Volume 68

Pre-Implementation Social Acceptability Assessment of Near-Zero-Carbon Community Energy Measures: A Multi-Agent Simulation Approach Jixiang Cai , Zhuoyue Shen , Yi Zhang

Abstract

Near-zero-carbon communities require coordinated deployment of distributed energy technologies—such as building-integrated photovoltaics (BIPV), shared energy storage, and electric vehicle (EV) charging infrastructure—across multiple plots and building types. While technical design has advanced significantly, measures satisfying carbon thresholds may face stakeholder resistance due to divergent economic interests and risk perceptions. Conventional assessment methods—expert panels, Delphi surveys, and post?occupancy evaluations—are costly, static, and incapable of simulating interactive stakeholder dynamics. This study proposes a large language model (LLM)-based multi-agent simulation framework for rapid, quantitative, pre-implementation social acceptability assessment. The framework comprises four modules: issue extraction, stakeholder agent construction with heterogeneous utility weight vectors, three-round structured deliberation, and synthesis with design revision. Validated on a near-zero-carbon residential project in Shenzhen, China, six stakeholder types (24 agents)deliberated over four energy issues. BIPV cost allocation exhibited the highest conflict with a mean score of 4.8, while EV charger distribution reached near?consensus at 7.6. The three-round protocol drove convergence with inter-agent standard deviation reduced by 18%, without inflating scores. Agent?generated revision strategies improved aggregate acceptability by 27.4%, elevating all issues above the 7.0 threshold. Expert validation (n = 6) yielded Spearman ρ = 0.82 (p < 0.01) with identical issue rankings. This framework bridges the gap between technical carbon compliance assessment and socio-economic implementability analysis, providing planners with a low?cost, repeatable screening tool for community-scale decarbonization.

Keywords near-zero-carbon community, social acceptability, multi-agent simulation, large language model, stakeholder analysis, energy policy