Energy audit and management of environmental GHG emissions based on multi-objective genetic algorithm and data envelopment analysis: An agriculture case

Taherzadeh-Shalmaei, Nahid; Rafiee, Mahta; Kaab, Ali; Khanali, Majid; Vaziri Rad, Mohammad Amin; Kasaeian, Alibakhsh

doi:10.1016/j.egyr.2023.08.020

Energy Reports

2023

DOI: 10.1016/j.egyr.2023.08.020

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Energy audit and management of environmental GHG emissions based on multi-objective genetic algorithm and data envelopment analysis: An agriculture case

Nahid Taherzadeh-Shalmaei,

Mahta Rafiee,

Ali Kaab

et al.

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2024

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Cited by 16 publications

References 66 publications

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Machine learning‐based life cycle assessment for environmental sustainability optimization of a food supply chain

Nikkhah,

Esmaeilpour,

Kosari‐Moghaddam

et al. 2024

Integr Envir Assess & Manag

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Effective resource allocation in the agri‐food sector is essential in mitigating environmental impacts and moving toward circular food supply chains. The potential of integrating life cycle assessment (LCA) with machine learning has been highlighted in recent studies. This hybrid framework is valuable not only for assessing food supply chains but also for improving them toward a more sustainable system. Yet, an essential step in the optimization process is defining the optimization boundaries, or minimum and maximum quantities for the variables. Usually, the boundaries for optimization variables in these studies are obtained from the minimum and maximum values found through interviews and surveys. A deviation in these ranges can impact the final optimization results. To address this issue, this study applies the Delphi method for identifying variable optimization boundaries. A hybrid environmental assessment framework linking LCA, multilayer perceptron artificial neural network, the Delphi method, and genetic algorithm was used for optimizing the pomegranate production system. The results indicated that the suggested framework holds promise for achieving substantial mitigation in environmental impacts (potential reduction of global warming by 46%) within the explored case study. Inclusion of the Delphi method for variable boundary determination brings novelty to the resource allocation optimization process in the agri‐food sector. Integr Environ Assess Manag 2024;20:1759–1769. © 2024 SETAC

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Machine learning‐based life cycle assessment for environmental sustainability optimization of a food supply chain

Nikkhah,

Esmaeilpour,

Kosari‐Moghaddam

et al. 2024

Integr Envir Assess & Manag

View full text Add to dashboard Cite

show abstract

Assessing the effect of industrial products on air pollution in Iran: a novel NDEA approach considering undesirable outputs

Fehresti-Sani,

GholamAzad,

GholamAzad

2024

Environ Dev Sustain

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Coupling life cycle audition and operation research methods to achieve sustainable rapeseed production system

Khoshhal-Zolpirani,

Majidian,

Banaeian

et al. 2024

Environ Dev Sustain

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Energy audit and management of environmental GHG emissions based on multi-objective genetic algorithm and data envelopment analysis: An agriculture case

Cited by 16 publications

References 66 publications

Machine learning‐based life cycle assessment for environmental sustainability optimization of a food supply chain

Machine learning‐based life cycle assessment for environmental sustainability optimization of a food supply chain

Assessing the effect of industrial products on air pollution in Iran: a novel NDEA approach considering undesirable outputs

Coupling life cycle audition and operation research methods to achieve sustainable rapeseed production system

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