Sector: Environment, Climate Resilience, Renewable Energy & ESG Infrastructure
Geography: Multi-region / Global Application
Solution Type: Predictive Analytics, Geospatial Intelligence & Agentic AI

Lead Research Advisor

Dr. Dilshan Silva – R&D & Innovation
Imperial College PhD | AI & enterprise engineering | Global product leadership

Problem Statement

Governments, climate funds, and environmental organisations face increasing challenges in responding to climate change, environmental degradation, and energy volatility using traditional, reactive systems.

 Key limitations included:

• Delayed detection of illegal environmental activity

• Limited early-warning capability for floods, fires, and climate disasters

• Inefficient industrial emissions management

• Manual, non-optimised renewable energy dispatch

• Lack of real-time, autonomous climate intervention system

Intervention Design

A next-generation Predictive Analytics & Agentic AI platform was designed leveraging:

• Large-scale geospatial data ingestion (satellite, sensors, environmental feeds)

• Predictive analytics models for forecasting future trends

Agentic AI systems capable of:

• Perceiving real-world data

• Planning optimised responses\

• Acting autonomously through connected systems

• Learning continuously through feedback loops

The platform enables closed-loop, autonomous climate and environmental response systems.

Measured Outputs

Environment & Biodiversity Protection

• Real-time detection of illegal logging, mining, and fishing

• Automated endangered species tracking

• Forest fire prediction with early-intervention triggers

• Identification of high-priority deforestation risk zones

Global Warming & Disaster Risk

• Detection and alerts for high-risk flood zones

• Continuous monitoring of sea-level rise since 2020

• Predictive modelling for coastal inundation and storm surge risk

Climate Modelling & Carbon Footprint

• Live monitoring of factory emissions and industrial processes

• Autonomous efficiency optimisation to reduce carbon output

• Real-time chemical leak detection

• Predictive maintenance for equipment failure prevention

• Climate simulations identifying highest-impact decarbonisation levers

Renewable Energy & Smart Grids

• High-accuracy forecasting of wind, solar, and grid conditions

• Autonomous dispatch of energy storage (batteries, EV fleets)

• Real-time supply–demand balancing on smart grids

Measured Outcomes

• Shift from reactive environmental response to proactive prevention

• Significant improvements in:

  • Illegal activity detection speed

  • Disaster risk anticipation

  • Industrial emission reduction

  • Renewable energy utilisation efficiency

• Reduced:

  • Environmental damage

  • Carbon leakage

  • Equipment waste and unplanned downtime

• Enabled autonomous, real-time climate intervention at system scale

Independent Verification (Applicable by Deployment)

• Satellite data validation

• Environmental authority enforcement confirmation

• Industrial emissions audit records

• Energy grid operational telemetry

• ESG and climate reporting frameworks

Sustainability & Long-Term Impact

• Enables national climate early-warning and response systems

• Supports net-zero industrial transformation

• Strengthens biodiversity protection infrastructure

Direct alignment with:

1. Climate adaptation

2. Decarbonisation

3. Disaster-risk reduction

4. ESG and impact investing mandates

Donor, Government & ESG Relevance

This case study demonstrates how predictive analytics and Agentic AI can:

• Prevent environmental destruction before it occurs

• Reduce loss of life and infrastructure damage from climate disasters

• Eliminate carbon inefficiencies at source

• Fully automate renewable energy optimisation

• Deliver measurable, system-level climate impact at scale