The problem with sustainability data
Sustainability has become a numbers industry. Companies publish emissions tables, progress dashboards and glossy reports filled with metrics that suggest precision. The problem is that most of these numbers describe an imaginary world. They are built from averages, assumptions and broad estimates that rarely touch the physical conditions they claim to represent. When the data does not reflect reality, neither investors nor policymakers can tell whether anything is working.
The issue is structural. Much of sustainability reporting treats the environment as homogeneous. It assumes that a tonne of carbon behaves the same in every context. It assumes that a practice produces the same effect on every farm. It assumes that a retrofit performs as modelled. None of this is true. The physical world is heterogeneous, local and sensitive to detail. When methods ignore this, the information collapses under even modest scrutiny.
You see this failure everywhere. Soil carbon projects deliver opposite outcomes on fields a few metres apart because texture, microbial activity and moisture differ. Forest credits built on static baselines unravel when actual disturbance regimes and species composition are examined. Building energy models overstate savings because occupancy and maintenance diverge from expectations. Waste reduction claims fall apart once real material flows are tracked. These are not edge cases. They are examples of how quickly high-level sustainability claims drift from the physical world.
This points to a deeper problem. The sustainability data that travels well is often the data that performs poorly. Investors favour simple metrics because they are easy to compare. Regulators accept coarse numbers because they are easy to process. Companies rely on generalised assumptions because they are easy to produce. Information spreads because it is tidy, not because it is true.
Groups like CarbonPlan, Calyx Global and parts of Sylvera have shown what happens when you reverse this logic. They begin with mechanism and observation. They treat uncertainty as information rather than a threat. Their work demonstrates that credibility comes from understanding how forests grow, how soils behave and how emissions actually move through systems. Once you pay attention to these mechanisms, you cannot pretend that a single rating or conversion factor can resolve the complexity.
If sustainability information is going to mean anything, it needs to reflect the physical world. That requires methods built around several principles. Observations must matter more than narratives. Measurements must relate to the mechanisms that produce change. Uncertainty must be quantified rather than ignored. Context must be a variable, not a footnote. Replicability must be prioritised over convenience. Claims must be traceable back to what was actually measured, not what was hoped for.
This is not an academic preference. It is a requirement for functioning climate finance. If investors cannot distinguish signal from noise, capital will continue to flow toward interventions that look good on paper but fail in practice. If policymakers rely on abstracted numbers, regulations will misfire. If communities adopt practices based on averages rather than their own conditions, outcomes will be unpredictable and often disappointing.
The field does not need better branding or louder commitments. It needs better ways of knowing. The next phase of climate action will depend on information that reflects real materials, real environments and real behaviour. The organisations that build methods capable of capturing this reality will shape the future because they will be able to show what is actually changing. The rest will continue to produce numbers that look impressive and mean very little.