Application of a Decision-Making Model to Reduce CO2 Emissions in Iran (Case Study: CHP-CCS technology and renewable energy)

Iran is one of the largest producers of CO2 in the world. Therefore, in order to lessen its greenhouse gas production, thus complying with the Intended Nationally Determined Contributions (INDCs), it should cut its CO2 emissions by about 4% by 2030, compared to 2010. Hence this paper aims at finding an early solution to this problem. Because the country's electricity sector is responsible for the highest annual CO2 emissions, the paper focuses on two technologies that can effectively reduce CO2 emissions from the electricity sector, namely renewable energy and Combined Heat And Power Plants (CHP) with CO2 capture and storage (CCS). Further it assesses adoption of these technologies and their impact on Iran's annual CO2 emission by 2030, considering two main scenarios: the optimistic scenario (OS) which assumes that the policies of the Sixth Development Plan (SDP) will be fully realized as well as the fair scenario (FS) which believes that SDP policies will be followed to some extent by the end of the program. To this end, twenty six micro-factors, affecting CO2 emissions, have been identified and classified into five different groups. The detected micro factors are then introduced to a Gradient Boosting Decision Tree (GBDT) Algorithm to identify the most important specific microscopic factors in Iran. The final detected micro-factors have finally been included in a Gaussian regression model to predict CO2 emissions in Iran by 2030. The findings suggest that if Iran intends to comply with the INDCs, CHP-CCS technology is a solution that has an early return, compared to renewable technologies.