Digital intelligence, environmental resilience: AI proves pivotal in sustainable local development

In a sweeping review of over a decade’s worth of academic research, a new study asserts that artificial intelligence (AI) is rapidly becoming a cornerstone of sustainable development across regional ecosystems.

The study, titled “The Impact of Artificial Intelligence on the Sustainability of Regional Ecosystems: Current Challenges and Future Prospects” by Sergiusz Pimenow and colleagues, was published in Sustainability in May 2025. It synthesizes findings from 155 peer-reviewed articles published between 2010 and March 2025, offering a comprehensive analysis of how AI is reshaping local governance, environmental resilience, and socio-economic equity.

How is AI currently shaping sustainability across regional ecosystems?

The review identifies nine major functional domains where AI is significantly altering sustainable practices, including environmental monitoring, agricultural productivity, aquatic and forest management, carbon tracking, and energy systems optimization. Technologies such as machine learning, remote sensing, digital twins, and IoT sensors have been deployed to predict and manage land degradation, forecast crop yields, model forest biomass, and optimize renewable energy networks.

For example, in agriculture, AI-driven remote sensing can detect crop stress weeks earlier than traditional field scouting methods, enhancing food security while minimizing agrochemical use. In forestry, tools like 3D Vision Transformers are used to monitor deforestation and predict fire risks. Similarly, aquatic ecosystems benefit from machine learning algorithms that assess water quality and forecast flood events under climate change scenarios.

Urban applications include real-time traffic modeling to reduce emissions, and predictive maintenance of infrastructure to increase longevity. AI also supports inclusive digital education and healthcare through tools such as federated learning, environmental awareness platforms, and disease surveillance systems.

What are the challenges and barriers to broader AI integration?

Despite its transformative potential, AI deployment in regional ecosystems faces several critical challenges. The study highlights institutional readiness, regulatory ambiguity, and technological inequality as primary barriers. Regions with underdeveloped digital infrastructure, limited scientific capacity, or weak governance frameworks struggle to scale up pilot projects into effective long-term programs.

There are also thematic and geographical gaps in existing research. Central and Eastern Europe, parts of the Americas, and post-Soviet states are underrepresented in AI-related sustainability studies. Moreover, there is a lack of longitudinal evaluations that explore how AI performs under real-world constraints like limited funding, data sparsity, or resistance to digital change.

Ethical concerns and public trust also loom large. Without explainable AI models and transparent data governance, the risk of opaque decision-making and algorithmic bias could undermine community engagement and policy legitimacy. This concern is particularly relevant in ecosystem services management, where socio-environmental trade-offs require inclusive deliberation.

What strategies and future directions does the study recommend?

To address these multifaceted challenges, the authors advocate for a holistic and interdisciplinary approach. AI must be embedded within a “socio-technical systems” framework that aligns technological capabilities with regional knowledge, institutional structures, and ethical norms. The report emphasizes the importance of local capacity building, open data infrastructure, and cross-sectoral collaboration as prerequisites for sustainable digital transformation.

Notable future directions include integrating AI with complementary innovations like blockchain for transparency, citizen science for data generation, and nature-based solutions for ecosystem restoration. Combining these paradigms could yield adaptive governance systems capable of responding to escalating climate risks while maintaining public accountability.

The study also calls for expanding case studies in underrepresented regions and sectors, and developing explainable AI benchmarks tailored to local environmental conditions. It recommends sustained public and scientific investment to ensure that AI remains an enabler, not a disruptor, of equitable sustainability.