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
The Digital Life Cycle Assessment and Environmental Impact Intelligence course blends cutting‑edge digital tools with data‑driven methodologies to empower you to evaluate, reduce, and communicate product footprints. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
The Digital Life Cycle Assessment and Environmental Impact Intelligence course blends cutting‑edge digital tools with data‑driven methodologies to empower you to evaluate, reduce, and communicate product footprints.
📋 Course Objectives
1. Translate AI in Sustainability & Climate theory into practical, reproducible analysis.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 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 demonstrable AI in Sustainability & Climate project for your research or industry portfolio.
• 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
Module 1 – Digital LCA Foundations & Open‑Source Workflows
Understand the core phases of Life Cycle Assessment and its role in ESG reporting • Explore open‑source LCA platforms such as openLCA and public datasets • Visualize environmental impact data with Python libraries (Pandas, Matplotlib, Plotly)
Module 2 – AI‑Powered Carbon Prediction & Hotspot Analysis
Build machine‑learning models to predict carbon footprints and energy demand • Conduct hotspot, sensitivity, and scenario‑based LCA analyses • Evaluate uncertainty and interpret results for sustainable decision‑making
Module 3 – Sustainability Dashboards, ESG Reporting & Decision‑Support
Design interactive dashboards for LCA and ESG metrics using Streamlit • Integrate LCA outputs into carbon disclosure and sustainability communication • Implement AI‑enabled decision‑support systems for product‑design and policy scenarios
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | openLCA |
| Covered Tool / Platform | GLAD |
| Covered Tool / Platform | Public LCA Dataset |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | Matplotlib |
| Covered Tool / Platform | Plotly |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Brightway |
Frequently Asked Questions
Scholar Feedback & Reviews
Based on 0 scholar submissions
Rating Breakdown
No verified reviews published yet. Be the first to share your academic experience.
Leave Scholar Feedback
Your rating will help prospective scholars. Ratings below 3 stars are routed privately to the faculty mentor for immediate response.
