Life Cycle Sustainability Assessment of Buildings: A Scientometric Analysis

Abstract

The significant environmental impact of the built environment, particularly concerning energy use, carbon emissions, and material consumption, coupled with its economic and social implications, has driven the demand for sustainable buildings. Life Cycle Sustainability Assessment (LCSA) offers a comprehensive approach to evaluating sustainability performance by integrating environmental, economic, and social dimensions across the building life cycle. However, the application of LCSA frameworks in the buildings sector remains limited due to the challenges in harmonizing different sustainability dimensions and addressing methodological inconsistencies. This study employs a scientometric analysis to systematically examine the research landscape on LCSA for buildings. Bibliographic records from the Scopus and Web of Science databases (1999–2024) were systematically analyzed using science mapping techniques and tools, including VOSviewer, CiteSpace, and Gephi. The analysis identifies key research trends, conceptual developments, influential academic sources, and collaboration patterns at the country level. The findings reveal a multi-faceted research landscape characterized by a predominance of environmental assessments, increasing attention to economic and social dimensions, the development of BIM-related methodologies, and emerging trend towards dynamic LCSA. Persistent barriers include insufficient standardization of methodologies, limited data availability, and the fragmented incorporation of the environmental, economic, and social dimensions of sustainability. The findings emphasize the need for advancing LCSA frameworks to achieve more effective integration of the triple bottom line, enabling robust decision-making and advancing sustainability in the built environment.

1. Introduction

The buildings and construction sector has a significant impact on the environment, economy, and society. Buildings account for approximately 30% of global final energy demand, increasing to 34% when including the energy required for the production of construction materials. Building operations and construction generate approximately 37% of global energy and process-related carbon dioxide emissions [1]. In addition, the buildings and construction sector contributes to economic and social growth, representing over 10% of the global Gross Domestic Product (GDP) and creating approximately 100 million jobs [2]. Therefore, achieving sustainability in the built environment requires a balanced approach that addresses social and economic needs while minimizing environmental impacts. This imperative has driven the development of frameworks to assess the influence of buildings on sustainability outcomes. As a result, the domain of sustainability assessment has expanded to include diverse methodologies, such as rating systems, sustainability indicators, energy analysis tools, and life cycle assessment-based methods, providing systematic measures to quantify the impacts of the built environment on ecosystems [3,4,5].

International sustainability strategies, such as the United Nations Sustainable Development Goals [6], alongside European strategies, including the Green Deal [7] and the Circular Economy Action Plan [8], emphasize the significant role of the buildings and construction sector in achieving global sustainability objectives to reduce the environmental footprint of the sector, enhance energy efficiency, and promote resource circularity. Although the application of these measures, including energy efficiency directives 2010/31/EU [9], 2012/27/EU [10], and 2018/2002/EU [11] has led to notable reductions in operational energy use, recent studies indicate a potential environmental load-shift to other life cycle phases of buildings [12]. This underscores the critical importance of adopting a holistic, whole life cycle approach to sustainability within the sector, accounting for both operational and embodied impacts.

Methodologies for assessing building sustainability can be categorized into frameworks derived from international standards and guidelines, Green Building Rating Systems (GBRSs), and methods proposed within academic literature. Widely recognized GBRSs, including the Building Research Establishment Assessment Method (BREEAM), Leadership in Energy and Environmental Design (LEED), Green Star, and other country-specific rating systems, assess compliance with predefined criteria and serve as benchmarks for issuing green building certifications [13]. However, these systems predominantly assess the environmental impacts of buildings, lack uniformity, and are highly subjective based on the location and nature of the analyzed buildings, leading to variability in their applicability and outcomes [14,15]. While some systems like Deutsches Gütesiegel für Nachhaltiges Bauen (DGNB) incorporate limited LCA and LCC components, social dimensions are typically restricted to comfort and health impacts on users, overlooking broader supply chain effects and stakeholder categories such as workers and local communities [16].

In addition to the GBRS methods, LCA has been extensively used in numerous studies to evaluate the environmental sustainability of buildings, structural systems, and building components across all phases of their life cycle [17,18]. Unlike GBRSs, which rely on predetermined criteria and are influenced by regional considerations, LCA provides a more quantitative and globally applicable approach for assessing environmental impacts by quantifying environmental pressures while considering the full life cycle of built assets [19]. Energy-based approaches, including exergy, emergy, and thermodynamic analyses of energy flows, alongside indicator-based methodologies, such as ecological footprints and indices, are extensively adopted to evaluate sustainability-related parameters, including energy, water, land use, and other resources [20,21,22]. However, these tools typically address only specific areas of sustainability. Despite progress in environmental assessment, a significant research gap remains in the development of a Life Cycle Sustainability Assessment (LCSA) framework, capable of integrating and evaluating the environmental, economic, and social dimensions of sustainability, and providing insights into potential upstream and downstream trade-offs related to environmental pressures, social considerations, and resource consumption [16,19].

Challenges persist in implementing life cycle sustainability thinking in building design and decision-making processes. These challenges arise from the complexity of assessing the interconnected environmental, economic, and social dimensions of sustainability, including difficulties in accurately quantifying and harmonizing different impacts, assigning weights to sustainability criteria, and addressing methodological uncertainties [23]. While many research works have been conducted to develop frameworks for Life Cycle Assessment (LCA), Life Cycle Costing (LCC), and Social Life Cycle Assessment (S-LCA), the absence of an integrated LCSA model remains a critical barrier to employing life cycle thinking in the decision-making process, thereby restricting its practical application in guiding sustainable design and policy development.

Scientometrics, as a robust methodology for analyzing research trends and mapping knowledge domains, has become increasingly applied in recent years across diverse fields, including the buildings and construction sector. Scientometric analyses have provided valuable insights into numerous research areas, such as global trends in sustainability and sustainable development [24], green construction [25], sustainable infrastructure [26], and green building research [27,28]. Additionally, studies have explored the circular economy within the buildings and construction sector [29], retrofitting of existing buildings [30,31], and the role of Building Information Modeling (BIM) in enhancing sustainability. Within the BIM domain, scientometric research has addressed BIM applications in sustainable construction [32], sustainable building management [33], and energy efficiency improvements [34], as well as the interoperability of BIM in architecture, engineering, construction, operation, and facility management contexts [35]. Furthermore, scientometric studies have investigated the application of LCA [36], BIM-integrated LCA for promoting a circular economy towards sustainable construction [37], and embodied energy in the construction industry [38]. Despite this extensive body of research, a significant gap remains in the analysis of the research landscape of LCSA.

LCSA, as an advanced methodology that integrates environmental, economic, and social dimensions of sustainability, offers a comprehensive approach to assessing the holistic impact of buildings throughout their life cycle. However, to date, no comprehensive scientometric study has systematically examined the evolution and thematic focus of LCSA research in the field of buildings. To address this gap, the study aims to: (1) systematically map the intellectual structure and evolution of the LCSA research within the domain of buildings; (2) identify and analyze the key research themes and prominent publication outlets shaping the LCSA of buildings research landscape; and (3) reveal research gaps and propose future research directions in the LCSA of buildings by synthesizing the findings of the scientometric analysis. Through science mapping techniques, including co-occurrence, co-citation, and co-authorship analyses, this study systematically identifies key research topics, reveals patterns of keyword interrelations, highlights leading academic journals, and maps the geographic regions most actively contributing to the research field. By offering a detailed and structured analysis, this study provides a comprehensive examination of the LCSA research landscape specific to buildings, identifies research gaps and limitations, and proposes directions for future scholarly investigation.

2. Materials and Methods

2.1. Scientometric Approach

Scientometrics, initially introduced by Nalimov and Mulchenko [39], refers to the application of quantitative methods of research on the development of science as an informational process. Hess [40] further defined it as the quantitative study of science, communication in science, and science policy. The objective of scientometric research is to generate insights into the evolution of scientific inquiry, whether focused on a specific topic, a broader field, or the entire body of scientific knowledge [41]. Key areas of focus in scientometric research include measuring research quality and impact, analyzing citation dynamics, mapping scientific fields, and applying indicators to inform research policy and management [42].

Scientometric mapping, an area within scientometrics, involves the application of quantitative methods to analyze and visually represent key metrics of scientific literature based on bibliographic data. By integrating data mining with visualization techniques, scientometric mapping facilitates an understanding of a research domain, its structural development, collaboration networks, influential articles or journals, and focal areas of research activity [43]. In this study, a scientometric approach was employed to systematically analyze the research landscape of LCSA specific to buildings.

2.2. Research Methodology

The research methodology involved the selection of science mapping tools, data collection, and the analysis, modeling, visualization, and interpretation of findings. Several science mapping tools exist, each with distinct strengths and capabilities. This study utilized VOSviewer (version 1.6.20) for constructing and visualizing bibliometric networks, Gephi (version 0.10.1) for advanced graph and network analysis, and CiteSpace (version 6.3.R3) for analyzing citation bursts, co-citation clusters, and visualizing the temporal evolution of research themes. The application of these tools has been validated in prior scientometric studies, including those by Darko et al. [44], Lixin et al. [45], and others.

Bibliographic data were sourced from the Scopus and Web of Science (WoS) databases, selected for their extensive coverage of high-quality scientific publications [46]. The search was conducted on 5 October 2024, employing a targeted keyword search to identify publications relevant to LCSA of buildings. The search was applied to publication titles, abstracts, and keywords using the following query string: (“life cycle” OR “life-cycle” OR “lifecycle”) AND (“sustainability assessment” OR “sustainability analysis” OR “sustainability evaluation”) AND (“building” OR “buildings”). The keywords sustainability assessment, sustainability analysis, and sustainability evaluation were selected to encompass diverse terminologies used in academic literature addressing sustainability assessment methodologies. The keywords life cycle, life-cycle, and lifecycle were included to capture studies focusing on the life cycle perspective, accounting for variations in spelling and hyphenation. The keywords building and buildings were employed to narrow the scope of the search to research specifically related to buildings. The initial search retrieved a total of 1131 publications, comprising 578 records from Scopus and 553 records from WoS databases.

To ensure the relevance and quality of the dataset, the following inclusion and exclusion criteria were applied. Only peer-reviewed journal articles were included to ensure the quality and reliability of the research. Articles published exclusively in English were included to ensure consistency in the analysis and interpretation of findings. No restrictions were imposed on the publication date to ensure comprehensive retrieval of relevant studies, given the relatively recent emergence of LCSA as a distinct research field.

These criteria resulted in the exclusion of publication types other than journal articles (e.g., conference proceedings, book chapters), non-English publications, and duplicate records identified across databases. The resulting 473 relevant publications comprised the dataset used for the scientometric analysis (Figure 1).

2.3. Scientometric Analysis Techniques

In this study, the following scientometric techniques were employed: (1) keyword co-occurrence analysis, identifying the co-occurrence of keywords within the dataset of research publications; (2) document co-citation analysis, identifying the documents that are frequently cited together, highlighting the relationships between scholarly works; (3) keyword citation bursts, depicting periods of rapid increase in the citation frequency of specific keywords, revealing emerging trends in the research field; (4) direct citation analysis of journals, examining the citation patterns between research outlets, emphasizing the direct citation links between influential publications; (5) country co-authorship analysis, exploring the collaborative networks between countries based on co-authorship of research publications, providing insight into international research collaboration.

A thematic analysis was carried out to systematically identify the main research themes and conceptual developments within the academic literature on the LCSA of buildings. Keyword co-occurrence analysis was employed to identify key terms based on the frequency and strength of keyword pairings across the dataset. Node centrality measures were computed to identify keywords exerting significant influence on the research focus. The structure and temporal evolution of document co-citation clusters were examined to map key research themes and analyze their development over time. Keyword citation burst analysis provided insights into prevailing research trends and emerging areas of interest. Research gaps were identified by investigating areas characterized by limited research activity. The analysis of co-occurrence and co-citation patterns highlighted underdeveloped or inconsistently applied themes and methodologies within the literature.

2.4. Network Structure and Measures

A bibliometric network consists of nodes and edges, where nodes represent entities such as keywords, journals, researchers, research institutions, or countries, and edges signify the relations between nodes. As bibliometric networks are often weighted, edges not only indicate the presence of a relation between two nodes but also represent the strength of the relation among nodes. The most studied types of relations are citation, keyword co-occurrence, and co-authorship relations [47].

The centrality measures of nodes provide quantitative metrics describing the structure and dynamics of the network. Centrality measures identify influential nodes (in co-occurrence analysis—keywords) that contribute to the structure of the network, information flow, and connectivity. In this study, key centrality metrics—degree centrality, weighted degree centrality, betweenness centrality, and closeness centrality—were computed to analyze the research landscape of LCSA of buildings.

Degree centrality is defined as the number of direct connections a node has in the network, reflecting the immediate influence of a node upon other nodes [48,49]. Nodes with high degrees often exhibit high centrality according to other measures, establishing degree as a reliable indicator of nodal importance within the network [50]. Degree can be formalized as:

$$C_D\left(i\right)=\sum _j^N{x}_{ij},$$

(1)

where N is the total number of nodes, i is the focal node, j represents all other nodes, and x is the adjacency matrix, in which the cell $x_{ij}$ is defined as 1 if node i is connected to node j, and 0 otherwise. Weighted degree centrality extends this measure by accounting for the strength of the connections by incorporating the edge weights, formalized as:

$$C_D^w\left(i\right)=\sum _j^N{w}_{ij},$$

(2)

where w is the weighted adjacency matrix, in which $w_{ij}$ is greater than 0 if the node i is connected to node j, and the value represents the weight of the edge.

In addition to the degree, the closeness and betweenness measures indicate the importance of a node based on different aspects of the shortest path distances within a network. Closeness centrality represents the extent to which a node is proximally located to all other nodes in the network, as it is determined by the total length of the shortest paths from a node to all other nodes; it is defined as the inverse of this total distance [51]. In contrast, betweenness centrality identifies the shortest paths between all pairs of nodes and measures the extent to which a given node lies on these paths, thereby capturing its role as an intermediary in the structure of the network. Based on Freeman [49], closeness and betweenness can be formalized as, respectively:

$$C_C\left(i\right)={\left[\sum _j^{N}d\left(i,j\right)\right]}^{-1},$$

(3)

$$C_B\left(i\right)={\displaystyle \frac{g_{jk}\left(i\right)}{g_{jk}}},$$

(4)

where $g_{jk}$ is the number of binary shortest paths between two nodes, and $g_{jk}\left(i\right)$ is the number of those paths that go through node i.

Metrics, including modularity and weighted mean silhouette values, can be used to assess the overall structural properties of the network. The modularity of a network quantifies the degree to which the network can be divided into distinct components or modules, with values ranging from 0 to 1. Higher modularity values indicate a clearer separation between clusters, signifying well-defined structural divisions within the network [52]. For a network divided into b clusters, the modularity Q is computed from the symmetric b × b mixing matrix D, formulated as [53]:

$$Q={\sum }_i\left[d_{ii}-{\left({\sum }_j{d}_{ij}\right)}^2\right],$$

(5)

where the elements of D along its main diagonal $d_{ii}$ indicate the fraction of links among the nodes within cluster i, and other elements of D, $d_{ij}$ (where i ≠ j) represent the fraction of links between nodes within two dissimilar clusters i and j. A silhouette value of a cluster, ranging from −1 to 1, assesses the quality of the clustering configuration. A positive silhouette value suggests well-defined and distinct clusters, while negative values indicate overlapping or poorly defined clusters.

The centrality metrics, including degree centrality, weighted degree centrality, betweenness centrality, and closeness centrality, were applied to identify influential keywords and nodes, highlighting their role in analyzing the research landscape of LCSA in buildings. Structural network measures, such as modularity and silhouette values, were utilized to evaluate the cohesiveness and quality of clustering within the network, facilitating the identification of distinct thematic clusters.

3. Results

3.1. Research Trends in LCSA of Buildings

Growth in Publications

The framework for LCSA was conceptualized by Klöpffer [54] in 2008, comprising three separate assessments based on consistent system boundaries, comprising environmental Life Cycle Assessment (LCA) [55,56], Life Cycle Costing (LCC) [57,58], and Social Life Cycle Assessment (S-LCA) [59]. The LCSA framework was subsequently refined by the UNEP/SETAC guidelines [60], which established a procedural structure for its application in sustainability assessments. This coincided with an increase in academic interest regarding LCSA in many research areas, including in the context of the buildings sector. The number of publications on the LCSA of buildings began to rise significantly from 2012, as evident in Figure 2.

A marked increase in publications occurred after 2017, with more than half of the papers published in the last five years. By October 2024, annual publications peaked at 59, reflecting the growing adoption of LCSA. This trend has been further driven by increasingly tightening regulatory frameworks for the buildings sector, influenced by European directives and national regulations [61,62].

3.2. Research Structure on LCSA of Buildings

3.2.1. Keywords Co-Occurrence Network

In scientometric analysis, co-occurrence refers to the simultaneous appearance of two or more specific entities within the same context, such as a document or dataset. The co-occurrence of keywords enables the identification of clusters that define focal points within a specific research field, allowing publications to be characterized and grouped based on shared keyword patterns, which reveal the underlying structure of research areas and specialties [63]. In the case of a keyword co-occurrence network, the nodes represent keywords and edges represent relations between the keywords. The strength of an edge indicates the number of publications in which two terms occur together.

A keyword co-occurrence network was created using VOSviewer (1.6.20) software, used for constructing and viewing bibliometric maps [64]. To ensure a reliable representation of the research focus, author keywords, instead of all keywords, were chosen for the analysis, with a total of 1490 keywords identified in the dataset. The value of the minimum number of occurrences of a keyword to be included in the network was set to 5. This inclusion criterion was based on multiple experiments to produce a network that is optimal in terms of control, clarity, and reproducibility. Out of 1490 keywords, 62 met the threshold and were further analyzed. Identical terms (e.g., LCA and life cycle assessment; BIM and building information modeling; MCDM and multi-criteria decision-making) were merged (as life cycle assessment, building information modeling, and multi-criteria decision-making, respectively). The resulting network consisted of 52 nodes and 330 edges, illustrated in Figure 3.

The network data, comprising information on edges and nodes, was exported from VOSviewer and subsequently imported into Gephi (0.10.1) software, designed for advanced graph and network analysis [65]. Using Gephi, centrality metrics for the nodes in the network were calculated, including degree, weighted degree, betweenness centrality, and closeness centrality, presented in

Table 1. Centrality of nodes in the network.

Rank	Node	Degree	Weighted Degree	Betweenness Centrality	Closeness Centrality
1	life cycle assessment	44	223	275.98	0.88
2	sustainability	35	135	151.19	0.76
3	sustainability assessment	32	101	114.08	0.73
4	life cycle sustainability assessment	29	79	72.39	0.70
5	buildings	25	67	63.47	0.66
6	building information modeling	23	79	38.08	0.65
7	life cycle cost	21	52	39.94	0.63
8	multi-criteria decision-making	21	37	31.59	0.63
9	analytic hierarchy process	19	34	13.74	0.61
10	environmental impact	17	33	17.27	0.60
11	residential buildings	17	30	16.68	0.60
12	life cycle costing	16	45	11.55	0.59
13	social life cycle assessment	16	37	12.19	0.59
14	life cycle	16	28	21.63	0.59
15	green building	16	22	13.96	0.59
16	construction	14	27	8.50	0.58
17	energy efficiency	14	17	8.40	0.58
18	triple bottom line	13	24	7.16	0.57
19	multiple-criteria decision analysis	13	20	9.38	0.57
20	life cycle analysis	13	18	15.34	0.56
21	benchmarking	13	17	5.54	0.57
22	circular economy	12	23	9.21	0.57
23	sustainable building	12	19	5.55	0.56
24	sustainable construction	12	19	5.24	0.57
25	sustainability indicators	11	20	6.93	0.55
26	rating systems	11	16	5.72	0.56
27	sustainable development	11	11	7.69	0.55
28	social impact	10	14	1.79	0.55
29	indicators	10	12	5.26	0.55
30	modular construction	10	12	1.70	0.55
31	framework	9	9	3.01	0.54
32	economic input-output analysis	8	13	1.97	0.54
33	optimization	8	10	3.22	0.53
34	assessment	7	16	2.10	0.50
35	renovation	7	14	2.62	0.53
36	energy consumption	7	11	1.55	0.54
37	sustainable design	7	11	0.84	0.52
38	building sustainability assessment	7	10	2.25	0.53
39	leed	7	9	2.34	0.53
40	environment	7	9	2.24	0.53
41	carbon emissions	7	8	1.57	0.53
42	energy	6	11	1.26	0.50
43	retrofit	6	7	0.86	0.53
44	topsis	5	10	0.08	0.52
45	mives	5	9	0.84	0.51
46	embodied energy	5	8	0.87	0.51
47	industrial ecology	5	7	0.10	0.53
48	ghg emissions	5	6	1.07	0.52
49	life cycle thinking	5	6	0.62	0.53
50	resource recovery	4	6	0.32	0.47
51	system dynamics	4	6	0.00	0.51
52	building materials	3	7	0.13	0.48

.

The findings indicate that life cycle assessment exhibits the highest centrality within the keyword network (degree: 44, weighted degree: 223, closeness centrality: 0.88), reflecting its position as the foundational methodology for building sustainability assessment [66,67]. The highest betweenness centrality (275.98) indicates the role of LCA in methodological integration, connecting various research areas within building LCSA.

Environmental impact (degree: 17, weighted degree: 33, betweenness centrality: 17.27, closeness centrality: 0.60) demonstrates relatively high centrality, underscoring the predominant role of the environmental dimension in building sustainability assessment. While a focus on operational energy [68,69] is evident with keywords including energy efficiency, energy consumption, and the presence of embodied energy [18], although with lower centrality, signals increasing recognition of whole-life cycle impacts. Terms including carbon emissions, and GHG emissions indicate a focus on comprehensive greenhouse gas accounting within building LCSA, encompassing both operational and embodied impacts.

Characterized by relatively high centrality, the keywords life cycle cost (degree: 21, weighted degree: 52, betweenness centrality: 39.94, closeness centrality: 0.63) and life cycle costing (degree: 16, weighted degree: 45, betweenness centrality: 11.55, closeness centrality: 0.59) represent the economic dimension in building sustainability assessment research. Economic input-output analysis contributes to the exploration of the economic dimension by providing a quantitative framework for assessing the environmental and economic impacts across the supply chain, although its lower centrality suggests a more peripheral role in the network.

The keywords such as social life cycle assessment (degree: 16, weighted degree: 37, betweenness centrality: 12.19, closeness centrality: 0.59) and social impact (degree: 10, weighted degree: 14, betweenness centrality: 1.79, closeness centrality: 0.55) indicate a growing recognition of the social aspects of sustainability. However, their relatively lower centrality compared to LCA and other core terms reflects limited integration.

Keywords such as benchmarking and rating systems, exhibiting moderate centrality (weighted degrees of 17 and 16, respectively), indicate the application of standardized evaluation criteria in sustainable construction practices. The emphasis on decision support is reflected in the focus on methods such as multi-criteria decision-making (weighted degree: 37) and, particularly, analytic hierarchy process (weighted degree: 34), which are employed for evaluating trade-offs between sustainability criteria. Conversely, tools such as TOPSIS and MIVES exhibit notably lower centrality (weighted degree of 10 and 9, respectively), suggesting limited adoption and integration within building sustainability assessments. These findings indicate that LCSA research is oriented towards enhancing the practical application of assessment results by translating complex findings into actionable insights for stakeholders in the buildings sector, thereby facilitating informed decision-making regarding sustainable building practices and policies.

The relatively high centrality of building information modeling (degree: 23, weighted degree: 79, betweenness centrality: 38.08, closeness centrality: 0.65) highlights the importance of integrated information management in advancing building sustainability research. These results are expected, as BIM facilitates the optimization of building performance across life cycle stages by enhancing data integration, simulation, and decision-making, particularly in optimizing construction activities through information exchange mapping [70]. This confirms the increasing importance of BIM not only as a design tool but also as a key enabler for LCSA implementation, facilitating data collection, analysis, and integration within LCSA studies.

The moderate centrality of keyword construction (degree: 14, weighted degree: 27, betweenness centrality: 8.50, closeness centrality: 0.58) suggests a prominent focus on new construction within LCSA research. However, the presence of keywords such as circular economy, industrial ecology, and resource recovery indicates a growing interest in closed-loop systems within the buildings sector, aligning with global sustainability objectives related to waste minimization and resource optimization. Similarly, the terms renovation and retrofit suggest an increasing emphasis on adaptive building practices that prioritize adaptability, resource efficiency, and life cycle extension, reflecting a concern for improving the performance of existing building stock [71,72,73,74]. Despite this emerging interest, the relatively low centrality of these keywords compared to established LCSA terms indicates a limited integration of these approaches into mainstream building LCSA practice.

In terms of building types, residential buildings (degree: 17, weighted degree: 30, betweenness centrality: 16.68, closeness centrality: 0.60) is the only prominently represented category, indicating the prioritization of sustainability assessments in the residential sector. In contrast, non-residential buildings are less analyzed in building sustainability research, indicating a potential gap in the literature.

3.2.2. Document Co-Citation Network

Citation reveals connections between research ideas, scholars, journals, and institutions, forming an empirical field or network that can be quantitatively analyzed [42]. Citation patterns among publications, represented by clusters formed through co-citation trends, provide insights into the structure of a scientific knowledge domain [75]. To explore these patterns, CiteSpace (6.3.R3) software was employed for its advanced clustering capabilities and its ability to analyze bibliographic data related to cited references, enabling the construction of a document co-citation network [76].

By employing text-mining algorithms, specifically latent semantic indexing (LSI), log-likelihood ratio (LLR), and mutual information (MI), CiteSpace automatically generates descriptive labels for the identified clusters. These labels characterize the nature of each cluster by extracting noun phrases from the titles, keyword lists, or abstracts of articles that cited the cluster. In this study, the LSI algorithm was chosen for generating cluster labels due to its ability to capture latent semantic relationships within the text, offering a more nuanced and context-aware identification of thematic concepts.

The modularity Q value of 0.7152 for the document co-citation network on LCSA of buildings indicates an effective division of the network into distinct clusters, demonstrating clear structural differentiation among thematic areas. The weighted mean silhouette S value of 0.8696 signifies a high degree of homogeneity within these clusters, reflecting the clarity and distinctiveness of the clustering configuration of the network.

The document co-citation analysis revealed 11 main clusters within the research on LCSA of buildings, with each cluster representing a thematic focus, or emerging area within the broader field of study, visualized in Figure 4.

The clusters consist of the following thematic areas: building information (Cluster 0), life cycle assessment (Cluster 1), life cycle (Cluster 2), economic input–output analysis (Cluster 3), construction and demolition waste (Cluster 5), cumulative energy demand (Cluster 6), scope-based carbon footprint (Cluster 7), residential buildings (Cluster 8), consensus building (Cluster 11), lca-carbon emission (Cluster 17), and climate impacts (Cluster 20).

The central area of the network is densely populated with several large, interconnected clusters, including Cluster 1 (Life Cycle Assessment), Cluster 2 (Life Cycle), Cluster 3 (Economic Input-Output Analysis), Cluster 6 (Cumulative Energy Demand), Cluster 7 (Scope-Based Carbon Footprint), and Cluster 17 (LCA-Carbon Emission). This dense interconnection indicates a strong core of research focused on LCA methodology, its various applications (e.g., energy and carbon), and related economic considerations. The close proximity of these clusters suggests significant cross-referencing and methodological overlap, representing the established knowledge base of building LCSA. However, the relatively distant position of Cluster 5 (Construction and Demolition Waste), Cluster 8 (Residential Buildings), Cluster 11 (Consensus Building), and Cluster 20 (Climate Impacts) indicates distinct but related research areas that are not yet fully integrated into the core LCSA framework. The detailed characteristics of the identified clusters are presented in

Table 2. Characteristics of thematic clusters in the Building LCSA research.

ID	Size	Silhouette Value	LSI Label	Other Significant Keywords
0	106	0.84	building information	building information modeling; industry foundation classes; building design process; data structure
1	92	0.822	life cycle assessment	environmental impact; life cycle sustainability assessment; sustainability assessment; life cycle thinking; modular building; multicriteria decision
2	90	0.767	life cycle	life cycle assessment; life cycle thinking; environmental performance; eco-efficiency analysis; life cycle cost; sustainability indicators; environmental impact; planetary boundaries
3	85	0.844	economic input-output analysis	hybrid lca; life cycle assessment; life cycle sustainability assessment; multi-criteria decision analysis; prospective lca; system dynamics
5	43	1	construction and demolition waste	life-cycle analysis; triple bottom line; sustainable jobs; unemployment
6	41	0.908	cumulative energy demand	life cycle assessment; global warming; buildings sustainability; dynamic energy simulation; optimization analysis; renewable energy
7	37	0.961	scope-based carbon footprint	life cycle assessment; carbon flow; environmental product declaration; stakeholder involvement; sustainability science
8	34	0.979	residential buildings	sustainable facades; multicriteria decision-making; sustainable assessment; sustainable development; residential high-rise buildings; sustainability indicators; analytic hierarchy process; urban density
11	23	1	consensus building	decision support systems; social construction of technology (scot); environmental indicators; green building
17	8	0.991	lca-carbon emission	emergy method; building system; digital twin; building glass industry
20	5	0.999	climate impacts	energy-efficiency; life cycle impact assessment; environmental-economic sustainability assessment; windows

.

The silhouette values, ranging from 0.767 to 1.0, demonstrate the internal consistency of the clusters, with higher values indicating well-defined and cohesive thematic groupings, particularly regarding Cluster 5, Cluster 7, Cluster 8, Cluster 11, Cluster 17, and Cluster 20.

The analysis of the document co-citation network reveals that LCA constitutes the foundational methodology for building sustainability research, as evidenced by two large, interconnected clusters: Cluster 1 (size: 92, silhouette: 0.822), focusing on the quantitative evaluation of environmental impacts, and Cluster 2 (size: 90, silhouette: 0.767), extending the LCA framework to include broader sustainability indicators and metrics such as eco-efficiency analysis and planetary boundaries. The overlap between the clusters suggests strong interconnectedness between research focusing on the overall life cycle concept and studies addressing the LCA methodology. The clusters underscore the role of LCA as a comprehensive tool for assessing environmental impacts and life cycle trade-offs, reflecting a systems-thinking approach by linking environmental performance to economic considerations through LCC analysis.

BIM emerges as a central platform for data management and design optimization within building sustainability research, forming the largest Cluster 0 (size: 106, silhouette: 0.84). This prominence underscores the increasing reliance on digital technologies to advance sustainable building practices. The inclusion of terms such as industry foundation classes and data structure indicates a focus on standardized digital frameworks for managing building life cycles and optimizing resource efficiency and design processes. The close proximity of Cluster 0 to Cluster 1 and Cluster 2 suggests that BIM is actively integrated into LCA studies and used as a tool for simulation, data management, and design optimization.

Research in Cluster 3 (size: 85, silhouette: 0.844) is focused on hybrid LCA approaches, combining economic input-output models with process-based LCA. Keywords such as system dynamics and prospective LCA highlight the predictive and scenario-based applications of these methodologies. The integration of economic flows with environmental impacts allows for a holistic evaluation of resource use, demonstrating the applicability of hybrid models to address macro-level sustainability challenges in the buildings sector.

Cluster 6 (size: 41, silhouette: 0.908) is focused on energy-related sustainability, with a specific focus on cumulative energy demand and global warming impacts. Terms such as dynamic energy simulation and renewable energy reflect an emphasis on optimizing energy performance through simulations and renewable technologies. The research underscores the need for energy-efficient design strategies that align with climate change mitigation objectives in the buildings sector.

Cluster 7 (size: 37, silhouette: 0.961) examines the carbon footprint of buildings, utilizing scope-based methodologies to quantify and trace carbon emissions. The emphasis on environmental product declarations and stakeholder involvement highlights the importance of transparency and inclusivity in carbon management. The strong silhouette value indicates that carbon footprint analysis has become a well-established approach to assessing environmental performance across building life cycles. Cluster 17 (size: 8, silhouette: 0.991) further addresses carbon emission reduction through targeted LCA applications. Keywords such as emergy method and digital twin highlight the use of advanced tools and methodologies for assessing energy and carbon flows.

In the analyzed document co-citation network, Cluster 5, Cluster 8, Cluster 11, and Cluster 20 are positioned more distantly from the core building LCSA research, indicating their current status as peripheral areas of inquiry. Cluster 5 (size: 43, silhouette: 1) addresses construction and demolition waste (CDW) management within the building life cycle. Focusing on quantifying and mitigating the environmental impacts of CDW, the studies support decision-making across all building life cycle phases—from initial project planning through construction, maintenance, refurbishment, and demolition—regarding material selection, work plans, waste treatment options, destination of waste, and reuse potential [70]. The inclusion of sustainable jobs and unemployment among keywords introduces a socio-economic dimension, suggesting broader sustainability objectives. Cluster 8 (size: 34, silhouette: 0.979) focuses on sustainability assessments specific to residential buildings, addressing high-rise structures, urban density, and sustainable design. Keywords such as analytic hierarchy process and multicriteria decision-making indicate a reliance on evaluation frameworks to guide sustainability decisions, addressing urban housing challenges. Cluster 11 (size: 23, silhouette: 1) highlights the role of decision-making processes in sustainability transitions, focusing on participatory approaches such as decision support systems (DSS) and the social construction of technology (SCOT). Cluster 20 (size: 5, silhouette: 0.999) emphasizes climate-related impacts in the building lifecycle.

The evolution of research on the LCSA of buildings reveals a clear progression in thematic priorities, reflecting the growing complexity and interdisciplinary scope of the field. The timeline visualization of document co-citation analysis, generated using CiteSpace (6.3.R3), reveals the temporal evolution of thematic clusters from 1965 to 2024, illustrated in Figure 5.

During the years 1965–1990, research on building sustainability assessment remained limited, with early studies focusing on themes such as economic input-output analysis (Cluster 3), scope-based carbon footprint (Cluster 7), and residential buildings (Cluster 8). The subsequent decade (1990–2000) witnessed a notable increase in research on construction and demolition waste (Cluster 5). However, this theme, along with consensus building (Cluster 11), which gained prominence around 1995, experienced a reduction in research emphasis after 2005. A significant expansion of research activity occurred post-2000, encompassing themes such as cumulative energy demand (Cluster 6) and the growing research on economic input-output analysis (Cluster 3). Life cycle assessment (Cluster 1) and Life Cycle (Cluster 2) grew significantly during this period, reflecting the adoption of LCA as a main tool for sustainability assessments. The period from 2010 onward witnessed a diversification of research themes, with the emergence of new areas of inquiry such as LCA-carbon emission (Cluster 17) and building information (Cluster 0). The latest emerged as the largest and most dominant cluster, highlighting the growing trend towards the integration of BIM [77,78,79,80,81,82] and other digital technologies, such as Digital Twin [83] to enhance data exchange, interoperability, and decision-making throughout the building life cycle.

3.3. Emerging Trends in Building LCSA Research

Keyword Citation Bursts

Citation bursts in keywords reflect periods when a particular concept or term gained significant attention or prominence in the literature, indicating emerging trends or shifts in the field. Using Citespace (6.3.R3), keywords with the strongest citation bursts were analyzed, presented in Figure 6.

The analysis of keywords with the strongest citation bursts highlights a clear transition from the singular focus on environmental impacts to more holistic building sustainability. Term life cycle assessment displayed a strong burst strength of 2.39 in 2017–2020, reflecting its maturity and the predominant role in building sustainability assessment. The keywords environmental impact (2015–2016, strength = 1.48), life cycle impact assessment (2015, strength = 1.92), global warming potential (2012–2014, strength = 1.13), energy consumption (2013–2018, strength = 1.55), energy performance (2014–2015, strength = 1.18) further underscore the centrality of environmental metrics in early sustainability research. In parallel, the burst of life cycle costing (2015, strength = 1.28) reflected the growing importance of financial feasibility in sustainability assessments. Social life cycle assessment experienced a citation burst in 2018–2019 (strength = 1.33), indicating different maturity levels of sustainability assessment methods.

Although the term triple bottom line, conceptualized by Elkington [84], experienced a notable citation burst between 2003 and 2014 (burst strength = 1.52) in building sustainability research, academic interest in life cycle sustainability assessment—a framework that integrates TBL principles—has significantly increased only in recent years. The rise of life cycle sustainability assessment, evidenced by the highest citation burst strength of 4.34 from 2021 to 2024, marks a significant paradigm shift in sustainability research, indicating that sustainability cannot be achieved solely through environmental optimization but must also consider financial viability and social equity.

Emerging keywords such as multi-criteria decision-making (2019, strength = 1.18) and analytic hierarchy process (2020–2024, strength = 1.34) indicate a growing emphasis on the integration of decision-support methodologies within LCSA research.

The bursts of keywords circular economy (2018, strength = 1.13) and resource recovery (2022, strength = 1.18) signify a growing interest in transitioning from linear to circular resource flows in construction, although currently, their integration into LCSA frameworks remains limited. This suggests that while the concept of circularity is gaining traction, further research is needed to fully embed circular economy principles into life cycle sustainability assessments, particularly in relation to material reuse, waste reduction, and the development of closed-loop systems in the buildings sector.

Modular construction (2019, strength = 1.18) and off-site construction (2019, strength = 1.18) show the growing emphasis on innovative construction techniques, reflecting a shift toward industrialized construction processes that optimize resource use, reduce on-site waste, and minimize construction timelines.

The increasing integration of digital technologies and data-driven approaches is evident in the citation bursts of keywords related to BIM and system-based methodologies. High bursts of building information modeling (BIM) (2020–2022, strength = 2.09) and building information modeling (2021–2024, strength = 1.77) underscored its critical role in optimizing building performance across life cycle phases.

The increasing emphasis on system dynamics (2023–2024, strength = 1.19) signals a shift towards predictive modeling within LCSA, enabling a more holistic understanding of sustainability impacts within broader systems. System dynamics enables the modeling of dynamic relationships between LCSA indicators, addressing the limitations of traditional LCSA frameworks in capturing interrelationships between social, environmental, and economic factors. Through techniques such as causal loop modeling and the quantification of feedback mechanisms, potential delays, and multidimensional causal relationships, system dynamics allows for a more in-depth analysis of sustainability implications and facilitates scenario-based projections for long-term policymaking [85].

3.4. Leading Academic Outlets

Journal Direct Citation Network

In this study, a direct citation analysis of journals was conducted to identify the leading academic outlets publishing research on the LCSA of buildings. VOSviewer was utilized for the analysis, with the type of analysis set to citation and the unit of analysis defined as sources. To achieve an optimum network, the thresholds for the minimum number of documents of a source and the minimum number of citations of a source were set to 3 and 10, respectively. Out of 165 identified sources, 32 met the thresholds. After removing 4 items which were not connected to each other, the network was developed, consisting of 28 nodes and 108 edges, visualized using Gephi (0.10.1), as illustrated in Figure 7.

The weighted degree, which reflects the sum of the weights of connections for each node, was employed as a key metric to assess the influence of journals in the network. This measure serves as an indicator of the strength and importance of connections of a journal within the network, providing a clear measure of its influence and prominence in disseminating research. In this analysis, weighted degree values were used to adjust the size and color of the nodes, where larger and darker-colored nodes signify higher levels of influence.

Table 3. Top 20 outlets for research on LCSA of buildings.

Rank	Journal	Number of Publications	Citations	Norm. Citations	Weighted Degree
1	Sustainability	45	1075	34.6554	117
2	Building and Environment	26	1358	33.6679	105
3	Journal of Cleaner Production	47	2464	69.457	96
4	Journal of Building Engineering	23	477	32.0788	87
5	International Journal of Life Cycle Assessment	24	1343	33.9517	80
6	Buildings	16	289	11.6312	50
7	Energy and Buildings	16	818	22.6977	46
8	Sustainable Cities and Society	9	286	11.4815	38
9	Automation in Construction	5	203	11.0385	19
10	Engineering Construction and Architectural Management	3	50	2.8435	18
11	Clean Technologies and Environmental Policy	6	96	2.654	12
12	Energies	5	80	2.0999	12
13	Environmental Impact Assessment Review	5	328	5.5342	12
14	Environmental Science and Pollution Research	6	36	4.2847	10
15	Journal of Construction Engineering and Management	6	121	4.2745	9
16	Sustainable Production and Consumption	7	64	10.1242	9
17	Construction and Building Materials	4	213	4.2149	8
18	Resources Conservation and Recycling	3	133	2.8756	7
19	Smart and Sustainable Built Environment	4	60	3.585	7
20	Solar Energy	3	78	3.1856	6

presents the top 20 journals publishing research on the LCSA of buildings, based on their weighted degree values within the network.

The results highlight Sustainability as the most influential journal in the network, with the highest weighted degree (117), 45 publications, and 1075 citations. Building and Environment follows closely with a weighted degree of 105, 26 publications, and 1358 citations, illustrating its strong contribution to advancing the field. Journal of Cleaner Production stands out for its exceptionally high normalized citation count (69.457), indicating its significant academic impact and widespread recognition, despite having a slightly lower weighted degree (96). This suggests that its publications are not only well-cited but also critical to shaping key discussions in LCSA research. The Journal of Building Engineering (weighted degree: 87) and the International Journal of Life Cycle Assessment (weighted degree: 80) also exhibit considerable prominence in the citation network, reflecting their relevance to LCSA research.

3.5. Global Collaboration in Building LCSA Research

Country Co-Authorship Networkk

A scientific collaboration network was created to identify the most active and influential countries in research related to the LCSA of buildings and to explore the collaborative dynamics among them. The analysis was conducted using VOSviewer, with the type of analysis set to co-authorship and the unit of analysis as countries, employing fractional counting to account for shared contributions among collaborating countries. To ensure the robustness of the network, thresholds were set at a minimum of 3 documents and 10 citations per country. Of the 69 countries identified in the dataset, 45 met the thresholds. After excluding 3 items due to the lack of connections with other nodes, the final network comprised 42 nodes and 178 edges, visualized in Gephi, as shown in Figure 8. In the visualization, the size and color of the nodes represent their weighted degree, with larger and darker nodes indicating countries that exhibit higher levels of collaboration and influence in the dissemination of research.

The data highlight the prominence of several European countries, particularly Italy, the United Kingdom, and Spain, as leading contributors to research in the field. Additionally, China and the United States emerge as significant contributors on a global scale, reflecting their strong research output and influence in advancing the LCSA of buildings. The geographic distribution of research on the LCSA of buildings is illustrated in Figure 9.

The metrics of the leading countries, including the number of publications and the corresponding weighted degree values, are presented in

Table 4. Top 20 countries collaborating in research on LCSA of buildings.

Rank	Country	Number of Publications	Weighted Degree
1	Italy	51	32
2	United Kingdom	44	29
3	Spain	55	25
4	China	48	24
5	USA	53	22
6	Australia	32	16
7	France	15	13
8	Germany	28	13
9	Portugal	37	12
10	Sweden	17	10
11	Brazil	13	9
12	Netherlands	12	9
13	Canada	24	8
14	Switzerland	9	8
15	Belgium	14	7
16	Finland	12	7
17	Austria	10	6
18	Iran	8	6
19	Saudi Arabia	10	6
20	Denmark	11	5

.

The results reveal that Italy emerged as the most influential country, with 51 publications and the highest weighted degree of 32, signifying its central role in international collaborations. The United Kingdom (44 publications, weighted degree 29) and Spain (55 publications, weighted degree 25) also demonstrated significant influence, underscoring their active contributions and strong connections within the research network. China (48 publications, weighted degree 24) and the United States (53 publications, weighted degree 22) followed closely. Other notable contributors include Australia (32 publications, weighted degree 16), France (15 publications, weighted degree 13), and Germany (28 publications, weighted degree 13). These countries exhibit robust collaboration patterns and play key roles in advancing research on the LCSA of buildings.

4. Discussion

The scientometric analysis of research on LCSA in the field of buildings provides a comprehensive evaluation of the academic body of knowledge, highlighting its thematic scope, methodological developments, and the contributions of leading journals and countries. By analyzing research trends, keyword co-occurrences, co-citation patterns, and publication dynamics, this study examines the intellectual structure of the domain, identifies emerging areas of inquiry, and reveals the collaborative networks. The upward trend in research publications underscores the increasing recognition of LCSA as a key methodology to quantitatively evaluate the environmental, economic, and social impacts of buildings throughout their life cycle. Regulatory measures, such as European directives and national regulations, have further reinforced the importance of LCSA in ensuring compliance with sustainability standards in the built environment.

4.1. Central Themes and Methodologies

Research on building sustainability assessment predominantly focuses on the environmental and economic dimensions, with comparatively limited attention given to social aspects [86]. Environmental LCA is identified as the foundational methodology within LCSA research, exhibiting the highest centrality in the keyword co-occurrence network. High centrality reflects the extensive applicability of LCA for quantifying environmental impacts across various building life cycle stages. The maturity of LCA methodology and established standardization, through the development of international standards and guidelines, such as [55,56], facilitate comparability and consistency across studies, ensuring the reliability and validity of assessment results for practical applications in building design, policy, and decision-making. This has driven the advancement of LCA methodologies and the use of LCA results to inform policy development and support evidence-based decision-making within the buildings sector.

The high centrality of terms related to environmental impact, such as carbon emissions and greenhouse gas (GHG) emissions, highlights the emphasis on addressing global environmental challenges, particularly related to climate change mitigation. This prominence demonstrates the significant influence of regulatory frameworks and international policies establishing targets for net-zero carbon emissions and energy-efficient buildings on research priorities. Research in this domain has prioritized the refinement of LCA methodologies to capture dynamic and context-specific environmental impacts. The research field encompasses aspects such as the development of dynamic LCA models to account for temporal variations in emissions and resource use, the adoption of hybrid LCA frameworks that integrate process-based and input-output approaches, and the incorporation of emergy analysis.

The analysis reveals a prominent focus on energy and resource efficiency, addressing both operational and embodied environmental impacts of buildings. Research efforts are directed towards the assessment of mitigation strategies, encompassing energy efficiency measures, the substitution of carbon-intensive materials with low-carbon alternatives, and the use of renewable energy systems. While keywords related to operational energy exhibit high centrality, reflecting the emphasis on reducing energy use during building operation, the comparatively lower centrality of keywords associated with embodied energy underscores the need for integrated assessments that consider trade-offs between operational and embodied energy, especially in the context of net-zero energy buildings, where embodied impacts become proportionally more significant.

The centrality of keywords such as life cycle costing and social life cycle assessment highlights the growing emphasis on the economic and social dimensions of LCSA. Although less central than LCA, the presence of these keywords signals a gradual shift toward more holistic assessments within the research landscape. While economic considerations are moderately represented in the research landscape through keywords such as life cycle cost and economic input-output analysis, their application remains secondary to environmental aspects. Although there is a focus on LCC, however, broader economic metrics such as macroeconomic impacts (e.g., contributions to GDP, employment effects) are not prominently addressed in the academic research.

The low centrality of S-LCA points to significant methodological challenges, including the absence of standardized metrics for social indicators. Despite the increasing interest in S-LCA, significant challenges remain, including its reliance on qualitative and often subjective assessments that can introduce biases and inconsistencies, particularly when stakeholders with conflicting priorities are involved, and the difficulty of obtaining comprehensive and reliable data due to the qualitative and context-dependent nature of social impacts [19]. The absence of standardized frameworks and consistent methodologies hinders benchmarking and the comparability of results, highlighting a significant area for future research and development within LCSA research.

Despite significant advancements in LCSA research, several persistent challenges hinder its wider adoption and application within the buildings sector, including incomplete and inconsistent life cycle data, particularly concerning the social and economic dimensions, as well as reliance on approximations, compromising the accuracy of impact calculations, particularly when considering dynamic aspects [83]. Challenges in the adoption of LCSA include unclear goal and scope definition, unclear assumptions and data selection in inventory analysis, and methodological controversies surrounding normalization and weighting methods in impact assessment. Integrating the triple bottom line (TBL) into a unified and transparent LCSA framework remains a key challenge, particularly due to the underdeveloped state of S-LCA, and differing scopes, data requirements, stakeholder focus, and functional units of S-LCA, LCC, and LCA, resulting in limited practical alignment.

While methodologies such as LCA, LCC, and S-LCA provide comprehensive assessments of environmental, economic, and social dimensions, they do not facilitate decision-making unless integrated into a decision support system (DSS). Without such integration, the interpretation of results and the selection of optimal design alternatives can become challenging due to the complex nature of multi-objective decision-making problems [87]. The moderate centrality of the term multi-criteria decision-making within the keyword co-occurrence network suggests a growing recognition of MCDM approaches such as AHP. By prioritizing the development of robust MCDM frameworks, research can address current LCSA limitations, including the insufficient integration of trade-off and co-benefit analyses among environmental, economic, and social dimensions, which undermines LCSA effectiveness [23]. The low centrality of keywords such as TOPSIS and MIVES indicates the need for further methodological diversification.

In order to account for the trade-offs between different sustainability dimensions, decision support frameworks based on system dynamics are proposed, integrated with the MCDM methods to facilitate dynamic sustainability assessments, emphasizing the evaluation of interdependencies among social, economic, and environmental criteria, while accounting for time-induced changes that impact the long-term sustainability performance of buildings [88]. Using a system dynamics-based Dynamic Life Cycle Sustainability Assessment (D-LCSA) framework, which accounts for time-induced changes in building characteristics, such as electricity consumption, energy mix, and material properties, it has been shown that neglecting time-dependent dynamic aspects can lead to a significant underestimation of up to 50% of sustainability impacts [89], highlighting the necessity of incorporating temporal dynamics within LCSA methodologies, particularly when evaluating the long-term sustainability performance of building assets.

The high centrality of BIM highlights its critical role in advancing LCSA research and applications. BIM improves data exchange, decision-making, and life cycle optimization, enabling collaboration between stakeholders and the integration of environmental, economic, and social considerations across the building life cycle. Research is increasingly focused on developing methods for seamlessly integrating building information models with LCSA workflows, leading to more efficient data collection, automated calculations, and improved accuracy in sustainability assessments. This integration also facilitates the exploration of design alternatives and the optimization of building performance from a life cycle perspective. Despite its prominent role, the full integration of BIM with LCSA is still in its early stages, facing challenges such as limited automation, interoperability, and the comprehensive inclusion of all life cycle stages. Recent advancements, including the integration of DT technologies, address the limitations of static models by capturing dynamic building performance data, allowing for more accurate and real-time sustainability assessments. The emergence of DT, in conjunction with BIM, suggests a move towards more advanced and data-driven LCSA approaches.

While current LCSA research demonstrates a strong focus on residential buildings, a significant gap exists in the application of LCSA across diverse building typologies. This underrepresentation of non-residential buildings (e.g., commercial, industrial, and institutional) limits the generalizability of research findings and the development of sustainability strategies for these building categories. Consequently, the lack of comparable data constrains the formulation of targeted policies and the implementation of effective sustainability strategies for the built environment, thereby hindering the achievement of broader sustainability goals. A holistic approach to sustainable construction necessitates research diversification to develop LCSA methodologies that effectively account for the specific characteristics of different building typologies.

The analysis reveals a significant thematic shift in the research domain, with an increasing emphasis on circular economy and resource recovery. These concepts align with sustainability objectives focused on waste reduction and material reuse, indicating a transition toward closed-loop systems in construction. In addition, keywords such as renovation and retrofit highlight the importance of adaptive reuse and extending the lifecycle of existing building stock, suggesting an increasing alignment of LCSA with sustainable development goals. However, the integration of these concepts into comprehensive LCSA frameworks remains an area for further exploration, particularly concerning material flows and waste management when assessing building life cycles, addressing the specific challenges and opportunities associated with new construction, renovation, and retrofit projects.

4.2. Publication Outlets and International Collaboration Networks

The direct citation analysis of journals indicates that journals such as Sustainability, Building and Environment, and the Journal of Cleaner Production stand out as the primary sources of research, with Sustainability emerging as the most influential journal in the field. The highest weighted degree and citation count reflects its role in disseminating key research on LCSA. Notably, the Journal of Cleaner Production has gained considerable academic impact despite a slightly lower weighted degree, suggesting that its publications are highly cited and significant within the scholarly community.

The country co-authorship analysis reflects the global nature of LCSA research, with leading contributions from European countries, China, and the United States. Consistent with the findings of [86], Italy emerges as the most influential country, with the highest number of publications and weighted degree, signaling its central role in advancing LCSA research. The United Kingdom, Spain, China, and the United States follow closely, indicating significant research output and collaborative influence. While Europe remains the dominant region for LCSA research due to established regulatory frameworks and sustainability policies, contributions from China and the United States demonstrate the growing global interest in applying sustainability assessments to the built environment. Expanding collaboration with underrepresented regions, including developing countries, could further enhance the applicability and robustness of LCSA frameworks, ensuring that research reflects a broader range of socio-economic and environmental contexts.

Enhancing the adoption of LCSA in regions with limited research output, such as developing countries, requires addressing key barriers, including limited access to data, expertise, and resources. To address these challenges, the establishment of regional life cycle inventory (LCI) databases, which incorporate data on locally available building materials, energy sources, and transportation methods, is necessary for ensuring the applicability and relevance of LCSA methodologies within the local context. The development of national LCSA guidelines and standards would provide a consistent framework for assessment and reporting. Context-specific adaptations of LCSA methodologies are needed to reflect the unique characteristics of building practices, materials, and socio-economic conditions. The effective implementation of these context-specific strategies, achieved through collaboration among researchers, policymakers, and industry stakeholders, is imperative for realizing the potential of LCSA in advancing global sustainability within the buildings sector.

5. Conclusions

This scientometric analysis critically examined the development and application of LCSA in the field of building sustainability. The buildings sector faces increasing sustainability challenges due to tightening regulatory frameworks, resource constraints, and growing demands for environmentally, economically, and socially responsible construction practices. This review highlights the transformative potential of LCSA as a holistic decision-support framework for optimizing building sustainability across life cycle stages.

By systematically examining research trends, thematic clusters, and emerging areas of inquiry, the study enhances the understanding of the LCSA knowledge domain, its thematic evolution, and interdisciplinary aspects. The analysis uncovered key trends and patterns in LCSA research, including its foundational reliance on environmental life cycle assessment (LCA), the rising prominence of circular economy principles, and the integration of BIM to enhance data-driven sustainability assessments. These advancements underscore the critical role of LCSA in aligning construction practices with global sustainability goals. Methodological gaps, such as the need for S-LCA standardization and improved frameworks for evaluating the interdependencies among social, economic, and environmental criteria, while accounting for temporal dynamics, are revealed, suggesting areas for future theoretical advancements. From a practical perspective, the findings offer insights for enhancing LCSA adoption and application in the buildings sector. The findings emphasize the importance of integrated decision-support frameworks, including MCDM approaches and dynamic modeling techniques, for addressing multi-dimensional trade-offs and supporting informed decision-making. However, further research is needed to fully integrate these concepts into holistic LCSA frameworks.

While this study provides valuable insights into the application of LCSA within the field of buildings, several limitations should be acknowledged. The bibliographic data analyzed were sourced from the Scopus and WoS databases, selected for their extensive coverage of academic literature. However, the exclusion of other databases may have limited the scope of the analysis, and future research should consider incorporating additional data sources. Furthermore, the search methodology employed a specific set of keywords applied to publication titles, abstracts, and keywords. While this approach was chosen to ensure a focused analysis of LCSA research specifically related to buildings, it is possible that some relevant publications employing alternative terminology may have been excluded. The restriction to English-language publications further limits the scope of this study, potentially overlooking valuable contributions published in other languages.

The recommendations for future studies include the adoption of dynamic modeling tools, enhanced interoperability in BIM-LCSA integration, and the development of frameworks that prioritize the early inclusion of economic and social dimensions in sustainability assessments. Implementing the proposed integrative frameworks and leveraging digital technologies such as DT, circular design principles, and decision-support tools can significantly advance LCSA research and practice. This will ensure that buildings not only meet current sustainability requirements but also adapt effectively to evolving environmental, economic, and societal demands, contributing to a resilient and sustainable built environment.

Future research in LCSA should focus on developing integrated frameworks that effectively align the environmental, economic, and social dimensions of sustainability. This integration enhances the accuracy and comprehensiveness of data used in assessments, accounting for the trade-offs and co-benefits of different sustainability aspects. Advancing digital tools to enable dynamic and automated sustainability assessments is also crucial for improving real-time data integration and decision-making processes. Future work should prioritize embedding circular economy principles into life cycle methodologies, allowing for the quantification of resource efficiency and waste reduction impacts throughout the building lifecycle. Establishing standardized metrics and databases for S-LCA is essential to improving the evaluation of social impacts. Expanding the research scope to encompass non-residential building typologies and underrepresented regions would enhance the applicability and generalizability of LCSA frameworks for promoting sustainability in the built environment.