Unveiling Individual Climate Behaviors Through Digital Text: A Scoping Review of Natural Language Processing Methods
TL;DR
NLP-based text analysis constitutes a scalable complement to surveys and a foundation for targeted climate interventions, and topic modeling and transformer-based models are the dominant techniques, typically combined with sentiment analysis.
Abstract
Climate change is one of the most serious global challenges, and greenhouse gas emissions continue to rise despite mitigation efforts. Household consumption accounts for approximately 72% of global emissions, indicating the central role of individual behaviors. Traditional measurement instruments, such as surveys and interviews, are costly, time-consuming, and subject to response biases. User-generated textual content provides an alternative source of evidence, and natural language processing (NLP) enables its analysis at scale. Despite this potential, existing reviews have not systematically mapped its use for individual-level climate behaviors. The main goal of this research is to address this gap through a scoping review following PRISMA-ScR guidelines. Systematic searches were performed in Web of Science, Engineering Village, and Google Scholar, covering January 2015 to April 2026. A total of 2580 records were screened, and ten studies met the inclusion criteria. These studies analyzed Twitter/X, Sina Weibo, Reddit, and e-commerce reviews, covering behaviors from green transportation to waste management. The findings demonstrate that topic modeling and transformer-based models are the dominant techniques, typically combined with sentiment analysis. Recent studies extend beyond describing climate discourse toward explaining behavior. NLP-based text analysis constitutes a scalable complement to surveys and a foundation for targeted climate interventions.