The effect of group decisions in heat transitions: An agent-based approach

Guerrero, G.D.C. Luteijn-Nava; Hansen, Helle Hvid; Korevaar, Gijsbert; Lukszo, Zofia

doi:10.1016/j.enpol.2021.112306

Cited by 10 publications

(28 citation statements)

References 40 publications

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“…Total energy demand (in 2049), factors that affect the demand (income & fuel price growth, external influences) [70] Explore the impacts of social interactions on weatherization decisions for households under pre-and post-weatherisation conditions Number of weatherized households (with and without Assistance Program, with and without community leader, for different memory lengths of agents, and network characteristics) [75] Explore socioeconomic conditions that could support the neighborhoods' heat transition over time while meeting the neighbourhood's heat demand Number of heating systems adopted at certain combination of time horizon for all, changes in natural gas price and electricity price, fraction of households that is able to compare combined investments [76] Explore how group decision-making in strata buildings could affect the heat transition in the owner-occupied share of the housing sector in the Netherlands Individual preferences for thermal systems at the beginning of the simulation, group lock out (when the Homeowner Association can't agree on the decision), cumulative heating costs over time [69] Explore the development of the renovation state of the building stock based on renovation behaviour of different types of homeowners Development of overall heat demand (GWh/a) and number of buildings renovated in the city over time [77] Analyse the effect of behavioural outcomes in different policy situation due to the influence of energy-saving behaviour and intentions…”

Section: Study Model Purposementioning

confidence: 99%

“…Majority of these models limit their agent population to the households that live in a single-family building, because installation of renewable energy in other types of housing (rented apartments, multi-family housing) is subject to additional legal or physical constraints. However, few models are exceptions: [3,61] differentiate agents into tenants and house owners, where only house owners can buy and install PV and tenants can choose from green electricity or community solar program; Nava Guerrero et al [76] attempts to represent group decision-making regarding heating system, insulation or RE system installation in multi-family houses. In other models, building (or building block) owner [60,69] and building agents [59] can make building-level decisions, i.e., adopting PV or renovation.…”

Section: Agentsmentioning

confidence: 99%

“…Majority of these studies consider the diffusion of a single technology: rooftop PV [50,59,60,62,64], feedback device (CO 2 meter) [66,67]. In some cases, there could be several options are available for agents: [3,61] let agents choose between buying PV via cash payment of a loan, adopting community solar (i.e., renewable energy community) or opting for green electricity; Ramshani et al [63] make agents choose the optimal solution for their rooftops-either rooftop PV or green roof; Nava Guerrero et al [76] introduces the combinations of technologies as "technology state" of a household (i.e., combination of heating system, insulation level, and appliances). Building insulation or renovation is addressed in three studies [69,70,75].…”

Section: Technologies and Policies Modelledmentioning

confidence: 99%

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Agent-Based Modelling of Urban District Energy System Decarbonisation—A Systematic Literature Review

et al. 2022

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There is an increased interest in the district-scale energy transition within interdisciplinary research community. Agent-based modelling presents a suitable approach to address variety of questions related to policies, technologies, processes, and the different stakeholder roles that can foster such transition. However, it is a largely complex and versatile methodology which hinders its broader uptake by researchers as well as improved results. This state-of-the-art review focuses on the application of agent-based modelling for exploring policy interventions that facilitate the decarbonisation (i.e., energy transition) of districts and neighbourhoods while considering stakeholders’ social characteristics and interactions. We systematically select and analyse peer-reviewed literature and discuss the key modelling aspects, such as model purpose, agents and decision-making logic, spatial and temporal aspects, and empirical grounding. The analysis reveals that the most established agent-based models’ focus on innovation diffusion (e.g., adoption of solar panels) and dissemination of energy-saving behaviour among a group of buildings in urban areas. We see a considerable gap in exploring the decisions and interactions of agents other than residential households, such as commercial and even industrial energy consumers (and prosumers). Moreover, measures such as building retrofits and conversion to district energy systems involve many stakeholders and complex interactions between them that up to now have hardly been represented in the agent-based modelling environment. Hence, this work contributes to better understanding and further improving the research on transition towards decarbonised society.

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Section: Study Model Purposementioning

confidence: 99%

Section: Agentsmentioning

confidence: 99%

Section: Technologies and Policies Modelledmentioning

confidence: 99%

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Agent-Based Modelling of Urban District Energy System Decarbonisation—A Systematic Literature Review

et al. 2022

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“…No policy; changes in natural gas price and electricity price are taken as proxies for market forces and policies [76] insulation, renewable heating investments in new technology Fiscal policy (i.e. linear growth of natural gas taxes, taxes on electricity, and regulated price of heat from networks) and disconnection from gas network.…”

Section: Investments In New Technologymentioning

confidence: 99%

“…In some cases, there could 234 be several options are available for agents: [3,61] let agents choose between buying PV via 235 cash payment of a loan, adopting community solar (i.e. renewable energy community) or 236 opting for green electricity; [63] make agents choose the optimal solution for their rooftops 237 -either rooftop PV or green roof; [76] introduces the combinations of technologies as 238 "technology state" of a household (i.e. combination of heating system, insulation level, and 239 appliances).…”

mentioning

confidence: 99%

Agent-Based Modelling of Urban District Energy System Decarbonisation – A Systematic Literature Review

Akhatova¹,

Kranzl²,

Schipfer³

et al. 2021

Preprint

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There is an increased interest in the district-scale energy transition within interdisciplinary research community. Agent-based modelling presents a suitable approach to address variety of questions related to policies, technologies, processes, and the different stakeholder roles that can foster such transition. This state-of-the-art review focuses on the application of agent-based modelling for exploring policy interventions that facilitate the decarbonisation (i.e., energy transition) of districts and neighbourhoods while considering stakeholders’ social characteristics and interactions. We systematically select and analyse peer-reviewed literature and discuss the key modelling aspects, such as model purpose, agents and decision-making logic, spatial and temporal aspects, and empirical grounding. The analysis reveals that the most established agent-based models’ focus on innovation diffusion (e.g., adoption of solar panels) and dissemination of energy-saving behaviour among a group of buildings in urban areas. We see a considerable gap in exploring the decisions and interactions of agents other than residential households, such as commercial and even industrial energy consumers (and prosumers). Moreover, measures such as building retrofits and conversion to district energy systems involve many stakeholders and complex interactions between them that up to now have hardly been represented in the agent-based modelling environment.

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An agent-based exploration of the effect of multi-criteria decisions on complex socio-technical heat transitions

et al. 2022

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The effect of group decisions in heat transitions: An agent-based approach

Cited by 10 publications

References 40 publications

Agent-Based Modelling of Urban District Energy System Decarbonisation—A Systematic Literature Review

Agent-Based Modelling of Urban District Energy System Decarbonisation—A Systematic Literature Review

Agent-Based Modelling of Urban District Energy System Decarbonisation – A Systematic Literature Review

An agent-based exploration of the effect of multi-criteria decisions on complex socio-technical heat transitions

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