Integrating Social Aspects into Energy System Modelling Through the Lens of Public Perspectives: A Review

Abstract

The energy system model (ESM) predominantly emphasises techno-economic factors and often overlooks the essential social dimensions that are crucial for successful energy transitions. This review investigates the integration of social aspects into energy system models (ESMs) and explores approaches for incorporating public perspectives. Through a systematic literature review utilising the Preferred Reporting Items for Systematic Reviews and Meta-Analyses approach (PRISMA), 79 relevant publications were identified. The analysis revealed that while some studies considered socio-economic and socio-environmental elements, these efforts are frequently insufficient to fully comprehend social dynamics. The review highlights the significance of public engagement in ESMs, with 29 studies demonstrating some form of public participation. However, current engagement strategies are often limited to information sharing and consultation, with minimal strong collaboration. This study advocates for the implementation of transparent collaborative approaches in ESMs, including co-creation, active participation, and iterative processes, to enhance the comprehensiveness and societal relevance of models. It also addresses challenges, such as time constraints and the conversion of qualitative inputs into quantitative parameters. The review concludes by calling for further research to develop transparent, iterative frameworks for collaborative approaches in ESMs, emphasising the inclusion of vulnerable population perspectives to ensure equitable energy transitions and more effective, socially acceptable outcomes.

1. Introduction

Energy is crucial for all forms of development, human health, and welfare. Renewable energy promotes sustainable environmental and economic development, mitigates carbon emissions, and generates employment and business opportunities for the general population and entrepreneurs. More than 90% of nations worldwide are striving to increase the proportion of renewable energy in their energy portfolios. The United States is playing a significant role in the global expansion of renewable energy capacity. In 2022, it joined China and the European Union (EU) in accounting for 75% of the global additions to renewable energy capacity [1]. Over the last decade, there has been a substantial increase in focus on renewable energy systems. This significant shift towards renewable energy systems necessitates consideration of the technical, economic, and social dimensions of these energy systems. Modelling tools, such as simulation models and optimisation models, play a crucial role in investigating the economic and technical aspects of the energy system and supporting policy decisions for future energy system analysis [2].

Substantial research has been conducted on the technical and economic aspects of energy systems. Modelling tools adopted by modellers to develop renewable energy systems vary significantly. These models support decision-making and cover sectors such as electricity, transport, buildings, and industry from a techno-economic perspective [3,4]. Traditionally, research has focused on techno-economic assessments for long-term energy system planning. Social aspects are frequently overlooked or considered external to these assessments [5]. Future energy systems should consider social aspects in conjunction with technical features [2,6]. The integration of social and environmental factors into ESM is increasingly recognised as essential [5,7,8]. The incorporation of social elements in techno-economic modelling provides a more accurate representation of societal realities during energy transitions [9]. Disregarding non-technical factors, such as political support and societal acceptance, may impede the achievement of the Paris Agreement goals. Therefore, researchers have developed innovative approaches to balance techno-economic, environmental, and social factors in energy models. This study reviews current ESM practices and identifies ways to integrate social aspects into these models.

Energy system modellers increasingly aim to integrate various stakeholder perspectives, including those from energy and environmental experts, social scientists, industry representatives, civil society organisations, citizens, and government bodies. This integration aims to incorporate diverse perspectives into the modelling process. Stakeholder involvement is necessary for a comprehensive representation of social aspects in energy models. Many studies advocate the inclusion of ordinary citizens in energy system modelling and planning, as they are directly impacted by conventional energy systems and sustainable energy policy formulation [10]. Citizen engagement methods are vital for comprehending public values and attitudes towards future energy systems [11]. This review examines methods for integrating public perspectives into ESMs. The public engagement process must be transparent and iterative to ensure that the model accurately reflects reality. However, to the best of the authors’ knowledge, there is a gap in the literature regarding transparent strategies for collaborative approaches that incorporate public perspectives into energy models along with those of other stakeholders.

This study conducted a comprehensive review of current ESM approaches, with a particular emphasis on the integration of social aspects and potential methodologies for their inclusion. It addresses the limitations of traditional models by exploring approaches that incorporate public perspectives. By outlining collaborative approaches for integrating public perspectives, this study contributes to the development of comprehensive and socially informed transition strategies. Moreover, this review identifies the challenges and elucidates considerations for future research in this domain.

Therefore, the aims of this study were:

• To review current ESM practices, focusing primarily on social aspect integration.
• To identify ways to incorporate social dimensions into ESM by addressing the limitations of traditional techno-economic models.
• To present collaborative approaches for integrating public perspectives into energy models.

2. Background

2.1. Energy System Model (ESM)

The energy system model was initially developed in the 1970s to design energy systems and to support long-term energy planning. Subsequently, it evolved into a tool for addressing greenhouse gas (GHG) emissions. The transition from a fossil fuel-based energy system to a low-carbon energy system is essential for mitigating the increase in global temperature. As noted by Lund et al. [12], the primary objective of energy models is to guide the design, planning, and implementation of future energy systems. Consequently, a comprehensive strategy integrating various dimensions (e.g., technological, economic, social, environmental, institutional, and political) is imperative for designing complex future energy systems. ESMs aim to develop alternative energy frameworks to increase the proportion of renewable energy and reduce carbon emissions. ESMs are frequently employed to design an economically efficient energy system; however, they often overlook socio-political considerations [5,13]. Primarily, ESMs focus on techno-economic aspects and require a comprehensive approach to incorporate socio-political factors [13]. Six distinct energy models incorporate social aspects into the modelling process: the energy system optimisation model (ESOM), energy system simulation model (ESSM), integrated assessment model (IAM), computable general equilibrium model (CGEM), partial equilibrium model (PEM), and agent-based model (ABM) [14,15].

Table 1. Overview of ESMs along with integration of social aspects (source: [14,15]).

Model Type	Function	Features	Application Scenarios	Integration of Social Aspects
Energy system optimisation model	Optimises energy systems for least-cost solutions	Cost-oriented, calculates prices endogenously	Scenarios requiring cost minimisation and technological detail	Limited representation of social aspects and microeconomic processes
Energy system simulation model	Simulates energy system behaviour over time	Captures dynamics without necessarily optimising	Exploring the impacts of different policies or changes over time	Allows exploratory analysis but may not focus on social factors
Integrated assessment model	Analyses long-term policy impacts by integrating human and natural dimensions	Recognises the interaction between the economic framework and climate system	Assessing climate policy impacts and systematic changes over long periods	Incorporates behaviour and lifestyle elements in narratives and scenarios
Computable general equilibrium model	Models’ economy-wide interactions between sectors and agents	Comprehensive view of economic interactions	Analysing economic policies and broader economic impacts	Considers behaviour, lifestyle, and diversity of participants within input parameters
Partial equilibrium model	Focuses on specific sectors or markets, assuming others remain unchanged	Detailed insights into specific markets	Detailed analysis of specific markets or sectors within the energy system	Typically lacks broader social aspect integration
Agent-based model	Emphasises decision-making processes of individual agents	Represents social phenomena at a microeconomic level	Studying complex social systems and individual behaviour impacts on energy transition	Integrates the diversity of stakeholders, and public ownership, emphasising the importance of social dynamics

provides a concise overview of each model type, highlighting their unique functions, features, and how they incorporate social aspects into energy modelling.

The ESOM, including HOMER, OSeMOSYS, MARKAL, and BALMOREL, provides more comprehensive technical information on energy systems [16]. The input and output of energy models encompass social aspects, including behaviour and lifestyle, as well as public acceptance and opposition [14]. Similarly, the ESSMs EnergyPLAN and LEAP primarily incorporate social aspects through narratives and the input and output discussion of energy models [14]. Optimisation and simulation of ESMs are prevalent methodologies for techno-economic energy system analysis to achieve net-zero emissions by 2050 [17]. The IAM exhibits characteristics similar to the ESOM and ESSM, encompassing technical aspects of the energy infrastructure (e.g., electricity and transportation systems) and costs associated with technology expenditures (including capital and O&M costs) [16]. Additionally, the IAM addresses social factors, particularly behaviour and lifestyle, in the input and output of the model [18,19]. The PEM exhibits significant similarities to the CGEM in terms of its framework and mechanism [15]. Bachner et al. [20] employed the CGEM to incorporate “behaviour and lifestyle” as social aspects within the input parameters. By contrast, the ABM offers alternative approaches that enable modellers to observe agent interactions [21]. It is recognised as a sophisticated tool for comprehending the complex societal challenges associated with transitioning energy systems [21,22].

A comprehensive approach to ESMs—one that encompasses not only technical and economic factors but also environmental, social, and political aspects—achieves a balance to realise societal benefits. The incorporation of socio-political aspects into ESMs presents a significant challenge. Addressing this challenge requires a multidisciplinary methodology that integrates specialists from various fields, including social sciences, environmental studies, and engineering. This collaborative initiative promotes a more thorough comprehension of energy systems, considering both the technical and societal dimensions. A pertinent illustration of this strategy entails the organisation of workshops that engage both citizens and policymakers. This approach not only democratises the modelling process but also enriches it through diverse viewpoints, which may result in more effective and widely acceptable outcomes [23]. However, the implementation of this comprehensive approach presents considerable challenges. These include ensuring equitable participation from all stakeholders and addressing communication disparities between disciplines. Traditional ESM approaches often prove inadequate for effectively addressing these challenges [13,24].

As highlighted by Dioha et al. [13], there are five recommended actions (outlined in Section 4.2) to integrate socio-political aspects into ESMs. Notably, collaborative approaches within ESMs, which have received less attention from the modelling community, have proposed an alternative strategy for enhancing a more comprehensive understanding of societal aspects towards a climate-friendly future. Energy system modellers, who are technical experts, tend to focus on technical dimensions while overlooking the social and political aspects that influence the energy system. Nevertheless, incorporating social aspects into ESMs is just as crucial as addressing techno-economic factors to facilitate a successful transition [13]. The effective incorporation of social aspects necessitates the implementation of collaborative methodologies that engage a diverse range of stakeholders throughout the modelling process. This approach not only enhances transparency and credibility but also ensures that models accurately represent the perspectives and concerns of communities directly affected by energy policies [25]. For example, collaborative techniques have demonstrated efficacy in reconciling technical expertise with local knowledge, thereby enhancing the relevance and feasibility of energy scenarios [26]. Moreover, acknowledging the socio-political dynamics can facilitate the addressing of energy equity issues, ensuring equitable resource distribution and participation in policymaking processes [27]. Ultimately, the integration of these social elements will result in more comprehensive and inclusive energy transition strategies that align with societal needs and aspirations.

2.2. Energy Scenario Design

The global landscape is undergoing a transition towards decarbonisation, shifting from the business-as-usual (BAU) or reference scenario to various scenarios influenced by societal pressure, technical innovation, and governmental initiatives [28]. Significant uncertainties regarding the potential infrastructure of energy systems include technical advancements, policy frameworks, economic growth, and reduced carbon emissions. Therefore, relying exclusively on a business-as-usual scenario is no longer viable, and it is imperative to explore multiple scenarios to accommodate future energy systems. The business-as-usual approach aims to validate energy system models and compare the output with that of a real-world scenario. Furthermore, it can serve as a reference for designing alternative scenarios. Validating the energy system model is crucial for establishing reliability and ensuring the accuracy of the outcomes elicited from alternative scenarios.

Research and governmental institutions evaluate the net-zero emission goals and renewable energy targets set by each country. Most studies on energy transition incorporate two distinct frameworks: (1) energy system modelling and (2) energy scenario analysis [29]. This combination allows for a comprehensive examination of potential pathways to achieve decarbonisation targets. This twin-framework methodology not only facilitates a comprehensive understanding of energy systems but also underscores the significance of integrating socio-economic factors into modelling efforts. For instance, while traditional energy system models predominantly focus on technological and economic variables, contemporary research advocates the inclusion of social dimensions such as consumer behaviour and community acceptance to enhance model accuracy and relevance [6]. By bridging quantitative modelling with qualitative insights from social sciences, researchers can develop more holistic scenarios that reflect real-world complexities, thereby improving policymaking processes [30]. ESMs aim to generate scenarios or compare energy alternatives to examine critical factors, such as energy demand and supply, renewable energy fractions, unmet load, costs, and carbon emissions. Mustonen [31] developed three scenarios using the LEAP model to examine the electricity demand in a rural village in the Lao People’s Democratic Republic. Scenarios can be employed to achieve specific objectives based on the context of a particular region. Notably, scenarios can facilitate the anticipation of future energy systems and address specific questions regarding potential developments, such as the prospective composition of future energy systems. The simulation models are fundamentally scenario models [12]. These scenario models have the potential to enhance our comprehension of future energy systems while facilitating citizen engagement in energy-related discourse. Consequently, these scenarios may serve as a guide towards achieving sustainable energy transitions.

Scenario analysis in modelling facilitates long-term energy planning and enables the analysis of environmental and climate systems. Cantarero [32] utilised the LEAP and EnergyPLAN models to evaluate future energy system planning in Nicaragua. They investigated three scenarios to increase the proportion of renewable energy in the Nicaraguan energy system, as follows.

• Business-as-usual (BAU) scenario: This scenario projects ongoing trends without new policies and functions as a reference point for evaluating alternative strategies. It assumes no substantial changes in energy sources or technological advancements, thus preserving the current energy production and consumption patterns. This scenario resulted in the highest costs and GHG emissions compared to the other scenarios.
• Electricity generation expansion plan (EXP) scenario: This scenario emphasises the advancement of renewable energy for electricity generation and yields diminished greenhouse gas emissions and lower unit electricity costs than those of the BAU scenario.
• Greener alternative (BIO) scenario: This scenario highlights the incorporation of biofuels and the adoption of efficiency-enhancing measures in the residential sector. Consequently, fuel costs are approximately 14% lower than in the BAU scenario, which is attributed to the biodiesel and ethanol blends used in transportation. The BIO scenario also resulted in carbon dioxide (CO₂) payments of approximately 14.6% less than the BAU scenario, reflecting a decrease in greenhouse gas emissions.

In an energy system model, scenarios can be formulated to conduct technical and economic evaluations of an energy system. This approach can enhance energy efficiency by incorporating renewable energy technologies and identifying the most cost-effective energy mix to minimise the overall energy cost.

3. Methods

A comprehensive review of key aspects of ESMs was conducted, with particular emphasis on the current integration of social aspects and public perspectives into energy models and the prospective steps for their incorporation in the modelling, following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) (http://www.prisma-statement.org/, accessed on 14 April 2024) approach. This approach comprises the following key steps [33]: (1) identification (defining search criteria and selecting databases), (2) screening (applying inclusion and exclusion criteria), and (3) eligibility (further assessment through full-text article review). The comprehensive search process for systematic reviews is presented in Figure 1. The initial phase of the research process involved a keyword search using the Scopus database. The search was designed to identify documents containing a combination of the following three strings.

• (“energy” OR “alternative energy” OR “renewable energy”) AND (“storage” OR “battery”) AND (“model” OR “modelling”) AND (“scenario” OR “pathway” OR “forecast” OR “projection”) AND (“socio” OR “acceptance” OR “opposition” OR “participation” OR “view”).
• (“energy” OR “alternative energy” OR “renewable energy”) AND (“storage” OR “battery”) AND (“model” OR “modelling”) AND (“scenario” OR “pathway” OR “forecast” OR “projection”) AND (“social” OR “perception” OR “preference” OR “perspective” OR “engage”).
• (“energy” OR “alternative energy” OR “renewable energy”) AND (“storage” OR “battery”) AND (“model” OR “modelling”) AND (“scenario” OR “pathway” OR “forecast” OR “projection”) AND (“society” OR “employment” OR “job” OR “growth” OR “lifestyle”).

The search was directed towards the subject areas outlined in

Table 2. Selected subject areas for the three search strings on the Scopus database.

Subject Area	Average Percentage
Energy	33.87%
Engineering	28.28%
Environmental Science	22.69%
Social Sciences	5.36%
Business, Management, and Accounting	3.31%
Economics, Econometrics, and Finance	3.19%
Decision Sciences	1.82%
Multidisciplinary	1.48%
Total	100%

. These subject areas were selected with careful consideration, taking into account the interconnection of energy models with disciplines such as energy, engineering, environmental science, and economics [34].

The comprehensive literature search yielded 2295 articles that were distributed as follows:

• The initial search, focusing on terms such as “socio” OR “acceptance” OR “opposition” OR “participation” OR “view,” generated 392 studies.
• The subsequent search, targeting terms including “social” OR “perception” OR “preference” OR “perspective” OR “engage,” resulted in 1223 studies.
• The final search, centred on terms such as “society” OR “employment” OR “job” OR “growth” OR “lifestyle,” generated 680 studies.

In the second stage, a thorough review of article abstracts was performed, leading to the selection of 134 pertinent publications. This screening process involved the application of the inclusion and exclusion criteria to eliminate irrelevant studies. This critical step reduced the number of articles to those deemed potentially relevant based on their abstracts. The selection criteria were formulated to select studies that explored the integration of technical, economic, environmental, and social aspects, policy analysis, future energy scenarios, an interdisciplinary approach, and stakeholder viewpoints on energy system modelling. This review aims to examine energy system modelling in urban, rural, and national contexts. However, the criteria excluded studies that focused on building energy simulations, power system modelling, and a broad range of other energy system modelling tools utilised for whole-economy modelling. Subsequently, a comprehensive analysis of the full text of these 134 articles was conducted. The review revealed that 79 of these publications addressed, either primarily or secondarily, the technical, economic, environmental, and social aspects of energy system models, as well as public perspectives. To achieve a successful energy sector transition, it is crucial to integrate social aspects into traditional ESMs, which typically emphasise technical and economic elements. The current methods of public involvement in ESMs are often restricted to sharing information and consultation approaches, lacking substantial collaborative efforts. Researchers have suggested adopting transparent and cooperative approaches to enhance the societal relevance of energy models.

Of the 79 articles examined, 50 addressed key aspects of ESMs, including the current and potential integration of social factors. The remaining 29 articles investigated the incorporation of public perspectives into ESMs. This review encompasses 79 publications, with a comprehensive overview provided in Appendix A (

Table A1. Existing studies addressing various aspects of ESMs (source: Scopus database).

Sl.	Authors	Year of Publication	Ref.	Country/Region	Sector Focused	Methods	Purpose of the Study
							Technical	Economic	Environmental	Social
1.	Benjamin K. Sovacool	2009	[118]	-	-	Semi-structured interviews	☑			☑
2.	Francis G.N. Li et al.	2015	[119]	-	-	STET	☑			☑
3.	Anurag Chauhan, R.P. Saini	2016	[120]	India	Electricity and cooking	HOMER	☑	☑
4.	Pouya Ifaei et al.	2017	[121]	Iran	Electricity	TESEMA	☑	☑	☑	☑
5.	Alexandros Gasparatos et al.	2017	[71]	-	Ecosystems and biodiversity	Millennium Ecosystem Assessment (MA) framework			☑
6.	Yashwant Sawle et al.	2018	[55]	India	Electricity	GA, PSO, BFPSO and TLBO	☑	☑		☑
7.	Yahya Z. Alharthi et al.	2018	[44]	Saudi Arabia	Electricity	HOMER	☑	☑
8.	Avila Sofia	2018	[68]	Americas, Africa, Asia, and Europe	Electricity	Environmental Justice Atlas (Ej-Atlas)			☑	☑
9.	Nasser Yimen et al.	2018	[45]	Cameroon	Electricity	HOMER	☑	☑
10.	Gabriela Iacobuta et al.	2018	[61]	-	Climate change mitigation	Comprehensive approach			☑
11.	Philippa Roddis	2018	[76]	UK	-	Binomial logistic regression				☑
12.	M.R. Elkadeem et al.	2019	[52]	Sudan	Agriculture	Systematic framework	☑	☑	☑
13.	O. Krishan and S. Suhag	2019	[122]	India	Electricity and agriculture	HOMER and MATLAB/Simulink model	☑	☑
14.	Evelina Trutnevyte et al.	2019	[7]	-	-	-				☑
15.	K. Murugaperumala and P. Ajay D Vimal Raj	2019	[123]	India	Electricity	ANN-BP, LM and HOMER	☑	☑
16.	Vijay Mudgal et al.	2019	[124]	India	Electricity	HOMER	☑	☑
17.	Andrea A. Eras-Almeida et al.	2020	[125]	Galapagos Islands	Electricity	HOMER Pro	☑	☑
18.	Nasser Yimen et al.	2020	[47]	Nigeria	Electricity	MATLAB	☑	☑	☑
19.	N. Takatsu and H. Farzaneh	2020	[50]	Japan	Electricity	PSO algorithm	☑	☑
20.	Samuel Carrara	2020	[69]	EU	-	Scenario-based	☑
21.	H. K. Pujari and M. Rudramoorthy	2021	[41]	India	Electricity	HOMER	☑	☑
22.	Jean-Michel Clairand	2021	[42]	Ecuador	Electricity	HOMER	☑	☑
23.	Faizan A. Khan et al.	2021	[126]	India	Electricity	HOMER Pro	☑	☑	☑	☑
24.	Connor McGookin et al.	2021	[77]	-	-	Systematic review				☑
25.	Hossam A. Gabbar et al.	2021	[54]	-	Maritime	Differential evolution algorithm (DEA)	☑	☑	☑
26.	P. K. Kushwaha and C. Bhattacharjee	2022	[36]	India	Electricity and telecommunications	HOMER	☑	☑	☑	☑
27.	Rakibul Hassan et al.	2022	[74]	Bangladesh	Electricity	NSGA-II	☑		☑	☑
28.	Saikat Saha et al.	2022	[35]	-	-	A comprehensive review of IRES	☑	☑	☑	☑
29.	Prashant Malik et al.	2022	[39]	India	Electricity	HOMER	☑	☑	☑
30.	R. Kumar and H. Channi	2022	[127]	India	Electricity	HOMER and TOPSIS	☑	☑	☑
31.	Jinze Li et al.	2022	[62]	China	Electricity	HOMER	☑	☑
32.	Djeudjo Temene Hermann et al.	2022	[66]	Africa	-	MOPSO	☑	☑	☑
33.	Diana Süsser et al.	2022	[5]	EU	-	Empirical and desk research			☑	☑
34.	Abdulla Al Wahedi and Yusuf Bicer	2022	[128]	Qatar	Electric vehicles	HOMER	☑	☑
35.	Alaize Dall-Orsoletta et al.	2022	[27]	-	-	Systematic review of SD energy system				☑
36.	Md. Mahai Menul Islam et al.	2022	[129]	Bangladesh	Healthcare	HOMER and PVsyst	☑	☑
37.	Mamoon Ur Rashid et al.	2022	[130]	Pakistan	Electricity	HOMER	☑	☑
38.	Faizan A. Khan et al.	2022	[46]	India	Electricity	HOMER and MATLAB/Simulink	☑	☑	☑	☑
39.	David J. Hess et al.	2022	[67]	North America	Electricity	Systematic comparative analysis			☑	☑
40.	Theresa Liegl et al.	2023	[6]	-	-	Systematic review				☑
41.	Upeksha Caldera et al.	2023	[70]	Sri Lanka	Electricity, heat, transportation, desalination	LUT-ESTM	☑		☑	☑
42.	Ibrahim AlHajri et al.	2023	[63]	Kuwait	Electricity and desalination	Mixed-integer linear programming	☑	☑	☑
43.	Matin Karbasioun et al.	2023	[38]	Iran	Electricity	MCDA	☑	☑	☑	☑
44.	Mohamed R. Elkadeem et al.	2024	[37]	Egypt	Electricity	HOMER and MPC	☑	☑	☑	☑
45.	Connor McGookin et al.	2024	[23]	-	-	Participatory approach				☑
46.	Pujari Harish Kumar et al.	2024	[131]	India	Electricity	HOMER	☑	☑	☑
47.	Surajit Sannigrahi	2024	[58]	India	Electricity, fresh water, and electric vehicles	MOPSO	☑	☑	☑
48.	Yasmine Ayed et al.	2024	[132]	Tunisia	Electricity	HOMER	☑	☑	☑
49.	Subhash Yadav et al.	2024	[133]	-	Electricity	HOMER	☑	☑
50.	Oladimeji Lawrence Oyewole et al.	2024	[57]	-	Electricity and transportation	AIMMS	☑	☑	☑

) and Appendix B (

Table A2. Different forms of stakeholder involvement approach in energy models.

Sl.	Year of Publication	Ref.	Types of Stakeholders Engaged in ESMs	Stakeholders’ Engagement Approach
1.	2007	[134]	University, local municipalities, energy management agency, local business, and citizens	Information (public representation of initial reports), consultation (expert working groups for technical analysis)
2.	2009	[98]	Citizens, energy experts, representatives from different groups/institutions, local experts, and local decision-makers	Information (providing information about existing energy scenarios), strong emphasis on consultation (interviews with stakeholders and experts, workshops, stakeholder feedback) and strong emphasis on collaboration (joint development of energy scenarios, participatory workshops)
3.	2010	[84]	Community leaders, regional partnership boards and members, members of NGOs, and university researchers	Information (presenting modelling results and scenario outputs to the participants), consultation (gathering input from participants on scenario development, surveying participants after the workshop), strong collaboration (involving stakeholders in scenario creation through the INSPECT process, working with stakeholders to identify critical knowledge gap and research needs)
4.	2010	[88]	Residents, tourists, park managers, forest user groups, guides, local key informants, research institutions and universities	Information (collecting data through surveys and questionnaires, providing information on energy consumption, indoor air quality), consultation (focus group discussion with local residents, interviewing key informants, scenario planning sessions) and collaboration (participatory modelling process involving stakeholders, discussing management scenarios with stakeholders)
5.	2011	[89]	Members of the public from diverse backgrounds across US and UK, researchers, local councils, and energy companies	Information (sharing background information about the town), consultation (asking participants to individually rank, gathering qualitative data through discussions), and collaboration (role-playing, encouraging participants to debate and discuss)
6.	2011	[90]	Energy consumers, experts, academics, and energy industry	Information (providing descriptions of energy visions and scenarios to stakeholders), consultation (eliciting stakeholders’ preferences for different energy visions), and collaboration (involving board members in defining study questions, visions, and criteria)
7.	2012	[135]	Residents, village councils, farmers, local mayor, and researchers	Information (initial citizens’ meetings to introduce the concept) and consultation (semi-structured interviews with 5 key actors, MCDA workshops where participants could express their preferences)
8.	2014	[136]	City authorities, citizen groups, market associations, technical and scientific experts	Information (providing information through GIS maps, sharing results of surveys) and consultation (conducting extensive surveys and analysing smart meter data)
9.	2014	[137]	Participants from metropolitan area, researchers, community organisations	Information (providing participants with homework materials about climate change and electricity technology, presenting a review of homework materials during group meetings), consultation (asking participants to answer knowledge questions about the material, collecting participants’ ratings on environmental statements), and partial collaboration (facilitating group discussions where participants shared their portfolios and opinions, allowing participants to revise their chosen opinions)
10.	2014	[138]	MSU students, administrations, resource management, energy consultancy, energy, and environmental engineers	Information (providing online primer about energy system on MSU campus presenting information), consultation (eliciting information from stakeholders), and collaboration (allowing participants to construct their own energy portfolio, involving students in decision-making about university’s future energy strategy)
11.	2015	[139]	Decision-makers, experts from various disciplines, moderators, analysts, and public/general population	Information (surveys), consultation (expert workshops, online surveys), and collaboration (stakeholders involved in the decision-making process)
12.	2016	[140]	Residents, academic researchers, local community organisations, local political representatives, and local business interests	Information (creating posters and models to communicate ideas, organizing an initial exhibition), consultation (gathering input from residents on local resources and feasible technologies, discussing various aspects of energy futures), and collaboration (jointly developing research pathways, collaboratively designing, and creating models)
13.	2016	[92]	City council, district heating company, natural gas and coal suppliers, citizens	Information (providing data on current energy situation, energy consumption, and potential of renewable resources), consultation (input through storytelling, feedback on desktop research results, input on scenario section), and collaboration (co-creation of scenarios, joint development of future visions and pathway participation in participatory workshops)
14.	2016	[91]	Local groups, researchers, state agencies, farmers, foresters, current/potential users of forest-based energy	Information (events and workshops for mutual learning and information sharing) and consultation (involve public and citizen advisory plan in decision-making, regular monitoring and evaluations)
15.	2017	[83]	Central and local government representatives, industry representatives, NGOs, community groups, and academia	Information (sharing model structures, presenting HEW-WISE web-based simulation tool to the participants), consultation (semi-structured interviews), and strong collaboration (participatory stakeholder engagement, GMB workshops, interactive simulation session, facilitation of discussions, and investment strategy through collaborative approach)
16.	2017	[141]	Local citizens, policymakers, and municipal officials	Information (providing information about renewable energy options, goals, displaying outcome indicators and charts on renewable energy production), consultation (gathering participants’ perceptions and preferences towards renewable energy through questionnaires), and collaboration (group discussion on potential locations for renewable energy installations, joint development of renewable energy strategies for the city collaborative learning about renewable energy options, requirements, and limitations)
17.	2017	[101]	Members of the British population, researchers from Cardiff University, UK Department of Energy and Climate change	Information (providing basic information about my2050 tool to the participants, presenting exemplar scenarios), consultation (using my2050 tool to elicit public preferences about energy future), and partial collaboration (engaging public in exploring and constraints in energy system planning)
18.	2018	[80]	Members of the municipal council and energy team, farmers, representatives of municipal administration, and private individuals	Information (project goals and methodology), consultation (eliciting feedback), and collaboration (stakeholders actively involved in shaping the project’s outcome)
19.	2018	[142]	Members of the public, researchers, council members, energy companies, and local communities	Information (sharing background information with the participants), consultation (gathering qualitative data through discussions and deliberations), and collaboration (role-playing, encouraging participants to propose energy developments)
20.	2018	[86]	Residents, research team, local business, and tourists	Information (initial surveys to gather baseline inform, presentation of technical energy plan scenarios to the community), consultation (focus groups to discuss energy-related topics, individual interviews, and feedback sheets), and strong collaboration (iterative process were finding from each phase informed subsequent phases, mutual learning between researchers and participants)
21.	2018	[143]	Informed citizen panel and researchers	Information (providing factsheets on electricity technologies), consultation (surveys to elicit participants’ preferences and knowledge), and partial collaboration (workshops and group discussions for participants to share perspectives)
22.	2018	[144]	Household members, energy experts, energy providers, and policymakers	Information (surveys to gather household energy usage, preferences, and knowledge), consultation (seeking household opinions on future energy mix), and no explicit collaboration (use only household preference to modify the scenario)
23.	2019	[26]	Scientist, researchers, and representatives from public and private organisations in energy and transport domain	Information (presentation of initial driving forces to the scenario team), consultation (brainstorming, qualitative assessment and feedback sessions), and strong collaboration (participatory creation of scenarios, iterative revisions, and discussions of scenarios with stakeholders and joint development of additional scenarios)
24.	2019	[145]	Informed citizen panels in Geneva, researchers, and policymakers	Information (providing factsheets on electricity technologies, conducting knowledge test) and consultation (eliciting citizen preference for individual electricity technologies)
25.	2020	[146]	Citizens, informed citizens, energy experts, and model-based scenario developers	Information (citizens were given minimal information, informed citizens received factsheet) and consultation (workshops for consulting to provide their preferred electricity supply scenario for 2035 using risk-meter tool)
26.	2020	[147]	Participants from the city of Zurich and researchers	Information (researcher explained goals, content and rules, posters), consultation (survey, risk-meter web tool, final survey), and partial collaboration (workshops such as serious board games, small group activities, and reflective group discussion)
27.	2020	[87]	Ordinary citizens of Suita city, future generations, experts, and municipal office	Information (experts provided background knowledge and information to participants about GHG emission reductions, energy-savings targets, and current trends), consultation (an online survey was conducted to gather public opinion), and strong collaboration (participants collaborated to construct logic trees and create storylines)
28.	2022	[85]	Citizens from three countries, policymakers, researchers, the energy modelling community, the European Commission, and national government	Consultation (online survey with citizens using risk-meter tool), suggestions for collaborative approach
29.	2023	[25]	School pupils, adult citizens, energy experts, and modellers	Information (providing factsheets and posters with information about electricity supply technologies, presenting PowerPoint slides about the context) and consultation (surveying pupils using Risk meter tool)

).

4. Results

4.1. Key Aspects of Current ESMs

This study examined a diverse array of studies utilising energy system models with various foci. Fifty articles were selected for analysis. The highest number of articles was published in 2022 (Figure 2), presumably due to global initiatives towards the United Nations’ Sustainable Development Goals and evolving climate policies [34]. These goals and policies have driven research on energy system modelling, reflecting an emphasis on sustainability, carbon emission reduction, and the integration of social aspects into ESMs beyond techno-economic factors [5]. Furthermore, this trend indicates that interdisciplinary collaboration is essential: engaging experts from economics, environmental science, and engineering could lead to more comprehensive models that address both technical and social dimensions of energy systems [14]. Most of these studies predominantly used HOMER Pro (https://homerenergy.com/; accessed on 10 May 2024) for their analyses, as shown in Table A1. The alternative models examined included MATLAB (https://www.mathworks.com/; accessed on 10 May 2024), EnergyPLAN (https://energyplan.eu/; accessed on 10 May 2024), LEAP (https://leap.sei.org/; accessed on 10 May 2024), NSGA-II (https://pymoo.org/; accessed on 10 May 2024), MARKAL (https://iea-etsap.org/; accessed on 10 May 2024), and PVsyst (https://www.pvsyst.com/; accessed on 10 May 2024). An overview of the study area, which is detailed in Appendix A (Table A1), primarily focuses on the electrification of the rural and urban power sectors. Several studies have expanded sector coupling to encompass the electrification of additional domains, including agriculture, cooking, heating, transportation, and desalination.

Researchers have employed diverse approaches to the design of renewable energy systems. As illustrated in Figure 3, sixteen out of the fifty research studies concentrated on technical and economic assessments for long-term energy planning and system modelling. Environmental impacts were considered in twelve studies, while seven studies incorporated social aspects with technical, economic, and environmental considerations. The Human Development Index (HDI) is one of the social parameters considered in ESMs. A higher HDI may correlate with greater acceptance and support for renewable energy initiatives [35]. The number of local jobs created by the hybrid renewable energy system (HRES) is a key factor for societal approval. Kushwaha and Bhattacharjee [36] highlight the use of excess energy for charging battery rickshaws, which contributes to local employment. This study assessed the social impact of various energy sources using specific employment factors: 0.11 jobs per GWh per year for utility grid energy and 0.01 jobs per GWh rated capacity for battery storage [37]. Public acceptance is another crucial social factor in energy system modelling, affecting the adoption of renewable energy technologies and the success of policy implementations [38]. Social factors play a crucial role in energy system modelling by influencing pollution reduction, industrial growth, economic stability, job creation, and renewable energy adoption. It is important to note that comprehensive research addressing the technical, economic, environmental, and social dimensions of energy models remains insufficient.

Figure 4 illustrates the factors commonly associated with energy models in the extant literature. This demonstrates that current studies predominantly focus on technical, economic, environmental, and social parameters utilised by energy system modellers to evaluate long-term energy planning and facilitate a successful transition towards decarbonisation goals.

In summary, extant energy system models (ESMs) predominantly emphasise technical and economic aspects with insufficient consideration of social elements. This disparity underscores the necessity for more comprehensive methodologies in future studies encompassing all four dimensions—technical, economic, environmental, and social—to formulate more holistic and efficacious energy transition strategies. While social factors such as employment generation, societal acceptance, HDI, and health advantages are included as external variables in energy models, their current integration fails to adequately represent social aspects. Future research should prioritise developing techniques that can effectively quantify and incorporate social dimensions into ESMs. For policymakers, these insights underscore the significance of considering a wider array of factors when designing energy policies, particularly emphasising the societal impact and public involvement. This more inclusive approach could result in more sustainable and socially acceptable energy transitions, potentially enhancing policy efficacy and public support for renewable energy initiatives.

4.1.1. Technical Aspects

The technical factors of energy systems include the design, technology selection, and performance of energy systems. These systems are typically modelled for both grid-connected and off-grid scenarios. The design of future energy systems in emerging and developed economies aims to facilitate energy transitions and address design constraints to ensure sustainable energy solutions. The transition to renewable energy has been addressed across various sectors, including electricity, transportation, agriculture, heating, and cooling. However, most recent studies have concentrated on the electricity generation sector to enhance efficiency and develop decarbonisation strategies [39,40,41,42,43].

Wind and solar energy sources constitute the foundation for designing renewable energy systems. For example, in the Kingdom of Saudi Arabia, the city of Yanbu executed a hybrid system that connected solar photovoltaic and wind power technologies, which were subsequently linked to an electrical grid [44]. This initiative has effectively reduced the city’s dependence on fossil fuels and mitigated its environmental impact. Yimen et al. [45] demonstrated the feasibility of utilising solar and wind energy as foundational elements in renewable energy systems for rural electrification in sub-Saharan Africa. Similarly, Khan et al. [46] combined stand-alone wind turbine and solar photovoltaic systems in rural India, emphasising the critical importance of solar and wind energy as primary energy sources. These investigations have predominantly focused on battery storage systems to dispatch energy during peak-load demand and intermittency periods, thereby ensuring the reliability of renewable energy systems [41,47,48,49]. Nevertheless, certain exceptions have considered pumped hydro-energy storage [45] and renewable hydrogen-based storage systems [50]. There has been a significant focus on the electrification of both stand-alone and grid-connected networks in emerging and developed countries using renewable energy systems to enhance renewable energy penetration [39,40].

4.1.2. Economic Aspects

Researchers have examined various economic factors, including the levelised cost of energy (LCOE), the annual cost of the system (ACS), net present cost (NPC), payback period, and return on investment (ROI) for analysing energy systems [35].

The ACS is calculated using Equation (1) [36]. This equation expresses the annual system costs, with NPC denoting the net present cost and CRF representing the capital recovery factor. The CRF is determined using Equation (2) [51].

$$\mathrm{ACS}=\mathrm{NPC}\times \mathrm{CRF}$$

(1)

$$\mathrm{CRF}={\displaystyle \frac{{\mathrm{i}(1+\mathrm{i})}^{\mathrm{N}}}{{(1+\mathrm{i})}^{\mathrm{N}}-1}}$$

(2)

Here, N signifies the number of years and “I” denotes the annual real interest rate.

NPC represents the comprehensive lifetime expense of the system, encompassing all financial inflows and outflows throughout the project duration [51]. The calculation of the system’s total NPC is calculated using Equation (3).

$$\mathrm{NPC}={\displaystyle \frac{\mathrm{ACS}}{\mathrm{CRF}}}$$

(3)

When evaluating the economic aspects of HRES, LCOE (USD /kWh) is considered a crucial metric [52]. LCOE represents the average expense per kWh for an energy system that produces usable electrical power [36]. The LCOE is calculated using Equation (4).

$$\mathrm{LCOE}=\mathrm{ACS}/\sum _{\mathrm{t}=1}^{8760}{\mathrm{P}}_{\mathrm{load}}$$

(4)

Here, ${\mathrm{P}}_{\mathrm{load}}$ is the total electrical power served (kWh) by HRES.

Another crucial factor in evaluating the economic feasibility or profitability of HRES as an investment is ROI [52]. This metric is defined as the annual cost savings relative to the initial capital expenditure of the reference system. The ROI is calculated using Equation (5) [53].

$$\mathrm{ROI}=\sum _{\mathrm{i}=1}^{{\mathrm{T}}_{\mathrm{proj}}}\left({\mathrm{C}}_{\mathrm{i},\mathrm{ref}}-{\mathrm{C}}_{\mathrm{i},\mathrm{curr}}\right)/[{\mathrm{T}}_{\mathrm{proj}}\times \left({\mathrm{C}}_{\mathrm{cap},\mathrm{curr}}-{\mathrm{C}}_{\mathrm{cap},\mathrm{ref}}\right)]$$

(5)

Here, the nominal annual cash flow of the reference system is denoted by ${\mathrm{C}}_{\mathrm{i},\mathrm{ref}}$, while ${\mathrm{C}}_{\mathrm{i},\mathrm{curr}}$ represents the nominal annual cashflow of the current system. Additionally, ${\mathrm{C}}_{\mathrm{cap},\mathrm{curr}}$ signifies the capital cost of the current system, and ${\mathrm{C}}_{\mathrm{cap},\mathrm{ref}}$ indicates the capital cost of the reference system.

In the process of decarbonising energy systems, substantial attention has been focused on economic aspects, primarily emphasising parameters such as the NPC and LCOE. Ref. [54] compared the NPC and LCOE of four different energy systems for marine transportation (as shown in Figure 5).

• Scenario 1: stand-alone fossil fuel-based energy systems.
• Scenario 2: renewable and fossil fuel hybrid energy systems.
• Scenario 3: stand-alone nuclear energy systems.
• Scenario 4: renewable and nuclear hybrid energy systems.

Gabbar et al. [54] demonstrated that Scenario 4 exhibited the most favourable economic performance, with the lowest NPC (USD 532.11 million) and LCOE (USD 164.45/MWh). Scenario 3 was the second-best performer. Fossil fuel-based systems (Scenarios 1 and 2) incurred significantly higher costs, with the stand-alone fossil fuel system presenting the highest NPC (USD 877.61 million) and LCOE (USD 277.96/MWh). The study concluded that Scenario 4 outperformed the other three energy systems in terms of both economic indicators (NPC and LCOE). However, a lower LCOE ensures a competitive grid supply and provides additional payback to the end user [46]. In certain instances, studies have aimed to minimise the value of NPC and LCOE to achieve an optimal renewable energy system design that meets the electricity load requirement [52,55].

Reduced initial capital cost and LCOE render renewable energy systems more accessible to consumers. These cost reductions enhance the competitiveness of renewable technologies compared to fossil fuels, thereby increasing their economic feasibility in the global energy market. IRENA reported that in 2021, the LCOE for utility-scale solar photovoltaics decreased by 13% compared to the previous year, while onshore and offshore winds experienced declines of 15% and 13%, respectively [1]. The diminishing costs associated with renewable electricity generation have positioned it as the most economically effective power source in several regions. As a result, 21 European Union member states witnessed an expansion in their renewable energy proportions between 2022 and 2023 [56].

4.1.3. Environmental Aspects

In the energy domain, dialogues on environmental impacts are predominantly focused on the necessity of reducing emissions of CO₂ and other greenhouse gases (GHGs) to mitigate climate change [5]. Studies have examined the environmental impacts of renewable electricity [39,47], utilising battery energy storage systems [35,57,58], and integrating electric vehicles (EVs) [59,60] in future energy systems. Researchers have been motivated to design renewable energy systems to curtail GHG emissions and mitigate global warming in the energy sector [61]. Germany’s strategic renewable energy initiatives, particularly in solar power, have significantly reduced carbon dioxide emissions. Between 1990 and 2017, the nation achieved a notable 27% decrease in greenhouse gas emissions, primarily due to the widespread adoption of sustainable energy solutions, including solar technology [61]. A case study conducted in western China demonstrated the implementation of a hybrid renewable energy system comprising solar, wind, and biomass resources. Notably, when operating in off-grid mode, the system averted approximately 120,284 kg of CO₂ emissions per annum by providing a stable power supply without dependence on fossil fuels [62]. Fossil fuel-based energy systems deteriorate environmental conditions, necessitating the consideration of renewable energy as a zero-emission source when modelling the emission cost of power generation units [63], while minimising the energy cost and maximising green energy in the overall energy mix. Several studies have utilised HOMER to calculate the annual carbon emission savings from renewable energy system design [62,64,65], whereas others have employed mathematical modelling [63,66].

Few studies have incorporated environmental justice into energy and climate policy. Ref. [67] provided a comprehensive analysis of justice-related issues in energy infrastructure opposition and offered solutions to address these challenges. Sofia Avila [68] emphasises the need for equitable transitions in climate and energy policy, addressing social and spatial issues, challenging existing power structures, and promoting alternative energy futures.

Raw materials are considered environmental constraints. The increasing demand for various sustainable energy technologies, including wind turbines, solar photovoltaic (PV) panels, and lithium-ion batteries, has led to an increased demand for critical raw materials (CRMs) [69]. Reliance on these raw materials presents potential environmental risks.

Furthermore, there is an increasing inclination towards evaluating the impacts of energy infrastructure on land, water resources, and biodiversity. Land use is becoming increasingly significant, particularly in the context of renewable energy system design due to its limited availability. For example, Ref. [70] explored the constraints of land use for extensive solar photovoltaic installations and wind energy facilities. Hydropower frequently raises concerns regarding biodiversity. In this context, Ref. [71] highlighted the ways in which hydropower affects biodiversity, primarily through alterations in habitats and interruptions to ecosystems. Finally, while decarbonised energy systems strive to mitigate carbon emissions, they must concurrently address the challenge of water availability to promote sustainable and resilient energy generation [5].

4.1.4. Social Aspects

There is an increasing emphasis on incorporating social dimensions into energy systems modelling, including behavioural patterns in energy consumption and cultural considerations in the adoption of renewable energy technologies [14,72]. For example, Vanegas Cantarero [73] utilised socio-economic indicators, such as consumer behaviour in the transportation sector, as input parameters to generate five scenarios. This methodology aims to decarbonise the transportation sector and foster synergy between the power and transportation sectors. The outcomes of this study encompass the enhancement of urban areas through the advancement of transportation systems and the creation of employment opportunities.

The resulting articles utilised various energy modelling tools, including HOMER Pro and EnergyPLAN, as well as artificial intelligence methods such as the PSO-DE algorithm and GOA validated by the PSO algorithm. These tools were employed to evaluate socio-economic indicators, namely, the Human Development Index (HDI), job creation (JC), and portfolio risk (PR) [35,36]. Irrespective of the modelling approach employed, employment opportunities or job creation constitute the most frequently integrated socio-economic aspects of energy models [46,55,74]). Khan et al. [46] designed a hybrid energy system that utilised a modelling tool to electrify rural consumers in northern India. The design emphasises a comprehensive technical analysis for a cost-effective and highly reliable renewable energy solution. The implementation of a hybrid energy system influences the social dynamics of rural residents by enhancing socio-economic parameters such as the HDI and generating local employment opportunities.

Costa Rica’s National Decarbonisation Plan (NDP) focuses on socio-economic advancement through the improvement of air quality, employment generation, poverty reduction, and ensuring equitable access to affordability [75]. Ref. [76] analysed the effect of community acceptance on onshore wind and solar farms in Great Britain, highlighting that the incorporation of social acceptance in decision-making processes is crucial for achieving a low-carbon energy transition and ensuring energy justice. The incorporation of socio-political factors (public approval, health considerations, air quality, and resilience) into the energy system provides decision-makers with an enhanced perspective for effectively managing communities and stakeholder groups. However, a review of current research indicates that although social evaluation criteria are gaining prominence in scientific investigations, there is a substantial need for better integration of social dimensions in energy models. To achieve comprehensive and credible energy system modelling and planning, it is essential to engage the public with the involvement of other stakeholders. This inclusive approach fosters a collaborative environment, wherein diverse perspectives contribute to more robust and widely accepted results (as detailed in Section 4.3).

4.2. Potential Approaches to Integrate Social Aspects

The findings of this study indicate that comprehensive incorporation of social aspects in ESMs necessitates collaboration between modellers and experts from diverse disciplines from the inception of the modelling process [6,14]. Additionally, Krumm et al. identified three potential strategies for integrating social aspects into models: (1) incorporating storylines, scenarios, and input parameters; (2) embedding social aspects within the simulation or optimisation process; and (3) discussing the social implications of modelling outputs [14]. Trutnevyte et al. [7] introduced an approach encompassing three strategies for integrating social science into energy models. Within this framework, the merging strategy offers a comprehensive perspective on the energy system, considering technological, economic, environmental, and societal factors. Liegl et al.’s [6] SWOT analysis of these linking strategies facilitated the identification of the most appropriate strategies for existing ESMs. McGookin et al. [77] conducted a comprehensive review of participatory methodologies in energy system modelling and planning. The principal advantage of this participatory approach is that it extends beyond technical and economic factors, situating ESMs within a socio-political context. This investigation elucidates the challenges inherent in integrating narrative scenarios with quantitative ESMs.

Recent research by McGookin et al. [23] introduced a framework for advancing participatory models by integrating inputs from various stakeholders at different stages of the modelling process. This approach aims to reflect the concerns and preferences of individuals in ESMs. Furthermore, this study provides guidance on incorporating stakeholders and public opinion into ESMs through participatory methodologies.

Table 3. Potential approaches to integrate social aspects in ESMs.

Ref.	Authors	Possible Integration Approach of Social Dimensions into Energy System Models (ESMs)
[7]	Trutnevyte et al., 2019	This study outlines three approaches to incorporating social aspects into ESMs: (1) bridging strategy, (2) iterating strategy, and (3) merging strategy.
[14]	Krumm et al., 2022	To comprehensively integrate social aspects into ESMs, this study proposes two approaches: (1) incorporating social dimensions into the modelling process from the outset, rather than treating them as socio-economic impacts, and (2) collaborating with social scientists to establish a connection between ESMs and social science disciplines.
[78]	Fodstad et al., 2022	Future directions for ESMs involve: (1) the integration of energy consumer behaviour to represent social aspects, which necessitates the development of more sophisticated theories regarding this behaviour, and (2) the promotion of collaboration across diverse academic disciplines to formulate a more advanced model.
[13]	Dioha et al., 2023	This study proposes several recommendations for enhancing traditional ESMs to incorporate social factors, which are as follows: (1) recognising socio-political factors as equally significant as techno-economic factors, (2) refining the framework of the modelling system, (3) developing new metrics for socio-political factors, (4) establishing connections among diverse types of models, and (5) necessitating interdisciplinary involvement in the modelling process.
[6]	Liegl et al., 2023	According to this study, the subsequent steps in integrating social aspects into ESMs encompass: (1) incorporating socio-economic factors such as “willingness to pay” to ensure a comprehensive representation of the energy system, and (2) utilising insights from current social science research essential for enhancing the depiction of ESMs and thus promoting interdisciplinary collaboration is crucial for a holistic representation of the energy transition.

outlines the potential steps for integrating social dimensions into ESMs to enhance the representation of energy transition towards a low-carbon economy.

As outlined by Ref. [77], there are two prevalent forms of shallow integration of the societal dimeson: (1) engaging in multidisciplinary collaborations to incorporate socio-technical theories, such as behavioural profiles, and (2) conducting public attitude surveys independently of energy system analysis. The results of these surveys do not influence the output of energy system analysis. This represents a significant limitation in comprehensively integrating social factors into the modelling process, as non-technical factors are becoming increasingly crucial for a successful transition to sustainable energy.

4.3. Approaches to Incorporate Public Perspectives

The extant literature increasingly advocates broader stakeholders and members of public inclusion [24,77,79] to ensure more equitable policy decisions and incorporate citizen perspectives into modelling. McGookin et al. [77] conducted a systematic review highlighting the involvement of various stakeholders including academics, governments, businesses, agriculture, and citizens. This review identified 28 articles that incorporated public participation in some form. The notion of public involvement in energy system modelling and planning encompasses individuals, groups, and organisations [77]. Further elaboration is provided on the roles of ordinary citizens and civil society entities such as social movements and non-governmental organisations (NGOs).

• Ordinary citizens: Ordinary citizens contribute diverse perspectives and personal experiences (including local perceptions, concerns, and preferences for any technology or policy), which can enrich the discussion. Citizens and other stakeholders are involved in the energy planning process [80,81]. However, ordinary citizens are sometimes not directly engaged in such processes [82].
• Organised groups: In contrast, citizens who are members of civil society organisations, such as social movements and NGOs, tend to play a more structured and strategic role. They are often better positioned to exert influence on the decision-making process and outcomes because of their systematic approach and targeted advocacy [83,84].

Xexakis and Trutnevyte [85] proposed a collaborative approach to stakeholder involvement, including members of the public and other relevant parties. Figure 6 illustrates the phases of a collaborative approach to integrating public perspectives into ESMs.

4.3.1. Co-Creation

In the context of ESMs, co-creation refers to a collaborative approach. From the development of research questions to the formulation and implementation of solutions, diverse stakeholders and the public must collaborate throughout the process. A diverse range of stakeholders is mentioned in Ref. [23], identifying key stakeholders to be included in the modelling process.

Amongst the twenty-nine articles examined, five demonstrated strong collaborative efforts. These include the joint development of scenarios [84,86], storylines [87], multiple rounds of scenario revisions and discussions [26,86], and interactive simulation sessions [83], which require substantial stakeholder involvement. Few studies have engaged closely with rural inhabitants in developing renewable energy projects within their communities [88,89] with a focus on building trust through improved local relationships. Further studies have sought to understand the expectations [90,91,92] or determine the preferences [80,81,84] of local populations regarding the selection of appropriate technologies.

4.3.2. Active Engagement

Stakeholder engagement provides contemporary data that facilitate the comprehension of public opinion, preferences, requirements, and expectations. Active stakeholder engagement aids in achieving carbon mitigation goals while ensuring environmental, climate, and energy justice, thereby contributing to sustainable energy systems. Energy system modellers face difficulties in designing systems that satisfy future demands and address social considerations. Researchers have used methods such as public focus groups, interviews, surveys, and social media analysis to engage stakeholders and design systems that meet their needs [93,94,95].

Rowe and Frewer [96] identified 100 distinct methodologies for stakeholder engagement; however, four approaches were predominantly utilised: surveys, interviews, workshops, and focus groups [82]. The most formal approach to data collection is through surveys and questionnaires [97,98]. Survey questionnaires provide quantitative information that is readily incorporated into energy models. However, they lack contextual depth. Conversely, interviews and workshops are more effective engagement methods for developing an understanding of individuals’ perspectives, although the insights gained are challenging to integrate into the energy system models.

Among the twenty-nine articles examined, ten employed strategies focused on information sharing and consultation, while three implemented a limited collaborative approach to engage stakeholders in the modelling process. A comprehensive analysis of the twenty-nine studies, including the types of stakeholders involved and the engagement approaches utilised in each project, can be found in Table A2. Trutnevyte and Stauffacher [99] distinguished three distinct categories of participation approaches for stakeholder involvement, which were subsequently elaborated on by Ref. [23].

• Information: Unidirectional communication involves researchers sharing their results with stakeholders through various means, such as initial data presentations, fact sheets, informative posters, basic information provision, and existing energy scenario descriptions. This approach allows for limited input from stakeholders, who are consequently unable to shape the research outcomes.
• Consultation: Bidirectional communication in which stakeholders provide their perspectives through surveys, interviews, and workshops. Stakeholders can shape the results of this research, but not the research objectives.
• Collaboration: Bidirectional communication in which stakeholders and researchers can guide research objectives. They have been involved since the project’s inception. They collaboratively develop energy scenarios through active engagement and contribute to shaping the project’s outcomes.

4.3.3. Insights from Engagement

Quantitative or qualitative analyses utilising statistical representation have been extensively employed to elucidate stakeholders’ and public perspectives and enhance the planning of future energy systems [11,100]. Surveys and interviews serve as valuable instruments for eliciting public perspectives on the key aspects of energy systems. Stakeholders and public inputs contribute to a more comprehensive understanding of how consumers perceive and prioritise energy-related issues. Demski et al. [101] emphasised the significance of public perspectives in comprehending the social opportunities and constraints associated with energy pathways. Engaging the public can facilitate the identification of socially acceptable configurations of system change with respect to energy security, affordability, and environmental protection. For example, the German energy transition, known as Energiewende, exemplifies the role of public participation in shaping energy policy [102]. The government has engaged citizens in discussions on renewable energy deployment and energy efficiency measures. This collaborative approach fostered substantial public support for sustainable energy, resulting in significant advancements in Germany’s energy systems.

Comprehensive inquiries encompassing key factors (technical, economic, environmental, and social dimensions) are essential for transitioning towards a renewable energy system [103]. Identifying key factors in the development of renewable energy systems is crucial for scenario planning [103]. These factors are vital for understanding public perceptions of environmental and societal issues.

Table 4. Key factors of the energy survey (source: authors).

Factors	Sub-Factors	Items
Key factors	Technical factors	(1) Adoption of energy efficient technologies (2) Renewable energy technologies (3) Electric vehicles
	Economic factors	(1) Energy price (2) Tariff rate (3) Feed-in-tariff (FiT)
	Environmental factors	(1) Environmental awareness (2) Carbon reduction
	Social factors	(1) Energy conservation behaviour (2) Energy usage patterns (3) Attitude towards energy tariff reformation (4) Energy price

presents an overview of key factors from the survey, interviews, and other approaches. Statistical analysis of the subfactors yielded valuable insights for scenario development.

4.3.4. Scenario Development

Scenario development is essential for strategic energy system planning because it enables modellers and government planners to anticipate potential changes in energy systems. Scenarios have been extensively utilised to explore low-carbon energy pathways [104] and provide essential visions for managing complex and uncertain energy systems [77]. Scenario development constitutes a salient example of modelling assumptions. To create energy scenarios, the energy model requires input data regarding future societal developments and their effects on energy demand and supply [105]. The process of scenario development encompasses the following stages [106]: (1) identification of potential future problems, (2) proposition of solutions utilising reference scenarios, (3) comparison of reference scenarios with alternative scenarios, and (4) evaluation of the outcomes.

The scenario development process engages public and multidisciplinary experts, including modellers and non-modellers [106], and collaborates through participatory approaches [107]. One study [26] focuses on the Danish transportation sector as a case study to illustrate how stakeholders contribute to scenario development. In this context, stakeholders, including citizens, politicians, and experts, participate in collaborative processes to deliberate on and shape the future of the transport system.

Table 5. Roles and contribution of diverse stakeholders.

Actors	Roles and Contribution to Scenario Development
Public	(1) Plays a crucial role in scenario development by providing local knowledge and perspectives that can shape energy policies. (2) Engaging the public can also help identify common interests and areas for collaboration, enhancing the overall effectiveness of the scenario development process.
Experts from the Energy Industry	(1) Input helps to develop the scenarios in practical realities and enhances their credibility. (2) Contribute specialised knowledge and insights into market trends, technological advancements, and potential barriers to implementation.
Modellers and Researchers	(1) Establish the frameworks for scenario generation. (2) Responsible for synthesising the inputs from various stakeholders and translating them into quantitative projections and qualitative narratives.

presents the roles and contributions of the various actors in the scenario development process. This process incorporates various techniques, including workshops, focus groups, interviews, and surveys. These methodological tools provide valuable insights into public attitudes and preferences [108,109]. Such insights are essential for modellers to effectively comprehend end-user perspectives and to plan energy transitions. The translation of survey responses or workshop outcomes into a model presents significant challenges for modellers, as it necessitates numerous additional assumptions. In some instances, modellers are unable to find a feasible solution for these inputs [110].

4.3.5. Scenario Integration

Scenarios are typically categorised as either qualitative or quantitative [77,111]. A qualitative energy scenario was developed through stakeholder surveys, interviews with questionnaires or other participatory methodologies [23]. It plays a significant role when information such as attitudes, behaviour, awareness, and preferences cannot be quantified [107]. These qualitative energy scenarios provide insights into the social, technical, economic, and environmental preferences of end users [112]. However, the majority of energy scenarios focus on technical details and neglect the interaction between socio-economic, economic, and technical factors [113].

The survey results obtained from stakeholder engagement can serve as input parameters for the development of energy model scenarios. Energy models incorporate social factors such as consumer energy behaviour and lifestyle, diversity of energy consumers and producers, acceptance and opposition, and public participation and ownership through the development of exogenous storylines. These storylines can be translated into input parameters that constitute the components of qualitative energy scenarios [14]. Qualitative scenarios represent potential future energy systems related to technical, policy, environmental, and societal change. Qualitative scenarios are often considered less rigorous because of their undocumented assumptions and reliance on expert judgement. In contrast, quantitative energy scenarios are regarded as more scientifically robust because their assumptions have been frequently published [114]. Energy system models (ESMs) are computer and mathematical models that are widely utilised in energy research to construct quantitative scenarios. Energy system modellers develop a quantitative scenario by inputting parameters and assumptions that provide the numerical details of that scenario [111].

Energy models typically provide quantitative outputs that facilitate the comprehension of energy system complexity. These models examine the interaction between energy and the environment, specifically how alternative energy systems contribute to the mitigation of global warming and enhance energy supply security. The optimisation and simulation models of energy systems typically incorporate the following key inputs: (1) energy demand, which encompasses the requirements for lighting, cooling, and other utilisation; (2) technical data, comprising technical and cost-related information; (3) resource data, such as energy resource potential; and (4) various energy scenarios based on energy or environmental policies or specific policy instruments [113]. The integration of scenarios with energy models, such as agent-based modelling or system dynamics, constitutes a methodology for incorporating assumptions regarding end-user perspectives on potential future societal contexts into energy models [105,115].

4.3.6. Iterative Process

The outcome of the modelling results necessitates monitoring, and the extent to which the modelling output aligns with public perspectives in shaping the energy system requires further evaluation. Recognising disparities between the perspectives of researchers and the public is crucial. Those engaged in the study should be allowed to provide critical input to the modelling process results through an iterative process. Stakeholders and public engagement are largely excluded at the end of the modelling process [23]. To maintain the transparency of modelling outcomes, it is imperative to develop a structured feedback mechanism for those involved in the research. Stakeholders and the public can be solicited to provide feedback regarding the modelling process through surveys, interviews, and focus groups. Collaboration between stakeholders, the public, and modellers will mitigate the traditional techno-economic modelling assessment and consider diverse options in the modelling process.

5. Discussion

This study employed a systematic review methodology to identify the recent and relevant literature on the social dimensions of energy models. A comprehensive array of articles is presented in Appendix A, which demonstrates that most studies incorporate technical and economic factors across different policy or pricing scenarios. These analyses frequently contribute to long-term energy planning and are aligned with the current trajectories. Krumm et al. [14] emphasised the importance of incorporating cost and technical considerations into optimisation and simulation models. Ongoing progress in solar and wind technologies promises enhanced efficiency and lower expenses, making renewable energy more accessible and effective in combating climate change [61]. Future renewable energy innovations are expected to yield positive environmental outcomes. Advanced methodologies such as carbon capture and storage (CCS) present supplementary strategies to alleviate greenhouse gas emissions by sequestering CO₂ from manufacturing facilities and power generation sites before their discharge into the atmosphere [62]. When implemented alongside renewable energy solutions, this technology can significantly reduce the impact of climate change. Critics contend that recent energy system studies have excessively focused on the techno-economic aspects of energy transition, neglecting crucial environmental and social factors [5]. Integrating environmental factors into energy models is essential to create comprehensive, accurate, and actionable insights that can guide effective energy transition policies and strategies. This integration ensures that models reflect the full spectrum of technological advancements and their potential environmental impacts, ultimately supporting the transition towards a sustainable and climate-neutral future.

Among the fifty reviews, seven integrated social analyses were conducted on technical, economic, and environmental aspects. These studies incorporated social dimensions by evaluating future project development through socio-economic and socio-environmental factors, including employment generation, improvements in the Human Development Index, reductions in carbon footprint, and enhanced well-being, exemplified by a decrease in particulate matter (PM) emissions, which may contribute to the mitigation of respiratory diseases. Section 4.1.4 addresses the current integration of social aspects with other factors in energy models. Our findings indicate that energy models frequently utilise exogenous assumptions to incorporate socio-economic factors into output discussions. This approach necessitates the internal integration of societal aspects with technical, environmental, and economic elements [7]. The integration of social dimensions into future energy system analyses is essential beyond technical considerations. However, these studies need to adequately address social aspects. Integrating social factors into energy models presents a significant challenge that requires more attention and improvement. Therefore, a collaborative approach is necessary to incorporate social dimensions into the energy models. Addressing social factors is essential for mitigating social inequalities and advancing technological innovations in future energy systems. Several researchers, including [6,13,14,78], have proposed potential methodologies for integrating social dimensions into energy system models (as outlined in Section 4.2). These scholars argued that conventional energy models, such as simulation and optimisation, require substantial restructuring to effectively include social factors. This integration process requires multidisciplinary collaboration in order to develop a comprehensive energy model. However, coordination with experts from diverse disciplines presents a challenge, as they operate with different methodologies and objectives. Furthermore, these studies did not provide guidance for facilitating effective interdisciplinary collaboration.

There is an increasing demand for the incorporation of public concerns and preferences into ESMs. A review of the literature identified twenty-nine articles that had engaged public participation in some form (as shown in Appendix B) in energy modelling, with 35% of the studies eliciting citizen perspectives through “information and consultation” strategies that limit citizens’ influence on the research direction. Few studies have involved public decision-making processes using a collaborative approach. The public must comprehend how their opinions and perspectives contribute to the research outcomes [87]. In the absence of such an understanding, the public may become disillusioned and sceptical of the model’s output. This challenge imposes more transparent methods as well as innovative approaches to dialogue and deliberation to foster trust and understanding. However, the citizen engagement process should be as transparent and iterative as possible to enable the model to represent reality accurately [116]. Therefore, collaboration between various stakeholders and the public may prove beneficial in mitigating the limitations of traditional modelling approaches and considering multiple dimensions in the energy transition.

In accordance with these challenges, a collaborative approach to ESMs engages a wider range of stakeholders and the public, highlighting the importance of incorporating diverse perspectives. Limited research has adopted collaborative approaches to incorporate diverse groups into the modelling process [77]. From the inception of a project, professionals across various disciplines and the public participate in surveys, group discussions, workshops, and other collaborative activities to explore methodologies and anticipated outcomes. It is important to establish a common approach (e.g., scenario creation) to define essential concepts and objectives through iterative refinements. This common framework enhances interdisciplinary communications. At the end of the project, it is essential to disseminate the results to stakeholders and solicit their feedback. This final step ensures that their contributions effectively address the issue and meet intended objectives. Studies have facilitated collaboration by enabling participants to jointly develop energy scenarios [83] or create narratives [87], and participants were allowed to provide their feedback on the research findings and the proposed energy scenarios. However, the following challenges persist in implementing collaborative approaches.

• Time-intensive procedures: Maintaining participation from project commencement to conclusion through dynamic involvement. However, this process requires substantial time.
• Interpretation of contributions: Converting qualitative feedback into quantifiable model parameters presents a significant challenge.
• Energy model appropriateness: Determining effective energy models that reflect public perspectives more accurately.
• Complexity: Non-experts could experience difficulties when attempting to contribute to technical modelling without adequate guidance.

Nevertheless, the principal findings of this review indicate that further research in this field is required.

• Trutnevyte et al. [7] suggested that new modelling approaches are required for a better representation of technical, economic, environmental, and social factors.
• Modelling structures must be developed further to enable the quantification of qualitative inputs from stakeholders and the public.
• A transparent, iterative framework for collaborative approaches is essential for engaging diverse stakeholders and the public from project inception to completion. Further exploration of collaborative stakeholder engagement methods is necessary [85,117].
• The inclusion of vulnerable populations remains insufficient, and without this, a fair energy transition would not be feasible.

6. Conclusions

Traditionally, energy models focus on techno-economic factors. The integration of social aspects into ESMs remains a significant challenge. This study analysed potential methods for integrating social factors, emphasising collaborative strategies involving diverse stakeholders and the public.

The review highlighted the significance of public engagement with other stakeholders in ESMs. However, current engagement strategies are often limited to information sharing and consultation, with minimal strong collaboration. The reviews presented here advocate for transparent collaboration strategies that incorporate public perspectives. Future research is still necessary in this critical area to better connect public perspectives in the modelling process. The integration of public perspectives into ESMs raises several questions. Future research could investigate: (1) the impact of public perspectives on the reliability of ESMs; (2) the utilisation of perspectives on the development of socially acceptable energy policies; and (3) the identification of energy models that most effectively integrate citizens’ perspectives.

The review also highlights that further research is essential to enhance modelling techniques that more accurately represent vulnerable groups, which is vital for ensuring an equitable transition in the energy sector. If energy system models can better incorporate diverse perspectives of the preferred future energy systems, the outcomes of the modelling will become reliable for guiding energy transition.