Understanding the human element in community integrated energy systems

Community Integrated Energy Systems (CIES) integrate distributed renewable energy in residential communities. While previous studies have investigated the technological or economic aspects of transitioning to sustainable community energy systems, the social aspect is less understood — but still important.

Zhang et al. explored the factors that influence residents’ willingness to adopt CIES. They extended a technology acceptance model, which describes how individuals accept emerging technologies, to include institutional, economic, social, and psychological factors. Then, the researchers tested the behavioral framework using survey data from 589 respondents to identify the main drivers and barriers influencing residents’ intentions to participate in CIES.

The authors found that residents’ attitude toward using CIES was the strongest predictor of their intention to adopt the system. The extent of government involvement was the second most influential factor, and perceived cost was the strongest barrier to adoption.

“These findings demonstrate that successful CIES deployment depends not only on technological performance but also on residents’ perceptions and the surrounding institutional environment,” said author Jianjun Wang. “We believe that integrating behavioral science with energy system planning will become increasingly important as countries accelerate the transition toward decentralized, renewable, and community-based energy systems.”

These findings could be useful for policymakers, community planners, and energy service providers developing policies and community engagement strategies to improve public acceptance of CIES.

“For example, reducing perceived costs, strengthening policy support, enhancing technology trust, and increasing public awareness may significantly encourage adoption,” Wang said.

Next, the authors plan to study how these intentions translate into actual adoption behavior, compare residents’ acceptance of CIES across different regions and countries, and incorporate additional behavioral and institutional factors into the analytical framework.

Source: “Modeling the adoption dynamics of community integrated energy systems: Behavioral drivers and policy implications for renewable energy deployment,” by Jingyi Zhang, Jianjun Wang, Huiru Nie, and Jiale Ruan, Journal of Renewable and Sustainable Energy (2026). The article can be accessed at https://doi.org/10.1063/5.0311504 .