## 1 Introduction
Currently, the spatial autocorrelation model is primarily employed to examine the spatial correlation of carbon emissions [17, 18, 19], and few studies have analyzed the spatial correlation of SIUS. Moreover, the relationship between stock and economy has attracted considerable attention [20, 21, 22]. Stock can directly demonstrate the physical basis of the economy [23]. [PERSON] et al. (2020) analyzed the decoupling relationship between SIUS and economic growth by using the decoupling index of resource consumption [24]. Current research shows that the relationship between SIUS and economic growth is complex [22], indicating the need to examine the decoupling relationship between SIUS and economic development. However, scholars typically focus on the decoupled state between the economy and the SIUS, and few studies reflect the role of relevant factors in the decoupling process. In summary, existing studies have made significant progress in SIUS, but there are still areas that warrant further investigation: (1) Prior research on SIUS has primarily explored their spatial and temporal characteristics. However, relatively few studies have examined their relationships with future SIUS trends and material flows at the provincial level. (2) Most spatial studies of SIUS focus on characterizing distribution patterns, leaving the in-depth exploration of spatial features insufficient. Additionally, while the spatial autocorrelation model is widely used in research on carbon emissions, it has not been applied to analyze the spatial characteristics of SIUS, hindering a deeper understanding of regional interactions. (3) Most scholars have explored the decoupling state between the economy and SIUS, but the factors driving this process remain unexplored and unquantified. To address existing research gaps, this study proposes a spatio-temporal characterization framework that integrates multiple models, including the bottom-up model, spatial autocorrelation model, Tapio-Logarithmic Mean Divisia Index (Tapio-LMDI) model, and stock-driven model. By combining temporal, spatial, and predictive analyses, this integrated framework offers a holistic perspective on SIUS. Building on previous research, the study further expands spatial analysis through the application of spatial autocorrelation models, enabling a deeper exploration of spatial patterns. In summary, this framework analyzes the spatio-temporal characteristics, decoupling relationships, and future trends of SIUS, providing a scientific basis for regional resource recycling strategies and policy development. Henan Province is located in the central region and is an emerging industrial province. Previous studies have shown that the central region will become a major supplier of waste steel after 2040 [14], and Henan Province has the third-largest total SIUS in China [10]. Therefore, this study chose Henan Province as the research subject. The province consists of 18 cities with varying economic, demographic, and administrative characteristics, which prompted this study to explore the internal heterogeneity of Henan Province. The paper is structured as follows. Section 2 describes the methods used in this study. Section 3 discusses the temporal and spatial characteristics of provincial SIUS over the years, the decoupling relationship and driving factors between SIUS and the economy, and the future steel stocks and scrap resources. Section 4 analyzes its uncertainty and puts forward policy suggestions. Finally, Section 5 presents the conclusion of this paper.