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The Textile Industry and Sustainable Development: A Holt–Winters Forecasting Investigation for the Eastern European Area

Author

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  • Dorel Paraschiv

    (International Business and Economics Department, Bucharest University of Economic Studies, Bucharest 010374, Romania)

  • Cristiana Tudor

    (International Business and Economics Department, Bucharest University of Economic Studies, Bucharest 010374, Romania)

  • Radu Petrariu

    (International Business and Economics Department, Bucharest University of Economic Studies, Bucharest 010374, Romania)

Abstract

To achieve sustainable development, massive changes towards fostering a clean and pollution-reducing industrial sector are quintessential. The textile industry has been one of the main contributors to water pollution all over the world, causing more than 20% of the registered levels of water pollution in countries like Turkey, Indonesia and China (among the G20 group of countries) and also in Romania and Bulgaria (in the Eastern European area), with even more than 44% in Macedonia. Given the controversy created by the textile industry’s contribution to pollution at a global level and also the need to diminish pollution in order to promote sustainable development, this paper comparatively investigates the contribution of the textile industry to the water pollution across Central and Eastern European countries, as well as developed countries. In addition, we employ the Holt–Winters model to forecast the trend of the total emissions of organic water pollutants, as well as of the textile industry’s contribution to pollution for the top polluters in Eastern Europe, i.e ., Poland and Romania. According to our estimates, both countries are headed towards complete elimination of pollution caused by the textile industry and, hence, toward a more sustainable industrial sector, as Greenpeace intended with the release of its 2011 reports.

Suggested Citation

  • Dorel Paraschiv & Cristiana Tudor & Radu Petrariu, 2015. "The Textile Industry and Sustainable Development: A Holt–Winters Forecasting Investigation for the Eastern European Area," Sustainability, MDPI, vol. 7(2), pages 1-12, January.
    • Handle: RePEc:gam:jsusta:v:7:y:2015:i:2:p:1280-1291:d:45131
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    References listed on IDEAS

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    1. Singh, Kunwar P. & Basant, Ankita & Malik, Amrita & Jain, Gunja, 2009. "Artificial neural network modeling of the river water quality—A case study," Ecological Modelling, Elsevier, vol. 220(6), pages 888-895.
    2. Sujit K. Sahu & Kanti V. Mardia, 2005. "A Bayesian kriged Kalman model for short‐term forecasting of air pollution levels," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(1), pages 223-244, January.
    3. Sarah Gelper & Roland Fried & Christophe Croux, 2010. "Robust forecasting with exponential and Holt-Winters smoothing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(3), pages 285-300.
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    Cited by:

    1. Ye Duan & Hailin Mu & Nan Li, 2016. "Analysis of the Relationship between China’s IPPU CO 2 Emissions and the Industrial Economic Growth," Sustainability, MDPI, vol. 8(5), pages 1-19, April.
    2. Cristiana Tudor, 2016. "Predicting the Evolution of CO 2 Emissions in Bahrain with Automated Forecasting Methods," Sustainability, MDPI, vol. 8(9), pages 1-10, September.
    3. Baogui Xin & Zhiheng Wu, 2015. "Neimark–Sacker Bifurcation Analysis and 0–1 Chaos Test of an Interactions Model between Industrial Production and Environmental Quality in a Closed Area," Sustainability, MDPI, vol. 7(8), pages 1-19, July.
    4. Salimeh Malekpour Heydari & Teh Noranis Mohd Aris & Razali Yaakob & Hazlina Hamdan, 2021. "Data-Driven Forecasting and Modeling of Runoff Flow to Reduce Flood Risk Using a Novel Hybrid Wavelet-Neural Network Based on Feature Extraction," Sustainability, MDPI, vol. 13(20), pages 1-16, October.

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