Abstract
Carbon dioxide (CO2) emissions forecasting is becoming more important due to increasing climatic problems, which contributes to developing scientific climate policies and making reasonable energy plans. Considering that the influential factors of CO2 emissions are multiplex and the relationships between factors and CO2 emissions are complex and non-linear, a novel CO2 forecasting model called SSA-LSSVM, which utilizes the Salp Swarm Algorithm (SSA) to optimize the two parameters of the least squares support sector machine (LSSVM) model, is proposed in this paper. The influential factors of CO2 emissions, including the gross domestic product (GDP), population, energy consumption, economic structure, energy structure, urbanization rate, and energy intensity, are regarded as the input variables of the SSA-LSSVM model. The proposed model is verified to show a better forecasting performance compared with the selected models, including the single LSSVM model, the LSSVM model optimized by the particle swarm optimization algorithm (PSO-LSSVM), and the back propagation (BP) neural network model, on CO2 emissions in China from 2014 to 2016. The comparative analysis indicates the SSA-LSSVM model is greatly superior and has the potential to improve the accuracy and reliability of CO2 emissions forecasting. CO2 emissions in China from 2017 to 2020 are forecast combined with the 13th Five-Year Plan for social, economic and energy development. The comparison of CO2 emissions of China in 2020 shows that structural factors significantly affect CO2 emission forecasting results. The average annual growth of CO2 emissions slows down significantly due to a series of policies and actions taken by the Chinese government, which means China can keep the promise that greenhouse gas emissions will start to drop after 2030.
Highlights
The issue of climate change has become the focus of attention of all countries in the world, and CO2 emissions are considered as the main factor of global warming [1]
A novel CO2 emissions forecasting model is proposed based on the LSSVM model, which is widely used to solve complex and non-linear problems, and the Salp Swarm Algorithm (SSA) method, which has superiority in solving single-objective optimization problems with unknown search spaces compared with the general optimization algorithm, namely, the SSA-LSSVM model
gross domestic product (GDP), population, energy consumption, economic structure, energy structure, urbanization rate, and energy intensity are all found to affect CO2 emissions in some context based on a summary of previous research
Summary
The issue of climate change has become the focus of attention of all countries in the world, and CO2 emissions are considered as the main factor of global warming [1]. It was found that the energy storage of cryogenic carbon capture contributes to a saving of the cycling costs He constructed a hybrid system including load-following coal and gas-fired power units, a CCC process, and wind generation. Taking China’s social and economic development, industry structure and energy structure adjustment, energy conservation and environmental protection efforts into account, the main factors of CO2 emissions are GDP, population, energy consumption, economic structure, energy structure, urbanization rate, and energy intensity in this paper, which are regarded as the input variables for the constructed SSA-LSSVM model. Energy structure, urbanization rate and energy intensity are taken into consideration in the proposed model as the driving factors of CO2 emissions, which reflect the orientation of China’s recent policies that aim to keep the promise of CO2 emissions reduction by.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
