Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework
Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework
复制标题
化学工业中人工智能的可持续性影响：一个概念框架
DOI：
10.1111/jiec.13214
复制
发表时间：
2021-11
影响因子：
5.9
通讯作者：
Mochen Liao;Kai Lan;Yuan Yao
中科院分区：
文献类型：
--
作者：
Mochen Liao;Kai Lan;Yuan Yao
人工智能(AI)是一项新兴技术，在降低化工生产的能耗、环境负担和操作风险方面具有巨大的潜力。然而，人工智能的大规模应用仍然有限。一个障碍是缺乏对不同人工智能应用程序的潜在好处和风险的量化理解。本研究回顾了相关的人工智能文献，并根据应用类型、影响类别和应用模式对这些案例研究进行了分类。大多数研究评估了人工智能应用程序的能源、经济和安全影响，而考虑到巨大的数据差距和选择适当评估方法的困难，很少有研究评估人工智能对环境的影响。在回顾化工行业案例研究的基础上，我们提出了一个概念框架，涵盖了工业生态学、经济学和工程学的方法，以指导人工智能影响的整体评估的绩效指标和评估方法的选择。这一框架可以成为支持化学品基础研究和实际生产中与人工智能相关的决策的有价值的工具。虽然这项研究的重点是化工行业，但文献综述和提出的框架的见解可以应用于其他行业和广泛的产业生态领域的人工智能应用。最后，这项研究指出了未来的研究方向，以应对在评估人工智能的影响和开发支持化学工业可持续发展的人工智能增强工具方面的数据挑战。
Artificial intelligence (AI) is an emerging technology that has great potential in reducing energy consumption, environmental burdens, and operational risks of chemical production. However, large‐scale applications of AI are still limited. One barrier is the lack of quantitative understandings of the potential benefits and risks of different AI applications. This study reviewed relevant AI literature and categorized those case studies by application types, impact categories, and application modes. Most studies assessed the energy, economic, and safety implications of AI applications, while few of them have evaluated the environmental impacts of AI, given the large data gaps and difficulties in choosing appropriate assessment methods. Based on the reviewed case studies in the chemical industry, we proposed a conceptual framework that encompasses approaches from industrial ecology, economics, and engineering to guide the selection of performance indicators and evaluation methods for a holistic assessment of AI's impacts. This framework could be a valuable tool to support the decision‐making related to AI in the fundamental research and practical production of chemicals. Although this study focuses on the chemical industry, the insights of the literature review and the proposed framework could be applied to AI applications in other industries and broad industrial ecology fields. In the end, this study highlights future research directions for addressing the data challenges in assessing AI's impacts and developing AI‐enhanced tools to support the sustainable development of the chemical industry.