Prediction of building carbon emission based on grey Markov theory

The increase in carbon emissions in the construction industry is one of the factors that lead to environmental problems and hinder the sustainable development of society and the construction industry. Therefore, there is an urgent need to rationally control carbon emissions by formulating scientific and effective energy-saving and emission reduction policies so as to safeguard the development of the industry. However, because of the uncertainty and complexity of CO2 emissions, more reliable prediction and assessment tools are needed to comprehensively analyze and predict CO2 emissions. This study combines the gray system theory and Markov principle to give full play to the advantages of the two methods, using Markov theory to determine the state transition probability, find out the characteristics of carbon emissions in the construction industry, and form a suitable gray Markov prediction model. The feasibility of the prediction model is demonstrated by calculating and examining the prediction model using data related to carbon emissions from the U.S. buildings from 2002 to 2022, which greatly improves the accuracy of CO2 emissions prediction.