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Uncertainty in the prediction and management of CO2 emissions: a robust minimum entropy approach

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A Correction to this article was published on 11 January 2021

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Abstract

CO2 emission control is one of the most vital parts of environment management. China owns the largest CO2 emission in the world. For the sake of clarifying China’s emission sharing responsibilities and set emission reduction targets, a considerable number of scholars have worked to project China’s embodied CO2 emission. Single Regional Input–output (SRIO) model is widely used for investigating CO2 emission issues. Considering the ubiquitous time lag of input–output data, entropy optimization model is introduced to estimate SRIO tables. However, the uncertainty in the corresponding model parameters necessarily has a serious impact on the estimation results. To consider the impact of uncertainties, we introduce robust optimization into entropy minimization model for SRIO table estimation. Based on three different uncertainty sets, we constructed three robust entropy minimization models to construct 2016 China’s SRIO tables and calculate China’s embodied CO2 emission based on those tables. The estimation results show that the model based on the ball uncertainty set has the best performance with less uncertainty, while the model based on the budgeted uncertainty set performances more ‘robust’ facing greater uncertainty, which means its performance is less volatile at different levels of uncertainty. Moreover, the embodied carbon emission is predicted to reach 9632.57 Mt CO2. The top emitter is the sector of supply of electricity, heating and water, which accounts for more than 40% of total CO2 emission.

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Authors and Affiliations

  1. University of Shanghai for Science and Technology, Shanghai, China

    Shaojian Qu, Hao Cai, Dandan Xu & Nabé Mohamed

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  1. Shaojian Qu
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  2. Hao Cai
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  3. Dandan Xu
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  4. Nabé Mohamed
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Correspondence to Hao Cai.

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Qu, S., Cai, H., Xu, D. et al. Uncertainty in the prediction and management of CO2 emissions: a robust minimum entropy approach. Nat Hazards 107, 2419–2438 (2021). https://doi.org/10.1007/s11069-020-04434-6

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  • DOI: https://doi.org/10.1007/s11069-020-04434-6

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