A Five‐Step Theory‐of‐Change–Cost–Benefit Protocol for Assessing the Socio‐Economic Benefits of Climate Information Services in Data‐Scarce Settings: A Cameroon Pilot

Governments and international agencies routinely justify investments in weather and climate services by citing a single global figure: every dollar spent returns several dollars in economic benefits. This figure is useful for advocacy but says nothing about whether a specific national service in a specific country is actually delivering value to the people and institutions it serves. The alternative—rigorous impact evaluation with measured baselines and counterfactual comparison groups—requires years of data collection that newly established service partnerships have not had time to accumulate. This leaves institutions with an evidentiary gap: not lacking value, but lacking the infrastructure to demonstrate it in local, measured terms. We address this gap by developing a five‐step protocol that translates a theory of change into measurable indicators, matches each causal link to an appropriate empirical method, aligns those methods with data sources institutions already hold, and combines the results into a single cost–benefit estimate while using independent institutional, administrative, and satellite data to cross‐check claims where formal baselines are absent. Applied to four Cameroonian institutions using national meteorological services, the protocol situates a benchmark‐derived national estimate—presented throughout as an external, order‐of‐magnitude comparator rather than as a result of the assessment—alongside case evidence that, including a substantial reported reduction in climate‐related losses at one enterprise, supports the benchmark directionally but cannot yet confirm it numerically because none of the institutions currently track the costs required. We argue that this outcome should not be read as a failure of the services themselves, but as a diagnostic signal of an early‐stage market, and we specify the minimal data practices that would close the gap. Because the protocol does not depend on country‐ or sector‐specific assumptions, it offers a transferable approach for any institution seeking to evaluate service value before formal impact‐evaluation data exist.