Impacts of heat metering and efficiency retrofit policy on residential energy consumption in China

Abstract

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.

This is a preview of subscription content, access via your institution.

Fig. 1

Notes

  1. 1.

    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.

  2. 2.

    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.

  3. 3.

    See Imbens and Wooldridge (2009) for a general overview of the methods used in ex-post-evaluation studies.

  4. 4.

    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.

  5. 5.

    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.

  6. 6.

    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.

  7. 7.

    Tibet, Hainan, Taiwan, Hong Kong, and Macau are excluded from this study, as some data information is missing in the statistics.

  8. 8.

    The publication of statistical yearbooks in China has 1-year delay, which means that the yearbook in 2004 reports the statistics of 2003.

  9. 9.

    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.

  10. 10.

    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.

  11. 11.

    The percentage change is calculated by using 100[eα,1], where α is the coefficient of the policy variable.

  12. 12.

    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.

  13. 13.

    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.

  14. 14.

    We use the values of total energy consumption and GDP reported in the statistics (NBS 2004–2012a, b; LNBL 2012).

References

  1. Adan H, Fuerst F (2015) Do energy efficiency measures really reduce household energy consumption? A difference-in-difference analysis. Energy Effic 9(5):1207–1219

    Article  Google Scholar 

  2. Allcott H, Kessler J (2015) The welfare effects of nudges: a case study of energy use social comparisons. NBER Working Paper No. 21671

  3. Alberini A, Bareit M (2017) The effect of registration taxes on new car sales and emissions: evidence from Switzerland. Resour Energy Econ. https://doi.org/10.1016/j.reseneeco.2017.03.005

    Google Scholar 

  4. Ameli N, Pisu M, Kammen DM (2017) Can the US keep the PACE? A natural experiment in accelerating the growth of solar electricity. Appl Energy 191:163–169

    Article  Google Scholar 

  5. Ashenfelter O, Card D (1985) Using the longitudinal structure of earnings to estimate the effects of training programs. Rev Econ Stud 67:648–660

    Google Scholar 

  6. Bao L, Zhao J, Zhu N (2012) Analysis and proposal of implementation effects of heat metering and energy efficiency retrofit of existing residential buildings in northern hating areas of China in “the 11th Five-Year Plan” period. Energy Policy 45:521–528

    Article  Google Scholar 

  7. Boomhower J, Davis L (2017) Do energy efficiency investments deliver at the right time? NBER Working Paper No. 23097

  8. Cai WG, Wu Y, Zhong Y, Ren H (2009) China building energy consumption: situation, challenges and corresponding measures. Energy Policy 37(6):2054–2059

    Article  Google Scholar 

  9. Chen S, Li N, Guan J, Xie Y, Sun F, Ni J (2008) A statistical method to investigate national energy consumption in the residential building sector of China. Energy Build 40:654–665

    Article  Google Scholar 

  10. Datta S, Filippini M (2016) Aanlysing the impact of ENERGY STAR rebate policies in the US. Energy Effic 9(3):677–698

    Article  Google Scholar 

  11. Ding Y, Tian Z, Wu Y, Zhu N (2011) Achievements and suggestions of heat metering and energy efficiency retrofit for existing residential buildings in northern heating regions of China. Energy Policy 39:4675–4682

    Article  Google Scholar 

  12. Fowlie M, Greenstone M, Wolfram C (2015) Do energy efficiency investments deliver? Evidence from the Weatherization Assistance Program. NBER Working Paper Series 21331

  13. Gillingham K, Harding M, Rapson D (2012) Split incentives in residential energy consumption. Energy J 33(2):37–62

    Article  Google Scholar 

  14. Horowitz M (2007) Changes in electricity demand in the United States from the 1970s to 2003. Energy J 28(3):93–119

    Article  Google Scholar 

  15. Houde S, Todd A, Sudarshan A, Flora JA, Armel KC (2013) Real-time feedback and electricity consumption: a field experiment assessing the potential for savings and persistence. Energy J 34(1):87

    Article  Google Scholar 

  16. Imbens GW, Wooldridge JM (2009) Recent developments in the econometrics of program evaluation. J Econ Lit 47(1):5–86

    Article  Google Scholar 

  17. Levinson A (2016) How much energy do building energy codes save? Evidence from California houses. Energy Policy 106(10):2867–2894

    Google Scholar 

  18. LNBL (2012) China Energy Databook Version 8.0. Lawrence Berkeley National Laboratory, Berkeley

    Google Scholar 

  19. MOHURD, MOF (2008) Implement opinion on promoting heat metering and energy efficiency retrofit of existing residential buildings in northern heating areas of China. Document of Department of Science and Technology of Mohurd (2008). http://www.mohurd.gov.cn/zcfg/jswj/jskj/200806/t20080613_171707.htm

  20. NBS (2004–2012a) China Statistical Yearbooks, Beijing

  21. NBS (2004–2012b) China Urban Life and Price Yearbook, Beijing

  22. Richerzhagen C, von Frieling T, Hansen N, Minnaert A, Netzer N, Russbild J (2008) Energy efficiency in buildings in China. Policies, barriers and opportunities, German Development Institute (Edit.)

  23. Sekitou M, Tanaka K, Managi S (2018) Household electricity demand after the introduction of solar photovoltaic systems. Econ Anal Policy 57:102–110

    Article  Google Scholar 

  24. Sheer J, Clancy M, Hogain SN (2013) Quantification of energy savings from Ireland’s home energy saving scheme: and ex post billing analysis. Energy Effic 6:35–48

    Article  Google Scholar 

  25. Tanaka K, Sekito M, Managi S, Kaneko S, Rai V (2017) Decision-making governance for purchases of solar photovoltaic systems in Japan. Energy Policy 111:75–84

    Article  Google Scholar 

  26. Tsinghua University, Building energy research center (2007) 2007 Annual Report on China Building Energy Efficiency. China Building Industry Press, Beijing

    Google Scholar 

  27. Xu X, Andon LD, Lee H (2016) Increasing residential building energy efficiency in China: an evaluation of policy instruments. Harvard Kennedy School Belfer Center for Science and International Affairs, Discussion Paper No. 2016-02

  28. Zhang L (2013) Model projections and policy reviews for energy saving in China’s service sector. Energy Policy 59:312–320

    Article  Google Scholar 

  29. Zhao Jing, Wu Yong, Zhu Neng (2009a) Check and evaluation system on heat metering and energy efficiency retrofit of existing residential buildings in northern heating areas of China based on multi-index comprehensive evaluation method. Energy Policy 37:2124–2130

    Article  Google Scholar 

  30. Zhao J, Zhu N, Wu Y (2009b) Technology line and case analysis of heat metering and energy efficiency retrofit of existing residential buildings in Northern heating areas of China. Energy Policy 37:2106–2112

    Article  Google Scholar 

  31. Zhao X, Li N, Ma C (2012) Residential energy consumption in urban China: a decomposition analysis. Energy Policy 41:644–653

    Article  Google Scholar 

  32. Zheng X, Wei C, Qin P, Guo J, Yu Y, Song F, Chen Z (2014) Characteristics of residential energy consumption in China: findings from a household survey. Energy Policy 75:126–135

    Article  Google Scholar 

  33. Zhou N, McNeil MA, Fridley D, Lin J, Price L, de la Rue du Can S, Sathaye J, Levine M (2007). Energy use in China: sectoral trends and future outlook. LBNL-61904. Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Lin Zhang.

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Keywords

  • Chinese residential sector
  • Difference-in-differences
  • Energy consumption
  • Policy evaluation
  • Heat metering and energy efficiency retrofit