Forecasting of future greenhouse gas emission trajectory for India using energy and economic indexes with various metaheuristic algorithms. (1st August 2022)

• Record Type: Journal Article
• Authors: Bakır, Hüseyin; Ağbulut, Ümit; Gürel, Ali Etem; Yıldız, Gökhan; Güvenç, Uğur; Soudagar, Manzoore Elahi M.; Hoang, Anh Tuan; Deepanraj, Balakrishnan; Saini, Gaurav; Afzal, Asif
• Is Part Of: Journal of cleaner production. Volume 360(2022)
• Publication Date: 2022-08-01
• Subjects: Indian GHG emissions -- Energy -- Environment -- Carbon footprint -- Metaheuristic algorithms; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2022.131946
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: The accelerating increment of greenhouse gas (GHG) concentration in the atmosphere already reached an alarming level, and nowadays its adverse impacts on the living organisms, environment, and ecological balance of nature have been well-understood. India is one of the top countries that contribute the most to global GHG emissions. Therefore, it is of great significance to forecast the future GHG trends of the country in advance and accordingly take measures against the parameters that cause these emissions, considerably. In this direction, the present research has centered on forecasting the greenhouse gas trajectory of India with various metaheuristic algorithms. In this framework, marine predators algorithm (MPA), lightning search algorithm (LSA), equilibrium optimizer (EO), symbiotic organisms search (SOS), and backtracking search algorithm (BSA) are used for modeling the future GHG emission trajectory of India. Accordingly, the significant economic and energy indicators of India such as renewable energy generation, electricity generation from coal, electricity generation from gas, electricity generation from oil, gross domestic product, and population between 1990 and 2018 are collected to make a nexus with GHG emissions. As GHG emissions, CO2, CH4, F-gases, N2 O, as well as total GHG emissions are separately forecasted by the year 2050. To make a better comparison, each GHG emission data in the last year five years is used for the testing phase of the algorithms, and then statistically discussed in terms of R 2, MBE, rRMSE, and MAPE benchmarks. In the results, it is found that the R 2 value changes between 0.8822 and 0.9923 for CO2, 0.2855–0.9945 for CH4, 0.9–0.9904 for F-gases, 0.4655–0.9964 for N2 O, and 0.9016–0.9943 for total GHG emission, and the results in terms of rRMSE are very satisfying for all algorithms. In the study, it is forecasted that the two greenhouse gas emissions with the highest increase rate in 2050 will be between 2.5 and 2.87 times for CO2 emissions and between 2.8 and 3.5 times for F-gases, compared to today's data. According to the results of the present paper, the total GHG emission for India is forecasted to be 2.1–2.4 times higher in the year 2050 as compared to today. Given all forecasting results together, it is seen that the MPA algorithm generally gives the best results according to the statistical metric results, while the LSA algorithm generally gives the worst results. Consequently, the present paper strongly reports that the decision-makers and policy-makers should take some serious steps in advance for the mitigation of GHG emissions, and to revise their energy investments in the upcoming years.