From monitoring to decision-making: How AI is transforming sustainable environmental governance

Artificial intelligence (AI) is becoming a driving force in reshaping how nature-based solutions (NBSs) are designed and applied for climate resilience, according to new research published in Atmosphere. The authors highlight how AI is evolving from a supportive analytical tool to a central engine for ecological governance, providing fresh strategies for addressing the global climate crisis.

The peer-reviewed study, “Advancing Nature-Based Solutions with Artificial Intelligence: A Bibliometric and Semantic Analysis Using ChatGPT,” explores the integration of AI into ecological interventions by analyzing 535 academic articles published between 2011 and 2024. The authors employ a hybrid approach that combines bibliometric mapping with semantic refinement powered by ChatGPT-4.0 to reveal thematic trends and research gaps at the intersection of AI and NBSs.

How AI is transforming nature-based solutions

The research underscores that NBSs, which rely on natural processes to address societal and ecological challenges, have gained international prominence as a sustainable alternative to traditional gray infrastructure. Applications such as green roofs, artificial wetlands, and urban green spaces already demonstrate significant benefits, from reducing stormwater runoff to improving air quality. However, widespread implementation has been hampered by technical, data-related, and governance challenges.

AI is emerging as a critical tool to address these limitations. Early applications focused on machine learning models for species distribution, land use classification, and remote sensing analysis, improving accuracy and reducing monitoring costs. More recently, deep learning and evolutionary algorithms have enabled long-term ecological forecasting, carbon emission reduction strategies, and optimized green infrastructure design. Transformer-based models have achieved significant advances in climate prediction, while convolutional neural networks have shown over 90 percent accuracy in endangered species monitoring.

Large Language Models (LLMs) represent a new phase in this trajectory. Unlike earlier AI systems that focused on computation, LLMs can integrate meteorological data, ecological monitoring, remote sensing imagery, and land use policies into coherent, policy-ready recommendations. This ability to connect fragmented datasets is pushing AI from calculation-driven approaches toward knowledge-driven governance systems. The authors note that AI-powered NBS strategies are not only more scalable but also more adaptable to diverse ecological and social contexts.

What the study reveals about research trends

The analysis shows that global research on AI-NBS has expanded exponentially since 2020, with the number of publications more than doubling by 2024. The authors divide this growth into three phases: an initial exploratory period between 2011 and 2016, a steady build-up between 2017 and 2021, and a rapid expansion from 2022 onward. The acceleration coincides with breakthroughs in AI technologies, heightened climate urgency, and supportive global policy frameworks such as the Kunming-Montreal Global Biodiversity Framework adopted in 2022.

From the bibliometric and semantic analysis, fifteen thematic clusters emerged as the backbone of AI-NBS research. These clusters range from intelligent ecological monitoring and urban heat island mitigation to carbon storage optimization, smart transportation, and sustainable building design. Research also highlights new domains such as blue carbon ecosystem monitoring, green manufacturing, and low-carbon infrastructure management. Together, these themes illustrate how AI tools are being adapted across diverse sectors of environmental governance.

The findings reveal that “machine learning,” “artificial intelligence,” “climate change,” and “green infrastructure” are the dominant keywords shaping the field. Research has progressively shifted from localized ecological monitoring to system-level optimization, where AI is being used to model, forecast, and guide complex interventions across scales. This reflects a significant transition: AI is no longer a supporting instrument but increasingly the backbone of decision-making in ecological governance.

Despite these advances, the study identifies persistent gaps. Ecological data remain regionally fragmented, limiting the ability of AI models to function across different geographical contexts. Many AI models still operate as opaque black boxes, raising concerns about transparency, interpretability, and trust in policymaking. Moreover, the high energy consumption and carbon footprint of large AI models pose new ethical challenges, calling into question the sustainability of deploying AI for climate-focused applications.

Why the findings matter for climate and policy

The study contributes both theoretical and practical insights for future research and policy. On the theoretical side, the hybrid bibliometric and semantic approach represents a methodological innovation, demonstrating how large language models can enhance the coherence and interpretability of bibliometric reviews. This framework offers a replicable model for analyzing cross-disciplinary fields where diverse datasets and fragmented knowledge often hinder clarity.

On the practical front, the results carry strong implications for climate policy and urban governance. AI-enhanced NBS strategies can improve real-time ecological monitoring, optimize the placement and performance of green infrastructure, and provide decision-makers with adaptive tools to respond to unpredictable climate events. For cities facing rising heat, frequent flooding, or deteriorating air quality, AI-driven approaches offer more precise, data-backed methods to design and manage sustainable interventions.

While AI is unlocking new levels of precision and scalability, future research must address its limitations, the research asserts. Key priorities include integrating generative AI into socio-ecological modeling, improving cross-regional transferability of ecological data, and ensuring transparency in model decision-making. Greater attention to social equity and public participation is also essential to ensure that AI-driven solutions benefit communities equitably and do not exacerbate existing inequalities.