Analyze climate policy data

Compile the climate policy analysis brief for the policy director and the advocacy lead: merge emissions projections, policy scenario inputs, and sensitivity runs into one workbook, rank the policy options by net emissions impact and economic cost, and write a two-paragraph executive summary that states the recommended option and the confidence level. Deliver by Wednesday noon after checking all scenario formulas.

Before sending the model to the advocacy team, make it easy to approve: put the headline emissions and cost trade-off up front, provide an uncertainty band for the central estimate, call out any strong assumptions that would make a reviewer hesitate (carbon price path, technology uptake), and include a one-row summary that policy wonks can paste into a memo.

I re-ran the policy scenarios and the 2035 emissions under our preferred package rose contrary to expectations. The policy director and two external partners will see this soon; I don't know whether the change came from an input tweak, a model bug, or a real sensitivity to a single assumption. What sequence of checks should I run immediately and what interim message do I send to the partners to keep trust without overcommitting?

Decision makers keep reversing endorsements because our briefs later reveal hidden sensitivities, creating missed opportunities and reputational damage. The pattern points to poor uncertainty communication and buried assumptions. Which one habit should I change first — require a sensitivity table in every brief, standardize a one-line confidence statement, or hold a short pre-brief with key decision makers — to reduce reversals? Recommend the single change and how to implement it for the next policy memo.