FORECASTING OF CO2 WITH THE EFFECT OF RENEWABLE ENERGY, NON-RENEWABLE ENERGY, GDP AND POPULATION FOR TURKEY: FORECASTING WITH NMGM (1, N) GRAY FORECASTING MODEL Cover Image

FORECASTING OF CO2 WITH THE EFFECT OF RENEWABLE ENERGY, NON-RENEWABLE ENERGY, GDP AND POPULATION FOR TURKEY: FORECASTING WITH NMGM (1, N) GRAY FORECASTING MODEL
FORECASTING OF CO2 WITH THE EFFECT OF RENEWABLE ENERGY, NON-RENEWABLE ENERGY, GDP AND POPULATION FOR TURKEY: FORECASTING WITH NMGM (1, N) GRAY FORECASTING MODEL

Author(s): Özlem Karadağ Albayrak
Subject(s): Energy and Environmental Studies, Methodology and research technology, Policy, planning, forecast and speculation
Published by: Kafkas Üniversitesi Sağlık, Kültür ve Spor Daire Başkanlığı Dijital Baskı Merkezi
Keywords: Multivariate grey prediction model; NMGM (1, n); econometric models; co2 emissions; renewable energy consumption; non-renewable energy consumption; GDP; population;

Summary/Abstract: Carbon dioxide emission is one of the important factors that have a negative impact on the environment. One of the reasons why policy makers produce incentive policies on renewable energy is that they want to reduce CO2 emissions. From this point of view, prediction of CO2 emissions must be made depending on different factors, and new policies can be developed and implemented according to the prediction results. In this article, a new approach from gray estimation models, NMGM (1, N) forecasting model, is used to measure the impact of renewable energy consumption, nonrenewable energy consumption, GDP and Population factors on CO2 emission over time. 2006-2015 data was simulation set and 2016- 2019 data was used as a test set. In addition to this method, estimation was made with GM (1, N) and econometric model, which is the multivariate gray estimation method, and the results were compared. As a result, NMGM (1, N) model has become a very effective estimation method with very low deviation values.

  • Issue Year: 12/2021
  • Issue No: 24
  • Page Range: 810-828
  • Page Count: 19
  • Language: English
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