Background

By focusing on emissions per unit of economic output, carbon intensity accounts for the fact that larger organizations or economies may naturally produce more emissions simply due to scale. This allows for a fair comparison of sustainability performance across different entities, regardless of size.

Accurate and timely carbon accounting and the development of robust measurement frameworks are essential for effective emission reduction strategies and the pursuit of sustainable development goals. While carbon accounting frameworks offer valuable insights into emissions quantification, they are not without limitations. One of those limitations is the frequency with which this information is released, generally at an annual frequency, while most companies’ economic indicators are made public on a quarterly basis. This is a perfect example in which temporal disaggregation can be used to bridge the gap between data availability and prompt economic and financial analyses.

In this application, the variable of interest is the GHG emissions for IBM between Q3 2005 and Q3 2021, at annual frequency, resulting in 17 data points (i.e., \(\mathbf{Y} \in \mathbb{R}^{17}\)). For the high-frequency data, we used the balance sheet, income statement, and cash flow statement quarterly data between Q3 2005 and Q3 2021, resulting in 68 data points for the 112 variables (after filtering). We remove variables that have a pairwise correlation higher than 0.99, resulting in a filtered dataset with 112 variables (\(\mathbf{X} \in \mathbb{R}^{68 \times 112}\)).

In this example, we employ the adaptive LASSO method (method = "adaptive-spTD") to select the best variables that can be used to recover the high-frequency observations, and we apply the aggMat = "first" aggregation method.