Journal
ENERGY POLICY
Volume 126, Issue -, Pages 177-189Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.enpol.2018.11.029
Keywords
Energy efficiency; Retrofit; Agent-based model; Multi-agent system; Principal-agent theory
Funding
- MOE (Ministry of Education in China) Grant of Humanities and Social Sciences [18YJCZH092]
- Shanghai Pujiang Program [17PJC061]
- Natural Science Foundation of China [71801023]
- Chongqing Science & Technology Commission [cstc2018jcyjAX0099]
- Fundamental Research Funds for the Central Universities [17JCYA08, 0204005201043]
Ask authors/readers for more resources
The building sector is responsible for a major share of energy consumption, with the most energy being consumed during the operation stage of buildings. Energy-efficiency retrofit (EER) policies have been promoted by numerous countries. However, the effectiveness of these incentive policies has been insufficient, a main reason being the agency problem between the government and building owners. In addition, most policies ignored the diversity of buildings and building owners, resulting in a lack of reaction from owners. To address this problem, this study proposed an agent-based model for policy making on EER. The model defined the government and owners as agents and their decision-making behaviors were modeled with principal-agent theory. A platform based on the proposed model was then developed and the incentive policy was optimized under different circumstances. To verify the effectiveness of the proposed model, three policy scenarios were compared on the platform, which are the policy by the proposed model, the incentive policy in Shanghai and Shenzhen, China. The results showed that the incentive policy based on the proposed model has the best performance on energy savings, returns on investment, and leverage effects. A sensitivity analysis indicated that the government should pay more attention to energy price.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available