Chapter 09 · Sustainability & AISustainability & AI
Machine learning
Definition
A branch of AI in which systems improve their performance at a task by learning statistical patterns from data, rather than following explicitly programmed rules.
References
This source provides part of the technical or institutional basis for the definition.
This source supports the explanation of how the term is applied, measured or governed in practice.
Overview
What it means
Machine learning spans supervised, unsupervised and reinforcement approaches, unified by the idea that the model's behaviour is fitted to examples. The Elements of Statistical Learning is the field's standard graduate reference; today's large models are its most compute-hungry expression.
How it is used
It is the workhorse behind nearly all AI in sustainability: forecasting, classification, anomaly detection and optimisation across energy, land, supply chains and reporting. Model quality depends on data quality — and model scale drives computational footprint.
Why it matters
Machine learning is what separates modern AI from earlier rule-based systems — and what makes its behaviour dependent on data provenance, representativeness and drift. Those dependencies are the recurring governance questions in every sustainability application.