Managing uncertainty in climate science and meeting sustainability constraints

Advanced Systems Analysis (ASA) Program researchers work on conceptualizing the notion of sustainability and exploring different approaches to evaluating and quantifying it.

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Systems thinking promotes the understanding of sustainability as a pattern of organization that favors self-reinforcing feedback processes interacting with other reciprocating feedback processes, as argued by [1]. Further, this paradigm suggests the use of network analysis approaches to quantify sustainability [2]. In [3] researchers emphasized the challenge of the local-global interface in defining sustainability and thus argued that a new globally consistent systems-analytical framework is needed to address the challenge of interactions between the Earth’s subsystems in the context of planetary environmental boundaries.

In this connection, researchers reviewed the use of indicators in ecological models. These indicators can be used to assess ecosystem health, ecosystem services, environmental management, or human-environment system analysis, to name a few applications [4]. Further research suggested a framework of indicators to evaluate the sustainability of development in a particular region, that is, to evaluate the state of environmental services; they demonstrated its application for a region in the Italian Alps which is under pressure of reforestation [5].

In [6] a conceptual model was introduced for developing eco-industrial parks; it extends the standard business model and considers local attributes such as facilities and environment; it also looked at how these are interlinked to benefit from by-product synergies and how the firm can promote a green image to customers and clients. Each of these aspects is explored in terms of key ideas, indicators, stakeholders, system boundaries, and barriers. The research provides insights into how the business community can incorporate sustainable development into their operations.

Greenhouse gas (GHG) emissions are a particular challenge for sustainability. In order to estimate the magnitude and distribution of current net emissions and their possible future paths, an evaluation is required of associated uncertainties resulting from the uncertainty in GHGs that are i) emitted into the atmosphere by various sources and ii) absorbed by terrestrial and aquatic systems. Proper accounting for both types of uncertainty is crucial for planning, analyzing, validating, and verifying mitigation efforts at global scale, and for analyzing scenarios of future emissions [7]. The IIASA Emissions-Temperature-Uncertainty (ETU) framework published by [8] allows any country to understand its current national level and near-term mitigation and adaptation efforts in a globally consistent long-term emissions-temperature context. The ETU suggests national emission target paths that are consistently embedded globally so that the cumulative emissions with their uncertainties comply with a future global warming target (ranging from 2–4°C) and, subsequently, with the associated risk of exceedance.

Thorough uncertainty analysis of the past, current, and future emissions is needed for when international environmental agreements are framed in order to better understand and inform policy. In 2014 ASA researchers made methodological advances by studying two important, though rather neglected, issues. Researchers analyzed the historical change in the total uncertainty of GHG emissions from stationary sources in the European Union reported annually by national inventory reports and gave examples of changes in total uncertainty due to structural changes in GHG emissions which can significantly influence the total uncertainty [9]. In [10] researchers addressed the issue of incompatibility of GHG inventories and thus incomparability of emission estimates if emissions are reported with different uncertainties. They provided a categorization and ranking of the inventories which can induce compliance checking conditions based on two groups of techniques – probabilistic treatment of uncertainties and a “fuzzy” approach.


[1] Fath BD (2014a). Sustainable systems promote wholeness-extending transformations: The contributions of systems thinking. Ecological Modelling, 293:42-48

[2] Fath BD (2014b). Quantifying economic and ecological sustainability. Ocean & Coastal Management. Article in press (Published online 16 July 2014)

[3] Jonas M, Ometto JP, Batistella M, Franklin O, Hall M, Lapola DM, Moran EF, Tramberend S, Queiroz BL, Schaffartzik A, Shvidenko A, Nilsson S, Nobre CA (2014). Sustaining ecosystem services: Overcoming the dilemma posed by local actions and planetary boundaries. Earth's Future, 2(8):407-420

[4] Burkhard B, Fath BD, Jorgensen SE, Li BL, Guest Editors (2015). Editorial: Use of ecological indicators in models. Ecological Modelling, 295:1-4 (Published online 1 November 2014)

[5] Hayha T (2014). Mapping ecosystem services: An integrated biophysical and economic evaluation. IIASA Interim Report IR-14-007

[6] Dean CA, Fath BD, Chen B (2014). Indicators for an expanded business operations model to evaluate eco-smart corporate communities. Ecological Indicators, 47:137-148

[7] Ometto JP, Bun R, Jonas M, Nahorski Z, Gusti M (2014). Uncertainties in greenhouse gases inventories - Expanding our perspective. Climatic Change, 124(3):451-458

[8] Jonas M, Marland G, Krey V, Wagner F, Nahorski Z (2014). Uncertainty in an emissions-constrained world. Climatic Change, 124(3):459-476

[9] Lesiv M, Bun A, Jonas M (2014). Analysis of change in relative uncertainty in GHG emissions from stationary sources for the EU 15. Climatic Change, 124(3):505-518

[10] Hryniewicz O, Nahorski Z, Verstraete J, Horabik J, Jonas M (2014). Compliance for uncertain inventories via probabilistic/fuzzy comparison of alternatives. Climatic Change, 124(3):519-534

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Last edited: 12 March 2015


Elena Rovenskaya

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Advanced Systems Analysis

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