Estimating Chinese energy-related CO2 emissions by employing a novel discrete grey prediction model

By Song Ding, Ning Xu, Jing Ye, Weijie Zhou, Xiaoxiong Zhang. 2020

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Abstract Under the background of green and low-carbon economy, comprehensively analyzing the influential factors and apply them to accurately estimate energy-related CO2 emissions make great sense for Chinese green-economy development. In this paper, the grey incidence analysis is employed to identify the influential factors that may generate strong and nonlinear effects on emissions. To consider these nonlinear effects, a new discrete grey power model is designed, through introducing grey power indexes into the model structure. Additionally, one-step ahead rolling mechanism is associated with the proposed technique so as to further improve its forecasting precision. To demonstrate its efficacy, the rolling discrete grey power model is utilized to forecast Chinese energy-related carbon emissions from 2011 to 2015, and then compared to projections provided by diversified competing techniques - the empirical applications show that the newly proposed rolling model clearly outperform other benchmark models in accordance with three measuring indices. Therefore, by using this optimum model, Chinese carbon emissions from 2016 to 2020 are quantified, which infers that it will rise to over 9936 million tons in 2020, and is consistent with those provided by other international agencies and scholars. Eventually, based on the forecasts, suggestions on reducing the CO2 emissions are provided for decision-makers.

Subfields: Economics and Econometrics, Environmental Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/9822810750e65a5cb1da74bb714973b269b71554