Forecasting of industrial structure evolution and CO2 emissions in Liaoning Province. (20th February 2021)

• Record Type: Journal Article
• Authors: Pan, Xiongfeng; Xu, Haitao; Song, Malin; Lu, Yuduo; Zong, Tianjiao
• Is Part Of: Journal of cleaner production. Volume 285(2021)
• Publication Date: 2021-02-20
• Subjects: CO2 emissions forecasting -- MIOM model -- Industrial emissions -- Emissions peak -- Technology progress; 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.2020.124870
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: A framework for predicting environment should focus on the sources of economic growth, behavioral issues of market participants and the changing structure of the regional economy over time. This paper constructed a multi-agent intertemporal optimization model (MIOM) that include consumer preference, technology input and knowledge accumulation to forecast CO2 emission trends of 13 industrial sectors in Liaoning Province from 2018 to 2030. With the premise of maximizing consumer benefit, the model realizes intertemporal optimization in sector output and investment and obtains the optimal path of economic growth driven by capital. The results show that: (1) Liaoning Province's economy will maintain the abidance growth driven by investment. Before 2030, the economic growth rate of Liaoning Province is on the rise. (2) Departments with higher consumption preferences will account for a higher proportion of total economic output. And the bigger the gap of consumption preference is, the more obvious the change of industrial structure will be. (3) In the current level of R&D investment and economic growth, the energy consumption structure of most energy-intensive industries in Liaoning will continue to decline in the future except oil industry. (4) Under the influence of R&D investment and knowledge accumulation, the emission intensity of most industrial sectors will continue to decline during the simulation period, and the annual growth rate of CO2 emissions is gradually decreasing. Highlights: Constructing a multi-agent intertemporal optimization model to forecast industrial CO2 emissions of Liaoning province. The function of knowledge capital accumulation and energy efficiency is introduced into the model. Analyzing the energy structure change and emissions peak of different industries. The effect of consumption preference and technology on the industrial structure and emission trend is also found.