Forecasting the carbon emissions in Hubei Province under the background of carbon neutrality: a novel STIRPAT extended model with ridge regression and scenario analysis

Abstract

The impact of global greenhouse gas emissions is increasingly serious, and the development of green low-carbon circular economy has become an inevitable trend for the development of all countries in the world. To achieve emission peak and carbon neutrality is the primary goal of energy conservation and emission reduction. As the core province in central China, Hubei Province is under prominent pressure of carbon emission reduction. In this paper, the future development trend of carbon emissions is analyzed, and the emission peak value and carbon peak time in Hubei Province is predicted. Firstly, the generalized Divisia index method (GDIM) model is proposed to determine the main influencing factors of carbon emissions in Hubei Province. Secondly, based on the main influencing factors identified, a novel STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) extended model with ridge regression is established to predict carbon emissions. Thirdly, the scenario analysis method is used to set the variables of the STIRPAT extended model and to predict the emission peak value and carbon peak time in Hubei Province. The results show that Hubei Province’s carbon emissions peaked first in 2025, with a peak value of 361.81 million tons. Finally, according to the prediction results, the corresponding suggestions on carbon emission reduction are provided in three aspects of industrial structure, energy structure, and urbanization, so as to help government establish a green, low-carbon, and circular development economic system and achieve the industry’s cleaner production and sustainable development of society.

Advances and future trends in research on carbon emissions reduction in China from the perspective of bibliometrics

“…The research contents and methods were relatively superficial and single, and further in-depth and specific discussions were required [ 56 ]. (2) Since 2009, research on China’s carbon emissions has developed rapidly, with many keywords emerging, such as the computable general equilibrium model [ 60 ], energy efficiency [ 61 ], shadow price [ 61 ], and the STIRPAT model [ 62 ]. Practical applications of various analytical models and tools enriched the research methodologies, showing increasing attention to empirical research.…”

Examining Carbon Emission Drivers in the Digital Economy Era: Empirical Insights from the Beijing-Tianjin-Hebei Urban Agglomeration, China

“…This method solved the multicollinearity problem by introducing an L2 regularization term. In addition, many scholars have applied this method to fit models for factors influencing carbon emissions [28][29][30]. Yang et al (2018) [31] and Huang et al (2023) [32] combined the STIRPAT model with principal component analysis and Lasso regression respectively to explore the influencing factors of carbon emissions.…”

Uncertainty Analysis and Optimization Modeling with Application to Supply Chain Management: A Systematic Review

“…The shortest route problem, minimum spanning tree problem, and other common network optimization issues come in a wide variety. Numerous researchers have been motivated to model network optimization issues mathematically by these appealing challenges [29,30]. When the sample size is too small or when there is no population for calculating a probability distribution, we must enlist the assistance of subject-matter experts in order to determine their level of confidence that each event will occur [31].…”