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The energy and power industry is an important field for CO2 emission reduction. The CO2 emitted by thermal power enterprises is a major cause of global climate change, and also a key challenge for China to achieve the goals of "carbon peaking and carbon neutrality." Therefore, it is essential to scientifically and accurately predict the CO2 emissions of key thermal power enterprises in the region. This will guide carbon reduction strategies and policy recommendations for leaders, and also provide a valuable reference for similar regions globally. This study utilizes the factor analysis method to extract the common factors influencing CO2 emissions based on the carbon verification data of 17 thermal power enterprises in Gansu Province. Additionally, the DISO (distance between indices of simulation and observation) index is employed to comprehensively evaluate three prediction models, namely multiple linear regression, support vector regression, and GA-BP neural network. Ultimately, this study provides a reasonable prediction of CO2 emissions for the aforementioned enterprises in Gansu Province. The results show that the three common factors obtained by factor analysis, namely energy consumption and output factor, energy quality factor, and energy efficiency factor, can effectively predict the CO2 emissions from thermal power enterprises. In the three prediction models, GA-BP neural network has the best overall performance with DISO value of 0.95, RMSE value of 11848.236, and MAE value of 7880.543. Over the period 2022–2030, CO2 emissions from 17 thermal power enterprises in Gansu Province are predicted to increase. Under the low-carbon, scenario baseline, and high-carbon scenarios, the CO2 emissions will reach 71.58 Mt, 79.25 Mt, and 87.97 Mt, respectively, by 2030. [ABSTRACT FROM AUTHOR]

To develop effective strategies to reduce CO2 emissions from electricity systems, this study accounted for carbon emissions from power systems (production, transmission, consumption) in Gansu Province based on life cycle theory. We used LMDI and SDA decomposition methods to analyze the driving force of carbon emissions and quantified the influence effect and action intensity of various factors on carbon emissions in corresponding links. Several results were found: (1) Direct carbon emissions during the stage of electricity production had the largest share of the entire electricity life cycle. (2) From the perspective of the cumulative contribution rate, electricity consumption and the electricity trade promoted carbon emissions in the stage of electricity production; the power structure, electricity efficiency, and fuel structure had opposite effects. (3) In the stage of electricity transmission, the higher the voltage level, the lower the net loss rate; high-voltage-level transmission lines effectively reduced the growth of implied carbon emissions. (4) Industrial restructuring and technological advances effectively offset the growth in carbon emissions due to population, economy, and electricity consumption. The results can provide a scientific basis for energy-saving and emission reduction policies in provincial government departments and the electric industry. [ABSTRACT FROM AUTHOR]