Carbon emission calculation method based on big data of electricity and combined SARDL-ECM model

Since the reform and opening up, China’s economy has grown at a rapid pace and urbanization has accelerated, making China the world’s largest energy consumer and carbon emitter. Establishing a scientific accounting method and systematically grasping the overall situation of China’s carbon emissions is an important prerequisite for fully implementing the major strategic decision of the Party Central Committee and the State Council on carbon peaking and carbon neutrality. This study adopts the improved Kaya constant equation and logarithmic mean Divisia index model, and innovatively decomposes electricity consumption and the share of renewable energy as influencing factors to quantify the drivers of carbon emissions. At the same time, the long-term equilibrium relationship between electricity and renewable energy share, energy consumption, and product output is explored, and a calculation framework of ‘calculating energy (production) by electricity and carbon by energy (production)’ is developed. The results show that the combined autoregressive distributed lag model considering seasonality and error correction terms (SARDL-ECM) has the best fit and the highest accuracy for energy consumption data. The monthly projections of carbon emissions in Shanghai and the steel industry in 2022 will support the government and enterprises to take more effective measures to reduce carbon emissions.