The assessment of greenhouse gases and air pollutants (indirect GHGs) emitted to and removed from the atmosphere is high on the political and scientific agendas. Building on the UN climate process, the international community strives after addressing the long-term challenge of climate change collectively and comprehensively, and to take concrete and timely action that proves sustainable and robust in the future. Under the umbrella of the UN Framework Convention on Climate Change, mainly developed but also other country parties to the Convention have, since the mid-1990s, published annual or periodic inventories of emissions and removals, and continue doing so after the Kyoto Protocol to the Convention has ceased in 2012. Policymakers use these inventories to develop strategies and policies for emission reductions and to track the progress of those strategies and policies. Where formal commitments to limit emissions exist, regulatory agencies and corporations rely on emission inventories to establish compliance records.
However, as increasing international concern and cooperation aim at policy-oriented solutions to the climate change problem, a number of issues centering around uncertainty have come to the fore, which were undervalued or left unmentioned at the time of the Kyoto Protocol but require adequate recognition under a workable and legislated successor agreement. Accounting and verification of emissions in space and time, compliance with emission reduction commitments, risk of exceeding future temperature targets, mitigation versus adaptation versus intensity of induced impacts at home and elsewhere, and accounting of traded emission permits are just a few to be mentioned.
The important point is that it must be expected that retrospective learning will depend on the spatial resolution of the historical data. This project aims at grasping this dependency.
Key Questions
We apply retrospective learning which requires processing historical data in a way that allows prognostic uncertainty to increase the more the further we look into the (historical) future. Toward identifying the optimal learning, we select granular computing as our primary / guiding approach. We anticipate that retrospective learning will provide answers to the following three questions:
The important point is that it must be expected that retrospective learning will depend on the spatial resolution of the historical data. This project aims at grasping this dependency.
February 2015 - January 2017
Project Funder
RESEARCH PARTNERS
Visiting Scholars
International Institute for Applied Systems Analysis (IIASA)
Schlossplatz 1, A-2361 Laxenburg, Austria
Phone: (+43 2236) 807 0 Fax:(+43 2236) 71 313