Scenario Prediction of China’s Natural Gas Consumption and Carbon Emissions in the Next Ten Years

Abstract

Based on Johansen Cointegration Test, this paper sheds light on the long-run equilibrium relationship between natural gas consumption, gas production, and GDP in China. Three different natural gas demand scenarios of low, medium and high rates in the next ten years are considered, and a Neural Network Autoregression Model is used to predict the future carbon dioxide emission. We conclude: (1) In all three scenarios, the growth rates of natural gas consumption are all higher than those of natural gas production, while the gap between demand and domestic supply will gradually turn broader and China will largely rely on imports ; (2) In the scenario of low-rate economic growth, natural gas consumption will grow slowly, and it will be difficult to realize the carbon emission reduction targets by 2030 due to low-rate substitution of natural gas for coal; (3) If medium-rate to high-rate economic growth sustains, coupled with rapid increase in natural gas consumption and production, China’s Carbon Emission Reduction Targets for 2030 can be achieved with high-rate substitution of natural gas for coal.

Forecasting carbon dioxide emissions: application of a novel two-stage procedure based on machine learning models

“…Compared against methods using only ANN, ANFIS, or multiple linear regression, the multi-stage approach yields the lowest forecast error. Wang & Zhu (2021) use the Johansen cointegration test and the neural network autoregression model to forecast China's CO 2 emissions based on assumptions of three levels of GDP growth rate, which results in a different amount increase in natural gas consumption and production. Morshed-Bozorgdel et al (2022) find that a two-level ensemble of ML algorithms captures the high variation in wind speed.…”