South African Industry Practitioners on Building Energy Simulation Software: Implementation Challenges and Opportunities

Abstract

Building Energy Modelling (BEM) practitioners play a crucial role in delivering energy-efficient buildings by analysing building performance using simulation tools. However, their experiences while using BEM software to predict building energy performance are understudied. In addition, research that directly engages with practitioners and stakeholders is particularly lacking in the Global South (GS), where the bulk of new building construction takes place. This study explores the implementation challenges and opportunities associated with BEM software among South African industry practitioners, focusing on their experiences in utilising BEM tools. Structured interviews were conducted with 19 South African industry specialists, supplemented by quantitative data collected through a questionnaire. Qualitative data from the interviews were analysed using MAXQDA 24 Analytics Pro to identify key themes, while quantitative data were visualised to compare software preferences. The analysis indicated that DesignBuilder is widely used, followed by BSIMAC. These tools highlight the largest opportunities for supporting active South African practitioners. The respondents highlighted the need for user-friendly interfaces, standardised methodologies, and improved training to address entry barriers and inconsistent simulation outcomes. Mixed opinions exist regarding the preference for tools with visual representations of 3D geometry, primarily influenced by the field of specialisation and how it impacts client engagement. The research concludes that while BEM software is critical for advancing sustainable design, its effective implementation is hindered in South Africa and potentially in the GS. Recommendations include developing more intuitive software interfaces, establishing standardised modelling approaches, and creating structured training programmes and professional forums to enhance practitioner proficiency, knowledge transfer across contexts, and industry-wide adoption.

1. Introduction

The built environment contributes significantly to global greenhouse gas (GHG) emissions, accounting for over 30% of the total, as estimated by the United Nations Environment Programme (UNEP) [1,2,3]. Building energy performance simulations offer a valuable opportunity to assess and proactively improve the energy efficiency of buildings. In recent years, technological advancements have significantly shaped the built environment, driving the adoption of sophisticated building energy modelling (BEM) tools. The growing adoption helps address sustainability challenges, promoting energy efficiency and mitigating energy-related emissions. Historically, BEM software development began in the 1960s, with early efforts in the United States focusing on analysing the thermal environment of fallout shelters in response to nuclear threats [4,5]. At the same time, the Stockholm Royal Institute of Technology developed BRIS, one of the first BEM programmes, marking a pivotal moment in the history of energy simulation [6]. Presently, BEM has evolved from manual calculations to advanced software solutions, transitioning through ten distinct phases [7], as summarised in

Table 1. The evolution of building energy simulation and modelling, adapted from [7].

Phases	Descriptions	Milestones
1. Basic Energy Calculations	Simple manual or spreadsheet-based calculations estimate energy consumption using basic building parameters, such as size and insulation.	1. Basic Energy Calculations
2. BEM Software:	Computer-based simulation software, such as EnergyPlus and DOE-2, enables the accurate prediction of energy performance using climate, occupancy, and system data.	2. Assessing design decisions
3. Integrated Design Approach:	Collaboration among architects, engineers, and modellers has integrated energy modelling into early design to optimise energy performance.
4. Performance-Based Building Codes:	Energy modelling has become essential for complying with energy-efficient building codes and certifications.	3. Building Codes Compliance
5. Parametric and Optimisation Tools:	Parametric modelling tools enabled the rapid evaluation of design iterations to identify energy-efficient solutions.	4. Life Cycle Assessment
6. Predictive Analytics and Machine Learning:	Predictive analytics and machine learning enhanced energy models with accurate predictions and operational optimisations using historical and real-time data.
7. Whole-Building Life Cycle Assessment:	Energy modelling expanded to include life cycle assessments, evaluating embodied energy and emissions across a building’s entire life cycle.
8. Cloud-Based and Collaborative Platforms:	Cloud-based platforms enabled real-time collaboration, centralised data management, and accessible computational resources for energy modelling.	5. Grid Integration
9. Digital Twins and Building Performance Monitoring:	Digital twins integrate energy modelling with real-time sensor data for continuous performance monitoring and predictive maintenance.
10. Smart Grid Integration:	Energy modelling evolved to optimise building-grid interactions, incorporating demand response, energy storage, and renewable energy systems.

. These phases range from basic energy calculations to sophisticated applications, such as digital twins and smart grid integration, reflecting a growing emphasis on mitigating the built environment’s contribution to climate change.

The BEM landscape of today features a diverse range of tools, including widely used platforms like EnergyPlus, TRNSYS, and eQUEST. These tools enable energy performance predictions by incorporating climate data, occupancy patterns, and system specifications [8]. Emerging technologies, such as cloud-based platforms (e.g., Autodesk Insight) and machine learning-enhanced processes (e.g., DesignBuilder with Artificial Intelligence algorithms), facilitate real-time collaboration and predictive analytics [9,10]. Tools such as OpenStudio can be integrated with 3D modelling workflows to enable the interrogation of a building’s design early in the project’s design phase [11,12]. Meanwhile, private sector parametric tools, such as Grasshopper, combined with Ladybug and Honeybee plugins, enable rapid design optimisation [8,13]. These advancements underscore BEM’s critical role in achieving energy-efficient designs and complying with performance-based codes and green building rating systems, such as Leadership in Energy and Environmental Design (LEED) and Building Research Establishment Environmental Assessment Method (BREEAM).

Despite this potential, discrepancies between simulated and actual building energy performance—often referred to as building energy performance gaps (BEPG)—persist, with variations reported to be as high as 250% or more [14]. Previous studies have outlined multiple causes of BEPG and proposed potential solutions [15,16]. The literature indicates that these causes can be classified into four main categories: process-related, product-related, policy-related and people-related issues [17]. However, it is paramount to examine BEM tools and their associated challenges and opportunities. In addition, the contributions of built environment stakeholders to the outcomes of BEM and energy efficiency remain understudied, especially the end-users of BEM software in the industry (i.e., modellers, practitioners, or professionals). Ultimately, industry BEM practitioners, as end-users of BEM software working directly on real-world building projects, are a critical component of the global collective effort to ensure energy efficiency in buildings. These identified gaps underscore the need to investigate the challenges faced by BEM practitioners, particularly in contexts such as South Africa, where research on BEM and BEPG is limited.

To address this knowledge gap, the study adopted an exploratory approach focusing on the following objective:

• Identify the challenges faced by simulation practitioners when operating BEM software and explore opportunities to enhance practices in the BEM industry by engaging directly with BEM software end-users.

By optimising BEM practices, the architecture, engineering, and construction (AEC) industry can enhance building energy performance, reduce operational energy consumption, and promote a more sustainable built environment. This aligns the AEC industry with global climate goals and initiatives. The research contributes to the discussion on the roles of different stakeholders and key actors across various regions (see Section 2.2) in enhancing an energy-efficient built environment. It provides evidence from the South African and Global South (GS) contexts.

2. Literature Review

2.1. Brief View of BEM Development and Existing Knowledge Gaps

The field of building energy modelling (BEM) has evolved significantly to address the complexities of building systems and the persistent mismatch between predicted and actual energy performance. This mismatch is often referred to as the building energy performance gap (BEPG). Early simulation tools laid the foundational work for improved energy forecasting. However, recent studies emphasise that these tools must address broader performance deficiencies beyond energy, such as operational and maintenance issues in new buildings.

Rongere and Gautier [18] introduced CLIM2000, a modular platform for integrating thermal and energy models to support innovative electrical systems, highlighting early efforts to simulate holistic performance. Crawley et al. [19] advanced this approach with EnergyPlus, replacing legacy mainframe programmes through variable time steps and modular envelope simulations, which enables more reliable predictions of in-use energy demands. Glaser et al. [20] complemented these by developing lighting simulation algebras for daylight optimisation, using visualisation to analyse multivariate data and reduce over-reliance on idealised assumptions.

Later research targeted specific performance facets, revealing gaps in real-world application. Laouadi [21] proposed a semi-analytical model for radiant heating and cooling in low-energy residences, focusing on thermal dynamics without overemphasising climate-specific adaptations. Bazjanac [22] advocated for interoperable software ecosystems to facilitate the exchange of HVAC data, underscoring the need for seamless integration across tools rather than isolated simulations. Vesanen et al. [23] embedded fuel cell models into IDA-ICE for sustainable systems, demonstrating how BEM can incorporate renewables to narrow energy discrepancies.

Contemporary evaluations increasingly benchmark BEM against operational realities. For instance, Foucquier et al. [24] compared physical, machine learning and hybrid methods for energy and indoor condition predictions, noting discrepancies up to five times higher in actual consumption. Nima et al. [25] reviewed 25 web-based tools, recommending context-specific selection to mitigate usability barriers. In another study, Coakley et al. [26] synthesised 20 calibration studies, attributing gaps to modeller subjectivity and incomplete data validation.

Despite the progress in the field of building energy simulations, critical gaps remain. Computational bottlenecks, such as high processing demands in co-simulations, exacerbate inaccuracies in large-scale models [27]. Emerging integrations, such as digital twins and machine learning, promise streamlined input [9,10]; however, frameworks for managing uncertain data remain a challenge [10]. Underrepresentation in the GS exacerbates the above issues amid rapid urbanisation, which drives 76% of new construction [28]. Most BEM studies draw from the Global North (GN) with its data-rich environments [17], often neglecting GS challenges like non-standard materials, extreme climatic conditions, and anticipated climate change challenges. The absence of energy efficiency codes for buildings is another major barrier [29]. Based on a study in Sri Lanka, Samarakkody et al. [30] determined that significant BEPGs also stem from post-handover neglect. Adapting the Soft Landings (SL) framework—originally developed in the UK—shows promise but requires modifications for local practices, such as shortening the post-construction aftercare period from three years due to resource constraints.

These studies highlight the prevailing focus of BEM and BEPG research on theoretical approaches to improve parameter optimisation and model accuracy. However, it is crucial to explore the role of stakeholders as key actors in the BEM process to develop a more holistic understanding of energy efficiency in buildings [31].

2.2. Stakeholder Perspectives on Building Energy Efficiency

Several researchers have investigated the relationship between BEM and how various role-players affect the anticipated energy performance of buildings. These studies employed different research methodologies to evaluate this complex relationship. One main approach is through direct engagement with specific groups of key actors to understand their perspectives regarding various BEM- and energy efficiency-related issues.

In a previous study by Igugu et al. [17], a series of bibliometric and systematic queries was conducted on the Web of Science platform, a leading indexing database, to identify articles at the intersection of BEM and BEPG. The systematic queries identified 331 relevant articles published between 2012 and 2023 as the sample size. For this literature review, articles were screened to identify publications that investigated the role and viewpoints of stakeholders by searching for keywords that reflect direct engagement methods, such as “interview”, “questionnaire”, or “survey”. Each identified paper was thoroughly reviewed to ensure its relevance. Using this approach and the 43 articles deemed relevant, this section frames the knowledge gap within existing research.

The review revealed the multifaceted nature of stakeholders in the building sector that affects building energy efficiency outcomes. For example, Brown et al. [32] interviewed property owners in Sweden and found that environmental impact awareness among building users is a significant driver of demand for energy-efficient, certified non-residential buildings. Similar studies across Estonia, Sweden, and Norway substantiated this claim, extending the factors to include perceptions about green premiums and investment payback periods [32,33,34,35]. Alencastro et al. [36] investigated the connection between construction flaws and BEPG in the residential sector by interviewing contractors in the United Kingdom (UK) to determine the challenges builders encounter. Willan et al. [37] further explored how UK contractors perceive and discuss energy performance goals in relation to performance discrepancies. Several studies have also engaged other stakeholder samples (using interviews or questionnaires), such as building occupants [38,39,40,41], regulators and researchers [42], and non-profit associations [36], as well as real estate managers and developers [43]. These studies focus on role-players often from specific regions or countries, including Italy [41], Canada [38], Germany [44], Finland [45], Qatar [46], Cyprus [47], Ghana [48], Denmark [49], Belgium [50], China [51], India [52], Malaysia [53], and so on.

In addition to the aforementioned studies and stakeholders, experts and professionals in the building industry are often the target population due to their critical role in facilitating the development of energy-efficient buildings. Table 2 summarises a compilation of 20 studies from a sample of 43 publications, highlighting each study’s approach, the various expert groups that have been explored, and the region where the studies originate. Essentially, Table 2 also demonstrates that interviews are a proven method to engage with experts in the field (which informed this study’s research design) and generate valuable insights. For example, by engaging with industry experts in Europe, Alhamami et al. [54] found that despite the potential of BIM to improve building energy efficiency outcomes, there is a need for upskilling practitioners to capitalise on the technological possibilities. A previous study also established that a lack of adequate guidance given to building owners and occupants regarding how low-carbon systems in their buildings can be properly operated and maintained leads to undesirable outcomes. It reduced the expected gains, diminished long-term performance, and adversely impacted confidence in these low-carbon systems [55].

Another insight is the demographics and range of the respondents. The participant mix often includes building owners, project professionals and managers, as well as occupants. However, the perspectives and experiences of building energy modellers as key actors in delivering energy-efficient buildings are understudied. In addition, most studies that engage with stakeholders are based in the GN region context. This represents a significant gap and necessitates additional evidence from the GS context.

Although their context-specific realities may differ, GN and GS BEM practitioners share core challenges. However, GN practitioners benefit from resource-rich environments, leveraging well-developed technologies and processes, such as BIM integration [56,57,58,59], AI-driven insights [9,13], and regulatory frameworks, including Passive House [60]. GS modellers, on the contrary, face amplified barriers, including unreliable web tools [25] and non-contextualised material specifications [22,24].

Mutual learning between GN and GS practitioners offers pathways to enhance BEM globally. For example, the GN can adopt GS’s resilient strategies, such as contextually appropriate adaptive designs [19,61], thereby fostering robust and universal tools. On the other hand, GS practitioners could benefit from the GN’s automated workflows [62], as well as available open-source platforms like OpenStudio [13] and ethical AI guidelines [9]. This could help reduce manual efforts and enhance scalability.

Table 2. Review of BEM studies directly engaging experts and professionals.

№	Year	Article Title	Method	Total No. and Role(s) of the Respondents	Region	Reference
1	2014	Innovation in low-energy residential renovation: UK and France	Case study and semi-structured interviews	19 professionals (UK: 14; France: 5)	UK & France	[63]
2	2015	The indispensability of good operation & maintenance (O&M) manuals in the operation and maintenance of low carbon buildings	Semi-structured interviews and surveys	Experts in the low-carbon buildings industry [number not specified]	UK	[55]
3	2016	A methodology for estimating rebound effects in non-residential public service buildings: Case study of four buildings in Germany	Case study projects and semi-structured interviews	21 building users and non-specified number of building managers	Germany	[39]
4	2017	Ambitions at work: Professional practices and the energy performance of non-residential buildings in Norway	Interviews	11 respondents which include the building owners, employees, users and managers	Norway	[35]
5	2017	Realizing operational energy performance in non-domestic buildings: Lessons learnt from initiatives applied in Cambridge	Interviews	Building client, users, and operators [number not specified]	UK	[40]
6	2018	Application of Soft Landings in the Design Management process of a non-residential building	Case study project and semi-structured interviews	Two building users and four professionals (quantity surveyor, sustainability manager, architect, and facilities manager).	UK	[64]
7	2018	Energy efficiency practices for Malaysian green office building occupants	Questionnaires and interviews	53 building users (questionnaire) and five managers and construction professionals (interviews)	Malaysia	[53]
8	2019	Collaboration between designers and contractors to improve building energy performance	Semi-structured interviews	Nine experts: designers, contractors, project managers, and facility managers	China	[65]
9	2019	Strategies for minimising building energy performance gaps between the design intend and the reality	Semi-structured interviews	13 building energy practitioners (including managers, engineers, architects)	Australia	[66]
10	2020	Promoting energy efficiency in the built environment through adapted BIM training and education	Workshop and semi-structured interviews	40 workshop participants and 15 industry experts	Europe	[54]
11	2020	The perception of Swedish housing owners on the strategies to increase the rate of energy efficient refurbishment of multi-family buildings	Workshops and semi-structured interviews	24 workshop participants and four professionals (CEO, representatives, and managers)	Sweden	[34]
12	2020	A facilities manager’s typology of performance gaps in new buildings	In-depth interviews, focus group interviews, and workshops	Four in-depth, two focus groups, and three workshops	Denmark	[67]
13	2020	Appropriateness of soft landings concept for avoiding malpractices in Sri Lankan building projects	Interviews	20 experts (engineers, managers, architects, quantity surveyors, accountants, directors, etc.)	Sri Lanka	[30]
14	2020	Innovative designs of building energy codes for building decarbonization and their implementation challenges	Semi-structured and in-depth interviews	19 experts, researchers, and regulators	Denmark, France, England, Switzerland, and Sweden	[42]
15	2020	Talking about targets: How construction discourses of theory and reality represent the energy performance gap in the United Kingdom	Interviews, observations, and document analysis	31 construction industry professionals	UK	[37]
16	2020	Analysis of factors and their hierarchical relationships influencing building energy performance using interpretive structural modelling (ISM) approach	Semi-structured interviews	12 experts (engineers, contractors, managers, equipment specialists)	China	[68]
17	2022	Does a knowledge gap contribute to the performance gap? Interviews with building operators to identify how data-driven insights are interpreted	Interviews	11 building operators	Canada	[69]
18	2023	Investigating the influence of quality management on building thermal performance	Face-to-face interviews	15 housing association representatives, main contractors & quality officers	UK	[36]
19	2023	Net-positive office commissioning and performance gap assessment: Empirical insights	Interviews	Energy advisors and building operators [number not specified]	Canada	[70]
20	2023	Application of Soft Landings concept in Sri Lanka to narrow the building performance gap, enablers and barriers	Two phases of face-to-face interviews	20 experts (engineers, managers, architects, quantity surveyors, accountants, directors, etc.)	Sri Lanka	[71]

Table 2. Review of BEM studies directly engaging experts and professionals.

№	Year	Article Title	Method	Total No. and Role(s) of the Respondents	Region	Reference
1	2014	Innovation in low-energy residential renovation: UK and France	Case study and semi-structured interviews	19 professionals (UK: 14; France: 5)	UK & France	[63]
2	2015	The indispensability of good operation & maintenance (O&M) manuals in the operation and maintenance of low carbon buildings	Semi-structured interviews and surveys	Experts in the low-carbon buildings industry [number not specified]	UK	[55]
3	2016	A methodology for estimating rebound effects in non-residential public service buildings: Case study of four buildings in Germany	Case study projects and semi-structured interviews	21 building users and non-specified number of building managers	Germany	[39]
4	2017	Ambitions at work: Professional practices and the energy performance of non-residential buildings in Norway	Interviews	11 respondents which include the building owners, employees, users and managers	Norway	[35]
5	2017	Realizing operational energy performance in non-domestic buildings: Lessons learnt from initiatives applied in Cambridge	Interviews	Building client, users, and operators [number not specified]	UK	[40]
6	2018	Application of Soft Landings in the Design Management process of a non-residential building	Case study project and semi-structured interviews	Two building users and four professionals (quantity surveyor, sustainability manager, architect, and facilities manager).	UK	[64]
7	2018	Energy efficiency practices for Malaysian green office building occupants	Questionnaires and interviews	53 building users (questionnaire) and five managers and construction professionals (interviews)	Malaysia	[53]
8	2019	Collaboration between designers and contractors to improve building energy performance	Semi-structured interviews	Nine experts: designers, contractors, project managers, and facility managers	China	[65]
9	2019	Strategies for minimising building energy performance gaps between the design intend and the reality	Semi-structured interviews	13 building energy practitioners (including managers, engineers, architects)	Australia	[66]
10	2020	Promoting energy efficiency in the built environment through adapted BIM training and education	Workshop and semi-structured interviews	40 workshop participants and 15 industry experts	Europe	[54]
11	2020	The perception of Swedish housing owners on the strategies to increase the rate of energy efficient refurbishment of multi-family buildings	Workshops and semi-structured interviews	24 workshop participants and four professionals (CEO, representatives, and managers)	Sweden	[34]
12	2020	A facilities manager’s typology of performance gaps in new buildings	In-depth interviews, focus group interviews, and workshops	Four in-depth, two focus groups, and three workshops	Denmark	[67]
13	2020	Appropriateness of soft landings concept for avoiding malpractices in Sri Lankan building projects	Interviews	20 experts (engineers, managers, architects, quantity surveyors, accountants, directors, etc.)	Sri Lanka	[30]
14	2020	Innovative designs of building energy codes for building decarbonization and their implementation challenges	Semi-structured and in-depth interviews	19 experts, researchers, and regulators	Denmark, France, England, Switzerland, and Sweden	[42]
15	2020	Talking about targets: How construction discourses of theory and reality represent the energy performance gap in the United Kingdom	Interviews, observations, and document analysis	31 construction industry professionals	UK	[37]
16	2020	Analysis of factors and their hierarchical relationships influencing building energy performance using interpretive structural modelling (ISM) approach	Semi-structured interviews	12 experts (engineers, contractors, managers, equipment specialists)	China	[68]
17	2022	Does a knowledge gap contribute to the performance gap? Interviews with building operators to identify how data-driven insights are interpreted	Interviews	11 building operators	Canada	[69]
18	2023	Investigating the influence of quality management on building thermal performance	Face-to-face interviews	15 housing association representatives, main contractors & quality officers	UK	[36]
19	2023	Net-positive office commissioning and performance gap assessment: Empirical insights	Interviews	Energy advisors and building operators [number not specified]	Canada	[70]
20	2023	Application of Soft Landings concept in Sri Lanka to narrow the building performance gap, enablers and barriers	Two phases of face-to-face interviews	20 experts (engineers, managers, architects, quantity surveyors, accountants, directors, etc.)	Sri Lanka	[71]

Considering the significant role and impact of modellers on the simulation outcomes [72,73], some studies aimed to support BEM practitioners by providing guidelines or recommending specific workflows. For example, Choi et al. [74] developed guidelines to help practitioners select suitable airflow and infiltration simulation models through a theoretical and historical analysis. Similar studies also focused on strategies to upskill learners at higher education institutions and modellers in industry [75,76], improve the interface of BEM software [77], and optimise the amount of time required to run building energy performance simulations [62]. Nevertheless, research that directly focus on the modellers as key role-players in the delivery of energy-efficient buildings remains sparse. Specifically, the end-users’ perspectives on building energy performance simulation software are lacking. This knowledge gap is further exacerbated by the limited amount of BEM and BEPG studies from the GS context, where the bulk of new buildings are being built [28]. Therefore, this study aims to partially address this knowledge gap, which could stimulate further research in the field. Ultimately, the paper advances resource efficiency in the built environment and the practice of energy-efficient building design by identifying:

(1)

Current challenges encountered by industry-based practitioners;

(2)

Opportunities to enhance and scale the practice of building energy simulation and efficiency.

3. Research Methodology

This study employed a mixed-methods approach to examine the challenges and opportunities associated with the use of BEM software. Firstly, a review of pertinent literature was conducted to examine previous research and determine the knowledge gap and research context. The second phase of the study involved direct engagement with industry BEM practitioners, focusing on South African practitioners as representative of the African context and the larger GS. Notably, the GS (i.e., emerging and developing countries) accounted for approximately 76% of all new residential and non-residential floor space added between 2010 and 2022 [28]. The responses from the participants who formed part of the study ensure that the insights are relevant to the developing built environment context. The methodology assisted in understanding software preferences, operational difficulties, and industry requirements. At the same time, it supports the study’s findings and recommendations.

3.1. Sampling Method

The study incorporated two purposive, non-probability sampling approaches to acquire research participants. According to Etikan et al. [78], a researcher can select respondents intentionally when a need exists for potential participants who meet predefined criteria (e.g., levels of expertise or field of specialisation). Campbell et al. [79] further state that “the reason for purposive sampling is the better matching of the sample to the aims and objectives of the research, thus improving the rigour of the study and trustworthiness of the data and results.” Expert sampling, a purposive, non-probability sampling technique, selects participants based on their expert understanding of a subject or theme [61,80]. The following criteria were used to define the target population for the study:

• Practitioners with BEM experience;
• Practitioners with their professional practices in South Africa;
• Practitioners with completed building projects in South Africa.

The research integrated snowball sampling as a second non-probability sampling technique. Snowball sampling is a process in which respondents recommend other potential participants who match the predefined criteria and may also be willing to participate in the study [81]. In essence, snowballing enlarges the potential pool of participants [82]. While snowballing could introduce a measure of bias [83], it was necessary and applicable as demonstrated by Schwarz et al. [42] due to the absence of a professional registration body for South African BEM practitioners. This absence introduced uncertainty and limited the reach of potential participants.

Figure 1 summarises the specific applications of the sampling methods. The study applied the expert sampling methodology by identifying potential participants from the following:

• LinkedIn profiles;
• Presenters of Continuous Professional Development (CPD) courses related to building energy efficiency;
• Sustainability consultancies that were listed in the Green Building Council South Africa’s (GBCSA) case study library.

The authors also sent an invitation to registered persons (RPs) through the South African Council for the Architectural Profession (SACAP). After the fifth respondent, snowball sampling leveraged the respondents’ personal networks to acquire additional participants, resulting in 19 study participants (12 from prospecting and seven referrals).

3.2. Data Collection, Processing, and Analysis

Both quantitative and qualitative data were collected to evaluate the study’s theme. Firstly, a Likert scale rating was administered through the electronic collection of data with SurveyMonkey, focusing on the following:

• Which software programmes do participants use to simulate building energy performance?
• How often do practitioners use different software programmes?
• How user-friendly do respondents consider the simulation software they are using?

Secondly, during the structured interviews, the 19 participants explained the reasons behind their preference for specific software and ratings. The authors analysed the quantitative data from SurveyMonkey to present visualisations, illustrating the responses of individual participants. The 19 structured interviews were recorded and transcribed using Microsoft Teams. Each transcript was carefully reviewed and organised in preparation for analysis using MAXQDA 24 Analytics Pro software (version 24.4.1). MAXQDA was selected as the analysis tool for this study due to the flexibility it provides in integrating quantitative and qualitative datasets [84], while also being relatively more straightforward to use [85]. Ultimately, the transcripts were coded in two stages. Using MAXQDA, one researcher analysed and coded the transcripts, identifying the unique themes. Afterwards, a different researcher reviewed the transcripts and codes to ensure relevance and alignment.

3.3. Overview of the Respondents

Each participant is represented using a respondent identifier (Resp ID) to preserve anonymity.

Table 3. An overview of the research respondent.

Resp ID	Field of Specialisation	Professional Registration Body	Highest Qualification Level	Experience (No. of Years)
				Professional	BEM
R1	Architecture	SACAP	Bachelor’s Degree	29	11
R2	Architecture	SACAP	Master’s Degree	8	4
R3	Architecture	SACAP	Master’s Degree	23	2
R4	Certified Building Energy Modelling Professional (BEMP)	ASHRAE	Master’s Degree	10	10
R5	Architecture	SACAP	Master’s Degree	45	7
R6	BEM Software (engineering background)	None	Honour’s Degree	4	4
R7	BEM Software + Business Operator	None	Bachelor’s Degree	12	12
R8	Engineering	ECSA	Honour’s Degree	16	12
R9	Engineering	ECSA	Bachelor’s Degree	25	11
R10	Heating, Refrigerating and Air-Conditioning	ASHRAE	Bachelor’s Degree	18	15
R11	Energy Engineering	AEE	Master’s Degree	22	18
R12	Architecture	SACAP	National Diploma and Advanced Certificate	31	20
R13	Glazing Specialist	None	Master’s Degree	11	6
R14	Green Building Rating Specialist	None	Master’s Degree	11	6
R15	Engineering	ECSA	Master’s Degree	9	3
R16	Architecture	SACAP	Bachelor’s Degree	12	5
R17	Engineering	ECSA	Honour’s Degree	11	11
R18	BEM Software + Business Operator	None	Bachelor’s Degree	15	15
R19	Engineering	ECSA	Bachelor’s Degree	2	2

presents an overview of the study’s participants, highlighting their field of specialisation, professional registration or certification status, and highest educational qualification level. It also outlines the total years of experience in the built environment profession (Prof Years) and the total years of experience using BEM on building projects (BEM Years).

The 19 respondents have either an architectural or an engineering background. Six respondents were registered with SACAP and another five with the Engineering Council for South Africa (ECSA) at the time of participation in the research. Two respondents were certified by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE). In addition, one respondent was registered with the Association of Energy Engineers (AEE). The final five respondents were not registered with any professional body at the time of participating in the study.

Collectively, the 19 participants have 314 years of professional experience in the building industry and 174 years of BEM experience. This translates to an average of 16.5 and 9.2 years, respectively. Despite the limited sample size due to the previously highlighted uncertainties in the total population (see Section 3.1.), variations in the participants’ registration and certification status, as well as the range of years of experience, indicate a diverse distribution of industry practitioners. This variation helps explore the research theme to generate useful findings and recommendations that are applicable across the industry.

4. Discussion and Findings

The findings presented in this paper were part of a larger study investigating the barriers to building energy performance. This section discusses the outcomes of four central questions posed to the 19 research participants about the use of BEM software. The first part discusses the quantitative elements while the second part focuses on the “why”, thus capturing the experiences of practitioners and lending a voice to BEM software users. Each section follows a similar structure, stating the interview question and discussing the participants’ responses.

4.1. BEM Software in the South African Industry

4.1.1. Software Used by Practitioners

In question I, participants were asked to indicate which BEM software package(s) they use to simulate building energy performance in their practices when working on actual projects for clients. The research participants were allowed to select or specify multiple software packages. Figure 2 highlights the 14 software packages identified by practitioners. Approximately 74% of BEM practitioners use DesignBuilder, making it the most used building energy performance simulation software among South African practitioners. The other programmes selected by the participants include Microsoft Excel (37%), the EDGE App (32%), and BSIMAC (21%). Autodesk Insight, Integrated Environmental Solutions–Virtual Environment (IES-VE), and OpenStudio are some of the least-used BEM software among the respondents.

The software preferences of BEM among the South African practitioners provide context for the study’s results. However, the study aimed to determine the prevalent BEM tools in use and explore the current challenges that practitioners face when using these tools. Furthermore, the study reveals that certain features practitioners complement in their preferred software indicate the elements that industry-based end-users prioritise when selecting and using BEM software. For instance, practitioners use commercially available Microsoft Excel spreadsheets to facilitate building energy code calculations through the prescriptive compliance route. Others engage BEM tools that are certified by Agrément South Africa for compliance calculations (e.g., DesignBuilder) [86].

4.1.2. Relative Usage Frequency

The second question asked each research respondent to rank their selected BEM software package(s) by frequency of use, by assigning a percentage value to each selection out of a cumulative of 100%. The purpose of this question was to provide context for the findings around the use of BEM software and its associated implementation. In Figure 3, participants ranked their use of preferred BEM software package(s), with 89% of practitioners utilising DesignBuilder or BSIMAC. The data indicate that these two programmes are used to simulate the building performance of 60% to 100% of their projects.

The distribution shown in Figure 3 further presents software developers and BEM researchers with market information on the utilisation of BEM software in South Africa. Should industry-level interventions be considered, these two software programmes will have the broadest impact in the South African built environment and potentially the larger African construction industry.

4.2. Experiences of Practitioners in South Africa

During the structured interview phase, the interviewer asked Questions III and IV as a follow-up. This provided each participant with the opportunity to elaborate on the reasons behind their preferences and their experiences when using BEM software. In question III, respondents were asked to describe their process of using energy modelling software packages to simulate building energy performance, and to explain the reason for their response in Question IV. The participants responded to Question III using a five-point Likert scale, as illustrated in Figure 4. Furthermore, their responses to Question IV were analysed as described in Section 3.2. Using the MAXQDA code system, Figure 5 classifies and highlights the commonality in the everyday experiences of practitioners. The blocks are weighted by size and colour, showing the level to which each respondent indicates that the identified theme influences their daily practice. Blocks that are larger and closer to red indicates more coded comments from the participant.

The research indicates that the majority of respondents (nine of 19) attributed some level of difficulty (i.e., somewhat, or very difficult) to using BEM software. Four respondents considered the process to be somewhat or very easy. Finally, six of the participants reflected on the process as neither easy nor difficult. The responses to Questions I, II and III were compared to determine whether the ranking distribution of ease of use for BEM software was generic or specific to certain users. The analysis reveals no direct correlation between the description of the process and the specific software used by the participants.

The following discussions unpack the experiences of BEM practitioners as described by the research participants and illustrated in Figure 5. Ultimately, it highlights the current challenges in a developing market and the opportunities that exist, which, if maximised, could lead to potential growth in the BEM industry.

4.2.1. Software Operation Challenges

The operation of BEM software emerged as the primary source of challenges for practitioners. These challenges affect the efficiency, accuracy, and usability of BEM tools in professional practice. The discussions highlighted several key themes, including the speed of data processing and analysis, the flexibility of modelling workflows, and the complexity of software interfaces and systems. Additional themes include the availability and utility of 3D visual representations, and the transparency of underlying calculation methods. These issues reflect a broader tension between the need for rapid, user-friendly tools that accommodate diverse project requirements and the demand for precise, reliable outputs that align with real-world building performance.

The South African Practitioners’ experiences underscore opportunities for software developers to enhance functionality and streamline processes for further implementation. The diverse needs of existing and future users can be addressed to improve resource efficiency in the built environment, particularly in contexts such as South Africa, Africa, and the broader Global South, where standards and project demands vary widely. Following the analysis of the interview transcripts, four challenges were identified, namely:

• Software speed, flexibility, and level of detail;
• User interface, tools, and system complexity;
• Influence of a visual 3D geometric representation;
• Transparency of calculation methods.

I. Software speed, flexibility and level of detail: The speed and flexibility of BEM software are critical concerns for practitioners. These factors have a direct impact on project turnaround times and overall productivity. The participants highlighted that software like BSIMAC allows faster model execution compared to DesignBuilder. In other words, BSIMAC enables the rapid delivery of projects when faced with tight deadlines.

A core concern for practitioners working with BEM software is the speed of data capture and analysis. For example, R11 and R15 preferred BSIMAC, with R15 claiming to run “…models much faster on BSIMAC than … on DesignBuilder; probably half the time…”. The speed of analysis results in a more rapid execution of projects. This factor is beneficial when working with clients who demand quick turnaround times, allowing practitioners to increase their output. Additionally, R11 developed in-house tools and spreadsheets to enhance data capture speed when using BSIMAC. This indicates opportunities for software developers to improve processes in their programmes or provide additional tools which will enhance the functionality and speed of BEM software.

Flexibility is another challenge because some software programmes impose rigid, linear workflows. It can lead to confusion, especially in complex projects with multiple zones or detailed specifications. It is evident from the research that practitioners noted the need for customisable library components and less prescriptive modelling methods to accommodate diverse project requirements. One respondent (R1), who uses DesignBuilder, stated:

“It’s also quite linear in its application … But it can result in confusion … If you don’t follow the procedure sort of verbatim, it’s easy to get confused and there’s not really another workaround to another method of getting to that information that you need to change.”

Subsequently, some practitioners commended the varying levels of detail available in software such as DesignBuilder. For example, R14 emphasised that a DesignBuilder user can choose between a simplified heating, ventilation, and air conditioning (HVAC) design or a detailed HVAC design. The choice depended on the project’s complexity, needs and scale. However, other practitioners, such as R19, prefer to switch between software(s) based on the project and client requirements, stating:

“… with DesignBuilder and EnergyPlus, we go more into detail. So, what EDGE would do in the background, we actually do that in terms of defining the base case, defining the actual building, doing the comparison, and so forth… It’s a lot more work …”

II. User interface, tools, and system complexity: The user interface and available tools have a significant influence on practitioners’ experiences with BEM software. The respondents’ opinions varied based on programme usability and complexity. Software like DesignBuilder was commended for its intuitive interface, simple drawing tools, and customisable libraries, which facilitate quick model building and data management. This was particularly evident for users who lacked a background in architecture.

Some of the respondents found specific interfaces, such as BSIMAC, less user-friendly due to their reliance on complex input screens rather than graphical representations. This led respondents to explore alternative software, such as Rhino and Climate Studio. The learning curve for BEM software was also a recurring issue, with practitioners noting a high barrier to entry due to the technical knowledge required. Inconsistent system operations and the need for extensive training further complicate usage, suggesting that software developers should prioritise creating more accessible, user-friendly interfaces while maintaining robust analytical capabilities.

Across the respondents, there were varying opinions about the software interfaces, available tools and the processes. For example, R6 commented on the user interface of DesignBuilder, stating:

“… Everything’s clear. … The drawing tools are very simple, especially for me, who wasn’t an architect, to just pick it, build a block… we can create our own libraries so we can match a product to what… we have here in South Africa… We can also set it up for our (South African National Standards) SANS XA schedules or in our ASHRAE schedules…”

Despite various functions available in the BEM software, such as DesignBuilder, some users experience challenges which result in the search for alternatives. As R16 mentions, these challenges can arise due to varying educational backgrounds and personal preferences:

“It’s a subjective answer because I’m used to architectural software … modelling with DesignBuilder, I found it to be quite horrible. … I’m trying to explore how to actually use Rhino and Climate Studio for energy modelling, which I find easier to work with… inputs are not as difficult, but putting together the whole form and zoning it and things like that, is a bit challenging and it doesn’t make one enthusiastic to use the software…”

Similarly, R5 referred to BSIMAC as a “fairly complex system”, stating the following:

“… it’s just a series of different screens that you would click, and you would enter various parameters like areas of window, areas of walls, and coefficients of conductivity, etcetera. And it’s not…, from that point of view, as user-friendly, but none of them [BEM software] are in a way.”

The contrasting responses of R16 and R5 demonstrate that practitioners prioritise the user-friendly nature of a BEM programme. This presents software developers with the opportunity to develop energy modelling software packages that are easy to use, accurate, and thorough in analysis. Furthermore, practitioners claimed that they sometimes encounter inconsistent software system operations, with R9 mentioning that their practice has developed a list of simulation steps not to take, stating, “… we’ve got a list of ‘don’t do this’…”.

Several practitioners spoke about the difficulties in learning BEM software and maximising the available tools. This includes the following statement from R10, on the barrier to learning BEM software:

“… [for] someone new into this experience… it’s not very easy to just pick up a software package and do building energy modelling. There’s a lot behind it... There’s a lot of technical understanding required. But once you’ve got that baseline experience, I mean, you can do a lot fairly quickly, but I think the barrier to entry is still quite high…”

According to R13 (using DesignBuilder) and R17 (using IES-VE), additional challenges that significantly affect the simulation process, include specifying the correct data and software settings. This is supported by a statement from R17, saying that it is crucial to “…understand the limitations of the software…” being used. The research results clearly suggest a shortage of adequate skill or training, as well as the high complexity levels of the BEM programme(s). These complexities can be simplified to make BEM more accessible to practitioners, as supported by R19, stating:

“… EDGE is a very nice tool which is gaining popularity because it’s very easy to use. All at once, it’s just inputs, and it gives you outputs … It doesn’t need the user to really do any technical work.”

The researchers believe software developers could provide these two options as integrated features within their programmes. However, this endeavour should be balanced with ensuring accuracy and minimising performance discrepancies between simulations and real-world performance.

III. Influence of visual 3D geometric representations: Practitioners are of the opinion that 3D representation in BEM software significantly impacts its usability and appeal. Software like DesignBuilder, which offers visual modelling capabilities, is favoured because it allows users to visualise the data by presenting the building’s geometry. This feature also enhances client communication and model comprehension. In contrast, BSIMAC’s non-graphical, input-driven approach was perceived as faster by some participants. Amongst these participants were users accustomed to CAD-based systems, who considered BSIMAC less intuitive. Supported by R15, indicating a preference for non-graphical models, while emphasising the numerical basis behind 3-D visualisations:

“I do models much faster on BSIMAC than … DesignBuilder; probably half the time … Because you don’t have to draw buildings … I’m not intimidated by non-graphical models. … clients and designers and architects want to work on a CAD-based package … but fundamentally behind the CAD sits equations; numerical, mathematical models... And that’s a fundamental basis of any thermal performance software or heat transfer model…”

Moreover, R8 also stated:

“… I hate sitting and building geometry on the software … building in Revit or whatever, DesignBuilder, I just don’t enjoy that.”

In contrast, as mentioned above, some users, such as R1, preferred using visual presentation, stating:

“You can visually model the building. It’s not just a bunch of data that you enter into cards or like spreadsheet slots. You actually have something to show the client at the end of the day. This is what your building looks like in terms of energy modelling.”

Another participant, R14, supported the argument, stating:

“… you build a model where you can actually see the geometry. You can actually understand what the building looks like from the perspective of an architect. BSIMAC, it doesn’t use any of that at all. It uses simply a series of inputs; they’re called cards, and they are just screens with different boxes where you would input information. So, there isn’t really a geometric model that you could look at to see.”

Practitioners with engineering backgrounds, such as R15 and R8, found non-graphical software packages efficient, while those with architectural training (R1) preferred visual interfaces. This divide underscores the importance of 3D representation for engaging diverse users, including clients, designers and AEC professionals. It highlights the need for BEM software that strikes a balance between speed and visual clarity, catering to the varying professional preferences.

IV. Transparency of calculation methods: The processing of the input data using calculation methods and models of the BEM software is a critical yet challenging aspect for practitioners. These calculations directly affect the reliability and interpretability of simulation results. Users expressed difficulty in understanding the background calculations, as the software offered multiple calculation methods, which adds to the complexity.

For example, R6 noted the challenge of selecting appropriate methods and inputs, particularly for detailed HVAC modelling, without clear insight into how the software processes data. This lack of transparency can lead to errors or meaningless results if users do not fully grasp the underlying mechanisms. The practitioner (R6) states:

“… it’s a very complicated… You have to really understand it. There’s a lot of different methods that it uses for calculation. That was the trickiest thing for me when I started, figuring out “how does it calculate this? How?

What is it doing in the background?” Cause if you don’t understand that, you don’t understand what you’re doing and you’re producing nonsense results. And whenever you do a calculation, there’s a drop-down with like six different methods of calculation. … they have all those different calculation methods because they’re done to different standards or to different procedures. And so, understanding those was very, very, very difficult for me.”

Practitioners emphasised the need to understand these processes better and to ensure more accurate modelling data. The research suggests that software developers should provide more precise documentation or simplified options to enhance user confidence, results and overall accuracy.

4.2.2. The Software User as a Factor

The background and preferences of the software user significantly influence their interaction with BEM tools, with notable differences mainly between engineers and architects. Engineers approach modelling with a cause-and-effect mindset, valuing scientific outcomes, while architects prioritise visualisation and practical implementation. This is supported by R9, stating:

“I’m an engineer, so I think a little bit different to an architect as in for me, it’s a matter of “if I do this, what’s the result?” And for an architect, it’s like “this is how it’s going to be. Make it work.”

The lack of formal training and mentorship in BEM was also a recurring concern, as many practitioners relied on self-teaching or brief courses, resulting in inconsistent proficiency. R9 stated:

“One of the training companies has a two-day course, which just basically shows you how to set up the model. But to actually understand it and do it and struggle and run a simulation and it crashes and you try to find it. It becomes difficult.”

Similarly, R13 argued:

“…it’s somewhat difficult. You need a lot of knowledge, and you need a lot of practice and a lot of experience to ensure that your simulations are actually correct.”

This high barrier to entry, coupled with the need for extensive practice experience required to master software like DesignBuilder or IES-VE, underscores the importance of user experience and education. The researcher believes that software developers and targeted training programmes could address these challenges by offering more structured learning paths tailored to diverse professional backgrounds.

4.2.3. The Need for Comprehensive Protocols

The absence of a standardised approach to BEM creates inconsistencies, particularly when aligning with national standards, building energy efficiency codes or addressing real-world complexities, such as air infiltration. Practitioners noted that compulsory building energy standards, such as the SANS 10400-XA:2021 [87], prioritise minimum compliance over comprehensive design guidance. Consequently, critical factors such as indoor thermal comfort are often overlooked. This leads to models that may not accurately reflect real-world performance, as seen in R12’s frustration with the simplified HVAC modelling.

R12 emphasised the modelling challenges that arise when considering an HVAC system for an office building, using only simplified information.

“… my modelling, like on the HVAC system, it’s still just using a simple HVAC. So, even when we model those notional buildings, so like if I’m modelling an office building, that notional building—and it’s why I stayed with simple modelling—is that that notional office building was only modelled with simple HVAC. So, you just set a COP of 2.8 for heating and cooling through that whole building, but it’s not accurately reflecting the complexities of a proper HVAC system, and all the diffusers and all the components that make it up, that all have an energy cost and an impact on the real efficiency of that HVAC system. We’re not doing zonal modelling in office buildings. So, I find that frustrating because we’re not…in office buildings, it’s very difficult to do.”

Furthermore, R12 highlighted HVAC modelling gaps and the lack of information. Although it might not be relevant, data from other countries are used to address contextual challenges.

“I need to explore that element of air infiltration”. … buildings do leak air. You can’t ignore that. But what default value do you use? … I followed Australia legislation …”

The participants contrasted the minimum regulatory requirements of the SANS 10400-XA:2021 with the ideal of achieving better building performance through the use of reliable, predictive modelling. The following statement from R12 supports the argument:

“… the function of an XA standard should be to assist architects to design better buildings with some level of certainty. … The function of the standard is to ensure a minimum elementary level of adequacy. It’s a low level …”

Although R18 stated it is possible to capture data step by step for an energy model, there are particular challenges when deviating from the typical:

“… an energy model is not necessarily intuitive … there’s a specific way of doing it … [but] as soon as there’s kind of any deviation of what is a typical project, it’s then difficult...”

The non-intuitive nature of specific modelling processes requires particular training, as deviations from typical projects often complicate implementation and real-world analysis. The research recommends a standardised, yet flexible methodology that incorporates realistic parameters and clear guidelines that could enhance model accuracy and support better BEM outcomes.

4.2.4. The Case for Improved Support

Practitioners also highlighted that inadequate technical and user support present a significant challenge when using BEM simulation programmes. The first type of support mentioned is training for mastering the software, often presented as CPD courses. Participants claimed that without adequate prior BEM knowledge, they were unsure of the right questions to ask during the CPD training sessions. When engaging in real-world projects following the training, R2 stated that “… you’ve got a whole lot of questions that you feel ignored”. Similarly, R6 stated that BEM “… is a lonely field…”, specifically in developing countries such as South Africa.

Practitioners with such BEM experiences occasionally quit using advanced simulation programmes or only use part of the software’s capacity. In worst cases, these practitioners settle for using spreadsheets. This reality highlights the need for structured forums as an alternative to supporting South African and GS practitioners in furthering BEM knowledge.

The suggested forum could be seen as either software-specific and/or function as a professional community for BEM practitioners. In addition, the researchers’ opinions state that structured forums could potentially increase the uptake of BEM software and present best practices for simulating building energy performance that are localised and contextualised. A properly constituted forum could benefit the practice of sustainable, energy-efficient design in South Africa, Africa, and the broader GS.

5. Conclusions

This study aimed to identify the multifaceted challenges that South African practitioners encounter in implementing BEM software, as well as the significant opportunities. The objective is to enhance the adoption and efficacy of sustainable and energy-efficient building design practices in South Africa, Africa, and the broader Global South. Using a combination of structured interviews and online questionnaires, this study directly engaged 19 South African industry experts to analyse software usage preferences and how practitioners perceive their use of BEM tools for simulating building energy performance. The findings reveal that while tools like DesignBuilder and BSIMAC are widely used, issues such as software speed, inflexible workflows, complex interfaces, limited 3D representation, and uncertain calculation methods hinder their effectiveness in a developing context.

The steep learning curve of BEM software(s), driven by inadequate formal training and the diverse professional backgrounds of end-users, amplifies the need for tailored support at both the tertiary and industry levels. In addition, early-stage simulations encountered knowledge, cost, and client resistance barriers, while industry practitioners in South Africa also have to balance complexity and delivery speed. These training and expertise gaps can hinder real-world efficiency. However, the gaps represent a unique value-creation opportunity for various industry actors in South Africa, such as software developers, to invest in upskilling practitioners and partnering with higher education institutions to train the future workforce. The barriers also limit the market pull for building energy simulation and efficiency expertise. However, policies can be enacted to incentivise the use of BEM for building performance analysis.

It is crucial to note the differing needs of modellers from industry who work directly with clients and those who engage BEM as a tool for research. The distinction is necessary because research often advances at a considerably faster pace than what is required by regulations or green building certification systems for industry practitioners. At the same time, the absence of standardised modelling approaches further exacerbates inconsistencies, particularly when aligning with national building energy standards such as the SANS 10400-XA:2021, which often prioritise minimum compliance over comprehensive design guidance and international best practice. To achieve real-world energy efficiency improvements, integrating research-based recommendations into industry workflows for practitioners is essential.

This study presents evidence from South African practitioners regarding challenges associated with the BEM process. To address the identified challenges, the researchers suggest that software designers prioritise the further development of user-friendly interfaces and integrate flexible software functionalities. Simultaneously, establishing targeted building energy simulation training programmes, professional forums, and standardised methodologies could empower BEM practitioners and increase current interest. It could also lead to greater collaboration, industry-wide adoption, and ultimately elevate built environment energy efficiency standards. The implementation of these recommendations could assist South African built environment practitioners to better utilise BEM as a tool to achieve energy-efficient design practices and address real-world building operation challenges. In addition, the study’s findings and recommendations can be adapted in the context of other African countries to foster the growth of energy-efficient practices. Ultimately, the research seeks to contribute to global efforts in mitigating climate change and achieving the 2030 United Nations Sustainable Development Goals (UN SDGs). Future research could investigate the applicability of these findings and potential unique challenges in other developing countries of the GS region.