In a significant advancement for the energy sector, researchers have developed a novel prediction model that could play a pivotal role in reducing carbon emissions from power grids in China. With the power sector accounting for approximately one-third of the nation’s total carbon emissions, achieving the ambitious “double carbon” goal—reaching peak carbon emissions before 2030 and achieving carbon neutrality by 2060—hinges on effective strategies for carbon emission reduction.
The study, led by Ye Fan from the State Grid Chongqing Economic Research Institute, combines multiple linear regression models with the GM(1,1) model to create a robust framework for predicting grid carbon emission factors. “Our model not only helps in understanding the current emission trends but also provides a predictive insight into future emissions, which is crucial for strategic planning in the energy sector,” Ye noted.
The researchers established a grid carbon measurement model based on the theory of carbon emission flow and an accounting model grounded in consumption data. By analyzing the grid carbon emission factor across three dimensions, they found that the carbon emission factor has steadily decreased from 0.719 kg CO2/(kW·h) in 2010 to 0.593 kg CO2/(kW·h) in 2022. This decline reflects the effectiveness of existing policies and technologies aimed at emissions reduction. Notably, the study highlighted that electricity consumption was a significant contributor to carbon emissions, peaking at 2.9824 million tons in 2014.
The introduction of the MR-GM(1,1) model demonstrated impressive accuracy, with an absolute error margin of just 15,000 tons and a maximum relative error of only 2.42%. These results provide a reliable tool for power grid enterprises to gauge their carbon emissions accurately. “By clarifying emission trends, our model can guide companies toward achieving their low-carbon goals, ultimately contributing to a more sustainable energy future,” Ye emphasized.
As the energy sector grapples with the pressing need to decarbonize, this research offers a pathway for companies to enhance their operational strategies and align with national climate objectives. The implications of such predictive modeling extend beyond compliance; they can drive innovation in energy efficiency and renewable energy integration, potentially shaping future investments and policy decisions.
Published in ‘Applied Mathematics and Nonlinear Sciences’, this research not only underscores the importance of data-driven approaches in tackling climate change but also highlights the commercial viability of sustainable practices in the energy sector. For more information on Ye Fan’s work, visit State Grid Chongqing Economic Research Institute.
