





Select carbon factors from trusted, UK-relevant sources, such as government conversion factor guidance, WRAP studies, or peer-reviewed research. Map your categories to the closest available product class—like small appliances or laptops—and note any substitutions. Avoid mixing regional datasets without explanation. Document the year of factors, as updates shift results. Where a product lacks a direct factor, apply a conservative analogue and explain why. This disciplined alignment turns volunteer-entered data into numbers that funders and policymakers can confidently endorse.
Carbon savings hinge on extra years of service. Record a realistic extension, then test sensitivity with shorter and longer scenarios. Acknowledge rebound risks, like improved performance leading to higher energy use, especially with older appliances. Where energy efficiency matters, pair repair with guidance on mindful use and simple maintenance. Include a small line item for repair activities—travel, parts—to avoid overstating benefits. Framed this way, your estimate becomes balanced and useful for comparing interventions, grant applications, and local climate action plans.
Communicate ranges, not just single numbers. Provide a central estimate with low and high bounds, and a short note about key drivers—weight, lifetime, and factor choice. Use approachable visuals, like shaded bars, to convey variability without fear. For community events, a simple legend explaining why ranges exist invites questions and learning. Decision-makers value humility backed by method. Over time, as your dataset grows, you can narrow ranges, update assumptions, and celebrate improved precision alongside the familiar joy of a well-timed fix.
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