Analysis and Prediction of Energy Consumption Structure in Tianjin City Based on Markov Chain

In response to the dual challenges of high external energy dependence and the “dual carbon” goals in Tianjin, this study employs a Markov chain model, utilizing energy consumption data from 2021 to 2024, to predict the evolution of the city’s primary energy consumption structure—encompassing coal, oil, natural gas, and renewable energy. Based on this prediction, three policy scenario optimization paths are designed. The results show that under the baseline scenario, coal consumption continues its downward trend and renewable energy usage increases steadily; however, the dominance of fossil fuels remains difficult to reverse in the short term. Accordingly, this study proposes a strategic energy development path for Tianjin: (1) prioritize the exploitation of local renewable resources such as wind, tidal, and solar energy; (2) promote the transformation of high energy-consuming industries to reduce energy intensity; and (3) implement policies such as tiered pricing for residential energy use to encourage conservation and reduce unnecessary consumption. The findings confirm the applicability of Markov chain models in dynamically predicting energy structure transitions and provide quantitative support for energy security and low-carbon transformation in the Beijing-Tianjin-Hebei region.