4.7 Article

Policy cognition is more effective than step tariff in promoting electricity saving behaviour of residents

期刊

ENERGY POLICY
卷 139, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.enpol.2020.111338

关键词

Step tariff; Policy cognition; Behaviour; Electricity consumption

资金

  1. National Science Fund for Distinguished Young Scholars [71625003]
  2. Yangtze River Distinguished Professor of MOE, National Key Research and Development Program of China [2016YFA0602504]
  3. National Natural Science Foundation of China [91746208, 71573016, 71403021, 71521002, 71774014]
  4. Humanities and Social science Fund of Ministry of Education of China [17YJC630145]
  5. China Postdoctoral Science Foundation [2017M620648]

向作者/读者索取更多资源

As a demand response plan, the step tariff policy for electricity has been fully implemented for several years in China. However, it is not clear whether the step tariff policy for electricity is effective. Therefore, we analysed and compared the effectiveness of step tariff policy from two aspects: step tariff and policy cognition. Specifically, combining macro-scale statistical data and microscopic research data, we constructed a regression discontinuity design and binary response model. First, we tested the correction effect of the step tariff to residential electricity consumption behaviour. Further, combined with cognitive behavioural theory, we analysed the role of policy cognition in the occurrence of electricity saving behaviour. Finally, based on the cognitive evaluation theory, the impact of residents' heterogeneity on their cognitive differences was identified. The specific conclusions are as follows: (1) The step tariff based on the Ramsay strategy cannot effectively correct electricity consumption behaviours as expected. (2) Compared with the step tariff, construction of correct policy cognition has a more significant effect on changing the customary behaviour of electricity consumption. (3) Policy cognition of residents is heterogeneous, and groups with weak policy cognition are accurately identified.

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