A systems-based analysis of energy demand reduction and efficiency policies using fuzzy cognitive maps

Energy Efficiency (EE) and Energy demand reduction (EDR) policies are crucial for addressing both energy security and climate change. However, despite their strategic importance, they often face significant barriers, including institutional resistance, limited funding, and short-term political priorities that tend to overlook long-term efficiency gains. This study utilizes fuzzy cognitive maps (FCMs) to model the factors influencing EE/EDR policy effectiveness across European countries. To capture the importance and interrelation of factors predefined from the literature, expert knowledge was leveraged through interviews with European energy agency representatives. Qualitative expert assessments were then transformed into numerical values, generating weighted causal matrices. Centrality measures subsequently identified key factors within an aggregated European model. Results revealed six pivotal factors: continuous financial support, favourable regulatory frameworks, consumer engagement, ease of implementation, long-term policy mandates, and support from industry and stakeholders. Scenario analysis explored the impact of three policy interventions: increased market actor support, weakened monitoring frameworks and increased energy literacy for end-consumers. The results of the interventions indicated that isolated interventions had limited impact on overall EE/EDR system outcomes, underscoring its complexity. However, consumer literacy initiatives mitigated negative behavioural effects, such as rebound effects and misaligned targeting, while weakened monitoring frameworks diminished policy coherence and increased redundancies. These findings reinforce the need for long-term policy stability, regulatory clarity, and robust end-user engagement. A systems-based approach, accounting for interdependencies and system dynamics, is crucial for effective EE/EDR policy design, as isolated interventions are insufficient.