Building a falsifiable strategic diagnosis

A strategic diagnosis explains a decision problem using evidence and allows the explanation to be disproved. 'We need to transition because markets are changing' identifies neither the problem nor a preferred response. Instead, connect an observed outcome to a possible cause and a constraint: demand growth exceeds low-emissions generation additions, so fossil generation continues to rise despite a cleaner mix, while network connection limits further additions. That hypothesis requires demand, generation, timing and connection evidence. It can be rejected if the evidence points elsewhere. Do not embed the preferred project in the problem definition. Network losses or flexible demand may explain the shortfall and call for a response different from building new supply.

Begin with a small set of reconcilable facts. Identify the period, unit and measurement boundary of each, and distinguish measurement, estimate and interpretation. Develop at least two competing explanations that imply different responses. If every explanation automatically favors the project the team already wants, alternatives have probably not been tested. Explanations are not equally strong merely because they appear in a list. State what each would predict and compare those predictions with observations. Where sources conflict, investigate differences in boundary, timing and method rather than select the convenient value. Preserve an influential unresolved conflict as a qualification until evidence or sensitivity analysis resolves its effect.

Initial solution: delivered electricity increased 20% and emissions increased 8%. Intensity fell from 150,000 ÷ 300,000 = 0.50 to 162,000 ÷ 360,000 = 0.45 tonnes per megawatt hour, a 10% decline. Fossil conversion efficiency, however, remained 250 ÷ 625 = 40% and then 270 ÷ 675 = 40%. The data do not support improved plant efficiency. The renewable share increased from 16.67% to 25%. Renewable additions were 40 gigawatt hours against demand growth of 60, so fossil generation rose by 20. Operations overlooks the improved mix, while marketing attributes improvement to a mechanism absent from the supplied data. The stronger diagnosis recognizes the specific progress alongside rising total emissions.

Examine the financial denominator too. Average revenue per megawatt hour increased from 100 to approximately 108.33 currency units. Price or tariff mix therefore contributed to revenue growth; falling emissions per unit of revenue cannot be interpreted as purely technical improvement. Applying base-year emissions intensity to current delivery gives 180,000 tonnes, which is 18,000 above actual emissions. This comparison illustrates the changed mix at current activity but does not establish what would actually have happened without the project. Fuel prices, network constraints or service requirements could have changed counterfactual dispatch. Name the comparison and its premise rather than turn it into a proven causal reduction or a saleable credit.

Translate the diagnosis into testable alternatives. Keeping current fossil delivery at 270 gigawatt hours while raising efficiency to 45% would require 600 gigawatt hours of fuel rather than 675 and produce 144,000 tonnes under unchanged assumptions. Adding 30 gigawatt hours of renewable generation that actually displaces equal fossil output at 40% efficiency would leave fossil delivery of 240 and fuel demand of 600, producing the same emissions. Equal annual results do not make the alternatives financially or operationally equivalent. The efficiency option requires performance evidence under relevant loads; the renewable option requires time alignment, connection and dispatch evidence. Their benefits cannot be added using separate unchanged baselines: implementing either changes the basis for evaluating the other.

Design a decisive test around the uncertainty most likely to reverse the choice. For efficiency, request simultaneous fuel and net-electricity measurements at representative load points, with acceptance conditions and uncertainty limits. A monthly average with changing load is weaker evidence. For renewables, test a time series of generation, demand and network curtailment, then calculate feasible displacement in the relevant periods. Where the data do not exist, the next step may be a bounded measurement study rather than full commitment. State which decision the test changes: passing a performance condition permits expansion; failure revises or rejects the assumption. Collecting information that changes no decision uses resources without improving the strategy.

Distinguish what can be authorized now from what requires a prior condition. Metering or a load-profile study may be approved when cost and scope are clear, while major equipment remains conditional on performance and connection tests. Identify responsibility for interpretation, the latest useful evidence date and the fallback consequence of delay. Governance prevents a temporary assumption becoming a permanent commitment. If management selects a more expensive alternative for reliability or flexibility, record those reasons and their limits. Do not silently modify the financial model to make the chosen alternative appear to have the highest return; doing so prevents useful learning when actual outcomes emerge.

Write a diagnosis that another reader can challenge. State the observation, the strongest explanation and its support, the remaining rival explanation and its evidence need, and the implication for the decision. Add a concrete assumption whose change reverses the recommendation, such as renewable output failing to displace fossil generation during its production hours. Keep environmental impact, service and cost distinct before combining the judgment. A carbon benefit can coexist with failure on water or peak service. 'Available data show no fuel-efficiency improvement' accurately describes the evidence; 'the plant cannot improve' asserts an untested technical limit. The diagnosis should make that boundary clear.