Scenario Analysis of Carbon Emissions of China’s Power Industry Based on the Improved Particle Swarm Optimization-Support Vector Machine Model

Abstract

Nowadays, one of the most important issues regarding environmental challenges is global warming, which stems from trapping greenhouse gases (GHS) in the atmosphere, causing climate changes at any point of the world [1]. Energy-related carbon emissions, as the primary source of GHS, undertake the main responsibility for climate change and environmental degradation. However, slightly more than 40% of the global energy-related carbon emissions are attributable to emissions from electricity and heat production [2]. Thus, the carbon emissions from power industry are of major environmental concern. As a supplier of national energy and power, the power industry is the pillar industry and the lifeblood of national economic development for China. Since implementation of the reform and opening up, the power industry has experienced more than 20 years of development and has

China’s carbon dioxide emission forecast based on improved marine predator algorithm and multi-kernel support vector regression

“…In order to make up for the shortcomings of a single model, many scholars used hybrid models to predict carbon dioxide emissions. Zhou et al used support vector machines and improved particle swarm optimization (IPSO) to forecast carbon dioxide emissions in the Chinese power sector (Zhou et al 2018). Qiao et al proposed a hybrid model based on the improved lion swarm optimizer and LSSVM for predicting carbon dioxide emissions.…”