Short-run forecast and reduction mechanism of CO2 emissions: a Chinese province-level study

Abstract

Rational prediction of future CO₂ at the regional level is essential to the carbon emission reduction targets in China. The primary aim of this study is to examine the applicability of an up-to-date forecast algorithm, namely dynamic mode decomposition (DMD), in provincial CO₂ emission prediction. The testing results validate the accuracy and application value of the DMD short-run forecast, which may provide method reference for relevant policy formulation and research areas. Moreover, the 2020 provincial economic situation and CO₂ emissions in China are projected via DMD. On this basis, the unqualified provinces regarding CO₂ emission reduction are identified considering the relative standard and absolute standard, and the corresponding mitigation paths are proposed through decoupling analysis and shadow price calculation. The results indicate that the unqualified provinces include Heilongjiang, Gansu, Shanxi, Hebei, Liaoning, Jilin, Shaanxi, and Inner Mongolia. The open-emission-reduction mechanism should be adopted in the first five provinces; the conservative one should be applied in the other provinces. Graphical abstract.

Keywords: Carbon intensity; Decoupling analysis; Dynamic mode decomposition (DMD); Emission-reduction mechanism; Shadow price; Short-run forecast.