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Forecasting greenhouse gas emissions of the Slovak Republic based on grey models

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Reducing greenhouse gas (GHG) emissions has become a necessity and not an option to sustain the environment in both human and natural systems. The Slovak Republic (SR), like the European Union (EU), aims to become greenhouse gas neutral by 2050. To reach this ambitious target, emissions will need to fall by 55% compared to those in the base year 1990. Therefore, forecasting GHG emission amounts is important. The grey model is one of the widespread mathematical forecasting methods. There exist studies that have used some types of grey models to predict GHG but not in the case of the Slovak Republic. We have optimized the length of the input sequence in the rolling mechanism to enhance the forecast accuracy of a new grey model combining the Bernoulli equation with the rolling mechanism. Standard grey model, nonlinear grey Bernoulli model, and grey model with rolling mechanism were used to prove the validity of our optimization and to compare prediction performance among grey models. The novel model was also used for a long-term forecast of GHG emissions in the SR for the years from 2020 to 2040 and compared with officially reported projections. Calculated values showed that the SR is on a good way to reach set targets towards climate change mitigation.
Rocznik
Strony
117--134
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
  • University of Žilina, Faculty of Operation and Economics of Transport and Communications, Department of Quantitative Methods and Economic Informatics, Univerzitná 1, 01026 Žilina, Slovakia
  • University of Žilina, Faculty of Operation and Economics of Transport and Communications, Department of Quantitative Methods and Economic Informatics, Univerzitná 1, 01026 Žilina, Slovakia
  • University of Žilina, Faculty of Operation and Economics of Transport and Communications, Department of Quantitative Methods and Economic Informatics, Univerzitná 1, 01026 Žilina, Slovakia
Bibliografia
  • [1] OECD, Working Party on Environmental Performance, Environmental Performance Reviews, Midterm progress report: Slovak Republic, available online: https://www.minzp.sk/files/omv/mid-termreview-environmental-performance-slovakia-2017.pdf (accessed on 22 June 2020).
  • [2] GHG Emissions by EU Country [Interactive Map], available online: https://www.greenmatch.co.uk/blog/2019/10/greenhouse-gas-emissions-by-country (accessed on 9 June 2021).
  • [3] GONZÁLEZ-SÁNCHEZ M., MARTÍN-ORTEGA J.L., Greenhouse gas emissions growth in Europe. A comparative analysis of determinants, Sust., 2020, 12, 1012. DOI: 10.3390/su12031012.
  • [4] VOJTEKOVA M., BLAZEKOVA O., Greenhouse gas emissions as a global problem of nowadays, Proc. International Scientific Conference on Globalization and its Socio-Economic Consequences, Zilina, Slovakia, 4–5 October 2017, 2888–2894.
  • [5] BLAZEKOVA O., VOJTEKOVA M., About one model of greenhouse gas emissions forecasting, Proc. International Scientific Conference on Knowledge for Market Use 2017: People in Economics – Decisions, Behavior and Normative Models, Olomouc, Czech Republic, 7–8 September 2017, 68–74.
  • [6] ABDULLAH L., PAUZI H.M., Methods in forecasting carbon dioxide emissions. A decade review, J. Tekn., 2015, 75 (1), 67–82. DOI: 10.11113/jt.v75.2603.
  • [7] RADOJEVIĆ D., POCAJT V., POPOVIĆ I., PERIĆ-GRUJIĆ A., RISTIĆ M., Forecasting of greenhouse gas emissions in Serbia using artificial neural networks, En. Sources A: Rec. Util. Environ. Eff., 2013, 35 (8), 733–740. DOI: 10.1080/15567036.2010.514597.
  • [8] RODRIGUES J.A.P., NETO L.B., COELHO P.H.G., Estimating greenhouse gas emissions using computational intelligence, Proc. International Conference on Enterprise Information Systems, Milan, Italy, 6–10 May 2009, 248–250.
  • [9] PAO H.-T., FU H.-C., TSENG C.-L., Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model, Energy, 2012, 40 (1), 400–409. DOI: 10.1016/j.energy.2012.01.037.
  • [10] LIN C.-S., LIOU F.-M., HUANG C.-P., Grey forecasting model for CO2 emissions. A Taiwan study, Appl. En., 2011, 88 (11), 3816–3820. DOI: 10.1016/j.apenergy.2011.05.013.
  • [11] LU I., LEWIS C., LIN S.J., The forecast of motor vehicle, energy demand and CO2 emission from Taiwan’s road transportation sector, En. Pol., 2009, 37 (8), 2952–2961. DOI: 10.1016/j.enpol.2009.03.039.
  • [12] HU Y.-C., JIANG H., JIANG P., KONG P., An improved grey multivariable verhulst model for predicting CO2 emissions in China, [In:] F.F.-H. Nah, K. Siau (Eds.), HCI in Business, Government and Organizations, Information Systems and Analytics Lecture Notes in Computer Science, Springer, 2019, 354–366.
  • [13] DING S., XU N., YE J., ZHOU W., ZHANG X., Estimating Chinese energy-related CO2 emissions by employing a novel discrete grey prediction model, J. Clean. Prod., 2020, 259, 120793. DOI: 10.1016/j.jclepro.2020.120793.
  • [14] DING S., DANG Y.-G., LI X.-M., WANG J.-J., ZHAO K., Forecasting Chinese CO2 emissions from fuel combustion using a novel grey multivariable model, J. Clean Prod., 2017, 162, 1527–1538. DOI: 10.1016/j.jclepro.2017.06.167.
  • [15] ŞAHIN U., Forecasting of Turkey’s greenhouse gas emissions using linear and nonlinear rolling metabolic grey model based on optimization, J. Clean. Prod., 2019, 239, 118079. DOI: 10.1016/j.jclepro.2019.118079.
  • [16] XU N., DING S., GONG Y., BAI J., Forecasting Chinese greenhouse gas emissions from energy consumption using a novel grey rolling model, Energy, 2019, 175, 218–227. DOI: 10.1016/j.energy.2019.03.056.
  • [17] XIE M., YAN S., WU L., LIU L., BAI Y., LIU L., TONG Y., A novel robust reweighted multivariate grey model for forecasting the greenhouse gas emissions, J. Clean. Prod., 2021, 292, 126001. DOI: 10.1016/j.jclepro.2021.126001.
  • [18] Fourth Biennial Report of the Slovak Republic (4BR SVK), available online: https://ghg-inventory.shmu.sk/documents.php (accessed on 12 June 2020).
  • [19] DENG J.L., Introduction of grey system theory, J. Grey Syst., 1989, 1, 1–24.
  • [20] LU J., XIE W., ZHOU H., ZHANG A., An optimized nonlinear grey Bernoulli model and its applications, Neurocomp., 2016, 177, 206–214. DOI: 10.1016/j.neucom.2015.11.032.
  • [21] LIU L., WANG Q., LIU M., LI L., An Intelligence Optimized Rolling Grey Forecasting Model Fitting to Small Economic Dataset, Abstr. Appl. Anal., 2014, 1–10. DOI: h10.1155/2014/641514.
  • [22] Slovak Republic – Annual Report 2020, available online: https://ghg-inventory.shmu.sk/documents.php (accessed on 12 June 2020).
  • [23] LEWIS C.D., Industrial and business forecasting methods: a practical guide to exponential smoothing and curve fitting, Butterworth Scientific, London 1982.
  • [24] Member States’ greenhouse gas (GHG) emission projections, available online: https://www.eea.europa.eu/data-and-maps/data/greenhouse-gas-emission-projections-for-6 (accessed on 2 June 2020).
  • [25] JONAS M., BUN R., NAHORSKI Z., MARLAND G., GUSTI M., DANYLO O., Quantifying greenhouse gas emissions, Mitig. Adapt. Strateg. Glob. Chang., 2019, 24, 839–852. DOI: 10.1007/s11027-019-09867-4.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b1050ebf-b083-4d4c-a223-778380c49ecd
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