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Review

Theoretical Framework for Carbon Trading in Construction Industry: A PROMISE Framework and System Dynamics (SD) Causal Loop Diagram (CLD) Approach

by
Augustine Senanu Komla Kukah
*,
Xiaohua Jin
,
Robert Osei-Kyei
and
Srinath Perera
Centre for Smart Modern Construction, School of Engineering Design & Built Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW 2751, Australia
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(22), 10342; https://doi.org/10.3390/su172210342
Submission received: 20 August 2025 / Revised: 25 October 2025 / Accepted: 10 November 2025 / Published: 19 November 2025
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Abstract

Carbon emissions trading from past studies has been recommended as effective in minimizing future levels of carbon emissions. The aim of this paper is to develop a theoretical framework for a construction industry carbon trading system by identifying the categorizations in the system and their influences. The theoretical framework in this study was developed using the PROMISE Framework. PROMISE is an acronym representing Personal, Relational, Organizational, Market, Institutional, Social, and Environmental. The Scopus database was used in the selection of articles. Using the System Dynamics (SD) Causal Loop Diagram (CLD) approach, the positive and negative influences among the variables in the seven categories were evaluated and illustrated. This study is significant and provides a foundation for future researchers to develop conceptual frameworks and models for carbon mitigation strategies. For policy makers, the proposed carbon trading framework assists in evaluating the key legal, economic, environmental, and political policies that can improve carbon trading projects in the built environment. When policy makers place significant emphasis on the influences identified in this study, it will contribute to them supporting regulations and policies that effectively mitigate these emissions.

1. Introduction

Carbon trading has been identified to be a significant solution in the fight against climate change through reducing the quantity of greenhouse gas emissions [1]. Carbon trading is both a policy-based and market-based instrument that is critical in the attainment of carbon neutrality targets and achieving net zero carbon [2]. Governments and other major stakeholders are the active participants in sustainability projects through carbon trading. The main aim of carbon emissions trading is to assign a price to carbon, and through market forces, transform these emissions into paid factor of production [3]. Carbon emissions trading policies have led carbon markets to receive increased attention as a solution to addressing climate change. The construction industry is a large contributor and source of global carbon emissions, and it faces constraints in meeting its carbon emissions reduction targets [4,5,6,7]. Application of carbon trading and carbon trading policies are expected to help the construction industry achieve its carbon mitigation targets [8].
Carbon emissions trading offers a global response in the fight against climate change [9]. Since it allows for offsets to be used, this leads to low compliance costs and helps jurisdictions in fighting climate change [10]. The Paris Agreement makes provisions for countries to work together in minimizing greenhouse gas emissions, and carbon trading is a market-based instrument that facilitates this [11]. Another important aspect of carbon trading is its ability to provide incentives in reducing emissions [12].
Carbon trading has been applied in different countries and sectors [13]. The main sectors that have applied carbon trading include oil and gas, manufacturing power, agriculture, aviation, and maritime [14,15,16]. In the United States, carbon trading was implemented through the acid rain program. It contributed to reducing pollution levels at lower costs than estimated [17]. In the European Union (EU), carbon trading has been used to create prices for carbon, thereby reducing emissions [18].
Even though carbon trading has been identified as a panacea for reducing greenhouse gases and subsequently mitigating the threats of climate change, past studies have shown that when the current carbon trading systems were developed, they did not focus on the construction sector [19,20,21,22,23]. The largest carbon trading system that has been implemented globally is the European Union Emissions Trading System. However, a cursory analysis of the trading scheme shows it does not cover much of the construction industry [24,25]. Other major trading systems have not focused much on the construction sector, which is a major gap. This study seeks to enhance knowledge by developing a theoretical framework for carbon trading in the construction industry based on the PROMISE Framework and systems dynamics.

2. Methodology

2.1. PROMISE Framework

The theoretical framework in this study was developed based on the PROMISE Framework. The PROMISE framework is a systems thinking approach to sustainability strategy developed by Massachusetts Institute of Technology (MIT) Sloan School of Management researchers John D. Sterman, Jayson Jay and Roberto Rigobon [26]. It is an acronym representing Personal, Relational, Organizational, Market, Institutional, Social and Environmental [27].
“Personal” and “relational” category input variables of carbon trading involve personal and relational aspects, respectively, of carbon trading in construction. Personal carbon trading underscores the rights and roles of consumers through household use of energy [28]. It operates on the idea that individuals are allocated a carbon quota. The quota can be used entirely or partially sold to others [29]. Relational category explains the relationship between stakeholders in the carbon trading scheme [30]. Stakeholders for carbon trading in the construction industry include central government, local government, construction firms, individuals, environmental protection departments, non-governmental organizations, financial institutions, etc. [31].
“Organizational” category in carbon trading describes elements that influence the way organizations/firms work and behave. According to [32], organizational factors in carbon trading include corporate culture, environmental attitudes of organization, historical engagement, leadership abilities, innovation adoption, research and development, and technology use.
“Market” category input variables explain where buying and selling takes place. Market category variables encompass economic trends that affect demand and supply. Market category explains the elements where parties engage in exchange. In carbon trading, market mainly relates to the production and buying and selling of carbon credits [33].
“Institutional” category input variables include the irreplaceable role of government in carbon emissions reduction through carbon trading schemes [34]. Institutional factors include executive orders, laws, penalties, rewards, incentives, and compensations related to carbon trading for construction [35]. Carbon emission targets and policy tools based on these emissions targets for the construction industry are encompassed in the category called institutional [36].
“Social” category input variables are the elements of carbon trading that affect people within a society. Variables in carbon trading under social category include population, urbanization, social cost, etc. [37]. “Environmental” categorization variables in carbon trading relate to the environmental considerations of carbon trading including emissions of carbon dioxide.
Incorporating these seven categories in sustainability projects leads to better and successful projects. At each level of the framework, variables can be optimized [27]. The PROMISE framework, however, demonstrates at each level the qualities of a system: interdependencies, uncertainty, and feedback loops [26]. Some recent studies on sustainability have adopted the PROMISE framework for classifying their variables in sustainability-related projects. A notable example is the study by [38]. The study conducted research on sustainability adoption in universities.

2.2. Search for Articles

Scopus was chosen as the database for this literature review due to its advantages over other databases. Scopus has 20 percent more documents than Web of Science and a higher index rate than Google Scholar and PubMed [39]. The title, abstract and keywords were used for the search. Search queries were created for all the seven categorizations of the framework. The next stage was the exclusion criterion where the search was subsequently refined and limited to specific parameters. Language was restricted to English, and publications in German, Chinese, Italian, French, Spanish, Russian, Finnish, Korean and Portuguese were excluded. There were no restrictions in the document types, and hence journals, book chapters, books, conference proceedings, technical notes and reviews were all included. Subsequently there was no restriction in the document sources, and therefore, this study included journals, conference proceedings, books, book series and trade reports. Inclusion criteria included articles published from the year 2010 to 2025. After filtering, the final number of documents that were used for reviewing the variables in the framework included 67 documents, 82 documents, 58 documents, 123 documents, 89 documents, 35 documents, and 54 documents, respectively, for personal, relational, organizational, market, institutional, social, and environmental categories. Content analysis was then undertaken on the selected articles to identify the variables and assess the relationships among them.

2.3. Systems Dynamic—Causal Loop Diagram (CLD)

System Dynamics (SD) was adopted for evaluating the relationships among the variables in the theoretical framework. This was chosen due to its advantages as compared to the other techniques. System dynamics (SD) is a methodology utilized for systems modelling and dynamic simulation. It analyzes the dynamic complexities that are involved in socio-economic systems. SD assists decision makers in understanding the dynamics and structure of complex situations. SD also designs influential policies to facilitate change, sustained improvement and successful implementation of projects [40]. SD has increasingly been adopted by researchers and academicians to better appreciate different environmental, economic and social systems through a holistic lens.
SD modelling is an appropriate modelling technique for dynamic environments with multi-dimensional size that have variables which are time-dependent such as energy and power systems [41]. Carbon trading is a multi-faceted area and cuts across the research areas of economic, ecosystems, transportation, and energy management. This study’s theoretical framework adopted Causal Loop Diagrams to illustrate the positive and negative influences among the variables. Causal loop diagrams in the model were used in describing the conceptual model structure obtained from the researchers’/modelers’ understanding of the system. According to Park, Nepal and Dulaimi [42] and Xia, Chen, Walliah, Buys, Skitmore and Susilawati [43], causal loop diagram (CLD) is used to illustrate the dynamic nature of variables in a study. For this study, diagrams were sketched to indicate the architecture and boundary of the model causal loop. Model causal loop diagrams provide an understanding of how variables relate with one another in the system.
Stella Architect version 3.7.1 was the software adopted for the Causal Loop System Dynamic Modelling. Stella is an abbreviation for Systems Thinking Experimental Learning Laboratory with Animation [44]. This software enables its users to run models in the form of graphical diagrams of a system [45]. As compared to other major system dynamic software such as Vensim and Insightmaker, Stella Architect has better presentations, and speed for simulation is fast [46,47].

3. Analysis and Discussion

3.1. Influence of “Personal” Variables on Variables in Same Category/Other Categories

Awareness of the role of carbon trading in mitigating emissions in the construction industry influences support for carbon trading initiatives in the construction industry [48,49]. As awareness increases, there is stimulation in policy adoption, public engagement, and emissions reductions, thereby creating a reinforcing loop of support for the initiatives that relate to carbon trading in construction [50,51].
Public doubts have an influence on support for carbon trading in the construction industry. Increased public doubts lead to reduced support for carbon trading initiatives in the construction industry [52]. Certain individuals or groups may have doubts about carbon trading schemes for the construction industry [53]. This can lead to the dissemination of false or misleading information on claims about the effectiveness or costs of carbon trading in the construction industry [54].
Ethical individuals influence adoption of carbon trading in construction. Ethical individuals encourage the prioritization of trust building among stakeholders regarding carbon trading [55]. They do so by ensuring ethical and transparent carbon trading activities in the construction industry [56]. The commitment by ethical individuals to honesty and integrity leads to credibility and trust [57,58]. This improves confidence of stakeholders in carbon reduction efforts by organizations, as well as their willingness to partake in carbon trading [59].
Accountability by stakeholders influences integrity in carbon trading. When stakeholders are accountable for any action they take, it motivates them to enhance transparency, which causes a positive increase in integrity [60]. This leads to the stakeholders disclosing relevant information openly without hiding it [61,62]. This results in increased credibility and trust in carbon trading system, thereby strengthening the commitment of stakeholders to transparency and accountability [63,64].
Personal capacity influences adoption of carbon trading. Personal capacity has an influence on the adoption of carbon trading. Personal capacity is the ability of individuals to access skills, resources, time, and finances that promote how they engage in carbon trading [65,66]. Personal capacity leads to increased participation in carbon trading, since individuals become actively involved in carbon trading initiatives in the construction industry [67].
Attitudes of individuals influence reduction in carbon emissions. Attitudes of individuals have an influence on the overall reduction in carbon emissions [68]. Individuals’ attitudes represent the values, beliefs, and perceptions that individuals have concerning carbon trading [69]. Positive attitudes regarding carbon trading do encourage individuals to actively partake in related activities like purchase of carbon credits and investment in emissions reduction projects [70,71].
Table 1 and Figure 1 below show the influences “personal” variables have on variables in same category/other categories.

3.2. Influence of “Relational” Variables on Variables in Same Category/Other Categories

Level of collaboration in carbon trading influences technological innovation. Level of collaboration in carbon trading in the construction industry has a significant impact on technological innovation within the construction industry. Collaboration in carbon trading facilitates exchange and knowledge sharing among stakeholders in the construction industry [89]. These include sharing of lessons learnt, best practices, and innovative technologies that relate to reduction in carbon emissions and trading [90,91].
Stakeholder engagement influences participation in carbon trading. Stakeholder engagement denotes the interaction, involvement, and commitment of different parties such as government agencies, construction firms, local communities, and non-governmental organizations (NGOs) in carbon trading projects [92]. It also depicts the level to which stakeholders actively partake in collaborative efforts, decision-making processes, and discussions that relate to carbon emissions reduction projects [93].
Knowledge sharing influences capacity building. As knowledge sharing increases in the construction sector, stakeholders have access to valuable experiences, information and insights that concern carbon trading practices [94,95]. Increased knowledge stimulates the understanding of stakeholders regarding carbon trading mechanisms, market trends, regulatory requirements, and technological innovations [96,97]. As stakeholders become more informed and knowledgeable on carbon trading, they are better equipped to engage in capacity building activities that develop their competencies, skills, and capabilities in the field [93].
Table 2 and Figure 2 below show the influences “relational” variables have on variables in same category/other categories.

3.3. Influence of “Organizational” Variables on Variables in Same Category/Other Categories

There is positive influence between research and development (R&D) and innovation capability of construction firms. Research and development (R&D) activities are significant in improving innovation capability of construction firms in carbon trading [101,109]. R&D investments ensure construction companies can adopt and develop innovative solutions and technologies that aim at mitigating carbon emissions in the construction lifecycle [51,110]. These include sustainable construction methods, advancements in materials that are energy-efficient, and integration of renewable energy [111].
Positive influence exists between subsidies and investment in energy efficiency. Subsidies have a significant influence on energy efficiency in carbon trading in the construction industry [112,113]. Subsidies provide financial incentives for construction companies to invest in energy-efficient materials, technologies, and practices [114,115]. These subsidies offset upfront costs that are associated in the implementation of energy-efficient projects, making these investments more financially attractive [116,117].
The number of carbon trading projects by construction firms influences certification of emissions reduction costs. The number of carbon trading projects undertaken by construction firms has an influence on certification of emissions reduction costs [118,119]. As the number of projects increase, there exist more opportunities in generating carbon credits through emissions mitigation activities [120,121].
Table 3 and Figure 3 below show the influences “organizational” variables have on variables in same category/other categories.

3.4. Influence of “Market” Variables on Variables in Same Category/Other Categories

Economic growth rate influences carbon emissions. According to Wang, Qu, Wang and Xie [142], higher growth rate in the economy translates to corresponding higher emissions in carbon dioxide by the construction industry. The influence that economic growth rate has on carbon emissions by the construction industry is significant [143]. As economic growth rate increases, there is corresponding increase in demand for construction activities, as well as new infrastructure like residential buildings and commercial properties [144]. Increased construction activities lead to higher carbon emissions levels, arising from construction processes like transportation, material extraction and on-site activities [145,146]. There is influence between economic growth and technological innovation. Economic growth influences technological innovation in the construction industry [147]. This occurs through adopting energy-efficient and sustainable practices [148]. Investments in Research and Development (R&D) by the construction industry lead to developing renewable energy technologies, green building materials and construction techniques that are energy efficient [149,150,151].
Demand for carbon credits influences transaction costs. High demand for carbon credits in the construction sector influences transaction costs [152,153]. This arises from competition among purchasers and regulatory requirements [54,154]. Increased demand for carbon credits causes more transaction activities, since construction companies seek to purchase carbon credits in order to achieve their compliance obligations and also offset their emissions [155,156].
Profit of firms in carbon trading influences GDP. When firms involved in carbon trading derive high profits, it leads to a positive impact on productivity and economic performance [153,157]. Increased profits translate into more investments by construction companies leading to economic growth and better GDP [144]. Increase in GDP is reflected in the total economic output that arises from the construction sector and from carbon trading [158,159]. As GDP increases, the right environment is provided for companies to make more profits, further leading to more carbon trading investments [160,161].
Table 4 and Figure 4 below show the influences “market” variables have on variables in same category/other categories.

3.5. Influence of “Institutional” Variables on Variables in Same Category/Other Categories

Investment in carbon offsetting projects influences generation of carbon credits. When construction companies invest in projects that remove or reduce carbon dioxide (CO2) emissions from the atmosphere, it leads to generation of carbon credits [182]. Carbon offsetting projects include methane capture, afforestation, reforestation, renewable energy installations and energy efficiency initiatives [183,184]. Depending on the scale and type of offset project as well its compliance with recognized standards like Clean Development Mechanism (CDM) or Verified Carbon Standard (VCS), it can generate carbon credits [185,186].
Government investment influences subsidies. Increase in government subsidies during carbon trading serves as a source of financial provision for construction firms that engage in carbon emissions reduction projects or for firms that purchase carbon credits [89,187,188]. The presence of subsidies serves as an incentive in the adoption of cleaner technologies, participation in carbon trading schemes, and investment in emissions reduction initiatives that result in carbon emissions [189,190].
Free allocation of permits influences profits by firms trading. Free allocation of permits in carbon trading enables firms to receive specific amounts of carbon emissions allowances for free [126]. This permits these firms to emit certain quantities of carbon without having to purchase additional permits [191]. In carbon trading, free allocation of permits minimizes operational costs that come with compliance with regulations [192,193]. This positively impacts the profits accrued by firms that partake in carbon trading [194,195].
Table 5 and Figure 5 below show the influences “institutional” variables have on variables in same category/other categories.

3.6. Influence of “Social” Variables on Variables in Same Category/Other Categories

Population influences population growth rate. Population growth rate also has an influence on population [205]. High population growth rate causes increased demand for buildings and housing. This causes construction activities to increase to accommodate the increasing population [206]. This results in increased levels of carbon emissions arising from transportation, energy use and building materials [207].
Increasing population growth rate is often associated with urbanization as people move from rural communities to urban towns and cities for better living conditions [208,209]. Urbanization causes increased expansion in amenities such as roads, buildings, and utilities [210]. This ultimately increases emissions arising from construction activities.
Population growth rate influences per capita GDP [211,212]. Population growth rate is the extent to which a country or area’s population increase with time [213]. Population growth rate is often measured as percentage change in population size from a period to another [214]. Population growth has an effect on demand and supply for construction labor [215]. Increasing population will cause high demand for construction activities leading to high levels of employment and high wages [216]. Population growth also has an influence on demand for infrastructure, buildings, and houses by consumers [206].
Table 6 and Figure 6 below show the influences “social” variables have on variables in same category/other categories.

3.7. Influence of “Environmental” Variables on Variables in Same Category/Other Categories

There exists influence between fossil fuel and CO2 emissions. Global energy supply for the built environment still mainly relies on fossil energy sources, which are mainly coal, oil, and gas [220]. As construction is one of the most energy consuming sectors in the world, it uses a lot of fossil fuels, thereby contributing to increased CO2 emissions [107].
Carbon emissions reduction influences cost savings. Adoption of sustainability practices results in lower carbon emissions as material waste and energy consumption are reduced [221]. Construction companies obtain cost savings over time since they consume lower energy, use less resources and incur lower expenses that are related to carbon offset purchases or emissions allowances [222,223].
CO2 emissions influence carbon trading volume. Influence of CO2 emissions on the carbon trading volume in the construction industry is diverse and there are both direct and indirect effects. When there are higher levels of CO2 emissions arising from the construction industry, this triggers very strict regulatory requirements towards reduction in emissions [224]. Regulatory bodies or governments will impose targets and caps on carbon emissions, thereby leading to increased carbon trading participation in compliance with the regulations [202].
Table 7 and Figure 7 below show the influences “environmental” variables have on variables in same category/other categories.

4. Theoretical Framework for Carbon Trading Based on PROMISE Framework

The theoretical framework was developed by explaining the interactions in all seven categories, and how they were combined to show the relationships among the variables in their categories and with other categories. In this study, the framework was based on the interaction and influences among the categorization of all the seven categories using the PROMISE framework. The foremost process in carbon trading is setting a cap. The cap implies the uppermost limit of greenhouse gas that is allowed in the trading scheme. It also represents the total number of allowances that covered entities can have access to. The setting of the cap corresponds with the baseline against which emissions must be reduced. In setting the cap, historical emissions are set against a base year or projected future emissions. A maximum cap is set by the government on the number of permits. These trading permits can be bought from other firms in the trading market or they can be purchased from the government [229]. The allowances from government are either free or auctioned. These variables have interactions among other variables in their categories and other categories. Some of the variables include emissions cap, carbon allowance price cap, free quota, carbon intensity reduction rate, CO2 emissions, auction allocation, level of free allocation of permits [3,126,181,231].
Purchasing of economic carbon credits or carbon offsets is another major process of carbon trading through which buildings in the construction industry can minimize their net environmental impact. These carbon credits come in the form of tradable certificates awarded to firms to lower the content of carbon released in the atmosphere. This can come in the form of carbon capture and sequestration. From the PROMISE framework categorization, the majority of the variables for purchasing carbon credits fall under market categorization. However, they have an influence on other factors in the market categorization and variables in the other categories. Some of these variables include carbon credit price, demand for carbon credits, level of carbon credit consumption, quantity of carbon allowances, profit of firms in carbon trading, carbon trading volume, transaction costs [8,33,136,165,169,188].
A further major process is Monitoring, Reporting and Verification (MRV). MRV consists of multiple steps involved in measuring emissions removed by polluting firms and certifying that the removals are truly real, additional, permanent, and verifiable. This is then reported to a third-party agent that is accredited to certify that the removal was completed according to the right MRV protocols. The verification of carbon credits is a rigorous process, and the end goal is to ensure legitimacy of the credits. This verification process starts with the project developers and firms that implement activities that reduce carbon emissions. The developers/firms must provide evidence of reduction in carbon through the monitoring of data, project reports and other required documents.
Figure 8 below shows the theoretical framework based on the PROMISE framework that is developed for construction industry carbon trading.

5. Validation of Theoretical Framework

5.1. Expert Forum Validation of Framework

To validate the theoretical framework for construction industry carbon trading, an expert forum was consulted. This was to seek expert opinions on the suitability of the framework. Ethics approval was sought from the University Human Research Ethics Committee (HREC). Twenty experts knowledgeable in system dynamics and carbon trading as well as sustainability were contacted from the Australian construction industry. The inclusion criterion was that they should have been working for at least ten years. Out of twenty expert forum guides sent out, twelve were returned, representing a response rate of sixty percent. Ten of the respondents had postgraduate degrees, with the remaining two having a first degree. For professional bodies they belonged to, these comprised Australian Institute of Project Management (AIPM), Australian Institute of Quantity Surveyors (AIQS), Australian Sustainable Built Environment Council (ASBEC), Royal Institution of Chartered Surveyors (RICS) and Engineers Australia (EA).
They were asked to rank four validation questions on a Likert scale of 1 to 5 with 1 being strongly disagree and 5 being strongly agree. They were also provided a box to provide any further comments they may have on the framework. The results are presented in Table 8 below. All respondents agreed on the suitability of the theoretical framework. When asked for further comments, one expert recommended all the colors in the framework be made black to ensure consistency. However, authors believe it is best to maintain the different colors since they represent different groups in the framework. Another expert suggested only initials of variables be used in the theoretical framework to keep things simple. The authors, however, believe that using only initials would make it difficult for the components of the framework to be understood, and hence, words should rather be maintained. The remaining experts indicated the framework was okay.

5.2. Databases for Stock and Flow Equations and Mathematical Modelling

The next stage after the theoretical framework is the quantitative modelling using stock and flow diagrams in system dynamics. Table 9 below presents some of the databases that will be adopted in deriving secondary data for the mathematical modelling. The predominant ones will be Australian Bureau of Statistics (ABS), Australian Renewable Energy Agency (ARENA), Australian National Environment Protection Council, Climate Change Authority, Bureau of Meteorology, Clean Energy Finance Corporation, Nationwide House Energy Rating Scheme (NatHERS), Carbon Footprint Factsheet, Centre for Climate and Energy Solutions, Green Building Council Australia (GBCA), CSIRO Energy, New South Wales State of Environment, Built Environment Carbon Database (BECD), Environmental Protection Agency (EPA), United Nations Framework Convention on Climate Change (UNFCCC).

5.3. Modelling Tests to Be Adopted During System Dynamic Simulations

Model testing is important since it checks for flaws in models and sets the tone for improving them [232]. Following the recommendation of Forrester [233], this study will perform several model tests such as boundary adequacy test, structural and behavior tests, dimensional consistency tests, extreme conditions test and behavior reproduction test. Structural and behavior tests will check if the system model under-predicts or over-predicts the inherent behaviors and patterns in the carbon trading model. Dimensional consistency tests will check if the equations used in the models are consistent and correspond to the linkages depicted in the causal loop diagrams. Boundary adequacy tests will check for dynamic changes in endogenous and exogenous variables. Behavior reproduction tests will check different modes of model behavior that are inherent in real systems. Extreme conditions test will involve ascertaining the validity of model equations by giving extreme values to the input factors.

6. Conclusions and Recommendations

This study sought to fill the gap of current trading schemes not focusing on construction by developing a theoretical framework based on the PROMISE framework. The developed framework in the study explains the mechanisms and relationships behind the constructs in a carbon trading framework. Carbon trading projects that are sustainability-oriented start with people (personal) and the interactions among stakeholders (relational). Firms are involved in these projects (organizational), and they are influenced by market conditions (market). The governments as well as other major stakeholders partake in the carbon trading projects (institutional). A broader group of stakeholders involves the society that is affected by these projects (social). Natural resources further have a role to play in sustainability-related projects (environment). The variables in these seven categories have an influence on each other. Causal loop diagrams (CLDs) were used to evaluate and illustrate the influences (positive and negative) among the relationships.
This research on carbon emissions trading has varying implications ranging from policy, theoretical, practical, and empirical contributions. This theoretical study contributes methodologically to investigating carbon reduction research. This theoretical technique minimizes subjectivities and biases that are often associated with purely systematic literature reviews. This study formulates sound policies and best practices to ensure the construction industry meets its carbon emissions reduction targets. Furthermore, the study provides a foundation for future researchers to develop conceptual frameworks and models for carbon mitigation strategies. For policy makers, the proposed carbon trading framework assists in evaluating the key legal, economic, environmental, and political policies that improve carbon trading projects in the built environment. When policy makers place significant emphasis on the influences identified in this study, it will contribute to them supporting regulations and policies that effectively mitigate these emissions.
In the next stage of this ongoing research, the remaining steps involved in the system dynamics (SD) technique will be used to model the relationship among the various components. Stock and flow diagrams will be developed next. The interactions in these models will comprise model verification, model development, model testing and model simulations.

Author Contributions

Conceptualization, A.S.K.K. and X.J.; methodology, A.S.K.K., X.J., R.O.-K. and S.P.; software, A.S.K.K.; writing—original draft preparation, A.S.K.K.; writing—review and editing, X.J., R.O.-K. and S.P.; supervision, X.J., R.O.-K. and S.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All the data used appears in this study.

Acknowledgments

The study acknowledges Western Sydney University for the kind funding of this study for which the first author is a recipient of the PhD scholarship scheme. As part of the funded PhD, this paper is part of several other publications sharing similar background but varying methodology, scope, and conclusions. Authors acknowledge anonymous reviewers for their inputs in the article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Sustainability 17 10342 g001
Figure 1. Causal loop diagram (CLD) for influence of “personal” variables on variables in same category/other categories.
Figure 1. Causal loop diagram (CLD) for influence of “personal” variables on variables in same category/other categories.
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Sustainability 17 10342 g002
Figure 2. Causal loop diagram (CLD) for influence of “relational” variables on variables in same category/other categories.
Figure 2. Causal loop diagram (CLD) for influence of “relational” variables on variables in same category/other categories.
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Sustainability 17 10342 g003
Figure 3. Causal loop diagram (CLD) for Influence of “organizational” variables on variables in same category/other categories.
Figure 3. Causal loop diagram (CLD) for Influence of “organizational” variables on variables in same category/other categories.
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Sustainability 17 10342 g004
Figure 4. Causal loop diagram (CLD) for influence of “market” variables on variables in same category/other categories.
Figure 4. Causal loop diagram (CLD) for influence of “market” variables on variables in same category/other categories.
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Sustainability 17 10342 g005
Figure 5. Causal loop diagram (CLD) for influence of “institutional” variables on variables in same category/other categories.
Figure 5. Causal loop diagram (CLD) for influence of “institutional” variables on variables in same category/other categories.
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Sustainability 17 10342 g006
Figure 6. Causal loop diagram (CLD) for influence of “social” variables on variables in same category/other categories.
Figure 6. Causal loop diagram (CLD) for influence of “social” variables on variables in same category/other categories.
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Sustainability 17 10342 g007
Figure 7. Causal loop diagram (CLD) for influence of “environmental” variables on variables in same category/other categories.
Figure 7. Causal loop diagram (CLD) for influence of “environmental” variables on variables in same category/other categories.
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Figure 8. Theoretical framework for carbon trading system based on PROMISE framework.
Figure 8. Theoretical framework for carbon trading system based on PROMISE framework.
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Table 1. Influence of “Personal” variables on variables in same category/other categories.
Table 1. Influence of “Personal” variables on variables in same category/other categories.
Preceding FactorCategorization of Preceding FactorInfluence (+/−)Sustainability 17 10342 i001Succeeding FactorCategorization of Succeeding FactorReferences
Ethical individualsPersonal+Adoption of carbon tradingPersonal[72,73]
Level of accountability by stakeholdersPersonal+Integrity in carbon tradingPersonal[60,74]
Ethical individualsPersonal+Innovation in carbon tradingOrganizational[75,76]
Level of awareness of carbon tradingPersonal+Adoption of carbon tradingPersonal[23,77]
Level of personal experiencePersonal+Adoption of carbon tradingPersonal[78]
Level of awareness of carbon emissionsPersonal+Energy consumption reduction actionsPersonal[3,79]
Level of personal capacityPersonal+Adoption of carbon tradingPersonal[80,81,82]
Individuals’ attitudes towards carbon tradingPersonal−Carbon emissionsEnvironmental[83,84]
Individual perception of risksPersonal−Adoption of carbon tradingPersonal[83,85]
Level of awareness of role of carbon tradingPersonal+Support for carbon trading initiativesRelational[23,86]
Level of public doubtsPersonal−Support for carbon trading initiativesRelational[54,64]
Personal networksPersonal+Adoption of carbon tradingPersonal[79,83]
Personal motivationPersonal+Adoption of carbon tradingPersonal[87,88]
Table 2. Influence of “Relational” variables on variables in same category/other categories.
Table 2. Influence of “Relational” variables on variables in same category/other categories.
Preceding FactorCategorization of Preceding FactorInfluence (+/−)Sustainability 17 10342 i001Succeeding FactorCategorization of Succeeding FactorReferences
Level of knowledge sharingRelational+Capacity buildingOrganizational[98]
Level of collaboration in carbon tradingRelational+Technological innovationOrganizational[89,99]
Level of community engagement Relational+Social impact of carbon tradingSocial[100,101]
Level of stakeholder engagement Relational+Participation in carbon tradingInstitutional [85]
Willingness to collaborate in carbon tradingRelational+Carbon priceMarket[102]
Level of awareness of carbon emissionsRelational−Energy consumptionOrganizational[3]
Formation of collaborations and partnershipsRelational+Implementation of carbon offset projectsInstitutional[101,103]
Level of collaboration with certification bodiesRelational+Participation in carbon tradingPersonal[85,100]
Level of engagement with carbon trading platformsRelational+Participation in carbon tradingPersonal[68,104]
Level of collaboration with suppliersRelational+Adoption of low carbon technologiesInstitutional[105,106]
Level of collaborative lobbyingRelational+Policy advocacy for carbon tradingInstitutional[68,103]
Willingness to collaborate in carbon tradingRelational+Demand for carbon creditsMarket[102]
Willingness to collaborate in carbon tradingRelational+Market liquidityMarket[83,84]
Level of stakeholder involvement in carbon tradingRelational+Decarbonization in built environmentEnvironment [107,108]
Table 3. Influence of “Organizational” variables on variables in same category/other categories.
Table 3. Influence of “Organizational” variables on variables in same category/other categories.
Preceding FactorCategorization of Preceding FactorImpact (+/−)Sustainability 17 10342 i001Succeeding FactorCategorization of
Succeeding Factor		References
Research and development (R&D)Organizational+Innovation capability of construction firmsOrganizational[99,122]
IncentivesOrganizational+Investment in energy efficiencyInstitutional[123]
SubsidiesOrganizational+Investment in energy efficiencyInstitutional[124]
Level of complexity of projectOrganizational+Transaction costsMarket[125]
Technological innovationOrganizational+Participation in carbon sequestration projectsOrganizational [99]
Incentives Organizational−CO2 emissionsEnvironmental[126,127]
Size of construction projectsOrganizational+CO2 emissionsEnvironmental[128,129,130]
Number of carbon trading projects by construction firmsOrganizational+Certification of emissions reduction costsInstitutional[131]
Number of carbon trading projects by firmsOrganizational+Verification of emissions reduction costsInstitutional[76,132]
Carbon quotas by firmsOrganizational+Demand for carbon creditsMarket[33,109,126]
Standardization of documents for carbon tradingOrganizational−Transaction costsMarket[133]
Technology transfer Organizational+Economic viability of carbon trading projectsMarket[134,135]
Technological innovationOrganizational+R&D of low-carbon technologiesOrganizational[136,137]
Technological innovationOrganizational+Clean energyEnvironmental[99]
R&DOrganizational+Demand for carbon creditsMarket[109,135,138]
R&D investmentOrganizational+Energy efficiencyOrganizational[109,135,138]
IncentivesOrganizational+Investment in energy efficiencyInstitutional[127,139]
Technological innovationOrganizational+Participation in carbon sequestration projectsEnvironmental[140]
IncentivesOrganizational+Carbon reduction projectsEnvironmental[127,141]
Table 4. Influence of “Market” variables on variables in same category/other categories.
Table 4. Influence of “Market” variables on variables in same category/other categories.
Preceding FactorCategorization of Preceding
Factor		Impact (+/−)
Sustainability 17 10342 i001		Succeeding FactorCategorization of Succeeding FactorReferences
Economic Growth RateMarket+Carbon emissionsEnvironmental[162]
GDPMarket+Investment in energy-reduction technologyInstitutional[134]
GDPMarket+Energy consumptionOrganizational[163,164]
GDP growth rateMarket+GDPMarket[136]
Carbon quota priceMarket+Revenue of carbon quotaMarket[33,126,165]
Number of trading entitiesMarket−Monopolistic behaviorMarket[162,166]
Carbon creditsMarket+Use of renewable energyOrganizational[136,138]
Purchasing Power Parity (PPP)Market+Energy demandMarket[167]
Carbon allowance demandMarket+Carbon allowance priceMarket[134]
Carbon allowance priceMarket+Carbon allowance demandMarket[134,168]
Revenue of firms in carbon tradingMarket+Profit of firms in carbon tradingMarket[169]
Carbon credit priceMarket−Demand for carbon creditsMarket [165]
Demand for carbon creditsMarket+Level of carbon credit consumptionMarket[138,165]
Carbon credit priceMarket+Producer price index for construction industryMarket[170]
Carbon credit priceMarket+Prices of building and construction materialsMarket[168,171]
Energy gapMarket+Energy policiesInstitutional [172]
Energy supplyMarket−Energy gapMarket [172]
Electricity demandMarket+GDPMarket[138]
Number of trading partners in emissions trading schemeMarket−Transaction costsMarket[152]
Demand for carbon creditsMarket+Carbon credit priceMarket[33,165]
Quantity of carbon allowancesMarket+Cost of legal transactionsInstitutional[134]
Demand for carbon creditsMarket+Transaction costsMarkets[173]
Profit of firms in carbon tradingMarket+GDPMarket[171,172,174]
Profit of firms in carbon tradingMarket+R&D investmentOrganizational[109,173]
Carbon trading volumeMarket+Non-performing quotaMarket[126,138,175]
Carbon emissions costs of firmsMarket−Profit of firms in carbon tradingMarket[134]
Carbon credit supplyMarket−Carbon credit priceMarket[33,134,165,176,177]
Demand for carbon creditsMarket+Purchase of carbon creditsMarket[33,178]
Economic growthMarket+Energy consumptionInstitutional[20,179]
GDPMarket+Per capita GDPMarket[180,181]
Table 5. Influence of “Institutional” variables on variables in same category/other categories.
Table 5. Influence of “Institutional” variables on variables in same category/other categories.
Preceding FactorCategorization of Preceding FactorImpact (+/−)Sustainability 17 10342 i001Succeeding FactorCategorization of Succeeding FactorReferences
Technology investmentInstitutional−CO2 emissionsEnvironmental[134,138]
CO2 emissions reduction targetInstitutional−CO2 emissionsEnvironmental[126,136,196]
Government investmentInstitutional+SubsidiesOrganizational[122,197]
Level of free allocation of permitsInstitutional+Profits by firms tradingMarket[107]
Unit subsidyInstitutional−Participation in carbon tradingPersonal[198]
Level of government interventionInstitutional−CO2 emissionsEnvironmental[197,199]
Fines Institutional+Compliance to carbon tradingInstitutional[200]
Auction allocationInstitutional+Demand for carbon creditsMarket[134]
Level of government commitment to decarbonizationInstitutional+Renewable energy adoptionOrganizational[108,201]
Carbon offset monitoringInstitutional+Decarbonization in built environmentEnvironment[136]
Carbon allowance price capInstitutional−Demand for carbon creditsMarket[135,177]
Level of political acceptabilityInstitutional+Carbon trading efficiencyOrganizational[126,136]
Level of government investment Institutional+Demand for carbon creditsMarket[108]
Emissions capInstitutional+Carbon credit priceMarket[105]
Carbon trading regulatory frameworksInstitutional+Transaction costsMarket[200,202]
CO2 emissions reduction targetInstitutional−Energy consumptionOrganizational[136,163]
Free quotasInstitutional+Carbon trading participationPersonal[33,202]
Cap on offsetsInstitutional+Cost savingsMarket[134]
Investment in energy-reduction technologyInstitutional−CO2 emissionsEnvironmental[126,134]
Level of government monitoringInstitutional+Non-compliance penaltiesInstitutional[203]
Level of government credibility during carbon tradingInstitutional+Rate of government regulatory successInstitutional[25]
Non-compliance penaltiesInstitutional+Enforcement costs of carbon trading Institutional[108,203]
Verification capacity of third-party agentsInstitutional+Rate of government regulatory successInstitutional[25]
Non-performing quotaInstitutional+Fines of firmsInstitutional[33,134]
Fines of firmsInstitutional+Carbon emissions costs of firmsMarket[3,204]
Free quotaInstitutional−Carbon credit priceMarket[33,138,165]
Emissions tax incomeInstitutional+Government incomeMarket[160]
Level of regulatory complianceInstitutional−Carbon emissions profileEnvironmental [202]
Level of Monitoring Reporting and Verification (MRV)Institutional+Capacity BuildingOrganizational[136,204]
Investment in carbon offsetting projects Institutional+Generation of carbon creditsMarket[160]
Table 6. Influence of “Social” variables on variables in same category/other categories.
Table 6. Influence of “Social” variables on variables in same category/other categories.
Preceding FactorCategorization of Preceding FactorImpact (+/−)Sustainability 17 10342 i001Succeeding FactorCategorization of Succeeding FactorReferences
Household sizeSocial+Energy consumptionOrganizational[217,218]
Population growth rateSocial+PopulationSocial[136,181]
UrbanizationSocial+Energy consumptionOrganizational[163]
UrbanizationSocial+Per capita incomeMarket[217]
Population growth rateSocial+Per capita GDP Market[180,181]
Social networksSocial+Adoption of carbon tradingPersonal[219]
Social networksSocial+Community impact of carbon tradingSocial[219]
Table 7. Influence of “Environmental” variables on variables in same category/other categories.
Table 7. Influence of “Environmental” variables on variables in same category/other categories.
Preceding FactorCategorization of
Preceding Factor		Impact (+/−)Sustainability 17 10342 i001Succeeding FactorCategorization of Succeeding FactorReferences
Increment of coalEnvironmental+Coal consumptionEnvironmental[138,220]
Embodied carbon construction materialsEnvironmental+CO2 emissionsEnvironmental[225,226]
CO2 emissionsEnvironmental−Carbon quotas by firmsOrganizational[33,136,163,227]
CO2 emissionsEnvironmental+Social cost of carbon tradingSocial[160,219]
Fossil energy consumptionEnvironmental+CO2 emissionsEnvironmental[164,220]
CO2 emissionsEnvironmental+Carbon trading volumeMarket[138,163]
Environmental impact
assessment (EIA)		Environmental−Stakeholders’
opposition		Relational[197,228]
Construction activitiesEnvironmental+Carbon emissions profileEnvironmental[20,160,229]
CO2 emissionsEnvironmental+Carbon intensity in the construction industryEnvironmental[230]
CO2 emissions reductionEnvironmental+Cost savingsMarket[230]
Table 8. Expert validation of framework results.
Table 8. Expert validation of framework results.
Nr.Validation QuestionsMeanStandard Deviation
1The theoretical framework developed is reasonable3.980.896
2The theoretical framework is feasible to be applied during stock and flow equation stage of system dynamics4.390.713
3The theoretical framework supports decision making regarding carbon trading 4.080.804
4The theoretical framework is sufficient to be implemented4.120.987
Table 9. Databases for secondary data to be used for modelling.
Table 9. Databases for secondary data to be used for modelling.
Nr.VariableDatabase
1CO2 emissions reduction targetUnited Nations Framework Convention on Climate Change (UNFCCC), Green Building Council Australia (GBCA)
2Population growth rateAustralian Bureau of Statistics (ABS)
3Monitoring, Reporting and VerificationInternational Carbon Action Partnership (ICAP), Australian National Environment Protection Council (NEPC)
4Penalties for non-complianceCentre for Climate and Energy Solutions, Carbon Market Institute (CMI)
5Carbon trading incentivesClean Energy Finance Corporation (CEFC)
6Allocation of carbon creditsClean Energy Regulator (CER)
7Carbon trading profitCarbon Market Institute (CMI)
8Transfer of technologyCSIRO Energy
9Carbon trading projects by construction firmsNational Australian Built Environment Rating System (NABERS)
10Collaboration with certification bodiesCentre for Climate and Energy Solutions
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MDPI and ACS Style

Kukah, A.S.K.; Jin, X.; Osei-Kyei, R.; Perera, S. Theoretical Framework for Carbon Trading in Construction Industry: A PROMISE Framework and System Dynamics (SD) Causal Loop Diagram (CLD) Approach. Sustainability 2025, 17, 10342. https://doi.org/10.3390/su172210342

AMA Style

Kukah ASK, Jin X, Osei-Kyei R, Perera S. Theoretical Framework for Carbon Trading in Construction Industry: A PROMISE Framework and System Dynamics (SD) Causal Loop Diagram (CLD) Approach. Sustainability. 2025; 17(22):10342. https://doi.org/10.3390/su172210342

Chicago/Turabian Style

Kukah, Augustine Senanu Komla, Xiaohua Jin, Robert Osei-Kyei, and Srinath Perera. 2025. "Theoretical Framework for Carbon Trading in Construction Industry: A PROMISE Framework and System Dynamics (SD) Causal Loop Diagram (CLD) Approach" Sustainability 17, no. 22: 10342. https://doi.org/10.3390/su172210342

APA Style

Kukah, A. S. K., Jin, X., Osei-Kyei, R., & Perera, S. (2025). Theoretical Framework for Carbon Trading in Construction Industry: A PROMISE Framework and System Dynamics (SD) Causal Loop Diagram (CLD) Approach. Sustainability, 17(22), 10342. https://doi.org/10.3390/su172210342

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