Making incentive policies more effective: An agent-based model for energy-efficiency retrofit in China

Xin Liang, Tao Yu, Jingke Hong, Geoffrey Qiping Shen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

34 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)177-189
Number of pages13
JournalEnergy Policy
Volume126
DOIs
Publication statusPublished - Mar 2019

Keywords

  • Agent-based model
  • Energy efficiency
  • Multi-agent system
  • Principal-agent theory
  • Retrofit

ASJC Scopus subject areas

  • Energy(all)
  • Management, Monitoring, Policy and Law

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