Harnessing Data Science For Sustainable Environmental Development: A Path Toward Smarter Ecosystems
DOI:
https://doi.org/10.64252/4mx7pt41Keywords:
data science, environmental sustainability, machine learning, artificial intelligence, geospatial analytics, big data, smart ecosystems, sustainable development goals (SDGs), anomaly detection, water stress, air quality monitoring, energy efficiency, climate adaptation, predictive modeling, decision intelligence, MLOps, governance, renewable energyAbstract
Environmental systems are complex, nonlinear, and data-scarce in places that need insights most. Recent advances in data science—spanning scalable data engineering, machine learning (ML), geospatial analysis, and MLOps—provide a unified toolkit to transform raw environmental data into actionable intelligence for climate mitigation and adaptation. This paper proposes an endtoend blueprint for “smarter ecosystems,” integrating heterogeneous data sources (satellites, IoT, administrative records), robust pipelines, interpretable ML, and decision support layers that close the loop from insights to action. Using synthetic but realistic examples, we demonstrate anomaly detection for urban air quality, geospatial heatmapping for water stress, and Pareto analysis for datacenter energy management. We also discuss uncertainty, fairness, governance, and reproducibility. The result is a practical, systems oriented approach to harnessing data science for sustainable environmental development.
