Using NLP to extract insights from corporate climate change messaging
Industrial activities, encompassing construction, production, transportation, and operations, are major contributors to climate change. Addressing this global issue requires transparent corporate pledges and genuine efforts aligned with global or local climate policies and goals. Some corporations, however, whether intentionally or not, fall short of meaningful action, sometimes projecting misleading impressions of their climate change commitments. Policymakers and investors, who seek to regulate or support corporations, on the other hand, are increasingly looking for objective and evidence-based insights to guide their decisions. The current landscape of corporate messaging and activities on climate change, however, is opaque due to the sheer volume of data - including corporate reports, promotional material, and environmental databases – and the limited resources available for comprehensive analysis.
Given the promising throughput of artificial intelligence (AI), particularly natural language processing (NLP) in analyzing documents, can these challenges be addressed? In this article, I’d like to outline how NLP can be used to analyze corporate messages and activities related to climate change, drawing on my research as a visiting scholar at Stanford University with several case studies.
