Qualitative vs Quantitative Analysis: From Description to Evidence

A sentence like this appears in a great many sustainability reports. It is usually true. It is also where the analysis begins rather than where it ends.

A reader who wants to understand what the sentence means for the company has further questions. Which assets, which hazards and over what time horizon? At what level of exposure? What would happen to operations? What might it cost? What evidence supports the conclusion and what assumptions sit behind it?

None of these questions demands a number for its own sake. Some can be answered qualitatively; some may not yet be answerable at all. Together they mark the difference between telling the reader that an issue exists and helping the reader understand it.

The distinction is often framed as qualitative against quantitative. That framing misses the point. The difference is not simply the presence or absence of numbers. It is the difference between describing an issue and building an evidence-based assessment of it, which can be done well or badly with or without numbers.

Qualitative analysis still has an important role

Qualitative analysis is often the right tool. Reliable quantitative data may not exist. The issue may be emerging, the evidence directional rather than numerical or the potential impact difficult to model. The methodology may still be developing. At the screening stage, where the purpose is to identify which risks deserve further work, a well-structured qualitative assessment does the job efficiently.

A strong qualitative assessment is still specific. It identifies the nature of the risk, the activities and locations affected, the time horizons involved and the mechanism by which the risk would reach the business. It sets out the evidence relied on, the management response, the limitations and the areas that need further analysis. A reader can follow its reasoning and see where it stops.

The reporting standards recognise this. IFRS S2 expects a company’s approach to climate scenario analysis to be commensurate with its circumstances; in practice that ranges from qualitative narratives to detailed modelling. For anticipated financial effects, a company need not give quantitative information where the effects are not separately identifiable or where measurement uncertainty is so high that a number would not be useful. It explains why and gives qualitative information instead.

The question is therefore not which method is superior. It is whether the level of analysis is appropriate to the conclusion being drawn.

From climate statement to climate analysis

Take a Malaysian example. Malaysia is exposed to rising temperatures and episodes of extreme rainfall; the IPCC’s Sixth Assessment Report expects heavy rainfall across much of Asia, including the Southeast Asian monsoon region, to intensify as warming continues. A company with sites across the country might begin with a description: extreme rainfall may cause flooding. That is a reasonable first level.

Each further level asks a narrower question. The second asks which facilities, assets or operations are exposed. The third asks how that exposure might change under different scenarios and time horizons. The fourth asks what flooding would mean operationally: lost access for staff and deliveries, interrupted production, damaged inventory, utility outages and the limits of business continuity plans. The fifth asks what those consequences could mean financially, in repair costs, downtime, insurance, lost revenue or asset values.

Each level needs evidence and assumptions the one before did not: site locations and hazard data, then scenarios, then operating information, then financial data. Each also narrows the conclusion until it says something management can act on, which is why the progression ends with a response.

Materiality: how was the conclusion reached?

The same pattern appears in materiality assessment. A report might state that material topics were identified through stakeholder engagement and benchmarking. That tells the reader a process existed. It does not explain why one topic ranked above another.

Compare “stakeholders consider climate change important” with an assessment that explains how many people responded, which stakeholder groups they represented, which topics were assessed, how responses were scored and weighted, how they were distributed, how management’s own assessment and external reference points were brought in and how the final ranking resulted.

A survey alone does not determine materiality. It is one input alongside management judgement, the company’s risk processes and external evidence. GRI 3 asks companies to describe the process they followed to determine their material topics, which is a request for exactly this kind of explanation. A materiality assessment earns its result by being able to give it.

A score is not automatically evidence

Scoring is one way to make that explanation possible. A materiality assessment might end in a short table:

Before the scores mean anything, the reader needs answers to the questions beneath them. A score of 4.7 with nothing behind it is a more precise-looking statement, not a more informative one.

Once the basis is documented, the score becomes useful because it can be examined. A reviewer can ask whether the weighting was reasonable, whether the respondent base was balanced and whether the ranking holds when one assumption changes. That is the main strength of quantification rather than a weakness.

A documented quantitative assessment gives a reviewer something tangible to examine, challenge and reproduce. Good analysis does not prevent challenge. It makes meaningful challenge possible.

Financial materiality: what does the ranking mean?

Financial materiality raises the stakes. Under IFRS S1, a sustainability-related risk is in scope when it could reasonably be expected to affect a company’s cash flows, its access to finance or its cost of capital over the short, medium or long term. A disclosure may say exactly that: climate-related risks may affect cash flows and financial performance.

The analytical questions follow from the sentence. Which cash flows? Under what circumstances and over what time horizon? What is the potential exposure and on what assumptions? Which mitigations have been considered? How does the exposure change under different scenarios? What uncertainty remains?

Answering them moves a risk along a chain from exposure to operational effect to financial effect; from there it leads to a response. Our article on connecting sustainability risks to financial effects follows that chain in more detail.

The danger of false precision

None of this is an argument for quantifying everything. A number can look authoritative while resting on very little. An unsupported probability, an arbitrary weighting, incomplete data, a poorly defined boundary or an unsuitable denominator can each produce a figure that is precise and wrong. A model can return a result to the nearest ringgit when the uncertainty in its inputs is far wider.

The second figure is less precise and more useful, because the reader can see what it depends on. The ISSB Standards allow quantitative information to be given as a single amount or a range. Their test of usefulness is that information is relevant and faithfully represents what it purports to represent, which a figure carried to seven significant digits on unstated assumptions may not.

Uncertainty does not mean analysis should stop

Forward-looking sustainability analysis is uncertain by nature. A future climate-related exposure may depend on climate conditions, the scenarios chosen, operational changes, prices, technology, policy, management responses and the remaining life of the assets involved. Results will change as better data becomes available, scenarios are updated, methods improve and the organisation learns more.

A changed result does not mean the original analysis was defective. More often it means the analysis is doing its job. IFRS S1 anticipates this: it asks companies to explain the judgements with the most significant effect on their disclosures. For amounts subject to high measurement uncertainty, it asks for the sources of that uncertainty and the assumptions made.

The aim is the most robust assessment the available evidence can reasonably support, with its assumptions, limitations and level of uncertainty documented alongside it.

The fear of being wrong

Some organisations hesitate to quantify forward-looking risks precisely because the estimate may change. The hesitation is understandable. It often rests on treating two different kinds of information as though they were the same.

A historical transaction is a fact. An invoice supports it; it should not change. A future climate exposure is an estimate. It is built from evidence and assumptions and is expected to move as they do.

An estimate is judged differently from a fact. The relevant questions are whether its basis was documented, its assumptions identified and its limitations explained; whether fact was kept distinct from estimate; whether the scenarios were appropriate; and whether the analysis is updated when the evidence changes. An estimate that meets those tests and later moves has not failed. The same logic applies to emissions inventories, where restating GHG emissions on better information is a sign of a working process rather than a broken one.

The same principle applies to Scope 3

A Scope 3 inventory is a familiar example of analysis under imperfect information. Supplier data is incomplete, many figures are estimates, secondary emission factors fill the gaps, methods change and boundary assumptions have to be made. The GHG Protocol’s Scope 3 Standard accepts this. It asks companies to describe their data sources, methods and assumptions for each category and to describe the quality of the data alongside the result.

Our GHG emissions series looked at where those choices matter: the spend-based calculation, the risk of double counting, the case for and against custom emission factors and the effect of trade-adjusted factors on imported goods. The common thread is the one in this article. The quality of a GHG inventory depends on the transparency of the methodology, the appropriateness of the data and the clarity with which limitations are documented.

From analysis to decision-making

Better disclosure is not the only reason to do this work. Analysis can help management understand what matters, where exposure exists and what may change. It can show what requires mitigation, where more information is needed, where capital or resources may be required and what should be monitored.

The progression runs from analysis to understanding, from understanding to decision and from decision to response. A descriptive statement can rarely support that chain. A well-supported assessment, qualitative or quantitative, can inform each step of it. That is also why, as our article on sustainability data and the sustainability report argued, the analysis increasingly needs to be under way before the report is written.

What good analysis looks like

Good analysis does not follow a single template. It should generally let a reader or reviewer see the evidence that supports the conclusion, the method by which it was reached, the assumptions it needed and its scope: what was included and what was left out. It should be clear about uncertainty, meaning what is known and what is not.

Two further tests follow from those five. Another reviewer should be able to understand the assessment and, in principle, reproduce it. The analysis should also say something relevant to a decision: what it tells management and the users of the report.

Qualitative and quantitative are not opposing methods

A mature assessment usually combines the two. In the flooding example, qualitative analysis says that flooding could disrupt access to a facility; quantitative analysis places the facility within an identified flood-exposure area, with modelled exposure under defined scenarios. Qualitative analysis explains that disruption could affect operations; quantitative analysis estimates the downtime and associated financial exposure on defined assumptions.

The qualitative explanation gives context and mechanism. The quantitative analysis provides scale where scale can reasonably be estimated. Each is weaker without the other.

Making the reasoning visible

The objective of sustainability analysis is not to produce numbers for their own sake. It is to develop an evidence-based understanding of an issue that can support decisions and disclosures.

A qualitative assessment can be rigorous. A quantitative assessment can be weak. The difference is not simply whether numbers appear on the page. Good analysis makes the reasoning visible: what evidence was used, how the conclusion was reached, what assumptions were required and where uncertainty remains.

Ace CSR supports Malaysian companies with materiality assessments, climate scenario analysis, financial exposure quantification and IFRS S1 and S2 advisory, including documenting the evidence, assumptions and limitations behind each conclusion.