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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Simulation Tests of Methods in Evolution, Ecology, and Systematics: Pitfalls, Progress, and Principles
Complex statistical methods are continuously developed across the fields of ecology, evolution, and systematics (EES). These fields, however, lack standardized principles for evaluating methods, which has led to high variability in the rigor with which methods are tested, a lack of clarity regarding their limitations, and the potential for misapplication. In this review, we illustrate the common pitfalls of method evaluations in EES, the advantages of testing methods with simulated data, and best practices for method evaluations. We highlight the difference between method evaluation and validation and review how simulations, when appropriately designed, can refine the domain in which a method can be reliably applied. We also discuss the strengths and limitations of different evaluation metrics. The potential for misapplication of methods would be greatly reduced if funding agencies, reviewers, and journals required principled method evaluation. Expected final online publication date for the Annual Review of Ecology, Evolution, and Systematics, Volume 53 is November 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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- NSF-PAR ID:
- 10348276
- Date Published:
- Journal Name:
- Annual Review of Ecology, Evolution, and Systematics
- Volume:
- 53
- Issue:
- 1
- ISSN:
- 1543-592X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1146/annurev-ecolsys-102320-093722
