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

IF 3 4区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Integrated Environmental Assessment and Management Pub Date : 2024-06-14 DOI:10.1002/ieam.4954

Amin Nikkhah, Mahdi Esmaeilpour, Armaghan Kosari-Moghaddam, Abbas Rohani, Farima Nikkhah, Sami Ghnimi, Nicole Tichenor Blackstone, Sam Van Haute

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引用次数: 0

Abstract

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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基于机器学习的生命周期评估，优化食品供应链的环境可持续性。

农业食品行业的有效资源分配对于减轻环境影响和实现循环食品供应链至关重要。最近的研究强调了将生命周期评估（LCA）与机器学习相结合的潜力。这种混合框架不仅对评估食品供应链很有价值，而且对改善供应链以实现更可持续的系统也很有价值。然而，优化过程中的一个重要步骤是定义优化边界或变量的最小和最大数量。通常，这些研究中优化变量的边界是通过访谈和调查得出的最小值和最大值。这些范围的偏差会影响最终的优化结果。为解决这一问题，本研究采用德尔菲法确定变量优化边界。在优化石榴生产系统时，使用了一个将生命周期评估、多层感知器人工神经网络、德尔菲法和遗传算法联系起来的混合环境评估框架。结果表明，在所探讨的案例研究中，所建议的框架有望大幅减少对环境的影响（全球变暖的潜在影响减少 46%）。采用德尔菲法确定变量边界为农业食品行业的资源配置优化过程带来了新意。Integr Environ Assess Manag 2024;00:1-11。© 2024 SETAC.

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来源期刊

Integrated Environmental Assessment and Management ENVIRONMENTAL SCIENCESTOXICOLOGY&nbs-TOXICOLOGY

CiteScore

5.90

自引率

6.50%

发文量

156

期刊介绍： Integrated Environmental Assessment and Management (IEAM) publishes the science underpinning environmental decision making and problem solving. Papers submitted to IEAM must link science and technical innovations to vexing regional or global environmental issues in one or more of the following core areas: Science-informed regulation, policy, and decision making Health and ecological risk and impact assessment Restoration and management of damaged ecosystems Sustaining ecosystems Managing large-scale environmental change Papers published in these broad fields of study are connected by an array of interdisciplinary engineering, management, and scientific themes, which collectively reflect the interconnectedness of the scientific, social, and environmental challenges facing our modern global society: Methods for environmental quality assessment; forecasting across a number of ecosystem uses and challenges (systems-based, cost-benefit, ecosystem services, etc.); measuring or predicting ecosystem change and adaptation Approaches that connect policy and management tools; harmonize national and international environmental regulation; merge human well-being with ecological management; develop and sustain the function of ecosystems; conceptualize, model and apply concepts of spatial and regional sustainability Assessment and management frameworks that incorporate conservation, life cycle, restoration, and sustainability; considerations for climate-induced adaptation, change and consequences, and vulnerability Environmental management applications using risk-based approaches; considerations for protecting and fostering biodiversity, as well as enhancement or protection of ecosystem services and resiliency.