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An Improved Grey Model and Scenario Analysis for Carbon Intensity Forecasting in the Pearl River Delta Region of China

Author

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  • Fei Ye

    (School of Business Administration, South China University of Technology, 510640 Guangzhou, China)

  • Xinxiu Xie

    (School of Business Administration, South China University of Technology, 510640 Guangzhou, China)

  • Li Zhang

    (School of Business Administration, South China University of Technology, 510640 Guangzhou, China)

  • Xiaoling Hu

    (Guangdong Food and Drug Vocational College, Guangzhou, China)

Abstract

In this paper, an improved grey model and scenario analysis, GA-GM(1,N) is proposed to forecast the carbon intensity in the Pearl River Delta (PRD) region, one of the most developed regions in China. Moreover, to show the advantage and feasibility of the proposed model, the forecasting results of the GA-GM(1,N) model are compared with that of a single-variable grey model (GM (1,1)) and a multivariable form (GM(1,N)). Data from one sample period (2005–2012) are used to develop the models, and data from another sample period (2013–2015) are used to test them. The mean absolute percentage error (MAPE) is applied to measure the accuracy of prediction. The results show that, of the three models, GA-GM(1,N) produces the best carbon intensity forecasts, with MAPEs of 0.4–1.4% and 0.04–0.4% in the development and testing periods respectively. This indicates that the optimization of the genetic algorithm is effective. The realization of carbon reduction targets in different cities is also explored by combining grey models with scenario analysis. Only Guangzhou could achieve its reduction target under all scenarios, and it can serve as a reference for other cities. Policy recommendations are provided based on these results.

Suggested Citation

  • Fei Ye & Xinxiu Xie & Li Zhang & Xiaoling Hu, 2018. "An Improved Grey Model and Scenario Analysis for Carbon Intensity Forecasting in the Pearl River Delta Region of China," Energies, MDPI, vol. 11(1), pages 1-16, January.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:1:p:91-:d:125004
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    as
    1. Wang, Zhaohua & Zhang, Bin & Liu, Tongfan, 2016. "Empirical analysis on the factors influencing national and regional carbon intensity in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 34-42.
    2. Pao, Hsiao-Tien & Fu, Hsin-Chia & Tseng, Cheng-Lung, 2012. "Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model," Energy, Elsevier, vol. 40(1), pages 400-409.
    3. Bhattacharyya, Subhes C. & Ussanarassamee, Arjaree, 2004. "Decomposition of energy and CO2 intensities of Thai industry between 1981 and 2000," Energy Economics, Elsevier, vol. 26(5), pages 765-781, September.
    4. Zhou, P. & Ang, B.W. & Poh, K.L., 2006. "A trigonometric grey prediction approach to forecasting electricity demand," Energy, Elsevier, vol. 31(14), pages 2839-2847.
    5. Zhu, Bangzhu & Wang, Kefan & Chevallier, Julien & Wang, Ping & Wei, Yi-Ming, 2015. "Can China achieve its carbon intensity target by 2020 while sustaining economic growth?," Ecological Economics, Elsevier, vol. 119(C), pages 209-216.
    6. Wang, John & Yan, Ruiliang & Hollister, Kimberly & Zhu, Dan, 2008. "A historic review of management science research in China," Omega, Elsevier, vol. 36(6), pages 919-932, December.
    7. Akay, Diyar & Atak, Mehmet, 2007. "Grey prediction with rolling mechanism for electricity demand forecasting of Turkey," Energy, Elsevier, vol. 32(9), pages 1670-1675.
    8. Liu, Xiuli & Moreno, Blanca & García, Ana Salomé, 2016. "A grey neural network and input-output combined forecasting model. Primary energy consumption forecasts in Spanish economic sectors," Energy, Elsevier, vol. 115(P1), pages 1042-1054.
    9. Wang, Run & Liu, Wenjuan & Xiao, Lishan & Liu, Jian & Kao, William, 2011. "Path towards achieving of China's 2020 carbon emission reduction target--A discussion of low-carbon energy policies at province level," Energy Policy, Elsevier, vol. 39(5), pages 2740-2747, May.
    10. Yu, Shiwei & Zhang, Junjie & Zheng, Shuhong & Sun, Han, 2015. "Provincial carbon intensity abatement potential estimation in China: A PSO–GA-optimized multi-factor environmental learning curve method," Energy Policy, Elsevier, vol. 77(C), pages 46-55.
    11. Jalil, Abdul & Mahmud, Syed F., 2009. "Environment Kuznets curve for CO2 emissions: A cointegration analysis for China," Energy Policy, Elsevier, vol. 37(12), pages 5167-5172, December.
    12. Yi, Wen-Jing & Zou, Le-Le & Guo, Jie & Wang, Kai & Wei, Yi-Ming, 2011. "How can China reach its CO2 intensity reduction targets by 2020? A regional allocation based on equity and development," Energy Policy, Elsevier, vol. 39(5), pages 2407-2415, May.
    13. Zhang, Wei & Li, Ke & Zhou, Dequn & Zhang, Wenrui & Gao, Hui, 2016. "Decomposition of intensity of energy-related CO2 emission in Chinese provinces using the LMDI method," Energy Policy, Elsevier, vol. 92(C), pages 369-381.
    14. Zhang, Kaiqi & Du, Haifeng & Feldman, Marcus W., 2017. "Maximizing influence in a social network: Improved results using a genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 478(C), pages 20-30.
    15. Kadier, Abudukeremu & Abdeshahian, Peyman & Simayi, Yibadatihan & Ismail, Manal & Hamid, Aidil Abdul & Kalil, Mohd Sahaid, 2015. "Grey relational analysis for comparative assessment of different cathode materials in microbial electrolysis cells," Energy, Elsevier, vol. 90(P2), pages 1556-1562.
    16. Li, Jianglong & Lin, Boqiang, 2016. "Inter-factor/inter-fuel substitution, carbon intensity, and energy-related CO2 reduction: Empirical evidence from China," Energy Economics, Elsevier, vol. 56(C), pages 483-494.
    17. Qunli Wu & Chenyang Peng, 2016. "Scenario Analysis of Carbon Emissions of China’s Electric Power Industry Up to 2030," Energies, MDPI, vol. 9(12), pages 1-18, November.
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