincer, I.; Rosen, M.A. Chapter 4.2—Life Cycle Assessment of Ammonia Production Methods. In Exergetic, Energetic and Environmental Dimensions; Dincer, I., Colpan, C.O., Kizilkan, O., Eds.; Academic Press: Cambridge, MA, USA, 2018; pp. 935–959. [Google Scholar] [CrossRef]
- Tassielli, G.; Notarnicola, B.; Renzulli, P.; Arcese, G. Environmental life cycle assessment of fresh and processed sweet cherries in southern Italy. J. Clean. Prod. 2018, 171, 184–197. [Google Scholar] [CrossRef]
- Groen, E.; Van Zanten, H.; Heijungs, R.; Bokkers, E.; De Boer, I. Sensitivity analysis of greenhouse gas emissions from a pork production chain. J. Clean. Prod. 2016, 129, 202–211. [Google Scholar] [CrossRef]
- Xiao, N.; Huang, H.Z.; Li, Y.; He, L.; Jin, T. Multiple failure modes analysis and weighted risk priority number evaluation in FMEA. Eng. Fail. Anal. 2011, 18, 1162–1170. [Google Scholar] [CrossRef]
- IEC. IEC 60812:2016 Failure Modes and Effects Analysis (FMEA and FMECA); Commission Electrotechnique Internationale: Geneva, Switzerland, 2016. [Google Scholar]
- Arabian-Hoseynabadi, H.; Oraee, H.; Tavner, P.J. Failure Modes and Effects Analysis (FMEA) for wind turbines. Int. J. Electr. Power Energy Syst. 2010, 32, 817–824. [Google Scholar] [CrossRef] [Green Version]
- Arvanitoyannis, I.S.; Savelides, S.C. Application of failure mode and effect analysis and cause and effect analysis and Pareto diagram in conjunction with HACCP to a chocolate-producing industry: A case study of tentative GMO detection at pilot plant scale. Int. J. Food Sci. Technol. 2007, 42, 1265–1289. [Google Scholar] [CrossRef]
- Papadopoulos, Y.; Walker, M.; Parker, D.; Rüde, E.; Hamann, R.; Uhlig, A.; Grätz, U.; Lien, R. Engineering failure analysis and design optimisation with HiP-HOPS. Eng. Fail. Anal. 2011, 18, 590–608. [Google Scholar] [CrossRef]
- Djekic, I.; Tomic, N.; Smigic, N.; Udovicki, B.; Hofland, G.; Rajkovic, A. Hygienic design of a unit for supercritical fluid drying-case study. Br. Food J. 2018, 120, 2155–2165. [Google Scholar] [CrossRef]
- Heiko, A. Consensus measurement in Delphi studies: Review and implications for future quality assurance. Technol. Forecast. Soc. Chang. 2012, 79, 1525–1536. [Google Scholar]
- Hasson, F.; Keeney, S.; McKenna, H. Research guidelines for the Delphi survey technique. J. Adv. Nurs. 2000, 32, 1008–1015. [Google Scholar] [Green Version]
- Avadí, A.; Nitschelm, L.; Corson, M.; Vertès, F. Data strategy for environmental assessment of agricultural regions via LCA: Case study of a French catchment. Int. J. Life Cycle Assess. 2016, 21, 476–491. [Google Scholar] [CrossRef]
- Bartl, K.; Verones, F.; Hellweg, S. Life cycle assessment based evaluation of regional impacts from agricultural production at the Peruvian coast. Environ. Sci Technol. 2012, 46, 9872–9880. [Google Scholar] [CrossRef]
- Vázquez-Rowe, I.; Villanueva-Rey, P.; Hospido, A.; Moreira, M.T.; Feijoo, G. Life cycle assessment of European pilchard (Sardina pilchardus) consumption. A case study for Galicia (NW Spain). Sci. Total Environ. 2014, 475, 48–60. [Google Scholar] [CrossRef]
- Ribal, J.; Ramírez-Sanz, C.; Estruch, V.; Clemente, G.; Sanjuán, N. Organic versus conventional citrus. Impact assessment and variability analysis in the Comunitat Valenciana (Spain). Int. J. Life Cycle Assess. 2017, 22, 571–586. [Google Scholar] [CrossRef]
- Skunca, D.; Tomasevic, I.; Nastasijevic, I.; Tomovic, V.; Djekic, I. Life cycle assessment of the chicken meat chain. J. Clean. Prod. 2018, 184, 440–450. [Google Scholar] [CrossRef]
- Borrisser-Pairó, F.; Kallas, Z.; Panella-Riera, N.; Avena, M.; Ibáñez, M.; Olivares, A.; Gil, J.M.; Oliver, M.A. Towards entire male pigs in Europe: A perspective from the Spanish supply chain. Res. Vet. Sci. 2016, 107, 20–29. [Google Scholar] [CrossRef] [Green Version]
- Bessou, C.; Basset-Mens, C.; Tran, T.; Benoist, A. LCA applied to perennial cropping systems: A review focused on the farm stage. Int. J. Life Cycle Assess. 2013, 18, 340–361. [Google Scholar] [CrossRef]
- Cerutti, A.K.; Beccaro, G.L.; Bruun, S.; Bosco, S.; Donno, D.; Notarnicola, B.; Bounous, G. Life cycle assessment application in the fruit sector: State of the art and recommendations for environmental declarations of fruit products. J. Clean. Prod. 2014, 73, 125–135. [Google Scholar] [CrossRef]
- de Vries, M.; de Boer, I.J.M. Comparing environmental impacts for livestock products: A review of life cycle assessments. Livest. Sci. 2010, 128, 1–11. [Google Scholar] [CrossRef]
- Carvalho, A.; Mimoso, A.F.; Mendes, A.N.; Matos, H.A. From a literature review to a framework for environmental process impact assessment index. J. Clean. Prod. 2014, 64, 36–62. [Google Scholar] [CrossRef]
- Lewandowska, A.; Foltynowicz, Z.; Podlesny, A. Comparative lca of industrial objects part 1: Lca data quality assurance—Sensitivity analysis and pedigree matrix. Int. J. Life Cycle Assess. 2004, 9, 86–89. [Google Scholar] [CrossRef]
- Chen, X.; Corson, M.S. Influence of emission-factor uncertainty and farm-characteristic variability in LCA estimates of environmental impacts of French dairy farms. J. Clean. Prod. 2014, 81, 150–157. [Google Scholar] [CrossRef]
- Groen, E.A.; Bokkers, E.A.M.; Heijungs, R.; de Boer, I.J.M. Methods for global sensitivity analysis in life cycle assessment. Int. J. Life Cycle Assess. 2017, 22, 1125–1137. [Google Scholar] [CrossRef]
- Konstantas, A.; Jeswani, H.K.; Stamford, L.; Azapagic, A. Environmental impacts of chocolate production and consumption in the UK. Food Res. Int. 2018, 106, 1012–1025. [Google Scholar] [CrossRef] [Green Version]
Severity | ||
Rank | Consequence | Description |
1 | None | No failure(s) |
2 | Minor | Failure(s) associated with results for one inventory |
3 | Low | Failure(s) associated with results within one subsystem |
4 | Major | Failure(s) associated with results within more than one subsystem |
5 | Severe | Failure(s) associated with results throughout the entire life-cycle |
Occurrence | ||
Rank | Probability | Description |
1 | Very unlikely | Minimal probability of occurrence of failure(s) as a result of force majeure |
2 | Unlikely | Occurrence of failure(s) only as a result of misuse of software |
3 | Possible | Occurrence of failure(s) only as a result of errors in man-made calculations/estimations |
4 | High probability | Occurrence of failure(s) will occur only for certain type of products |
5 | Certain | Occurrence of failure(s) in each subsystem of the entire life-cycle |
Detection | ||
Rank | Criteria | Description |
1 | Very high | Failure(s) associated with results is easily detected |
2 | High | Failure(s) associated with results is detected during inventory phase |
3 | Low | Failure(s) associated with results is detected during impact assessment phase |
4 | Remote | Failure(s) associated with results is detected during interpretation phase |
5 | Never | No possibility of identifying failure(s) associated with results of LCA |
No | Stage | Non-Conformity | What Might Occur? | Potential Failure Effect? | Severity (S) | Occurrence (O) | Detection (D) | Risk |
---|---|---|---|---|---|---|---|---|
1 | Scope and goal | Inadequate system boundaries | LCA does not include all product stages | Results will show only partial life-cycle of the food product | 5 | 3 | 1 | 15 |
2 | Scope and goal | Inconsistent functional units | Calculation and interpretation of the results in wrong functional units | No possibility of comparing results throughout the food supply chain and benchmark with other | 4 | 3 | 2 | 24 |
3 | Scope and goal | Inappropriate data collection method | Inadequate collection methods (measurement, estimation, combination, re-calculation, …) | Wrong/un-useful data collected | 3 | 3 | 2 | 18 |
4 | Scope and goal | Low level of data quality | Primary/secondary sources, time related dimension (data for specific time periods), consistency of data quality in the entire life-cycle | Bad results during validation of data and when relating the data to processes/products/FUs | 3 | 3 | 4 | 36 |
5 | Scope and goal | Wrong limitations | Exclusion of important data/stages/system boundaries/environmental impacts | Results will show only partial life-cycle of the food product | 3 | 4 | 3 | 36 |
6 | Inventory analysis | Material and energy flows | All material/energy flows (primary/converted energy, inputs from nature/from technosphere) not included | Results will show only partial life-cycle of the food product | 3 | 3 | 3 | 27 |
7 | Inventory analysis | Waste streams | Incorrect calculations of outputs to nature/technosphere | Results will show only partial life-cycle of the food product | 3 | 3 | 3 | 27 |
8 | Inventory analysis | Imprecise allocation | Incorrect allocation of the total emissions and material consumption attributed to each specific product | Results will show only partial life-cycle of the food product | 5 | 2 | 4 | 40 |
9 | Impact assessment | Lack of an accepted official list of environmental impacts | Subjective choice of environmental impacts | Wrong/un-useful environmental impacts calculated | 5 | 2 | 4 | 40 |
10 | Interpretation of results | Uncertainty | Quality of data, subjective choice system boundaries, allocation rules, functional units, environmental impacts | Results will show only partial life-cycle of the food product | 3 | 3 | 4 | 36 |
11 | Interpretation of results | High sensitivity | Sensitivity analysis shows that the input data and methodological choices influence the results of a LCA too much | Results will show only partial life-cycle of the food product | 4 | 2 | 4 | 32 |
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Djekic, I.; Pojić, M.; Tonda, A.; Putnik, P.; Bursać Kovačević, D.; Režek-Jambrak, A.; Tomasevic, I. Scientific Challenges in Performing Life-Cycle Assessment in the Food Supply Chain. Foods 2019, 8, 301. https://doi.org/10.3390/foods8080301
Djekic I, Pojić M, Tonda A, Putnik P, Bursać Kovačević D, Režek-Jambrak A, Tomasevic I. Scientific Challenges in Performing Life-Cycle Assessment in the Food Supply Chain. Foods. 2019; 8(8):301. https://doi.org/10.3390/foods8080301
Chicago/Turabian StyleDjekic, Ilija, Milica Pojić, Alberto Tonda, Predrag Putnik, Danijela Bursać Kovačević, Anet Režek-Jambrak, and Igor Tomasevic. 2019. "Scientific Challenges in Performing Life-Cycle Assessment in the Food Supply Chain" Foods 8, no. 8: 301. https://doi.org/10.3390/foods8080301