[Analysis of the Temporal and Spatial Evolution of Carbon Emissions in the Provincial Logistic Industry in China from the Perspective of Shared Responsibility].

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2 of the OLS model ranged from 0.476 970 to 0.716 380. Additionally, the AICc values of the GWR model were consistently lower than those of the OLS model, indicating that the GWR model provided a significantly better fit and could better explain the spatiotemporal heterogeneity between various influencing factors and logistics industry carbon emissions. The heterogeneity results of the influencing factors showed that logistic energy intensity, freight turnover, and logistic industry per capita GDP were significantly positively correlated with logistic industry carbon emissions. Therefore, the spatiotemporal heterogeneity of influencing factors on carbon emissions should be completely considered and differentiated emission reduction policies for different provinces should be formulated.

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