Day 1: Defining Environmental Data Requirements

Environmental Aspects and Indicators

• Decision points for environmental aspects
• Trend changes checked against operating context
• Evidence trails supporting reported metrics
• Monitoring coverage across environmental assets

Monitoring Sources and Frequency

• Ownership expectations for sources and frequency
• Measurement methods recorded for consistency
• Environmental indicators linked to material impacts
• Sustainability measures tied to verified sources

Sustainability Reporting Boundaries

• Performance signals around sustainability reporting boundaries
• Reporting boundaries defined before analysis
• Exceptions recorded prior to final submission
• Anomalies investigated before formal reporting

Day 2: Analyzing Environmental Performance with AI

Trend and Anomaly Detection

• Evidence requirements for anomaly detection
• Environmental data quality prior to reporting
• Data gaps disclosed with clear limitations
• Source records retained for assurance

Emission and Resource Forecasting

• Decision points for resource forecasting
• Sensor quality checked prior to interpretation
• Emission indicators tracked against baselines
• Threshold breaches triggering environmental review

Narrative Reporting Assistance

• Application limits around narrative reporting assistance
• Human review prior to sustainability disclosure
• Assumptions tested before final conclusions
• Business question tied to decisions

Day 3: Assuring Credible Sustainability Reporting

Data Verification and Traceability

• Validation checks for data verification
• Query logic checked against requirements
• Missing values requiring deliberate treatment
• Data lineage informing result traceability

Materiality and Disclosure Review

• Review criteria for disclosure review
• Uncertainty limits around analytical conclusions
• Consistent definitions for key measures
• Visual outputs informing decision clarity

Environmental Intelligence Dashboard

• Review criteria for environmental intelligence dashboard
• Explanation boundaries communicated to decision-makers
• Validation checks before using results
• Outliers reviewed prior to model use

• Peer review challenging assumptions around sources and frequency
• Practical lab developing resource forecasting for operational use
• Decision simulation testing data verification in context
• Hands-on workshop applying environmental intelligence dashboard to realistic tasks