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Artificial intelligence solutions for environmental and social impact assessments

In: Handbook of Environmental Impact Assessment

Author

Listed:
  • Atiyah Curmally
  • Blaise W. Sandwidi
  • Aditi Jagtiani

Abstract

This chapter has two objectives: first, to review artificial intelligence solutions to address constraints to the Environmental and Social Impact Assessment (ESIA) process and, second, to propose an environmental, social, and governance domain-specific natural language processing model–esgNLP–trained by the International Finance Corporation to review environmental and social (E&S) text. esgNLP is applied to a sample of ESIAs and associated reports to identify E&S risk terms and to conduct sentiment analysis. The chapter also illustrates the relevance of sentiment analysis scores as proxies for project risk and predictors of future E&S performance. These findings reinforce the value of artificial intelligence and machine learning solutions to support ESIA review and analysis. Such insights can direct resources, assign technical capacity, determine legal requirements, and develop comprehensive environmental and social action and remediation plans. The chapter concludes with a description of model limitations and recommendations for future applications.

Suggested Citation

  • Atiyah Curmally & Blaise W. Sandwidi & Aditi Jagtiani, 2022. "Artificial intelligence solutions for environmental and social impact assessments," Chapters, in: Alberto Fonseca (ed.), Handbook of Environmental Impact Assessment, chapter 9, pages 163-177, Edward Elgar Publishing.
    • Handle: RePEc:elg:eechap:20383_9
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