Eco-Conscious Best-First Search for Environmental AI: Advancing Sustainable Computation through the ECO-GH Framework

Achieving environmental sustainability in AI systems requires algorithms that balance computational efficiency with energy conservation. Best-First Search (BFS), a foundational heuristic-based graph traversal technique, plays a pivotal role in areas such as robotics, automated planning, and symbolic reasoning. This work presents a comparative evaluation of five recent advances in BFS, highlighting innovations in parallelism, heuristic learning, and integration with large language models (LLM). Building on these insights, we propose ECO-GH (Energy Constrained Opportunistic Greedy Heuristic Search), a novel variant of BFS tailored for climate-aware computation. ECO-GH incorporates energy-sensitive tie-breaking, opportunistic node expansion, adaptive parallelism, and dynamic heuristic switching. The experimental results on standard planning benchmarks reveal that ECO-GH achieves up to 34% lower energy consumption and a greater 25% reduction in runtime compared to state-of-the-art approaches, without compromising the quality of the solution. These findings position ECO-GH as a scalable and sustainable search framework for environmentally responsible applications, including renewable energy planning, autonomous electric mobility, and intelligent infrastructure systems.