Fig. 1: Ordinary Least Squares (OLS) regression coefficients for (a) scope 1 emissions ratio (CDP 2023/2018) with n = 78 (b) and climate target ambition with n = 89.
From: The negligible role of carbon offsetting in corporate climate strategies
The graph displays the estimated regression coefficients (\({\hat{\beta }}_{i}\)), with error bars representing their 95% confidence intervals (CI) with \({\text{CI}}_{{\beta }_{i},0.95}=\left[{\hat{\beta }}_{i}-{t}^{*}\cdot \,\text{SE}\,({\hat{\beta }}_{i}),{\hat{\beta }}_{i}+{t}^{*}\cdot \,\text{SE}\,({\hat{\beta }}_{i})\right]\) and t* the critical value from the t-distribution. In (a), positive regression coefficients indicate a negative relationship between the explanatory variables (on y-axis) and decarbonisation speed (x-axis), suggesting that as the explanatory variables increase, we observe a decreased decarbonisation speed. In (b), positive regression coefficients indicate a positive relationship between the explanatory variables (y-axis) and climate target ambition (x-axis), suggesting that as the explanatory variables increase, we observe an increased climate target ambition. The sectoral categorical variables are relative to the aviation sector, and the geographic categorical variables are relative to headquarters in Asia. The label of retired carbon credits is written in bold as it represents the study’s primary outcome variable of interest.
