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Sensitivity And Uncertainty Analysis In Mrio Modelling; Some Empirical Results With Regard To The Dutch Carbon Footprint

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  • Harry C. Wilting

Abstract

Environmental multi-regional input--output (MRIO) models require large amounts of data that all have their specific uncertainties. This paper presents a sensitivity and uncertainty analysis in order to gain an understanding of the directions in which efforts should be made to reduce these uncertainties. The analyses were carried out for an MRIO model to calculate the Dutch carbon footprint. A sensitivity analysis of the technical coefficients showed that changes in the coefficients in the domestic blocks and in the Dutch import blocks had the largest effects on the calculated footprint. The uncertainty analysis consisting of a Monte Carlo simulation based on probability distributions around the model coefficients showed a relatively low degree of uncertainty in the total Dutch carbon footprint; uncertainties in the carbon emissions allocated to regions, sectors and products were larger. Both analyses showed that, in certain cases, it is justified to apply a partial MRIO analysis.

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  • Harry C. Wilting, 2012. "Sensitivity And Uncertainty Analysis In Mrio Modelling; Some Empirical Results With Regard To The Dutch Carbon Footprint," Economic Systems Research, Taylor & Francis Journals, vol. 24(2), pages 141-171, September.
    • Handle: RePEc:taf:ecsysr:v:24:y:2012:i:2:p:141-171
    DOI: 10.1080/09535314.2011.628302
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    1. Linden, Jan A. van der, 1995. "Fields of influence of technological change in EC intercountry input-output tables, 1970-80," Research Report 95D17, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    2. repec:dgr:rugsom:95d17 is not listed on IDEAS
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    Cited by:

    1. Daniel Moran & Richard Wood, 2014. "Convergence Between The Eora, Wiod, Exiobase, And Openeu'S Consumption-Based Carbon Accounts," Economic Systems Research, Taylor & Francis Journals, vol. 26(3), pages 245-261, September.
    2. Shaojian Qu & Hao Cai & Dandan Xu & Nabé Mohamed, 2021. "Uncertainty in the prediction and management of CO2 emissions: a robust minimum entropy approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(3), pages 2419-2438, July.
    3. de Koning, Arjan & Bruckner, Martin & Lutter, Stephan & Wood, Richard & Stadler, Konstantin & Tukker, Arnold, 2015. "Effect of aggregation and disaggregation on embodied material use of products in input–output analysis," Ecological Economics, Elsevier, vol. 116(C), pages 289-299.
    4. Zhen, Wei & Qin, Quande & Zhong, Zhangqi & Li, Li & Wei, Yi-Ming, 2018. "Uncovering household indirect energy-saving responsibility from a sectoral perspective: An empirical analysis of Guangdong, China," Energy Economics, Elsevier, vol. 72(C), pages 451-461.
    5. Umed Temurshoev, 2015. "Uncertainty treatment in input-output analysis," Working Papers 2015-004, Universidad Loyola Andalucía, Department of Economics.
    6. Jihoon Min & Narasimha D. Rao, 2018. "Estimating Uncertainty in Household Energy Footprints," Journal of Industrial Ecology, Yale University, vol. 22(6), pages 1307-1317, December.
    7. Hanspeter Wieland & Stefan Giljum & Nina Eisenmenger & Dominik Wiedenhofer & Martin Bruckner & Anke Schaffartzik & Anne Owen, 2020. "Supply versus use designs of environmental extensions in input–output analysis: Conceptual and empirical implications for the case of energy," Journal of Industrial Ecology, Yale University, vol. 24(3), pages 548-563, June.
    8. Mattila, Tuomas & Koskela, Sirkka & Seppälä, Jyri & Mäenpää, Ilmo, 2013. "Sensitivity analysis of environmentally extended input–output models as a tool for building scenarios of sustainable development," Ecological Economics, Elsevier, vol. 86(C), pages 148-155.
    9. Caroline Hambÿe & Bart Hertveldt & Bernhard Michel, 2018. "Does consistency with detailed national data matter for calculating carbon footprints with global multi-regional input–output tables? A comparative analysis for Belgium based on a structural decomposi," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 7(1), pages 1-22, December.
    10. Ling Li & Ling Tang & Junrong Zhang, 2019. "Coupling Structural Decomposition Analysis and Sensitivity Analysis to Investigate CO 2 Emission Intensity in China," Energies, MDPI, vol. 12(12), pages 1-23, June.
    11. Jonas Karstensen & Glen Peters & Robbie Andrew, 2015. "Allocation of global temperature change to consumers," Climatic Change, Springer, vol. 129(1), pages 43-55, March.
    12. Johanna Ruett & Lena Hennes & Jens Teubler & Boris Braun, 2022. "How Compatible Are Western European Dietary Patterns to Climate Targets? Accounting for Uncertainty of Life Cycle Assessments by Applying a Probabilistic Approach," Sustainability, MDPI, vol. 14(21), pages 1-21, November.
    13. Bruno Casella & Richard Bolwijn & Daniel Moran & Keiichiro Kanemoto, . "Improving the analysis of global value chains: the UNCTAD-Eora Database," UNCTAD Transnational Corporations Journal, United Nations Conference on Trade and Development.
    14. Caggiani, Leonardo & Ottomanelli, Michele & Dell’Orco, Mauro, 2014. "Handling uncertainty in Multi Regional Input-Output models by entropy maximization and fuzzy programming," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 71(C), pages 159-172.

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