Within the context of the “30·60 dual carbon” goal, China’s low-carbon sustainable development is affected by a series of environmental problems caused by rapid urbanization. Revealing the impacts of urbanization on carbon emissions (CEs) is conducive to low-carbon city construction and green transformation, attracting the attention of scholars worldwide. The research is rich concerning the impacts of urbanization on CEs but lacking in studies on their spatial dependence and heterogeneity at multiple different scales, especially in areas with important ecological statuses, such as the Han River Ecological Economic Belt (HREEB) in China. To address these gaps, this study first constructed an urbanization level (UL) measurement method. Then, using a bivariate spatial autocorrelation analysis and geographically weighted regression model, the spatial relationships between UL and CEs from 2000 to 2020 were investigated from a multiscale perspective. The results were shown as follows. The total CEs in the HREEB witnessed an upsurge in the past two decades, which was mainly dispersed in the central urban areas of the HREEB. The ULs in different regions of the HREEB varied evidently, with high levels in the east and low levels in the central and western regions, while the overall UL in 2020 was higher than that in 2000, regardless of the research scale. During the study period, there was a significant, positive spatial autocorrelation between UL and CEs, and similar spatial distribution characteristics of the bivariate spatial autocorrelation between CEs and UL at different times, and different scales were observed. UL impacted CEs positively, but the impacts varied at different grid scales during the study period. The regression coefficients in 2020 were higher than those in 2000, but the spatial distribution was more scattered, and more detailed information was provided at the 5 km grid scale than at the 10 km grid scale. The findings of this research can advance policy enlightenment for low-carbon city construction and green transformation in HREEB and provide a reference for CE reduction in other similar regions of the world.

For a long time, the mismatch between material flow and capital flow in the supply chain operation management practice is very prominent, which has led to the inefficiency of supply chain operation and hindered the exertion of supply chain’s advantages. In the field of supply chain management theory research, the information completeness in capital market has long been a hypothetical premise, which has led to the separation of corporate financing and operational decision-making research. In addition to production, inventory, procurement, pricing and other strategies in supply chain operations, payment options and credit incentives are also important decisions for both parties, especially for products with long production and sales cycles; different payment methods directly affect corporate capital flows and the enterprise’s long-term development. On the basis of summarizing and analyzing previous works, this paper analyzed the research status and significance of supply chain financing and operational comprehensive decision-making, expounded the development background, current situation, and future challenges of the fractal dimension of fractional Brownian motion; elaborated the principles and methods of the scaling properties of fractional Brownian motion and the phase space reconstruction of time series, established a financial management analysis model based on the fractal dimension of fractional Brownian motion, performed the analysis of the agglomeration degree, time series and multi-fractal characteristics of supply chain financing, explored the coupling relationship between the comprehensive operational decision-making and Brownian motion’s scaling properties. The final empirical analysis showed that when own funds are sufficient, the production should be carried out with the goal of maximizing profits, and full consideration should be given customer channel stickiness, relative costs of offline and online channel products, and product profitability; the proposed analysis model can achieve the optimal order quantity in supply chain, and reach risk and benefit sharing among financial institutions, retailers, and suppliers by setting the financing interest rate, wholesale price, repurchase price and other parameters, thereby improving supply chain performance. This study results of this paper provided a reference for further researches on the application of fractal dimension of the fractional Brownian motion to the supply chain financing and operational comprehensive decision-making.