AI for Sustainability Management and Systems (AIB556)

Artificial intelligence (AI) is increasingly deployed to support sustainability management across complex socio-technical systems that span industries, public institutions, and markets. This course focuses on how AI-enabled systems are designed, governed, and applied to manage sustainability outcomes, rather than on firm-level competitive strategy or innovation leadership. The course examines how AI supports sustainability management through system-level mechanisms such as technology governance, policy instruments, data infrastructures, and cross-sector coordination. Students analyse the role of AI in enabling measurement, accountability, and decision-support across interconnected systems, including public–private arrangements and regulatory contexts. Through applied sectoral case studies—such as smart recycling systems, tourism water-energy management, and electric mobility—students learn to interpret AI-driven sustainability analytics, including ESG monitoring, resource and footprint optimisation, and lifecycle-based carbon assessment. By the end of the course, students will be able to critically evaluate AI applications for sustainability management, assess system-level trade-offs, and interpret data-driven insights to support sustainability-oriented decisions in complex and carbon-constrained environments.

• AI Technology Management in Organisational Contexts
• AI-Enabled R&D and Innovation Processes
• Organisational Learning and Human–AI Collaboration
• AI Applications and Commercial Deployment Models
• Policy Instruments and Regulatory Approaches for AI
• Public–Private Coordination and Governance Structures
• Comparative AI Policy and Governance Frameworks
• Open Collaboration and Data-Sharing Models
• Ethical, Legal, and Accountability Issues in AI Systems
• AI Systems for Sustainability Monitoring and Management
• AI Applications for Resource Efficiency and Circular Systems
• AI for Environmental Footprint and Lifecycle Assessment

• Appraise the role of AI-enabled systems in shaping sustainability management across industrial, sectoral, and socio-technical systems.
• Assess the role of policy instruments, governance arrangements, and ethical frameworks in shaping AI deployment for sustainability outcomes.
• Synthesise AI, data analytics, and systems thinking to support sustainability-oriented technology management and decision-making.
• Critique industrial and sectoral case studies to assess how AI-enabled systems influence sustainability performance and system-level value outcomes.
• Compare international AI policy and governance frameworks to identify strengths, limitations, and contextual suitability for sustainability management.
• Recommend AI-, big data-, and IoT-enabled sustainability analytics to support evidence-based sustainability decisions.