Exploring Carbon Emissions in the Construction Industry: A Review of Accounting Scales, Boundaries, Trends, and Gaps

Abstract

The construction industry, characterized by high energy consumption and carbon emissions, plays a pivotal role in climate change mitigation. This paper employs bibliometric analysis, based on 282 articles from the SCIE and SSCI in the Web of Science spanning 1992–2022, to explore research trends and themes in Carbon Emissions of Construction Industry (CECI). A manual review was conducted to identify challenges and possibilities concerning accounting scales, objects, boundaries, and methods in CECI research. Key findings include (1) temporal and thematic evolution, with a notable increase in research activity since 2015, primarily focusing on energy efficiency, sustainable development, green building technologies, and policy evaluation; (2) scale-specific gaps, as 80.7% of studies are conducted at macro (national/regional) or micro (building/material) levels, while city-scale analyses are significantly underrepresented, with only 13 articles identified; (3) object granularity deficiencies, with 74.8% of studies not distinguishing between building types, resulting in rural residential, educational, and office buildings being significantly underrepresented; (4) system boundary limitations, as few studies account for emissions from building demolition or the disposal and recycling of construction waste, indicating a substantial gap in life-cycle carbon assessments. Furthermore, the predominant reliance on the carbon emission factor method, along with embedded assumptions in accounting processes, presents challenges for improving carbon accounting accuracy. This review synthesizes insights into prevailing research scales, object classifications, system boundaries, and methodological practices, and highlights the urgent need for more granular, lifecycle-based, and methodologically diverse approaches. These findings provide a foundation for advancing CECI research toward more comprehensive, accurate, and context-sensitive carbon assessments in the construction sector.

1. Introduction

Since the industrial revolution, the emission of various types of greenhouse gases, especially CO₂, from human production and life has led to significant changes in the concentration of greenhouse gases in the atmosphere. Climate change caused by the greenhouse effect poses a great challenge to environmental protection and even human survival and development [1]. Construction activities are characterized by high energy consumption [2], and the production of building materials [3], construction processes [4], building operations [5], and the disposal of waste from demolition [6] all contribute significantly to energy use and carbon emissions. The construction industry consumes one-third of the world’s energy demand, and the entire building sector accounts for 39% of CO₂ emissions from energy use [7].

According to the World Cities Report 2022, the urban population accounted for 56% of the total global population in 2021 and is expected to grow to 68% by 2050. The increase in population will inevitably increase the construction of cities, and high construction demand will drive more Carbon Emissions from the Construction Industry (CECI) [8,9]. Against this backdrop, the decarbonization of the construction industry has garnered widespread attention. The methods of literature review, systematically summarizing the research findings of predecessors, deeply reflecting on the current state of carbon emission growth in the construction industry, and exploring effective strategies to curb further increases in its carbon emissions have become hot research directions.

Many scholars have summarized the development of construction technology and proposed a range of carbon emission management measures, from building materials and construction processes to operational energy efficiency, to curb the growth of CECI [10]. In terms of building materials, the use of low-embodied-energy materials has become a common solution [11]. Some scholars, through summarizing material properties, have found that replacing high-carbon components such as cement and aggregates in traditional concrete can effectively reduce material carbon emissions without compromising performance [12]. The continuous progress of alternative binders, new adhesives, and energy-saving production technologies has driven the research and application of zero-carbon concrete [13]. Meanwhile, as an emerging technology, bio-concrete can sequester CO₂ through carbonation, reducing carbon emissions while enhancing concrete strength [14].

Some scholars have focused on construction processes and pointed out that employing BIM technology for decision-making in the initial stage of a project can significantly promote the sustainability of buildings [15]. The use of prefabricated components can reduce embodied carbon emissions by 15.6% and operational carbon emissions by 3.2% [16]. More scholars have focused on building operational efficiency. Chastas reviewed the correlation between energy efficiency levels and carbon emissions in residential buildings [17], while Atmaca assessed the carbon footprint of residential buildings, finding that residents’ daily energy use is a major contributor and needs to be closely managed [18]. Gao reviewed studies on carbon emissions in public buildings, finding that building characteristics, structure, and the application of digital technologies are key focuses in current carbon emission management [19].

The aforementioned studies are mostly centered on carbon reduction strategies and focus on specific subjects. Few studies have systematically reviewed and comprehensively summarized the development of CECI accounting research from the carbon source definition. In fact, the source definition is the foundation for the quantification and assessment of CECI. Some scholars have summarized the research progress in this field through bibliometric analysis or meta-analysis. Lai reviewed the quantification process of CECI and outlined the definition of carbon sources in existing studies from a stage perspective [20]. Wang divided the definition of carbon sources in existing studies of the construction industry into two aspects: stage and scope through bibliometric analysis [21]. To ensure the accuracy of CECI accounting results, it is essential to select appropriate research objects, determine the proper scale, and clarify the accounting boundaries [22].

Although some existing studies have recognized the importance of carbon source definition in CECI research, none have yet provided a comprehensive summary within an integrated framework. This review innovatively categorizes the carbon source definition into three dimensions: research object, scale, and boundary. It systematically reviews the development, current gaps, and limitations of CECI accounting studies. Specifically, Section 2 introduces the data sources and the methods used in the bibliometric and manual reviews. Section 3 presents the results of the bibliometric analysis. Section 4 summarizes the evolution of carbon source definition in CECI accounting based on manual evaluation. Section 5 provides an outlook on potential future research directions in the field. Finally, Section 6 presents the conclusions.

The main contributions of this study are fourfold. (1) It outlines the core research themes and development trends in CECI accounting. (2) It identifies the lack of attention to cities as medium-scale accounting units and emphasizes the importance of urban-scale studies for regional carbon mitigation goals. (3) It highlights the current neglect of end-of-life processes such as demolition and material recycling, underscoring the need to extend accounting boundaries to the full building lifecycle. (4) It points out the methodological limitations of relying solely on emission factor-based approaches and stresses the need for data transparency and diversified accounting methods to advance future research.

2. Literature Searching and Review Method

2.1. The Literature Retrieved

The data sources and retrieval strategies in this study are based on Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) in the WOS Core collection. These two indices are regarded as important data sources for bibliometric research.

Figure 1 shows the process of literature retrieval. Firstly, define the search terms and criteria to be used for retrieving the relevant publications, including the keywords, author names, publication years, and publication types. The publication period of the search literature is set to be 1992–2022, as research on carbon emissions has developed since 1992, when the United Nations Framework Convention on Climate Change was adopted.

To ensure a comprehensive and thematically relevant dataset, a structured search strategy is applied. The search query combined carbon-related terms (“carbon emission*”, “carbon peak”, “carbon neutral*”, “carbon footprint”, “CO₂ emission*”, etc.) with construction-related terms (“building sector”, “construction sector”, “building industry”, “construction industry”). Here, the use of the asterisk (*) serves as a wildcard, allowing for the retrieval of various word forms and derivatives. The search was conducted using the Topic (TS) field, which retrieves records based on matches in the title, abstract, author keywords, and Keywords Plus. In addition, combinations with Title (TI) and Keywords Plus (KP) fields were used to improve specificity and ensure inclusion of high-relevance articles.

Five search terms were constructed according to different related phrases, and, by combining these search terms to simplify the search results, 367 publications were recorded. The search terms are shown in

Table 1. Search term used in WOS Core collection.

No.	Search Term	Publication	Article	Reviews	Other
#1	TS = (“carbon emission*” OR “carbon peak” OR “carbon neutral*” OR “carbon footprint”)	40,944	36,878	3115	951
#2	TS = (“CO2 emission*” OR “CO2 peak” OR “CO2 neutral*” OR “CO2 footprint”)	48,390	45,058	2794	538
#3	TS = (“building sector” OR “construction sector” OR “building industry” OR “construction industry”)	17,842	16,051	1448	343
#4	TI = (“building sector” OR “construction sector” OR “building industry” OR “construction industry”)	2815	2339	217	259
#5	KP = (“building sector” OR “construction sector” OR “building industry” OR “construction industry”)	718	633	83	2
#6	#1 AND #3	977	856	117	4
#7	#2 AND #3	934	821	109	4
#8	#1 AND #4	189	170	17	2
#9	#1 AND #5	89	77	12	0
#10	#2 AND #4	181	162	18	1
#11	#2 AND #5	86	80	6	0
#12	#8 OR #9	265	235	28	2
#13	#10 OR #11	255	230	24	1
#14	#12 OR #13	367	325	39	3

, and the final expressions for #6–#14 are shown in Appendix A,

Table A1. Final expressions of search terms used in the WOS core collection.

No.	Search Term
#1	TS = (“carbon emission*” OR “carbon peak” OR “carbon neutral*” OR “carbon footprint”)
#2	TS = (“CO2 emission*” OR “CO2 peak” OR “CO2 neutral*” OR “CO2 footprint”)
#3	TS = (“building sector” OR “construction sector” OR “building industry” OR “construction industry”)
#4	TI = (“building sector” OR “construction sector” OR “building industry” OR “construction industry”)
#5	KP = (“building sector” OR “construction sector” OR “building industry” OR “construction industry”)
#6	TS = ((“carbon emission*” OR “carbon peak” OR “carbon neutral*” OR “carbon footprint”) AND (“building sector” OR “construction sector” OR “building industry” OR “construction industry”))
#7	TS = ((“CO2 emission*” OR “CO2 peak” OR “CO2 neutral*” OR “CO2 footprint”) AND (“building sector” OR “construction sector” OR “building industry” OR “construction industry”))
#8	TS = (“carbon emission*” OR “carbon peak” OR “carbon neutral*” OR “carbon footprint”) AND TI = (“building sector” OR “construction sector” OR “building industry” OR “construction industry”)
#9	TS = (“carbon emission*” OR “carbon peak” OR “carbon neutral*” OR “carbon footprint”) AND KP = (“building sector” OR “construction sector” OR “building industry” OR “construction industry”)
#10	TS = (“CO2 emission*” OR “CO2 peak” OR “CO2 neutral*” OR “CO2 footprint”) AND TI = (“building sector” OR “construction sector” OR “building industry” OR “construction industry”)
#11	TS = (“CO2 emission*” OR “CO2 peak” OR “CO2 neutral*” OR “CO2 footprint”) AND KP = (“building sector” OR “construction sector” OR “building industry” OR “construction industry”)
#12	((TS = (“carbon emission*” OR “carbon peak” OR “carbon neutral*” OR “carbon footprint”) AND TI = (“building sector” OR “construction sector” OR “building industry” OR “construction industry”)) OR (TS = (“carbon emission*” OR “carbon peak” OR “carbon neutral*” OR “carbon footprint”) AND KP = (“building sector” OR “construction sector” OR “building industry” OR “construction industry”)))
#13	((TS = (“CO2 emission*” OR “CO2 peak” OR “CO2 neutral*” OR “CO2 footprint”) AND TI = (“building sector” OR “construction sector” OR “building industry” OR “construction industry”)) OR (TS = (“CO2 emission*” OR “CO2 peak” OR “CO2 neutral*” OR “CO2 footprint”) AND KP = (“building sector” OR “construction sector” OR “building industry” OR “construction industry”)))
#14	(((TS = (“carbon emission*” OR “carbon peak” OR “carbon neutral*” OR “carbon footprint”) AND TI = (“building sector” OR “construction sector” OR “building industry” OR “construction industry”)) OR (TS = (“carbon emission*” OR “carbon peak” OR “carbon neutral*” OR “carbon footprint”) AND KP = (“building sector” OR “construction sector” OR “building industry” OR “construction industry”))) OR ((TS = (“CO2 emission*” OR “CO2 peak” OR “CO2 neutral*” OR “CO2 footprint”) AND TI = (“building sector” OR “construction sector” OR “building industry” OR “construction industry”)) OR (TS = (“CO2 emission*” OR “CO2 peak” OR “CO2 neutral*” OR “CO2 footprint”) AND KP = (“building sector” OR “construction sector” OR “building industry” OR “construction industry”))))

. Research articles are selected for analysis, as they generally provide higher-quality and more detailed information than other types of publications. Review papers, non-research articles, and non-English literature are excluded. A total of 325 research articles are retrieved from the WOS database, each including metadata such as authors, affiliations, country/region, publication year, source journal, title, abstract, and keywords.

To ensure the relevance of the selected literature, titles and abstracts are manually screened. Articles are excluded based on the following criteria: (1) the study is unrelated to carbon emissions; (2) the study is unrelated to the construction sector; and (3) the study involves multiple sectors without clearly distinguishable data for the construction industry. The screening is conducted sequentially according to these criteria. As a result, 43 articles are excluded, and 282 research articles are included for further analysis.

2.2. Bibliometric Tool

With the development of bibliometrics, many different types of knowledge mapping tools have been developed. VOSviewer, Publish or Perish, CiteSpace, HistCite, etc., are commonly used analysis tools, which have different characteristics and limitations.

Many literature databases have developed their own literature statistics systems relying on the huge literature foundation, such as Scopus, Google Scholar, and WOS. Users can obtain analysis results through simple operations while sub-searching the literature, which is convenient and fast. These web tools can only provide some simple bibliometric analysis, often only classifying the literature by publication date, publication, author, or type. The development of AI technology has also brought convenience to bibliometrics. Several AI technology-based web platforms have developed bibliometric tools. Such as “AMiner” and “wisdom.ai”, which can automatically search the literature and generate analysis charts by simply entering keywords. However, these tools suffer from small literature databases and unclear data sources. There are also platforms that are similar to traditional bibliometric software and use records from literature databases to analyze the literature, such as “bibliometric.com”.

Compared to emerging bibliometric platforms, traditional bibliometric software is complex to operate but has more powerful features. VOSview 1.6.20, which was developed at Leiden University in the Netherlands, has been selected for analyzing the contributions and collaborations of institutions and authors. It performs well in visualizing contributions and collaborations. The CiteSpace software (6.3.R1 Basic), invented by Prof. Chaomei Chen, is characterized by co-occurrence and co-citation analysis, which is advantageous in trend detection. Therefore, this software is used in this chapter for the co-occurrence of keywords and topics and the co-citation analysis of journals and authors.

2.3. The Process of Manual Review

The focus of this paper is on identifying key gaps in CECI research. Therefore, a more detailed manual review is conducted on the retrieved literature. To ensure the accuracy and relevance of the dataset, a manual review is conducted on all retrieved articles. In addition to reviewing titles, abstracts, and keywords, the main text is also examined, when necessary, to determine the study’s primary focus. This process allows for more precise identification and classification of research themes.

To facilitate structured analysis, each article is manually coded along four thematic dimensions: research scale, research object, accounting boundary, and accounting method. These dimensions are selected based on commonly used classifications in the existing CECI literature and are refined through iterative reading of the main text.

The research scale is closely related to the object. The changes in the scale and object of research on CECI can well reflect the development of research in this field. For research scale, studies are classified into national, regional, city, or building levels. Articles without a clearly defined scale are marked as “undefined”. For research objectives, studies are categorized into four types: the whole construction industry (WCI), multiple types of civil buildings (MTCB), a single type of civil building (STCB), and materials and others. STCB includes studies focused on a specific building type, such as residential, educational, or office buildings. If different types of civil buildings are included in the study objectives, they will be classified as MTCB.

The determination of the accounting boundary is the most important part of carbon emission research. Different accounting boundaries for the same research objectives will produce different accounting results. The accounting boundaries of previous studies are reviewed, and the accounting methods are discussed based on them. The accounting boundary is categorized into eight types: Construction (C), Materials (M), Operation (O), Waste (W), Construction & Materials (C&M), Materials & Waste (M&W), Construction, Materials & Operation (C, M&O), and Whole Life Cycle. Ambiguous cases are marked as “Other” or “Not Mentioned”. For accounting methods, studies are classified into four groups: emission factor methods, carbon intensity methods, hybrid methods, and input-output models, depending on how carbon emissions are quantified.

Each article is assigned to one or more categories within these dimensions through a consistent manual coding process. The frequency distributions of these classifications are analyzed statistically to identify dominant themes and research gaps, which are discussed in the subsequent results section.

3. Results of Bibliometric Analysis

3.1. Trends in Publishing

The distribution of publication dates for research articles on CECI provides insights into the pace of development and knowledge accumulation in this field. Based on the retrieved literature, the research on CECI began in 1998 and showed an increasing trend after 2006. Related publications grew rapidly after the Paris Climate Conference in 2015. The Paris Agreement has a huge impact on the energy policies of all countries, and the CECI has gradually become a hot topic.

Figure 2 displays publishing trends in 12 countries that have published more than five papers on the topic. China leads the way with 168 published papers, which is over half of the total. The annual number of published papers in China has been steadily increasing, reflecting the Chinese government’s policy direction. The 13th Five-Year Plan for Controlling Greenhouse Gas Emissions in 2016 and the “Dual Carbon” Target in 2020 have been the driving forces behind this rapid growth. England, the United States, and Australia are also major contributors, with an increasing number of publications. These indicate that CECI research has entered a rapid development phase, particularly driven by major international climate agreements and corresponding policy influence.

3.2. Contribution and Collaboration

The analysis of the author’s contribution and collaboration in CECI research can show the author’s influence, which is conducive to quickly understanding the research situation of CECI. There are 884 authors who contributed to the study of CECI. A total of 13 authors have more than five papers;

Table 2. The top authors with the most published articles in the field of CECI.

Rank	Author	Article	Citations	TLS
1	Cai Weiguang	22	1239	75
2	Ma Minda	12	890	30
3	Du Qiang	10	184	44
4	Chen Jindao	8	387	27
5	Hong Jingke	8	268	33
6	Ren Hong	8	284	33
7	Shen Liyin	8	603	30
8	Huo Tengfei	7	291	33
9	Shi Qian	6	354	19
10	Bai Libiao	5	53	22
11	Feng Wei	5	216	20
12	Lu Yujie	5	237	12
13	Wu Min	5	114	21

lists these authors who made major contributions. Cai Weiguang, a professor at Chongqing University in China, has published 22 articles with a total of 1239 citations. Prof. Cai has not only conducted in-depth research on the CECI but also has a great influence in this field. Prof. Ma Minda, from Lawrence Berkeley National Laboratory, has a high average number of citations, indicating that his research is widely recognized. Prof. Du Qiang from Chang’an University has published 10 articles and made great contributions to the research and development of CECI.

Figure 3 visualizes the total link strength, showing eight large clusters derived from the analysis of 110 authors who published more than two articles, as imported into VOSviewer. The connection between Cluster 1 and Cluster 5 indicates that extensive collaboration already exists in the CECI research area. The largest cluster is led by Prof. Cai Weiguang, and influential scholars, such as Prof. Huo Tengfei, Prof. Ren Hong, and Prof. Feng Wei, are members of the cluster. Ma Minda, another well-published professor, works closely with Prof. Cai and is the core of Cluster 2. Cluster 6, Cluster 7, and Cluster 8 exist independently, with less collaboration with scholars outside the cluster; this means that their research may have some uniqueness.

As shown in Figure 4, Chongqing University, as the core of the largest cluster, ranks first in both the total number of publications and total citations. They have contributed to a total of 43 published papers, accounting for 15.2% of the overall publications, which indicates their outstanding contributions to the field of CECI. Moreover, Chongqing University has established close collaborative relationships with other institutions in the CECI field, as evidenced by a total link strength of 86. Thirteen research institutions have published more than six research papers related to CECI, as shown in

Table 3. The top organizations with the most published articles in the field of CECI.

Rank	Organization	Country/Region	Article	Citations	TLS
1	Chongqing University	China	43	2041	86
2	Hong Kong Polytech University	Hong Kong, China	17	684	35
3	Tsinghua University	China	16	484	23
4	Lawrence Berkeley National Laboratory	USA	14	789	27
5	Tongji University	China	14	598	22
6	Chang’an University	China	12	195	9
7	Southeast University	China	8	253	15
8	Dalian University of Technology	China	7	77	10
9	China Association of Building Energy Efficiency	China	6	553	14
10	Chinese Academy of Sciences	China	6	159	12
11	Hebei University of Technology	China	6	152	10
12	North China Electric Power University	China	6	192	3
13	Tianjin University	China	6	134	7

. Hong Kong Polytech University, Tsinghua University, and Lawrence Berkeley National Laboratory ranked 2–4. They all belong to Cluster 1 and come from different countries or regions, indicating that cooperation in the field of CECI has been widely carried out in different regions. The four institutions ranked 5–8 are the cores of the other four clusters. The four clusters are not closely connected, and the inter-agency cooperation is mainly carried out within the clusters.

The author and institution network analysis highlights a small number of highly influential researchers and organizations dominating the field. While certain clusters exhibit strong internal collaboration, overall global collaboration remains fragmented. Future research would benefit from more cross-institutional and international cooperation to enhance the diversity of perspectives and broaden methodological development.

3.3. Research Trend and Frontier

The use of keywords to understand research key points and frontiers is considered feasible and credible [23]. Co-occurrence analysis was performed using CiteSpace, with keywords proposed by the authors and “Keyword plus” provided by the journals as sources of keywords. Using one year as a slice, the top 10% of keywords appearing in each slice were selected for analysis. A total of 150 keywords were analyzed. As shown in Figure 5, these keywords were divided into 10 clusters, covering areas such as energy policy, life cycle assessment, structural path analysis, building energy consumption, sustainability, energy efficiency, decoupling analysis, supply chain, and the application of artificial intelligence. The larger the keyword node, the higher its burst intensity. Topics such as “performance”, “decomposition analysis”, “life cycle assessment”, “CO₂ emission”, “energy savings”, “emissions mitigation”, and “scenario analysis” have been extensively studied.

Research on carbon accounting in the construction industry has witnessed a growing number of keywords since 2010, falling into four categories: carbon accounting methods, research objects, research purposes, and analysis methods. The methods include “input-output” and “life cycle assessment”, while the research objects encompass “China” and the “construction sector”. The research purposes involve “climate change mitigation” and “sustainability”, and analysis methods encompass “decoupling analysis”, “bp neural network model”, and “decomposition analysis”. These categories reflect the wide interest in carbon accounting research within the construction industry.

From the evolution of keywords over time, carbon accounting research in the construction industry is experiencing a phase of rapid development, with research content gradually shifting from a single-dimensional to a multi-dimensional analysis and an increasingly diverse range of exploration angles. Since 2015, the research focus on energy efficiency, sustainable development, green buildings, and policy evaluation has intensified, reflecting global and national efforts to mitigate carbon emissions in the construction sector. Below is a detailed analysis of these emerging trends:

Energy Efficiency: Hydrogen fuel cell combined heat and power (CHP) systems and smart technologies (such as BIM and digital twins) have become essential tools for enhancing building energy efficiency, optimizing energy use, and reducing carbon footprints.

Sustainable Development: The rise of climate-positive and circular buildings is driving architectural design towards low emissions, material reuse, and waste reduction, helping to minimize the environmental impact of construction.

Green Buildings: The adoption of zero-energy and zero-water technologies, green roofs, and degradable or recyclable materials is further advancing the green building movement, lowering resource consumption and carbon emissions.

Policy Evaluation: Policy supports and interdisciplinary research play a crucial role in optimizing green building policies, offering effective incentives and guidance to steer the construction industry towards a low-carbon future.

Scenario Analysis: Scenario analysis and predictive modeling enable researchers to simulate future carbon emission trends, providing scientific support for policy development.

The keyword analysis further reveals a growing diversification in research themes, with an evolution from single-dimensional to multi-dimensional analytical frameworks. However, there is still a need to deepen methodological integration, especially in the application of artificial intelligence, decoupling analysis, and predictive modeling. In addition, the overlap and segmentation among thematic clusters suggest untapped opportunities for more interdisciplinary synthesis and cross-sectoral dialogue in future CECI research.

These emerging trends illustrate a shift in the construction industry towards integrating advanced technologies and adopting a holistic approach to reduce carbon emissions. Notably, keywords such as “BIM”, “prefabrication”, “renewable energy”, and “low-carbon materials” have surged in frequency since 2015, frequently co-occurring with research themes like “LCA”, “energy efficiency”, and “scenario analysis”. This pattern suggests that technological advancements are not only being studied as research subjects but also enabling new methodological approaches and driving the evolution of CECI research. In parallel, industrial policies such as China’s Dual Carbon strategy, the 14th Five-Year Plan for Building Energy Efficiency, and green building evaluation standards have actively promoted the adoption of these technologies, further reinforcing their presence in academic discourse. A feedback mechanism has thus emerged, wherein policy drives technological deployment, which in turn shapes academic focus, and research outcomes feed back into policy refinement. Recognizing this interplay is essential for understanding how CECI research has evolved into a more integrated, data-informed, and practice-oriented field.

3.4. Research Hotspot

In the academic literature, citations are used to demonstrate the connection between the cited literature and the citing literature, while co-citations show the relationship between citing literatures. The frequency of co-citation not only reflects the relevance of the study but also highlights emerging hot topics.

Table 4. Top 10 most co-cited references.

No.	Cited References	Co-Citation Times	Burst, Strength, Begin-End	Citation Times
1	Lu et al., 2016 [24]	33	2.12, 2018–2022	131
2	Shi et al., 2017 [25]	30	-	122
3	Huang et al., 2018 [26]	30	3.09, 2019–2022	263
4	Tan et al., 2018 [27]	27	-	106
5	Zhou et al., 2018 [28]	26	2.73, 2020–2022	169
6	Lin & Liu, 2015 [29]	25	6.00, 2017–2020	91
7	J. Chen et al., 2017 [30]	24	-	96
8	Hong et al., 2016 [31]	24	1.64, 2016–2017	129
9	Y. Wu et al., 2018 [32]	22	-	134
10	P. Wu et al., 2019 [33]	21	3.91, 2020–2022	66

displays the top 10 co-cited papers in research on CECI, including the time of the co-citation outbreak. Total citations for these papers were obtained from the WOS database, and all 10 papers were published after 2015.

How to find the drivers of CECI is the hottest topic. Lu applied the LMDI method in CECI research, identifying the fact that construction materials were the largest contributor to increased emissions, while energy intensity reduced emissions [24]. Lu’s research provides a valuable reference for future studies and initiates a co-citation burst in 2018. Based on this research, P. Wu introduced the concept of the whole life cycle, suggesting high development density and improved energy efficiency as ways to reduce operational emissions [33]; Y. Zhang et al. incorporated non-combustion processes into LCA and proposed controlling unnecessary construction activities and improving material production efficiency as means to reduce China’s CECI [34]; Li et al. decomposed CECI drivers in different provinces, and strategies for achieving peak CECI in China were proposed [35]. The structural decomposition analysis is another way to find drivers of carbon emissions that has also attracted a lot of attention. By using this method, Shi found that final demand is the largest contributor to CECI growth, and that the high reliance on the construction sector in the Chinese economy is driving the growth of CECI [25]. Building on Shi’s method, Chen argued that the energy sector and the building materials sector are the main contributors to China’s CECI [30], which is also recognized and ranked seventh in Table 4.

How to account for CECI is another hot topic. The use of regional input–output analysis to examine the carbon footprint has attracted wide attention. Huang [26] and Hong [31] accounted for global and China CECI by this method, discovered that China emerges as the largest contributor to CECI, and the cross-regional importation of building materials has led to the central regions of China assuming the carbon emissions for the eastern coastal regions. The use of predictive models to account for carbon emissions from building operations has also gained acceptance. Tan’s bottom-up model considered important variables, such as population, urbanization rate, and building size, and suggested that cross-sectoral synergies are more effective than single-sectoral reductions [27]. Zhou, utilizing the Berkeley Lab’s China 2050 Demand Resource Energy Analysis Model to project building growth rates and building envelope efficiency in China, acknowledges the potential of building energy efficiency technologies, systems, or practices to reduce carbon emissions [28]. However, Zhou also emphasizes that strict policies are essential to address the multiple barriers hindering the implementation of these technologies.

In addition, the decoupling analysis of CECI from economic development [32] and the policy-based carbon reduction potential assessment are also hot topics [29].

Co-citation analysis identifies the most influential studies and research themes in the field. These studies have established methodological foundations for carbon accounting and driver identification. However, the dominance of studies from China and the limited representation of policy-focused comparative studies indicate a need for broader geographical inclusion and more policy-oriented analyses to guide practical decarbonization strategies.

4. Manual Review

According to bibliometric analysis, research on CECI is a vast and complex topic that has led to many related studies. Due to the diverse research objectives, scales, and objects, as well as differences in accounting and analytical methods, a manual review is necessary to explore further and determine the research status, identify gaps, and uncover future research direction in the area of CECI.

4.1. Research Scales and Objects

Carbon emission studies rely on the calculation of energy and emission-related data, and the lack of basic data is an important factor limiting the development of comprehensive and accurate carbon emission accounting. As shown in Figure 6 and Figure 7, the collected literature is categorized separately by research scale and research object; residential, public, commercial, educational, and office buildings are widely studied civil buildings.

(1)

National scale

Exploring the carbon emissions of civil buildings on a national scale is the earliest study. In 1998, Koomey conducted an operational carbon emissions projection for U.S. residential buildings based on U.S. Department of Energy report data for commercial and residential buildings throughout the United States [36]. Up to now, civil buildings are still the mainstream research object, and residential building is the hot spot [37]. Calculating CECI for residential buildings at the national scale to explore the costs and strategies for carbon reduction has long been a focus [38,39]. In recent years, what strategy to adopt and how to develop a reasonable carbon reduction roadmap have become hot topics [40].

With the focus on climate issues, other types of civil buildings have also been studied at a national scale, with a focus on energy efficiency and improvement strategies [5,41]. Ma’s team focused on the drivers of carbon emissions from the operation of public buildings [42], commercial buildings [43,44] in China based on the CMBECSS 2.0 database, and compared the differences between China and the USA [45].

The WCI is the main object of national-scale research. In the early days, researchers were focused on the impact of patents, policies, behaviors, standards, and materials on the construction industry [46,47], without accounting for the energy consumption or carbon emissions of the WCI. After Kucukvar and Tatari introduced input–output models to assess the sustainability of the construction industry [48], major carbon-emitting countries and even the globe’s CECI were widely carried out [49,50]. To date, the exploration of emission reduction pathways at the national scale [49,51] and carbon transfer between countries [52] remains a hot topic.

(2)

Regional scale

Research at the regional scale began in 2006, and its development history is similar to that of the national scale. Initially focusing on MTCB, Johansson conducted a study of carbon emissions from electricity and primary energy demand for heating residential and office buildings in southern Sweden [53,54]. As research progressed and data became publicly available, STCB studies were conducted. Kahn examined commercial building electricity carbon emissions in the western United States [55], and Ma studied the carbon intensity and operational carbon transition of commercial buildings in the top five city clusters in China [56,57]. The regional scale WCI carbon emissions study started in 2017. Hu compared the CECI performance by states in Australia [58]. Li accounted for China’s provincial CECI and predicted carbon peaking [59]. Accounting for and comparing carbon emissions and influencing factors across Chinese provinces has been widely explored [60].

Compared with the national scale, the study of civil buildings at the regional scale is carried out later. The reason for this is the non-disclosure and lack of data. Kahn’s research was made possible by a partnership with the power company [55]. Regional scale CECI research in China began to break out after 2016, thanks to the publication of China Building Energy Consumption Study Report, which proposed a series of data processing methods to remedy the problems of changing caliber, missing indicators, and incomplete data of statistical yearbooks [61]. Since then, research on energy consumption and carbon emissions of civil buildings at the regional scale has been better carried out [62].

(3)

City scale

Only a few studies have examined CECI at the city scale. Hung quantifies WCI carbon emissions in Hong Kong, China [63]. Kamei assesses and predicts city transformations in Tokyo, Japan, where carbon emissions from buildings are an important indicator [64]. Balali assesses passive measures to reduce energy consumption in buildings using Shiraz, Iran, as an example [65]. Residential buildings and commercial buildings are popular research objects. Ma et al. evaluated the CECI of commercial buildings in four Chinese municipalities, Beijing, Shanghai, Chongqing, and Tianjin [56]. Esch-sur-alzette is the second most populous city in Luxembourg, and Mastrucci assessed the potential environmental impact of the renovation of residential buildings in this city [66].

These studies have been conducted for large cities or cities with special status, and few studies have investigated ordinary cities. City-scale CECI has not yet been in-depth. This is caused by the difficulty of obtaining municipal-level data, for which Shan proposed a method to estimate the city-level through provincial-level data [67]. Zhao applied this method to accounting for CECI in Hangzhou, China [22]. Some scholars attempted to address the problem using a downscaling approach, estimating city-scale carbon emissions from provincial energy data and gridded socioeconomic parameters [68].

(4)

Building scale

The study of building scale has been a hot topic of research [69]. Compared to other scales, building scale research has been widely studied due to the single object and the fact that complete data can often be obtained directly from the builder, user, etc.

These studies have usually focused on the building itself, without extending the results to the construction sector and without using “construction sector” as a keyword. In the literature retrieved in this paper, such studies are conducted from the perspective of an STCB, using a bottom-up approach to carbon accounting by analyzing the energy, material demand, and C&D waste output of a specific building. Atmaca conducted a carbon emission assessment of two residential buildings in Turkey based on construction drawings, energy consumption data, etc., provided by the construction company [70]. Huang used a life-cycle assessment of Fuzhou University’s dormitory buildings based on tender information, utility bills, and maintenance reports provided by the university, with carbon emissions as one of the indicators [71].

It is now the mainstream to conduct studies on the carbon emissions of the WCI at a large scale, like national or regional. Micro-scale carbon emission accounting with case buildings as the research object has also received extensive attention. In contrast, city-scale studies at the meso-level are limited, and limited to a few cities, leaving a lack of basis for assessing the development of the city-scale construction industry and formulating policies. As an important implementation unit for the carbon target, it has become imperative for cities to strengthen the accounting of CECI at the city scale.

4.2. Accounting Boundaries and Methods

Accounting boundaries and methods for carbon emissions from the construction industry often determine each other. As shown in Figure 8, the accounting boundaries of previous studies are reviewed, and the accounting methods are discussed based on them. Among the single-phase boundaries, building operation was the most frequently used, followed by construction and materials, accounting for 29.6%, 9.1%, and 9.5% of the total publications, respectively. Only four studies focused on the demolition phase, indicating a research gap in carbon emissions from building demolition and waste reuse. In addition, some studies accounted for carbon emissions in multiple phases, with C&M and whole life cycle being the most common, accounting for 28.9% and 6.3% of the total publications, respectively.

Building operations serve as the largest contributor to carbon emissions throughout the entire lifecycle of a building [59], and are a key focus for carbon reduction efforts. Carbon emissions during this stage primarily originate from daily energy consumption by users to maintain a comfortable living environment and support household and work-related activities. In studies with detailed energy consumption data, the carbon emission factor method is the most commonly used accounting method [72], as shown in Formula (1). Secondary energy sources, such as electricity and heating, as well as primary energy sources, like natural gas and liquefied petroleum gas, are the primary forms of energy consumed. The carbon emission factors for secondary energy are typically sourced from energy supply sectors or government data [73], while the emission factors for primary energy are calculated using Formula (2).

$$CE={\sum }_1^n{Q}_q\cdot {EF}_q$$

(1)

$$EF=V_c·V_u·R_c·44/12$$

(2)

where CE denotes carbon emission, Q_q denotes the energy q consumption, EF_q denotes the carbon emission factor of energy q, V_c denotes low heating values, V_u denotes carbon content per unit of heating value, R_c denotes the rate of carbon oxidation, and 44/12 denotes the transformation coefficient from carbon to CO₂.

When energy consumption data for building operations are missing or when carbon emissions need to be predicted for a yet-to-be-built structure, the carbon emission intensity method is often employed. As shown in Formula (3), this method uses floor area and carbon emission intensity to estimate the CECI of building operations [74]. Carbon emission intensity is typically obtained from historical data or similar studies. Tan used the heating energy consumption data in the statistical yearbook to measure carbon emission intensity, and to account for carbon emissions and reduction potential [27]. Ma uses the Kaya equation and LMDI method to measure the carbon intensity of buildings to account for the operational carbon emissions of commercial buildings in China [43].

$$CE=FA\cdot {CI}_{area}$$

(3)

where FA denotes floor area and CI_area denotes the carbon emission intensity.

Using software or models to predict operational carbon emissions is another common approach. The incorporation of these tools allows for the consideration of more variables in the accounting process, thereby obtaining carbon emission data with higher resolution. Cellura uses TRNSYS to model buildings and simulate the impact of climate change on building operating energy [69]. Satre-Meloy uses Scout, an open-source software program developed by the U.S. Department of Energy, to estimate carbon emissions [75]. This program provides hourly data and is widely used. In addition, the C³IAM/NET-Building mode [76], AIM/Enduse model [77], etc., are also applied to operational carbon accounting.

The production process of building materials is considered the most carbon-intensive stage in the whole lifecycle of buildings, due to the significant emissions released during raw material extraction, manufacturing, transportation, and other related processes. Carbon emission accounting with building materials as the boundary often relies on the carbon emission intensity method. As shown in Formula (4), the total carbon emissions of building materials are calculated by material consumption and material carbon intensity. Material carbon intensity data are often obtained from manufacturers or previous studies. The life cycle assessment method, which splits the carbon emission process of materials into production, transportation, and installation processes, is a common method in studies that use materials as the accounting boundary [78]. Relying on this method, prediction models for building materials carbon emission have also been developed and applied. These models predict the carbon emissions of building materials by calculating the demand for those materials [46].

$$CE={\sum }_1^n{M}_i\cdot {CI}_i$$

(4)

where M_i denotes demand for building materials i and CI_i denotes the carbon emission intensity of building materials i.

The construction of buildings is an extremely broad activity, encompassing a series of engineering tasks ranging from site preparation, foundation construction, main structure construction, to interior decoration and equipment installation [74]. During these activities, carbon emissions primarily originate from the consumption of fossil fuels such as diesel and gasoline by heavy machinery and transport vehicles, as well as from the electricity consumption of some smaller equipment [79]. For the purpose of construction cost accounting, the construction party will meticulously record energy consumption [80]. Consequently, the process of carbon emission accounting with construction as the boundary is straightforward, often directly adopting the carbon emission factor method. The accounting results are often used to analyze the relationship between the construction industry and social and economic development [81].

The relationship between building materials and construction is closely intertwined, as the process of material use is essentially the process of building construction. Some scholars combine these two boundaries and calculate carbon emissions from both materials and construction together, calling it carbon emissions of the building materialization process, or C&M boundary carbon emissions. As shown in Formula (5), some studies calculate construction and materials boundary emissions separately, then sum them to obtain C&M boundary emissions.

Based on this approach, some studies have considered the material reuse, as in Formula (6). Some scholars refer to this approach as the simplified LCA method [32], stating it includes the carbon emissions of C, M&W boundaries. However, this paper classifies these calculations as C&M, since this method only deducts carbon emissions from reused materials and does not consider emissions from waste treatment and material recycling.

$$CE={\sum }_1^n{Q}_q\cdot {EF}_q+{\sum }_1^n{M}_i\cdot {CI}_i$$

(5)

$$CE={\sum }_1^n{Q}_q\cdot {EF}_q+{\sum }_1^n{M}_i\cdot {CI}_i\cdot (1-{\epsilon }_i)$$

(6)

where ε_i denotes the reuse rate of material i.

In recent years, input–output models have increasingly been applied in the field of carbon emissions. As shown in Formula (7), this method establishes the relationship between demand in the construction industry and the supply from related sectors, calculating the carbon emissions from material, energy, and other sectors driven by construction demand [22]. These emissions are typically referred to as indirect CECI. Adding the fossil energy carbon emissions from the construction site results in the C&M boundary carbon emissions [82]. Some studies further include operational carbon emissions on top of the C&M boundary [33], forming the C, M&O boundary analysis.

$${CE}_I={\sum }_1^n(C_{a,b}/X_{a,b})\cdot (X\cdot k_{a,b})$$

(7)

where C_I denotes indirect carbon emission, C_a,b denotes the direct carbon emissions of sector a in region b, X_a,b denotes the total output of sector a in region b, C_a,b/X_a,b represents carbon emission factor, X denotes the total output of the construction sector, and k_a,b denotes the construction industry’s complete consumption coefficient to sector a in region b.

While input–output models provide a macro-level understanding of indirect carbon emissions, they are limited by low data granularity and sectoral aggregation. The IO tables used are often based on national or regional economic statistics, which lack the spatial, temporal, and technological detail required for building-level analysis. In addition, the use of averaged emission factors across sectors introduces uncertainties and may lead to biased results, particularly when comparing different project types or locations.

Studies that use the whole life cycle of buildings as the accounting boundary are the most typical. These studies usually use LCA combined with carbon emission factor method to divide the whole life cycle of a building from “cradle to death” into several stages and obtain the whole life cycle carbon emissions of a building by accounting for carbon emissions in each stage and summing them up, such as Formula (8). This approach is often referred to as a process-based method. Evangelista divided the whole building life cycle into pre-operational phase, operational phase, and post-operational phase, to account for the carbon performance of a typical residential building in Brazil [83]. Huang evaluated the environmental costs of campus buildings in China by accounting for five phases [71]. Based on process-based LCA methods, several CECI accounting tools have been developed, and it has become a popular method to quickly calculate CECI with the help of accounting tools [84].

$$CE={\sum }_1^m{CE}^s={\sum }_1^m{\sum }_1^n{Q}_q\cdot {EF}_q$$

(8)

where CE^s denotes carbon emission from stages.

Hybrid LCA methods that combine different approaches to accounting for carbon emissions have also been used in some studies [85]. Zhang employed the input–output method to account for carbon emissions in the construction and demolition phases, and the carbon emission factor method to account for carbon emissions in the operation phase [86]. This hybrid approach is sometimes referred to as input–output LCA. Although hybrid LCA attempts to balance the detail of process-based LCA with the comprehensiveness of input-output models, it still faces critical challenges. The integration of data from heterogeneous sources can result in inconsistencies in emission boundaries, units, or system assumptions. Moreover, hybrid methods partially inherit the data quality limitations of IO models, including coarse sector classifications and outdated statistical inputs. These issues reduce the comparability and reliability of hybrid LCA outcomes, especially in cross-regional or cross-sector analyses.

However, as studies use Waste as the accounting boundary, which is still in the emerging stage [87], accounting for carbon emissions in the construction industry over the whole life cycle has some limitations. Some studies exclude the disposal of C&D waste from the boundary, calculating only the carbon emissions from the demolition activity [88]. Others assume all C&D waste is disposed of in landfills, overlooking the carbon reduction potential of material reuse [89]. This is largely due to the lack of a well-developed accounting framework. In the limited research available, process-based LCA methods are often used, calculating carbon emissions by dividing waste generation, recycling, and final disposal into separate stages to assess energy consumption. The disposal of concrete and metals is a key focus [90]. Some studies also consider both materials and C&D waste, using an emission factor approach to calculate carbon emissions from production, transportation, disposal, and recycling of materials based on material recycling concepts [91].

The determination of accounting boundaries is the basis of carbon emission studies, and the standardization of the boundary is particularly important. In single-phase boundary studies, the calculation process and methods are simple, with carbon emission factor methods and carbon intensity methods being commonly used. For multiple phase boundary studies, the accounting boundary is complicated, and the carbon emission accounting is usually performed by mixing multiple methods or summing up after accounting in phases.

Research on CECI is still in its early stages, and several issues have been highlighted by previous studies. First, there is a degree of ambiguity in boundary definitions, as building demolition, waste disposal, and recycling are often left out [92]. Accounting methods are also still uncertain; different accounting methods for the same boundary often yield different results [93]. Moreover, accounting methods are highly dependent on carbon emission factors or carbon intensity factors. These data are typically sourced from suppliers or from previous studies based on individual building or project measurements. Using data from different sources will likely lead to biased accounting results, making it difficult to compare data between different regions, organizations, and projects.

Although national- and building-scale studies dominate current CECI research, our analysis reveals a significant lack of studies at the city scale. This gap is critical because cities are often the key operational units for implementing carbon reduction targets, such as urban carbon peaking and low-carbon planning initiatives. Without reliable city-scale emission accounting models and data, urban policymakers may face difficulties in designing evidence-based interventions. Similarly, the limited research on specific building types may hinder the development of targeted policies for schools, offices, or rural residential buildings. These findings suggest a misalignment between academic research and policy needs, highlighting the importance of future studies that more directly support the formulation and evaluation of low-carbon policies.

5. Future Research in CECI

This study reviews the literature on CECI from 1992 to 2022, summarizing it from the perspectives of study subjects, scales, accounting methods, and boundaries, with a view to providing directions for CECI research. The literature search was conducted through WOS and does not cover all studies completed.

While current research primarily focuses on macro and micro scales, there is a significant gap at the city scale. Cities are the main spaces of population concentration [94,95] and production activities [96], and they are also areas of concentrated resource consumption [97] and environmental load [98,99]. Studies have demonstrated the inequity in carbon emissions between cities due to transboundary CO₂ flows [100]. With environmental policies tightening in more developed cities, high-emission industries often relocate to neighboring cities. Differences in energy technology and industrial standards in these areas may further increase overall emissions [22]. Policymakers should consider city-scale perspectives when developing policies, identifying emission sources and locations, revealing intercity carbon flows, and uncovering the driving forces of carbon emissions to prevent undesirable carbon leakage.

In contrast, there are positive examples, such as Tianjin Eco-City, which integrated various boundary-based carbon reduction strategies from its initial planning stages. These strategies include the use of energy-efficient building materials, prefabrication techniques during construction, and a smart energy management network during operation [101]. In particular, the complementary relationship between intercity energy supply and carbon reduction has enabled an annual reduction of 100,000 tons of CO₂ emissions solely through green electricity trading with surrounding areas [102].

Residential buildings have been fully explored from the perspective of carbon reduction cost [38], technology application [103], and regional differences [104], and the accounting boundary has covered all stages of the whole life cycle [84]. However, these studies do not distinguish between urban and rural areas or take urban dwellings as the subject of study [105]. Rural housing and urban housing have differences in structure, materials, thermal performance, etc., and there is a large stock of rural housing [106]. Research on commercial buildings has also been carried out in depth [107]. Ma has conducted carbon accounting, driving force analysis, carbon emission reduction prediction, and economic decoupling analysis for commercial buildings at national, regional, and city scales, respectively [57]. This research demonstrated that China’s series of building energy-saving efforts have made substantial contributions to carbon reduction in commercial buildings, suggesting that these efforts should be further implemented [43,44].

Fewer studies have been conducted for office buildings and educational buildings. With the development of urbanization, the demand for office buildings will increase. Educational buildings are different from other civil buildings in terms of indoor environment demand and usage behavior due to their special characteristics [108]. With the strengthening of carbon emission control and energy conservation needs [109], further segmentation of civil building types in research will help stimulate technological innovation and promote technological development. Future research within CECI should focus on the carbon emissions of rural residential buildings, educational buildings, and office buildings, using modeling to analyze carbon characteristics by building type. This approach would support the development of more tailored carbon reduction strategies.

In terms of accounting boundaries for carbon emissions, the material, construction, and operational boundaries have been extensively studied, while studies of the demolition phase are often conducted through assumptions. One study estimates that energy consumption during demolition accounts for only 9% of the construction phase [86], another estimates approximately 90% [110], and yet another suggests that demolition represents just 0.2% of a building’s total lifecycle primary energy consumption [70]. The substantial disparity between the estimated percentages of energy consumption during the demolition phase of a building among various studies greatly exacerbates the level of ambiguity in carbon accounting. The carbon emissions from waste disposal and material recycling are often neglected [111]. This reveals a gap in the research on carbon emissions from building demolition and waste disposal processes. However, a study from Kitakyushu, Japan, demonstrated that comprehensive recycling of construction waste could provide up to 6.8% carbon reduction potential for the entire construction industry [87]. Given the large number of old buildings currently facing demolition or renovation, and the negative environmental impact of solid waste landfills [7,112], there is an urgent need to supplement recycling of building demolition and construction waste within the CECI study’s accounting boundary.

In addition, the research on CECI also suffers from a deficiency of accounting methods. The standardization of carbon emission research methods in the construction industry is low, and a set of reliable and scientific assessment methods and index systems cannot be formed to comprehensively assess CECI. Current accounting methods rely heavily on the carbon emission factor method, the LCA method, the input–output method, and the carbon intensity method, all of which are accounting methods based on the carbon emission factor method. Accounting often uses previously measured carbon emission factors, which do not accurately evaluate the status of carbon emissions and demonstrate the differences between building projects. It will be a major issue to introduce methods such as the monitoring method [113] and mass balance method [114,115], which have been fully applied in other disciplines, into the carbon accounting of the construction industry and to establish a standard and reliable carbon accounting system. At the same time, local policy assessment, knowledge sharing, and data standardization should be strengthened, municipal policies should be assessed, regional best practices should be adopted, and an open and standardized database of emission factors should be created in order to improve the accuracy of carbon accounting and to enhance cooperation on carbon emission reduction.

Given the identified gaps, future research should focus on expanding the scope of city-scale studies to better represent the complex carbon dynamics across cities. This could involve using spatial downscaling techniques or multi-source data integration to account for municipal-level emissions more accurately. For underrepresented building types, such as educational and office buildings, further investigation can leverage BIM to analyze specific energy use patterns and materials. Addressing the demolition phase in the lifecycle requires process-based LCA methods combined with waste recycling assessments to capture carbon flows in construction waste management effectively. Integrating these methodologies can enrich the current CECI framework and enhance accounting accuracy across diverse building categories and stages.

6. Conclusions

The study of CECI is a complex process. As one of the main contributors to carbon emissions, reducing carbon emissions throughout the whole lifecycle of buildings has become an urgent and practical issue. In this study, we conducted a bibliometric analysis and manual review of 282 previous studies on CECI. The aim was to understand the challenges and possibilities in this field. The focus on carbon emission accounting objects, scales, and boundaries is the innovation point of this paper.

The bibliometric analysis revealed that the research system on CECI is in a stage of vigorous development. Core keywords were identified, including “performance”, “decomposition analysis”, “life cycle assessment”, “driving forces”, “embodied carbon”, and so on, which can be classified into the following categories: carbon accounting methods or systems, research objects or accounting boundaries, research purposes, and carbon emissions analysis methods.

Through manual review, it was found that (1) large and small scales of research objects, such as nations or buildings, have been emphasized in carbon emission research, while medium-scale research, represented by cities, has been neglected. Cities are important units for implementing carbon caps and carbon neutrality. Conducting carbon emission accounting at the city scale will be a key focus of future research. (2) Civil buildings have been widely studied. Different types of civil buildings have significant differences in carbon emissions. One future research direction for CECI is to investigate how to specifically conduct carbon emissions research on rural residential, office, and educational buildings. (3) The material production, construction, and operation of buildings are currently the main accounting boundaries used in carbon emission research. Processes such as building demolition and waste recycling have not been considered by most studies and represent research gaps that need to be urgently addressed. (4) The carbon accounting methods used in current research are single and highly dependent on carbon emission factor methods. Developing or introducing other methods will be a major challenge in the future.

It is worth noting that data are an important factor limiting the development of research on CECI. The relevant data sources are relatively limited, and the evaluation standards are not uniform, which makes it difficult to assess the level of CECI comprehensively and accurately. The publicization and standardization of carbon emission and energy data will greatly promote the development of research on CECI.

This study also has some limitations. The search strategy was designed to focus on carbon emissions accounting, using keywords such as “carbon emission”, “carbon footprint”, and “CO₂ emission”. While this ensures thematic consistency, it may exclude relevant literature using broader terms, like “NetZero”, “decarbonization”, or “low carbon”, which could reflect related but more conceptual or policy-driven perspectives. Future research may consider expanding the keyword scope or incorporating multi-level search strategies to include a wider range of carbon reduction studies while maintaining methodological focus.

Research on CECI is an important and complex field that requires interdisciplinary and comprehensive research approaches and methods. This review helps to deepen our understanding of the status and future directions of research on CECI, providing a reference and guidance for CECI research.