Tracking Green Industrial Policies with LLM s: A Demonstration (2025)

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 actively 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 GreenSpyder to U.S. Legislative Records from the 117th Congress, paving the way for more comprehensive LLM-based GIP documentation in the future.

## introduction climate change represents one of the most significant challenges of our time @xcite . crucial to the mitigation and adaptation efforts are green industrial policies (gips), which are "strategic government measures that aim to promote new economic sectors and accelerate structural change" towards a green economy (united nations environment programme, 2024). gips encompass a wide range of governmental interventions, including targeted incentives, regulations, and investments in clean technologies. as economists and policy makers generally agree, these policies serve as the foundation for transitioning economies toward more sustainable practices while maintaining economic growth @xcite @xcite . despite their significance, there remains a substantial gap in the systematic documentation and analysis of gips. current research predominantly examines isolated instances of gips rather than providing comparative analyses. for example, partnership for action on green economy (2019); @xcite studied eco-industrial parks in china, while @xcite studied the effectiveness of carbon trading in south korea. a comprehensive cross-jurisdictional and temporal analysis would undoubtedly contribute to the formulation of evidence-based best practices and policy recommendations. unlike other climate policy instruments such as nationally determined contributions (ndcs), which are centrally documented through international frameworks like the paris agreement (united nations, 2015), gips lack a centralized repository. instead, they are dispersed across various government publications, legislative records, and policy announcements, making comprehensive analysis challenging. furthermore, the technical and domain-specific nature of these documents requires specialized knowledge to properly identify and categorize relevant policies, traditionally necessitating expensive expert annotation. to address these challenges, we propose greenspyder, a large language model (llm)-based workflow designed to monitor, classify, and annotate gips from opensource information. our approach leverages recent advances in natural language processing (nlp) to automate much of the labor-intensive work of policy identification and classification, potentially en-1 abling more comprehensive and timely analysis of gips worldwide. in this paper, we first evaluate the capability of llms in classifying and annotating gips using new industrial policy observatory (nipo), a small expert-curated dataset on industrial policies @xcite . our evaluation reveals that while llms perform well on differentiating gips from general industrial policies, and coarse annotation tasks, they still face limitations when handling more nuanced policy distinctions. building on these insights, we demonstrate a practical application of our approach by applying greenspyder to u.s. legislative records from the 117th congress, successfully identifying and annotating gips within this substantial corpus of legislative text. our work contributes to the growing intersection of nlp and climate policy @xcite @xcite by providing a scalable method for gip documentation, potentially enabling researchers, policymakers, and advocates to better track, compare, and analyze green industrial policies across different contexts. this improved visibility could ultimately support more effective policy design and implementation in the global effort to address climate change. ## results ## conclusion in this paper, we introduced greenspyder, an llmbased workflow designed to systematically monitor, classify, and annotate green industrial policies from diverse government sources. our evaluation of gpt-4o on the expert-curated nipo dataset demonstrated promising capabilities in distinguishing gips from general industrial policies and performing coarse-grained annotations, though challenges remain for more nuanced classification tasks. by successfully applying greenspyder to u.s. legislative records from the 117th congress, we have demonstrated its practical utility in identifying and categorizing gips within large legislative corpora, offering a foundation for future advancements in automated gip tracking.