Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
This program offers a comprehensive introduction to ESG-oriented AI, helping participants understand how artificial intelligence can be developed and deployed responsibly in a world increasingly shaped by sustainability goals, social justice concerns, and emerging regulations. Over three intensive days, the course explores the environmental footprint of AI systems, the societal impact of algorithmic bias and exclusion, and the governance frameworks required for transparent and compliant AI adoption. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This program offers a comprehensive introduction to ESG-oriented AI, helping participants understand how artificial intelligence can be developed and deployed responsibly in a world increasingly shaped by sustainability goals, social justice concerns, and emerging regulations. Over three intensive days, the course explores the environmental footprint of AI systems, the societal impact of algorithmic bias and exclusion, and the governance frameworks required for transparent and compliant AI adoption.
📋 Course Objectives
1. Put AI in Sustainability & Climate techniques to work on real datasets and case studies.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI in Sustainability & Climate
• R&D engineers and working professionals applying AI in Sustainability & Climate in industry
• Academics and educators building research or teaching capacity in AI in Sustainability & Climate
🚀 Key Learning Outcomes
• A portfolio-grade AI in Sustainability & Climate deliverable you can defend and extend.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
The Environmental Cost of AI
• Training against inference energy, and why inference dominates at scale
• Data centre PUE, water consumption for cooling and grid carbon intensity
• Published estimates and the reason most reported figures are not comparable
Quantifying Impact
• CodeCarbon and similar tooling for measuring a training run
• Scope 1, 2 and 3 emissions and where embodied hardware falls
• Market-based against location-based accounting and the offsetting critique
Harms Beyond Carbon
• Algorithmic bias, exclusion and the distribution of error across groups
• Data labelling labour conditions in the supply chain
• Displacement, access inequality and concentration of compute
Documentation and Reporting
• Model cards, datasheets for datasets and system cards
• GRI, CSRD and ESG reporting frameworks as they begin to cover AI
• Distinguishing a substantive disclosure from greenwashing
Making It Operational
• Impact assessment before deployment rather than after an incident
• Efficiency levers: model size, quantisation, scheduling and siting
• Procurement criteria and internal accountability that survives a deadline
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | SimaPro |
| Covered Tool / Platform | GaBi |
| Covered Tool / Platform | Python |
| Covered Tool / Platform | HOMER Pro |
| Covered Tool / Platform | EnergyPlus |
| Covered Tool / Platform | LCA Tools |
| Covered Tool / Platform | GIS |
Frequently Asked Questions
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