Hello @Michaelweber If you need hourly marginal data, then try electricityMap. Their machine-learning algorithm is described by Carradi (2018). ElectricityMap must have historical data to train their algorithm but I don’t know how far back that goes.
Notwithstanding, I recently asked electricityMap about the problem of unaccounted cross-border carbon (shorthand for GHG) flows, anti‑parallel to the energy flows if you like, but have yet to receive an answer. I think the underlying allocation problem is probably quite tough and interactions at the margins do not capture the information being sought.
If you need annualized average country‑specific data, then perhaps using national economic accounts, including input/output tables might prove a better approach? Perhaps CGE modelers can help out here? They could also project forward. I am not familiar with the literature here though. But you will probably need to apply decomposition techniques. Patterson et al (2006) might offer clues? When I coded up their tutorial problem in Matlab, I got a different solution. I told Murray that but he wasn’t the slightest bit interested.
This question is quite topical because Germany claims its carbon trajectory is lousy because it now exports significant quantities of hard coal generation to the Netherlands, Great Britain, and elsewhere. HTH, R.
References
Corradi, Olivier (3 July 2018). Estimating marginal carbon intensity with machine learning. Tomorrow. Copenhagen, Denmark. Blog.
Patterson, Murray G, Graeme C Wake, Robert McKibbin, and Anthony O Cole (15 March 2006). “Ecological pricing and transformity: a solution method for systems rarely at general equilibrium”. Ecological Economics. 56 (3): 412–423. ISSN 0921-8009. doi:10.1016/j.ecolecon.2005.09.018.
