Optimizing Incentive Policy of Energy-Efficiency Retrofit in Public Buildings: A Principal-Agent Model

Author:

Liang Xin,Shen Geoffrey Qiping,Guo Li

Abstract

The building sector consumes most energy in the world, especially public buildings, which normally have high energy-use intensity. This phenomenon indicates that the energy-efficiency retrofit (EER) for public buildings is essential for energy saving. Incentive policies have been emphasized by governments in recent years, but their effectiveness has not been sufficient. A major reason is agency problems in EER and that the government and building owners have asymmetric information. Furthermore, most policies apply identical standard to existing buildings of different types, resulting in resistance from owners and tenants. To mitigate this issue, this study proposes a principal–agent model to optimize incentive policy in EER. The proposed model defines two pairs of principal–agent relations (i.e., the government-owner and owner-tenant) and models their behaviors under different scenarios as per principal–agent theory. The results indicate the optimal incentive policies for different scenarios. In addition, critical factors of policy making, such as cost, risk, uncertainty, and benefit distribution are discussed. This study has implications for policy that will benefit policy makers, particularly in promoting EER by mitigating the agency problem found for the different scenarios.

Funder

MOE (Ministry of Education in China) Grant of Humanities and Social Sciences

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development

Reference42 articles.

Cited by 11 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Saving Energy and Maintaining Indoor Comfort Level: A Reinforcement Learning Based Approach;2023 IEEE 11th International Conference on Smart Energy Grid Engineering (SEGE);2023-08-13

2. A Reinforcement Learning Based Approach to Recommend Appropriate Occupant Behaviours;2023 IEEE 6th International Conference on Big Data and Artificial Intelligence (BDAI);2023-07-07

3. Risks Caused by Information Asymmetry in Construction Projects: A Systematic Literature Review;Sustainability;2023-06-23

4. Communication challenges and blockchain in building energy efficiency retrofits: Croatia case;Engineering, Construction and Architectural Management;2023-06-13

5. Pricing of Credit Default Swaps from the Perspective of Credit Enhancement in PPP Projects;Journal of Construction Engineering and Management;2023-06

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3