China’s 11th Five-Year Plan introduced various policy instruments to address carbon mitigation; however, the ex-post-policy impacts need to be investigated in a scientific and systematic way to guide future policy design. In this paper, we estimate the impacts of the heat metering and energy efficiency retrofitting policy (HMEER) on residential energy consumption in Chinese provinces using a difference-in-differences approach. Our results suggest that the HMEER policy reduces energy consumption in the treated regions by 10% per year on average, with an annual reduction in CO2 of approximately 50 Mt. We conclude that the HMEER policy contributes to household energy conservation.
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According to Zhou et al. (2007), there is a lack of detailed information on demand by end use in China’s official energy statistics; therefore, it is difficult to break down the energy use by sectors. In this paper, we utilize the residential energy use data from China Energy Databook, which provides robust end-use sector energy consumption data compared to the official statistics.
The provinces covered by the HMEER policy are Beijing, Gansu, Hebei, Heilongjiang, Henan, Inner Mongolia, Jilin, Liaoning, Ningxia, Qinghai, Shaanxi, Shandong, Shanxi, Tianjin, and Xinjiang.
See Imbens and Wooldridge (2009) for a general overview of the methods used in ex-post-evaluation studies.
Recently, the University of California–Berkeley, the Massachusetts Institute of Technology, and the University of Chicago have started an interesting initiative to promote randomized experiments for the evaluation of energy efficiency policy measures. See http://e2e.haas.berkeley.edu/about-mission.html.
The DID approach has the advantages of removing unobservable individual effects and common macro effects and taking into account approximation errors and random behavior through the statistical noise.
The usual model specification in a DID setting is Y = β0 + β1 × [Time] + β2 × [policy] + β3 × [Time × policy] + β4 × [Covariates] + ε. In model (1), the policy variable (dPOL) is obtained as an interaction term between time and the policy group dummy. Furthermore, the policy group dummy is not included in the model specification, because this variable is time invariant and, therefore, absorbed by the individual effects.
Tibet, Hainan, Taiwan, Hong Kong, and Macau are excluded from this study, as some data information is missing in the statistics.
The publication of statistical yearbooks in China has 1-year delay, which means that the yearbook in 2004 reports the statistics of 2003.
In China, there are five different climate zones. In the subsample model, we exclude the hot-summer–warm-winter zone (provinces in this zone include Guangdong, Guangxi, and Fujian) and the temperate zone (Yunnan) from the control group as they do not need heating systems in general. We also exclude the severe-cold zone (provinces in this zone include Jilin, Liaoning, Heilongjiang, and Xinjiang), as provinces in this zone require significantly higher heating services. All the other provinces are spread along the Qinling-Huaihe line, which is the line for the official heating division.
People living in the central and southern provinces (in the control group) tend to use more energy for heating over time compared to the treated group. However, this is not an issue in this study as the DID approach captures such differences. To note, the values reported in Fig. 1 refer to the consumption at the end of each year.
The percentage change is calculated by using 100[eα,1], where α is the coefficient of the policy variable.
Unfortunately, no information on the emission coefficient is available at the province level. For this reason, the emission coefficient of each province is approximated by the national emission coefficient, which is obtained by dividing the total CO2 emissions by the total energy consumption. The data used for the calculation of the emission coefficient are obtained from the World Bank Database.
This reduction of CO2 emissions has been calculated for each province by multiplying the emission coefficient with the amount of energy savings. For instance, the residential energy savings of Beijing in 2007 are about 1.2 Mtce, and the emission coefficient is 2.42 Mt/Mtce; therefore, the total emission reduction can be calculated by multiplying the two numbers, namely 2.9 Mt CO2 equivalent.
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Filippini, M., Zhang, L. Impacts of heat metering and efficiency retrofit policy on residential energy consumption in China. Environ Econ Policy Stud 21, 203–216 (2019). https://doi.org/10.1007/s10018-018-0227-8
- Chinese residential sector
- Energy consumption
- Policy evaluation
- Heat metering and energy efficiency retrofit