Spatio-temporal Uncertainty Assessment of GHG Emission Inventories with the Specific Focus on Austria and Ukraine

The focus of this 2-year research is on learning, more specifically, on the spatio-temporal assessment of uncertainty in GHG emission inventories and how our knowledge increases in terms of predictability across space and time, and whether our insights into learning will eventually allow to better handle (reduce?) uncertainty in prognostic emission scenarios or, more generally, scenarios of global change.

© Vicki France | Dreamstime.com

© Vicki France | Dreamstime.com

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.

ASA’s and LPNU’s Scientific Expertise


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:

  1. Given the one reality – the past – a system has experienced, can we develop an understanding of uncertainty, which proves reliable in the future, i.e., under prognostic conditions, irrespective of how the system unfolds dynamically?
  2. How far into the future will our understanding of uncertainty prove reliable?
  3. What are the practical consequences: Can we apply our improved understanding of uncertainty in earth system sciences models that project future change and even contribute to handling (reducing?) uncertainty in these models?

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.


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Last edited: 02 November 2015

CONTACT DETAILS

Matthias Jonas

Senior Research Scholar

Advanced Systems Analysis

T +43(0) 2236 807 430

Timeframe

February 2015 - January 2017

International Institute for Applied Systems Analysis (IIASA)
Schlossplatz 1, A-2361 Laxenburg, Austria
Phone: (+43 2236) 807 0 Fax:(+43 2236) 71 313