A Distributed Computational Model for Estimating the Carbon Footprints of Companies

A new approach based on input–output (IO) analysis has emerged to estimate the carbon footprints of companies and their products from cradle to gate. While the approach relies on the same principles as the GHG Protocol, it uses a distributed iterative framework to improve the footprint estimations and reduce their uncertainty. While optimal estimations would result if all the world’s companies would enter such a system, this paper shows how such a distributed system could apply to the real world where many enterprises would stay out of the system. We show how the quality of the estimations with respect to the GHG Protocol would be increased by integrating scope 1 and scope 2 data from the value chains in the footprint estimations and progressively reducing the part of the remaining scope 3 data. To help with analyzing uncertainty, we show how to use the scope 1/2/3 decomposition to estimate the biases and the standard deviations of the computed production carbon intensities. We illustrate the model on macroeconomic data for 44 sectors and two regions (Europe and Rest of World), using the Inter-Country Input–Output database from the OECD. Such a system would necessarily rely on Information and Communication Technology, since the companies would be permanently interconnected in a large-scale meshed network, using an application protocol for data exchange.