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.
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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.
Hi Donald,
Thank you for your answer. However I still do not understand why there exists (non-default) temperature scores above 3.2C : (one of) the objective of a company when referencing a target to SBTi is to reduce its temperature score (the previous company score being the default score 3.2C). Therefore, if a company takes the time to reference a target, but at the end obtain a temperature score above the one it would get by not providing any target, it is in the interest of the company to delete its previous target, so its temperature score would return to 3.2 (which would be an improvement !).
Can you confirm the above reasoning is correct ? It is hard for me to believe that you encourage some companies to delete their newly set targets.
Hi @Max,
Apologies for the late follow-up.
Yes, you are correct, but we do not encourage any company to delete their targets. If the company gets a target that is above 3.2 it simply means that the target isn’t ambitious enough. The company objective should not be to reduce its temperature rating but to reduce emissions in line with the Paris agreement. A company setting a target that gets a rating above the default score is clearly not doing that.
Hi Donald - I’ve just been searching through the forum looking for some guidance on this issue. We have a few companies which are SBTi validated, but for some reason are getting very high scores above 3.2. It appears as though some of these are cement/steel companies, where perhaps the regression model being used has the high parameter as found above. The results appear to be counterintuitive versus the SBTi validation status. I know these do not always align, but there is a big gap between the SBTi validation and a score of say 3.7!