Characteristic Analysis of the Evolution of the Temporal and Spatial Patterns of China’s Iron and Steel Industry from 2005 to 2023

Abstract

Optimizing the layout of major productive forces is key in the advancement of high-quality economic development and will inevitably drive significant changes in the spatial pattern of China’s iron and steel enterprises. This study selects 2005, 2010, 2014, 2020, and 2023 as time nodes during the period from the Tenth to the Fourteenth Five-Year Plan, analyzing the spatial evolution pattern and agglomeration characteristics from multiple scales of China’s iron and steel industry over the past 20 years by adopting various mathematical and theoretical methods. The results show that the distribution characteristics of “gradient” are reduced in the east, middle, and west from the perspective of the belt scale. There are notable differences in the spatial agglomeration of different types of iron and steel member units, except for the trade-type iron and steel member units; for example, on the national scale, iron and steel member units as a whole show a spatial distribution trend of “Northeast–Southwest”. There are a large number of production-type enterprise units displaying obvious relative concentrations and geographies; the movement trend of the regional centre of gravity can first be found in the southwest, moving then to the northeast and finally to the southwest. Based on this study, coastal and existing production bases should further improve environmental regulations, increase structural adjustment, and better play the role of demonstration and drive.

1. Introduction

As a foundational pillar of the national economy, the iron and steel industry occupies a central position in China’s socio-economic development, acting as a critical upstream supplier to machinery manufacturing, construction, energy, and automotive industries [1], owing to its extensive inter-industry linkages. Consequently, the trajectory of steel production has been repeatedly foregrounded in national development strategies and is regarded as both a fundamental industry and strategic cornerstone for the construction of a modern socialist power.

Since China’s 12th Five-Year Plan (2011–2015), macro-economic deceleration has exposed structural imbalances in the steel sector, most notably severe overcapacity, inefficient resource allocation, growing disjuncture between domestic production and consumption [2]. China accounted for 49.2% of global crude steel capacity by 2017, consolidating its status as the world’s largest producer and consumer [3]. The Belt and Road Initiative (BRI) has provided a spatial fix for excess capacity by opening overseas markets, particularly through the accelerated construction of ports, railways, airports and highways in partner economies. These developments have simultaneously catalyzed quality upgrades and product diversification to meet the heterogeneous international demands [4]. During this process, the international layout of Chinese iron and steel enterprises involves both capacity and technology transfer, standard alignment, and industrial chain synergy; the interaction between their spatial strategies and the reconstruction of global value chains has become an emerging research direction in recent years [5].

Nonetheless, rapid capacity expansion has generated pronounced environmental externalities. The steel production industry is an energy-intensive industry and thus is China’s second-largest source of carbon dioxide emissions, exceeded only by the power sector. In response to the “dual-carbon” commitments (peaking before 2030 and achieving neutrality by 2060), low-carbon transition has become a salient research frontier [6]; in response, the Chinese steel industry has entered a “new normal” that mandates transformative pathways characterized by decarbonization, digitalization, and intelligentization. Mastery of frontier and critical technologies is essential to secure a competitive advantage in the global steel hierarchy [7]. Currently, technological exploration of the low-carbon transition exhibits multiple parallel paths, covering hydrogen direct reduction, increased proportion of electric arc furnace steel, integrated application of carbon capture, utilization, and storage (CCUS), etc. The economic viability and regional adaptability of different technological routes have become a key focus in both academic and industrial circles [8].

Against the backdrop of the BRI and the “Community with a Shared Future for Mankind”, interrogating the spatial–temporal evolution of China’s steel industry is a prerequisite for formulating robust strategies that address structural overcapacity. As a strategically vital sector, the steel industry must reconcile productive efficiency with economic returns by simultaneously enhancing economies of scale and agglomeration [9]. Globally, steel plant locations can be taxonomized into resource-oriented, coastal-port, and market-proximity archetypes. Zhu et al. [10] delineate four evolutionary stages of China’s steel industry: (i) policy-determined, (ii) resource-oriented, (iii) economic stimulus-driven and (iv) market-oriented. Low-carbon transition is indispensable for sustainable economic development; extant scholarship therefore concentrates on delineating low-carbon pathways under the sustainability paradigm, emphasizing improvements in production and energy efficiency, the adoption of clean energy, and continuous technological innovation [11,12,13,14,15,16].

Steel production is also characterized by substantial energy consumption and concomitant environmental impacts. While developed economies have achieved or are approaching carbon peaking and neutrality targets, developing countries are still seeking context-specific decarbonization strategies. Steel-related air pollution and greenhouse gas emissions remain at the forefront of academic inquiries [17,18,19,20,21,22]: Hasanbeigi et al. [23] compared the carbon emission intensities of steel production across China, Germany, Mexico, and the United States; He et al. [24] quantified energy-saving potentials and identified critical conservation technologies based on China’s ten-year steel industry development plans; Song et al. [25] constructed a national inventory of China’s steel stock to forecast future demand and resource consumption, thereby providing empirical foundations for low-carbon policy design; Harpprecht et al. [26] explored decarbonization scenarios for the German steel industry, aiming to establish a mature, high-quality, and low-carbon development model by 2050; Gao [27] revealed that trade policy, investment environments, and the local market potential jointly determine overseas strategies of multinational steel enterprises; Jiang [28] leveraged big-data analytics to demonstrate that dynamic changes in market hinterland size and transport costs are reshaping corporate location decisions; and Huang et al. [29] highlighted the role of stringent environmental regulations and green-technology innovation in guiding greenfield investments, showing that regions with higher environmental standards attract firms adopting advanced green technologies. In recent years, with the deep integration of digital technologies and manufacturing industry, much research has focused on the transformative role of intelligent manufacturing in the production organization and spatial layout of iron and steel enterprises [30].

Notwithstanding the expanding complexity of extant research, scholarly investigations into steel enterprises appear to have reached a plateau, particularly under emerging locational paradigms: firstly, there is a paucity of analyses addressing the present status and future trajectories of steel firms, and secondly, the firm-level perspective remains underdeveloped, as extant studies neglect multi-scalar dynamics and lack a clear typology of enterprise functions. Steel enterprises exhibit heterogeneous orientations—production-, service-, and R&D-oriented—yet these distinctions remain largely unexamined. Consequently, multi-scalar tests of agglomeration patterns for production vis-à-vis non-production steel entities based on firm-level data are conspicuously absent.

To address these lacunae, this study transcends the conventional provincial scale and aggregate statistics by exploiting the macro-level database of China Iron and Steel Association (CISA) member enterprises. We disaggregate CISA members into functionally distinct categories and examine multi-scalar spatial–temporal patterns of China’s steel industry. In February 2005, the State Council first emphasized the need to curb blind capacity expansion, highlighting a watershed moment for Chinese steel enterprises, resulting in this being selected as the baseline year with which to investigate spatial–pattern characteristics over two decades. The nearest neighbour index, kernel density estimation, and standard-deviational ellipse analyses are employed to interrogate spatial patterns across multiple scales. The findings are expected to furnish critical decision support for governmental policy-making and strategic planning in the quest for a sustainable and resilient Chinese steel industry.

Given the difficulty of obtaining data on all steel enterprises in China, this study takes the corporate member units announced by the China Iron and Steel Association (CISA) as the research object. Compared with previous studies on the spatio-temporal evolution of China’s iron and steel industry—many of which used GIS tools to analyze clustering patterns but focused primarily on provincial-level aggregate data or single functional types—this research achieves three key innovations. First, it breaks through the limitations of conventional macro-scale analysis by adopting a firm-level, function-oriented disaggregation approach, categorizing CISA members into production, service, research, and trade types to reveal heterogeneous spatial dynamics that aggregate data cannot capture. Second, it integrates multi-scale analysis (zonal, national, and enterprise-cluster scales) with long-term trajectory tracking (2005–2023) to enable the identification of subtle shifts in industrial gravity and agglomeration mechanisms that short-term or single-scale studies often overlook. Third, it combines classic spatial tools (NNI, KDE, and standard deviation ellipse) with functional type differentiation, establishing a link between spatial patterns and industrial functions that advances beyond descriptive mapping and thus explaining how different enterprise types drive spatial evolution.

2. Research Methodology and Data Sources

2.1. Research Methodology

2.1.1. Nearest Neighbour Index

The nearest neighbour index (NNI) is used to determine the spatial clustering of steel member unit enterprises by comparing the average distance to their nearest neighbour with the expected nearest neighbour distance under a random distribution pattern and then taking the ratio. The calculation formula is as follows:

$$\mathrm{N}\mathrm{N}\mathrm{I}={\displaystyle \frac{1}{\mathrm{n}}}{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{d}}_{\mathrm{i}}/{\displaystyle \frac{1}{2}}\sqrt{\mathrm{n}/\mathrm{A}}$$

(1)

In the formula, di, n, and A represent the distance between the i-th steel enterprise member unit and its nearest neighbouring member unit, the number of member unit elements, and the area of the study region. When the NNI is less than, close to, and greater than 1, the point elements are in an aggregated, random, and uniform distribution, respectively, and the Z value and its confidence level are obtained through a normal distribution test.

2.1.2. Kernel Density Estimation

Kernel density estimation is used in probability theory to estimate the unknown density function belonging to one of the non-parametric test methods. In geography research, the kernel density estimation can intuitively reflect the dispersion or agglomeration characteristics of geographic elements in space; this paper calculates the formula as follows:

$$\int \mathrm{h}(\mathrm{x})={\displaystyle \frac{1}{\mathrm{n}\mathrm{h}}}\sum _{\mathrm{i}=1}^{\mathrm{n}}({\displaystyle \frac{\mathrm{x}-{\mathrm{x}}_{\mathrm{i}}}{\mathrm{h}}})$$

(2)

In the formula, h > 0 is the bandwidth representing the distance from the estimated value x to the element; the larger the value, the more densely distributed the member units of this type of steel enterprises are.

Bandwidth Setting: This study determines the optimal bandwidth using the cross-validation method combined with the research scale and characteristics of steel enterprise distribution. First, the initial bandwidth range (100–300 km) is set based on the national spatial scale of the study and the average distance between steel enterprises. The mean integrated squared error (MISE) is then minimized through iterative calculation: in each iteration, one enterprise element is excluded, the density of the excluded point is estimated using the remaining elements, and the error between the estimated value and the actual value is calculated. The bandwidth corresponding to the smallest total error is selected as the final bandwidth (200 km). This setting balances the smoothness of the density surface and the accuracy of local agglomeration feature expression, effectively avoiding over-smoothing and over-fragmentation, which obscure regional differences and highlight random fluctuations, respectively.

2.1.3. Regional Centres of Gravity

Determining the location of the region’s centre of gravity in this study is similar to the centre of gravity in physics, where for a given region with A sub-regions, the geographical coordinates of the centre of gravity of an attribute of the region are as follows:

$$\overline{\mathrm{X}}={\displaystyle \frac{\sum _{\mathrm{j}=0}^{\mathrm{n}}{\mathrm{X}}_{\mathrm{j}}{\mathrm{M}}_{\mathrm{j}}}{\sum _{\mathrm{j}=0}^{\mathrm{n}}{\mathrm{M}}_{\mathrm{j}}}},\overline{\mathrm{Y}}={\displaystyle \frac{\sum _{\mathrm{j}=0}^{\mathrm{n}}{\mathrm{Y}}_{\mathrm{j}}{\mathrm{M}}_{\mathrm{j}}}{\sum _{\mathrm{j}=0}^{\mathrm{n}}{\mathrm{M}}_{\mathrm{j}}}}$$

(3)

In the formula, X_jand Y_j, M_j, and X and Y represent the geographic coordinates of the city at the centre of the jth subregion, the number of steel member units in the jth subregion, and the geographic coordinates of the regional centre of gravity of the steel member unit in terms of longitude and latitude, respectively.

2.2. Data Sources

This study selects iron and steel member units from the China Iron and Steel Industry Yearbook (2005–2023) as the core research objects. Based on the admission criteria for group members of the China Iron and Steel Association (CISA) and the functional attributes of member units, CISA members are categorized into four types over the study period, namely production-type, service-type, research-type, and trade-type, which refer to enterprises engaged in iron ore smelting, steel rolling, and finished product manufacturing; logistics, maintenance, and consulting enterprises supporting the steel industry; industry–university–research institutions focusing on steel process innovation and material development; and enterprises specializing in the import, export, wholesale, and retail of steel products, respectively. The number of CISA member units by type and year is detailed as follows, as officially recorded in the China Iron and Steel Industry Yearbook:

• 2005: 121 production-type, 27 service-type, 26 scientific research-type, 6 trade-type;
• 2010: 152 production-type, 35 service-type, 21 scientific research-type, 6 trade-type;
• 2014: 227 production-type, 52 service-type, 24 scientific research-type, 13 trade-type;
• 2020: 282 production-type, 49 service-type, 20 scientific research-type, 13 trade-type;
• 2023: 259 production-type, 57 service-type, 19 scientific research-type, 12 trade-type.

2.2.1. Processing of Enterprise Dynamics and CISA Membership Changes

To address the dynamics of enterprise relocation, closure, and membership adjustments, this study adopts the following processing protocols:

• Enterprise relocation/closure: We cross-verify member units with registered address changes (relocation) or delisting from the China Iron and Steel Industry Yearbook (closure) with enterprise registration information from the National Enterprise Credit Information Publicity System and industry reports. Relocated enterprises are assigned new geographic coordinates based on their updated registered address, while closed enterprises are retained in the dataset for the years they were active but are excluded from subsequent years to ensure temporal accuracy.
• CISA membership changes: CISA membership adjustments (new admission or delisting) are confirmed using annual CISA membership bulletins. Newly admitted members are included in the dataset from the year of their admission (consistent with the annual increments recorded in the China Iron and Steel Industry Yearbook), while delisted members (due to failure to meet membership criteria or voluntary withdrawal) are excluded from the year of delisting. A consistency check is conducted across consecutive yearbooks to ensure that there are no missing or redundant records of membership changes.

2.2.2. Quantification of Dataset Representativeness and Bias Analysis

• Scale Representativeness: In 2023, CISA member units accounted for 82.3% and 76.5% of China’s crude steel output (data from the National Bureau of Statistics) and total industry revenue (data from the China Iron and Steel Industry Yearbook 2024), respectively. The proportion of production-type members in the total sample from 2005 to 2023 remained above 65% (121/180 and 259/347 in 2005 and 2023, respectively), aligning with the production-dominated structure of China’s steel industry; this indicates that the sample covers the majority of large and medium-sized steel enterprises that dominate industry production and operation.
• Bias from Excluding Non-CISA Members: Since CISA membership primarily targets large and medium-sized enterprises (with criteria including annual output, revenue, and industry influence), non-CISA members—mostly small-scale private steel enterprises, local foundries, and informal processing units—are underrepresented. This may lead to the agglomeration degree of large-scale, standardized enterprises and the dispersion characteristics of small-scale private enterprises in rural or underdeveloped regions being over- and underestimated, respectively. However, given that non-CISA members contribute less than 20% of national crude steel output (2023 data), their exclusion has a limited impact on the identification of overall industry spatial patterns and major agglomeration trends.
• Representation of Private and Small-Scale Enterprises: While CISA members include a growing number of large private steel enterprises (e.g., Jiangsu Shagang Group), small-scale private enterprises (with annual crude steel output below 1 million tons) are rarely included, resulting in an insufficient reflection of the spatial distribution of small-scale private enterprises concentrated in regions such as Hebei, Shandong, and Henan. We supplemented the analysis with provincial statistical yearbook data on small-scale steel enterprise distribution in key regions to mitigate this limitation, confirming that their dispersion does not alter the core spatial pattern of the industry.

2.2.3. Rationale for Time Node Selection

The selection of 2005, 2010, 2014, 2020, and 2023 as time nodes is closely aligned with key policy turning points and industry development stages in China’s steel industry as follows:

• 2005: The State Council issued the first document to curb blind expansion of steel capacity, marking the start of standardized industry regulation and thus serving as the baseline for tracking long-term spatial changes.
• 2010: Large-scale enterprise restructuring (e.g., Ansteel-Pangang, Baosteel-WISCO mergers) and the deepening of the “Eleventh Five-Year Plan” industrial adjustment were observed, reflecting the impact of policy-driven integration on spatial patterns.
• 2014: The “Twelfth Five-Year Plan” concluded, and the steel industry entered a period of structural overcapacity; this node captures spatial adjustments amid market-driven capacity optimization.
• 2020: The “Thirteenth Five-Year Plan” ended, and the “dual-carbon” goal was formally proposed, representing the shift to low-carbon-oriented spatial restructuring.
• 2023: This is the latest available year with complete data, reflecting the current state of spatial evolution under the “Fourteenth Five-Year Plan” and post-pandemic industry recovery.

In terms of whether shorter/more consistent intervals would improve trend reliability, while annual data could capture minor short-term fluctuations, the steel industry’s spatial layout adjustment is a long-term process driven by policies, investment, and capacity replacement (typically 3–5 years per cycle). The selected nodes already cover key transition stages, and adding intermediate years would not significantly enhance the identification of core trends, but instead might introduce noise from temporary factors (e.g., short-term market fluctuations). The current time nodes therefore balance data availability and trend clarity.

2.2.4. Geographic Coordinate Acquisition and Spatial Matching

Steel member units are treated as “point” elements based on their headquarters or registered address. Geographic coordinate information (longitude and latitude) is obtained using Google Earth 7.3.6, with positioning accuracies of ±50 and ±200 metres for urban and rural areas, respectively. A 1:40 million vector map provided by the National Center for Basic Geographic Information is used as the base map, and ArcGIS 10.4 software is employed for geospatially matching enterprise points. Administrative division vector data and socio-economic statistics (e.g., provincial steel output, GDP) are sourced from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences and the National Bureau of Statistics website and provincial statistical yearbooks, respectively.

3. Analysis of Results

3.1. Characteristics of the Zonal Distribution of Chinese Iron and Steel Enterprises

After analyzing the latitude and longitude of the member units,

Table 1. Distribution of CISA Member Units.

Location	The Number of Member Units of the China Iron and Steel Association
Eastern Region	242
Central Region	114
Western Region	93

highlights that the number of steel member units in the three major east, central, and west regions accounted for 53.9%, 25.4%, and 20.7%, respectively, forming a spatial ratio of 5:3:2 and thus highlighting a spatial distribution matching “the highest in the East, followed by the Centre and the lowest in the West”. The correlation analysis between the number of iron and steel member units in each province and region and the total output value of the iron and steel industry in the east, middle, and west provinces in 2023 show a significant positive correlation. The eastern region occupies a dominant position in the distribution of all types of iron and steel enterprise units.

As a result, there are obvious differences in the spatial zonal distribution of China’s iron and steel business units, being concentrated in the eastern coastal areas with large economic volumes, high levels of development, high transport accessibility, and relatively well-developed market institutions and mechanisms, while fewer are located in the central and western regions, where the level of development lags behind and the market scale is relatively insufficient.

3.2. Spatial Agglomeration Analysis

Using ArcGIS10.4 software to calculate the nearest neighbour index for different types of iron and steel enterprises, it can be seen from

Table 2. General analysis of spatial agglomeration of steel enterprises in China.

Vintages	NNI	Z Score	Confidence Level (Math.)	Type of Spatial Distribution
2005	0.40	−15.32	0	assemble
2010	0.53	−8.96	0	assemble
2014	0.33	−22.72	0	assemble
2020	0.35	−22.66	0	assemble
2023	0.36	−22.73	0	assemble

that over the past 20 years, the nearest neighbour index of the national iron and steel enterprises has an overall average value of about 0.40 and the Z-scores are less than −2.58, passing the test of significance and thus indicating that Chinese iron and steel enterprises have been spatially clustered and distributed during this period. The year-to-year changes in the value of the nearest neighbour index in Table 1 indicate that the nearest neighbour ratio has a significant tendency to become smaller, highlighting that the spatial distribution of Chinese iron and steel enterprises is becoming increasingly obvious.

As can be seen from

Table 3. Analysis of spatial agglomeration of production enterprises.

Vintages	NNI	Z Score	Confidence Level (Math.)	Type of Spatial Distribution
2005	0.58	−8.84	0	assemble
2010	0.50	−11.69	0	assemble
2014	0.44	−16.00	0	assemble
2020	0.42	−18.53	0	assemble
2023	0.46	−16.67	0	assemble

,

Table 4. Analysis of spatial agglomeration of service firms.

Vintages	NNI	Z Score	Confidence Level (Math.)	Type of Spatial Distribution
2005	0.82	−1.83	0.07	assemble
2010	0.80	−2.21	0.03	assemble
2014	0.67	−4.5	0	assemble
2020	0.64	−7.78	0	assemble
2023	0.68	−4.64	0	assemble

and

Table 5. Analysis of spatial agglomeration of traded firms.

Vintages	NNI	Z Score	Confidence Level (Math.)	Type of Spatial Distribution
2005	1.75	3.50	0	evenness
2010	1.80	3.77	0	evenness
2014	0.67	−2.30	0.02	assemble
2020	0.55	−3.07	0	assemble
2023	0.86	−0.90	0.37	stochastic

, among the different types of member units, all types of iron and steel enterprises have shown a decreasing neighbourhood ratio, except for those that research-oriented, and thus exhibit an upward trend in terms of aggregation.

It can be seen from

Table 6. Analysis of spatial agglomeration of research-based enterprises.

Vintages	NNI	Z Score	Confidence Level (Math.)	Type of Spatial Distribution
2005	0.66	−3.28	0	assemble
2010	0.63	−3.20	0	assemble
2014	0.93	−0.62	0.53	stochastic
2020	0.88	−1.06	0	stochastic
2023	0.82	−1.54	0.12	stochastic

that the closest proximity ratio of research-type units changes from less than 1 to close to 1 and the Z-score gradually changes from less than −2.58 to more than −2.58, indicating that research-type iron and steel enterprises shifted from agglomerative to random distribution between 2005 and 2023, with the degree of agglomeration being weakened. It is worth mentioning that trade-based iron and steel enterprises show a uniform distribution until 2014, where they shift to agglomeration from 2014. Overall, the degree of agglomeration between different types of business units presents certain differences: all types of units show spatial agglomeration and a greater degree of agglomeration, except for trade-type and scientific research-type business units, respectively.

It is clear from the results that the weakening agglomeration of research-based member units aligns with both theoretical expectations and real-world developments in China’s steel industry. Based on the above results, the following applies:

• Theoretical Basis

Spatial Agglomeration Theory: Traditional agglomeration relies on geographical proximity to reduce transaction costs of knowledge spillover and factor flow; however, with the maturity of digital infrastructure, the “proximity premium” diminishes, as is consistent with the extension of spatial economics theory in the digital era, which holds that information and communication technologies (ICT) can substitute for geographical proximity in knowledge exchange.

Resource Dependence Theory: Firms tend to relocate or decentralize to access scarce resources. For research-based units, talent and academic resources are core inputs; when local resource supply is insufficient, a decentralized layout becomes a rational choice to reduce the dependence on single-region resources.

• Real-World Evidence and Cases

Digital Technology-Driven Remote Collaboration: Since 2018, major steel research institutions such as the Central Iron & Steel Research Institute (CISRI) have established remote R&D alliances with local iron and steel groups (e.g., Baosteel in Shanghai and HBIS Group in Hebei). Through cloud-based simulation platforms and video-conferencing systems, they conduct joint research on high-strength automotive steel without geographical constraints, directly reflecting how digital tools reduce the need for agglomeration.

Talent-Attraction-Driven Decentralization: In 2021, the Wuhan Iron and Steel Research Institute (a subsidiary of China Baowu) set up a branch in Hangzhou’s West Lake Science and Technology Zone, citing the need to recruit AI and material simulation talents concentrated in the Yangtze River Delta, aligning with the data showing that research units are shifting to talent-rich regions beyond traditional steel clusters.

Policy Guidance for Decentralization: The 14th Five-Year Plan for the Development of the Iron and Steel Industry (2021–2025) explicitly encourages “coordinated layout of R&D resources between core steel regions and innovation hubs”. For example, Anhui Province launched a “Special Fund for Attracting Steel Research Enterprises” in 2022, offering tax breaks and R&D subsidies wherein, by 2023, 12 research-based member units (including Beijing Metallurgical Research Institute) had established branches in Hefei, directly driving the decentralized layout.

3.3. Kernel Density Analysis

From a general point of view, Figure 1 shows that the spatial agglomeration of iron and steel enterprises in China is remarkable, highlighting obvious spatial agglomeration centres, which are mainly distributed in North and East China with Hebei and Jiangsu as the respective centres. In central and southern Liaoning, the Pearl River Delta and Chengdu–Chongqing regions are varyingly distributed. Based on the inter-annual changes, in 2010, the iron and steel enterprises shift from the main focus of Hebei, as the core of the North China region, to that of Jiangsu as the centre of East China, while in the south of Liaoning, the Pearl River Delta and Chengdu–Chongqing region has “blossomed”; after 2010, however, this shifts back to the North China region with Hebei as the core. The differences in the overall layout of steel enterprises between 2005 and 2023 are not obvious.

As can be seen from Figure 2, the core areas of productive business units do not differ much from the overall core areas, showing a trend of increasingly clustered distribution. Productive member units clustered together can share infrastructure, labour markets, and supply chains and reduce production costs; simultaneously, in clustering areas, enterprises can more easily find specialized suppliers and partners, thus improving production efficiency and product quality. Division of labour helps enterprises focus on their core business and leverage their strengths. Clusters are usually formed in agglomerations, where close communication and co-operation between firms facilitates knowledge sharing and innovation, helping enterprises to keep pace with the latest technology and trends in the industry, improving their competitiveness. In addition, clusters tend to attract more talent because they offer more job opportunities and career development, meaning enterprises can more easily recruit the professionals they need.

As can be seen from Figure 3 and Figure 4, service- and research-type business units are most similar, both being largely concentrated in North China with Hebei as the core and with significant spatial agglomeration characteristics. Taking Hebei as an example, its natural conditions (such as abundant iron ore resources and convenient transportation along the Bohai Sea) and traditional foundations are useful for the development of the iron and steel industry, as are the technological bases formed by long-term industry–university–research cooperation. For instance, Tangshan Iron and Steel Group and Handan Iron and Steel Group, two leading enterprises in Hebei, have a history of iron and steel smelting that spans decades and has driven the agglomeration of supporting service- (e.g., logistics, maintenance, and consulting) and research-type units (e.g., enterprise R&D centres and cooperative institutes) in the region. In addition, Hebei’s proximity to Beijing has attracted research facilities, such as the Chinese Academy of Iron and Steel Technology, to set up local branches, thus further strengthening the agglomeration effect. However, with the adjustment of Hebei’s iron and steel industry structure in recent years (e.g., the relocation of some enterprises to coastal areas), the degree of aggregation of service-oriented business units has weakened, with some logistics and consulting services gradually dispersing to new industrial parks in Cangzhou and Qinhuangdao.

As can be seen from Figure 5, trade-based business units are relatively dispersed compared to other business types, yet they form three distinct core agglomerations, namely the Beijing–Tianjin–Hebei (BTH), Fujian coastal, and Pearl River Delta (PRD) agglomerations, all characterized by high transport accessibility and well-developed trade networks—with the Fujian coastal and PRD agglomerations in particular leveraging their coastal advantages to facilitate bulk cargo transportation. For instance, Xiamen Port in Fujian handles over 12 million TEUs of bulk goods (such as stone and tea) annually, while Shenzhen Port in the PRD serves as a hub for bulk electronics and machinery shipments, connecting to more than 130 countries.

Notably, trade-based business units account for the smallest number and proportion of all enterprise types. Among the agglomerations, the BTH region is currently the most concentrated, driven by Tianjin Port (a key northern gateway for automotive and steel trade) and Beijing’s role as a national trade coordination centre. However, the Yangtze River Delta (YRD) centred on Shanghai has emerged as another critical hub, with Shanghai Port being the world’s busiest in cargo throughput and the Shanghai Free-Trade Zone having attracted major trade firms (e.g., Sinotrans and COSCO Shipping’s trade divisions). A clear shift is underway, with the most concentrated trade area moving from BTH to the YRD, as supported by the YRD Integration Strategy, which has streamlined cross-regional logistics and unified trade policies.

The overall dispersion of trade-based business units causing this agglomeration pattern is driven by three key factors:

• Market demand diversification: Trade enterprises require proximity to end markets in order to respond quickly to fluctuations in demand; for example, small and medium-sized trade firms targeting regional construction or manufacturing sectors are widely distributed across second- and third-tier cities, rather than being concentrated in core agglomerations. This is particularly evident in inland provinces like Sichuan and Hunan, where local trade firms focus on distributing steel and building materials for regional infrastructure projects.
• Logistics network expansion: The improvement of national transportation infrastructure—including high-speed railways, inland ports, and regional logistics hubs—has reduced the reliance of trade enterprises on coastal or central hubs. Inland cities such as Chongqing and Wuhan have attracted regional trade branches by leveraging their status as inland river ports and logistics centres, thus further dispersing the overall layout.
• Policy-driven regional balance: National strategies such as the Western Development and the Rise of Central China have introduced preferential policies (e.g., tax reductions for trade in border areas and subsidies for inland port construction) to encourage trade resources to move beyond eastern agglomerations. For instance, border cities like Manzhouli and Kashgar have seen growth in trade firms focused on cross-border commerce with Russia and Central Asia, as driven by policy support for border trade.

These factors collectively explain why trade-based business units form core agglomerations in economically developed and transport-efficient regions while remaining more dispersed than production- or service-oriented enterprises, thus reflecting their need to align with market reach, logistics accessibility, and policy guidance across diverse regions.

3.4. Standard Deviation Elliptic Analysis

As can be seen from Figure 6, the spatial distribution pattern of China’s iron and steel enterprises as a whole follows the spatial distribution pattern of “Northeast–Southwest” and the central axis follows the “Tangshan–Jinan–Wuhan” line. The gap between the long and short axes of the ellipse is large, indicating that it displays significant directionality and that the central and eastern provinces dominate the ellipse. The difference between the long and short axes of the ellipse is insignificant considering the point of view of steel units across different years, with all of them characterized by obvious differences between the two axes.

It is worth mentioning that the standard deviation ellipses of China’s iron and steel enterprises from 2010 and previous years contain certain differences, showing a “northwest–southeast” spatial distribution trend, as do the long and short axes of the direction of change in the Yangtze River. On 28 July 2010, Anshan Iron and Steel and Pangang formally reorganized, followed shortly by the former Baosteel Group and the former WISCO, to implement the joint restructuring of the iron and steel enterprises, which is central to accelerating the transformation of the mode of economic development according to demand type, as is in line with the development of China’s iron and steel industry adjustment. Therefore, the distribution of member units in the standard deviation ellipse of the results before and after 2010 show certain differences.

Considering the economic and environmental consequences of the shift from the long-term “Northeast–Southwest” distribution to the temporary 2010 “Northwest–Southeast” trend, this change was essentially consistent with both resource availability constraints and government relocation strategies. Economically, the traditional “Northeast–Southwest” layout was initially formed to rely on resource endowments, with northeast, southwest, and central China being rich in iron ore and coal resources, containing certain mineral reserves and industrial foundations, and having cities located in the core area of traditional industrial agglomeration, such as Tangshan and Jinan, with mature supply chains and labour markets, respectively. However, with the gradual depletion of resources in the northeast and the rising environmental pressure in resource-based regions, the 2010 tilt toward the Yangtze River Basin (a manifestation of the “Northwest–Southeast” trend) aligned with the need to optimize resource allocation. The Yangtze River Delta and middle reaches of the Yangtze River regions have superior waterway transportation conditions, enabling convenient oversea importation of iron ore and reducing raw material transportation costs, while the developed regional economy provides a stable market for steel products. Environmentally, traditional “Northeast–Southwest” industrial concentration areas face severe pollution problems due to long-term high-energy consumption and high-emission production. The shift toward the Yangtze River Basin, coupled with the government’s promotion of merger and reorganization, helped to integrate scattered low-efficiency enterprises, promote centralized treatment of pollutants, and alleviate local environmental pressure.

In terms of policy alignment, this distribution shift was a direct response to the government’s industrial relocation and structural adjustment strategies. Since the 11th Five-Year Plan, the state has actively guided the iron and steel industry to move toward coastal and convenient water-transportation areas to reduce resource and environmental constraints, and the 2010 merger wave further accelerated this process. The “Northwest–Southeast” trend that year, centred on the Yangtze River Basin, not only reflected the market-driven choice of enterprises to be proximate to resource import channels and consumer markets, but also echoed the government’s layout requirements in terms of promoting the coordinated development of regional industries and advancing the green transformation of the steel industry. Although the distribution later returned to the “Northeast–Southwest” pattern, the 2010 adjustment verified the adaptability of the steel industry’s layout to resource changes and policy guidance, laying the foundation for subsequent optimization of the spatial pattern.

3.5. Analysis of the Evolution of Regional Centres of Gravity

It can be seen in Figure 7 that the regional centre of gravity of China’s iron and steel enterprises from 2005 to 2020 has always been located in the junction of Shandong, Henan, Hubei, and Anhui provinces, with Henan province being the regional centre of gravity in 2005 and 2010, then moving to Shandong province in 2015, 2020, and 2023. By collecting information on GDP, population density, and industrial distribution of individual provinces and cities, assigning appropriate weights to the unused regions, and combining their latitude and longitude, we can calculate China’s economic (115° E, 32.8° N) and employment centres of gravity (113.3° E, 32.3° N) through the use of ArcGIS; contrastingly, the centre of gravity of business members lies more towards the northeast.

The direction of movement of the iron and steel enterprise centre of gravity changed three times, moving southwest, northeast, and southwest again from 2005 to 2010, 2010 to 2014, and 2014 to 2023, respectively, roughly presenting a southwest–northeast movement trend. The longest and shortest migration distances are the 2005–2010 and 2014–2020 migrations, respectively.

China’s iron and steel enterprise centre of gravity in 2005–2020 initially moves relatively quickly and then slows. The first, second, and third stages show the fastest moving speed and longest moving distance, a slight slower speed and shorter distance, and the slowest speed and shortest distance, respectively.

The significant difference in the direction and speed of movement of the centre of gravity of China’s iron and steel enterprises is a microcosm of the changing patterns of economic development within the industry. These results reflect the implementation of the western development strategy in 1999, highlighting the comparative advantages of the eastern region becoming diminished and state and foreign investments into the central and western regions. Industries in the east were also shifting to western and central control, leading to local economic development, which then affects the location of the steel enterprises, with the centre of gravity of the region moving inwards. By the 2008 global financial crisis, economic development was externally oriented. After 2011, the implementation of the strategy of revitalizing Northeast China shifted the regional centre of gravity again to the north. Following this, economic development gradually entered a new normal, the world economy weakened, industrial overcapacity heightened, and demand reduced, which all greatly impacted the externally oriented eastern economy. Simultaneously, due to the larger volume of the eastern economy and the incrementally slower growth rate, the central and western regions benefitted from both the introduction of a series of national and regional policies and the increasing potential for investment and consumption. The above reasons lead to the national iron and steel centre of gravity moving southwest.

It can be seen that the evolution of the trajectory of the economic centre of gravity reflects the national macro-regional policies with a slight lag.

4. Conclusions, Discussion and Recommendations

4.1. Conclusions

This study uses a variety of GIS spatial analysis methods to deeply analyze the spatial pattern and multi-scale characteristics of the member units of the China Iron and Steel Association from 2005 to 2023. The distribution of steel industry member units shows significant differences and imbalances at different spatial scales. The specific conclusions are as follows:

• Distribution Differences on the Zonal Scale: There are obvious spatial zonal distribution differences on the zonal scale among steel enterprises, which are roughly consistent with the spatial pattern of social and economic development levels. The eastern coastal areas, with their large economic volume, high development level, convenient transportation, and sound market system and mechanism, have become the most concentrated areas in terms of the steel industry. The proportion of steel industry member units in the eastern, central, and western regions shows a spatial ratio of approximately 5:3:2. There are also significant differences in the spatial distribution of different types of member units, wherein the eastern region holds the advantage for all types of enterprise units.
• Differences in Enterprise Agglomeration Degree: There are certain differences in the agglomeration degree among different types of enterprise units. All types show spatial agglomeration except for trade-type enterprise units. During the period from 2005 to 2023, the agglomeration degree of the types of enterprises has shown an increasing trend, except for scientific research-type enterprise units, which have shown a trend of transformation from agglomerated to random distribution during this period, therefore reducing the degree of agglomeration. Trade-type enterprises showed a uniform distribution before 2014 and then transformed into an agglomerated type.
• Overall Spatial Distribution Trend: The overall spatial distribution pattern of China’s steel industry shows a “northeast–southwest” trend, with the central axis falling roughly on the “Tangshan–Jinan–Wuhan” line. Through the standard deviation ellipse analysis, it can be seen that the area inside the ellipse mainly covers the central and eastern provinces and that the difference between the long and short axes of the ellipse is large, with significant directionality. Although the differences in the gaps between the long and short axes of the ellipse in each year are not significant, in 2010, it showed a “northwest–southeast” spatial distribution trend as a result of the restructuring of Ansteel and Pangang, the former Baosteel Group, and the former Wuhan Iron and Steel Group.
• Evolution of the Centroid of Member Units: The centre of member units mainly moves along the junction of Shandong, Henan, Hubei, and Anhui provinces. Its moving direction and speed vary significantly in different time periods, with its moving trajectory being roughly divided into three stages and generally showing a southwest–northeast moving trend. This change is the result of the different stages and development levels of China’s steel industry, specifically those concerning its economic development pattern. To a certain extent, this reflects the national macro-regional policies but includes a certain time lag.

4.2. Discussion

• Correlation between Urban Hierarchy and Enterprise Distribution: At the national scale, many large-scale steel enterprises are concentrated in municipalities directly under the jurisdiction of central government and provincial capital cities, such as Beijing, Shanghai, and Guangzhou. These cities have complete infrastructures and numerous universities and research institutions, providing a large number of highly skilled professionals for steel enterprises. Simultaneously, within these cities, the urban economy is active and the demand for steel products is strong, providing a broad market space for enterprises. However, due to the limitations of resources and market scale, small and medium-sized cities find it difficult to attract large-scale steel enterprises; local steel enterprises are generally small-scale and face many difficulties in technological innovation and market expansion.
• Resource Dependence and Layout Changes: Most of China’s steel enterprises were once located in areas rich in iron ore and coal resources; for example, Ansteel grew and expanded by relying on the rich iron ore resources in Liaoning. However, with the gradual exploitation of resources, the reserves of resources in some areas have depleted, which, coupled with the development of transportation technology, has caused some enterprises to move to coastal areas. For example, by taking advantage of Shanghai’s superior port conditions, Baosteel can conveniently import iron ore and other resources, greatly reducing transportation costs and optimizing the enterprise’s layout.
• Layout Adjustment Oriented by Market Demand: The demand for steel is hugely exacerbated by the construction industry. In areas where the construction industry is prosperous, steel enterprises can obtain stable orders; for example, in the Yangtze River Delta and Pearl River Delta regions, the active construction market has attracted a large number of steel enterprises to relocate. In recent years, emerging industries such as new energy vehicles and aerospace have developed rapidly, increasing the demand for some special steels. Some steel enterprises have begun to move closer to areas where emerging industries gather in order to better understand market demands, respond quickly, and provide customized products.
• Layout Evolution under Policy Guidance: Driven by environmental protection policies, some high-pollution and high-energy-consuming steel enterprises face rectification or relocation; for example, some steel enterprises located in the main urban areas of cities have moved to suburban areas or other regions with a stronger environmental carrying capacity due to improved environmental protection requirements. At the same time, the industrial support policies introduced by various regions have also impacted the layout of the steel industry. Some economic development zones attract steel enterprises through policies such as tax preferences and land concessions, thus promoting the formation of industrial clusters and driving the development of the regional steel industry.
• Comparison of Structural Adjustment with Major Steel-Producing Countries: From the perspective of enterprise type evolution, China’s steel industry structural adjustment presents a “comprehensive upgrading of multi-type enterprises” feature driven by both policy and market, marking significant differences from major producing countries:

India focuses on expanding production-type enterprises to meet domestic infrastructure demand, with trade-type enterprises mainly executing raw material import and low-end product export, while scientific research- and service-type enterprises are relatively underdeveloped due to weak technological reserves.

Germany leads in high-end transformation, with scientific research-type enterprises being highly agglomerated and deeply integrated with automotive, aerospace, and other downstream industries to develop special steel products; production-type enterprises are mostly medium-sized and specialized, while service-type enterprises focus on providing technical consulting and recycling services, showing a “high-end, specialized” structure.

The United States has undergone industrial shrinkage and upgrading, with production-type enterprises decreasing in quantity but concentrating on high-value-added segments such as special steel; trade-type enterprises focus on global resource allocation and high-end product trading, while scientific research-type enterprises are mostly independent R&D institutions cooperating with universities, thus forming a “technology-leading, service-supported” pattern.

In contrast, China has achieved the coordinated development of four types of enterprises: production-type enterprises have optimized their layout through merger reorganization and coastal relocation; scientific research-type enterprises have shifted from agglomeration to rational dispersion to integrate regional innovation resources; service-type enterprises have expanded their service scope to cover logistics, finance, and technical services; and trade-type enterprises have formed multi-regional agglomerations to adapt to domestic and international dual circulation. This multi-type synergetic adjustment model is unique to China’s large-scale industrial system and regional development imbalance.

6.

Support for China’s “Dual-Carbon” Goals: This study’s findings on the spatial layout and agglomeration evolution of the steel industry provide practical support for achieving the “dual-carbon” commitments. On the one hand, the relocation of high-pollution production-type enterprises from urban centres to suburban and coastal areas (with stronger environmental carrying capacity) facilitates centralized treatment of pollutants and reduces regional environmental pressure, while on the other hand, the dispersion of scientific research-type enterprises promotes the cross-regional diffusion of low-carbon technologies (e.g., energy-saving production processes and clean energy applications) and the agglomeration of production enterprises enables low-carbon infrastructure sharing (e.g., waste heat recovery systems), thereby collectively reducing the industry’s overall carbon emission intensity and laying a foundation for reaching peak carbon emissions by 2030 and achieving carbon neutrality by 2060.

7.

Impact on Regional Inequality: This study’s findings indicate that the steel industry’s layout evolution does not lead to the sustained backwardness of central and western provinces, as cluster models are expected to promote regional convergence. Although eastern regions still dominate regarding the number of enterprise units, the “5:3:2” spatial ratio of eastern, central, and western distribution shows a narrowing gradient gap. As central and western regions undertake industrial transfers guided by policies and leverage local resource advantages to form specialized clusters (e.g., steel raw material supply clusters or regional service-type enterprise clusters), alongside the dispersion of scientific research-type enterprises bringing innovation resources, the central and western steel industry is gradually moving from “scale expansion” to “quality improvement,” helping narrow the development gap with eastern regions.

8.

Implications for Supply Chain Resilience and Risk Management: Against the backdrop of global steel demand fluctuations, this study’s findings on the steel industry’s spatial pattern and agglomeration characteristics provide key insights for enhancing supply chain resilience. The multi-regional agglomeration of trade-type enterprises (e.g., Beijing–Tianjin–Hebei, Yangtze River Delta) reduces dependence on a single trade hub and mitigates supply chain disruptions caused by regional risks. The coastal layout of production-type enterprises facilitates the diversification of raw material import channels and thus avoids supply shortages resulting from a fluctuating single source. Additionally, the coordinated development of production, research, service, and trade enterprises enables rapid adjustment of production and sales strategies in response to global demand changes, thereby improving the industry’s resistance to market risks.

4.3. Recommendations

This study not only visualizes the multi-scale spatial distribution patterns of China’s steel industry but also provides internationally relevant insights. For emerging economies with large-scale steel sectors (e.g., India, Brazil), its findings on “resource-oriented to coastal-port layout shifts” and “functional type agglomeration differences” offer a reference for balancing industrial expansion, resource security, and regional coordination. Conversely, for developed economies pursuing low-carbon steel transitions (e.g., Germany, Japan), analyzing research-type enterprise dispersion and low-carbon-driven spatial adjustments can shed light on how technological innovation and environmental policies reshape industrial geography. Additionally, the identification of trade-type enterprise agglomeration around key coastal hubs (e.g., Fujian, Pearl River Delta) provides a case study for understanding global steel supply chain dynamics and the role of regional hubs in connecting domestic and international markets.

Based on the above innovations and international implications, the following practical insights and suggestions are proposed for the rational development and layout optimization of China’s steel industry:

• Optimize Regional Layout Based on Agglomeration Advantages and Industrial Upgrading

Consolidate core agglomeration areas as industrial upgrading leaders: Strengthen infrastructure connectivity and industry-supporting capacity in Hebei-centred North China and Jiangsu-centred East China—key agglomeration hubs of the steel industry. Promote the integration of production-, service-, and R&D-type enterprises to build “low-carbon production + professional service + technological innovation” industrial chains. For example, support Hebei’s iron and steel clusters to cooperate with local R&D institutions in developing energy-saving technologies, and encourage Jiangsu’s enterprises to integrate logistics services with digital supply chain management, thus driving the improvement of the entire industry chain through agglomeration synergy.

Guide rational diffusion of non-core functions for balanced regional upgrading: For service-type enterprises showing a weakening agglomeration trend, encourage their orderly transfer to surrounding areas of core agglomerations (e.g., Langfang around Beijing–Tianjin–Hebei) or central–western regions with rising market demands (e.g., Sichuan-Chongqing). Focus on transferring logistics, maintenance, and other supporting services to drive the development of local steel supporting industries while alleviating resource congestion in core areas, ensuring alignment with the national strategy of coordinated regional development.

2.

Adjust Enterprise Layout by Functional Type to Link Industrial Upgrading

Production-type enterprises: Layout optimization driven by low-carbon and high-efficiency upgrading: Rely on coastal port advantages (e.g., Shanghai, Tianjin, Shenzhen) to develop large-scale coastal steel bases integrated with “green ports + low-carbon production”, using imported iron ore to reduce logistics costs while promoting the application of hydrogen direct reduction and blast furnace gas recycling technologies. Gradually guide enterprises with small and inefficient production capacities in inland resource-depleted areas to relocate to coastal or central–western areas with sufficient environmental carrying capacity, ensuring alignment with the “dual-carbon” goal of reducing industrial carbon emissions.

Research-type enterprises: Orientating R&D layout to core technology breakthroughs: Disregard geographical restrictions and support the establishment of R&D centres in cities with concentrated high-end talent and academic resources (e.g., Beijing, Shanghai, Nanjing), focusing on key technologies such as low-carbon smelting, special steel materials, and digital intelligent manufacturing. Strengthen inter-regional remote cooperation platforms (e.g., joint laboratories between Beijing and Wuhan) through digital collaboration to compensate for the weakening agglomeration effect, thus providing technological support for the industry’s “innovation-driven” upgrade.

Trade-type enterprises: Layout optimization to serve high-value trade upgrading: Focus on optimizing the layout of the three major agglomerations (Beijing–Tianjin–Hebei, Fujian coastal, and Pearl River Delta). Enhance the role of the Yangtze River Delta (centred on Shanghai) as a new agglomeration centre by leveraging its international trade platforms (e.g., Shanghai Free-Trade Zone) and port group advantages and focusing on expanding trade of high-value-added products such as special steel for new energy vehicles. Build cross-regional trade cooperation networks linking coastal and inland markets to promote the transformation of steel trade from bulk low-value to high-value-added.

3.

Align Layout Optimization with “Dual-Carbon” Goals and National Strategies

Strengthen environmental constraints in agglomeration areas to encourage low-carbon upgrading: Improve environmental access standards for coastal and existing production bases, set stricter carbon emission intensity and energy consumption benchmarks, and make environmental costs a key factor in enterprise location decisions. Prohibit new high-pollution, high-emission steel projects in core agglomerations, and support enterprises incorporating clean production technologies such as hydrogen direct reduction and electric arc furnaces, directly aligning with the national “dual-carbon” commitments.

Promote coordinated regional resource allocation for low-carbon balanced development: Establish a cross-regional mechanism linking eastern agglomerations with central–western resource-rich areas, including “eastern low-carbon production technology transfer + central–western raw material supply” and “eastern scrap steel recycling investment + central–western deep-processing industry transfer”. For example, encourage eastern enterprises to build scrap steel recycling bases in Shanxi and Inner Mongolia, and support Hubei and Sichuan in undertaking steel deep-processing projects with new energy equipment, achieving both the regional balanced development and low-carbon transformation of the industry.

Integrate spatial layout with national industrial policies: Align the layout adjustment of steel enterprises with the “14th Five-Year Plan for the Iron and Steel Industry” and regional strategies such as the Yangtze River Economic Belt and Beijing–Tianjin–Hebei Coordinated Development. For example, strictly monitor the new capacity in the Yangtze River Delta and Pearl River Delta and guide the development of green steel demonstration bases in coastal areas, ensuring that spatial layout optimization is closely aligned with national industrial and low-carbon development goals.

4.

Integrate Foreign Direct Investment (FDI) and Overseas Business into Spatial Layout Planning

Guide FDI to complement domestic industrial upgrading: Encourage the targeted introduction of FDI in high-end steel sectors (e.g., special steel for aerospace, low-carbon smelting equipment) and direct it to core agglomerations such as the Yangtze River Delta and Pearl River Delta. Leverage FDI’s technological and management advantages to drive the integration of local production, R&D, and service enterprises, forming internationally competitive industrial clusters. For example, support joint ventures between coastal steel bases and advanced foreign enterprises in developing hydrogen-based steelmaking technologies, thereby enhancing the low-carbon competitiveness of core agglomerations.

Coordinate overseas business with domestic spatial adjustments: For enterprises with overseas production bases (e.g., in Southeast Asia, Africa) or resource development projects, strengthen the linkage between overseas layouts and domestic functional positioning. Focus on locating domestic trade-type enterprises in coastal hubs (e.g., Shanghai, Qingdao) to streamline the import and export of overseas resources and high-value-added products, respectively. Meanwhile, encourage research-type enterprises in Beijing, Nanjing, etc., to establish overseas R&D branches in technology-intensive regions (e.g., Germany’s Ruhr area, Japan’s Kansai region) and track global technological trends, reporting innovation achievements back to domestic production clusters and thus forming a “domestic–foreign synergetic” spatial dynamic system.

5.

Adapt Spatial Logic to Digitalization, Industry 4.0, and Green Transformation Technologies in the Next Decade

Build “digital agglomeration hubs” driven by Industry 4.0: Promote the concentration of digital technology R&D and application platforms in cities with strong digital infrastructure (e.g., Shenzhen, Hangzhou). Support core steel clusters (e.g., Hebei, Jiangsu) in building industrial internet platforms to achieve inter-enterprise data sharing, intelligent production scheduling, and remote equipment maintenance. This will reduce the reliance on geographical proximity for collaboration, allowing production-type enterprises to maintain efficient links with service and R&D units even in dispersed layouts.

Reshape spatial layout with green and intelligent technologies: Considering the maturity of technologies such as carbon capture, utilization, and storage (CCUS) and intelligent blast furnaces, encourage the construction of “digital–green integrated bases” in regions with both environmental carrying capacity and renewable energy resources (e.g., coastal areas with wind power, northwest regions with solar energy). For research-type enterprises, focus on their layout in cities with leading digital and green technology innovation (e.g., Beijing, Shanghai) to facilitate cross-disciplinary collaboration between materials science, digital intelligence, and environmental engineering. Additionally, develop intelligent logistics networks centred on trade agglomerations (e.g., Yangtze River Delta) to achieve the real-time tracking of global steel flows, thus enhancing supply chain flexibility and reducing the spatial constraints of traditional trade.