Analyzing the Environmental, Economic, and Social Sustainability of Prefabricated Components: Modeling and Case Study

Abstract

The building industry has significant environmental, economic, and social impacts. The trend of construction industrialization to promote sustainable development is becoming increasingly evident. As an essential component of this process, prefabricated components provide a foundation for studying the sustainability of prefabricated buildings. This study proposes a life cycle sustainability assessment (LCSA) model involving environmental, economic, and social aspects to assess the sustainability of prefabricated components. The sustainability impacts on the raw material extraction and production, off-plant transport, material processing and component manufacturing, on-plant transport, and component storage stages are comprehensively assessed. The LCSA model is applied to four types of prefabricated components: interior wallboards, precast stairs, composite beams, and composite floor slabs. The results show that the precast stairs exhibit the highest sustainability score and that the performances of environmental, economic, and social pillars of four components are slightly different. These findings enhance our understanding of the sustainability of prefabricated components and broaden the scope of LCSA applications. The established sustainability assessment model is expected to help guide prefabrication scheme designs and production plan optimization, further encouraging the wider adoption of sustainable practices in construction.

1. Introduction

The construction industry exerts positive and negative influences across economic, social, and environmental spheres. In several countries, the construction industry plays a crucial role in stimulating economic growth, with GDP growth contributions ranging from 5% to 15% [1]. It also creates living conditions for society and affects people’s health and well-being. However, the construction industry is a major consumer of natural resources and a producer of pollutants [2], accounting for 33% of global CO₂ emissions and representing approximately 40% of global energy and material consumption [3]. Therefore, the sustainability of the construction industry has attracted much attention [4]. This requires satisfying the demands of the people without compromising the ability to satisfy the demands of future generations [5], that is, achieving functionality while reducing adverse effects. Many countries have implemented sustainable construction policies, such as the Green Bank in the United States, the Energy Performance in Buildings Directive in the European Union, and the Sustainable Development Strategy in China.

Over the past decade, prefabricated buildings have rapidly developed worldwide [6] and are regarded as important breakthroughs in realizing construction sustainability [7]. Prefabricated buildings are constructions in which building components are prefabricated in a factory and then assembled and installed on-site. The Chinese government established a policy that 30% of new buildings should use prefabricated structures by 2025 [4]. Prefabricated buildings are widely recognized to have the potential to improve construction efficiency and quality [4], reduce waste and operating costs, and ensure worker safety and health [8]. Its environmental, economic, and social impacts are observed and interrelated. A reduction in environmental burden will lead to an increase in government support. Social benefits can provide the guidance for economic and environmental improvement [7]. Therefore, it is meaningful to evaluate the sustainability of prefabricated buildings.

A significant difference between prefabricated and cast-in-place buildings is the use of prefabricated components. Prefabricated components refer to building elements that are premanufactured in a factory and later transported to the construction site for installation and assembly [9]. The production process for prefabricated components typically includes mold cleaning and installation, reinforcement binding, concrete pouring, curing, treatment, and arrangement [10]. Owing to the streamlined and sequential nature of these processes, prefabricated components can be mass-produced in factories; the production process is relatively standardized and easy to control and manage. As basic elements, prefabricated components can be individually studied to provide accurate and convenient results for the sustainability assessment of prefabricated buildings.

Life cycle sustainability assessment (LCSA) is a widely used method and it integrates three pillars: life cycle assessment (LCA), life cycle cost (LCC), and social life-cycle assessment (S-LCA). Research on LCA and LCC began early and resulted in mature systems and standards, such as ISO 14040 [11], ISO 14044 [12], ISO 15686-5 [13], and the GB tools. S-LCA focuses on the coexistence of individuals, communities, and societies [14]. Related research is helpful in promoting the fulfillment of social responsibility. However, the S-LCA is still in its infancy and has not yet formed a formal international standard. In addition, the aggregation of LCA, LCC, and S-LCA across the three pillars and systematic decision making remains controversial [15].

Regarding the practical application of the LCSA method in the construction field, relevant research covers the macro, meso, and micro levels. At the macro level, scholars have focused on assessment models for the construction industry [14] and have implemented practical applications in countries, including the United States [16] and China [17]. At the meso level, LCSA plays a role in sustainability evaluation and management of individual buildings at different stages, including guiding design schemes [18] and improving construction plans [19]. Limited studies have been conducted at the micro level for building materials, such as ceramic tiles [20] and concrete [21]. Unfortunately, LCSA studies on prefabricated components are scarce and their sustainability levels are ambiguous [22].

To address the insufficient understanding of the sustainability of prefabricated components, this study establishes a comprehensive LCSA model and applies it to assess the environmental, economic, and social sustainability of four types of prefabricated components. The remainder of the paper is structured as follows. A literature review is presented in Section 2. Section 3 presents the methodology. Section 4 applies the proposed framework in practice. Section 5 discusses the results. Finally, the conclusions are presented in Section 6.

2. Literature Review

2.1. Sustainability Assessment Research of Prefabricated Buildings

(1)

Life cycle sustainability assessment

The LCSA aims to evaluate the environmental, economic, and social performance of a product and/or service during its life cycle. It is usually expressed in the form of “LCSA = LCA + LCC + S – LCA” [23], where LCA is the environmental assessment, LCC is the evaluation of the product’s life cycle cost, and S-LCA quantifies social impacts. LCSA includes the evaluation and results of these three aspects. The Life Cycle Initiative published the guidelines Towards A Life Cycle Sustainability Assessment: Making Informed Choices on Products [24] in 2011. Over the last decade, LCSA has gained widespread recognition and has been used in many fields to evaluate sustainability, including the energy [25], dairy [26], manufacturing [27], transportation [28], and construction industry [29].

(2)

The environmental pillar

LCA is widely used to quantify the environmental impacts of prefabricated buildings, with energy and carbon emissions as the main focus. Wen et al. [30] assessed the embodied energy and global warming potential of prefabricated constructions. Du et al. [31] compared the carbon emissions of prefabricated and traditional buildings and analyzed the influence of the prefabrication rate. Zhu et al. [32] used a hybrid LCA method to explore the energy efficiency of prefabricated buildings. From the perspective of research boundaries, various life-cycle stages are involved in current LCA studies. Zheng et al. [33] proposed a carbon-emission calculation model covering the construction, operation, and abandonment stages. Tian and Spatari [34] evaluated the environmental benefits from raw material extraction to construction processes.

(3)

The economic pillar

The LCC research focuses on the basic and incremental costs of prefabricated construction at various stages. Hong et al. [35] comprehensively evaluated cost-effectiveness at the design, manufacturing, transportation, and on-site installation stages. Qi et al. [36] established an incremental cost-calculation model for prefabricated buildings. Samani et al. [37] analyzed the life cycle cost of prefabricated masonry buildings.

(4)

The social pillar

In contrast, there are fewer S-LCA studies on prefabricated buildings, which may be owing to the subjective, qualitative, and situational nature of social evaluation [38]. Here are a few research examples. Radziejowska and Sobotka [39] evaluated the social quality of prefabricated buildings from residents’ perspectives. Li et al. [40] compared work stress and job satisfaction of prefabricated and traditional construction workers. Liu and Qian [41] systematically constructed an S-LCA framework for buildings with four stakeholders (workers, residents, local community, and society) systematically considered.

(5)

Integration studies

Some scholars attempted to integrate these three pillars to conduct multidimensional assessment research. Wu et al. [42] established sustainability evaluation indices and models and converted them into direct, environmental, and social costs. Liu et al. [1] analyzed the comprehensive sustainable benefits of prefabricated buildings using system dynamics and model simulations. Kamali and Hewage [43] combined literature reviews and questionnaires to determine appropriate indicators for sustainability assessment. Compared with single-pillar research, integration studies focus more on the overall theoretical framework, evaluation indicator selection, and balance between the three pillars.

2.2. Sustainability Assessment Research of Prefabricated Components

(1)

The environmental pillar

Among the three pillars of sustainability evaluation, LCA research on prefabricated components is relatively abundant. Some scholars [8] have highlighted those environmental impacts during component production accounting for a significant share. Liu et al. [44] quantified carbon emissions of prefabricated components during production. Xu et al. [45] developed an energy analysis method for the component production. Some scholars have focused on more life-cycle stages of the components. Li et al. [46] assessed carbon emissions from prefabricated stairs during production, transportation, and construction. Hong et al. [47] investigated the energy usage of six typical prefabricated components, from manufacturing to demolition.

(2)

The economic pillar

Research on understanding the cost of prefabricated components is growing. Kurpinska et al. [10] calculated the costs of precast walls, ceilings, and staircases, including transportation and installation costs. Jeong et al. [48] evaluated the cost of prefabricated concrete columns during the procurement to construction stages. Some studies [35,36] have itemized component costs when evaluating prefabricated construction costs.

(3)

The social pillar

Currently, social assessments of prefabricated components are mostly conducted from the perspective of workers rather than multiple stakeholders and primarily focus on workers’ health and safety issues arising from component production, transportation, and other activities. Cui et al. [49] developed a risk assessment model for workers’ health caused by environmental releases in prefabricated component factories. Li and Guo [50] proposed a transportation safety evaluation model for prefabricated components. Therefore, a comprehensive social sustainability indicator system involving various stakeholders is required.

(4)

Integration studies

Three pillars are seldom considered in the multidimensional sustainability evaluation of components. Only a few studies have integrated environmental and economic indicators. Zhang et al. [51] assessed the economic costs and greenhouse gas emissions of two prefabricated concrete components. Cheng et al. [52] evaluated the environmental and economic performance of manufacturing processes of prefabricated components. A comprehensive quantitative sustainability assessment model that aggregates appropriate indices for the three aspects of prefabricated components must be developed.

3. Methodology

This paper developed an environmental, economic, and social sustainability assessment model for prefabricated components, as shown in Figure 1. The first step was to define the research goals and scope of the evaluation. Next, models for the three pillars were established and their assessment results were integrated by weighting. The index of relative sustainability (IRS) is adopted to quantify the comprehensive sustainability level of prefabricated components.

3.1. Goal and Scope Definition

The goal is to provide a sustainability assessment model and steps for producing prefabricated components. Following the cradle-to-gate research concept, the evaluation scope covers five stages: raw material extraction and production, off-plant transport, material processing and component manufacturing, on-plant transport, and component storage. Furthermore, the material processing and component manufacturing stage typically can be divided into six processes: material processing, mold cleaning and installation, installation of rebar binding and embedded parts, concrete pouring and vibration, concrete curing, and component cleaning and placement. All activities that occurred during these processes fell within the system boundaries.

The per-unit volume of the prefabricated components is defined as the functional unit of assessment. Referencing pertinent environmental impact studies on building materials [53,54] and considering a large amount of resource consumption and pollutant emissions during the production of prefabricated components, the environmental impact categories included the global warming potential (GWP), acidification potential (AP), eutrophication potential (EP), photochemical ozone creation potential (POCP), and Chinese resource-depletion potential (CADP). The LCC covers the costs of materials, machinery, manpower, and others. The social assessment was conducted from the perspectives of four stakeholders: workers, the local community, consumers, and society. Detailed information is presented in the following sections. EXCEL software (v2016) was employed for data processing and results computation.

3.2. Environmental Assessment Model

3.2.1. Consumption Analysis

To conduct an LCA, it is essential to analyze the consumption of materials, machinery, and manpower within the system boundaries. Based on the five stages proposed in Section 3.1, an analysis was performed, as shown in Figure 2, and the related calculation formulas are provided below.

(1)

Materials consumption

Many materials are consumed during the production of prefabricated components, including rebars, concrete, and molds. Their consumptions were calculated using Equation (1). Material loss and turnover cannot be avoided and are included in the equation. Data for the parameters $n_i$, $s_i$, and $Q_i$ were obtained through field surveys and interviews.

$$M_{Mi}=\frac{1}{n_i}\times \left(1+s_i\right)\times Q_i,$$

(1)

where $M_{Mi}$ represents the consumption of the i^th material, $s_i$ is the scrap rate of the i^th material, $Q_i$ is the net consumption of the i^th material, and $n_i$ is the average turnover of the i^th material.

(2)

Energy consumption of transportation machinery

Transportation activities can be categorized into two types: off-plant transport (from material production factories to the component prefabrication factory) and on-plant transport (inside the component prefabrication factory). The choice of transportation vehicles is influenced by the material type and transportation distance. Off-plant transportation commonly employs heavy-duty and flatbed trucks, whereas on-plant transportation often uses concrete transport trucks, forklifts, and flatbed transport vehicles. The energy consumed during transportation includes diesel, gasoline, kerosene, and electricity [55]. The energy consumption of the materials and component transportation are represented by Equations (2) and (3), respectively. Considering the possible variations in transportation distance due to traffic congestion, $d_i$ and $d_m$ are introduced into the equations. The values of $M_{Pm}$, $d_i$, $d_m$, $L_i$, and $L_m$ were obtained from field surveys, whereas the values of $P_{i,j}$ and $P_{m,j}$ were acquired from official vehicle data or on-site measurements.

$$M_{TMj}=\sum _i{M}_{Mi}\times P_{i,j}\times d_i\times L_i,$$

(2)

where $M_{TMj}$ represents the consumption of the j^th energy when transporting materials, $P_{i,j}$ represents the j^th energy consumption per hundred kilometers for transporting the i^th material; $d_i$ is the coefficient of change in the transportation distance of the i^th material, and $L_i$ is the transport distance of the i^th material.

$$M_{TPj}=\sum _m{M}_{Pm}\times P_{m,j}\times d_m\times L_m,$$

(3)

where $M_{TPj}$ represents the consumption of the j^th energy when transporting the per unit volume of components, $M_{Pm}$ is the weight of the m^th unit volume component, $P_{m,j}$ represents the j^th energy consumption per hundred kilometers for transporting the m^th component per unit weight, $d_m$ is the coefficient of change in the transportation distance of the m^th component, and $L_m$ is the transport distance of the m^th component.

(3)

Energy consumption of operational machinery

Various machines are required for the processing and manufacturing of components, such as concrete mixers, rebar cutters, vibration tables, and curing kilns. These machines typically utilize diesel, gasoline, and electricity. $o_k$ was introduced to account for machinery idle time due to factors such as inefficient production planning or insufficient operator skills. The energy consumption of machinery can be calculated using Equation (4). The values of $o_k$, $T_k$, and $N_k$ were obtained through field surveys, and the data of ${\mathrm{P’}}_k$ can be obtained from official equipment data.

$$M_{Ej}=\sum _k{o_k\times T}_k\times {\mathrm{P’}}_k\times N_k,$$

(4)

where $M_{Ej}$ represents the consumption of the j^th energy owing to the operating machinery, $k$ is the machinery serial number required for the production system, $o_k$ is the operation rate of the k^th machinery, $T_k$ is the effective running time of the k^th machinery, ${\mathrm{P’}}_k$ is the operating power of the k^th machinery, and $N_k$ is the number of the k^th machinery.

(4)

Manpower

Workers are primarily engaged in processing and component manufacturing. Considering activities such as rest and preparation, parameter $u$ is introduced. The manpower input is represented in terms of labor hours, as shown in Equation (5). The values of $EW$ and $u$ are derived from the field surveys.

$$M_W=\frac{EW}{u},$$

(5)

where $M_W$ represents the input of manpower, $EW$ is the effective working hours of workers, and $u$ is the utilization rate of the effective working hours.

3.2.2. Environmental Impact Assessment

According to ISO14044 [12], LCA includes life cycle inventory (LCI) and life cycle impact assessment (LCIA) of these two key steps. LCI analyzes product-related inflows and outflows to establish an input–output inventory. In the manufacturing of prefabricated components, input inventories include fossil fuels, limestone, iron ore, and so on. The output inventories cover pollutant emissions to the atmosphere, water, and soil. The quantity of inventory substances was obtained using consumption (materials, machinery, and manpower discussed in Section 3.2.1) and background inventory data, as shown in Equation (6). Background inventory data can primarily come from various mature databases, such as Ecoinvent. The literature, statistical yearbooks, and industry reports can offer secondary data for supplementation.

$${IS}_z=\sum _i{M}_{Mi}\times E_{i,z}+\sum _j{{(M}_{TMj}+M_{TPj}+M_{Ej})\times E}_{j,z}+M_W\times E^{\prime }\times A,$$

(6)

where ${IS}_z$ is the z^th inventory substance; $E_{i,z}$ is the z^th inventory substance due to the i^th material; $E_{j,z}$ is the z^th inventory substance due to the j^th energy; and $\mathit{E’}$ is the artificial carbon footprint, which is 1.18 kg CO₂/person-hour [55]. $A$ = 1 when $z$ is CO₂; otherwise, $A$ = 0.

LCIA quantitatively assesses and compares various environmental impact categories of the prefabricated components. The CML (Centrum voor Milieukunde Leiden) midpoint method was adopted to conduct LCIA, a life cycle impact assessment method developed by the Center for Environmental Sciences at Leiden University in The Netherlands. It provides a structured approach for describing and quantifying the environmental burden of mid-point impact categories during the LCIA phase. This includes three steps: classification, characterization, and normalization. The classification step assigns the LCI results to different environmental impact categories. As introduced in Section 3.1, GWP, AP, EP, POCP, and CADP categories are involved. The characterization step quantifies the contribution of inventory substances to each environmental impact category. Related characterization factors were obtained from the literature [56]. Subsequently, the normalization step made environmental data of different categories comparable. The normalization factors can be acquired by referring to the literature [57] with the Chinese situation considered. The final environmental impacts were calculated using Equation (7). The specific inventory substance, characterization factors, and normalization factors are summarized in Table S1 in the Supplementary File.

$$EI=\sum _m\frac{\sum _z{F}_{mz}\times {IS}_z}{R_m},$$

(7)

where $EI$ is the environmental impact result of LCA, $m$ represents the environmental impact category, $F_{mz}$ is the characterization factor of the z^th inventory substance to the m^th impact category, and $R_m$ is the normalization factor of the m^th impact category.

3.3. Economic Model

An economic model was used to evaluate the cost of prefabricated components. The related consumptions (including manpower, materials, and machinery) are analyzed in Section 3.2.1 and their corresponding costs are calculated using Equations (8)–(10). The values of $P_i$ can be identified from material suppliers, construction material markets, relevant organizations, and prefabrication factories. The data of $C_{\mathrm{u}\mathrm{s}\mathrm{a}\mathrm{g}\mathrm{e}}$ and $P_{hour}$ can be identified from component factories.

$$C_{\mathrm{M}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{a}\mathrm{l}}=\sum _i{M}_{Mi}\times P_i,$$

(8)

where $C_{\mathrm{M}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{a}\mathrm{l}}$ denotes the cost of the materials and $P_i$ denotes the price of the i^th material per weight.

$$C_{\mathrm{M}\mathrm{a}\mathrm{c}\mathrm{h}\mathrm{i}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{y}}=\sum _j{(M}_{MTj}+M_{PTj}+M_{Ej})\times P_j+C_{\mathrm{u}\mathrm{s}\mathrm{a}\mathrm{g}\mathrm{e}},$$

(9)

where $C_{\mathrm{M}\mathrm{a}\mathrm{c}\mathrm{h}\mathrm{i}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{y}}$ is the cost of machinery utilization; $P_j$ is the actual price of the j^th energy per unit; and $C_{\mathrm{u}\mathrm{s}\mathrm{a}\mathrm{g}\mathrm{e}}$ includes driver fees for transportation equipment, equipment depreciation, repair costs, etc.

$$C_{Manpower}=M_W\times P_{hour},$$

(10)

where $C_{Manpower}$ is the labor cost and $P_{hour}$ is the hourly wage of workers, including basic salary, bonuses, allowances, subsidies, and insurance premiums.

In addition to material, machinery, and manpower costs, other costs occur during the production process, which is $C_{\mathrm{O}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{r}}$ in this paper. It includes labor costs for workshop management personnel, detailed design fees for component drawings, safety production costs, environmental protection expenses, and others. Related data can be obtained from field surveys. The LCC composition is represented by Equation (11).

$$C_{\mathrm{P}\mathrm{r}\mathrm{o}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}=C_{\mathrm{M}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{a}\mathrm{l}}+C_{\mathrm{M}\mathrm{a}\mathrm{c}\mathrm{h}\mathrm{i}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{y}}+C_{\mathrm{M}\mathrm{a}\mathrm{n}\mathrm{p}\mathrm{o}\mathrm{w}\mathrm{e}\mathrm{r}}{+C}_{\mathrm{O}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{r}},$$

(11)

where $C_{\mathrm{P}\mathrm{r}\mathrm{o}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}$ is the total cost of producing prefabricated components, namely, the LCC result.

3.4. Social Model

3.4.1. Social Impact Categories and Indicators

Based on the Guidelines for Social Life Cycle Assessment of Products published by the UNEP/SETAC Life Cycle Initiative [58] and other studies [59,60,61], this study conducted interviews with experts, scholars, and practitioners in the construction field and identified four stakeholders for social impact assessment: workers, local community, consumers, and society. Furthermore, 14 subcategories and 21 evaluation indicators were identified, which were divided into quantitative and semi-quantitative categories, as summarized in

Table 1. Social impact stakeholders and indicators.

Stakeholder	Subcategory	Indicator
		Label	Measurement	Type *
Workers	Freedom of association	Labor dispute ratio	Number of workers involved in labor disputes/Number of total workers	√
		Union participation ratio	Number of unionized workers/Number of total workers	√
	Forced labor	Percentage of weekly overtime hours	Average weekly overtime hours/Prescribed weekly overtime hours limit	√
	Fair salary	Average salary ratio	Average salary of workers in the assessed factory/Average salary in the industry	√
		Minimum salary worker ratio	Number of workers earning the local minimum salary/ Number of total workers	√
		Timely salary payment	Number of times salaries are paid on time/ Number of times salaries are due	√
	Working hours	Whether the total weekly working hours exceed the legal working hours	YES/NO	×
	Equal opportunities /Discrimination	Female labor force ratio	Number of female employees/Number of male employees	√
		Female employee salary coefficient	Average salary for female employees/Average salary for male employee	√
		Discrimination in hiring	Gender, race, household registration, marital status	×
	Health and safety	Workplace injury accident	YES/NO	×
		Usage rate of protective equipment	Number of workers properly performing production protection/ Number of total workers	√
	Social benefits	Insurances and housing fund proportion	Number of workers contributing to insurances and housing fund/ Number of total workers	√
Local community	Local employment	Proportion of local employees	Number of local employees/ Number of total workers	√
	Living conditions	Impact of production activities on local living environment	YES/NO	×
Consumers	Construction quality	Building quality improvement	YES/NO	×
	Construction period	Save construction time	Reduced construction time/Original construction time	√
Society	Economic development	Jobs created percentage	Number of jobs employed by the assessed factory/Average number of jobs absorbed	√
		Annual gross product contribution level	Annual gross product of the assessed factory/Annual average gross product	√
	Responses to sustainability issues	Response to hot social issues	YES/NO	×
	Technology development	Application of new technology, material, process, and equipment	YES/NO	×

* Note: “√” represents quantitative index and “×” represents semi-quantitative index.

. The quantitative indicators were measured using the ratio and the semi-quantitative indicators were measured based on whether the behavior was implemented. The selection of indicators accounts for the characteristics of prefabricated component production, taking the local employment subcategory as an example. In a traditional construction scenario, the uncertainty of the project location leads to the periodic flow of many migrant workers [62]. In the prefabricated construction scenario, the location of a prefabrication factory is fixed and can absorb more local labor. Therefore, the ratio of local employees is identified as an indicator.

With respect to the values of the involved indicators, site-specific data are highly suggested because different geographical locations, economic conditions, etc., may influence the values. This study combined enterprise and industry/country data to achieve a comprehensive evaluation. Enterprise data such as the average salary of workers were obtained through surveys of managers and frontline production personnel in prefabrication factories. Industry/country data are typically acquired from the China Statistical Yearbook, laws, and regulations.

3.4.2. Social Impact Assessment

The social impact assessment quantifies the positive and negative social impacts of the prefabricated components from the perspectives of the four stakeholders. This includes the characterization, normalization, and weighting of the three steps.

(1)

Characterization

Characterization represents social information using interpretable indicators and the related measurement methods are presented in Table 1.

(2)

Normalization

Normalization transforms quantitative and semi-quantitative indicator values into a comparable range from −1 to 1. Referring to previous social assessment research [63,64,65,66], the normalized values in this study are shown in

Table 2. Normalization values of S-LCA indicators.

Indicator	Indicator Value	Normalized Value
Quantitative indicator
Labor dispute ratio; Percentage of weekly overtime hours	0~20%	1
	20~40%	0.5
	40~60%	0
	60~80%	−0.5
	80~100%	−1
Union participation ratio; Average salary ratio; Minimum salary worker ratio; Timely salary payment; Female labor force ratio; Female employee salary coefficient; Usage rate of protective equipment; Insurances and housing fund proportion; Proportion of local employees; Jobs created percentage; Annual gross product contribution level	0~20%	−1
	20~40%	−0.5
	40~60%	0
	60~80%	0.5
	80~100%	1
Save construction time	0~10%	−1
	10~20%	−0.5
	20~30%	0
	30~40%	0.5
	40~100%	1
Semi-quantitative indicator
Whether the total weekly working hours exceed the legal working hours; Discrimination in hiring; Workplace injury accident; Impact of production activities on local living environment	Yes	−1
	No	1
Building quality improvement; Response to hot social issues; Application of new technology, material, process, and equipment	Yes	1
	No	−1

. Quantitative indicators were normalized into five levels based on the percentages their values belong to, with positive indicators (i.e., average salary ratio) assigned positive values and negative indicators (i.e., labor dispute ratio) assigned negative values. Owing to the relatively small values of the construction time-saving indicator, interpolation with smaller intervals was used for the assignment. The semi-quantitative indicators were assigned to two levels based on the level of implementation. For indicators with positive impacts (i.e., building quality improvement), “YES” was assigned a score of +1, whereas “NO” was assigned a score of −1. For indicators with negative impacts (i.e., workplace injury accidents), the scoring rule was reversed.

(3)

Weighting

This step obtains weights according to the importance of each indicator and then uses them to integrate the normalized values. The analytic hierarchy process (AHP) method was used to calculate weights. A hierarchical structure was constructed with social sustainability at the target level, stakeholders at the criterion level, and subcategories at the scheme level, making it suitable for executing the AHP method [64]. The importance of the indicators was compared and scored using a five-point scale by 14 invited experts. The expert panel comprised individuals from government agencies, enterprises, and research institutions, with 58% of the experts having more than ten years of work experience and 65% holding a master’s degree or higher. Detailed information on these experts is presented in

Table 3. Background information of surveyed experts.

Workplace	Shares	Working Years	Shares	Educational Background	Shares
Administrative departments	7%	<3 years	7%	Junior college	14%
Prefabrication factories	29%	3–5 years	14%	Undergraduate	21%
Scientific research institutions	50%	5–10 years	21%	Postgraduate	29%
Other	14%	>10 years	58%	Doctoral candidate	36%

. Yaanp software (v2.5.9226.16159) was used for data processing and automation of the AHP process. The judgment matrices for each hierarchical indicator are presented in Tables S2–S6 in the Supplementary File.

AHP also provides consistency testing to ensure logical coherence of the results. Equations (12) and (13) were used to calculate the consistency ratio ($CR$) indicator. When $CR$ < 0.1, the target matrix was considered consistent. In our study, $CR$ = 0, indicating that all matrices satisfied consistency requirements. The final weighting factor values are shown in Figure 3.

$$CI=\frac{{\lambda }_{max}-n}{n-1},$$

(12)

$$CR=\frac{CI}{RI},$$

(13)

where $CI$ is the consistency coefficient; ${\lambda }_{max}$ is the corresponding maximum eigenvalue of the judgment matrixes, $n$ represents the size of the corresponding judgment matrix, $CR$ is the consistency ratio, and $RI$ denotes the average consistency index.

These three steps enable the calculation of final social impact, as shown in Equation (14) [67].

$$S=\sum _e\frac{\sum _{eq}{NV}_{eq}}{MAX\left(eq\right)}\times W_e,$$

(14)

where $S$ is the S-LCA result, $W_e$ is the weighting factor of the e^th subcategory, $eq$ is the q^th indicator of the eth subcategory, and ${NV}_{eq}$ is the normalized value of the q^th indicator.

3.5. LCSA Scores

The LCSA framework comprised three pillars: LCA, LCC, and S-LCA. They collectively evaluated the sustainability of prefabricated components. Multicriteria decision analysis (MCDA) was adopted to aggregate the three pillars and obtain LCSA scores. However, the units and orders of magnitude of the results for the three pillars differed and their internal relationships were unclear. To solve this issue and achieve dimensionless and comparable evaluation scores, the following data processing framework was designed:

First, the results of LCA, LCC, and S-LCA were transformed into a numerical scale of 0–100 using the Z-score normalization method, which eliminates the need for external target definitions and conversion factors by relying on the mean and standard deviation [68]. This approach preserves the distributional information of the data and exhibits a certain level of robustness towards outliers. Given that environmental and economic results are negatively correlated while social results are positively correlated with sustainability, they are processed separately according to Equations (15) and (16).

$$R_{LCA/LCC}=\left\{\begin{array}{c}0,if \frac{\left(\mu +\sigma \right)-E}{2\sigma }<0\\ \frac{\left(\mu +\sigma \right)-E}{2\sigma }\times 100,if 0\le \frac{\left(\mu +\sigma \right)-E}{2\sigma }\le 1\\ 100,if\frac{\left(\mu +\sigma \right)-E}{2\sigma }>1\end{array},\right.$$

(15)

where $R_{LCA}$ is the LCA score, $R_{LCC}$ is the LCC score, and $E$ represents $EI$ or $C_{\mathrm{P}\mathrm{r}\mathrm{o}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}$.

$$R_{S-LCA}=\left\{\begin{array}{c}100,if \frac{\left(\mu +\sigma \right)-S}{2\sigma }<0\\ \frac{\mathrm{S}-\left(\mu -\sigma \right)}{2\sigma },if 0\le \frac{\left(\mu +\sigma \right)-\mathrm{S}}{2\sigma }\le 1,\\ 0,if\frac{\left(\mu +\sigma \right)-\mathrm{S}}{2\sigma }>1\end{array}\right.$$

(16)

where $R_{S-LCA}$ is the score of S-LCA.

Second, the impact scores of three pillars were aggregated using weighting factors. According to a survey conducted among 54 decision makers, the importance levels of LCA, LCC, and S-LCA were 35.3%, 33.5%, and 31.2%, respectively [69]. The final LCSA score was measured using the IRS indicator, which was obtained using Equation (17).

$$IRS=w_{LCA}\times R_{LCA}+{w_{LCC}\times R}_{LCC}+w_{S-LCA}\times R_{S-LCA},$$

(17)

where $w_{LCC}$, $w_{S-LCA}$, and $w_{LCA}$ are the weights of the LCC, S-LCA, and LCA, respectively, and $IRS$ is the relative sustainability index.

4. Case Study

4.1. Basic Information

This study applied the proposed methodology to components produced in a prefabrication factory located in the Zhejiang province. The factory had fixed lines, assembly lines, and specialized workshops for partial-encasing composite (PEC) structures. Four prefabricated components in these workshops were selected as evaluation objects: interior wallboards, precast stairs, composite beams, and composite floor slabs. Field investigations and interviews were conducted and information on the four components is shown in

Table 4. Information of evaluated components.

Serial Number	Component Type	Volume	Production Organization Mode
1WQB-3	Interior wallboard	1.19 m3	Fixed line workshop
LTLT1	Precast stair	0.62 m3	Fixed line workshop
PCL1	Composite beam	1.10 m3	PEC workshop
2DBS-145	Composite floor slab	0.44 m3	Assembly line workshop

and Figure 4.

4.2. Data Collection

Production consumption data including material consumption, transportation distance information, and working hours and powers of machinery were collected from the factory and were presented in Tables S7–S9, respectively, in the Supplementary File. The background inventory data were obtained from the Chinese Life Cycle Database and the literature [57]. LCC-related data were obtained from the factory. With these data and models in Section 3.2 and Section 3.3, LCA and LCC assessment can be conducted.

From a social perspective, the enterprise data were derived by conducting surveys with the financial, personnel, and general management departments. The industry/country data were obtained from International Labor Standards, China Statistical Yearbook, and Labor Law of the People’s Republic of China. Since these four prefabricated components were produced in the same factory, the impacts on workers, society, and the local community were the same. But the social impacts on consumers vary because these four components were used in different projects at various locations. The values of social indicators were presented in Tables S10 and S11 in the Supplementary File. The weights of social indicators were presented in Section 3.4.

4.3. Analysis of Results

4.3.1. Environmental Sustainability Analysis

The LCA results of the four prefabricated components were assessed and shown in Figure 5. The higher the LCA result, the greater the negative impact on the environment. Among the four components, the interior wallboard had the lowest LCA result. The results for the precast stairs, composite beams, and composite floor slabs were 39%, 36%, and 52% higher, respectively, than that for the interior wallboards. Among the five impact categories assessed, CADP was the most influential, accounting for more than 35%. It was followed by GWP, which accounted for 23–25%. EP and POPC accounted for a small proportion, indicating a low likelihood of COD and CO discharge.

4.3.2. Economic Sustainability Analysis

The LCC results for the four prefabricated components are presented in Figure 6. The four components are ranked from high to low as the interior wallboards, composite beams, precast stairs, and composite floor slabs. The highest total cost occurred for the interior wallboards, reaching 1873.23 yuan/m³. The composite floor slabs had the lowest cost (1636.40 yuan/m³) and shared only 87% of the highest value. Among the four types of costs, the material cost and manpower cost were relatively higher and their shares in total costs are presented in Figure 7. The material costs accounted for the largest proportion, ranging from 54% to 62%, with the composite beams being the component with the highest material cost. Manpower cost ranked second, at approximately 20%. The precast stairs had the highest manpower cost.

4.3.3. Social Sustainability Analysis

The S-LCA results for the four components are shown in Figure 7. From highest to lowest, the components were ranked as precast stairs, interior wallboards, composite beams, and composite floor slabs. The higher the S-LCA result, the more sustainable the corresponding component at the social level. Among the four stakeholders, the society stakeholder attained the highest result, not only because it holds the greatest weight within the social sustainability framework but also because prefabricated components facilitate economic and technological advancements, driving the resolution of sustainable issues. The results of workers and consumers stakeholders were at the medium levels. The S-LCA results for workers indicate that the production of prefabricated components satisfied the partial demands of construction workers and companies and the comprehensive protection of workers’ labor rights should be enhanced. The results for the consumer stakeholder highlight the requirement for further optimization of prefabricated components to enhance the quality of prefabricated construction and expedite project timelines, ultimately satisfying consumer demands. The impacts on the local community were minimal and negative, indicating that the interests of local people have been neglected. To mitigate this issue, more local workers must be employed.

4.3.4. Comprehensive Sustainability Results

Based on the environmental, economic, and social sustainability results and the aggregation framework in Section 3.5, the comprehensive LCSA scores of the prefabricated components were calculated, as shown in Figure 8. The closer the LCSA score is to 100, the higher the comprehensive sustainability level of the corresponding prefabricated component, aligning more closely with the objectives of environmental friendliness, cost-effectiveness, and social benefits. The scores show that the four types of components are from high to low in terms of comprehensive sustainability as precast stairs, interior wallboards, composite floor slabs, and composite beams, with IRS values of 66.39, 56.56, 38.35, and 22.72, respectively. The LCSA scores of all prefabricated components were low, which was consistent with the characteristics of the construction industry such as high input, high energy consumption, and high emissions. The use of prefabricated components and buildings can alleviate these problems to a certain extent. Further technological improvements and progress are required in the construction industry.

The shares of the three pillars in the total final LCSA scores for the four prefabricated components were significantly different. Thus, the assessment scores can provide guidance for determining design schemes and promoting prefabricated components. For example, the production of interior wallboards has lower cost benefits but the positive social and environmental effects are evident. Promotion should be strengthened and subsidies should be provided. Prefabrication factories may prefer composite floor slabs because of their higher cost benefits. However, their lower environmental and social benefits should arouse alarm. Therefore, in practice, the indiscriminate promotion of all components must be avoided.

5. Discussion

5.1. Sensitivity Analysis

The sustainability of prefabricated component production is a composite indicator affected by multiple factors. To further explore the key factors affecting sustainability, nine parameters involved in the LCA, LCC, and S-LCA models were selected for a sensitivity analysis. These are the material scrap rate, transport–distance variation coefficient, machinery operation rate, effective working utilization rate, machinery cost, minimum worker salary ratio, proportion of local employees, saved construction–time ratio, and percentage of jobs created.

The change in the LCSA score was quantified when each parameter was changed by 10%; the results are summarized in

Table 5. Sensitivity analysis results.

Parameter	Rangeability	Interior Wallboard	Precast Stairs	Composite Beam	Composite Floor Slab
Material scrap rate	−10.00%	−1.58	1.19	6.01	−0.80
	10.00%	2.78	−0.85	−3.78	0.75
Transport distance variation coefficient	−10.00%	0.00	−0.06	−0.04	0.08
	10.00%	0.00	0.06	0.04	−0.08
Machinery operation rate	−10.00%	0.00	0.72	−1.37	0.43
	10.00%	0.00	−0.61	1.19	−0.33
Effective working utilization rate	−10.00%	−0.15	−1.51	0.86	0.00
	10.00%	0.13	0.96	−0.54	0.00
Machinery cost	−10.00%	0.41	−0.15	−0.38	0.00
	10.00%	−0.41	0.15	0.39	0.00
Minimum worker salary worker ratio	−10.00%	0.00	0.00	0.00	0.00
	10.00%	0.00	0.00	0.00	0.00
Proportion of local employees	−10.00%	0.00	0.00	0.00	0.00
	10.00%	0.00	0.00	0.00	0.00
Saved construction–time ratio	−10.00%	−4.07	0.00	1.19	1.19
	10.00%	3.73	0.00	−0.78	−0.78
Percentage of jobs created	−10.00%	0.00	0.00	0.00	0.00
	10.00%	0.00	0.00	0.00	0.00

. The results indicate that the nine parameters have different degrees of impact on the LCSA scores. The parameters were classified into three types based on the magnitude of change in LCSA scores. (1) If the change in the LCSA score exceeded three, the corresponding parameter was considered a highly sensitive factor for sustainability and was highlighted in red. The material scrap rate and saved construction–time ratio are highly sensitive factors in the production of composite beams and interior wallboards, respectively. Improving these two factors is important for the sustainability of prefabricated components. (2) If the change in the LCSA score falls within the range 0–3, the corresponding parameter is a low-sensitivity factor and is highlighted in green. The following parameters had low sensitivity for many components: the material scrap rate, transport distance variation coefficient, machinery operation rate, effective working utilization rate, machinery cost, and saved construction–time ratio. (3) If the change in a parameter did not result in any change in the LCSA score, it was highlighted in blue. However, this does not imply that sustainability is insensitive to these factors. This may be because the data-processing method assumes a normal distribution and applies segmented processing. The results before and after ±10% variation still conform to the same normal distribution.

5.2. Model Adjustment and Parameter Selection

This study proposes a comprehensive LCSA model for prefabricated components involving environmental, economic, and social aspects. The social dimension considers both positive and negative impacts, whereas the environmental dimension considers only negative impacts. This is because, currently, the production of prefabricated components in China is not highly mature and the process has few positive environmental impacts. In the future, advanced technologies such as carbon capture and waste recovery may provide more environmental benefits. Then, the proposed LCSA assessment model must be adjusted to incorporate these factors.

When the proposed LCSA model is applied in practice, the parameter data are selected according to specific circumstances. The following parameter values are noteworthy. The background inventory data and normalization factors in the LCA model and the industry/country data in the S-LCA model have regional characteristics. However, in this study, the average data for China were applied to these parameters. Uneven regional development was not considered, which might lead to inaccurate results owing to uneven regional developments. Therefore, the use of local data must be prioritized. The data of many parameters in the LCC and S-LCA models are identified according to the actual situation in Zhejiang province. If the models are applied to other cases, modifications are suggested. In addition, the weights of the three pillars were determined based on the literature. These can be adjusted according to the user’s perspective. If users have a stronger focus on environmental issues, the weight of the environmental pillar can be increased appropriately.

5.3. Analysis of Results across Multidimensions

When analyzing the results of sustainability assessments, researchers are confronted with the issue of evaluating multidimensional results. Currently, three approaches are frequently adopted. (1) Some scholars have chosen to directly analyze the results of different indicators [28,70]. This method provided a more detailed understanding of the situation. However, the results were analyzed in a single dimension without considering the overall impact. Comparisons across multiple dimensions among many schemes were not allowed. (2) Some studies have summarized only certain results using indicators such as environmental costs [51] and cost–benefit ratios [71]. Partial summarization can reveal the correlation between two dimensions; however, it cannot make decisions that involve overall sustainability. (3) Others combine the three dimensions into a composite indicator, typically in monetary [42] or numerical forms [72]. This approach comprehensively assessed the performances of the schemes; thus, it is easily understandable and can support decision making. Unfortunately, this approach inevitably involves subjective factors, such as the indicator-conversion scheme and weight allocation.

This study adopted weights to aggregate the results into three dimensions and make them comparable, employing the concept of weak sustainability. The weak sustainability view states that improving the results in one dimension can compensate for the deterioration of results in other dimensions. In contrast, a strong sustainability concept assumes a hierarchical relationship between multiple dimensions and does not allow compensation. As the prefabricated component production in China is still in the developmental stage, it is difficult to completely avoid its impacts on the environment, society, and economy. Adopting the concept of weak sustainability can encourage manufacturers and users to care for overall sustainability, which is not limited by high costs or negative environmental impacts.

6. Conclusions

This study established a comprehensive LCSA model for prefabricated components, including environmental, economic, and social sustainability. In the LCA model, the environmental impacts of five categories (GWP, AP, EP, POCP, and CADP) were quantified. The LCC model estimated the economic costs, including material, machinery, manpower, and other costs. The S-LCA model involved four stakeholders (workers, local community, consumers, and society) and a social assessment indicator system was established. The evaluation results of LCA, LCC, and S-LCA were aggregated into a single indicator using weightings.

The developed LCSA model was applied to four types of prefabricated components: interior wallboards, prefabricated stairs, composite beams, and composite floor slabs. The sustainability levels of the four components were compared and ranked as follows: precast stairs, interior wallboards, composite floor slabs, and composite beams. Among the environmental impacts, the CADP had the greatest effect. In terms of cost composition, material costs accounted for the largest proportion. In terms of social assessment, the society stakeholder attained the highest results, while the local community stakeholder had negative impacts. Among the four components, the interior wallboards, composite floor slabs, and prefabricated stairs had the highest environmental, economic, and social sustainability, respectively.

This study developed a comprehensive quantitative model for the environmental, economic, and social sustainability of prefabricated components. It augments our comprehension of the sustainability aspects associated with prefabricated components and can also provide valuable references for designing prefabrication schemes and optimizing production plans in practice. This will contribute to improving the sustainability of prefabricated construction and guiding the widespread adoption of prefabricated buildings.

However, this study has some limitations. Data collection and processing of weights and social indicators were identified based on the literature, which may be subjective to some extent. In addition, only four types of components in Zhejiang province were assessed; thus, a broader range of prefabricated components in other locations should be investigated in the future. The construction of a sustainability database for prefabricated components is important.