Year: 2025
Type: conference-paper
Abstract: Green industrial policies (GIPs) are government interventions that support environmentally sustainable economic growth through targeted incentives, regulations, and investments in clean technologies.As the backbone of climate mitigation and adaptation, GIPs deserve systematic documentation and analysis.However, two major hurdles impede this systematic documentation.First, unlike other climate policy documents, such as Nationally Determined Contributions (NDCs) which are centrally curated, GIPs are scattered across numerous government legislation and policy announcements.Second, extracting information from these diverse documents is expensive when relying on expert annotation.We address this gap by proposing GreenSpyder, an LLM-based workflow that monitors, classifies, and annotates GIPs from open-source information.As a demonstration, we benchmark LLM performance in classifying and annotating GIPs on a small expert-curated dataset.Our results show that LLMs can be quite effective for classification and coarse annotation tasks, though they still need improvement for more nuanced classification.Finally, as a real-world application, we apply Green-Spyder to U.S. Legislative
Author: Yucheng Lu
Institution: New York University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
OpenAlex ID: W4412944300