Decomposition and Analysis of Driving Forces of GHG Emissions and Emission Reduction Potentials in Iran

Climate change cannot control unless by reduction of GHG emissions to secure level, therefore it is important to identify driving forces and possible scenarios based on targets. In this research, the Logarithmic Mean Divisia Index decomposition approach in combination with Extended Kaya Identity (EKI) are applied to investigate five factors could affect emissions during 1971-2012 in Iran. These factors include population, GDPcapita, energy and carbon intensity and share of fossil fuels. Chaining, non-chaining methods and three emission reduction scenarios (4, 8 and 12%) based on Iran INDC forms the other parts of research. Based on results, carbon intensity, activity effect, and population contribute to increase and share of fossil fuels has a decreasing effect on emissions. The future targets of government should emphasis on change of fuel type and economic development toward low carbon. Chaining analysis could be used as a calendar, show the effect of events on parameters while non-chaining present a clear image by deleting noises. Among proposed reduction scenarios, the best and logical option emphasize on reduction of fossil fuel share (up to 40% compared to 2020) and development of renewable energies. Improvement of carbon and energy intensity (each as 50% compared to 2020) are the next options. JEL Classification: Q54, Q01 Keywords: Climate Change, LMDI, Population, Activity Effect, Carbon Intensity, Energy Intensity.