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.
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Review of impact categories and environmental indicators for life cycle assessment of geotechnical systems
Life cycle assessment (LCA) has only had limited application in the geotechnical engineering discipline, though it has been widely applied to civil engineering systems such as pavements and roadways. A review of previous geotechnical LCAs showed that most studies have tracked a small set of impact categories, such as energy and global warming potential. Accordingly, currently reported environmental indicators may not effectively or fully capture important environmental impacts and tradeoffs associated with geotechnical systems, including those associated with land and soil resources. This research reviewed previous studies, methods, and models for assessment of land use and soil‐related impacts to understand their applicability to geotechnical LCA. The results of this review show that critical gaps remain in current knowledge and practice. In particular, further development or refinement of environmental indicators, impact categories, and cause–effect pathways is needed as they pertain to geotechnical applications—specifically those related to soil quality, soil functions, and the ecosystem services soils provide. In addition, many existing methods emerge from research on land use and land use change related to other disciplines (e.g., agriculture). For applicability to geotechnical projects, the resolution of many of these methods and resulting indicators need to be downscaled from the landscape/macro scale to the project scale. In the near term, practitioners of geotechnical LCA should begin tracking changes to soil properties and report impacts to land and soil resources qualitatively.
more » « less- NSF-PAR ID:
- 10360380
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Journal of Industrial Ecology
- Volume:
- 24
- Issue:
- 3
- ISSN:
- 1088-1980
- Page Range / eLocation ID:
- p. 485-499
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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