Beyond dollars and cents: why socio-political factors matter in energy system modeling

Traditional energy system modeling (ESM) has predominantly focused on techno-economic factors such as costs and efficiency. However, this narrow perspective fails to capture the intricate interplay between energy systems and socio-political dynamics that significantly influence their real-world impact. This piece underlines the importance of incorporating socio-political factors into ESM and highlights the need for a more comprehensive approach. We identify several reasons why socio-political factors are often overlooked in ESM, including technical bias, complexity, data standardization challenges, time and resource constraints, and societal biases. These factors must be addressed to improve the relevancy of ESM, reduce social injustice, and promote innovation in the design of future energy systems. Incorporating socio-political factors into ESM offers several benefits. To achieve these benefits, the paper suggests a shift towards a more comprehensive and value-driven approach and offers several options for improving ESM including: (I) recognizing that socio-political factors are just as important as techno-economic factors (II) improving the structure of the modeling process (III) developing new and innovative metrics for socio-political factors (IV) linking different types of models (V) emphasizing the need for interdisciplinarity in ESM. By prioritizing the aspects of the energy system that concern people to the same degree we prioritize cost, we can derive value-driven insights to assist in creating a more sustainable future for all.


Introduction: Energy system modeling (ESM) must go beyond cost for greater impact
Energy system models that are used to inform the energy transition tend to focus on cost-effective transition pathways and disregard the social and political aspects of the energy system (Süsser et al 2022b, Dioha andMutiso 2023).However, ESM is a complex and multi-dimensional field, involving a wide range of interdependent variables and uncertainties.There is a growing recognition of the importance of incorporating socio-political factors-the various behavioral and cultural dimensions that shape energy consumption patterns, energy policy, and technology adoption-into ESM (Freeman et al 2022, Vågerö andZeyringer 2023).Yet many of the models that support policymaking for the energy transition tend to neglect those socio-political factors, yielding cost-optimal solutions that are incomplete representations of the energy system and how it interacts with society (Jewell and Cherp 2020, 2023, Brutschin et al 2021, Süsser et al 2022a, 2022b).
Energy systems are deeply intertwined with society.They affect people's lives, livelihoods, and the environment.Neglecting socio-political factors in the modeling process leads to results that fail to capture the intricate interactions between the energy system and society.While techno-economic models focus on cost-optimal solutions, empirical evidence has shown that such a basis may be misleading.For example, in the context of clean cooking energy technology decisions in developing countries, factors extending beyond cost, such as gender roles, education levels, household size, access to the internet, and the practice of fuel stacking, have been demonstrated to exert significant influence on the choice of cooking energy technology (Sehjpal et al 2014, Emodi et al 2022, Shari et al 2022).Although cost-optimal solutions are technically feasible pathways to decarbonization, they fall short of providing insights that are socially and politically feasible.A value-driven approach to ESM-one that integrates factors beyond cost, such as culture, environmental sustainability, political and public acceptance, resilience, and energy justice-would provide pragmatic alternatives to the status quo.While the goal of a cost-optimal approach to ESM is to achieve the lowest possible overall cost while still meeting energy demand and other specified techno-economic constraints, the goal of a value-driven approach to ESM is to strike a balance between economic, environmental, and socio-political factors to maximize overall societal benefit.There is a growing body of literature that highlights value-driven modeling efforts across disciplines entwined with climate and energy systems research.For example, researchers that have evaluated the feasibility of Integrated Assessment Models (IAMs) emphasize that IAMs often fail to characterize the tradeoffs decision-makers face when responding to the conflicting priorities of their constituents or investors (Peng et al 2021).Other researchers have stressed the importance of integrating social mechanisms into modeling segments of the energy system, such as the building sector, and have introduced innovative methods to integrate human behavior as well as metrics that quantify disparate energy system impacts (Cong et al 2022, Verrier et al 2022).Yet, there are no comprehensive mechanisms to systematically infuse these findings into energy system models that serve the policymaking process.
Figure 1 illustrates that the sole reliance on techno-economic parameters in determining the desired energy system overlooks a multitude of critical considerations that can significantly impact the long-term viability and success of a decarbonization pathway.The examples under socio-political factors in figure 1 are a small subset of the many socio-political factors that will determine the viability of energy system transformation.The meaningful integration of these factors into ESMs will vary across regions-the factors identified in figure 1, for example, might be relevant in the context of a U.S. city burdened by the presence of oil and gas infrastructure, while factors like political corruption or lack of energy access may be more relevant in emerging economies with weak political institutions.As others have argued, place-based modeling efforts that consider the system as a whole are necessary to capture feasible future energy systems (Peng et al 2021, Verrier et al 2022).The following sections identify the areas in which ESM continues to fall short and call for new mechanisms to ensure ESM that integrates sociopolitical factors is prioritized in policy-and decisionmaking processes.
2. Energy systems exist to serve societyso why does ESM primarily focus on the techno-economic factors that support the energy transition, but not the sociopolitical factors that shape the energy transition?
ESM is predominantly handled as a purely technical problem led by technical experts who prioritize mathematical formulations and computations.Their approach can lead to technical biases that overlook the interplay of social, cultural, and political factors that shape energy systems and lead technical experts to unconsciously favor readily quantifiable and concrete technical factors.As a result, the crucial, yet more abstract, and often qualitative, factors are viewed as separate from the technical planning of energy systems and are relegated to secondary importance.
However, factors such as cultural values, social norms, and political dynamics are difficult to incorporate into ESM given their more subjective, intricate nature and the challenges involved in measuring them.The challenge of complexity can be categorized in two ways: First, most energy system models optimize for a value with mathematical tractability.Therefore, socio-political factors must be quantified in some way to be congruent with the techno-economic factors of energy systems that can be readily measured, quantified, and analyzed using data and math.Second, the more factors we attempt to fit into a model, the more computationally difficult it becomes to understand the relationships between them.The comprehensive inclusion of socio-political factors exponentially increases solve time and costs, making an already resourcedemanding process even more so.As a consequence, modelers have to prioritize and limit the number of variables and model resolution.For example, the U.S. Energy Information Administration's model-The National Energy Modeling System (NEMS)represents the entire United States using inputs from only four census regions and nine census divisions.A single NEMS run can take up to 18 hours (US EIA 2023).Therefore, every additional layer of representation could increase the time requirement by several days, even months, depending on the temporal and spatial resolution.
The standardized, data-driven nature of ESM adds to this complexity, as socio-political factors are not only difficult to quantify, but such data is also difficult to collect, validate, and integrate into models.Even in the best cases, challenges arise from insufficient data, incomplete datasets, and the inherent context-dependency of data that allows for standardization or comparison.Consider, for example, the concept of 'energy poverty' which is typically understood as the lack of access to modern energy services, such as electricity or clean cooking facilities, that are necessary for basic human needs and economic development (IEA 2017).The definition of energy poverty, however, varies widely across different contexts and regions.In some cases, energy poverty is defined based on income and household expenditure on energy, while in others it is based on the percentage of the population with access to electricity or modern cooking facilities (González-Eguino 2015).Inconsistencies in defining data points such as 'energy poverty' complicate effective data harmonization across analyses.Further, fundamental biases and gaps in data collection can also mask appropriate socio-political representation.
The ESM community, like many other professional domains, has historically grappled with underrepresentation.This lack of diversity can result in unintentional blind spots and a limited perspective when it comes to understanding the complex relationships between energy systems and socio-political dynamics.A recent report from Clean Air Task Force revealed that underrepresentation of African voices in the global energy modeling community has undermined Africa's development imperatives in net-zero emissions energy system scenarios (Blimpo et al 2023).Different communities may have distinct energy needs, access barriers, and preferences that can profoundly impact the results and implementation of insights from ESM.Moreover, the funding schemes supporting research in the field are biased and have not prioritized funding for Social Sciences and Humanities (SSH)-another form of underrepresentation.Many funding agencies and organizations tend to allocate a disproportionate share of resources to the natural sciences, engineering, and technologyrelated research in energy, while often underinvesting in SSH (Royston and Foulds 2021).This unbalanced funding allocation is reflective of biases in research priorities and can limit the resources available for understanding and addressing social and behavioral aspects in ESM.

A value-driven approach to ESM could lead to more meaningful, policy-relevant outcomes
Incorporating socio-political factors into ESM greatly enhances the accuracy of these models by providing more realistic assumptions based on real-world contexts (Dioha et al 2020, Krumm et al 2022, Süsser et al 2022b).In doing so, models are better equipped to account for the several other forces that influence the adoption of energy-efficient technologies and as a result, more equitably distribute the impacts and benefits of their adoption.For instance, energy system models that incorporate the attitudes and preferences of different stakeholder groups, the availability of incentives, and the influence of socio-economic circumstances would provide a more realistic representation of adoption rates of electric vehicles (EVs) in developing regions such as Sub-Saharan Africa.The success of EVs in this region largely depends on their ability to overcome socio-economic challenges such as low incomes, high unemployment rates, energy access, an unreliable electrical grid, and local air pollution (Dioha et al 2022).In contrast, EV adoption rates in developed economies with robust infrastructure and reinforcing policy processes are more likely to be driven by targeted policies and incentives in line with decarbonization goals.By accounting for these factors, energy system models can deliver more useful insights to enable decision-makers to accelerate the adoption and implementation of energy technologies.
Integrating socio-political factors into ESM can provide additional insights into how decision-makers can effectively manage the potential impacts on various communities and stakeholder groups.A changing energy system may disproportionately distribute the benefits and burdens of adopting or updating energy technologies.Taking this more comprehensive approach will ensure that the benefits and costs of energy system transformation are more equitably distributed.Incorporating socio-political factors including, but not limited to, energy affordability, cumulative health impacts, and access to energy-efficient technologies, can aid in highlighting the potential effects on low-income communities, communities of color, and other underrepresented groups.By identifying these impacts, energy system models can inform more equitable transition strategies, such as targeted incentives and policies, community engagement and participation, and improved access to energy services.
Additionally, the ESM community can foster innovation and creativity by involving a more diverse set of perspectives in the modeling process and encouraging new approaches to energy system design and development.By elevating diverse perspectives throughout the modeling process, ESM can facilitate a better understanding of the socio-political and cultural contexts in which energy systems operate.Doing so could lead to new approaches to decarbonization including the development of experimental business models, the valuation of technologies' co-benefits, and the co-creation of energy system design with local communities.In 2019 for example, Costa Rica released its National Decarbonization Plan which was developed via the country's Long Term Strategy (LTS).Costa Rica's LTS was co-designed with stakeholders and multi-sectoral representatives using open-source data and modeling tools.Its design considered socio-economic priorities such as poverty eradication, job creation, affordable energy access, and improved air quality.In 2022, $USD 2.4 billion in concessionary finance was mobilized to execute the plan as a result (Jaramillo et al 2023).

Incorporating socio-political factors into energy system models can be challenging, but there are several approaches to close the existing gap
Integrating socio-political factors into energy system models is a complex process that requires careful consideration and robust methodologies.We suggest five actions that could help to close the existing gap between ESM and socio-political factors: (I) recognizing that socio-political factors are just as important as techno-economic factors in shaping the energy systems of the future (II) improving the structure of the modeling process (III) developing new and innovative metrics for socio-political factors (IV) linking different types of models, and (V) emphasizing the need for interdisciplinarity in ESM.
The first step to incorporating socio-political factors into ESM is the need to recognize the problem and acknowledging socio-political factors as being equally as important as techno-economic factors.Often, technically minded modelers underestimate the value of incorporating socio-political factors in ESM and this sort of misunderstanding undermines the results of the models for meaningful policymaking.It could be a 'hard pill' to swallow, but the reality is that socio-political factors play a pivotal role in shaping energy policies and regulations.Government decisions, driven by political ideologies and public sentiment, can significantly impact the energy sector.Ignoring these factors can lead to a misunderstanding of the actual policy landscape.It is important to highlight that the ESM community is increasingly aware of this issue.There have been several articles to acknowledge the significance of socio-political factors in the modeling process (Krumm et al 2022, Süsser et al 2022a, 2022b, Vågerö and Zeyringer 2023).However, it remains essential to maintain ongoing advocacy and research efforts to meaningfully integrate sociopolitical factors into policy-relevant ESM.
The second approach is to improve the structure of the modeling process by treating socio-political factors more holistically in models beyond treating them as external factors.This requires modelers to deeply engage with the challenges of integrating social aspects and to be open to new modeling approaches.Modelers should be willing to reevaluate the structure of their models to reflect the behavior of various actors accurately.This may involve exploring alternative mathematical formulations to better represent societal dynamics, as well as adding new modules or features to existing models.There have been efforts to improve model structure such as integrating techniques like Modeling-to-Generate-Alternatives (Price and Keppo 2017).However, cost remains the main determinant of such a technique.The development of entirely new models could be warranted.This has also been advocated by several scholars (Krumm et al 2022, Dioha andMutiso 2023).These models should be designed to not only capture social factors, but to also involve a broader range of perspectives and enhance transparency regarding assumptions, inputs, and results.We can draw inspiration from existing integration strategies, such as the BLUE model by (Li and Strachan 2017), which account for the plurality of socio-political factors.
Thirdly, there is a need to develop new and innovative metrics for socio-political factors.Given the value-based nature of defining socio-political factors, any study that delves into the topic must include a clear definition and metric, particularly when dealing with ESM or other quantitative analysis (Vågerö and Zeyringer 2023).Socio-political factors encompass a wide range of elements, from government policies and public sentiment to regulatory changes and political stability.Thus, no single metric could address these issues comprehensively.This necessitates engagement with stakeholders, such as community representatives, NGOs, policymakers, and industry leaders who affect and are affected by energy systems to better understand what the factors of concern are.It can help ensure that the metrics developed for ESM reflect the needs and priorities of stakeholders and identify place-based solutions that are socially and politically acceptable (Süsser et al 2022a(Süsser et al , 2022b)).Different regions and contexts may require different metrics to account for their unique socio-political dynamics.Tailoring metrics to specific situations can yield more accurate and relevant insights for energy modeling.While we recognize that there have been some efforts to develop some metrics such as the Environmental Justice Indexes 1 , more needs to be done to capture the wide range of factors across the socio-political factors spectrum.For example, GISbased modeling can be used to identify and map energy poverty, energy access, energy consumption, and energy infrastructure at different spatial scales, from the household level to the country level (Khavari et al 2023).
The fourth approach is to link (both softand hard-linking) energy system models to capture socio-political factors adequately.It is important to recognize that models and model types have their unique abilities when it comes to representing socio-political factors and there are limitations to how extensively these factors can be integrated (Köhler et al 2018).To address the inherent limitations of individual models in capturing social factors, the practice of linking various models and model types can significantly allow the incorporation of socio-political factors into ESM.The have been some modeling efforts already moving in this direction.For instance, combining Agent-Based Models with Energy System Models, Integrated Assessment Models, or Computable General Equilibrium models presents opportunities to provide insights into human behavior and account for the diversity of actors within these broader model types, as exemplified by projects like EXIOMOD 2.0 and BSAM (Krumm et al 2022).It is essential for modelers to collaborate and harness the full potential of each model's capabilities for incorporating socio-political factors by establishing links between models and different model types.
The fifth approach involves emphasizing the need for interdisciplinarity in ESM.There is growing evidence to show that, to a certain degree, a disconnect exists between energy system modelers and social scientists (Turnheim et al 2015, Krumm et al 2022, Pianta and Brutschin 2022).It has become increasingly evident that there is a pressing need to enhance the participation of social and behavioral scientists in ESM.Thus, collaboration between energy system modelers and social scientists is essential for improving ESM.This allows experts from both fields to contribute their knowledge and expertise to the modeling process.This will consider involving social scientists from the beginning of the modeling process, starting with the definition of research questions.This ensures that the modeling efforts align with the social and behavioral aspects of energy transitions.
Social scientists can provide valuable insights, theories, and empirical data that can inform the modeling process, and modelers should be open to integrating these insights into their models to make them more accurate and comprehensive.Collaborating on developing behavioral models that can simulate how individuals and communities respond to changes in energy systems is also important.These models can incorporate insights from psychology, sociology, and economics.There is also a need to establish feedback loops between modelers and social scientists, allowing for continuous refinement of models based on new research findings and changing societal dynamics.Finally, both fields need to maintain open and transparent communication channels, ensuring that each understands the goals, assumptions, and limitations of the other's work.

Concluding remarks
We need to improve the structure, design, and configuration of current modeling methods, and we need to design new methods, metrics, and tools rooted in socio-political dynamics to better support decisionmaking around energy transitions.Energy system researchers and analysts all over the world are working to improve existing methods, some of which are expanding these methods beyond techno-economic optimization.However, many of the models used in policy and decision-making processes continue to be optimized for cost without any robust consideration for the socio-political aspects of energy systems.Existing modeling methods, such as agentbased modeling, can integrate multiple objectives, criteria, and stakeholders into decision-making processes.Other methods, such as spatial analysis or scenario analysis can be used to explore how the impacts of modeled decarbonized futures may vary across time and geographic scales.In addition to these methods, researchers are developing metrics-such as the energy equity gap and the affordability ratio, among others-that can aid in measuring the impact of decarbonized futures in terms of equity outcomes; and tools that allow for stakeholder engagement, like interactive online platforms, can help build the capacity of typically underrepresented groups to participate in designing their energy futures.These methods, among many others not listed, exist primarily in academia with very little permeation into the spaces where policy decisions are made.By promoting more inclusive decision-making processes, ESM can ensure that the benefits and costs of energy systems are distributed fairly.By prioritizing the aspects of the energy system that concern people to the same degree we prioritize cost, we can derive value-driven insights to assist in creating a more sustainable future for all.

Figure 1 .
Figure 1.Factors beyond cost influencing energy system decisions.