Assessing the Relationship Between Carbon Emissions and National Average Temperature in China multiple Based on linear regression model

Amid growing concern over carbon emissions in China, the systematic monitoring of emission patterns has become increasingly essential. However, empirical investigations linking temperature trends to carbon emissions at the national level remain scarce. This study employs national annual average temperature as a proxy indicator to reflect variations in carbon emission levels. Temperature data are compiled from annual averages recorded in the thirty-four major cities in China, with the national average derived as the arithmetic mean of valid city-level observations. Following an initial Exploratory Data Analysis (EDA) and visual inspection, the study applies time series decomposition using the Hodrick–Prescott (HP) filter. A multiple linear regression model is then developed to assess the positive association between temperature variation and carbon emissions. To determine the causal relationship, the Granger causality test is employed. The analysis also includes mediation effects through GDP and further validates the model using cross-validation procedures. Results reveal a robust long-term correlation between rising carbon emissions and increasing national average temperatures. A statistically significant relationship is identified, reinforcing the importance of integrating temperature metrics in carbon monitoring strategies. In addition, the variables included in the model of this paper also include the penetration rate of new energy vehicles in China, the consumption of oil, natural gas, and coal, as well as China's GDP. Moreover, the data on the penetration rate of new energy vehicles has a 4-year lag.