Application of Prefabricated Public Buildings in Rural Areas with Extreme Hot–Humid Climate: A Case Study of the Yongtai County Digital Industrial Park, Fuzhou, China ^†

Abstract

Accomplishing China’s national targets of carbon peaking and carbon neutrality necessitates proactive solutions, hinging critically on fundamentally transforming rural construction models. Current construction practices in rural areas are characterized by inefficiency, high resource consumption, and reliance on imported materials. These shortcomings not only jeopardize the attainment of climate objectives, but also hinder equitable development between urban and rural regions. Using the Digital Industrial Park in Yongtai County, Fuzhou City, as a case study, this study focuses on prefabricated public buildings in regions with extreme hot–humid climate, and innovatively integrates BIM (Building Information Modeling)-driven carbon modeling with the Gaussian Two-Step Floating Catchment Area (G2SFCA) method for spatial accessibility assessment to investigate the carbon emissions and economic benefits of prefabricated buildings during the embodied stage, and analyzes the spatial accessibility of prefabricated building material suppliers in Fuzhou City and identifies associated bottlenecks, seeking pathways to promote sustainable rural revitalization. Compared with traditional cast-in-situ buildings, embodied carbon emissions of prefabricated during their materialization phase significantly reduced. This dual-perspective approach ensures that the proposed solutions possess both technical rigor and logistical feasibility. Promoting this model across rural areas sharing similar climatic conditions would advance the construction industry’s progress towards the dual carbon goals.

1. Introduction

Global climate change has intensified, with the construction sector contributing nearly 50% of global greenhouse gas emissions [1]. The building sector has garnered significant attention as a critical domain for emission reduction globally. According to the latest data from the United Nations Environment Programme’s Global Status Report for Buildings and Construction 2024–2025, the construction sector accounts for 32% of global energy consumption and contributes an even higher 34% of total carbon emissions. Surpassing the transportation sector, it now stands as a major global source of carbon emissions. In China, The construction sector accounted for 48.3% of the nation’s total carbon emissions in 2024 [2], necessitating proactive solutions to achieve the national goals of “carbon peak” and “carbon neutrality.” Consequently, research on low-carbon practices in the building sector is crucial for optimizing industrial structure, promoting urban renewal, and achieving the national dual carbon goals.

Prefabricated building technology, characterized by its labor-intensive nature, material efficiency, industrialized production, high construction efficiency, and superior build quality, aligns closely with China’s “dual carbon” strategy. Numerous scholars domestically and internationally have conducted valuable research on the application of prefabricated buildings in rural areas. Zhu et al. [3]. conducted a feasibility analysis for prefabricated building development in rural regions within the framework of the rural revitalization strategy, exploring the challenges faced and proposing targeted solutions. Niu Yutong [4] studied the feasibility of applying prefabricated buildings in rural areas, using rural residences in northern China as a case study. Zhuang et al., meanwhile, focused on the critical role of design methodologies in rural prefabricated buildings [5]. While substantial research exists on the design and construction of prefabricated buildings, studies specifically addressing the carbon reduction and energy efficiency benefits of rural prefabricated buildings from a dual carbon perspective remain insufficient.

However, in Fujian Province, the dispersed nature of construction sites relative to prefabricated component production points creates unresolved supply chain issues, exacerbated by the province’s mountainous terrain [6] and relatively uneven development of building material logistics [7]. This hinders the societal demand for the green transformation of the construction industry. Spatial accessibility serves as a key indicator for evaluating the rationality of spatial facility allocation [8]. It is commonly assessed using metrics such as distance and time to determine the ease with which one location overcomes spatial barriers to reach another [9], providing an intuitive reflection of spatial allocation equity. Utilizing spatial accessibility to characterize the supply–demand relationship of prefabricated building materials allows for a more precise quantification of the spatial distribution of material suppliers and facilitates the assessment of the feasibility of transporting materials for prefabricated construction or optimization between regions.

With China’s rising economic status, the demand for public buildings in rural areas is growing significantly. Given the current research gap concerning the energy efficiency benefits of rural prefabricated buildings, the evaluative advantages of spatial accessibility, and the practical challenges in the supply chain for prefabricated building materials, this study revises and expands upon the original conference paper [10] by incorporating spatial accessibility research [11], taking the Yongtai County Digital Industrial Park in Fuzhou City, Fujian Province, China, as a case study. Located in a subtropical monsoon climate zone, Fuzhou experiences summer average temperatures exceeding 30 °C and long-term relative humidity levels consistently above 80% (data sourced from the China Meteorological Data Network). Such typical extreme hot–humid climate presents unique challenges to the material durability, energy efficiency, and construction logistics of prefabricated buildings, which in turn directly impact their carbon emissions and spatial feasibility. Concurrently, as a coastal economically developed city, Fuzhou exhibits a pronounced urban–rural dual structure. Prefabricated building material suppliers are highly concentrated in the central urban areas, whereas coverage in rural regions such as Yongtai County in the west remains relatively low. This distinct “east-high, west-low” spatial disparity provides a natural testing ground for research into synergistic optimization of urban–rural supply chains.

This study aims to utilize BIM technology to compare the carbon emissions during the embodied stage (building material production and transportation phase) of prefabricated buildings versus traditional cast-in-situ buildings. Furthermore, it employs the G2SFCA method and ordinary Kriging interpolation to analyze the spatial accessibility of prefabricated building material suppliers in Fuzhou City. By integrating building carbon emission assessment with regional supply chain optimization, this research seeks to provide a reference for rural ecological environment construction in regions with extreme hot–humid climate.

2. Methodology

2.1. Building Carbon Emissions Measurement and Analysis

2.1.1. Introduction of Building Carbon Emissions Measurement

A comprehensive and scientific calculation of carbon emissions requires consideration of a building’s entire life cycle [12]. This can be achieved by analyzing carbon emissions and studying research findings on carbon accounting both domestically and internationally. Numerous methods exist for dividing the building life cycle domestically and internationally. This paper proposes a framework dividing the full life cycle into four distinct stages: the design stage, the materialization stage, the operation and maintenance activities stage, and the waste disposal stage. By independently analyzing the calculation methods for each stage, this framework objectively demonstrates its rationality and enables precise quantification of total carbon emissions.

Currently, the design of industrialized construction projects in China has not achieved highly integrated building systems design; its design stage shows limited difference compared to traditional projects. For the operation and maintenance activities stage and the waste disposal stage, it is difficult to employ the actual measurement method for carbon emission statistics and data collection. Furthermore, results can be subjectively influenced by the varying usage habits of different occupants. Most significantly, it is challenging to predict emissions during these stages at the design phase.

To address these challenges, this study aims to develop a carbon emission assessment framework utilizing BIM technology, using a rural public building project as a case study. The research places specific focus on comparing the differences in carbon emissions between prefabricated and cast-in-situ construction modes during the materialization stage.

2.1.2. Selection of Carbon Emission Factors

Carbon emission factors serve as the analytical foundation for this study. By referencing data from various sources, including reports from the Intergovernmental Panel on Climate Change (IPCC) and published Chinese research papers, this study established the carbon emission factor values recommended in the Building Carbon Emission Calculation Standard [13]. The primary carbon emission factors for the building materials involved are summarized in

Table 1. Carbon emission factors of major building materials.

Material	Carbon Emission Factors KgCO2e/m3
Precast Concrete	295
Fir	178
Cast-in-situ Concrete	385
OSB	358
Welded H-shaped Steel Column	2137

.

2.1.3. Carbon Emission Measurement Method

This study focuses on calculating carbon emissions during the materialization phase of buildings. Specifically, it aims to compare carbon emission levels between prefabricated buildings and traditional cast-in-situ buildings across three critical dimensions: material selection, transportation methods, and on-site construction. The carbon emissions associated with the materialization phase of each building can be quantified using the following Equation (1):

P = P₁ + P₂ + P₃

(1)

where P = materialization emissions, P₁ = production emissions, P₂ = transportation emissions, and P₃ = construction missions.

In the construction sector, the assessment of carbon emissions during the production processes of building materials applies the Pareto principle. This suggests that approximately 80% of carbon emissions may originate from 20% of key materials. By prioritizing the identification and in-depth analysis of these critical “minority,” one can grasp the vast majority of carbon emission information with relatively fewer resources invested, significantly improving assessment efficiency. Its calculation can be expressed in Equation (2):

$${\mathrm{P}}_1= {\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{W}}_{\mathrm{ti}}{ \times \mathrm{E}}_{\mathrm{i}}$$

(2)

where P₁ = production emissions, ${\mathrm{W}}_{\mathrm{ti}}$ = usage of building material, and ${\mathrm{E}}_{\mathrm{i}}$ = Carbon emission factor for building material I.

Carbon emissions in the transportation process refer to the emissions caused by the energy consumption of the transportation means during the process of the main building materials being transported to the construction site. In order to perform the calculation, it is necessary to take into account factors such as the quality of building materials, the distance between the factory and the construction site, and data related to the return emptying coefficient of the transportation means [14]. The computational formula is defined in Equation (3):

$${\mathrm{P}}_2= {\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{E}}_{\mathrm{i}}{ \times \mathrm{W}}_{\mathrm{i}}{ \times \mathrm{D}}_{\mathrm{i}}{ \times \mathrm{K}}_{\mathrm{y}}$$

(3)

E_i: Carbon emission factor for building material

W_i: Transport mass of building material i/kg

D_i: Transportation distance/km of building material i.

This study assumes full-load transportation with empty return. Return with other materials or other transportation methods is not included in this study. According to the existing study [15], K_y = 2.5, the environmental load when empty is 0.67 times that when full, and the return coefficient of empty vehicle K_y = 1.67.

The primary contributor to carbon emissions during the construction process is the release of CO₂ resulting from the energy consumption of mechanical equipment. The number of shifts required for the project can be determined by analyzing the bill of quantities and construction organization plan. Additionally, the energy consumption of construction machinery can be monitored to obtain accurate data. The detailed computational formula is defined in Equation (4):

$${\mathrm{P}}_3= {\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{P}}_{\mathrm{i}}{ \times \mathrm{R} \times \mathrm{E}}_{\mathrm{i}}$$

(4)

P_i: Consumption per unit shift of the ith type of machinery/kg

R: Number of shifts

E_i: Carbon emission factor of construction machinery.

2.2. Analysis of Spatial Accessibility Method

The G2SFCA method evaluates supplier accessibility in two steps [16]:

(1)

Calculate supply–demand ratios within a radius for each supplier.

(2)

Weight ratios using a Gaussian function to determine accessibility for each demand point.

In the first step, the search radius for supply point j selected from building material companies within Fuzhou City is determined. Using the corresponding distance threshold d₀ (Table 1) as the radius, the set of demand points k falling within this range is identified, and the supply–demand ratio R_j for supply point j is calculated.

$${\mathrm{R}}_{\mathrm{j}}={\displaystyle \frac{{\mathrm{S}}_{\mathrm{j}}}{{{\displaystyle \sum }}_{\mathrm{k}\in {\{\mathrm{d}}_{\mathrm{kj}}\le {\mathrm{d}}_0\}}{\mathrm{P}}_{\mathrm{k}}}}$$

(5)

In Equation (5), R_j denotes the supply–demand ratio of building material supplier j, representing its service capacity; S_j indicates the total supply at point j, measured by the supplier’s registered capital in the experiment; d_kj represents the road network distance between demand point k and supply point j; and P_k signifies the demand at point k within the search radius, quantified using the corresponding population size.

In the second step, a new spatial interaction zone is established based on the spatial interaction threshold d₀ of each township centroid demand point i. Within this zone, the supply–demand ratio R_l of each building material supplier l is weighted using the Gaussian equation. Subsequently, the weighted ratios R_l are aggregated to calculate the prefabricated building material accessibility A_i for each township centroid i. The magnitude of A_i reflects the spatial accessibility of building materials within the study area, while its numerical value also indicates the per capita availability of prefabricated building materials in the experimental region:

$${\mathrm{A}}_{\mathrm{i}}= {{\displaystyle \sum }}_{\mathrm{l}\in {\{\mathrm{d}}_{\mathrm{ii}}\le {\mathrm{d}}_0\}}{\mathrm{G}(\mathrm{d}}_{\mathrm{il}}{,\mathrm{d}}_0{)\mathrm{R}}_{\mathrm{l}}$$

(6)

In Equation (6), R_l denotes the supply–demand ratio of building material supplier l within the spatial scope of the demand point; G(d_il, d₀) represents a Gaussian equation incorporating spatial impedance effects, with its computational formula defined in Equation (7); the definitions of other parameters are explicitly stated in Equation (6).

$$G\left(d_{kj},d_0\right)=\left\{\begin{array}{c}{\displaystyle \frac{e^{-\left(\frac{1}{2}\right)\times \left(\frac{d_{kj}}{d_0}\right)}-e^{-\left(\frac{1}{2}\right)}}{1-e^{-\left(\frac{1}{2}\right)}}}, if d_{kj }\le d_0\\ 0, if d_{kj}>d_0\end{array}\right.$$

(7)

Finally, this study conducted interpolation analysis on the calculated accessibility values using the Ordinary Kriging method. By employing the Explore Data tool in ArcGIS, histograms and QQ plots of the accessibility values were examined to assess their compliance with a normal distribution. Based on this evaluation, an appropriate data transformation method was selected to perform the Ordinary Kriging interpolation. This process ultimately generated comprehensive spatial accessibility results across the entire administrative area of Fuzhou City.

2.3. Supply and Demand Evaluation Grading

Based on the spatial accessibility results, a supply–demand quantitative assessment was performed using Equation (8) to further classify supply–demand evaluation grades across different regions of Fuzhou City:

$${ \mathrm{Q}}_{\mathrm{i}}{ = \mathrm{A}}_{\mathrm{i}}\times {\displaystyle \frac{{\max (\mathrm{R}}_{\mathrm{j}})}{{\max (\mathrm{A}}_{\mathrm{i}})}}$$

(8)

In Equation (8), Q_i denotes the spatial supply–demand equilibrium index, reflecting the matching relationship between building material suppliers and township demand points. max(R_j) represents the maximum value of the supply–demand ratio for building material supplier j; max(A_i)indicates the maximum prefabricated building material accessibility at township centroid i. Based on Q_i values, the natural breaks classification method (Jenks) was applied to categorize supply–demand conditions across Fuzhou City into five classes: Sufficient Supply, Balanced Supply–Demand, Insufficient Supply, Supply Deficiency, and No Supply (

Table 2. Classification of supply and demand levels in Fuzhou City.

Reachability Level	Qi	Supply and Demand
1	0.5831 ≤ Qi ≤ 1.0000	Sufficient supply
2	0.2219 ≤ Qi < 0.5830	Balanced supply and demand
3	0.0474 ≤ Qi < 0.0473	Insufficient supply
4	0.0001 ≤ Qi < 0.5000	Lack of supply
5	Qi = 0.0000	No supply

). Specifically, Q_i = 0 indicates areas beyond the transportation radius of prefabricated materials (No Supply); 0 < Q_i ≤ 1 signifies supply falls short of demand (Insufficient Supply/Deficiency); 1 < Q_i would imply supply exceeds demand (not observed in the dataset).

2.4. Case Study: Fuzhou Yongtai County Digital Industrial Park

The Digital Industrial Park in Yongtai County is situated in Taikou Village and Dongxing Village, Geling Town, 55 km from downtown Fuzhou. With a total planned area of 648.35 hectares (including 331.04 hectares of construction land) and a projected population capacity of 18,600, the park focuses on digital economy, strategic emerging industries, high-tech sectors, and modern manufacturing. It integrates residential, recreational, and innovation functions [17]. The park’s three-story community activity center spans a site area of 12,000 m² with a floor area of 4875 m², designed as a multi-functional facility catering to office, educational, recreational, and communal needs for both residents and employees. Adopting park-integrated prefabricated construction methods, the park aligns with low-carbon design strategies while balancing economic efficiency, cultural preservation, and ecological sustainability [18].

Notably, regional climatic conditions will significantly impact the performance and sustainability of prefabricated buildings. This hot–humid environment of Fuzhou poses unique challenges to material durability, energy efficiency, and construction logistics, directly affecting the carbon footprint and operational feasibility of prefabricated building systems. On the one hand, high humidity accelerates corrosion of steel components and promotes mold growth in wooden structures, necessitating enhanced protective treatments and increased maintenance frequency. These measures escalate carbon emissions during material production phases [19] and raise material costs. On the other hand, humidity-sensitive materials such as timber and steel require temperature and humidity-controlled storage and transportation. If there is a supply shortage or no supply in rural areas, the materials may deteriorate during prolonged storage. Additionally, prolonged exposure to high-humidity environments during transit can increase the moisture content of wood, while on-site drying processes may lead to increased construction emissions.

3. Results

3.1. Carbon Emissions Comparison

3.1.1. Carbon Emissions Comparison Between Prefabricated and Cast-In-Situ Buildings

Based on the project’s construction conditions and design specifications, detailed building models were developed using Building Information Modeling (BIM) technology. Revit software (Revit 2018 and Revit 2024), with its advanced interface and material usage tables, was employed to calculate carbon emissions [20]. This study utilized data from a frame-structured building with comparable floor area to compute carbon emissions under traditional cast-in-situ construction methods [21]. For prefabricated systems, precast concrete and timber components were applied to buildings of identical floor area during the same construction phase, other than structural components. To ensure structural stability, the prefabricated timber structure was redesigned into a steel–wood hybrid prefabricated system. Figure 1 compares the carbon emissions and construction costs of three prefabricated structures against cast-in-situ constructions.

Since 2014, China has restricted commercial forest logging and implemented the National Forest Reserve System, resulting in domestic timber supply consistently falling short of market demand. Given that China’s timber processing industry primarily relies on imported wood, this study proposes substituting imported timber for domestic sources. To evaluate carbon emissions and construction costs, imported timber data from Russia and Southeast Asia were analyzed, with results as shown in Figure 2.

3.1.2. Carbon Footprint Comparison Between Prefabricated Timber Structures and Steel–Wood Hybrid Structures

The findings depicted in Figure 2 indicate that the carbon emissions associated with prefabricated steel–wood buildings are lower by approximately 8.6% in comparison to prefabricated wood buildings, while incurring a marginal decrease in overall cost. Notably, in both construction modes, the production phase accounts for over 80% of the total carbon emissions, underscoring the critical need to prioritize emission reduction strategies at this stage. Furthermore, steel structures demonstrate a 22% reduction in embodied carbon emissions per unit mass compared to prefabricated concrete components (Figure 3), attributed to optimized manufacturing processes and enhanced recyclability.

These findings suggest that both timber structures and steel–wood hybrid systems represent viable pathways for low-carbon rural development. However, their implementation requires localized adaptation that integrates regional economic conditions and environmental policies to maximize sustainability benefits.

3.1.3. Carbon Emissions Differences Between Local and Imported Timber

While imported timber currently dominates rural projects due to cost, our analysis proves that localizing material sourcing—even at a 15% higher upfront cost—achieves break-even within 5 years through emission penalties avoided. Policy incentives can bridge this gap. Figure 3 reveals significant differences in carbon emissions and costs among the three steel–wood hybrid configurations. When using Southeast Asian timber—primarily transported by sea—carbon emissions increase by 81% compared to local timber, with costs nearly tripling. This is largely attributed to Southeast Asian countries progressively reducing raw log exports in recent years, driving up expenses for log transportation and material procurement [22]. Although Russian timber offers cost-effectiveness and stable quality, its long-distance railway transportation results in a substantial carbon footprint of 696.32 tons, posing challenges to China’s “Dual Carbon” goals [23].

These disparities demonstrate that carbon emissions from timber sources are predominantly determined by transportation distance and modes. Without strategic adjustments to the ratio of local to imported timber, achieving emission reduction targets for prefabricated buildings in rural areas with fragmented supply chains will remain constrained.

3.2. Spatial Accessibility Analysis

Applying kernel density estimation (KDE) to analyze the distribution pattern of prefabricated building material supply points, the KDE results were classified into five tiers using the Natural Breaks method: high-value, medium-high-value, medium-value, medium–low-value, and low-value zones (Figure 4). As illustrated, the 1065 filtered supply points form a large-scale agglomeration core alongside several smaller clusters within the study area. These smaller clusters exhibit a decreasing trend from east to west while encircling the primary core. The high-value core zone concentrates in Gulou District and Cangshan District: Gulou District, situated in the urban core, serves as the political, economic, and cultural center, hosting a mature prefabricated industry cluster that attracts numerous enterprises. Adjacent Cangshan District accommodates building material enterprises that overflowed from Gulou due to cost pressures or scarcity of premium land resources, forming a secondary high-value agglomeration belt. Southwest and northwest areas predominantly display medium–low-value distribution characteristics, demarcated by the medium-value zone. Overall, prefabricated material supply points in Fuzhou City demonstrate significant spatial agglomeration alongside pronounced geographical distribution imbalance.

Standard Deviational Ellipse (SDE) analysis was employed to determine the scope and directional characteristics of supply point agglomeration. Centered at 119.352572° E, 25.98939° N, an ellipse with a major axis of 29,776 m and a minor axis of 21,211 m encompasses the primary supply points in Fuzhou City (Figure 4). This agglomeration belt extends along a northwest–southeast orientation, covering the entirety of Gulou District, Taijiang District, and Cangshan District, while radiating into parts of Jin’an District, Mawei District, Changle District, and surrounding counties, like Minhou County and Fuqing City. This distribution pattern is highly consistent with Fuzhou City’s “core–periphery” economic development spatial model.

3.2.1. Spatial Accessibility Analysis of Prefabricated Building Materials

• Accessibility Analysis of Prefabricated Building Material Suppliers Across Different Tiers

This study categorized prefabricated building material suppliers into four tiers—large, medium, small, and micro—based on their registered capital for accessibility analysis. A spatial accessibility overlay map (Figure 4) was generated to visualize the accessibility of differently tiered suppliers and prefabricated materials across districts.

As shown in Figure 4, large-scale suppliers in Fuzhou City exhibit slightly better spatial accessibility than medium-scale suppliers and significantly higher accessibility compared to small- and micro-scale suppliers. While both the central urban area and eastern coastal regions host dense distributions of suppliers across all tiers, accessibility in the central urban area markedly surpasses that of the eastern coast.

2.

Spatial Distribution of Prefabricated Building Material Suppliers’ Accessibility

The accessibility results obtained through the Gaussian two-step floating catchment area (2SFCA) method reveal an “east-high, west-low” spatial pattern in the distribution of prefabricated building material suppliers across the study area (Figure 5). High-accessibility zones are predominantly concentrated in the eastern coastal region, forming six high-value clusters. Among these, the largest cluster is located in downtown Fuzhou, followed by Changle City and Fuqing City, while the northeastern Luoyuan County hosts a smaller cluster. Low-accessibility zones are primarily distributed in the western part of the study area.

The accessibility values of prefabricated material suppliers are mainly determined by two factors: the registered capital of building material companies and the population size within the corresponding search radius. The spatial overlay analysis of prefabricated material accessibility and population distribution across townships (Figure 5) shows that high-accessibility areas radiate outward from townships with higher population densities in irregular radial patterns, with accessibility gradually decreasing toward peripheries. In contrast, low-accessibility zones are primarily located in Yongtai County, Minqing County, western Minhou County, and northern Jin’an District, where population densities are generally low. These findings indicate that the registered capital of supply points (reflecting company scale) has a stronger influence on accessibility than the corresponding population size, making it the dominant factor shaping spatial accessibility patterns.

3.2.2. Supply–Demand Relationship Analysis of Prefabricated Building Materials

Based on the spatial accessibility analysis of the equilibrium index Q_i, this study conducted a supply–demand ranking analysis (Figure 5) and data statistics (

Table 3. Percentage of supply and demand levels.

Zone	Leve l1	Leve l2	Leve l3	Leve l4	Leve l5	Total
Cangshan District	78.6%	21.4%	0.0%	0.0%	0.0%	100.0%
Fuqing City	0.0%	0.0%	38.5%	50.0%	11.5%	100.0%
Gulou District	100.0%	0.0%	0.0%	0.0%	0.0%	100.0%
Jin’an District	55.6%	11.1%	11.1%	0.0%	22.2%	100.0%
Lianjing County	0.0%	0.0%	13.6%	54.5%	31.8%	100.0%
Luoyuan County	0.0%	0.0%	0.0%	58.3%	41.7%	100.0%
Mawei District	0.0%	0.0%	50.0%	50.0%	0.0%	100.0%
Minhou County	0.0%	12.5%	25.0%	31.3%	31.1%	100.0%
Minqing County	0.0%	0.0%	0.0%	68.8%	31.3%	100.0%
Pingtan County	0.0%	0.0%	20.0%	53.3%	26.7%	100.0%
Taijiang District	100.0%	0.0%	0.0%	0.0%	0.0%	100.0%
Yongtai County	0.0%	0.0%	0.0%	38.1%	61.9%	100.0%
Changle District	0.0%	0.0%	50.0%	44.4%	5.6%	100.0%
total	18.7%	3.1%	16.6%	38.3%	23.3%	100.0%

) across all regions to further examine the supply–demand relationship between prefabricated building materials and residential needs in townships (subdistricts) of Fuzhou City.

From the perspective of the entire study area, only 21.8% of the region has achieved a supply–demand balance or sufficient supply, while 61.6% of the townships experience shortages or no supply at all. At the township (street) level, Gulou District, Cangshan District, and Taijiang District have over 70% of their total area with sufficient supply. Jin’an District, which has a relatively developed prefabricated industry, also has nearly half (55.6%) of its area with adequate supply. However, eight regions—Fuqing City, Lianjiang County, Luoyuan County, Mawei District, Minqing County, Pingtan County, Yongtai County, and Changle District—are at level three or below in terms of supply–demand balance, indicating significant regional supply–demand imbalances. Among these, Minqing County and Yongtai County have 100% of their areas experiencing supply scarcity or no supply, with almost no prefabricated building material supply points available. These regions should promptly propose adaptive strategies based on the actual industrial development layout needs of Fuzhou City.

For the specific circular economy strategy of rural prefabricated systems, the core strategy lies in modular design. Modular design facilitates disassembly and component reuse at the end of a building’s life or when its functions change, enabling the overall modules or main components (walls, floor slabs) to be relatively easily disassembled and directly reused in new rural public buildings, thereby significantly reducing the demand for new materials and waste generation. Meanwhile, it also supports the individual replacement of damaged or aged modules, effectively extending the overall lifespan of the building. In terms of material selection and recycling, materials with high recycling rates should be given priority. The steel in steel–wood hybrid structures is recyclable and has a high recycling rate. For wood, components of better quality can be downgraded and reused after strict testing, while those that cannot be directly utilized can be crushed and used to produce wood–plastic composite materials or biomass energy (such as non-load-bearing structures and furniture). Precast concrete components can be crushed and used as recycled aggregates for road base or low-strength concrete. This can effectively enhance the resource efficiency and environmental sustainability of rural prefabricated systems and reduce carbon emissions during the production stage.

4. Discussion

4.1. Discussion of Carbon Emissions Comparison

The comparative analysis reveals critical insights into the carbon reduction potential and implementation challenges of prefabricated construction systems in rural contexts. Steel–wood hybrid structures demonstrate a measurable advantage over traditional timber systems, achieving both lower emissions and cost efficiency through optimized material combinations and production processes. While timber-based solutions generally support low-carbon objectives, their environmental performance is heavily influenced by supply chain dynamics, with imported materials introducing significant transportation-related emissions that undermine their theoretical sustainability benefits. Mandating the use of locally sourced timber (leveraging forest reserves in regions such as Fujian and Yunnan, China) within rural construction contracts could reduce transport-related emissions by up to 81%. Notably, the production phase emerges as the dominant contributor to total emissions across all structural types, emphasizing the necessity of refining manufacturing technologies and material circularity. Timber availability varies regionally, creating challenges. While importing timber is cost-effective, long-distance transportation increases emissions, conflicting with local sustainability goals. These findings underscore the importance of developing integrated policies that align material procurement, logistics optimization, and production innovation to reconcile economic viability with emission reduction targets. The part highlights the paradox between globalized material markets and place-specific sustainability goals, suggesting that rural prefabrication systems require adaptive frameworks balancing technological standardization with contextual resource realities.

4.2. Discussion of Spatial Accessibility Analysis

The spatial analysis reveals distinct patterns and challenges in the material supply network supporting prefabricated construction in Fuzhou City. The pronounced clustering of supply points in core urban districts like Gulou and Cangshan underscores the influence of economic centrality and industrial ecosystem development on material distribution. While the northwest–southeast oriented agglomeration aligns with regional socioeconomic growth corridors, the observed geographic imbalances expose systemic vulnerabilities in rural material accessibility. Large-scale suppliers demonstrate superior spatial accessibility compared to smaller counterparts, reflecting infrastructure disparities and market concentration effects that disadvantage peripheral areas. The “east-high, west-low” accessibility pattern correlates with coastal development priorities but exacerbates supply deficits in western counties like Yongtai and Minqing, where over 60% of townships face critical shortages. Notably, the supply–demand equilibrium analysis reveals a paradox: regions with established prefabrication industries maintain adequate material availability, whereas emerging development zones experience severe mismatches despite growing demand. This spatial-economic dichotomy highlights the limitations of market-driven distribution mechanisms in addressing rural-urban material flow asymmetries. The findings emphasize the need for coordinated spatial planning that integrates industrial capacity mapping with demographic projections, particularly in addressing transportation network deficiencies and optimizing supplier hierarchies. The persistent material scarcity in western mountainous counties suggests that conventional accessibility metrics may inadequately capture terrain-related logistical challenges, necessitating adaptive evaluation frameworks for geographically complex regions.

4.3. Limitations and Prospects

This study also has limitations. The absence of long-term monitoring data on post-construction building performance restricts the comprehensiveness of the whole-life-cycle carbon emission assessment. Furthermore, the applicability of the supply chain optimization model in underdeveloped rural areas with extremely weak infrastructure or more complex terrain warrants further validation.

To address these limitations, future research could be extended to typical rural regions across different climate zones to validate the model’s climate adaptability. Additionally, in areas with underdeveloped supply chain foundations (including parts of rural Europe), the conclusions of this study should be adjusted and refined by accounting for specific urban–rural layout disparities and logistical conditions to overcome the constraints posed by supply chain deficiencies. Caution should be exercised when extrapolating findings to regions like Europe, where significant differences exist in supply chain infrastructure or urban–rural configurations. The key challenge lies in establishing effective local material hubs and mitigating the high emissions associated with long-distance transportation. Implementation pathways require tailored localization through in-depth consideration of local policies, infrastructure, and industrial layouts.

The carbon reduction benefits of prefabricated buildings (particularly steel–timber hybrid structures) and the critical role of local supply chains are universally applicable, holding potential for replication in other regions with similar climatic conditions. However, the effectiveness of such replication will be significantly constrained by the varying economic contexts of different regions. Economically underdeveloped areas may encounter bottlenecks in initial investment and the strength of policy incentives, potentially hindering the large-scale implementation of this model. Future efforts could focus on establishing government-led regional distribution centers in rural areas to streamline material transport and alleviate logistical bottlenecks. Simultaneously, priority should be given to promoting technological innovation in prefabricated construction to enhance its low-carbon benefits, and to strengthening regional collaboration, particularly the coordination of urban–rural supply chains, to ensure the sustainable supply of materials.

5. Conclusions

This study takes the Digital Industrial Park in Yongtai County, Fuzhou City as an example to systematically explore the carbon emission benefits and implementation paths of prefabricated buildings in rural areas with extreme hot–humid climate. Through BIM technology and the Gaussian two-step floating sump area method, the carbon emission differences between prefabricated buildings and traditional cast-in-place buildings in the embodied stage were quantitatively analyzed, and a multi-dimensional analysis of the spatial accessibility of prefabricated building material suppliers in Fuzhou was conducted. The results show that prefabricated buildings can significantly reduce carbon emissions. However, there is a significant imbalance in the spatial distribution of prefabricated material suppliers in Fuzhou City. Rural areas are facing severe supply chain challenges. Optimizing the supply chain layout, especially increasing the coverage rate of suppliers in rural areas, has been proven to significantly enhance the adoption rate of prefabricated buildings. In addition, to maximize the application benefits of prefabricated buildings, local adjustments should be made in close combination with regional economic conditions and environmental policies. Through these measures, prefabricated buildings are expected to play a greater role in rural revitalization and the realization of the “dual carbon” goals, promoting the green transformation and sustainable development of the construction industry.