Next Article in Journal
Suggestions for a Collapse-Resistant Design for Frame–Masonry Hybrid Buildings Based on the Concept of Balancing Seismic Shear Forces
Previous Article in Journal
The Evaluation of Spatial Allocation and Sustainable Optimization Strategies for Sports Venues in Urban Planning Based on Multi-Source Data: A Case Study of Xi’an
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Buildings
Volume 15
Issue 8
10.3390/buildings15081356
buildings-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Visualization Analysis of Research on Inefficient Stock Space by Mapping Knowledge Domains

by
Wangyang Gui
1,
Xu Li
1 and
Bin Xu
2,*
1
School of Architecture and Planning, Anhui Jianzhu University, Hefei 230601, China
2
China Architecture Design & Research Group, Beijing 100044, China
*
Author to whom correspondence should be addressed.
Buildings 2025, 15(8), 1356; https://doi.org/10.3390/buildings15081356
Submission received: 10 March 2025 / Revised: 15 April 2025 / Accepted: 16 April 2025 / Published: 18 April 2025
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
Browse Figures
Versions Notes

Abstract

:
Inefficient stock space use in urban and rural areas causes economic losses and environmental harm, needing better solutions. Currently, this field is constrained by a relatively underdeveloped research history, which has led to the lack of a comprehensive theoretical framework and established solution methodologies. Therefore, it is crucial to clarify the principles of spatial evolution within theoretical approaches to promote the rapid advancement and practical application of subsequent theories. This effort will improve the understanding of the effective utilization of inefficient inventory space and encourage critical analysis by systematically reviewing the developmental trajectory of previous research. This study aims to conduct a thorough analysis of the developmental trajectory, evaluation frameworks, and strategies for the effective utilization of inefficient space by utilizing the Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) databases. Through the application of CiteSpace for visualization and analysis, the research investigates the pertinent literature on inefficient stock space, covering the period from 2004 to the present. The results show that research on inefficient stock space exhibits diverse characteristics, with WOS publications focusing on four primary dimensions, namely land space reuse, the establishment of evaluation systems, environmental governance, and urban and rural development planning. Conversely, CNKI publications tend to prioritize spatial optimization design and the mechanisms of planning and development. In relation to policy frameworks and evolutionary trends, the study of inefficient stock space in urban and rural contexts has evolved through three distinct phases, the embryonic stage (2004–2013), the exploration stage (2013–2020), and the growth stage (2020–present). While the effective utilization of currently inefficient stock space in urban areas has been addressed through various initiatives, there remains a significant gap in research focused on rural areas, highlighting the necessity for an enhanced exploration of urban–rural coupling mechanisms. Additionally, the efficient utilization of inefficient stock space in both urban and rural environments is a multidisciplinary challenge that requires the development of innovative urban and rural development models aligned with the principles of sustainable development, drawing insights from disciplines such as economics, architecture, and urban planning.

1. Introduction

Factors such as an inability to adapt to external changes and the presence of aging infrastructure have resulted in a significant amount of inefficient stock space in both urban and rural areas, including urban villages and obsolete buildings that serve no functional purpose. This phenomenon contributes to an irrational urban spatial structure and an imbalance in regional functions, leading to challenges such as traffic congestion, inadequate healthcare services, and increased fire safety risks. Moreover, the prolonged idleness of inefficient stock space can result in resource waste, increased maintenance costs, and diminished investment attractiveness in surrounding areas, particularly in rural regions. This situation may exacerbate population outflow, leading to economic contraction. Additionally, it can cause spatial fragmentation and ecological risks in both urban and rural areas, disrupting the continuity of functional planning. This disruption can negatively impact cultural landscapes, contribute to ecological degradation, reduce biodiversity, and leave behind pollution that poses a threat to residents’ health. It is particularly important to transform inefficient urban spaces through strategic planning and enhance the potential of both urban and rural areas.

1.1. Evolutionary Process of Research on Inefficient Stock Space

Due to limited research time, the concept of stock space remains in the rapid development stage, and there is no unified understanding of its conceptual division and scope of recognition. Some scholars argue that stock space planning encompasses two primary fields, which are urban renewal and land management. These fields feature relatively complex definitions and types, including land use concepts and functional categories such as idle land, urban stock construction land, and urban low-efficiency land [1]. Other scholars contend that stock space includes not only construction space but also non-construction space throughout the entire area [2]. However, in reality, inefficient stock space is further categorized based on the identification of urban stock space, which does not fully leverage its potential value. This encompasses construction land and functional layouts across multiple levels of economy, society, and ecology, as well as space types with low utilization rates [3]. Although scholars from various academic fields are actively proposing strategies to address the challenge of inefficient stock space from different perspectives, influenced positively by government policies, most methods exhibit significant one-sidedness. This limitation results in fundamental performance deficiencies in many strategies when applied in practice. Consequently, research on planning and design guidelines for inefficient stock spaces lags behind the evolving demands of the contemporary era. There is an urgent need to deepen the theoretical foundations, policy mechanisms, and practical explorations of updates [4]. Developing a more comprehensive theoretical framework remains crucial for addressing the issue of inefficient stock spaces in urban areas.

1.2. Research on Inefficient Stock Space by Using Mapping Knowledge Domain

Bibliometric analysis serves as a statistical method that offers a quantitative perspective on the academic literature. Among the essential bibliometric tools, CiteSpace is prominently utilized for conducting quantitative analyses within academic research. It facilitates the swift extraction of critical textual information, clarifies research hotspots at both the journal and global/regional levels, identifies developmental processes, and assesses contributions [5]. Additionally, CiteSpace is capable of producing visual knowledge maps [6]. Its application has been widespread across diverse fields, including the investigation of trends in zero-energy building research [7], the adaptive evolution of marine organisms [8], and the forecasting of scientific and technological advancements [9,10].
Caused by its relatively recent emergence, research on inefficient stock space has predominantly utilized traditional literature review and statistical methodologies. A limited number of studies have incorporated advanced visualization software to analyze policies and other relevant dimensions. Some researchers still have employed conventional literature review techniques to investigate the evolution of inefficient stock spaces in China, focusing on aspects such as definitions, experiences with renewal strategies, the exploration of evaluation models, institutional supply, and challenges related to property rights [11,12]. Conversely, other scholars have utilized CiteSpace software to conduct a systematic analysis of the policy development trajectory concerning China’s inefficient stock spaces [13]. This approach has enabled the identification of key stages in policy evolution and the formulation of improvement recommendations grounded in actual developmental trends. The current literature review on inefficient stock space is hindered by limitations in its efficiency, resulting in an incomplete analysis that restricts the ability to fully integrate the existing body of the literature within the broader research framework. As a result, there is an urgent requirement for a more thorough and integrative examination that takes into account the global developmental trajectory of ineffective urban stock space policies.

1.3. Objectives and Scope of Work

In order to analyze the spatial–temporal evolution process of inefficient stock space research, this study utilizes literature and policy data obtained from the China National Knowledge Infrastructure (CNKI), Web of Science (WOS), and various government websites. A quantitative analysis was conducted using CiteSpace software. Furthermore, based on the findings from the knowledge graph analysis, practical recommendations are proposed for future research and application in the field of inefficient stock space.
This paper investigates the following research topic:
  • The spatial–temporal evolution process of inefficient stock space research;
  • Limitations in the current research process and suggestions for future development.
Through the analysis presented above, we can visually comprehend the development process of inefficient stock space research and gain a more systematic understanding of the advantages and disadvantages associated with related theoretical research processes. Consequently, this understanding enables us to propose more effective research suggestions for inefficient stock spaces that align with future urban development, thereby providing theoretical support and guidance for subsequent research.

2. Research Methodology

The methodology employed in this study for the analysis of the knowledge network map and the spatial–temporal evolution of inefficient stock space is illustrated in Figure 1. The pertinent literature and policy information were sourced from the CNKI and WOS databases, as well as various governmental websites, to establish a foundational text database. It should be noted that the literature from the WOS database and CNKI database was initially selected using three criteria, namely subject terms, research fields, and publication dates. Before finalizing the text database, it should be manually reviewed again based on the abstracts and titles. After that, the textual data from the WOS database, CNKI database, and governmental websites were exported to RIS format and RefWorks format, respectively, and entered into CiteSpace 6.3.R1 software. These data included the literature title, author, institutions, and abstract text information, which were processed using utilities to address de-emphasis. Based on the processed text data, the co-occurrence network will be obtained by using the G-index selection criteria in CiteSpace software, and the spatial–temporal evolution process map also could be obtained by using the Timeline Layout and Burrstones Option functions. Furthermore, the spatial and temporal evolution of inefficient stock space within the CNKI and WOS databases was derived by integrating this analysis with the policy evolution processes of various governmental departments. Based on the findings from this research, appropriate recommendations are proposed for the future development of inefficient stock space.

3. Data Collection

Considering the geographical influence of language on the research literature regarding inefficient stock space, the Web of Science (WOS) database, which predominantly features English-language publications, was chosen as the primary source. Additionally, China is one of the countries with the highest number of megacities, many of which are currently in development or renewal; the China National Knowledge Infrastructure (CNKI) database, which includes the Chinese literature, was selected as a supplementary source. The investigation into inefficient stock space encompasses a variety of keywords and spans an extensive temporal range. In the process of reviewing the literature, this study meticulously filters relevant publications from the CNKI and WOS databases, as illustrated in the data collection processing presented in Figure 2, the asterisk (*) in this figure represents any sequence of characters. The findings indicate that research on inefficient stock space can be traced back to 2004 within the CNKI database. To facilitate a comparative analysis of the spatial and temporal evolution of the two types of the literature, the search parameters for both the CNKI and WOS databases were established to cover the period from 1 January 2004 to 31 March 2025, aligning with the timeline for urban inefficient stock space studies. Additionally, after reviewing these articles on stock space from WOS and CNKI databases, significant keywords pertinent to urban inefficient stock space, such as “stock space”, “inefficient space”, and “idle space”, were employed, with the disciplinary focus set on architecture and engineering sciences. Subsequently, CiteSpace’s built-in de-duplication feature, along with manual screening, selected a total of 986 relevant publications from the CNKI database and 1177 from the WOS database. These publications were utilized for the spatio-temporal analysis and visualization of urban inefficient stock space evolution presented in this paper.

4. Visualization Analysis of Inefficient Stock Space Studies

4.1. Research Hotspots of Papers from CNKI Database

4.1.1. Co-Occurrence Network Analysis Result of Keywords

CiteSpace software is proficient in conducting frequency statistics and relevance analyses of keywords within the literature, subsequently representing these findings through co-occurrence knowledge graphs. In this graph, the dimensions of the key nodes and the coloration of the outer circles convey distinct meanings: larger nodes indicate a higher frequency of occurrence, while darker outer circles signify a greater degree of centrality. Specifically, a purple circle denotes that the centrality of the intermediary node exceeds 0.1, categorizing it as an important node [14].
A statistical analysis was performed on 986 documents related to inefficient stock space within the CNKI database utilizing CiteSpace, which resulted in the creation of a keyword co-occurrence knowledge graph, as depicted in Figure 3. This figure reveals that the keywords associated with the literature on inefficient stock space in the CNKI database are ranked by frequency from highest to lowest as follows: urban renewal, stock space, remaining space, stock planning, and idle space. The keywords demonstrating the highest centrality, in descending order, are stock space, remaining space, reuse, urban renewal, and public space. It should be noted that research on inefficient stock space in urban renewal, as recorded in the CNKI database, primarily focuses on enhancing the efficient utilization of stock space to improve quality of life. This research explores strategies for intensive land use from multiple perspectives and aims to optimize land resource utilization [15,16]. As for enhancing the utilization of stock space within existing buildings, the analysis concentrates on two key topics, namely the reuse of public space and the efficient utilization of existing surplus space. Furthermore, investigations into the efficient use of inefficient stock space are predominantly approached from the perspectives of urban planning and architectural disciplines, with a notable lack of research and strategic solutions from a broader macro perspective.

4.1.2. Clustering Result of Keywords

The utilization of CiteSpace software for the clustering of keywords within the research literature pertaining to inefficient stock space facilitates the direct extraction of prominent terms associated with various topics across different years. Furthermore, the validity of the clustering map can be assessed through the clustering module value (Q) and the average profile value (S). Specifically, a Q value within the range of 0.3 to 0.7, coupled with an S value exceeding 0.7, signifies that the clustering results are reliable, thereby mitigating the potential impact of invalid clustering on the overall results [17].
A clustering analysis was conducted on the keywords from 986 research papers pertaining to inefficient stock space research within the CNKI database. The cluster module value was Q = 0.6675, and the average profile value was S = 0.8554, which means the clustering results are reliable. The cluster ranking number, strength, keywords, buzzwords, and hotspot years are presented in Table 1. Notably, the year 2018 exhibited the highest clustering scale, primarily focusing on the concept of remaining space. These research papers from the CNKI database concern the topics of emphasizing the utilization of remaining or idle space within existing structures. Furthermore, the efficacy of sustainable adaptive reuse strategies in fostering regional development also becomes another hot topic [18]. In conclusion, the classification of topics within the CNKI papers can be delineated into two primary categories, namely spatial optimal design and planning, as well as planning and development mechanisms, based on the attributes of the keywords and buzzwords (top five).
  • The spatial optimal design and planning includes five cluster keywords as follows: #0 remaining space, #1 idle space, #4 stock space, #6 old community, and #7 ecological restoration. The center values of the top five buzzwords of these keywords are 0.73, 0.43, 0.43, 0.32, and 0.12, respectively. This indicates that the frequency of keywords and the centrality of trending topics remain consistent. Researchers continue to focus primarily on the stock space, with comparatively less attention given to more detailed analyses. The issue of inefficient stock space has garnered significant attention, Chinese scholars try to study its effective utilization considering spatial features. This focus has resulted in a series of investigations centered on spatial optimization and design, with the objective of enhancing spatial utilization efficiency through a multi-system. From a macro perspective, existing research emphasizes the comprehensive planning of spatial resources. It explores the mechanisms of land spatial coordination [19], the adaptive utilization of inefficient land use [20], and technological pathways for spatial transformation [21]. Optimization strategies are proposed from the viewpoints of policy management, development models, and other aspects to support high-quality urban development. These studies have led to the formulation of reuse strategies for existing structures that serve predominant spatial functions, including community spaces, historic buildings [22], and industrial heritage sites [23]. Furthermore, researchers want to facilitate the coexistence and synergy of diverse urban spaces, thereby ensuring efficient urban operations and bolstering urban adaptability by evaluating spatial activities based on demand [24], exploring innovative methodologies for repurposing existing buildings, improving urban thermal environments [25], and advocating for a demand-driven approach that encompasses multiple methods [26].
  • In contrast, the planning and development mechanisms include three cluster keywords as follows: #2 Stock planning, #3 National land space, #5 Urban renewal, and #8 City planning. The center values of the top five buzzwords of these keywords are 0.43, 0.36, 0.73, and 0.05, respectively. This indicates that in the process of spatial planning analysis and research, although the frequency of references to older communities is relatively low, the coupling mechanisms and evaluation systems associated with them are increasingly being studied by researchers. In contrast to spatial optimization, planning and development mechanisms focus on improving the efficient utilization of suboptimal stock space from a macro-level perspective. These studies emphasize the importance of rational planning and design, which involve the regulation of land use zoning [27] and the reinforcement of intrinsic connections between planning zones to promote functional integration across various blocks [28]. Nevertheless, the current building stock significantly surpasses the volume of newly planned urban developments; research focused on enhancing the efficiency of utilizing inefficient stock space in this context has predominantly been conducted in earlier years.

4.2. Research Hotspots of Papers from WOS Database

4.2.1. Co-Occurrence Network Analysis Result of Keywords

An analysis of 1177 scholarly articles concerning the research on inefficient stock space within the Web of Science (WOS) database has yielded a keyword co-occurrence knowledge map, as illustrated in Figure 4. The figure results shows that the frequency ranking of keywords in the research papers from the WOS database is as follows: City, Rural revitalization, Urban, Areas, and China. For the keyword centrality, the ranking from highest to lowest is Health, Community, Impact, Environment, and City. In comparison to the findings from the CNKI database, the literature derived from the WOS database predominantly highlights a diversified approach to the research on inefficient stock space. It should be noted that over half of the keywords are concerned with specific areas and locations, signifying a transition in research topics from traditional analyses of residual urban spaces to investigations of the urban–rural coupling mechanisms. This change emphasizes the activation of the potential of inefficient stock spaces in rural and township areas as emerging focal points for future research. Furthermore, the prominence of the keyword “China” in the frequency statistics indicates that the literature on inefficient stock space within the WOS database significantly incorporates contributions from Chinese scholars. However, the results of keyword centrality statistics are different, as these studies propose strategies that focus on enhancing the utilization of inefficient stock space in both urban and rural contexts from multi-dimensional and multi-level perspectives.

4.2.2. Clustering Result of Keywords

By using CiteSpace, these keywords from 1177 research papers pertaining to the study of inefficient stock space were clustered, sourced from the Web of Science (WOS) database. The spatial clustering results are presented in Table 2, where the clustering module value is Q = 0.6383, while the average profile value was S = 0.8267. It should be noted that the year with the largest clustering strength was 2020, which emphasizes the efficient utilization of inefficient stock space in rural revitalization, followed by 2017, which pertains to the spatial evaluation methodologies for inefficient stock. In contrast to the clustering results derived from the CNKI database, the clustering keywords for the various literature within the WOS database exhibit significant temporal variation. This indicates a trend towards a multi-dimensional, multi-level, and multi-scale study of the efficient use of inefficient stock space, demonstrating a more comprehensive development compared to the literature found in the CNKI database. Furthermore, the clustered keywords were consolidated based on their semantic similarities, resulting in four primary categories of land space reuse, space efficiency evaluation systems, environmental governance, and urban and rural development planning.
  • The classification of land spatial reuse encompasses three primary clustering keywords as follows: #4 Urban Regeneration, #8 Urban renewal, and #9 Land Use. The center values of the top five buzzwords of these keywords are 0.45 and 0.20, which are consistent with the current trend of urban renewal as the primary focus of development. In the context of global urban contraction, adaptive land reuse has emerged as a vital strategy for enhancing urban resilience and is deemed a fundamental component of sustainable urban development [29]. International research has increasingly focused on urban renewal policies and the objectives of sustainable urban development, with scholars primarily investigating the mechanisms driving spatial differentiation, identifying factors that influence sustainable building utilization strategies [30], and examining community-level spatial decision-making systems to facilitate sustainable urban renewal [31]. Some studies adopt a results-oriented perspective, drawing spatial impact conclusions that contribute to the broader discourse on sustainable development [32]. Furthermore, industrial buildings are recognized as significant spatial resources that are crucial for the sustainable urban renewal of densely populated post-industrial metropolitan areas, where spatial resources are limited [33,34].
  • The space efficiency evaluation system is characterized by two clustering keywords as follows: #6 Coupling mechanism and #7 Space efficiency evaluation systems. The center values of the top five buzzwords of these keywords are 0.08 and 0.11. It is shown that researchers are more concerned with evaluation systems, which are more important for application. Studies on spatial coupling mechanisms have emerged as a significant area of inquiry within the early international research literature focus on enhancing the efficiency of inefficient urban stock space. Research utilizes calculations of spatial function coupling mechanisms, multi-factor quantitative analyses of environmental and urban spatial benefits [35], and the formulation of evaluation models [36] and development-driven models [37]. A prominent achievement in this field is the creation of the “Comfort-Demand” spatial analysis model [38], which has played a crucial role in establishing a comprehensive evaluation framework for sustainable urban development, thereby promoting a more effective utilization of urban space. Another significant achievement is the proposal of a new method that focuses on evaluating public satisfaction. This method combines a post-use evaluation system guided by public demand [39] with a multi-scale model.
  • Environmental governance includes three clustering keywords as follows: #1 Carbon emission, #2 Built Environment, #3 Urban Ecology, and #4 Environmental Justice. The center values of the top five buzzwords of these keywords are 0.49, 0.31, 0.75, and 0.45, respectively. Ecology remains the most important factor to consider in urban renewal, and carbon emissions also play a significant role due to the impact of government policies. The quality of the spatial environment is a key factor influencing public space utilization rates. Research in this area focuses on quantifying ecological resilience indicators, analyzing urban ecosystem networks [40], and examining the relationship between built environment design and social sustainability in urban renewal. Studies further explore sustainable design elements and propose the development of essential infrastructure to support the creation of intelligent and sustainable cities, ultimately enhancing residents’ quality of life [41]. Simultaneously, the development of existing buildings is a dynamic process that involves both demolition and renovation [42]. To aid in decarbonizing the construction industry and promoting sustainable urban development, current research on existing buildings integrates digital construction technology [43] and green material [44]. This approach aims to effectively manage both the embodied carbon and operational carbon throughout the entire building lifecycle, thereby reducing carbon emission intensity. Additionally, optimization and renewal strategies for the social built environment have emerged as key research directions within this category.
  • Lastly, the category pertaining to urban and rural development planning comprises three clustering keywords as follows: #0 Rural Revitalization and #10 Prioritization. The center values of the top five buzzwords of these keywords are 0.99 and 0.04, respectively. Due to the limited research on inefficient stock space in rural areas, there has been a trend towards a single center. There exist considerable disparities in demand between urban and rural regions, resulting in notable variations in functional configurations when compared to purely urban or rural environments. The optimization of space utilization at urban–rural interfaces has emerged as a crucial element in enhancing the efficiency of inefficient stock space. Developed nations have experienced significant urbanization prior to China; the international literature has primarily examined urban–rural development planning through social [45], institutional, and economic benefits [46]. Central themes of this analysis include the drivers of urban–rural development models and the associated regional economic benefits. It should be noted that citizen science has significant potential to enhance both research and policymaking. This approach facilitates a comprehensive consideration of various factors, including regional culture, religion, and history [47]. Additionally, it promotes sustainable urban development and fosters a just social environment [48]. Furthermore, within the framework of comprehensive urban renewal, research advocates for the implementation of innovative decision support systems that focus on refining and optimizing renovation strategies for existing urban spaces. These systems offer guidance to policymakers regarding decision-making models [49] and investigate the impact of macro-level factors on the promotion of sustainable urban development.

5. Evolutionary Process of Research of Inefficient Stocky Space

5.1. Evolutionary Process of Government Policy

Figure 5 illustrates the fluctuations in the volume of the literature pertaining to inefficient stock space research as recorded in the CNKI and WOS databases. The data indicate that the quantity of the related literature has generally followed a trajectory characterized by a gradual development in the initial stage, a period of rapid expansion in the intermediate stage, and a stage of explosive growth in recent years. This temporal and spatial evolution can be categorized into three distinct stages, corresponding to the release of government policies on inefficient stock space published by various countries, involving the embryonic stage from 2004 to 2013, the exploration stage from 2013 to 2020, and the growth stage from 2020 to the present.
  • Embryonic stage (2004 to 2013): Notable examples of policy initiatives include the Industrial Business Zone Plan (IBZ) released by New York City in 2006 and the Outline of the National Overall Land Use Plan published by China in 2008. During this period, the primary emphasis of governmental policies across various nations has been on the activation or revitalization of existing spatial resources, particularly addressing the challenges associated with the adverse effects of traditional urbanization models. However, scholarly investigations have yet to provide comprehensive analyses regarding the activation of inefficient stock space. Consequently, research in this area has exhibited a relatively stagnant development trajectory, characterized by a lack of systematic inquiry into the rational utilization and activation of inefficient stock space.
  • Exploration stage (2013 to 2020): Notable instances of policy initiatives include the Affordable Housing Plan introduced by the mayor of New York in 2014 [50] and China’s Guiding Opinions on Implementing Pilot Projects for the Development of Urban Low-Utility Land, issued in 2013 (Guofa [2013] No. 3). These initiatives indicate that the government is earnestly striving for the systematic and rational integration of inefficient urban spaces, with a focus on regulating and facilitating the reuse and development of urban areas that exhibit low utility. Since then, researchers have increasingly engaged in the study of the reuse of low-efficiency stock space in different regions, proposing effective strategies that consider the requirement from multiple dimensions, including functionality, ecological sustainability, economic viability, and community traffic [51], which could promote the sustainable development of urban villages and industrial remnants [52]. Furthermore, researchers from diverse disciplinary backgrounds have proposed relatively scientific and reasonable methodologies for the efficient utilization of inefficient stock space, drawing from their respective fields of study. Thus, the volume of research articles in this area has experienced significant growth, escalating from an average of approximately 10 publications per year during its initial stage to around 100, representing a tenfold increase in total scholarly contributions. However, the lack of interdisciplinary collaboration has hindered the effective resolution of the complex challenges associated with the efficient use of such spaces.
  • Growth stage (2020 to the present): Notable initiatives include Singapore’s Draft Master Plan 2025 and China’s 2023 Circular on the Ten-Point Work for the Redevelopment of Low-Use Land. With comparing the variances in different land stock patterns, governments have integrated concepts of spatial resilience and urban well-being to improve the long-term sustainability of urban communities [53] and enhance the overall quality of life [54]. Concurrently, governments are implementing innovative policy measures focusing on optimizing the use of inefficient urban spaces [55]. Moreover, the investigation of urban inefficient stock has emerged as a prominent research focus. Researchers are increasingly engaging in research of inefficient stock space with multidisciplinary theories and are utilizing a variety of theoretical frameworks to develop more scientifically informed and rational strategies for the effective utilization of stock spaces. Consequently, there has been a marked increase in the related scholarly literature, escalating from an average of approximately 10 articles per year to nearly 300.

5.2. Evolutionary Process of Research Papers from CNKI Database

An analysis of 986 research papers sourced from the CNKI database reveals the spatiotemporal evolution process of research hotspots pertaining to inefficient stock space. This analysis is contextualized within the framework of three developmental stages of various governmental policies addressing inefficient stock space in urban environments. Figure 6 illustrates the research hotspots, clustering intensity, and evolutionary characteristics of research papers from the CNKI database observed during the embryonic, exploration, and growth stages.
  • Embryonic stage (2004 to 2013): China’s policies regarding the repurposing of inefficient stock space have predominantly been articulated through planning schemes, lacking the implementation of specific regulatory measures for this situation. The academic community has pursued independent advancements in this field, concentrating on the revitalization of stock space through diverse methodologies, including the utilization of subterranean spaces [56] and the adaptive reuse of cultural heritage sites [57]. Furthermore, significant progress has been made in research domains such as stock planning, ecological restoration, and urban planning [58]. Nonetheless, a notable absence of concentrated research themes was evident during this period, suggesting a fragmented and varied research environment.
  • Exploration stage (2013 to 2020): The issuance of the Guiding Opinions on Launching Pilot Projects for Redevelopment of Inefficient Urban Land (Guofa [2013] No. 3) in 2013 marked a significant transformation in China’s approach to addressing inefficient stock space through specialized policies. This initiative has catalyzed an increase in scholarly research focused on inefficient stock space. Between 2015 and 2018, the topics of residual space, stock space, and urban renewal have become hotspots [59], while previous ones have disappeared. During this time, Chinese research efforts increasingly concentrated on spatial utilization and urban planning, with the objective of conducting a comprehensive analysis of the reutilization of inefficient stock space and developing a theoretical framework that incorporates multiple stakeholders [60]. Concurrently, bolstered by extensive research findings, Chinese governmental policy evolved from planning outlines into more detailed guidelines for redevelopment and utilization. This evolution signifies a transition of research and practical applications related to the reutilization of inefficient stock space into an exploratory phase.
  • Growth stage (2020 to the present): Research on the inefficient utilization of stock space in China has entered a phase of comprehensive development as of present. Within this domain, spatial optimization and planning design remain paramount areas of inquiry, while there is an increasing emphasis on ecological restoration and the revitalization of spatial production [61]. Initially characterized by broad and generalized methodologies, studies of residual space have progressively shifted the focus towards aging urban communities that contain substantial stock space [62]. This transformation focuses on enhancing demand-driven mechanisms for the renewal of these older communities and to better align resident needs with policy frameworks [63]. In addition, in accordance with policy mandates, it is essential to investigate the factors influencing urban development from a global perspective. The implementation of organic updates has become another important change [64]. Consequently, the investigation of the efficient reutilization of inefficient stock space in China has evolved from a singular analytical perspective to a multi-dimensional and comprehensive framework. However, foundational spatial evaluation methods and theoretical underpinnings have yet to emerge as central themes within this research landscape.

5.3. Evolutionary Process of Research Papers from WOS Database

An analysis of 1177 research articles obtained from the Web of Science (WOS) database, alongside an examination of three developmental phases of various governmental policies concerning inefficient stock space, elucidates the spatiotemporal evolution of research hotspots in the realm of international studies on this topic, as illustrated in Figure 7. The findings presented in this figure indicate that the research hotspots, clustering intensity, and evolutionary process are associated with the embryonic, exploration, and growth stages. From the perspective of spatiotemporal keyword evolution, it should be noted that international research hotspots exhibit a continuous distribution across all time periods, with no significant emergence of dominant research themes at any particular stage.
  • Embryonic stage (2004 to 2013): Preceding 2013, international scholarship predominantly concentrated on evaluation methodologies for inefficient stock spaces, which culminated in the establishment of theoretical frameworks that included mechanisms for spatial function coupling, evaluation models, and development-oriented models. Research during this period emphasized the assessment of adaptive potential [65], a focus on addressing issues related to fragment planning and development, and advocating for the formulation of adaptive strategy frameworks [66]. This effort effectively established a robust theoretical basis for the future repurposing of inefficient stock spaces. On this basis, international researchers further studied the integration of land use, urban renewal, urban ecology, and the built environment, thereby illustrating a multifaceted and concurrent evolution across various research domains.
  • Exploration stage (2013 to 2020): The substantial rise in the academic literature concerning rural revitalization has led to an intensified focus on urban–rural coupling mechanisms within international research. Simultaneously, investigations into inefficient stock spaces have begun to integrate the interdependent relationship between cultural heritage and contemporary urban development [67], thereby promoting a holistic enhancement of urban inclusivity, safety, adaptability [68], and sustainability [69]. This integration has further elaborated the functional implications of sustainable spaces, providing a more nuanced understanding of the functional characteristics associated with inefficient stock spaces [70]. Additionally, inspired by Chinese research on urban renewal and the reutilization of inefficient stock spaces, a number of stock planning studies authored by Chinese scholars have emerged as pioneering contributions during this period. Building on previous diverse developmental processes, international studies have refined key research directions, thereby continuously enriching their theoretical frameworks. Furthermore, scholars have investigated strategies aimed at enhancing the quality of life for citizens and reducing the environmental impacts of urbanization [71].
  • Growth stage (2020 to the present): In this period of rapid growth, the research on inefficient stock space has become markedly diverse. In contrast to the Chinese literature, which predominantly elaborates on concepts such as residual space, international research has concentrated on enhancing existing theoretical frameworks. This includes the continuous refinement of initial strategies aimed at improving the efficiency of stock space reutilization. Notable advancements encompass the development of evaluation methods for spatial coupling mechanisms, the establishment of multidimensional interdisciplinary index assessment frameworks, and the modification of policy mechanisms to facilitate strategic reconstruction [72]. The coupling and coordination of new urbanization with rural political consultative conferences is a key area of research aimed at promoting the comprehensive development of urban and rural areas [73]. Additionally, recent progress in the study of urban–rural coupling mechanisms has increasingly highlighted issues of rural inequality, thereby shifting the research paradigm from an urban-centric viewpoint to a more equitable urban–rural or rural-focused analysis. It is essential to strengthen government support and planning control by integrating spatial openness [74], digital innovation [75], talent acquisition [76], industrial upgrading [77], and other factors to promote a balanced economic output between urban and rural spaces. The focus should be on achieving a high-quality integration of economic, ecological, and social dimensions [78] while also alleviating the pressures of inequality between urban and rural areas. Researchers have also investigated approaches to align energy–environment transformations with the preservation of rural heritage values [79].

6. Discussion

The synthesis of comparative analyses of prominent keywords within the literature of inefficient stock space from the Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) databases, alongside an analysis of the evolution process of significant government policy, reveals several deficiencies in current domestic research concerning inefficient stock space. These deficiencies include the absence of a comprehensive evaluation system, reliance on a singular development model, inadequate mechanisms for urban–rural integration, and an unclear relationship between theoretical frameworks and practical applications. Consequently, this paper proposes several recommendations for future research on inefficient stock space, as follows:
  • Establish a scientific evaluation system for inefficient stock space. Current assessments of spatial value predominantly rely on demand-driven metrics characterized by their partiality. However, inefficient stock spaces diverge from traditional spaces, necessitating both the enhancement of existing functions and the introduction of new functionalities. Therefore, it is imperative to assign new functional values to inefficient spaces, thoroughly investigate the intrinsic logic and technical frameworks associated with these stock spaces, and prioritize theoretical and methodological research that possesses broad applicability. This includes the development of standardized evaluation criteria for spatial assessment and the evaluation of the interrelationship between spatial features.
  • Provide a comprehensive development strategy for the optimization of inefficient stock space. It is imperative to engage in multi-dimensional and multi-level investigations regarding the strategies for the efficient utilization of inefficient stock space. In terms of government policy, it is vital to establish innovative transformation models for spatial governance that prioritize multi-dimensional synergy. Regarding economic policy, it is important to study the relationship between urban stock space and economic growth, with the objective of identifying the most effective pathway for enhancing the economic viability of inefficient stock space within urban environments. In the realm of social policy, research should concentrate on the interconnections between the mechanisms that renew spatial functions and the driving forces behind urban development. This should involve methodologies focused on the “spatial function coupling mechanism” to evaluate the internal factors that influence the revitalization of inefficient stock space.
  • Enhance the analysis of urban–rural coupling mechanisms. A significant portion of the current literature focuses on the study of renewal strategies pertaining to specific locales or architectural spaces, predominantly emphasizing urban built environments while overlooking the ecological significance of inefficient areas in rural settings. Moreover, the proportion of research on inefficient stock space in rural areas within the China National Knowledge Infrastructure (CNKI) is approximately 16%. In contrast, the proportion of research on inefficient stock space in rural areas in Web of Science (WOS) is about 39%, with a focus on the years 2020 to 2023. Compared to urban areas, research on inefficient stock spaces in rural regions is more marginalized, often limited to case analyses or small-scale pilot studies, leading to an uneven allocation of academic resources. Academic research in urban areas is frequently driven by policies, capital, and technology, following a relatively standardized process. In contrast, research in rural areas tends to emphasize ecological and value-oriented perspectives, characterized by relatively singular policies and a lack of coordinated market mechanisms, resulting in a more fragmented research landscape. Furthermore, existing studies often analyze inefficient stock space in urban and rural areas separately, neglecting the potential for capital flow, policy coordination, and functional complementarity between the two areas. It is evident that the inefficient use of stock space in rural areas is often overlooked, lacking appropriate policy support and practical implementation. Future research endeavors should prioritize the interconnections and relationships between the assessment and utilization of these inefficient stock spaces and the revitalization efforts in rural regions. These topics would contribute to a more comprehensive dialog regarding development planning for both urban and rural contexts, and have an advantage at both the policy and economic levels in these areas.
  • The identification of effective strategies for transitioning from theoretical frameworks to practical applications is essential. The notion of inefficient stock space has implications across multiple disciplines, including landscape architecture, urban and rural planning, and architecture. Interdisciplinary analyses can offer novel insights into the transformation and utilization of these inefficient stock spaces. By enhancing the integration of theoretical constructs with practical implementations, leveraging comprehensive examples and data, and addressing technical obstacles, it is feasible to encourage collaborative initiatives among diverse research institutions, academic groups, and professional sectors. Such a collaboration may yield innovative approaches to the repurposing of inefficient stock space.
As illustrated in Figure 5, research on inefficient stock space plays an important role in the study of urban renewal, and there is an annual influx of government policies and scholarly publications. Because of this, the conclusion drawn from this study is characterized by its timeliness. It is essential to conduct an annual analysis of the relevant literature and significant policies through knowledge mapping to accurately capture the true spatial–temporal evolution of inefficiency in stock space. The literature selected from the CNKI and WOS databases exhibits distinct geographical characteristics, which may overlook contributions from non-English-speaking countries and consequently limit the conclusions of this paper. Furthermore, the issue of inefficient stock space intersects multiple disciplines, and the process of keyword screening within the literature may result in incomplete subject coverage, leading to the omission of certain potential associations or emerging trends. Collectively, these factors undermine the global and objective integrity of the review. Therefore, future research efforts should focus on expanding data sources, improving search methodologies, and fostering interdisciplinary collaboration.

7. Conclusions

To investigate the evolutionary process of research concerning inefficient stock space, a total of 2163 journal articles from the CNKI and WOS databases, along with governmental policies from various countries, were analyzed using CiteSpace. Utilizing the results of the visualization, an analysis of the co-occurrence network and the evolutionary trajectory of these buzzwords within the journal articles has been conducted. Subsequently, the following significant conclusions have been drawn:
  • There is a notable disparity between the research on inefficient stock space as represented in the CNKI and WOS databases. The articles sourced from the CNKI database predominantly emphasize stock space in alignment with Chinese government policies. Conversely, the research papers from the WOS database encompass a broader range of focal points within this area of study.
  • The governmental policies addressing inefficient stock space across various nations can be categorized into three distinct phases, namely the embryonic stage, which spans from 2004 to 2013; the exploration stage, covering the period from 2013 to 2020; and the growth stage, which extends from 2020 to the present.
  • Existing research is deficient in studies of the mechanisms of inefficient stock space and potential solutions to address these inefficiencies. Furthermore, there is a notable gap in the exploration of urban–rural coupling mechanisms, and the application of multidisciplinary theoretical frameworks to address the challenges associated with inefficient stock space has been largely neglected.
  • Future research on urban inefficient stock space should prioritize human subjectivity by considering the implementation of multi-dimensional coupling mechanisms in planning, environmental fairness and justice, and functional mechanism coupling. Additionally, it should enhance the investigation of the relationship between theoretical frameworks and practical applications. This approach aims to facilitate a comprehensive reutilization of urban inefficient stock space.

Author Contributions

Conceptualization, W.G. and X.L.; methodology, W.G.; software, X.L.; validation, W.G., X.L. and B.X.; formal analysis, X.L.; investigation, W.G.; resources, W.G. and B.X.; data curation, X.L.; writing—original draft preparation, X.L.; writing—review and editing, W.G. and B.X.; visualization, X.L.; supervision, W.G. and B.X; project administration, W.G. and B.X.; funding acquisition, W.G. and B.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the China National Key R&D Program “Land-Based Evaluation and Enhancement of Environmental Quality in Livable Cities” (Grant No. 2023YFC3805301), and the Anhui Philosophy and Social Sciences Planning Project “Measurement and Driving Mechanisms of the Coordinated Development between Rail Network Growth and Urban Space Driven by Public Transport Priority Strategies” (Grant No. AHSKY2024D062).

Data Availability Statement

Some or all of the data that support the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Abbreviations

The following abbreviations are used in this manuscript:
CNKIChina National Knowledge Infrastructure
WOSWeb of Science

References

  1. Chen, S.S.; Wang, J. Stock Space Planning and Governance System under the Background of High-Quality Development: Taking Neijiang Practice as an Example. J. Hum. Settl. West China 2023, 38, 80–86. [Google Scholar] [CrossRef]
  2. Huang, M. Added Value of Built-up Space: Transition and Implementation Path of Territorial Space Detailed Planning. Planners 2023, 39, 9–15. [Google Scholar] [CrossRef]
  3. Cao, G.R.; Song, J.; Liao, R.; Liu, Z.; Zhang, H. Research on the evaluation indicator system for identifying inefficient urban stock spaces from the CSPON perspective. Nat. Resour. Informatiz. 2025, 1, 1–11. [Google Scholar]
  4. Chen, S.R.; Ye, D.; Li, F.P.; Tang, C.S.; Li, W. The Characters and Strategy of Built-up Space Renewal in the Context of High Quality Development, Fujian Province. Planners 2023, 39, 126–132. [Google Scholar] [CrossRef]
  5. Ahmadvand, A.; Kavanagh, D.; Clark, M.; Drennan, J.; Trends, L.N. Visibility of “digital health” as a keyword in articles by JMIR publications in the new millennium: Bibliographic-bibliometric analysis. J. Med. Internet Res. 2019, 21, e10477. [Google Scholar] [CrossRef] [PubMed]
  6. Chen, C. Searching for intellectual turning points: Progressive knowledge domain visualization. Proc. Natl. Acad. Sci. USA 2004, 101, 5303–5310. [Google Scholar] [CrossRef]
  7. Wei, J.; Li, J.; Zhao, J.; Wang, X. Hot Topics and Trends in Zero-Energy Building Research—A Bibliometrical Analysis Based on CiteSpace. Buildings 2023, 13, 479. [Google Scholar] [CrossRef]
  8. Mao, N.; Zhu, Y.; Wang, J.; Shen, X.; Hou, J.; Miao, L. Adaptive Evolution of Marine Organisms: A Bibliometric Analysis Based on CiteSpace. Water 2023, 15, 4162. [Google Scholar] [CrossRef]
  9. Liu, X.; Zhao, S.; Tan, L.; Tan, Y.; Wang, Y.; Ye, Z.; Hou, C.; Xu, Y.; Liu, S.; Wang, G. Frontier and Hot Topics in Electrochemiluminescence Sensing Technology Based on CiteSpace Bibliometric Analysis. Biosens. Bioelectron. 2022, 201, 113932. [Google Scholar] [CrossRef]
  10. Li, H.; Xiang, Y.; Yang, W.; Lin, T.; Xiao, Q.; Zhang, G. Green Roof Development Knowledge Map: A Review of Visual Analysis Using CiteSpace and VOS viewer. Heliyon 2024, 10, e24958. [Google Scholar] [CrossRef]
  11. Zhao, W.M.; Li, Z.; Li, Y.Y. A Review of Researches on Urban Renewal in Contemporary China and the Future Prospect:Integrated Perspectives of Institutional Capacity and Property Rights Challenges. Urban Plan. Forum 2021, 265, 92–100. [Google Scholar] [CrossRef]
  12. Yang, J.M.; Chen, Y. Review on the Development of Urban Regeneration in China from 1949 to 2019. City Plan. Rev. 2020, 44, 9–19+31. [Google Scholar]
  13. Mo, Z.X.; Ye, Q.; Zhao, Y. The Evolutionary Context of Policies, Theories and Practices for the Utilization of Stock Construction Space in China. Econ. Geogr. 2022, 42, 156–167. [Google Scholar] [CrossRef]
  14. Xie, C.; Feng, T.; Hu, L. A Bibliometric Analysis of the Outdoor Thermal Environment Based on CiteSpace. Buildings 2024, 14, 1384. [Google Scholar] [CrossRef]
  15. Xu, H.; Xue, B. Key Indicators for the Resilience of Complex Urban Public Spaces. J. Build. Eng. 2017, 12, 306–313. [Google Scholar] [CrossRef]
  16. Chen, B.; Hashimoto, S. Integrate Brownfield Greening into Urban Planning: A Review from the Perspective of Ecosystem Services. Urban For. Urban Green. 2025, 104, 128642. [Google Scholar] [CrossRef]
  17. Rousseeuw, P.J. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 1987, 20, 53–65. [Google Scholar] [CrossRef]
  18. Wang, J.; Zakaria, S. Morphological Characteristics and Sustainable Adaptive Reuse Strategies of Regional Cultural Architecture: A Case Study of Fenghuang Ancient Town, Xiangxi, China. Buildings 2025, 15, 119. [Google Scholar] [CrossRef]
  19. Yan, Y.; Ye, H.Y.; Yin, Y.X. The Content System Composition and Key Technical Paths of Urban Renewal Special Planning. Planners 2024, 40, 8–16. [Google Scholar] [CrossRef]
  20. Xiang, S.Q.; Chen, W.; Li, Y.J. From the “three old” transformation to the redevelopment of inefficient land: Exploration on high-quality development of stock land in Zhuhai under the background of national land space. Urban Dev. Stud. 2023, 30, 127–136. [Google Scholar] [CrossRef]
  21. Yun, S.; Chen, Y.Q.; Zhang, Q.D. Research on the transformation thinking of territorial spatial detailed planning for implementation. Planners 2024, 39, 274–286. [Google Scholar] [CrossRef]
  22. Luo, S.; Shang, W.; Yan, Z.; Bi, S. Research on Factors Influencing the Style of Chinese Historic Districts Based on the Mask R-CNN Deep Learning Model. Buildings 2024, 14, 420. [Google Scholar] [CrossRef]
  23. Xiong, X.; Wang, Y.; Ma, C.; Chi, Y. Ensuring the Authenticity of the Conservation and Reuse of Modern Industrial Heritage Architecture: A Case Study of the Large Machine Factory, China. Buildings 2023, 13, 534. [Google Scholar] [CrossRef]
  24. Song, Y.; Han, C.; Zhao, Y. A Study on Tourist Satisfaction Based on the Conservation and Reuse of Alleyway Spaces in Urban Historic Neighborhoods. Buildings 2024, 14, 1324. [Google Scholar] [CrossRef]
  25. Guo, L.; Zhao, C. “Internal Envelope”, a Practical Exploration of Contemporary Interior Space Regenerated in Traditional Buildings. J. Asian Archit. Build. Eng. 2021, 20, 1–11. [Google Scholar] [CrossRef]
  26. Pan, W. What Type of Mixed-Use and Open? A Critical Environmental Analysis of Three Neighborhood Types in China and Insights for Sustainable Urban Planning. Landsc. Urban Plan. 2021, 216, 104221. [Google Scholar] [CrossRef]
  27. Li, C.; Gao, X.; Wu, J.; Wu, K. Demand Prediction and Regulation Zoning of Urban-Industrial Land: Evidence from Beijing-Tianjin-Hebei Urban Agglomeration, China. Environ. Monit. Assess. 2019, 191, 7. [Google Scholar] [CrossRef]
  28. Zheng, X.; Heath, T.; Guo, S. From Maslow to Architectural Spaces: The Assessment of Reusing Old Industrial Buildings. Buildings 2022, 12, 2033. [Google Scholar] [CrossRef]
  29. Vardopoulos, I. Critical Sustainable Development Factors in the Adaptive Reuse of Urban Industrial Buildings. A Fuzzy DEMATEL Approach. Sustain. Cities Soc. 2019, 50, 101684. [Google Scholar] [CrossRef]
  30. Chen, Z.; Fan, J. Influence of Grouping and Driving Mechanisms on Urban Renewal Performance Using a Six-Dimensional Benefit Framework. J. Urban Plan. Dev. 2024, 150, 04024033. [Google Scholar] [CrossRef]
  31. Riera Pérez, M.G.; Laprise, M.; Rey, E. Fostering Sustainable Urban Renewal at the Neighborhood Scale with a Spatial Decision Support System. Sustain. Cities Soc. 2018, 38, 440–451. [Google Scholar] [CrossRef]
  32. Monika, S.; Sundaram, A.M. Efficient and Optimum Design of Native Architecture—A Means for Sustainability. Case Study of Residential Units in Kottar, Kanyakumari. Energy Build. 2023, 298, 113586. [Google Scholar] [CrossRef]
  33. Sun, M.; Qin, T.; Kuang, Y.; Lv, J. Identifying Industrial Buildings as a Spatial Resource for Sustainable Urban Regeneration in High-Density Post-Industrial Metropolitan in Asia. J. Asian Archit. Build. Eng. 2025, 24, 1–18. [Google Scholar] [CrossRef]
  34. Vardopoulos, I. Industrial Building Adaptive Reuse for Museum. Factors Affecting Visitors’ Perceptions of the Sustainable Urban Development Potential. Build. Environ. 2022, 222, 109391. [Google Scholar] [CrossRef]
  35. La Rosa, D.; Privitera, R.; Barbarossa, L.; La Greca, P. Assessing Spatial Benefits of Urban Regeneration Programs in a Highly Vulnerable Urban Context: A Case Study in Catania, Italy. Landsc. Urban Plan. 2017, 157, 180–192. [Google Scholar] [CrossRef]
  36. Zhao, P.; Ali, Z.M.; Ahmad, Y. Developing Indicators for Sustainable Urban Regeneration in Historic Urban Areas: Delphi Method and Analytic Hierarchy Process (AHP). Sustain. Cities Soc. 2023, 99, 104990. [Google Scholar] [CrossRef]
  37. Liu, N.; Zhang, H. A Comparative Study of Traditional Village Renewal Characteristics Driven by Different Entities from the Perspective of Place-Making. Buildings 2024, 14, 1520. [Google Scholar] [CrossRef]
  38. Wang, M.; Yang, J. Sustainable Renewal of Historical Urban Areas: A Demand–Potential–Constraint Model for Identifying the Renewal Type of Residential Buildings. Buildings 2022, 12, 1210. [Google Scholar] [CrossRef]
  39. Ouyang, Q.L.; Liu, F.X.; Wang, Y.Y. Operationalizing Sustainability in Old Community Renewal: A Post-Occupancy Evaluation of Resident Satisfaction in Chengdu, China. Buildings 2025, 15, 725. [Google Scholar] [CrossRef]
  40. Zhao, R.; Fang, C.; Liu, H.; Liu, X. Evaluating Urban Ecosystem Resilience Using the DPSIR Framework and the ENA Model: A Case Study of 35 Cities in China. Sustain. Cities Soc. 2021, 72, 102997. [Google Scholar] [CrossRef]
  41. Yıldız, S.; Kıvrak, S.; Gültekin, A.B.; Arslan, G. Built Environment Design—Social Sustainability Relation in Urban Renewal. Sustain. Cities Soc. 2020, 60, 102173. [Google Scholar] [CrossRef]
  42. Yang, J.J.; Wang, Z.Y.; Li, G.C.; Wu, C.J.; Liu, F.Y.; Chen, X.Y. AutoBPS-Carbon: A Tool to Estimate Multi-Year Dynamic Embodied and Operational Carbon Emissions in the Building Sector. J. Build. Eng. 2025, 101, 111942. [Google Scholar] [CrossRef]
  43. Ullah, H.; Zhang, H.; Huang, B.; Gong, Y. BIM-Based Digital Construction Strategies to Evaluate Carbon Emissions in Green Prefabricated Buildings. Buildings 2024, 14, 1689. [Google Scholar] [CrossRef]
  44. Lu, X.; Lu, Z. How Does Green Technology Innovation Affect Urban Carbon Emissions? Evidence from Chinese Cities. Energy Build. 2024, 325, 115025. [Google Scholar] [CrossRef]
  45. Chen, H.; Zhu, Y.; Du, X.; Yan, H.; Fu, G. Identification and Simulation of the Influencing Factors of Private Capital Participation in Urban and Rural Infrastructure Transformation Based on System Dynamics. Buildings 2023, 13, 2327. [Google Scholar] [CrossRef]
  46. Huang, L.; Zheng, W.; Hong, J.; Liu, Y.; Liu, G. Paths and Strategies for Sustainable Urban Renewal at the Neighborhood Level: A Framework for Decision-Making. Sustain. Cities Soc. 2020, 55, 102074. [Google Scholar] [CrossRef]
  47. Liu, Y.; Tang, C.; Lian, L.; Zeng, Y. A Review of Chinese and Western Research on Community Garden Design: A Visualized Analysis Based on CiteSpace. J. Asian Archit. Build. Eng. 2024, 23, 453–464. [Google Scholar] [CrossRef]
  48. Beck, D.; Mitkiewicz, J. A Systematic Literature Review of Citizen Science in Urban Studies and Regional Urban Planning: Policy, Practical, and Research Implications. Urban Ecosyst. 2025, 28, 85. [Google Scholar] [CrossRef]
  49. Mercader-Moyano, P.; Camporeale, P.; Serrano-Jiménez, A. Integrated Urban Regeneration for High-Rise Multi-Family Buildings by Providing a Multidimensional Assessment Model and Decision Support System. J. Build. Eng. 2023, 76, 107359. [Google Scholar] [CrossRef]
  50. Pennell, G.; Newman, S.; Tarekegne, B.; Boff, D.; Fowler, R.; Gonzalez, J. A Comparison of Building System Parameters between Affordable and Market-Rate Housing in New York City. Appl. Energy 2022, 323, 119557. [Google Scholar] [CrossRef]
  51. Sacirovic, S.; Ketin, S.; Vignjevic, N. Eco-Industrial Zones in the Context of Sustainability Development of Urban Areas. Environ. Sci. Pollut. Res. 2019, 26, 24346–24356. [Google Scholar] [CrossRef] [PubMed]
  52. Yuan, D.; Bao, H.; Yau, Y.; Skitmore, M. Case-Based Analysis of Drivers and Challenges for Implementing Government-Led Urban Village Redevelopment Projects in China: Evidence from Zhejiang Province. J. Urban Plan. Dev. 2020, 146, 3. [Google Scholar] [CrossRef]
  53. Li, W.; Meng, M.; Llewellyn, C.; Sun, T. Developing an Indicator Framework for Sustainability Assessment in Urban Community Regeneration: A Case of Shanghai. Dev. Built Environ. 2024, 18, 100476. [Google Scholar] [CrossRef]
  54. Levine, D.; Ayalon, S.Y. Back and Forth from Urban Renewal: The Spatial Parameters of Affordable Housing in Two Cities. Buildings 2024, 14, 3324. [Google Scholar] [CrossRef]
  55. Meng, P.; Shen, J.; Shen, C.; Li, J.; Kang, J.; Li, T. Construction Engineering in China under Green Transition: Policy Research Topic Clusters and Development Forecasts. Energy Build. 2024, 323, 114758. [Google Scholar] [CrossRef]
  56. Zhang, P.; Chen, Z.; Yang, H.; Wang, H. On Utilization of Underground Space to Protect Historical Relics Model. Tunn. Undergr. Space Technol. 2009, 24, 245–249. [Google Scholar] [CrossRef]
  57. Zhu, Y.; Yan, Y.; Zheng, F.; Ge, J.; Gu, Y. Research on the Renovation of Historical Buildings and Improvement of the Residential Environment of Hangzhou Zhuyangxin Plaster Store. J. Asian Archit. Build. Eng. 2010, 9, 395–402. [Google Scholar] [CrossRef]
  58. Shi, Y.; Sun, X.; Zhu, X.; Li, Y.; Mei, L. Characterizing Growth Types and Analyzing Growth Density Distribution in Response to Urban Growth Patterns in Peri-Urban Areas of Lianyungang City. Landsc. Urban Plan. 2012, 105, 425–433. [Google Scholar] [CrossRef]
  59. Cui, J.; Lin, D. Utilisation of Underground Pedestrian Systems for Urban Sustainability. Tunn. Undergr. Space Technol. 2016, 55, 194–204. [Google Scholar] [CrossRef]
  60. Yung, E.; Chan, E.; Xu, Y. Community-Initiated Adaptive Reuse of Historic Buildings and Sustainable Development in the Inner City of Shanghai. J. Urban Plan. Dev. 2014, 140, 3. [Google Scholar] [CrossRef]
  61. Zhang, X.; Ren, Y. Revitalization of Urban Industrial Heritage from a Perspective of Spatial Production Theory: The Case Study of “Old Market” Project. J. Asian Archit. Build. Eng. 2024, 23, 1–17. [Google Scholar] [CrossRef]
  62. Sun, G.; Zhang, H.; Feng, J. Factors Driving Social Capital Participation in Urban Green Development: A Case Study on Green Renovation of Old Residential Communities under Urban Renewal in China. Buildings 2025, 15, 221. [Google Scholar] [CrossRef]
  63. Xu, S.; Chen, M.; Yuan, B.; Zhou, Y.; Zhang, J. Resident Satisfaction and Influencing Factors of the Renewal of Old Communities. J. Urban Plan. Dev. 2024, 150, 1. [Google Scholar] [CrossRef]
  64. Zhang, M.S.; Liu, C.H.; Sun, Y. Construction of Urban Renewal Planning and Operation System from the Perspective of Supply and Demand Matching. South Archit. 2024, 43, 24–32. [Google Scholar] [CrossRef]
  65. Teo, E.; Lin, G. Building Adaption Model in Assessing Adaption Potential of Public Housing in Singapore. Build. Environ. 2011, 46, 1370–1379. [Google Scholar] [CrossRef]
  66. Teng, M.; Wu, C.; Zhou, Z.; Lord, E.; Zheng, Z. Multipurpose Greenway Planning for Changing Cities: A Framework Integrating Priorities and a Least-Cost Path Model. Landsc. Urban Plan. 2011, 103, 1–14. [Google Scholar] [CrossRef]
  67. Misirlisoy, D.; Günçe, K. Adaptive Reuse Strategies for Heritage Buildings: A Holistic Approach. Sustain. Cities Soc. 2016, 26, 91–98. [Google Scholar] [CrossRef]
  68. Aigwi, I.; Egbelakin, T.; Ingham, J.; Phipps, R.; Rotimi, J.; Filippova, O. A Performance-Based Framework to Prioritise Underutilised Historical Buildings for Adaptive Reuse Interventions in New Zealand. Sustain. Cities Soc. 2019, 48, 101547. [Google Scholar] [CrossRef]
  69. Hasik, V.; Escott, E.; Bates, R.; Carlisle, S.; Faircloth, B.; Bile, M. Comparative Whole-Building Life Cycle Assessment of Renovation and New Construction. Build. Environ. 2019, 161, 106218. [Google Scholar] [CrossRef]
  70. Gosling, J.; Sassi, P.; Naim, M.; Lark, R. Adaptable Buildings: A Systems Approach. Sustain. Cities Soc. 2013, 7, 44–51. [Google Scholar] [CrossRef]
  71. Ludevid, J. Towards the Generalization of the Comprehensive or Architectural Refurbishment of Building Stock in Spain. Inf. Constr. 2015, 67, nt001. [Google Scholar] [CrossRef]
  72. Mercader-Moyano, P.; Morat, O.; Serrano-Jiménez, A. Urban and Social Vulnerability Assessment in the Built Environment: An Interdisciplinary Index-Methodology towards Feasible Planning and Policy-Making under a Crisis Context. Sustain. Cities Soc. 2021, 73, 103082. [Google Scholar] [CrossRef] [PubMed]
  73. Zhang, L.; Li, W.; Chen, Z.; Yin, Z.; Hu, R.; Qin, C.; Li, X. Coupling Coordination Measurement and Obstacle Diagnosis of New Urbanization and Rural Revitalization in the Basin Area of Sichuan Province, China. Sustainability 2024, 16, 9209. [Google Scholar] [CrossRef]
  74. Yao, W.; Zhang, T.; Fukuda, H.; Wang, M.; Yang, J. Assessing Utilization and Comfort in a Village Scenic Park: Implications for Rural Revitalization. Buildings 2024, 14, 1538. [Google Scholar] [CrossRef]
  75. Tian, Y.; Liu, Q.; Ye, Y.; Zhang, Z.; Khanal, R. How the Rural Digital Economy Drives Rural Industrial Revitalization-Case Study of China’s 30 Provinces. Sustainability 2023, 15, 6923. [Google Scholar] [CrossRef]
  76. Li, M.; Zhang, L.; Huang, J.; Lu, Y. A Quantitative Evaluation Study on Return-to-Hometown Entrepreneurship Policies in 16 Provinces (Municipalities) and Autonomous Regions in China under the Rural Revitalization Strategy. Sustainability 2024, 16, 10283. [Google Scholar] [CrossRef]
  77. Li, H.; Zhu, M.; Wang, Z.; Hong, J.; Wang, Y. Synergy Level of Urban Resilience and Urban Land Use Efficiency in the Yellow River Basin: Spatial-Temporal Evolution Characteristics and Driving Factors. Heliyon 2024, 10, e31456. [Google Scholar] [CrossRef] [PubMed]
  78. Li, G.; Zhang, X.E. The Spatial-Temporal Characteristics and Driving Forces of the Coupled and Coordinated Development between New Urbanization and Rural Revitalization. Sustainability 2023, 15, 16487. [Google Scholar] [CrossRef]
  79. Philokyprou, M.; Michael, A. Environmental Sustainability in the Conservation of Vernacular Architecture. The Case of Rural and Urban Traditional Settlements in Cyprus. Int. J. Archit. Herit. 2021, 15, 1741–1763. [Google Scholar] [CrossRef]
Buildings 15 01356 g001
Figure 1. Research process and methodology.
Figure 1. Research process and methodology.
Buildings 15 01356 g001
Buildings 15 01356 g002
Figure 2. Process of data collection from WOS and CNKI databases.
Figure 2. Process of data collection from WOS and CNKI databases.
Buildings 15 01356 g002
Buildings 15 01356 g003
Figure 3. Co-occurrence network of journal articles from CNKI database.
Figure 3. Co-occurrence network of journal articles from CNKI database.
Buildings 15 01356 g003
Buildings 15 01356 g004
Figure 4. Co-occurrence network of journal articles from WOS database.
Figure 4. Co-occurrence network of journal articles from WOS database.
Buildings 15 01356 g004
Buildings 15 01356 g005
Figure 5. Evolutionary process of government policies of different countries.
Figure 5. Evolutionary process of government policies of different countries.
Buildings 15 01356 g005
Buildings 15 01356 g006
Figure 6. Evolutionary process of buzzwords form CNKI database research paper.
Figure 6. Evolutionary process of buzzwords form CNKI database research paper.
Buildings 15 01356 g006
Buildings 15 01356 g007
Figure 7. Evolutionary process of buzzwords form WOS database research paper.
Figure 7. Evolutionary process of buzzwords form WOS database research paper.
Buildings 15 01356 g007
Table 1. Keyword clustering result of research articles from CNKI database.
Table 1. Keyword clustering result of research articles from CNKI database.
NumberStrengthTimeKeywordsBuzzwords (Top Five)
0312018Remaining spaceHighway, renewal, multiple subjects, urban furniture, spatial design
1282017Idle spaceReuse, cultural consumption, ageing population, abandoned plants, old buildings
2272015Stock planningTransaction costs, high density, China (Shanghai) Pilot Free Trade Zone, mechanism, competition
3252019National land spaceGuiyang City, Shrinking City, Adaptive reuse, Pingyuan, City park
4232019Stock spaceUrban public abandoned space, landscape vitality enhancement, small and medium-sized cities, optimizing transformation, pocket parks
5202018Urban renewalSpirit of place, shrinking cities, unused vacant rural land, urban villages, natural disasters
6192020Old communityDemand-orientation, Xiangtan, spatial syntax, evaluation system, inefficient industrial land use
7182013Ecological restorationCultural and tourism integration, synchronization index, sand mining abandoned areas, interaction, urban green space
8142013City planningNew from the past, creative urban areas, development mechanisms, typologies, action planning
Table 2. Keyword clustering result of research articles from WOS database.
Table 2. Keyword clustering result of research articles from WOS database.
NumberStrengthTimeKeywordsBuzzword (Top Five)
0462020Rural revitalizationRural development, Discourse analysis, Spatial differentiation, Smart shrinkage, Landscape pattern
1372017Carbon emissionSustainable construction method, Arid area, Resident participation, Digital twins, Analytical hierarchy process
2262012Built environment Geographical detector, Mountainous area, Logistic regression, Rural settlements, Landscape pattern
3262016Urban ecologySustainable development, Waterlogging control, Risk factors coupling, Sponge city, Urban modelling
4242016Urban regenerationUrban regeneration, Regeneration program, Sustainable development, Goals, Sustainable regeneration, Xintai county
5222017Environmental justiceEnvironmental justice, Participatory process, Vacant rural homesteads, Spatial differentiation, Disproportionate impacts
6172012Coupling mechanismCoupling mechanism, Monument, Food environments, Urban land use, Social support
7162013Multi-scale modelMultifunctional system, Interaction effects, Development pattern, Rural region function, Space poverty
8152015Urban renewalUrban renewal, Central business district, Urban sustainability metrics, Land-use change detection, Land-use diversity
9152015Land useProject, Regeneration, Customer satisfaction, Perceived quality, Model
10132016Prioritization Brownfield redevelopment, Sustainable development, Urban planning, Urban modelling, Urban development
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Gui, W.; Li, X.; Xu, B. Visualization Analysis of Research on Inefficient Stock Space by Mapping Knowledge Domains. Buildings 2025, 15, 1356. https://doi.org/10.3390/buildings15081356

AMA Style

Gui W, Li X, Xu B. Visualization Analysis of Research on Inefficient Stock Space by Mapping Knowledge Domains. Buildings. 2025; 15(8):1356. https://doi.org/10.3390/buildings15081356

Chicago/Turabian Style

Gui, Wangyang, Xu Li, and Bin Xu. 2025. "Visualization Analysis of Research on Inefficient Stock Space by Mapping Knowledge Domains" Buildings 15, no. 8: 1356. https://doi.org/10.3390/buildings15081356

APA Style

Gui, W., Li, X., & Xu, B. (2025). Visualization Analysis of Research on Inefficient Stock Space by Mapping Knowledge Domains. Buildings, 15(8), 1356. https://doi.org/10.3390/buildings15081356

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop