Hi,

my question concerns the regression models available in the GitHub (SBTi/inputs/regression_model_summary.xlsx). I see for example that the model with variable INT.emKyoto_gdp, slope15, model=4, samplesize=128 has an intercept parameter close to 4.1. This means that a target corresponding to this regression model can obtain a temperature score close to 4.1 (or at least > 3.2) if its annual reduction rate is really small ? How do you handle these situations ? I do not understand why some regressions models have an intercept parameter > 3.2.

Hi @Max,

Welcome to the community. If a company has set a target that is not very ambitious at all, they can get a temperature rating that is higher than the current default score of 3.2C.

3.2C is currently used as the default score as it is where the planet is likely to end up in a BAU scenario. That does not necessarily mean that some industries may not be more harmful than others.

In general, SBTi rates temperatures based on the ability to actually reduce emissions. So a cement company which would have a very hard time reducing emissions, due to technical limitations in actually producing cement, may need a flatter slope in its ambition than let’s say an a retailer, that may have more tools available to be able to reduce emissions, to achieve the same temperature rating. So, we are applying a ability based temperature rating, based on industry.

So, a company in an industry that should be able to reduce emissions more easily that sets a really low ambition target would get a rating higher than 3.2C.