Improved Forecasting of CO2 Emissions Based on an ANN and Multiresolution Decomposition

  • Lida BarbaEmail author
  • Nibaldo Rodríguez
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 713)


The sustainability of the environment is a shared goal of the United Nations. In this context, the forecast of environmental variables such as carbon dioxide (CO2) plays an important role for the effective decision making. In this work, it is presented multi-step-ahead forecasting of the CO2 emissions by means of a hybrid model which combines multiresolution decomposition via stationary wavelet transform (SWT) and an artificial neural network (ANN) to improve the accuracy of a typical neural network. The effectiveness of the proposed hybrid model SWT-ANN is evaluated through the time series of CO2 per capita emissions of the Andean Community (CAN) countries from 1996 to 2013. The empirical results provide significant evidence about the effectiveness of the proposed hybrid model to explain these phenomena. Projections are presented for supporting the environmental management of countries with similar geographical features and cultural diversity.


Carbon dioxide Multiresolution decomposition Stationary wavelet transform Artificial neural network Forecasting 



Thanks to Animal Production and Industrialization (PROANIN) Research Group of the Universidad Nacional de Chimborazo for supporting this work through the project Artificial Neural Networks to predict the carcass tissue composition of guinea pigs.


  1. 1.
    World Bank Group repository. (2017)
  2. 2.
    Kimball, B.A., Pinter Jr., P.J., Garcia, R.L., LaMorte, R.L., Wall, G.W., Hunsaker, D.J., Wechsung, G., Wechsung, F., Kartschall, T.: Productivity and water use of wheat under free-air CO2 enrichment. Glob. Change Biol. 1(6), 429–442 (1995)CrossRefGoogle Scholar
  3. 3.
    Tao, F., Feng, Z., Tang, H., Chen, Y., Kobayashi, Z.: Effects of climate change, CO2 and O3 on wheat productivity in Eastern China, singly and in combination. Atmos. Environ. 153, 182–193 (2017)CrossRefGoogle Scholar
  4. 4.
    Pérez-Suárez, R., López-Menéndez, A.: Growing green? Forecasting CO2 emissions with environmental Kuznets Curves and logistic growth models. Environ. Sci. Policy 54, 428–437CrossRefGoogle Scholar
  5. 5.
    Pao, H-T., Tsai C-M.: Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in Brazil. Energy 36, 2450–2458CrossRefGoogle Scholar
  6. 6.
    Wu, L., Liu, S., Liu, D., Fang, Z., Xu, H.: Modelling and forecasting CO2 emissions in the BRICS (Brazil, Russia, India, China, and South Africa) countries using a novel multi-variable grey model. Energy 79, 489–495 (2015)CrossRefGoogle Scholar
  7. 7.
    Shensa, M.: The discrete wavelet transform: wedding the a Trous and Mallat algorithms. IEEE Trans. Signal Process. 40(10), 2464–2482 (1992)CrossRefGoogle Scholar
  8. 8.
    Grossmann, A., Morlet, J.: Decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM J. Math. Anal. 15(4), 723–736 (1984)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Nason, G., Silverman, B.: Wavelets and statistics, the stationary wavelet transform and some statistical applications. In: Wavelets and Statistics, pp. 281–299. Springer, New York (1995)CrossRefGoogle Scholar
  10. 10.
    Mallat, S.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)CrossRefGoogle Scholar
  11. 11.
    Hornik, K., Stinchcombe, X., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)CrossRefGoogle Scholar
  12. 12.
    Svozil, D., Kvasnicka, V., Pospichal, J.: Introduction to multi-layer feed-forward neural networks. Chemometr. Intell. Lab. Syst. 39(1), 43–62 (1997)CrossRefGoogle Scholar
  13. 13.
    Rojas, I., Pomares, H., Bernier, J.L., Ortega, J., Pino, B., Pelayo, F.J., Prieto, A.: Time series analysis using normalized PG-RBF network with regression weights. Neurocomputing 42(1–4), 267–285 (2002)CrossRefGoogle Scholar
  14. 14.
    Roh, S.B., Oh, S.K., Pedrycz, W.: Design of fuzzy radial basis function-based polynomial neural networks. Fuzzy Sets Syst. 185(1), 15–37 (2011)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Liu, F., Ng, G.S., Quek, C.: RLDDE: A novel reinforcement learning-based dimension and delay estimator for neural networks in time series prediction. Neurocomputing 70(7–9), 1331–1341 (2007)CrossRefGoogle Scholar
  16. 16.
    Scarselli, F., Chung, A.: Universal approximation using feedforward neural networks: a survey of some existing methods, and some new results. Neural Netw. 11(1), 15–37 (1998)CrossRefGoogle Scholar
  17. 17.
    Gheyas, I.A., Smith, L.S.: A novel neural network ensemble architecture for time series forecasting. Neurocomputing 74(18), 3855–3864 (2011)CrossRefGoogle Scholar
  18. 18.
    Levenberg, K.: A method for the solution of certain non-linear problems in least squares. Quart. J. Appl. Math. 2(2), 164–168 (1944)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Marquardt, D.: An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Math. 11(2), 431–441 (1963)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Hahn, B., Valentine, D.: Essential MATLAB for engineers and scientists, 6th edn, pp. 333–339. Academic Press, Elsevier (2013)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Facultad de IngenieríaUniversidad Nacional de ChimborazoRiobambaEcuador
  2. 2.Escuela de Ingeniería InformáticaPontificia Universidad Católica de ValparaísoValparaísoChile

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