An Overview of Environmental Performance Indicators in the Construction Industry

Abstract

This paper analyzes environmental performance indicators (PIs) in the construction and building industry using bibliometric and content analysis, particularly in the fields of architecture and civil engineering. The paper aims to present a framework for environmental performance in the construction industry, focusing on projects and their impacts. It addresses which research fields are most focused on this area, whether the topic is currently relevant, whether it shows a positive or negative trend, what related topics exist, and what general overlaps or gaps are present. It also examines which PIs are most frequently mentioned and whether the topics and indicators align with the United Nations Sustainable Development Goals (UN SDGs). The results reveal a fragmented research area, with both complex PIs and very narrow PI applications, highlighting the need to bridge these gaps and address the challenge of insufficient data. The research uses QtoQ Target Mapping to map the PIs to the UN SDGs and provide an overview of coverage. The findings indicate that this topic is highly important and researched across various disciplines, and that the PIs and their analysis further contribute to the Sustainable Development Goals.

1. Introduction

The construction industry is a major contributor to economies [1,2]. However, beyond its economic significance, there is growing attention to other aspects of construction, such as heritage, environmental, and climate impacts [1]. Thanu et al. [3] note that construction is one of the leading sectors contributing to pollution. Environmental impacts are particularly related to greenhouse gas emissions, resource depletion, global energy and material use [4,5,6], as well as the effects of completed projects on all spheres of sustainability [7]. The use of modern materials and transportation makes construction a sector with intensive energy and material consumption, as the materials used have high embodied energy and carbon footprints. Working on reducing impact in resource consumption and minimizing waste have a positive effect on environmental impact [8].

Construction projects can have both immediate and long-term effects, including serious impacts, such as human fatalities [9]. Zhang and Pu [10] found a strong correlation between air pollution and respiratory diseases, as well as between long-term exposure to pollutants and higher mortality rates. Air, noise, sewage, and waste are the four major environmental issues that require impact evaluation at both management and operational levels, as some of the most common complaints concern vehicle emissions, noise, air pollution, and liquid waste [11]. Nevertheless, Ibrahim et al. [12] note that environmental performance accounts for only 15.75% of the reviewed literature on construction performance.

One way to evaluate and monitor potential impacts is by using evaluation models and performance indicators (PIs). Evaluation is effective for improving quality control procedures, especially in water, pollution, ecosystem monitoring, and health [13]. PIs have a dynamic nature as their development follows advancements in the construction industry and depends on the project’s characteristics and context [14]. Krajangsri and Pongpeng [15] emphasize the need for sustainable infrastructure that balances economic growth with environmental preservation, but also note that sustainable construction is not yet well defined.

Indicators are also important for public monitoring, implementation of environmental management plans (EMP), and public education. Project monitoring can be tailored to the needs of all stakeholders, but still faces significant limitations, such as a lack of staffing [9]. Detailed estimation and monitoring of indicators require complex measurements, sensors, and evaluations. This is also challenging due to the variety of construction types [4]. Cha and Kim [14] note the problem of data availability. In fact, tools and databases vary and are especially lacking in developing countries, while more data are available for the EU and USA, such as those related to environmental product declarations (EPDs) and Ecodesign [16]. However, Sambataro et al. [17] note that even environmental product declarations (EPDs) do not allow for a detailed comparison due to dynamic changes in data. Moyo et al. [18] find that sustainable construction indicators require innovative technical support systems, including innovation for construction sustainability, adequate sustainability expenditure and skills training support, adequate project economic assessment and governance support, adequate circularity and environmental technical support, climate change literacy and supplier assessment support, and adequate decent work support. Sustainable infrastructure assessments have a significant effect on construction project success, and the most important criteria for sustainable infrastructure are environmental impacts on surrounding areas and transport [15,19]. However, sustainable construction practices face barriers to implementation, including a lack of green technology and techniques, the need for collaboration among actors, a lack of resources for technological change, additional costs, a lack of understanding from stakeholders, difficult procurement of sustainable practices [20,21], and challenges in verification.

Multiple papers elaborate on certifications such as: Leadership in Energy and Environmental Design (LEED) by the US Green Building Council, the Green Building Assessment Tool (GBTool) by the Green Building Challenge project, Building Research Establishment Environmental Assessment Methodology (BREEAM) in the UK, Comprehensive Assessment System for Building Energy Efficiency (CASBEE), Green Star by the Green Building Council South Africa (GBCSA), International Initiative for Sustainable Built Environment (SBTool) [6,22], Balanced Score Card (BSC), European Foundation for Quality Management (EFQM) framework, Project Quarterback Rating (PQR) [23], LEED-India, Indian Green Building Council (IGBC), Green Rating for Integrated Habitat Assessment (GRIHA) [3], PassiHouse, DGNB, EDGE, CASA Guatemala, Estidama Pearl Certification in the UAE, Green Star in Australia, Living Building Challenge 4.0 Standard (USA), Greenmark in Singapore, Global Sustainability Assessment System (GSAS) in the Middle East, and Green Building Index (GBI) in Southeast Asia [4,24]. They note that environmental certifications impact both procedures and products, but usually lack country-specific aspects.

Cha and Kim [14] analyzed several benchmarking systems for the construction industry, such as benchmarking metrics in the Construction Industry Institute (CII) USA, which are based only on complex indicators and company and project functionality; and Key Performance Indicators (KPIs) in the Constructing Excellence (CE) UK, also based on complex indicators. Wang and Liu [25] found that both traditional and demand-side environmental regulations positively influence green construction.

Environmentally friendly firms were found to be successful, but public awareness is important for influencing construction demand as well as government policies [26]. Kaklauskas et al. [27] identified the key factors influencing Norway’s success and the factors other countries lack in this regard, finding that poor management, cultural values, and outdated technologies contribute to construction waste. Further, Erdenekhuu et al. [28] noted difficulties in data procurement and a lack of adequate IT tools, while Wen et al. [29] highlighted the need for high-tech solutions to reduce emissions, promote informatization and automation, and implement 3D printing and 5D-BIM (building information modeling) technology. Smart buildings and smart technologies are useful for monitoring energy use, the reading and evaluation of efficiency and sustainability, as well as user experience [30].

Poor performance and a lack of consensus on what constitutes a successful project characterize the construction industry [23]. Typically, evaluations focus on time, cost, and safety, while customer satisfaction, environmental impact, and communication management are rarely assessed. Hassanain et al. [30] suggest that various aspects of performance can be evaluated and monitored, including technical performance (such as sanitation, safety, security, and thermal comfort), functional performance (such as efficiency of circulation and accessibility), and behavioral performance.

The existing literature mostly presents complex indicators evaluated by stakeholders, reflecting the nature of complex systems evaluations on the topic [3,9,11,14,23,28,31,32,33,34,35,36]. Many evaluation tools in research address only certain phases or aspects [1]. Different KPIs may be used for various project types and local contexts [9,37]. For some indicators, it is difficult to determine whether or not they include environmental aspects [38].

Punctual measurable indicators are used mostly in the evaluation of performance related to punctual improvements [39,40,41]. Yu et al. [39] also note that studies typically define up to 30 indicators to maintain a manageable quantity of indicators, while Zhou et al. [40] highlight that too many indicators prove to be difficult to use. Consequently, many variables affecting contractors’ environmental performance are challenging to measure [42]. Positioning of the research: Although there is a substantial quantity of scientific sources, this topic is analyzed by many different disciplines, and the actual state of the art is unclear regarding how this topic should be considered in architectural and civil engineering approaches, as the literature appears very fragmented. In particular, it is not clear which areas are more or less covered by PI literature, or how they are addressed. It is also unclear how environmental PIs connect with strategic goals such as the UN Sustainable Development Goals (UN SDGs). Therefore, it was necessary to attempt to map the environmental PIs onto a strategic goals framework to determine whether this was possible, to identify which goals are better covered, and where attention is lacking. In this context, the PIs are considered from an architectural and civil engineering perspective, encompassing both direct and indirect environmental impacts that could inform the design, including construction site design, of construction projects. This paper provides a holistic overview of the interdisciplinary knowledge, identifies the state of the art in environmental PIs, and evaluates them with respect to the UN SDGs.

Therefore, the aim of the paper is to give a framework for environmental performance in the construction industry, particularly regarding projects and their impacts. The research questions (RQs) are designed to clarify which fields and perspectives define the research area and its niches; the relevance of this research area; the areas and perspectives that overlap or are currently not covered in the literature; the type, coverage, and utility of common PIs from architectural and civil engineering perspectives; and the relevance of the topic to the UN SDGs. Therefore, the aim of the paper is to give a framework of environmental performance in the construction industry, particularly regarding the projects and their impacts. The research questions (RQs) are as follows:

RQ1: Which research areas most frequently address performance, as well as environmental and social performance, in the construction industry—for example, environmental sciences, economics, engineering, or other fields? RQ2: Is this topic currently relevant, and does it show a positive or negative trend? RQ3: What topics are related to environmental performance, and what general overlaps or gaps exist? RQ4: What are the most frequently mentioned environmental performance indicators (or groups of indicators) in the construction industry for project evaluation? RQ5: Are the topics and indicators compatible with the UN SDGs, and if so, with which ones primarily? Are there gaps in certain categories?

This research draws on the existing scientific literature to understand the state of the art on this topic, particularly from the architectural and civil engineering perspectives, with regard to the design and realization of projects. The paper uses a dual approach of bibliometric and content analyses, combining qualitative and quantitative techniques to address the research questions and establish a framework for evaluating the environmental PIs of construction projects.

2. Materials and Methods

The analysis uses the Web of Science (WOS) database, VOSviewer 1.6.20 [43], and Biblioshiny 5.0 [44] bibliometric software, as well as content analysis. The analysis is conducted in several work phases, following the RQs. The research flowchart is given in Figure 1.

Figure 1. Research flowchart.

For RQ1, the analysis was conducted using different keyword queries and Boolean operators on the literature indexed in the WOS database, and the results were compared. The queries ranged from more general to more detailed, as shown in Table 1. The WOS database was selected based on previous research [45,46] on a similar topic, which showed that the papers in this database were more relevant to the aims of this study.

Table 1. Queries in WOS base.

	Aspects of Construction Industry	Query	First Year of Publication	Number of Publications	Year with Most Publications	Number of Papers in Last 20 Years	Number of Highly Cited Papers
1	General performance aspects	(“construction” or “building”) and (“technology” or “industry”) and “performance”	1966	110,076	2024	110,025	15,690
2	General evaluation performance aspects	(“construction” or “building”) and (“technology” or “industry”) and “performance” and “indicators”	1991	3237	2024	3131	19
3	Environmental evaluation performance aspects	(“construction” or “building”) and (“technology” or “industry”) and (“environment” or “environmental”) and “performance” and “indicators”	2000	1243	2024	1203	8
4	Social evaluation performance aspects	(“construction” or “building”) and (“technology” or “industry”) and (“social” or “cultural”) and “performance” and “indicators”	1993	631	2022	612	6
5	Environmental evaluation performance aspects related to projects	(“construction” or “building”) and (“technology” or “industry”) and (“environment” or “environmental”) and “performance” and “indicators” and (“assessment” or “evaluation”) and “project”	2001	340	2024	325	4

The question of which research areas address performance and environmental performance in the construction industry is answered through a series of queries, ranging from the most general to the most specific.

The first level concerned general performance aspects and used the following query: (“construction” or “building”) and (“technology” or “industry”) and “performance”. The second level addressed general evaluation performance aspects and used the query: (“construction” or “building”) and (“technology” or “industry”) and “performance” and “indicators”. The third level addressed environmental evaluation performance aspects and used the query: (“construction” or “building”) and (“technology” or “industry”) and (“environment” or “environmental”) and “performance” and “indicators”. The fourth level—the variation in level 3—addressed the social evaluation performance aspects and used the query.: (“construction” or “building”) and (“technology” or “industry”) and (“social” or “cultural”) and “performance” and “indicators”. The fifth and most relevant level from the architectural and civil engineering perspective, which addressed the topic of environmental performance evaluation aspects related to projects, used the query: (“construction” or “building”) and (“technology” or “industry”) and (“environment” or “environmental”) and “performance” and “indicators” and (“assessment” or “evaluation”) and “project”. This analysis provided insight into which research areas address this topic and at what level, helping to better understand the topic’s relevance or distance from the authors’ field of research.

For each level, it was important to see the data on: first year of publication in the query, number of publications, year with the most publications, number of papers in the last 20 years, number of highly cited papers, main field, number of papers in the field, country, number of papers, field of the top cited paper, and the number of papers in the main field among the 10 top cited papers. All types of papers were analyzed (articles, conferences, and so on). It was important to determine whether the topic is of growing importance, the main fields of research, and especially whether the highest cited and top papers are in the same field as the most frequent field.

For RQ2, the analysis was conducted using both bibliometric software, including results provided as graphs, tables, and Biblioshiny 5.0 [44]; AI Gemini powered generated textual interpretations to assess whether this topic is relevant today and to determine the direction of its trend.

The level 5 query was used for the bibliometric analysis, based on data from the Web of Science (WOS) for the period 2005–2024, focusing on “Article” and “Review Article” document types. The bibliometric analysis was conducted using Biblioshiny, a visual tool based on the Bibliometrix package in RStudio 4.4.2. It statistically analyzes bibliographic data for a given dataset, such as papers extracted with the WOS query. Biblioshiny provides descriptive statistics, three-field plots showing connections among sources, authors, and keywords, authors’ production over time, corresponding authors’ country collaborations, most locally cited documents, reference publication year spectroscopy, trend topics and topic clustering, knowledge structures, a country collaboration world map, and other data in table, graphic, and textual formats. The textual format is generated through integration with Gemini AI via the Biblio module within Biblioshiny, which connects Biblioshiny objects to Gemini AI. It uses tables from Biblioshiny to create textual descriptions of the same data, ensuring that no additional data is used that could lead to hallucinations or different outcomes.

The three-field plot was created for the keywords and for a 20-year period. Local source impact was assessed using the H-index as the impact measure. Trend topics were analyzed for all keywords over the entire 20-year range. A co-occurrence network was performed using all keywords, automatic layout, Walktrap diagram, and normalization by association. Clustering by coupling was performed using keywords and local citation score as the impact measure, with a minimum cluster frequency of 5 (a default value), community repulsion set to 0, and the Walktrap clustering algorithm. The Walktrap algorithm is a graph-based method for detecting clusters in networks, based on community detection algorithms. It relies on the concept of short random walks on the graph, which are expected to identify denser connections within communities (referred to as clusters). This algorithm is used in Biblioshiny software due to its suitability for networks with a smaller number of nodes, as is common in bibliographic analysis. After the random walks determine the clusters, the clusters are aggregated and ordered hierarchically. In the graph, nodes of the same color belong to the same cluster.

For RQ3, the analysis to determine the topics related to environmental performance, as well as general overlaps or gaps, was conducted using content analysis of bibliographic sources. Bibliographic analysis is effective for gaining insights into the research area based on metadata, such as publication trends and citation networks. However, it does not give an in-depth analysis of current research practices regarding the actual use of methodologies, applied research tools, frameworks, and current research questions. Understanding these aspects was considered crucial for establishing their relevance in the fields of architecture and civil engineering, as well as for identifying potential applications and gaps. For this, a qualitative reading and manual analysis of the papers was performed. The initial attempt was to strictly follow the PRISMA procedure, reading through titles and abstracts, then reading the selected papers in full. This approach was tested on several papers to identify topics and perspectives (specifically architectural and civil engineering perspectives) from the abstracts and to compare these findings with those obtained from reading the full papers. The process revealed the necessity of reading both the abstracts and the full papers, as the abstracts often lacked information about tools, methods, indicators, and the required perspectives. Because of that, the researchers decided on a different approach: screening, evaluation, classification, reading, annotation, and writing (SECRAW). All collected papers were read, and the researchers identified the topics within each paper. The relevance of each paper was evaluated on a scale from 5 to 1 (from most to least relevant) for the perspective and positioning of this research, by each researcher who read the papers. When a new topic was identified, a new column was created in Excel, employing an inductive approach to the literature review, which was considered more suitable for exploring the broad research area. All identified topics in the papers were recorded in the Excel table. Papers with the highest scores were described in this paper, and for topics with fewer papers, those rated lower were also included. During this process, some papers were found to be irrelevant, and some could not be located by the researchers or their institution. After reading and note-taking, the most relevant aspects of the papers were condensed based on the identified topic categories. The smallest categories were aggregated for easier reading. Finally, overlaps and gaps were identified.

For RQ4, the analysis of environmental PIs (or groups of PIs) in the construction industry for project evaluation was conducted using content analysis of bibliographic sources, based on selected papers from architectural and civil engineering perspectives. The indicators were grouped and several analyses were conducted: the impact (how important the group of indicators appears in the list of indicators), the ordering of the group of indicators based on prevalence in the sources, hierarchy analysis, and principal component analysis (PCA).

For RQ5, the analysis was performed by comparing the indicators found with the UN SDGs [47]. All the UN SDGs and their targets were evaluated in relation to the indicators identified in the literature, using qualitative terms, and then assessed using an equal-weight sum model.

The analysis of whether the topics and indicators examined are compatible with the UN SDGs, which SDGs they align with most, and whether there are gaps in certain categories was conducted using a multi-criteria UN SDGs alignment assessment. This assessment was defined by researchers as the Qualitative-to-Quantitative conversion method for PIs to UN SDGs Target Mapping (QtoQ Target Mapping), which is based on a qualitative-to-quantitative ordinal scoring methodology of quantitizing. An example of this is the Likert scale, where ordinal data is treated as interval data. Here, the approach is treated as a simplified multicriteria decision aid (MCDA) model, where the PIs are considered alternatives that are assessed with respect to the subgoals [48,49].

As qualitative evaluations in general, and qualitative-to-quantitative evaluations in particular, have a characteristic of subjectiveness, the quantification method had to be simple so as not to give the impression of being more objective by using an elaborate mathematical method. However, there is often a need to use the QtoQ approach, as quantitizing methods are used in various scientific fields, from medicine and sociology to project assessments related to UN SDGs, because they allow for the general comparison of analyzed alternatives that have more qualitative characteristics than a person can assess at a glance, and where quantitative information is unavailable or the data are mixed. Ordinal scaling provides a structured method when quantitative metrics are unavailable. It also allows for aggregation across multiple subcriteria while maintaining transparency. For example, the Initiative for Climate Action Transparency (ICAT) methodology uses the scales of major, moderate, and minor for different evaluated quantities of impacts—substantial, moderate, and insignificant. This approach also includes likelihood assessments [50].

The evaluators assigned the scores (minor, moderate, major, insignificant) during brainstorming by considering the texts, results of the indicator analysis, and PCA of the indicators. This approach was chosen because both the indicators and their use in the papers, as well as the UN SDGs, are complex and require interpretation. Therefore, simpler methods such as direct mapping using PCA (table) or frequency of indicators in the papers (table) could not be used. The evaluation was conducted at the lowest level—the level of targets (sub-goals). This was performed after completing the previous phases of the research to ensure a comprehensive overview of the results from those phases and the research questions. The evaluations were then quantitized. The proposed QtoQ Target mapping uses four categories: major contribution = 3, moderate contribution = 2, minor contribution = 1, and insignificant or no contribution = 0.

The second phase was aggregation at the level of the main indicator: the goal level. This was performed using the arithmetic average with equal weighting, which maintains transparency and is expressed as a percentage (%). Equal weighting for subcriteria [51] within groups also results in different weighting of higher-order criteria [52], but it also allows for the assignment of different weights for each of the higher-order criteria.

3. Results and Discussion

3.1. Analysis of Research Areas—RQ1

3.1.1. Analysis of Research Areas—Results

From the results presented in Table 2, it can be seen that the research areas most focused on performance, as well as environmental and social performance in the construction industry, ranged from civil engineering (the broadest) to environmental sciences (the narrowest). The most cited papers, however, were primarily from physics and robotics, and only in more specific cases from construction, technology, science and technology, and other topics.

Table 2. Main fields of research.

	Main Field	Number of Papers in the Field	Country	Number of Papers	Field of the Top Cited Paper	Papers in the Main Field/10 Top Cited Papers
1	Civil engineering	22,575	People’s Republic of China (PRC)	1136	Physics	0
2	Civil engineering	77,900	People’s Republic of China (PRC)	1267	Robotics	0
3	Environmental sciences	33,700	People’s Republic of China (PRC)	453	Robotics	1
4	Environmental sciences	15,700	People’s Republic of China (PRC)	232	Construction and Building Technology	4
5	Environmental sciences	9100	People’s Republic of China (PRC)	150	Science and Technology—Other Topics	7

Although civil engineering is the most prevalent field in broader research, there are no papers from civil engineering among the top-cited papers. More specifically, when examining environmental sciences as a leading field, construction and technology become more relevant among the top-cited papers.

3.1.2. Analysis of Research Areas—Discussion

The results of the comparison among research fields show that although papers from the fields of civil engineering and environmental sciences are prevalent, the most cited papers are related to other fields. This implies that other research areas have a greater impact in terms of citations.

A high number of journals indicated the multidisciplinary nature of the topic, but there is a clearly preferred journal, which featured frequent publishing and relatively high citation counts. International research increased visibility, especially with several affiliations to high-impact institutions. The paper with the highest citation count was from 2018, which may be related to the time required to accumulate citations.

Table 1 shows that the majority of papers were published in the last 20 years, with an average age of 4.52 years, indicating that this is a relatively new research area. The fact that most papers were published in recent years further confirms this. The first, general query produced a significant number of papers, while the more specific query yielded a much smaller number of papers, showing a niche and focused nature of the topic and a very active research area. The number of papers addressing environmental aspects is double that of those addressing social aspects, suggesting that the “social” niche is an interesting potential area for future research.

Table 2 shows that there was an important shift from the field of civil engineering to environmental sciences in more specialized queries, showing a lack of papers in research areas, such as architecture and civil engineering, that should actually implement evaluation and PIs in the project (a perspective and positioning of the research). Even though the research area is new, the majority of papers in environmental sciences indicate the consolidation of this emerging field, making it even more important to make this topic accessible to architecture and civil engineering.

3.2. Bibliographic Analysis—RQ2

3.2.1. Major Countries or Regions—Results

The most cited papers are listed in Table 3. Six out of ten publications were in the Journal of Cleaner Production, indicating the significant impact of this journal. This journal also had the most papers on the topic, as shown in Table 4. Two of the papers were from 2018, four from 2017, two from 2016, and the remaining two from 2015 and 2012. Figure 2 from Excel shows the increasing number of publications.

Table 3. List of the most cited papers.

Publication Year	Title	Authors	Journal	No. Citations
2018	Critical success factors of sustainable project management in construction: A fuzzy DEMATEL-ANP approach	Mavi, R. K., Standing, C.	Journal of Cleaner production	220
2017	A review on the application of Trombe wall system in buildings	Hu, Z., He, W., Ji, J., Zhang, S.	Renewable and Sustainable Energy Reviews	191
2017	Development of performance criteria for sustainability evaluation of modular versus conventional construction methods	Kamali, M., Hewage, K.	Journal of Cleaner Production	187
2018	A recycled aggregate concrete high-rise building: Structural performance and embodied carbon footprint	Xiao, J., Wang, C., Ding, T., Akbarnezhad, A.	Journal of Cleaner Production	169
2016	Sustainable material selection for construction industry—A hybrid multi criteria decision making approach	Govindan, K., Shankar, K. M., Kannan, D.	Renewable and Sustainable Energy Reviews	168
2015	Improving the CO2 performance of cement, part I: utilizing life-cycle assessment and key performance indicators to assess development within the cement industry	Feiz, R., Ammenberg, J., Baas, L., Eklund, M., Helgstrand, A., Marshall, R.	Journal of Cleaner Production	167
2017	Environmental assessment of green concretes for structural use	Marinković, S. Dragaš, J., Ignjatović, I., Tošić, N.	Journal of Cleaner Production	157
2016	An integrated BIM-based framework for minimizing embodied energy during building design	Shadram, F., Johansson, T. D., Lu, W., Schade, J., Olofsson, T.	Energy and Buildings	150
2017	Life Cycle Assessment for supercritical pulverized coal power plants with post-combustion carbon capture and storage	Petrescu, L., Bonalumi, D., Valenti, G., Cormos, A.-M., Cormos, C.-C.	Journal of Cleaner Production	119
2012	Sustainability performance evaluation in industry by composite sustainability index	Zhou, L., Tokos, H., Krajnc, D., Yang, Y.	Clean Technologies and Environmental Policy	111

Table 4. Most relevant journals by published articles.

Journal	Number of Articles
Journal of Cleaner Production	24
Sustainability	19
Journal of Building Engineering	12
Ecological Indicators	11
Building and Environment	8
Energy and Buildings	8
Buildings	7
Construction and Building Materials	7
Engineering Construction and Architectural Management	6
Science of the Total Environment	6

Figure 2. Documents by publication year.

Bibliographic data was obtained from a 20-year period, 2005–2024, through the Web of Science (WOS) database. The analysis using Biblioshiny [44] provided several conclusions.

This field of research had an annual growth rate of 20.2%, which is very high [44], and covers 155 different journals, reflecting the multidisciplinary nature of the research. The average age of publication was 4.52 years, which also indicates that this is a growing field. The average citations per document was 21.28. A high number of authors and international co-authorships indicated frequent collaboration in the field. The author collaboration network consisted of several distinct clusters with limited connections, suggesting that researchers tend to work within isolated groups.

The Walktrap algorithm identified several communities, representing schools of thought. International collaboration reflected partnerships among researchers from China, the United States, Europe, and Australia. Researchers from China mostly collaborated with those from the United States, the United Kingdom, and Australia. Researchers from the United States mainly collaborated with those from the United Kingdom and China. Researchers from the United Kingdom collaborated with those from the United States and China. Researchers from Europe tended to collaborate with other European researchers. Researchers from Australia collaborated with those from Europe and China.

Authors from China published 146 articles, but only 20.5% with multinational collaboration. Authors from Spain published 19 articles, and those from the United Kingdom published 14. Although Ireland, Lithuania, and Sweden have the highest rates of international collaboration, the most cited authors are from China, as shown in Table 5, while the most cited affiliations are from Universitat Politècnica de Catalunya, as shown in Table 6.

Table 5. Countries with most citations.

Country	Number of Citations
China	2759
United Kingdom	463
Sweden	355
Australia	319
Spain	241
Canada	214
Serbia	200
USA	189
Denmark	185
Romania	126

Table 6. Most cited affiliation.

Affiliation	Number of Articles
Universitat Politècnica de Catalunya	14
Hong Kong Polytechnic University	12
Chinese Academy of Sciences	8
Hohai University	8
South China University of Technology	7
State University System of Florida	7
Universidade de Coimbra	7
Universidade Federal de Santa Catarina (UFSC)	7
University of Hong Kong	7
Dalian University of Technology	6

3.2.2. Keyword Network Visualization—Results

Keyword network visualization in Figure 3 shows that the network was centered around the keywords design, performance, and life-cycle assessment. Different community colors indicate the presence of different sub-topics.

Figure 3. Keyword network visualization.

• Blue Cluster: performance, design, energy, environment, optimization, sustainability assessment, quality, challenges, simulation
• Orange Cluster: energy, buildings, residential buildings, systems
• Red Cluster: consumption
• Purple Cluster: indicators, framework, management, evaluation, China
• Brown Cluster: sustainability impacts, construction, circular economy, innovation
• Green Cluster: assessment, technology, KPIs
• Pink Cluster: BIM
• Gray Cluster: Life Cycle Assessment (LCA).

The bibliographic analysis showed a connection between the keywords:

• Design and Performance: evaluating or optimizing design for improved performance
• Life-cycle Assessment: for assessing the environmental impacts of products, services, or systems for the evaluation of the environmental performance of designs
• Indicators: for measuring performance
• Construction and Sustainability: sustainable practices in the construction industry.

3.2.3. Keyword Frequency—Results

Keyword frequency was calculated based on the authors’ keywords using descriptive statistics in bibliographic software that aggregates the keywords and counts the frequency. The most common keywords, as shown in Table 7 and Figure 4, were performance, indicators, construction, design, life-cycle assessment, and sustainability. The field showed granularity through keywords such as efficiency, emissions, key performance indicators, sustainability assessment, systems, environment, simulation, and construction industry.

Table 7. Keywords by occurrence.

Words	Occurrences
performance	72
indicators	37
construction	36
design	36
life-cycle assessment	35
sustainability	35
framework	32
management	28
model	26
impact	25
energy	20
life cycle assessment	20
industry	19
optimization	19
buildings	15
sustainable construction	15
system	15
efficiency	14
emissions	14
key performance indicators	14
sustainability assessment	14
systems	14
environment	13
simulation	13
construction industry	12

Figure 4. Keywords’ frequency over time.

3.2.4. Citation Bursts and Trend Evaluation—Results

Strategic Map Framework, Figure 5, Table 8, visualizes keyword clusters based on their centrality (relevance) and density (development/maturity):

Figure 5. Thematic map of research keywords by centrality and development.

Table 8. List of research keyword and their frequency.

Cluster	Cluster Label	Keywords
Cluster 1	exposure	exposure (3), index (3), air-pollution (2), areas (2), deep learning (2)
Cluster 2	construction	construction (36), life-cycle assessment (35), energy (20), life cycle assessment (20), optimization (19), buildings (15), efficiency (14), emissions (14), environmental performance (12), concrete (11), decision-making (11), china (10), consumption (9), energy-consumption (9), lca (9), residential buildings (9), energy efficiency (8), life cycle assessment (lca) (8), behavior (7), embodied energy (7), greenhouse-gas emissions (7), environmental assessment (6), environmental impacts (6), generation (6), impact assessment (6), tool (6), building (5), demolition waste (5), durability (5), aggregate (4), assessment lca (4), carbon footprint (4), cement (4), co2 emissions (4), conventional concrete (4), cost (4), demand (4), embodied carbon (4), fiber-reinforced concrete (4), office buildings (4), building-materials (3), building information modelling (3), carbon emissions (3), climate-change (3), comfort (3), construction sector (3), eco-innovation (3), footprint (3), internet (3), mitigation (3), multicriteria decision-making (3), power (3), products (3), recycling (3), thermal insulation (3), uncertainty (3), water (3), ash (2), asphalt (2), biomass gasification (2), carbon (2), coarse aggregate (2), compressive strength (2), concrete structures (2), construction materials (2)
Cluster 3	simulation	simulation (13), assessment (7), classification (6), energy performance (6), temperature (6), thermal comfort (6), machine learning (5), risk (5), ventilation (5), soil (4), air-flow (3), algorithm (3), area (3), cfd (3), climate (3), daylight (3), energy demand (3), monitoring (3), multiobjective optimization (3), sensitivity-analysis (3), support vector machine (3), thermal performance (3), air-quality (2), building energy performance (2)
Cluster 4	metrics	metrics (4), key performance indicator (3), asset management (2), building construction (2), co2 emission (2), comprehensive performance (2), economic-growth (2)
Cluster 5	mechanical-properties	mechanical-properties (6), strength (4), energy consumption (3), fly-ash (3), microstructure (3), slag (3), solid waste (3), alkali activation (2)
Cluster 6	COVID-19	COVID-19 (3), transmission (3), transport (3), droplets (2)
Cluster 7	performance	performance (72), indicators (37), design (36), sustainability (35), framework (32), management (28), model (26), impact (25), industry (19), sustainable construction (15), system (15), key performance indicators (14), sustainability assessment (14), systems (14), environment (13), construction industry (12), project management (12), quality (11), performance indicators (10), challenges (9), circular economy (9), criteria (9), projects (9), bim (8), impacts (8), innovation (8), performance evaluation (8), sustainable development (8), technology (8), analytic hierarchy process (7), life-cycle (7), sustainability indicators (7), benchmarking (6), construction projects (6), critical success factors (6), health (6), infrastructure (6), selection (6), topsis (6), adoption (5), barriers (5), evaluation (5), green building (5), methodology (5), performance measurement (5), perspective (5), prediction (5), satisfaction (5), sector (5), technologies (5), waste (5), ahp (4), assessment tools (4), green (4), hierarchy process (4), implementation (4), information (4), integration (4), models (4), participation (4), perception (4), performance assessment (4), policies (4), risk-assessment (4), risk assessment (4), risk management (4), stakeholders (4), structural equation model (4), success (4), architecture (3), benefits (3), built environment (3), case study (3), circular construction (3), cloud model (3), competitiveness (3), construction safety (3), corporate social responsibility (3), decision (3), drivers (3), empirical-analysis (3), energy management (3), energy savings (3), environmental assessment methods (3), facilities management (3), governance (3), green buildings (3), green construction (3), identification (3), improvement (3), indicator (3), infrastructure projects (3), multi-criteria decision-making (3), post-occupancy evaluation (3), project (3), project-management (3), project success (3), public procurement (3), responsibility (3), sensors (3), shield machine (3), sponge city (3), time (3), waste management (3), 0 (2), assessment framework (2), augmented reality (2), balanced scorecard (2), bim technology (2), building energy efficiency (2), building information modeling (2), building performance (2), building projects (2), composite sustainability index (2), construction-industry (2), construction management (2), construction project (2), corporate social-responsibility (2), corporate sustainability (2), critical project success factors (2), culture (2), delay (2), delphi method (2), dematel (2), determinants (2), diffusion (2), dynamics (2), economy strategies (2)
Cluster 8	eco-efficiency	eco-efficiency (5), data envelopment analysis (4), dea (3), environmental efficiency (3), growth (3), resource efficiency (3), composite indicators (2)
Cluster 9	degradation	degradation (2), digital twin (2)

• Performance—Motor Theme: focused on performance, indicators, design, environmental impact assessment, civil engineering management, and general management engineering
• Construction—Basic Theme: focused on construction, life-cycle assessment, and energy
• Simulation—Basic Theme: focused on simulation, assessment, and classification. New cluster
• Eco-efficiency: focused on efficiency, environmental impacts
• Mechanical-properties: focused on applied research, material properties, building or structural performance
• Metrics: focused on ecological indicators, environment
• COVID-19—Niche Theme: focused on COVID-19 pandemic, transmission, transport
• Exposure—Emerging or Declining Theme: focused on exposure, index, air pollution The older publication dates indicated a declining theme, but newer publications indicate ongoing interest
• Degradation—Emerging or Declining Theme): focused on degradation, digital twin.

Strategic Maps showed how topics were positioned based on their centrality (relevance) and density (development).

The trends showed a shift from geographic focus, growing environmental awareness, the importance of digital transformation, and the diversification and specialization of topics.

The factorial map showed several potential clusters:

• Cluster 1: LCA and Embodied Energy, including: lca, life cycle assessment, embodied energy, emissions
• Cluster 2: Energy Consumption and Building Performance, including: energy-consumption, buildings, simulation, design, life cycle assessment (lca), energy, optimization, concrete
• Cluster 3: Performance and Assessment, including: assessment, system performance indicators, performance, quality, China, impacts
• Cluster 4: Project Management and Sustainable Construction, including: performance evaluation, framework, model indicators, project management, sustainable construction, projects, criteria.

The dynamics of different research topics—trend evaluation, as shown in Table 9 and Figure 6, show that for recent trends (2021–2023), three main keywords are: ‘mechanical-properties’, ‘durability’, and ‘impacts’. In 2021, the trends were construction projects, performance, design, management, construction, sustainability, framework, and indicators, which continued thereafter. Keywords such as life-cycle assessment, key performance indicators, and sustainable development were trends between 2019 and 2021. Keywords including impact assessment, environmental assessment, and sustainability assessment were trends in 2019. Keywords such as life-cycle, consumption, methodology, and building were trends in 2017. Keywords such as energy efficiency, embodied energy, and evaluation were trends in 2015.

Table 9. Trend evaluation.

Period	Motor Themes	Niche Themes	Emerging or Declining Themes	Basic Themes
2005–2010	Hong Kong, Sustainability Assessment	-	-	-
2011–2015	Construction, Energy Efficiency, Performance, Indicators, Sustainability	Participation, Sustainability Assessment, Technologies	Monitoring, Metrics, Key performance indicators	Sustainability
2016–2020	Indicators, Performance	Construction Materials, Environmental Impacts	Thermal comfort, Health, Technology, Sponge City	Sustainability
2021–2024	Construction, Framework, Life-cycle Assessment, Management	Technology, Performance Evaluation, Eco-Efficiency, Digital Twin	Anthropogenic, Analysis, Modeling, Slag, Strength	Behavior, Decision-making
2021–2024	Construction, Framework, Life-cycle Assessment, Management	Technology, Performance Evaluation, Eco-Efficiency, Digital Twin	Anthropogenic, Analysis, Modeling, Slag, Strength	Behavior, Decision-making

Figure 6. Trend topics.

The newer approach focused more on general topics, digital technologies, and materials. Between 2005 and 2010, Hong Kong and Sustainability Assessment appeared. In the 2011–2015 period, topics such as Construction, Key Performance Indicators, Management, and Performance appeared. Performance remained an ongoing topic. In the 2016–2020 period, Life Cycle Assessment (LCA) and Sustainability Assessment appeared. From 2021 to 2024, BIM (Building Information Modelling), Challenges, Concrete, Energy Consumption, Mechanical Properties, Simulation, and Thermology appeared. LCA and performance continued to be ongoing topics.

3.2.5. Bibliographic Analysis—Discussion

The topic is relevant today, as shown by the growing number of papers. It features several stable main themes, such as sustainability, LCA, and performance, while the focus has shifted from themes like Hong Kong, energy efficiency, embodied energy, and evaluation to others, including BIM, challenges, concrete, energy consumption, mechanical properties, and simulation.

At this moment, the motor themes were: construction, framework, life-cycle assessment, management; basic themes: behavior, decision-making; niche themes: technology, performance evaluation, eco-efficiency, digital twin; and emerging or declining themes: anthropogenic, analysis, modeling, slag, strength.

All the major themes—performance, indicators, construction, design, LCA, sustainability, and framework are still important, indicating that general topics related to establishing the framework are still relevant, with LCA being the most recognizable tool. Themes such as simulation, assessment, classification, construction, LCA, and energy are basic themes that are not yet well developed and have room for further growth. Some themes are gaining importance, including digital twins and modeling (which can likely be interpreted as digitalization), as well as the triple orientation: anthropogenic, ecological, and technological. Both basic and emerging themes are expected to increase in importance with the development of new information tools (simulation, assessment, LCA, digital twins, modeling) and a shift in perspective toward architecture and civil engineering topics (construction, technology, classification, environmental aspects, or spheres of sustainability, and energy).

3.3. Topics Related to Environmental Performance, and What Are Some General Overlaps or Gaps (Bibliographic and Content Analysis)—RQ3

3.3.1. Topics Related to Environmental Performance Indicators in Construction Projects, General Overlaps or Gaps—Results

Based on the review of the abstracts and papers, 75 out of 327 papers were found to be unrelated to construction projects. The remaining 252 papers were further analyzed to identify research topics using the SECRAW approach, as shown in Table 10. Literature analysis revealed that some of the most common overlapping topics include design principles, environmental impacts, indicators, prioritization/optimization, energy, surveys of local stakeholders, emissions, life-cycle assessment, circular economy, green building, statistical analysis, and BIM.

Table 10. Identified topics in research area.

Design Principles	Environmental Impacts	Indicators	Prioritization	Energy	Survey of Local Stakeholders	Emissions	LCA	Circular Economy	Green Building	Statistical Analysis	BIM	POE	Climate
147	128	90	64	47	46	43	34	33	27	17	16	9	4

3.3.2. Design Principles

Cai et al. [53] show the importance of solar thermoelectric generator bricks with double phase change materials (STEGB-DPCM) for green buildings. Xu et al. [54] analyze research progress on the mechanical properties of GRAC. Kljajić et al. [55] highlight the potential of heat pumps in district heating, while Amaral et al. [56] examine the relationship between urban morphology, density, forms, open spaces, and energy use in NZEDs. Kerdan et al. [57] show that exergy analysis indicates good energy performance but also lower performance in other aspects for Passivhaus. Escandón et al. [58] and Marović [59] show the vulnerability of social housing to overheating in relation to housing design characteristics such as orientation, area, form ratio, height, window-to-wall ratio, and similar factors.

Dahalan et al. [60] evaluate the characteristics of roads and highways, while Bakos and Schiano-Phan [61] provide an overview of CE indicators and CE evaluation tools for the built environment, define Circular Building Indicators, Circular Campus Guidelines, and benchmarks for project-specific indicators. This is based on the existing ReSOLVE Framework [62]. Cha and Kim [14] provide the PI for residential buildings.

Garay et al. [4] note significant climate impacts from the construction industry, particularly due to greenhouse gas emissions, and provide an evaluation model for wood prefabrication based on material and energy consumption. Willar et al. [20] present evaluation indicators for sustainable construction practices in infrastructure projects. Tam et al. [11] propose measures for noise mitigation, alternative construction plants, and air pollution control at construction sites.

Ge et al. [63] indicate that distributed energy systems (DES)—including photovoltaic panels, ground source heat pumps, gas turbines, absorption heat pumps, and thermal storage tanks—could positively impact the decarbonization of the construction industry. Wilson et al. [64] find that, in addition to traditional environmental and economic indicators, optical and visual performance indicators can be used to enhance the integration of BIPV systems into building designs.

Dahalan et al. [60] highlight the necessity of Environmental Management Plans (EMPs) in road and highway construction projects. Pan and Zhang [65] find that modular construction reduces waste and improves efficiency. Mohammed et al. [66] emphasize deconstructability for environmental sustainability based on design principles such as the use of reusable or recyclable materials, prefabricated assemblies, and minimization of the number and types of building components and composite or complex materials. These principles help define a model [17] for environmental benchmarks in the cement industry to analyze the carbon footprint of aggregates or to compare [67] the sustainability performance of prefabrication and conventional building technologies.

Singh et al. [68] find that during the construction and operational phases, the largest impacts come from the use of stainless steel and electricity consumption for materials production. Ge et al. [67] conclude that prefabrication results in better sustainability during the construction phase, but there are differences in experts’ perceptions—manufacturers rate prefabrication highest for sustainability, while developers and designers rate it lowest. Xu et al. [69] note that previous studies on rural residential buildings have primarily focused on energy efficiency, without addressing other environmental aspects such as acoustics and air quality. In such cases, retrofitting existing buildings must take into consideration the characteristics of each building [70], as layouts can greatly influence the energy, lighting, and ventilation potential of buildings. For large buildings, the efficacy of HVAC systems is also very important. Feiz et al. [71] find that even variations in a material can create significant differences in environmental impacts; for example, different types of cement can produce concrete with varying emissions.

Ruuska and Hakkinen [72] consider Abiotic Depletion Potential (ADP) as the potential for depletion of resources such as land use, energy, and emissions, expressed as the percentage of scarce materials used. They estimate embedded or operational emissions and energy, and note that advanced building systems even use rare earth materials. They also observe significant uncertainties in determining the impact of materials due to the lack of a standardized method for estimating material efficiency.

3.3.3. Environmental Impacts

Environmental impacts are associated with every phase of the construction process, making it difficult to gather the information from all sources in all phases [71]. Selecting appropriate materials is important for efficient use of energy and resources. Therefore, Ruuska and Hakkinen [72] emphasize the impacts during Life Cycle Assessment (LCA) of availability or scarcity of materials, renewable and non-renewable resources, technology, political situation, environmental and economic aspects, procurement, land use, and GDP. They highlight potential impacts including global warming, ozone depletion, soil and water acidification, eutrophication, resource depletion, and land-use effects.

Nonetheless, environmental factors are still considered less important by stakeholders [11,38], especially compared to social aspects [3]. Environmental factors mostly concern the impact of the project on the external environment, while social impacts primarily relate to company operations [12]. There is also difficulty in determining the scope of environmental impacts [56]. Environmental considerations in evaluation are among the developing aspects [23] and are becoming an increasingly important topic, especially as environmental indicators and related policies for reducing pollution gain importance due to health risks for the population [10].

Environmental evaluation has gained renewed interest post-COVID-19 [31], as there is a need for a new approach to project evaluation that places greater emphasis on sustainability requirements, particularly those related to human development, such as improved workforce protection in terms of health, safety, and continuity. Xiahou et al. [32] evaluate the impact of a project addressing these requirements on the population.

The most significant environmental impacts are related to energy and stormwater [26]. Zabalza et al. [8] report the impacts of various materials and find that the use phase (52%) and the production phase (43%) have the greatest environmental impact, while the construction and end-of-life phases each contribute less than 3%. Xu et al. [54] state that the use of geopolymer recycled aggregate concrete (GRAC) can reduce environmental impacts.

Although high densities reduce traffic, there are also negative aspects such as a lack of solar exposure and overpopulation [56]. Equipment breakdown was the most prominent risk factor for environmental impacts, such as water pollution, water consumption, or air pollution [28].

3.3.4. Indicators

Numerous authors provide an overview of indicators presented in various studies and agree on the importance of area-specific characteristics of the indicators [1,3,4,6,9,10,11,13,14,17,18,19,20,21,22,24,34,65,66,67,73,74,75,76]. Some authors find that not only the selection of indicators and the determination of weights are subjective, but also that some indicators influence each other, making the evaluation more complex [24]. For example, thermal and electric load impact each other and the carbon emissions produced in the processes [63].

Dahalan et al. [60] find different correlation patterns between KPIs for different types of projects (roads and highways), while Zhang and Pu [10] find that there are no standard rules for computing indicators, even for air quality, which has been measured for a very long time. This makes comparisons across time and space very difficult or impossible.

Further, Agyekum et al. [6] define ten categories of KPIs: environmental compliance and management, impact on soil and land resources, effect on air quality, noise pollution, light pollution, effect on water quality, water use and conservation, impact on ecology and biodiversity, energy use and conservation, and construction and demolition waste management.

Ugwu and Haupt [73] emphasize the importance of socioeconomic aspects, health, safety, and environmental sustainability. They highlight the African context, stressing the significance of manpower development, management, international collaboration, social dimensions and partnerships, and evaluations. They also note the lack of micro-level design and project evaluation.

Ruuska and Hakkinen [72] note the impacts from transport, on-site land use and materials, surface permeability, biodiversity loss, and noise, and observe that local impacts can differ even for the same material. Kajjoba et al. [22] identify eight groups of indicators (35 in total): land and site ecology, water, energy, materials, indoor environmental quality, management, waste, and economic and social aspects. They further note that environmental and social factors are weighted less by local experts, while the most important considerations are optimization of energy use, advanced design and construction technology, natural ventilation, building operation energy costs, and lighting systems efficiency.

Kljajić et al. [55] identify indicators such as climate change, ozone depletion, terrestrial acidification, freshwater eutrophication, terrestrial ecotoxicity, agricultural land occupation, and others. Owusu-Manu et al. [26] note that the most significant environmental impacts are related to energy and stormwater. Herein, studies explore the expectation of green building to demonstrate environmental ethics, quality of life, and cost savings [77], as well as other important indicators [60], including geological factors, pollution, environmental change, ecological change, and public safety. Zhang and Pu [10] focus more on quality index, water quality index, and soil quality index; Liu and Wang [70] on evaluation aspects of cement production; and Erdenekhuu et al. [28] on resource consumption and pollution. Ugwu and Haupt [73] provide 55 indicators covering environment, economy, and society, while Yu et al. [39] present 31 indicators for eight phases: Initialization, Design and planning, Construction, Monitoring and control, Completion and turnover, Operation, Maintenance, and Demolition. Criteria categorization is based on qualitative categories or percentages.

Oliveira et al. [74] note that although CO₂ data is the most important indicator for emissions, CO₂ databases are incomplete. They mention the development of databases such as the French INIES (base-inies.fr) and the EU Ecoinvent database. Guo et al. [31] define 31 performance indicators, mostly organizational, while environmental results are important for understanding project restrictions. Overall, 25 KPIs are divided into five categories, with the most important being: occupational health, safety, and environment (HSE) goals achieved; relevant regulations and design requirements; technology; environmental protection; and sustainability in environment, society, and economy. Thanu et al. [3] observe that evaluations place high importance on energy efficiency, indoor environmental quality, recycling, recharging, water reuse, site management and sustainability, and materials, but less on transportation, health, well-being, and innovation. Zabalza et al. [9] give 39 KPIs, with 18 major KPIs organized into four groups: geological, pollution, environmental changes, and ecological. Cha and Kim [14] give 27 PIs in eight categories based on literature review, expert surveys, and benchmarking systems. Xiahou et al. [31] offer 18 indicators for impacts on the population, covering both social and environmental aspects.

Gunduz and Abu-Hijleh [32] identify poor site logistics and management, as well as high noise levels, as risks. Garay et al. [4] give 25 indicators for the environmental, social, and economic dimensions of wood products and structures. Willar et al. [20] give organizational indicators for sustainable construction practices, such as the implementation of an environmental management system in construction work, the concept of minimizing construction waste during construction, engagement of sub-contractors and suppliers who support sustainable principles, progress in accordance with contract documents, testing of materials and work results, and the creation of a manual for utilization and maintenance.

Further, Ugwu and Haupt [13] define which assessment method works for each key indicator and its subcategories. Tam et al. [11] give indicators for monitoring construction sites, including environmental site planning, energy consumption, equipment maintenance, air, noise, water, waste pollution control, site environment, regulatory compliance, and auditing capacity. Cha and Kim [15] present 10 assessment criteria with subcriteria for community, environmental impacts on surrounding areas, transport, materials and resources, waste management, energy, water, location, project management, and innovation and technology.

Li et al. [24] give indicators categorized as environment-centric (energy and indoor environmental quality (IEQ)) and human-centric (household satisfaction surveys). Wilson et al. [64] classify PIs into economic, energy, environmental, and optical/visual categories. Environmental PIs measure non-renewable energy consumption and greenhouse gas emissions. Optical/visual PIs assess the appearance and integration of BIPV systems. Yao et al. [42] identify eleven environmental PIs using the F-EPS model.

Dahalan et al. [60] give 18 KPIs for roads and 21 PIs for highways, identifying the most important as dust appearance, clogged drainage, changes in the color of runoff water, excessive cut and fill, changes in the color of bodies of water, increased schedule waste, open burning, excessive noise, traffic accidents on construction sites, and irregular floods. Azapagic [78] provides economic, environmental, social, and integrated PIs for mining and minerals extraction. Zhang and Pu [10] propose the Air Quality Index (AQI), Water Quality Index (WQI), and Soil Quality Index (SQI), based on data from the China National Bureau of Statistics. Pan and Zhang [65] define 11 PIs for concrete and steel modular construction systems in urban environments. Montalban-Domingo et al. [35] use benchmarks from the Global Reporting Initiative (GRI) to normalize the indicators, but note the need for industry-specific indices, while Jiang [67] provides 16 PIs for modular prefabrication.

3.3.5. Tools and Methods—Prioritization and Statistical Analysis, BIM, Life Cycle Analysis (LCA)

Owusu-Manu et al. [26] use statistical analysis, including mean score analysis and one-sample t-test. Ingle and Mahesh [23] apply a multivariate data analysis technique. Dahalan et al. [60] use mean score ranking, factor analysis, and agreement analysis for indicator ranking. Kajjoba et al. [22] use Interrater Agreement (IRA) and observe that expert agreement decreases as more details are considered. Escandon et al. [58] statistically analyze the characteristics of social housing based on the public database. Xu et al. [70] use the fuzzy analytic network process (FANP) to evaluate the greenness of rural residential buildings. Wang and Liu [25] employ the slack-based measure data envelopment analysis (SBM-DEA) model and regression analysis to explore the relationships between two types of environmental regulations and their influence on green construction practices.

Escandon et al. [58] use Parametric Building Simulation Models (PBSM) based on the SLABE method (Simulation-based Large-scale Uncertainty/Sensitivity Analysis of Building Energy Performance) to evaluate the energy efficiency of social housing. Erdenekhuu et al. [28] use a procedure based on risk factors, Monte Carlo simulations combined with the AHP method (for ranking environmental impact indicators), and the EMV approach (to estimate outcomes) to evaluate critical risk factors in construction, while Ingle and Mahesh [23] used the Project Quarterback Rating (PQR) model to assess the impact of construction projects. Feiz et al. [71] use the Weidema–Wesnes method to evaluate the impact of CO₂ in cement production.

Yu et al. [39] developed the Construction Project Sustainability Assessing System (CPSAS) based on three aspects of sustainability. Zhou et al. [40] used the Driver–Pressure–State–Impact–Response (DPSIR) framework to evaluate healthy building technologies. This framework applies the projection pursuit model (PPM), a data mining algorithm based on border collie optimization. Gu et al. [31] identified critical success factors (CSFs) using an expert survey, the AHP method, and structural equation modeling (SEM).

Zhang and Pu [10] use AHP to rank pollutants. Oliveira et al. [74] use MCDA to select building technology based on sustainability requirements. The evaluation is proposed for a partial LCA (from design to use), and then the methods MCDA Smart, Promethee, AHP, Electre, and multi-attribute utility theory (MAUT) are applied.

Azevedo et al. [38] use the MCDA-C method and means-end relationship maps to define indicators. Marović et al. [2] develop the Building Performance Score (BPS) model based on LCA. Thanu et al. [1] define the evaluation model based on LCA, expert opinions, AHP, and Sustainable Assessment Tools (SATs).

Dahalan et al. [9] use an evaluation model based on statistical analysis, agreement analysis, overlap analysis, factor analysis, and fuzzy synthetic evaluation (FSE), combining probability and impact to give 39 complex environmental indicators. Ibrahim et al. [12] give a model based on project management maturity models (PMMMs) and the construction performance index (CPI), combining the iron triangle of time, cost, and quality with non-financial impacts such as safety, environment, technology, and stakeholders.

Xiahou et al. [31] use the Fuzzy Analytical Hierarchy Process (FAHP) to reduce subjectivity in expert weight elaboration. Ugwu and Haupt [13] develop an evaluation model based on the weighted sum model and the MCDA additive utility model, and assess which model—Method A (credit-based scoring system), Method B (scaled scoring), Method C (comparison with a benchmark or other available options), Method D (credit system), or Method E (subjective marking)—performs best for evaluating indicators.

Tam et al. [11] analyze the effectiveness of Environmental Performance Assessment (EPA) using Environmental Operational Indicators (EOIs) and Environmental Performance Indicators (EPIs). Krajangsri and Pongpeng [15] use structural equation modeling (SEM) to evaluate infrastructure project construction. Wen et al. [29] analyze the spatial distribution and innovation-driving factors of construction carbon emissions (CCE) based on Moran’s I index and spatial econometric models. Yao et al. [42] propose a fuzzy-analysis-based scoring (F-EPS) method for assessing contractors’ environmental performance to help identify areas of poor performance and mitigate subjectivity in the evaluation process.

Dahalan et al. [59] use various statistical methods to determine the reliability of the evaluation, such as the Cronbach alpha method, Mean Score Ranking method, Rank Agreement Factor, Overlap Analysis, Mann–Whitney test, Spearman Correlation Analysis, and Factor Analysis. Azapagic [78] develops a model based on the Global Reporting Initiative (GRI) for corporate sustainability reporting and LCA.

Kaklauskas et al. [27] base their evaluation model on COPRAS (Complex Proportional Assessment) and INVAR (Degree of Project Utility and Investment Value Assessments). Zhang and Pu [10] use AHP, factor analysis, and principal component analysis (PCA) to determine detailed quality indexes based on an expert survey. Moyo et al. [18] use various analytical methods: Fuzzy Synthetic Evaluation (FSE), Shapiro–Wilk test, Kruskal–Wallis H test, mean score ranking, normalization value, and factor analysis. Besklubova et al. [34] use confirmatory factor analysis (CFA) to validate their survey. Jiang et al. [67] use projection pursuit (PP) and the Real-Coded Accelerating Genetic Algorithm (RAGA) to define their evaluation model.

Hashemi et al. [79] use a model based on the Triangular Intuitionistic Fuzzy (TIF) Decision-Making Approach, a type of multi-criteria group decision-making method, in which each expert is assigned a risk attitude. Li et al. [36] use principal component analysis (PCA) to evaluate sustainability indicators. Barrak et al. [80] propose a model called ECI-MCDA, based on LCA and MCDA, using the Electre I and Electre TRI methods. Krigsvoll et al. [81] highlight the importance of international standards, such as ISO 14000 and EPDs, in enhancing environmental management. In particular, they note the need to harmonize standards from a performance-based perspective rather than a prescriptive one, to allow for context adaptability.

Liang et al. [82] give an overview of the use of BIM in building life cycles. Mohammed et al. [66] develop a BIM-based Deconstructability Assessment Score (BIM-DAS). Wang and Yuan [76] develop a BIM-based visualized digital construction safety risk model using the FAHP method (fuzzy comprehensive evaluation (FCE) and AHP). Liu and Wang [70] use a BIM-based model to evaluate architectural layouts, environmental livability, safety, durability, health, comfort, and resource conservation. Cohen et al. [83] evaluate environmental PIs (cloud map, wind simulation and pressure, distribution of daytime and nighttime light, noise distribution, and indoor ventilation) for green buildings based on BIM.

Agyekum et al. [6] give an overview of resource consumption in the construction industry based on LCA and argue for the need for a simplified LCA method due to the highly complex nature of construction processes. Some responses to this issue include excluding certain indicators or phases or using qualitative LCA [6,71]. LCA demonstrates [72] the need for longer-lasting materials, which leads to fewer renovations.

Ortiz et al. [16] give a review of an LCA-based evaluation of sustainability in the construction industry. Liang et al. [82] provide an overview of the use of BIM in building life cycles. Kerdan et al. [57] present a combination of ExRET-Opt exergoeconomic analysis and life cycle cost analysis (LCCA). Yu et al. [39] provide an evaluation based on LCA.

Oliveira et al. [74] develop an evaluation model based on LCA and regulations for sustainable performance ISO 21.921-1:2011 [84], using kilograms of CO₂ as the primary indicator for global warming and ozone depletion. Garay et al. [4] give the evaluation of wood production based on LCA and international standards. Zabalza et al. [8] use ENergy Saving through promotion of LIfe Cycle assessment in buildings (ENSLIC) methodology, which is based on LCA. This method is used to evaluate energy savings and environmental impacts, but it is noted to be very complex and uncertain.

3.3.6. Stakeholders Involvement—Expert Opinion, POE—User Opinion

The frequent methodology involves gathering the potential indicators from a literature review and then using a stakeholders survey and statistical analysis or prioritization methods to assign weights [1,2,4,9,10,13,14,15,18,19,20,21,22,23,26,28,31,32,34,39,41,60,65,67,73,78].

The Delphi technique and statistical methods are used to identify agreements and engage experts [22,23,69]. The Likert scale can be used to evaluate expert weights [4,13,20,41,60]. Indicators can be defined by comparing LEED, GRIHA, and IGBC, and then weights are assigned by experts [3]. During the Delphi method, brainstorming and expert consultation can be used [69], as well as public surveys on perceptions of GM and Green living [77].

In Ugwu and Haupt [73], stakeholders agree on health and safety, but there is a lower level of agreement on the weights of environment, economy, project management, and administration.

Post-occupancy evaluation (POE) assesses energy performance, indoor environmental quality, occupant satisfaction, water use, heating, cooling, fire control, humidity, lighting, and design satisfaction. It can be conducted using methods such as occupant satisfaction surveys, focus group meetings, structured interviews, and visual records.

Ref. [83], based on the UK Post-occupancy Review of Buildings and their Engineering (PROBE), investigates energy performance, internal environment, and occupant satisfaction. They use occupant surveys with the Energy Assessment and Reporting Method EARM) and the Office Assessment Method (OAM). The study also highlights a major problem in the building industry: the lack of prototyping and frequent issues during the first year.

Post-occupancy evaluation (POE) of green buildings through household satisfaction surveys finds that most POE studies focus on environmental quality (68.3%), environmental load (25.0%), and economic benefits (6.7%) [24,63]. POE of office buildings in Singapore shows that the benefits of Green Mark are correlated with awareness of green buildings and lifestyle [77]. Sustainability requirements for building components—such as façade durability for waste reduction, product adaptability and durability, maintenance facilities and costs, and functional performance safety—impact POE results [74]. POE of a low-carbon building indicates the importance of real operational data over theoretical models, which tend to exceed the design data [75].

3.3.7. Energy and Emissions

Heat pumps in district heating demonstrate energy savings [55]. Amaral et al. [56] analyze the connection between morphology and energy use in NZEDs. Exergy analysis shows energy use reduction for Passivhaus [57]. Bakos and Schiano-Phan [61] provide guidelines for reducing energy use. Building energy efficiency retrofit (BEER) highlights the importance of retrofitting, particularly for existing and large buildings, which have much higher energy use per area than residential buildings [41]. Technical and electrical equipment significantly impact the environmental performance of buildings, but must be studied in detail as there are big differences between simple and detailed analyses [85]. Halilović and Berković [86] suggest that reintroducing vernacular features would greatly improve environmental impacts, particularly energy use related to production. Sadrolodabaee et al. [87] analyze the potential for reducing energy and environmental impacts of façade panels, especially due to production factors and energy sources. Spudys et al. [88] examine the use of digital twins for energy performance and indicate that the main limitations are related mostly to technology. Yu et al. [89] propose improvements to energy evaluation and certification.

Xu et al. [54] show that GRAC can partially or completely replace cement in concrete and reduce emissions. Guidelines for reducing emissions are provided [61]. Greenhouse gas (GHGs) emissions and energy consumption of a material have a global impact [72]. A holistic analysis of carbon emissions from cement production is presented [17]. Sadrolodabaee et al. [87] analyze the potentials and pitfalls of façade panel production. Spudys et al. [88] highlight the importance of asset rating for controlling emissions. The costs required to significantly reduce emissions from residential buildings are estimated in [90].

3.3.8. Circular Economy

Medina and Fu [91] define a framework for the circular economy (CE) in construction projects that considers production, manufacturing, consumption/operation, waste management, recovery/circularity, and innovation. The framework uses the Inventory of Carbon and Energy Database and design documentation to calculate emissions. It also calculates the Waste Index by comparing linear output flows and circular output flows.

Antwi-Afari et al. [92] identify the main CE indicators (18 at the project level and 20 at the organizational level) through an SLR, and define project-level and organizational-level CE indicators related to input, output, people, and processes. Wang et al. [93] analyze existing literature and drivers of eco-efficiency, eco-effectiveness, and existing methods for CE, then propose a guidelines framework for CE and the Methods Essence Wheel for analyzing circularity across technical, social, business, legislative, economic, innovation, and environmental aspects.

CE indicators and CE evaluation tools for the built environment, as well as definitions of Circular Building Indicators and Circular Campus Guideline and benchmarks, are established for project-specific indicators [61]. This is based on the existing ReSOLVE Framework [62].

Exergy analysis of the passive house retrofit highlights both positive and negative aspects, particularly regarding high costs and a non-comprehensive approach [50]. Lei et al. [94] evaluate the evaluation of CE at the regional level in southwest China using DEA and rank-sum ratio methods, indicating that material demand persists even with slower growth. BIM for deconstruction (BIMfD) implementation and the BIM-based Deconstructability Assessment Score (BIM-DAS) are defined [66].

The evaluation method for the triaxial mechanical performance and environmental impact of multi-recycled concrete (Multi-RAC) required for CE is defined [95]. Geopolymer recycled aggregate concrete (GRAC) shows distinct benefits in substituting cement in concrete [54]. The use of recycled coarse aggregate (RCA) and nylon waste fibers (NWF) in high-performance concrete (HPC) shows great potential [96]. Barrak et al. [80] combine different analyses and circularity indicators (C-indicators) to evaluate CleanTechBlock (which contains recycled material), based on the 10Rs (Refuse, Rethink, Reduce, Reuse, Repair, Refurbish, Remanufacture, Repurpose, Recycle, Recover).

3.3.9. Green Building and Climate

Locally, the most important green construction practices and indicators are energy management, stormwater management, water preservation, and sanitation [26]. Of the green building projects, 74% perform better, 12% perform similarly, and 14% perform worse than non-green counterparts [24]. The key factors in greening rural buildings are the indoor thermal environment, indoor light environment, biogas facilities, and green building materials [69]. POE studies show that the performance of green buildings still does not meet the requirements of the certification process [24]. They also note a lack of adequate training in technologies and the impact of occupant behavior on green building performance.

Both traditional and demand-side environmental regulations positively influence green construction [25]. Digital technologies can support green building in various phases, enabling simulations of water and energy use, solar aspects, carbon emissions, and other elements of green building [82]. The Green Mark (GM) approach needs to be transferred from industry to other users, particularly for features related to social and psychological well-being [77].

Nearly zero-energy district (NZED) analysis shows that aspects such as urban morphology, urban density, building and urban form, and public spaces influence their energy performance [56]. Energy efficiency is also shown to be necessary for the improvement of social housing retrofitting [58].

A solar thermoelectric generator brick with double phase change materials (STEGB-DPCM) can be a significant improvement for green buildings [53]. The mechanical properties of GRAC show notable progress [54]. A heat pump has proven to be very useful in district heating [55]. Exergy analysis indicates that the performance of green buildings is lower than expected [57].

There is an important vulnerability to overheating in social housing [58]. Guidelines for climate-adapted Campus are provided [61].

3.3.10. Topics Related to Environmental Performance Indicators in Construction Projects, General Overlaps or Gaps—Discussion

Topics related to environmental performance indicators in construction projects include design principles, environmental impacts, indicators, prioritization, energy, survey of local stakeholders, emissions, LCA, circular economy, green building, statistical analysis, BIM, POE, and climate.

The common overlaps are:

• design principles, indicators, circular economy, LCA, BIM
• survey of local stakeholders and prioritization
• design principles, circular economy, green building, LCA, BIM
• design principles, green building, POE

There are also several noticeable gaps:

• Lack of data
• Lack of data gathering methods
• Discrepancy between complex PIs and single-topic indicators
• Gaps between single-phase and holistic building process

Additional aspects, not necessarily negative but defining this research area, can be noted. The research area mostly uses complex indicators that require expert evaluations, which are prone to subjectivity. There is significant complexity in related fields, making it difficult to determine how some PIs can be established. There is also complexity in methods—for example, LCA—and a lack of standardization in methodologies. Additionally, there is a lack of connection to the UN SDGs: even though the research field is very broad, there is a gap in research related to the UN SDGs, which are addressed in only six papers.

3.4. The Most Mentioned Environmental PIs (or Groups of) in Construction Industry for Projects and Evaluation of the Projects—RQ4

3.4.1. Environmental PIs (or Groups of) in Construction Industry for Projects and Evaluation of the Projects—Results

Synthetic overview of PIs is given in Appendix A, grouped in Energy, Light, Water, Comfort, Construction site, Resources, Safety, Pollution—air pollution, Pollution—in general, Waste, Transport, Green and Bio, Technology, Soil, Habitats, Economy indicators due to environmental factors or impacts, Social indicators due to environmental factors or that impact them, Building, Land, and Procedures.

The table shows the representation of the indicator groups in the papers in Table 11. It also presents the principal component analysis (PCA) of the indicators in Table 12.

Table 11. Representation of the indicator groups in the papers.

Indicator Groups	Number of Indicators in Total	Number of Papers
Pollution—air pollution	28	33
Energy	23	31
Habitats	19	23
Resources	38	23
Waste	25	22
Social indicators due to environmental factors or that impact them	41	22
Water	14	19
Comfort	9	19
Technology	24	18
Soil	14	16
Building	24	15
Pollution—in general	33	14
Construction site	7	12
Light	6	12
Safety	10	11
Transport	12	10
Green and bio	7	10
Economy indicators due to environmental factors or impacts	10	9
Procedures	11	7
Land	10	5
Climate change	6	4

Table 12. Principal component analysis (PCA) of the indicators.

Factor	Variance/Information	Cluster	Description
PC1	10.60%	Resource Circularity and Waste Management	Recycled content (all types), Storage/collection of recyclables, Construction waste management, Resource reuse
PC2	5.79%	Local Site Disruption and Health Impact	Noise, Water quantity, Habitat destruction, Loss of habitats/feeding grounds, Water pollution, Short-term health
PC3	4.85%	Social Value, Cohesion, and Regulation	Public satisfaction, Stakeholders’ satisfaction, Delivery of social-economic benefits, Maintain social cohesion, Occupational health/safety/environment (HSE) goals achieved, Aesthetic quality
PC4	3.97%	Environmental Ecotoxicity and Acidification	Terrestrial ecotoxicity, Freshwater ecotoxicity, Marine ecotoxicity, Photochemical oxidation, Soil acidification
PC5	3.96%	Building Design and Indoor Environment	Occupant density, Building orientation, Outdoor environment, Window-to-wall ratio, Use of shading devices, Building shape, Building airtightness
PC6	3.82%	Construction Safety and Site Control	Public safety, Road safety hazard, Construction Waste Rate, Traffic accidents on construction site, Site cleanliness.
PC7	3.74%	Water Resource Management and Reuse	Measure of water saved, Water use, Water reuse, Recycled water, Water pollution
PC8	3.50%	Technology and Life Span	Innovations, Green technology/materials, Resource use, Fossil, Resource use, minerals and metals, Durability of structure.
PC9	3.42%	Emission Quantification (Gases)	Emissions, Calcination, Combustion, Electricity
PC10	3.25%	Transport and Site Accessibility	Alternative transportation, Public transport accessibility, Commuting mass transport, Green transport, Local transport, Vehicular access

The PCA shows high fragmentation of the research area, which is confirmed by the low information level of the top category, PC1 (10.60%), and even lower levels for other categories. The dominant indicator group is Resource Circularity, where the indicators are highly inter-correlated. The top 10 categories only connect 46.9% of the indicators. This highlights the need for a comprehensive framework to unify these topics. The next 10 categories contribute an additional 28.16% of the total variance, bringing the cumulative variance to 75.07%. These additional groups include topics such as Process/Input, Local Impact, Process/Compliance, Chemical Impact, Design/Form, Process/Risk, Input/Efficiency, Technology/Durability, Output/Quantification, and Site/Accessibility.

3.4.2. Environmental PIs (or Groups of) in Construction Industry for Projects and Evaluation of the Projects—Discussion

For RQ4, most indicators relate to energy use, resource use, pollution, waste, and water. Many are also concerned with design aspects and site organization. Some indicators are directly related to the environment, while others indicate the impacts of certain aspects (such as energy or water use) on the environment, and others on human lives (such as daylight or indoor air quality). For many factors, social and environmental aspects overlap. Besides those related to pollution, most indicators are complex and require expert evaluation. There are many PIs that are similar but oriented to catch different aspects of the topic, which produces a huge number of non-standardized PIs. Depending on the broader topic of the paper, similar indicators can be formulated, highlighting one aspect more than another, for example, “energy use” or “energy saved”.

Most PIs are complex and therefore difficult to quantify for even a single phase of the life cycle, and even more difficult to analyze for the entire cycle, requiring expert judgment. Other PIs can be used only for specific aspects because they require very detailed data.

There are several groups of PIs: more classical use of resources and waste production-related PIs; PIs related to site organization and, to a lesser extent, design; and PIs concerning user quality. Some PIs are both environmentally and socially related. Although most PIs are socially oriented, resources and pollution—in general, the most widely mentioned are pollution, air pollution, energy, habitats, and resources—show that some groups of PIs are more widely considered. PCA shows a very fragmented area that is less dominated by specific topics. The only group with high interconnectedness is Resource Circularity, which shows maturity. Other PI clusters represent potentially new research areas.

The low level of variance and information in the top 10 clusters further confirms the lack of unifying aspects that could be applied in architectural and civil engineering fields dealing with complex phenomena. The findings also indicate a highly fragmented research area with a lack of data and spanning multiple fields, making it difficult to standardize the architectural and civil engineering approach across countries. This area still relies heavily on expert knowledge, both local and general.

3.5. Connection of PIs and UN SDGs—RQ5

3.5.1. Topics and Indicators Compatible with UN SDGs—Results

Appendix B shows the connection of the PIs to the UNSDGs. The last row represents the % of the coverage of the goals and targets by the PIs. Table 13 shows the contribution of the PIs to the UN SDGs, with the most impacted being SDG 6, and the least impacted being SDG 17.

Table 13. Contribution of the PIs to UN SDGs—synthetic table.

UN SDGs	Contribution to the Goal	%
Goal 6: Ensure availability and sustainable management of water and sanitation for all	Major	71%
Goal 7: Ensure access to affordable, reliable, sustainable and modern energy for all	Moderate	53%
Goal 12: Ensure sustainable consumption and production patterns	Moderate	45%
Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation	Moderate	38%
Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all	Moderate	31%
Goal 11: Make cities and human settlements inclusive, safe, resilient and sustainable	Minor	27%
Goal 15: Protect, restore and promote sustainable use of terrestrial ecosystems…		17%
Goal 16: Promote peaceful and inclusive societies for sustainable development…	Minor	14%
Goal 13: Take urgent action to combat climate change and its impacts	Minor	13%
Goal 3: Ensure healthy lives and promote well-being for all at all ages	Minor	10%
Goal 14: Conserve and sustainably use the oceans, seas and marine resources for sustainable development	Minor	10%
Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture	Minor	8%
Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all	Minor	7%
Goal 10: Reduce inequality within and among countries	-	3%
Goal 1: End poverty in all its forms everywhere	-	0%
Goal 5: Achieve gender equality and empower all women and girls	-	0%
Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development	-	0%

3.5.2. Topics and Indicators Compatible with UN SDGs—Discussion

For RQ5, all indicators were compatible with the UNSDGs, but PIs related to water, energy, general sustainability, and those related to green and circular building contributed most to the UN SDGs, particularly to goals 6: Ensure availability and sustainable management of water and sanitation for all; 7: Ensure access to affordable, reliable, sustainable and modern energy for all; and 12: Ensure sustainable consumption and production patterns. They contributed somewhat less to goals 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all; 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation; 11: Make cities and human settlements inclusive, safe, resilient and sustainable; and 15: Protect, restore and promote sustainable use of terrestrial ecosystems, and very little to the other goals. In particular, the following goals, 1: End poverty in all its forms everywhere, 5: Achieve gender equality and empower all women and girls, 10: Reduce inequality within and among countries, and 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development, were those where the PIs have the least impact.

The single sector-oriented goals had the most coverage, while more complex topics, particularly those related to social goals, were less researched. Two types of gaps were identified: the first relates to the lack of coverage of some goals, and the second concerns gaps in methodologies that require intensive human interpretation and lack quantitative data. All goals still have to be adequately researched, even the more researched ones, as this paper shows.

The limitations are related to the subjectivity of the experts (researchers). When multiple evaluators conduct the assessment, inter-rater reliability can be measured to indicate agreement levels; however, even high agreement does not guarantee the correctness of the evaluation. Additionally, the UN SDGs themselves are open to interpretation and introduce a degree of subjectivity. Another limitation concerns the complexity and interconnectedness of the UN SDGs. Future research on evaluation could explore the role of agreement among different experts, the application of sensitivity analysis, a local interpretation of the UN SDGs, or interrelations between the goals.

4. Conclusions

The construction industry is important for both national and international economies and affects both the natural and built environments. Various scientific disciplines publish research in this field; although civil engineering and environmental sciences are the most prevalent, with the top papers coming from physics and robotics. This may suggest that traditional disciplines have difficulty in keeping pace with the impact of basic or rapidly expanding sciences. The topic is relevant and continues to see a growing number of published papers. While the core subject remains consistent, niche and emerging themes—such as technology, performance evaluation, eco-efficiency, digital twins, and anthropogenic factors—are gaining attention. Notable subtopics include design principles, environmental impacts, indicators, prioritization and optimization, energy, surveys of local stakeholders, emissions, life-cycle assessment, circular economy, green building, statistical analysis, and BIM. The emergent themes identified are Digital Twin, BIM, modeling, and simulation, but also materials and technologies.

Content analysis reveals a trend: the use of multiple statistical and prioritization tools that are well-published but do not provide additional content on the topic. Most indicators are complex and require expert evaluation. There is a gap between simple, measurable indicators and more complex ones, which are usually needed for evaluating complex systems, such as construction projects in challenging environments. This finding confirms the existing literature [12,97]. Similarly, this study shows a high number of non-standardized PIs, reflecting the complexity of the research area. Comparison with the UN SDGs indicates that many more areas could be addressed with PIs, suggesting a direction for further research. As such, this research both follows the existing literature but also differs from it by analyzing the research and not policy or project impacts [50,98,99].

The analyzed topic represents a new and highly active research area with a high citation rate, necessitating a systematic review, particularly from the architectural and civil engineering perspectives. There is a significant gap in the research papers: the most prominent field is environmental science, even though architecture and civil engineering are the disciplines that should implement the analyzed PIs. The next proposed step is to make this topic more relevant and applicable to fields of architecture and civil engineering.

The PIs cover all aspects of sustainability, which are in line with existing research [100,101]. The most common PIs found in this research are in line with existing research [102]. The same is visible for LCA analysis, which was found to be the most scrutinized method [103].

The challenges of the research were in content analysis, where the standard PRISMA approach had to be substituted for a more analytical screening, evaluation, reading, classification, note-taking, and writing (SECRAW). Similarly, the QtoQ Target Mapping based on Multicriteria Decision Analysis and Quantitizing, based on brainstorming by experts on all available data (papers, representations, overlaps, gaps, PCA) was chosen as simpler and direct methods were inadequate to evaluate, even at the basic level, the complex topics (impact of analyzed PIs) in relation to complex environment (UN SDGs).

The limitations of the study include the limited database, the subjectivity of expert evaluation of the topics, and the interpretation of source material content.

Further development could include analyzing sources from other bibliographic databases, using qualitative analysis tools to examine the sources, conducting additional research on the tools and methods that could create more usable and comparable PIs for design phases—particularly in countries with the biggest lack of data—further research on digital twins and BIM technology in construction project evaluation, and the integration of social and other complex themes.