The Influence Mechanisms of Carbon Emissions for Prefabricated Buildings in the Context of China’s Urban Renewal

Abstract

Prefabricated buildings, known for their energy efficiency, environmental benefits, and industrial advantages, play a crucial role in urban renewal. Previous studies on the carbon emissions of prefabricated buildings mainly concentrate on the assessment and auditing of carbon emissions at the materialization and construction phase. Few of them have analyzed the carbon emissions at the operational phase or the influence mechanisms of prefabricated buildings on carbon emissions in urban renewal. Thus, this paper explored the factors and mechanisms that influence carbon emissions in prefabricated buildings in China’s urban renewal. Firstly, the factors that influence the carbon emissions of prefabricated buildings in China’s urban renewal were identified through meta-analysis. Secondly, the theoretical model was developed to illustrate the influence paths of prefabricated buildings on the carbon emissions of urban renewal. Finally, the structural equation model (SEM) was used to test the hypotheses in the theoretical model using data collected from questionnaires. The results show that the carbon emission reduction potential of prefabricated buildings is influenced by four aspects, namely, socioeconomic factors, policy regulations, building operation, and materialization. Policy regulations have the greatest impact on the carbon emissions of prefabricated buildings. They not only directly affect the carbon emissions of urban renewal but also influence carbon emissions indirectly through the social economy aspect. The direct impact of social economy on the carbon emissions of prefabricated buildings is insignificant, while it can indirectly affect the carbon emission reduction in prefabricated buildings by influencing building operations and the materialization stage. The findings could help provide strategies for prefabrication and enhance the reduction potential of urban renewal.

1. Introduction

Under the “carbon peaking and carbon neutrality” goals, reducing carbon emissions in the construction industry is not only essential for achieving a green transformation of urban renewal but also a necessary condition for sustainable development [1]. Technological advancements and policy optimization in the construction industry are particularly significant in urban renewal. Considering that urban renewal nowadays is an important development direction of urban planning, construction, and governance, it is vital to investigate the impact of building and construction renewal strategies on carbon emissions [2,3,4].

Prefabricated buildings (PBs), also known as offsite construction, originate from the manufacturing domain and transform the construction process into industrialization. The basic categories of prefabrication, defined by the percentage of offsite, include component, non-volumetric, volumetric, and modular. In comparison with traditional construction methods, PBs can effectively reduce the negative effects of construction projects on the environment and promote low-carbon construction and development [5]. Specifically, technological advancements and policy optimization for carbon emission reduction in PBs help fulfill the transition towards low-carbon urban development. For example, Liu et al. [6] examined the effect of prefabricated building supply chains on carbon reduction. Buildings with prefabrication have been widely promoted for achieving the “carbon peaking and carbon neutrality” goals. Thus, in urban renewal, it is essential to use PBs for renovating existing structures, implementing PBs in new construction projects, and minimizing carbon emissions, as presented in studies such as Liu et al. [6].

The need for a distinct carbon emission framework for urban renewal, compared with conventional construction, arises from the unique complexities and opportunities inherent in redeveloping existing urban areas. Conventional frameworks often focus narrowly on the operational energy efficiency and embodied carbon of new structures on greenfield sites, overlooking critical factors specific to renewal projects. Urban renewal involves interacting with existing infrastructure, embodied carbon in standing structures, dense site logistics, and socioeconomic impacts. Demolishing existing buildings releases significant embodied carbon without accounting for this “carbon debt”, whereas adaptive reuse preserves it. Renewal projects also face challenges like constrained site access, requiring inefficient material transport and extended construction phases with higher ongoing emissions from equipment and worker commutes. Furthermore, renewal occurs within functioning communities, where temporary relocations or service disruptions can increase indirect emissions (e.g., longer resident commutes). A tailored framework must prioritize carbon stewardship—emphasizing building retention, low-impact retrofits, and circular material use—while accounting for phased construction emissions, avoided carbon through reuse, and broader urban system efficiencies (e.g., integrated transit or district energy). Ignoring these factors risks underestimating the true carbon footprint of renewal or favoring carbon-intensive demolition over sustainable refurbishment, undermining climate goals in cities where renewal is most critical.

However, the potential of carbon emission reduction from prefabricated buildings (PBs) in urban renewal is shaped by multidimensional interactions, such as socioeconomic conditions, policy framework, and operational practices. Liu et al. [6] quantified socioeconomic drivers via a structural equation model (SEM), revealing that economic conditions rank as the second-most critical driver (after technology) for PB carbon reduction. Socioeconomic conditions determine both the scale of construction activities and the adoption of low-carbon technologies, e.g., income levels and environmental awareness, cost efficiency, and resource optimization. Policy interventions are pivotal in overcoming market barriers and incentivizing low-carbon transitions, e.g., fiscal and regulatory tools, technical standards, and industrial restructurings. Timilsina [7] cautions that policies like carbon taxes face political resistance in urban renewal projects, where short-term costs provoke public opposition. Operational energy use refers to energy consumed during building use, such as heating, cooling, lighting, and ventilation. The operation phases account for most buildings’ lifecycle emissions; thus, energy-efficient operation practices are essential for the energy performance of PBs. Although the potential of carbon emissions reduction from PBs has received extensive attention, specific gaps remain in the research focusing on the mechanisms influencing carbon emissions from PBs in urban renewal. Few studies examined the effect of prefabrication on urban renewal, and even fewer explored the influence mechanism of prefabrication on the “carbon peak and carbon neutrality” target. The identification of the influencing factors is beneficial for uncovering the internal mechanisms of carbon emissions from PBs in urban renewal and sets the prerequisite for the influence paths of PBs’ carbon emissions. This paper aims to analyze the factors and mechanisms influencing carbon emissions from PBs in urban renewal and provide references for governments and relevant construction enterprises on the strategies of sustainable urban renewal.

2. Research Background

The problem of the existing building stock in cities has become increasingly prominent, and the issue of carbon emissions from urban buildings has become more serious. Urban renewal has evolved from physical revitalization to a strategic approach for reducing carbon emissions in existing building stocks. Ramnarine et al. propose urban digital twins (e.g., the CANVAS framework) to optimize carbon-neutral transitions, demonstrating how Energy Use Intensity (EUI) can decrease in Tokyo’s Nihonbashi district through operational improvements and photovoltaic integration [8]. PBs have become an important practical pathway for achieving low-carbon transformation and sustainable development in the urban renewal process. Guo and Zhang emphasized “green renewal” as a financial strategy, linking spatial patterns—such as building aspect ratios and density—to reduced residential energy consumption in Chinese cities [3]. A series of studies have been conducted in the fields of urban renewal and carbon emissions from PBs. Urban renewal, an important tool for optimizing urban spatial structure and enhancing urban functions through renovating existing buildings and improving infrastructure, has become a crucial approach for achieving the low-carbon transformation of cities. Xiao et al. developed an integrated analysis framework to discover that the construction of New-type Urbanization has reduced the carbon emissions of urban buildings [9]. Zhang et al. used Building Information Modeling (BIM) to construct a carbon budget estimation system that aims to achieve low-carbon urban renewal [10]. Urban development has gradually shifted from large-scale incremental construction in the past to emphasizing the optimization of both renovated and newly constructed buildings. PBs play a crucial role in this transformation process. In the policy documents promoting urban renewal, many countries emphasize that PBs should be actively encouraged to achieve the green transformation of construction projects. An increasing number of studies focus on PBs in the context of urban renewal. Li et al. incorporated PBs and atmospheric pollutants into the same research framework to explore the significant role of PBs in energy conservation and emission reduction [11].

Regarding carbon reduction for PBs, various research methods exist for studying carbon emission issues. Hao et al. developed a carbon emission calculation model for the materialization stage of PBs, including three stages, i.e., production, transportation, and construction. The production of concrete and steel reinforcement account for 42.43% to 41.04% of the total carbon emissions of PBs in the materialization stage [12]. Telesca et al. [13] found that prefabricated materials that contain adhesive products are beneficial for resource conservation and environmental protection. Ding et al. documented a 20.11% reduction in carbon emissions per unit volume (764.87 kg/m³ vs. cast-in-place components) due to efficient material use. Ma et al. quantify this at 336 kg/m² for semi-prefabricated projects versus 368 kg/m² for conventional methods, with material production dominating emissions (85%) [14]. Wang et al. revealed PBs reduce energy consumption by 7.54% and carbon emissions by 7.17% (81.08 kg CO₂/year·m²), primarily during operation and construction phases, while lowering costs by 10.62% [15]. Dong et al. [16] conducted a case study of a private residential building in Hong Kong and found that prefabricated concrete helps reduce carbon emissions in construction. Ding et al. developed a carbon emission measurement system for the materialization phase of PBs, showing that the total carbon emissions per unit volume of all prefabricated components were 764.87 kg/m³, which was 20.11% lower than that of cast-in-place components in a PB project [17].

Studies also examined the factors influencing carbon emission reduction in PBs. Based on the theory of Environment–Economy–Energy system, Zhu and Li identified five influencing factors for carbon emission reduction in the materialization stage of PBs, including government policy, management mode, technology level, economic input, and energy structure [18]. Zhan et al. analyzed the factors influencing the production stage of PBs, which include four aspects, i.e., material selection, energy consumption, material storage, and carbon emissions [19].

In addition, policy and governance frameworks are critical for scaling PBs. Cabannes documented PB in 1700+ cities globally, enabling community-driven projects that enhance service delivery and governance [20]. Sinervo et al. highlighted PB’s role in environmental sustainability, such as funding green urban regeneration and climate adaptation. In Brazil, PB redistributed resources to marginalized neighborhoods but faced constraints like inadequate funding and institutional resistance [21]. Leshinka et al. identified PPPs as vital for financing sustainable cities, integrating governance, innovation, and stakeholder engagement (“Public–Private–People Partnerships”) [22]. Zhang et al. noted financial pressure drives PPP adoption in China, particularly for economic infrastructure [23].

In sum, scholars have conducted extensive research on urban renewal theory and carbon emission accounting of PBs. However, few studies examined the mechanisms of carbon emission reduction from PBs in urban renewal. Urban renewal projects frequently entail complicated site conditions, renovation of existing buildings, and owners’ needs for the quality of the living environment. These attributes lead to considerable differences in the carbon emission reduction paths and influencing factors of PBs compared with traditional construction projects. A systematic framework needs to be developed to illustrate the influencing mechanism of PB’s carbon emissions and carbon emission reduction potential in urban renewal scenarios.

3. Methodology

Firstly, this study selected the most relevant literature on the carbon emission of PBs in the context of urban renewal from the Web of Science (WOS) database and identified the factors influencing PBs’ impact on carbon emission reduction. The keywords are “carbon emission/energy consumption”, “community renovation/urban renewal/old residential building”, and “prefabricated building/precast/modular/volumetric”. After retrieval, a total of 97 papers were obtained. Subsequently, the abstracts of the papers were read to conduct a preliminary screening of the documents, and 30 documents with relevant content were selected. Then, the publications with the most citations were finalized. Finally, through two rounds of screening, 14 documents were chosen as the core documents for the induction of influencing factors. The key influencing factors of carbon emission reduction in the context of urban renewal were identified and categorized into social economy, policy and regulation, building operation, and materialization of PBs.

Secondly, hypotheses were proposed to make assumptions about the relationships between the influencing factors of the carbon emissions from PBs in the context of urban renewal. This study developed a hypothetical model to explore the mechanisms influencing carbon emission reduction from PBs in urban renewal, in order to examine the internal relationships among variables and factors of social economy, policy and regulation, building operation, and materialization of PBs. A pilot study was conducted to verify the hypothesis model and the observed indicators. Five experts with over 10 years of work experience on prefabrication have been invited to check the description, clarity, relevance, and feasibility of all observed indicators, and to modify the questions and hypotheses based on participant feedback. The pilot study was also used to finalize the instrument and research plan for the main study. The SEM was used to develop the hypothesized paths among latent variables. Next, the developed model is evaluated, and the hypotheses are tested.

Subsequently, a questionnaire survey was conducted from September to November 2024 to gather expert insights on the perceived significance of factors influencing carbon emissions from prefabricated buildings (PBs) in urban renewal projects. Utilizing a purposive and snowball sampling approach, targeted respondents (n = 120) were recruited from key stakeholder groups directly involved in China’s urban renewal and prefabricated construction sector. Participants comprised professionals, including architects, project managers, construction engineers, sustainability consultants, developers, policy makers, and researchers with a minimum of 5 years of relevant experience and direct involvement in at least one urban renewal or PB project. The questionnaire employed a Likert five-point scale to measure the perceived influence level of each factor, where 1 = No significant influence, 2 = Weak influence, 3 = Moderate influence, 4 = Strong influence, and 5 = Significant influence. It consisted of three parts: (1) Introductory Remarks, outlining the research purpose and significance; (2) Respondent Demographics, capturing detailed information such as company type (e.g., construction firm, design institute, real estate developer, government agency), respondent category, educational attainment, current position, years of field experience, and number of urban renewal/PB projects participated in; and (3) Core Questionnaire Body, focusing specifically on the impact of various factors related to PBs in urban renewal on carbon emissions and general understanding of these p prefabricated projects in urban renewal.

The overall design concept is to formulate questions based on the influencing factor framework identified in the above Meta-analysis across four dimensions: social economy, policy and regulations, building operation, and the materialization of PBs. The materialization of PBs can be further categorized into four parts: production, transportation, assembly, and demolition. Based on the collected survey data, the accuracy and reliability of the data were tested. A fitness analysis was conducted on the effects of various factors influencing carbon emissions from PBs in urban renewal based on the SEM. The overall methodology roadmap is shown in Figure 1.

4. Results of the Data Analysis

4.1. Dimensions of Influencing Factors of Carbon Emission Reduction from PBs in Urban Renewal Based on Meta-Analysis

This study selected publications related to the carbon emissions of PBs in urban renewal by using search field titles, abstracts, and keywords that contain “urban renewal”, “carbon emissions of PBs”, and “sustainable development” and were published during the years 2015–2024. In total, 14 papers are chosen as the essential literature for identifying the influencing factors of carbon emissions from PBs. Through the meta-analysis of the selected literature, it is found that the research on the carbon emissions of PBs in urban renewal is relatively more focused on the manufacturing and construction stages of PBs. Only a few studies consider the operational stage of PBs, primarily focusing on aspects like lighting and heating. Some studies also examine the impact of policy regulations and the social economy on the overall carbon emissions system. Based on the identified influencing factors from the meta-analysis, it was found that the initial number of these factors was quite large and that certain coupling relationships existed among them. Incorporating the literature review of the projects and the characteristics of literature concerning carbon emissions from PBs in urban renewal, the initial influencing factors were summarized. Ultimately, the system of factors influencing carbon emissions from PBs in urban renewal was established, as specifically illustrated in

Table 1. Influencing factors of carbon emission reduction in PBs in urban renewal.

Dimension (Latent Variable)	Influencing Factors (Observed Variables)	References
Social Economy (SE)	Urban Population (SE1)	[24]
	Urbanization Rate (SE2)	[25]
	Gross Domestic Product (SE3)	[24,25,26,27,28]
	Per Capita Disposable income (SE4)	[24,27]
	Public Low Carbon Awareness (SE5)	[24,25,26,28]
Policy and Regulations (PR)	Building Code Policy Development (PR1)	[14,24,25,26,27,29]
	Policies to Guide Construction Enterprises (PR2)	[14,24,25,26,27,29]
	Scientific and Technological Innovation Input (PR3)	[24,25,27]
	Raise Public Awareness (PR4)	[24,26,27,28]
Building Operation (BO)	Illuminating System (BO1)	[28,30]
	Heating System (BO2)	[28,30,31]
	Gas System (BO3)	[31,32]
	Refrigerating System (BO4)	[31,32]
	Other Electrical Appliances (BO5)	[28,30,31]
	Daily Maintenance Energy Consumption (BO6)	[2,33,34]
	Domestic Rubbish Disposal (BO7)	[14,32,34]
	Low-carbon Technology Application (BO8)	[14,25,26,27,28,31]
Materialization of PBs (MPB)	PB Production (MPB1)	[2,14,30,32,33,34,35]
	PB Transportation (MPB2)	[2,14,27,30,32,34,35]
	PB Assembly (MPB3)	[2,24,29,32,33]
	PB Demolition (MPB4)	[14,30,32,34]
	PB Technical Level (MPB5)	[14,35]

Notes: Bonamente and Cotana, 2015, [30], Chen et al., 2022, [2]. Cheng et al., 2025, [24]. Du et al., 2021, [26]. Han et al., 2022, [33]. He et al., 2023, [31]. Jayawardana et al., 2023, [27]. Luo et al., 2022, [32]. Ma et al., 2021, [14]. Onyszkiewicz and Sadowski, 2022, [28]. Teng and Pan, 2020, [35]. Tumminia et al., 2018, [34]. Wang et al.,2022, [29]. Wu et al., 2019, [25].

.

4.2. Hypothetical Model of the Influence Mechanism of Carbon Emission Reduction from PBs in Urban Renewal

Based on existing research and relevant policy documents, the following hypotheses are proposed concerning the influencing mechanisms among the four dimensions: socioeconomic factors, policy regulations, building operation, and the materialization aspects of PBs mentioned earlier [14,28,36].

H1. 

Policy and regulations have a positive impact on social economy factors.

H2. 

Social economy factors have a positive impact on building operations.

H3. 

Social economy factors have a positive impact on the materialization aspects of buildings.

H4. 

Policy and regulations have a positive impact on building operations.

H5. 

Policy and regulations have a positive impact on the materialization aspects of PBs.

In addition to the influence of four dimensions, each dimension may also impact the final carbon emissions of PBs in urban renewal. Based on an analysis of existing research and relevant policy documents, the following hypotheses are proposed regarding the influencing mechanisms between each dimension and the total carbon emissions [25,37]:

H6. 

Social economy factors have a positive impact on the carbon emissions of PBs in urban renewal.

H7. 

Policy regulations have a positive impact on the carbon emissions of PBs in urban renewal.

H8. 

Building operation has a positive impact on the carbon emissions of PBs in urban renewal.

H9. 

The materialization aspects of PBs have a positive impact on the carbon emissions of PBs in urban renewal.

Based on the above analysis, research hypotheses can be formulated regarding the relationships among the four dimensions of socioeconomic factors, policy regulations, building operation, and the materialization aspects of PBs, as well as the impact of these four dimensions on the total carbon emissions of PBs in the context of urban renewal. According to the nine hypotheses mentioned above, the hypothesized model is shown in Figure 2.

4.2.1. Results of Validity and Reliability Test

In total, 39 paper questionnaires and 231 online questionnaires were gathered, resulting in 270 valid questionnaires and an 80.12% response rate. The profile of the respondents is presented in

Table 2. Data collected from the questionnaire.

Category	Frequency	Proportion
Category of the Company/Enterprise where one works
Construction companies	53	19.63%
Equipment manufacturing companies	16	5.93%
Urban planning and design companies	33	12.22%
Government or industry institutions	79	29.26%
Universities or research institutions	55	20.37%
Real estate and developers	34	12.59%
Occupational position
Organizational decision—making level	24	3.38%
Organizational management level	79	22.25%
The organizational implementation level	140	59.15%
Researchers in universities or research institutions	27	15.21%
Educational background
High school or below	24	8.89%
Junior college	52	19.26%
Undergraduate	148	54.81%
MPhil and above	46	17.04%
Work experience
Less than one year	19	7.04%
1–3 year	84	31.11%
4–5 year	86	31.85%
6–10 year	63	23.33%
10 year or more	18	6.67%
The number of PB projects
1–2	89	32.96%
3–5	82	30.37%
6–10	51	18.89%
10 or more	48	17.78%
The number of prefabricated projects applied in urban renewal projects
0	46	6.47%
1–2	82	23.07%
3–5	90	37.97%
6–10	29	16.32%
10 or more	23	16.17%
Total	270	100%

. Nearly 70% of the respondents have over 4 years of work experience. All of the respondents have participated in one or more PB projects. 71.8% of the respondents have a bachelor’s or higher degree. The remaining 28.2% of the respondents mainly come from construction and manufacturing companies with experience in prefabrication production, transport, and construction. The profile of the respondents suggests all respondents have relevant experience in prefabrication and justifies the validity of the data collection.

Using the statistical software SPSS 26.0, the Cronbach’s α coefficient for the total scale measuring carbon emissions of PBs in urban renewal is calculated to be 0.964, which exceeds 0.90, indicating an excellent overall reliability of the questionnaire. By calculating the Cronbach’s α coefficients for each sub-scale, it is found that the coefficients of the investigated variables are all greater than 0.70. Thus, the reliability levels for each dimension of the questionnaire are also relatively high.

In the next step, by deleting a certain measurement item, a change in the Cronbach’s α coefficient is observed, as shown in

Table 3. Cronbach’s $\mathsf{\alpha }$ coefficients of total scale and various dimension scales after deleting a certain item.

Social Economy		Policy Regulations		Building Operation		Materialization of PBs
Item	Coefficient After Deleting the Item	Item	Coefficient After Deleting the Item	Item	Coefficient After Deleting the Item	Item	Coefficient After Deleting the Item
SE1	0.906	PR1	0.800	BO1	0.930	MPB1	0.873
SE2	0.910	PR2	0.816	BO2	0.928	MPB2	0.856
SE3	0.913	PR3	0.803	BO3	0.930	MPB3	0.855
SE4	0.901	PR4	0.800	BO4	0.929	MPB4	0.870
SE5	0.901			BO5	0.928	MPB5	0.856
				BO6	0.928
				BO7	0.931
				BO8	0.929
Total coefficient 0.964		Total coefficient 0.964		Total coefficient 0.964		Total coefficient 0.964
Coefficient of This Dimension 0.924		Coefficient of This Dimension 0.846		Coefficient of This Dimension 0.937		Coefficient of This Dimension 0.890

. The total scale of the carbon emissions of PBs in the context of urban renewal has a high level of reliability, strong stability, and sufficient reliability; thus, the next step of the validity test can be carried out.

4.2.2. Results of the Confirmatory Factor Analysis

• Convergent Validity

As shown in

Table 4. KMO and Bartlett sphericity test of scales.

Indicate		Result
KMO measure of sampling adequacy		0.964
Bartlett Test of Sphericity	Approximate Chi-Square	4774.496
	Degree of Freedom	276
	Significance	0.000

, the KMO value for the investigated variables and the significance of Bartlett’s sphericity test indicate that the questionnaire can be utilized for SEM analysis.

According to calculations performed with SPSS 26.0, the results show that the characteristic roots of the first five factors exceed 1, and the cumulative contribution rate reaches 72.383%, surpassing 60%. At the same time, the factor loadings of all items are greater than 0.400. Therefore, all five latent variables outlined in the theoretical model above pass this test, which is shown in

Table 5. Scale factorization explains variance.

	Initial Eigenvalue			Sum of Squared Loadings Extracted			Sum of Squared Loadings After Rotation
	Total	Variance %	Cumulative %	Total	Variance %	Cumulative %	Total	Variance %	Cumulative %
1	13.106	54.607	54.607	13.106	54.607	54.607	5.163	21.513	21.513
2	1.252	5.217	59.824	1.252	5.217	59.824	3.749	15.620	37.133
3	1.219	5.081	64.905	1.219	5.081	64.905	3.719	15.495	52.629
4	1.080	4.501	69.406	1.080	4.501	69.406	3.111	12.961	65.590
5	0.767	3.198	72.604	0.767	3.198	72.604	1.683	7.014	72.604

.

The four initial single-factor models were evaluated using the maximum likelihood method with Amos 27 software. The operational results are shown in

Table 6. Fit indicators for each dimension.

Dimension	${\mathit{\chi }}^2/$df	GFI	AGFI	CFI	RMSEA
Social economy	2.427	0.981	0.942	0.993	0.073
Policy and regulations	1.311	0.995	0.976	0.999	0.034
Building operation	1.245	0.978	0.960	0.997	0.030
Materialization of PBs	1.819	0.987	0.960	0.994	0.055

. All indicators meet the requirements, indicating that the four sub-models of social economy, policy regulations, building operation, and materialization carbon of PBs possess strong fitting properties. Therefore, each sub-item effectively measures the conditions of its corresponding dimension, and the scale of each sub-dimension demonstrates strong structural validity.

• Discriminant Validity

When testing for discriminant validity, the AVE method is generally employed. If the square root of the AVE value of each latent variable exceeds its correlation coefficient with other latent variables, then there is strong discriminant validity between the two dimensions. As shown in

Table 7. Scale convergence validity.

Latent Variable	Observed Variable	Factor Loading	AVE	CR
Social economy	SE1	0.838	0.710	0.924
	SE2	0.819
	SE3	0.799
	SE4	0.881
	SE5	0.872
Policy and Regulations	PR1	0.770	0.580	0.847
	PR2	0.742
	PR3	0.763
	PR4	0.770
Building operation	BO1	0.825	0.652	0.937
	BO2	0.824
	BO3	0.805
	BO4	0.807
	BO5	0.825
	BO6	0.788
	BO7	0.777
	BO8	0.809
Materialization of PBs	MPB1	0.747	0.620	0.891
	MPB2	0.841
	MPB3	0.837
	MPB4	0.771
	MPB5	0.736
Carbon emissions	CE1	0.805	0.663	0.797
	CE2	0.823

and

Table 8. Correlation coefficient matrix and AVE arithmetic square root.

Dimension	Social Economy	Policy Regulations	Building Operation	Materialization of PBs	Carbon Emissions
Social economy	0.842
Policy and regulations	0.758 ***	0.761
Building operation	0.808 ***	0.764 ***	0.808
Materialization of PBs	0.744 ***	0.698 ***	0.774 ***	0.788
Carbon emissions	0.745 ***	0.745 ***	0.767 ***	0.740 ***	0.814

*** indicates significance at the 0.01 level.

, the correlation coefficient for each dimension satisfies the research requirements. Therefore, the observed variables with strong discriminant validity confirm the validity of the scale used in this paper.

4.3. Analysis of the Influence Mechanism

4.3.1. Analysis of the Degree of Fitting

Once the theoretical models for each sub-dimension have successfully passed the reliability and validity tests, it is essential to evaluate the goodness of fit for the overall structural model.

Combining with the theoretical model and research hypotheses of the carbon emissions of PBs in urban renewal, this paper analyzes the influence relationships among variables in social economy, policy regulations, building operation, materialization of PBs, and carbon emissions, and finally proposes a structural equation model, as shown in Figure 3.

Amos 24.0 was used to analyze the degree of fit between this structural equation model and the data sample. Three commonly used types of indices—absolute fit indices, incremental fit indices, and parsimony fit indices—are selected to evaluate the goodness of fit for the structural model. The results of the calculation and analysis using the maximum likelihood method are shown in

Table 9. Overall fitness evaluation results.

Model Fit Index	Fit Indices Thresholds	The Fit Indices Values	Evaluate
${\chi }^2/$df	<3.000	1.239	good
GFI	≥0.800	0.913	good
AGFI	≥0.800	0.893	good
RMR	<0.080	0.067	good
RMSEA	<0.080	0.030	good
CFI	≥0.900	0.988	good
NFI	≥0.900	0.939	good
IFI	≥0.900	0.988	good
PNFI	≥0.500	0.827	good
PGFI	≥0.500	0.740	good

.

4.3.2. Analysis of Hypothesis Testing

The calculated results using the maximum likelihood method are shown in

Table 10. Estimated values of observed variable parameters.

Latent Variable	Path	Observed Variable	Estimate	Standardized Estimates	S.E.	C.R.	p
CE1	<---	Carbon missions	1.000	0.804
CE2	<---	Carbon missions	1.007	0.822	0.077	13.162	***
SE1	<---	Social economy	0.985	0.838	0.054	18.101	***
SE2	<---	Social economy	0.950	0.818	0.055	17.338	***
SE3	<---	Social economy	0.925	0.8	0.056	16.666	***
SE4	<---	Social economy	1.078	0.878	0.055	19.748	***
SE5	<---	Social economy	1.000	0.869
PR1	<---	Policy regulations	1.014	0.764	0.081	12.555	***
PR2	<---	Policy regulations	0.973	0.744	0.08	12.187	***
PR3	<---	Policy regulations	1.040	0.759	0.083	12.463	***
PR4	<---	Policy regulations	1.000	0.768
BO1	<---	Building operation	0.938	0.788	0.062	15.252
BO2	<---	Building operation	1.003	0.823	0.062	16.303	***
BO3	<---	Building operation	0.978	0.804	0.062	15.716	***
BO4	<---	Building operation	0.980	0.808	0.062	15.847	***
BO5	<---	Building operation	1.015	0.827	0.062	16.417	***
BO6	<---	Building operation	1.000	0.826			***
BO7	<---	Building operation	0.957	0.776	0.064	14.914	***
BO8	<---	Building operation	1.036	0.807	0.066	15.802	***
MPB1	<---	Materialization of PBs	1.000	0.748
MPB2	<---	Materialization of PBs	1.144	0.839	0.082	13.94	***
MPB3	<---	Materialization of PBs	1.145	0.837	0.082	13.904	***
MPB4	<---	Materialization of PBs	1.051	0.771	0.083	12.707	***
MPB5	<---	Materialization of PBs	0.971	0.737	0.08	12.103	***

*** indicates significance at the 0.01 level.

and

Table 11. Results of path analysis in Structural Equation Model.

Path	Estimate	Standardized Path Coefficient	S.E.	C.R.	p
Social economy ← Policy regulations	0.856	0.758	0.077	11.047	***
Building operation ← Social economy	0.542	0.532	0.077	7.075	***
Building operation ← Policy regulations	0.433	0.377	0.088	4.926	***
Materialization of PBs ← Social economy	0.456	0.496	0.081	5.656	***
Materialization of PBs ← Policy regulations	0.358	0.345	0.092	3.904	***
Carbon missions ← Building operation	0.239	0.244	0.107	2.244	**
Carbon missions ← Materialization of PBs	0.260	0.239	0.101	2.573	***
Carbon missions ← Social economy	0.173	0.173	0.114	1.508	Non-significance
Carbon emissions ← Policy regulations	0.297	0.263	0.125	2.386	**

** indicates significance at the 0.05 level, and *** indicates significance at the 0.01 level.

. Through the structural equation, this paper identified the path relationships among the latent variables on carbon emissions from PBs in urban renewal. As shown in Table 10, the path relationship coefficients among the latent variables in the model are used to test the causal relationship hypotheses among the latent variables. The standardized results of the overall model are shown in Figure 4. The results of the path analysis show that the standardized path coefficients for all observed variables range from 0.65 to 0.99. The four dimensions of the socioeconomic aspect, policy and regulations, building operation, and the materialization of carbon of PBs significantly impact the carbon emissions of PBs in urban renewal. The results of the path analysis show that all the hypotheses except for H6 are valid.

5. Discussion

In the context of urban renewal, the mechanisms driving carbon emission reduction in prefabricated buildings (PBs) reveal a complex, interconnected system. Policies and regulations (PR) emerge as a primary catalyst, evidenced by a standardized path coefficient of 0.263. This significance stems from the hybrid effect of policy instruments—subsidies, taxes, R&D incentives, and public awareness campaigns—which collectively enhance prefabrication adoption and emission reduction, aligning with findings by Shi et al. [38] and Yan et al. [37]. Critically, industrial policies (e.g., fiscal, technical, and ecological measures) can elevate PB market shares by up to 52% and reduce emissions by 12.56 million tons, as demonstrated in Xi’an’s system dynamics simulations [33]. However, mandatory policies alone risk neglecting quality and safety; thus, a blend of incentives and mandates is optimal for sustainable PB development [39].

PR’s influence extends beyond direct impacts (path coefficient: 0.263) to potent indirect pathways via socioeconomic factors, with a notably higher coefficient of 0.76. This corroborates Tumminia et al. [34], showing that policy-driven incentives shape public and corporate willingness to adopt PBs. Socioeconomic factors (SE), while lacking a direct emissions pathway, exert substantial indirect effects through building operations (BO) and materialization processes (MP). For instance, SE boosts low-carbon PB development by channeling investments into R&D and new energy technologies [39], with Liu et al. confirming economy-driven factors as secondary only to technological innovation in emission reduction [6]. Path analysis further reveals that SE’s mediated impact—via BO and MP—accounts for >90% of its total effect, underscoring the necessity of “through-path” methodologies to capture indirect relationships [40]. In sum, while social economy factors shape where or why prefabricated units are deployed, they do not change the carbon footprint of prefabrication in manufacturing and transporting modules, or the carbon footprint of a factory-made wall panel. Their impact is indirect, but the emissions themselves arise from technical/logistical decisions, not social goals. Social factors can influence emissions indirectly from both policy incentives and design compromises. Social equity goals might accelerate the adoption of PB for affordable housing, indirectly scaling low-carbon technologies. In addition, community demands might reduce floor space, lowering material use.

Building operations (BOs) and materialization (MP) are unequivocal focal points for direct emission reduction. BOs contribute 28% of global carbon emissions, but hybrid renewable systems (e.g., BIPVs + wind turbines) can slash operational emissions by 65.9%. Meanwhile, MP’s embodied carbon varies from 2% to 80% of a building’s lifecycle emissions, emphasizing centralized production’s role in minimizing waste and energy inefficiency [41]. Zhu and Li [18] identified MP-stage optimization as critical, while BIM and building performance simulations (BPSs) enable precise quantification of these impacts.

Urban renewal uniquely amplifies PBs’ advantages over traditional buildings. Demolition–reuse cycles in renewal projects exacerbate waste in conventional construction, whereas PBs’ modular design reduces material waste by 30–50% [42]. Traditional urban renewal often erodes regional cultural identity through uniform demolition, but PBs align with renewal goals by balancing efficiency with heritage conservation. Empirical studies confirm that economic investments—particularly public-private partnerships (PPP)—accelerate low-carbon PB transitions via spatial spillover effects, enhancing infrastructure efficiency and industrial upgrading. For example, road PPP projects boost low-carbon economic efficiency by 15–20% more than traditional investments [43].

6. Conclusions

This paper uses the meta-analysis method to summarize the framework of factors influencing carbon emissions from PBs in the context of China’s urban renewal. The identified factors influencing carbon emissions of PBs in urban renewal can be categorized into four dimensions: social economy, policy regulations, building operation, and the embodied carbon of PBs. There are 22 influencing factors in these four dimensions. SEM was used to test the hypothesized model using the data collected from the questionnaire survey. The results confirm the existence of the pathway from policy regulations to carbon emissions, with a standardized estimated value of 0.263 for the path coefficient. Policy regulations were proven to have an impact on social economy, building operations, and the materialization of carbon of PBs, with path coefficients of 0.758, 0.377, and 0.345, respectively. In addition, the building operation has a positive correlation with carbon emissions, with a standardized estimated path coefficient value of 0.244. Building operations are influenced by both the social economy aspect and policy regulations, with path coefficients of 0.532 and 0.377. The standardized estimated value of the path for the materialization of PBs to the carbon emissions coefficient is 0.239. The building operation is influenced by both the social economy and policy regulations, with path coefficients of 0.496 and 0.345. The direct influence of socioeconomic factors on carbon emissions is insignificant, while it exerts its effect on PBs’ carbon emissions through the building operation and materialization stage. The findings help shed light on the potential action plans for achieving sustainable urban renewal in the building and construction sector.

Despite the findings of the study, some shortcomings still exist in this paper. This paper explores the relationships among various influencing factors from a macroscopic perspective. There is a lack of investigation into the action paths of factors at the microscopic level and their interrelationships regarding carbon emission reduction in PBs in urban renewal. Future research also needs to investigate the transmission mechanisms and action paths of macro-influencing factors, such as social economy and policy regulations, on the carbon emissions of PBs at the building or community level.