Forecasting of industrial structure evolution and CO2 emissions in Liaoning Province

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. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This study constructed a MIOM model to forecast CO2 emission trends of 13 industrial sectors in Liaoning Province, showing that the province's economy will maintain stable growth, sectors with higher consumption preferences will have a larger share of the total economic output, energy consumption structure will continue to improve, and emission intensity of industrial sectors will decrease over time.