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Stochastic models for greenhouse gas emission rate estimation from hydroelectric reservoirs: a Bayesian hierarchical approach

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  • Vinicius P. Israel
  • H�lio S. Migon

Abstract

Herein, we propose a fully Bayesian approach to the greenhouse gas emission problem. The goal of this work is to estimate the emission rate of polluting gases from the area flooded by hydroelectric reservoirs. We present models for gas concentration evolution in two ways: first, by proposing them from ordinary differential equation solutions and, second, by using stochastic differential equations with a discretization scheme. Finally, we present techniques to estimate the emission rate for the entire reservoir. In order to carry out the inference, we use the Bayesian framework with Monte Carlo via Markov Chain methods. Discretization schemes over continuous differential equations are used when necessary. These models applied to greenhouse gas emission and Bayesian inference for this purpose are completely new in statistical literature, as far as we know, and contribute to estimate the amount of polluting gases released from hydroelectric reservoirs in Brazil. The proposed models are applied in a real data set and results are presented.

Suggested Citation

  • Vinicius P. Israel & H�lio S. Migon, 2012. "Stochastic models for greenhouse gas emission rate estimation from hydroelectric reservoirs: a Bayesian hierarchical approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(5), pages 1069-1086, October.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:5:p:1069-1086
    DOI: 10.1080/02664763.2011.636417
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    1. Ge, Zewen & Geng, Yong & Wei, Wendong & Jiang, Mingkun & Chen, Bin & Li, Jiashuo, 2023. "Embodied carbon emissions induced by the construction of hydropower infrastructure in China," Energy Policy, Elsevier, vol. 173(C).

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