Journal
ENERGY ECONOMICS
Volume 94, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.eneco.2020.105076
Keywords
Increasing block electricity pricing; Policy cognition; Endogenous switching regression; Treatment effect; Price cognitive bias
Categories
Funding
- pilot programs for major science, technology and innovation projects toward 2030 of China Energy Investment Corporation-Clean and efficient utilization of coal: Research on medium and long-termcarbon emission reduction paths and energy structure optimizati [GJNY2030XDXM19-20.1]
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The study shows that policy cognition can reduce electricity consumption by about 42.25%, with price cognitive bias being a crucial way to achieve this effect. Especially for residents with low incomes, low electricity consumption, and over 50 years old, this energy-saving effect is more significant.
Consumers' policy cognition is often ignored in evaluating policy effects. Taking the increasing block electricity pricing (IBP) system implemented in China as an example, and based on 2917 micro-survey data of residents from four first-tier cities, this paper uses the endogenous switching regression (ESR) model to investigate the impact of residents' policy cognition on electricity consumption. The results show that the treatment effect of the treatment group is -0.564, the treatment effect of the control group is -0.529. The average treatment effect is -0.549, which means that policy cognition can reduce electricity consumption by about 42.25%. The mechanism analysis results show that price cognitive bias is a crucial way for policy cognition to affect electricity consumption. Further analysis of heterogeneity shows that this energy-saving effect is larger among residents with low incomes, low electricity consumption, and residents over 50 years old. These results suggest that the government should increase the publicity of the block electricity price policy and strengthen energy conservation awareness education for price-insensitive residents to take the initiative and continue to save electricity. (C) 2020 Elsevier B.V. All rights reserved.
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