Analysis of CO₂ Emission Drives Based on Energy Consumption and Prediction of Low Carbon Scenarios: a Case Study of Hebei Province

Abstract

The rapid consumption of energy has caused a surge in carbon emissions and led to serious ecological problems. This paper takes Hebei Province as the research area. First, carbon emissions related to energy consumption are calculated from 2001 to 2015, and then the decomposition analysis of the carbon emissions data is performed by using the LMDI method based on the extended IPAT model. Finally, according to the potential drivers derived from the decomposition results and the future trend of low-carbon development, three scenarios are set up, namely inertial emission reduction scenarios, relative emission reduction scenarios, and absolute emission reduction scenarios. The results show that the overall carbon emissions from 2001 to 2015 are on an upward trend, increasing by 4.08 times during the study period. Economic progress is the main driving factor for the rising CO 2 in emitter, followed by energy consumption structure and population size, and the effect of technological progress has inhibited the increasing carbon emissions. The gradual optimization of Hebei's industrial structure has changed the industrial structure effect from promotion to suppression. The final scenario analysis indicates that the relative emission reduction scenario and absolute emission reduction scenario can both achieve the Copenhagen emissions reduction target and the Paris Agreement target, but the relative emissions reduction scenario has become the most reasonable low-carbon pathway. Finally, by designing and implementing a local carbon emissions trading system, regional development could be encouraged to be closer to a relative emissions reduction scenario. Low-carbon transformation in other regions would be exerted by the avenue opened by this paper.

Strategies for Mitigating Urban Residential Carbon Emissions: A System Dynamics Analysis of Kunming, China

“…According to the analysis in Section 3.2 of the study, factors like economic development, urbanization processes, energy consumption levels and structures, low-carbon awareness, and scientific research investments have a significant impact on changes in household carbon emissions in Kunming. After an in-depth examination of these critical factors, the study formulates three scenarios, drawing on references [57][58][59][60] to precisely depict the prevailing conditions in Kunming. As shown in Table 6, these scenarios rigorously simulate the influence of several policy interventions on the trend of residential carbon emissions in Kunming, with a specific focus on energy conservation and emission reduction.…”

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

“…Based on these methods, an extended LMDI model with scale effect of scientific research was established, and the results showed that improving scientific research efficiency and optimizing industrial structure can effectively reduce carbon emissions (Gao et al 2020 ). Similarly, the LMDI method via an extended IPAT model was proposed by Ge et al ( 2020 ) to made an analysis for carbon decomposition and forecast the carbon emissions by setting three different scenarios, and the results showed that economic progress is the main cause of increased carbon emissions, with population growth and energy consumption pattern following closely behind, and industrial structure and technological progress significantly contribute to the benefits reduction. The IDA method was first used to investigate what causes carbon emissions from household energy use in Guangdong Province (Xie et al 2020 ).…”