Deep learning for sustainable development: cross-disciplinary applications and responsible AI

Abstract

Global climate change, energy shortages, food security crises, and urban sprawl pose multifaceted governance challenges for sustainable development. Leveraging its capacity for hierarchical representation learning from high-dimensional, heterogeneous data, deep learning has emerged as a core digital technology enabler for advancing the Sustainable Development Goals (SDGs). Employing a systematic literature review methodology and drawing on open-access publications from Google Scholar (2018-2026), this paper examines deep learning applications across four key domains: climate action, sustainable energy, smart agriculture, and smart cities. Through four comparative tables analyzing model suitability, data types, application outcomes, and challenges, the study identifies key bottlenecks in technology deployment from a "Responsible AI" perspective. It identifies three major industry trends: the large-scale deployment of physics-AI hybrid modeling, the growing dominance of deep reinforcement learning in energy dispatch, and the integration of federated learning with edge AI for distributed ecological monitoring. Current implementation efforts remain constrained by four critical issues: regional data divides, a lack of model interpretability, algorithmic bias, and the high carbon footprint associated with large-scale models. By outlining corresponding optimization pathways, this paper provides a theoretical framework and practical guidance for green AI and sustainable digital governance.