Forecasting based on sectoral energy consumption of GHGs in Turkey and mitigation policies

SÖZEN A., Gulseven Z., ARCAKLIOĞLU E.

ENERGY POLICY, cilt.35, sa.12, ss.6491-6505, 2007 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 35 Sayı: 12
  • Basım Tarihi: 2007
  • Doi Numarası: 10.1016/j.enpol.2007.08.024
  • Dergi Adı: ENERGY POLICY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
  • Sayfa Sayıları: ss.6491-6505
  • Anahtar Kelimeler: greenhouse gas emissions, sectoral energy consumption, mitigation, CO2 EMISSIONS, INDICATORS, DEMAND
  • Gazi Üniversitesi Adresli: Evet

Özet

Recently, global warming and its effects have become one of the most important themes in the world. Under the Kyoto Protocol, the EU has agreed to an 8% reduction in its greenhouse gas (GHG) emissions by 2008-2012. The GHG emissions (total GHG, CO2, CO, SO2, NO2, E (emissions of non-methane volatile organic compounds)) covered by the Protocol are weighted by their global warming potentials (GWPs) and aggregated to give total emissions in CO2 equivalents. The main subject in this study is to obtain equations by the artificial neural network (ANN) approach to predict the GHGs of Turkey using sectoral energy consumption. The equations obtained are used to determine the future level of the GHG and to take measures to control the share of sectors in total emission. According to ANN results, the maximum mean absolute percentage error (MAPE) was found as 0.147151, 0.066716, 0.181901, 0.105146, 0.124684, and 0.158157 for GHG, SO2, NO2, CO, E, and CO2, respectively, for the training data with Levenberg-Marquardt (LM) algorithm by 8 neurons. R-2 values are obtained very close to 1. Also, this study proposes mitigation policies for GHGs. (C) 2007 Elsevier Ltd. All rights reserved.