Carbon emission factor decomposition and carbon peak prediction based on multi-objective decision and information fusion processing

Global warming caused by excessive carbon dioxide emissions has seriously threatened the sustainable development of human society. How to reduce carbon dioxide emissions has become a common problem faced by the international community. This article aims to study the decomposition of carbon emission factors and the prediction of carbon peaks from the perspective of multi-objective decision-making and information fusion processing. The sample collection method and statistical analysis method are used to collect samples and simplify the algorithm. A collection experiment of carbon emission factors based on the industry of City A is designed. The experimental data collection takes into account the conversion of coal and oil products into standard coal and carbon dioxide the resulting emissions impact. The experimental results in this paper show that the simulated and real values of my country’s petroleum carbon emissions have both increased from 2000 to 2015, and the decline will be controlled in 2017. Both the simulated value and the real value of my country's coal carbon emissions have been on the rise from 2000 to 2015, and the decline will be controlled in 2017. The carbon emissions of coal are far greater than those of petroleum. The research on carbon emission factor decomposition and carbon peak prediction based on multi-objective decision-making and information fusion processing has been completed well. The research results can be used for industrial carbon emission factor decomposition and carbon peak prediction in other cities across the country.

Industrial Electrification and Efficiency: Decomposition Evidence from the Korean Industrial Sector

For this study, we conducted a decomposition analysis of industrial electricity consumption based on the logarithmic mean Divisia index approach. An empirical dataset consisting of 11 industrial sectors in Korea from 2000 to 2018 was used. The three-factor decomposition equation was extended to include four factors by decomposing the energy intensity effect into electrification and electricity consumption efficiency effects. The empirical results are summarized as follows: The increase in electricity consumption in the Korean industrial sector from 2000 to 2018 is mostly caused by the production effect. While the structure effect decreases electricity consumption, the intensity effect increases it. The key findings indicate that the hidden electrification effect can be confusing to researchers with regard to the intensity effect. The empirical evidence suggests that the intensity effect has a positive effect on electricity consumption induced by the electrification effect, although the efficiency effect continuously decreased electricity consumption. The decomposition results of some sectors show that electrification, rather than the production effect, contributed the most to the increase in electricity consumption. This implies that while replacing fuel with electricity has been successfully achieved in several sectors, there are still challenges regarding increasing energy efficiency and expanding clean electricity generation.

Input Structure Effect of Total Factor Productivity Growth of Animal Husbandry

This paper uses the input-output panel data of China's animal husbandry industry from 1997 to 2017, based on the total factor decomposition framework of total factor productivity (TFP), and uses the Hicks-Moorsteen index completely decompose the growth of animal husbandry TFP. By measuring the effect of mixed efficiency on the development of TFP in animal husbandry and then evaluating the input structure effect of TFP growth in animal husbandry. The results show that the impact of input structure on the TFP growth of animal husbandry has also changed from negative to positive. From 1997 to 2007, the input structure of the Huanghuaihai region alone contributed to the growth of TFP in animal husbandry, and the rest of the region was the opposite. From 2008 to 2017, the input structure of the Mengxin Plateau region hindered the growth of TFP in animal husbandry, while the rest of the region was the opposite.