Forecasting CO2 emissions of China's cement industry using a hybrid Verhulst-GM(1,N) model and emissions' technical conversion

Jeffrey Ofosu-Adarkwa, Naiming Xie and Saad Ahmed Javed

Renewable and Sustainable Energy Reviews, 2020, vol. 130, issue C

Abstract: The cement industry is a significant contributor to anthropogenic CO2. For China, the cement industry is crucial for development, considering the surging urbanization. CO2 emissions from the industry are detrimental to the planet, its ecosystem, and inhabitants. Forecasting of the emissions is a critical step in the emissions' mitigation strategies, and to achieve sustainable development. However, the level of uncertainty accompanying CO2 estimates leads to discrepancies in predictions. The current work aims to study the estimation of cement industry CO2 emissions from an uncertainty-driven technical perspective, and present a forecast for the Chinese cement industry emissions using a novel grey prediction model. By modeling the framework of China's cement industry and the CO2 emissions estimation techniques as grey systems with partially known information, this study develops an interval grey number-based approach to calculate the relative uncertainty. A grey sequence is generated from the whitenization of the interval grey numbers to represent annual emissions from different sources. The proposed approach is more flexible than the conventional midpoint estimate-based approach recommended by JCGM. The proposed model, V-GM(1,N), is found to give the highest accuracy of 97.29% in simulating the actual cement industry CO2 emissions data from 2005 to 2018. Comparative analysis of the proposed model with other forecasting models revealed the superiority of the model. The proposed framework, involving the forecasting model and uncertainty analysis approach, is likely to facilitate the decision-makers in making realistic and reliable forecasts at reasonable computational costs.

Keywords: CO2 emission; Cement industry; Emissions' technical conversion; China; Grey forecasting model; Uncertainty (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (18)

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DOI: 10.1016/j.rser.2020.109945

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