Uncertainty in the prediction and management of CO 2 emissions: a robust minimum entropy approach
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Uncertainty in the prediction and management of CO2 emissions: a robust minimum entropy approach Shaojian Qu1 · Hao Cai1 · Dandan Xu1 · Nabé Mohamed1 Received: 16 July 2020 / Accepted: 17 November 2020 © Springer Nature B.V. 2020
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 C O2 emission. Single Regional Input–output (SRIO) model is widely used for investigating C O2 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. Keywords Environment management · CO2 emission · Single regional Input–output model · Entropy minimization · Robust optimization
1 Introduction In the 4 years since the Paris Agreement in 2016, global enthusiasm for peaking and reducing carbon emissions to combat climate change has reached unprecedented heights. According to the data of China Emissions Accounting Databases (CEADs) (Shan et al. 2018), China’s carbon emissions have reached 9265.1 Mt, accounting for 28.17% of the total global carbon emissions, surpassing the United States to become the world’s largest * Hao Cai [email protected] 1
University of Shanghai for Science and Technology, Shanghai, China
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emitter. China has assumed an important responsibility in the Paris Agreement. Hence, calculating and predicting China’s CO2 emission to better set China’s carbon reduction targets has become a focus for many scholars. Originally proposed for the study of linkages between various sectors of the national economy, Single Regional Input–output (SRIO) model can be used to introduce a clear representation of a complex commodity flow system, including different logistics nodes and cargo transportation volumes, and also reflects the supply and demand of goods in different p
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