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Impacts of heat metering and efficiency retrofit policy
on residential energy consumption in China
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
Keywords: Chinese residential sector; difference-in-differences; energy
consumption; policy evaluation; heat metering and energy efficiency retrofit
ETH Zurich and University of Lugano
City University of Hong Kong
Hong Kong, China
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In the 11th Five-Year-Plan (FYP) period between 2006 and 2010, the
Chinese Government had planned to reduce its energy intensity by 20%. Several
energy policy measures were implemented in order to achieve this goal, some of
which were implemented in the residential sector. In 2011, residential energy
consumption consumed 414.54 Mtce of energy, which accounted for 11% of total
energy consumption in China (LBNL 2012).
Bao et al. (2012) show that in the
northern part of China, a very high proportion of the urban residential buildings
have low levels of energy efficiency. Furthermore, the share of total building
energy consumption used for heating purposes is high in these provinces (Cai et
al. 2009). Tsinghua (2007) reports that heating energy consumption in terms of
per square meter of building space in China is about 1-1.5 times more than that
in Northern Europe. This difference is mainly due to poor insulation, inefficient
heating systems and lack of heat metering facilities.
To reduce energy intensity in the residential sector, in 2007, the Chinese
Government introduced a policy measure to promote heat metering and energy
efficiency retrofitting (HMEER) in the northern areas of China. The program was
directed and supervised by the joint efforts of the Ministry of Housing and Urban-
Rural Development (MOHURD) and the Ministry of Finance (MOF). The
performance of the provinces covered in the policy was evaluated by the
investment assessing centers and energy efficiency testing organizations, which
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
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reported to the MOHURD and MOF, respectively. The main aim of the HMEER
policy was to promote the installation and retrofitting of heat meters for
temperature regulation of heating systems and to promote energy efficiency
retrofitting of building envelopes (MOHURD and MOF 2008). The HMEER
policywas implemented in the northern part of China from 2007 until 2010.
Heating systems are legally required for buildings located in northern
China, with both district heating and decentralized heating systems. However,
in southern China, there are generally no district heating systems and heating
systems are not legally required. In these regions, space heating in winter is
generally obtained using electrical radiators or air conditioners, which are also
used for cooling during the summer. Therefore, the energy service of “space
heating” is generally produced with different heating technologies in the north
and south of China. However, this difference does not affect our policy analysis
because, as discussed in more detail later in the paper, we are interested in
analyzing the impact of the HMEER policy on energy consumption.
According to Richerzhagen et al. (2008), the Chinese Government utilized
multiple channels to raise funds for improving energy efficiency in buildings.
Overall, in the period of the 11th FYP, the government offered a total of 24.4
billion RMB for application of the HMEER policy directly or indirectly through
central financing, local leveraging, and other social sources. Although there are
papers that discuss the organizational issues, challenges, and difficulties (Zhao
The provinces covered by the HMEER policy are Beijing, Gansu, Hebei, Heilongjiang, Henan,
Inner Mongolia, Jilin, Liaoning, Ningxia, Qinghai, Shaanxi, Shandong, Shanxi, Tianjin,
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et al. 2009a,b) and calculate the implementation effects based on technical
numbers (Ding et al. 2011; Bao et al. 2012), there is still a lack of an economic
approach to investigate the impacts of the HMEER policy.
We are interested in the HMEER policy for several reasons. Firstly, China
relies heavily on command-and-control measures for policy design and the
HMEER policy is one typical example of this type. Secondly, the HMEER policy
was designed and supervised by the central government but implemented by
local governments. A study of the policy effects can determine the level of
efficiency of the top-down system for achieving the designed policy target. Thirdly,
as the residential sector is contributing to an increasing share of total energy
consumption, a clear understanding of the policy implemented in the residential
sector is helpful for future management of energy conservation.
The goal of this paper is to perform an empirical analysis to evaluate the
impacts of the introduction of the HMEER policy on residential energy
consumption in Chinese provinces. For this purpose, we employ the difference-
in-differences (DID) approach proposed by Ashenfelter and Card (1985) using
Although the policy is now relatively outdated, it is important to
rigorously study the effectiveness of the policy in order to assist policymakers to
design successful energy efficiency policy in the future. For the empirical
analysis we obtained data for 29 provinces between 2003 and 2011 on residential
energy use, the presence of the HMEER policy and various economic, social, and
See Imbens and Wooldridge (2009) for a general overview of the methods used in ex-post evaluation studies.
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The main contribution of this paper to the literature is to provide a post-
policy evaluation of the effectiveness of the HMEER policy using a rigorous
evaluation method such as the DID approach. To our knowledge, this is one of
the few studies applying a causal identification method in the evaluation of an
energy policy measure adopted in China, and the first analysis related to HMEER.
Further, this paper contributes to the literature on the analysis of the impact of
the introduction of energy policy measures in emerging countries.
The paper is organized as follows. Section 2 reviews previous studies in
the literature and Section 3 introduces the model specifications. In Section 4, we
provide a descriptive summary of the data used for this analysis and Section 5
presents and discusses the estimation results. Section 6 concludes the paper.
2 Literature review
This paper contributes to the literature on policy evaluation. During the
last few years, several studies on the impact of energy policy measures on the
adoption of energy efficient technologies or renewable energy sources have been
published. In this section, we review some of the studies that have a direct or
indirect relation to the policy measure analyzed in this paper. The literature
contains both studies that have used randomized control trials and studies that
have used a DID approach based on disaggregate as well as aggregate data sets.
For instance, recently, some researchers have performed randomized control
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trials to evaluate energy-related programs.
Fowlie et al. (2015) evaluated the
impact of a large energy saving program, the Weatherization Assistance Program,
that was implemented in the US residential sector. The results of this analysis
showed that the economic benefits obtained with this program were lower than
the costs. Also, Allcott and Kessler (2016) presented a social welfare evaluation
of home energy reports through a randomized control trial and suggested larger
energy savings from survey respondents. In another US study, Boomhover and
Davis (2015) estimated the change in electricity consumption due to the
residential air conditioner program in Southern California. They found that
electricity savings occurred disproportionately during hours when the value of
electricity was high. Gillingham et al. (2012) explored the heating/cooling
incentives and insulation incentives between owners and occupiers of residential
dwellings, indicating that overall energy savings may be small for correcting the
split incentive issues, while Houde et al. (2013) conducted a field experiment to
estimate the impacts of real-time feedback technologies, which showed that
access to feedback leads to a reduction of 5.7% in energy use. In Japan, using
survey data, Tanaka et al. (2017) analyzed the factors that affected the
purchasing decision time for solar photovoltaics (PV), and highlighted the
importance of the availability of information in the process of decision making.
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
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They also noted that, on average, consumers spend four months making their
Studies that have investigated the impact of energy policy measures using
a DID approach include that of Horowitz (2007), who used a DID approach to
study the impacts of the demand side management programs on electricity
demand and electricity intensity using aggregate data for the US states from the
1970s to 2003, which confirmed that the energy efficiency programs dramatically
reduced state electricity intensity. Datta and Filippini (2016) also used a DID
approach to investigate the impacts of ENERGY STAR rebate policies in the US
using aggregate data from 2001 to 2006, and concluded that the rebate policies
increased the uptake of energy-efficient appliances. Another study using DID
was that of Alberini and Bareit (2017), who used DID to analyze the effect of the
introduction of a bonus-malus system on the adoption of energy efficient cars in
some Swiss cantons using aggregate panel data. The basic idea of this bonus-
malus program is to differentiate the annual car registration tax depending on
the energy efficiency of a car, where efficient cars receive a discount and
inefficient cars have a surcharge imposed. The empirical analysis confirmed a
positive effect on the adoption of energy efficient cars, although the effect was
observed to be rather small.
Further studies on the evaluation of energy efficiency policies include that
of Sheer et al. (2013), who quantified the energy saving from Ireland’s home
energy saving scheme using data from 210 households. They found that all
dwellings in the study underwent energy efficiency improvements. Likewise,
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Adan and Fuerst (2015) confirmed that energy efficiency measures in UK homes
decreased energy consumption significantly. In the US, Ameli et al. (2017)
conducted a natural experiment in northern California based on DID and
regression discontinuity to test how the Property Assessed Clean Energy (PACE)
program helps to promote solar PV installations. The results from this study
show that PACE has been effective in promoting residential solar PV installations.
Sekitou et al. (2018) evaluated how the installation of a solar PV system affects
the electricity use in Japanese households, and showed that in monetary terms,
Japanese households can save 334 Japanese yen annually for each additional 1
kW increase in battery capacity.
To the best of our knowledge, this paper is one of the first studies that
uses an evaluation method such as the DID method to estimate the ex-post
effects of the HMEER policy in the Chinese residential sector.
sector is an important component of total energy consumption in China,
therefore several studies have analyzed China’s residential energy consumption
using other methods. Chen et al. (2008) introduced a data aggregation method
to investigate national energy consumption in the residential building sector of
China, while Zhao et al. (2012) implemented a decomposition analysis of China’s
urban residential energy consumption for the period 1998-2007, Zheng et al.
(2014) developed a comprehensive survey of 1450 households in 26 provinces in
2012 to study residential energy consumption in China, and Xu et al. (2016)
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.
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introduced a set of six criteria to evaluate through a scorecard method the four
types of policies implemented in urban residential buildings in China.
Our findings shall attract a broader readership as both national and
provincial governments have responsibility for saving energy and reducing
emissions from both domestic and international perspectives.
3 Empirical strategy and data
As discussed previously, this study employs the difference-in-differences
(DID) approach to estimate the impacts of the HMEER policy on provincial energy
consumption of the residential sector.
Since publication of the work authored by Ashenfelter and Card (1985),
the use of DID methods has been widespread. The general setup of DID is that
the outcome variables are observed for at least two groups and for at least two
periods (before and after the policy intervention). One group, the “treated group”,
is exposed to a treatment (policy shock) in the second period but not in the first
period. The other group, the “control group”, is not exposed to any treatment
during either period. In this paper, we investigate the average causal impact of
the HMEER policy on provincial energy consumption. In our case, we consider
the HMEER policy as a natural experiment, with the northern provinces receiving
a treatment and the southern provinces receiving no treatment. As already
anticipated, the HMERR policy had the goal of supporting the installation of heat
meters and promoting energy efficiency retrofitting of building envelopes.
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By adopting a DID approach, the policy impact can be estimated using the
it i dT t dPOL it it
E dT dPOL X
where Eit is the energy consumption for province i in time t,
dichotomous variable equal to 1 if the HMEER policy has been adopted by
province i in time t, and
is the error term. The province-specific fixed effects
allow us to control for time invariant unobserved heterogeneity.
Finally, dTt is
time fixed effects and Xit is a set of socioeconomic and climatic variables that
influence the level of residential energy consumption. The residential energy
consumption and the continuous variables included in Xit are expressed as logs.
Levinson (2016) shows how the decline in energy use in California, which
is purported to be a result of energy efficiency policies, is , in fact, driven by other
factors, such as demographic factors. Those potential confounding factors need
to be taken into account when attempting to identify the true policy effects.
Including both time and province-specific fixed effects and other independent
variables, Xit, such as energy price, income, heating and cooling degree days,
population, and household size in the model enables us to disentangle the
impact of the HMEER policy from socioeconomic and climatic determinants, time
invariant provincial characteristics and time effects.
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. Further, the policy group dummy is not included in the model specification because this variable
is time invariant and, therefore, absorbed by the individual effects.
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In a DID approach, the coefficient of interest is POL, which in our case
provides an estimate of the average effects of introducing the HMEER policy on
provincial energy consumption. We expect a negative impact of the HMEER
variable, as the HMEER policy promotes installation and retrofit of heat meters
in the heating systems and energy efficiency retrofitting of buildings, which are
measures that should reduce the demand for energy services.
This article utilizes a balanced Chinese panel data set for a sample of 29
observed over the period 2003 to 2010 (
2003-2010). We limit our
analysis to provinces (including provinces, autonomous regions, and
municipalities according to China’s administrative classification) in mainland
China, and exclude Tibet and Hainan from this study due to incomplete
statistical information. The main data source is the China National Bureau of
Statistics reports “China Statistical Yearbook” (2004-2011),
which records most
of the provincial macro data, including income, population, households,
temperature, urbanization, etc. The final energy consumption data in the
residential sector is obtained from China Energy Databook V8 (LBNL 2012). The
price index information is taken from “China Urban Life and Price Yearbook”
(2004-2011). Table 1 presents descriptive statistics of the dependent and
independent variables used for the empirical analysis.
Tibet, Hainan, Taiwan, Hong Kong, and Macau are excluded from this study, as some data information is missing in
The publication of statistical yearbooks in China has one-year delay, which means the yearbook in 2004 reports the
statistics of 2003.
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Table 1: Descriptive statistics of the dependent and independent variables
Residential energy consumption
Energy price index (Year
Real income (100 million yuan)
Average household size (persons)
Heating degree days
Cool degree days
As discussed previously, the HMEER policy has been implemented in the
northern provinces of China. The classification of the groups of provinces that
were in the treated and control groups are listed in Table 2. All treated and
untreated provinces considered in the main empirical analysis are listed in the
upper part of Table 2 and the treated and untreated provinces located along the
border between northern and southern China are listed in the lower part of Table
2. The provinces listed in the lower part of the table make up a subgroup of
provinces that were used in a robustness analysis in order to verify the results
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obtained using all provinces.
The idea is that the provinces located along the
border between northern and southern China are relatively similar with respect
to several time variant unobserved characteristics. Of course, from an
econometric point of view, the problem with this subsample is that it is relatively
Table 2: Provinces and the HMEER policy
Provinces that belong to
Provinces that belong to treated
Anhui, Chongqing, Fujian,
Guizhou, Hubei, Hunan,
Jiangsu, Shanghai, Sichuan,
Beijing, Gansu, Hebei,
Heilongjiang, Henan, Inner
Mongolia, Jilin, Liaoning,
Ningxia, Qinghai, Shaanxi,
Shandong, Shanxi, Tianjin,
along the border
Anhui, Chongqing, Guizhou,
Hubei, Hunan, Jiangsu,
Shanghai, Sichuan, Zhejiang
Beijing, Gansu, Hebei, Henan,
Ningxia, Qinghai, Shaanxi,
Shandong, Shanxi, Tianjin
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
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Two important identification assumptions need to be fulfilled to use the DID
method. The first is that in the absence of treatment, there is a parallel trend in
the outcome variable for both the treatment and control groups. If this
assumption is violated, the estimated effects of policy intervention could be
biased. The second assumption is that the assignment of treatment has to be
exogenous. This may be violated if the selection is based on unobserved
characteristics of the units.
3.1 Parallel trend
To verify the parallel trend assumption, we estimated equation (1) using
only the data for the pre-policy period and introduced a new variable given by
the interaction between a time trend and a dummy variable that indicates
whether a province belongs to the treated group or not. By doing this, it is
possible to test if the coefficient of the interactions variable is equal to zero and,
therefore, verify if the parallel trend assumption is satisfied. In our case, the
coefficient of the interaction variable is not statistically significant (p-value
0.934). This illustrates that the common trend is similar for the two groups prior
to the policy period. In addition, we verify the common trend assumption by
substituting the time trend with time dummies and create a series of interaction
variables obtained by multiplying the time dummies with the policy dummy. The
reported value of the F statistic is 2.39, with p-value 0.076. This value confirms
that the common trend is similar for the two groups prior to the policy period.
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The north (treated) and south (non-treated) regions may present different
technological patterns due to the differences in the technologies (heating and
building systems) used in the production of space heating services. To verify this,
we estimate a model that includes an interaction variable between the time trend
and a dummy variable that indicates whether a province belongs to the treated
group or not. The estimation results show that the coefficient of the interaction
variable is not statistically significant (p-value 0.30). Therefore, we are confident
that there exists no different technology trend between the treated and control
groups. In Figure 1 we illustrate the average energy consumption over time for
both the treated and control groups.
3.2 Exogenous choice of treated group
Another assumption that should be satisfied in a DID approach is that the
assignment to the treatment group is exogenous. This assumption can be
violated if there are unobserved characteristics of the provinces that affect both
the outcome variables and the policy decisions. We believe that this should not
be an issue in our case as the treated group includes all the provinces within the
official heating regions. There are two reasons there being no endogeneity issue
related to the policy variable. Firstly, the decision to introduce the HMEER policy
into a province was taken by the central government and included in the national
development plan of the 11th FYP as a mandatory regulation for regional
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 Figure 1 refer to the consumption at the end of each year.
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governments. Secondly, as China sets ambitious targets for reducing emissions
over time, there is considerable political pressure behind the application of the
HMEER policy and its goals of energy saving and emission mitigation in these
Fig. 1: Average residential energy consumption of treated and control
groups over time
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4 Empirical results
The empirical analysis of this paper is composed of two parts. The first
part shows a simple difference-in-differences calculation of the energy
consumption data through a descriptive analysis and the second part, the most
important part of this paper, involves the econometric estimation of equation (1).
The DID estimate of the effects of the HMEER policy on energy
consumption can be computed using the following formula:
where E is the outcome variable, the subscript 1 denotes the pre-policy period
and 2 denotes the post-policy period, T denotes the treated group, and C denotes
the control group. The value of DID is obtained by using group average values
for treated and control provinces of the two outcome variables.
We can now compute the effect of the implementation of the HMEER policy
on energy consumption based on these mean values and using expression (2).
Of course, we are aware that this approach does not control for other variables
that may influence the outcome variable and does not allow verification if the
treatment effect is statistically significant. However, we believe that this simple
approach is informative and provides a first-hand potential outcome of the policy.
The results of calculations based on expression (2) are displayed in Table 3.
The highlighted number in the bottom right corner provides the DID value.
A negative value indicates that the policy reduces the energy consumption of the
treated group compared to the control group. It shows that the HMEER policy
induces, on average, a 0.48 Mtce reduction in energy consumption.
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Table 3: The results of simple DID calculation of Model A
In Table 4 we present the empirical results obtained by estimating
equation (1). Column (A) includes the results obtained using all the provinces,
while column (B) shows the results obtained by considering only the border
provinces. Overall, the values of the coefficients of the models have the expected
signs and are statistically significant at the 10% level. Both province and year
fixed effects are considered in the estimation.
The estimation results reported in column (A) indicate that the HMEER
policy has a statistically significant negative impact on the residential energy
demand. By transforming the log-linear form of the coefficient for the HMEER
policy, we find that the implementation of the HMEER policy contributes, on
average, an approximate 10% reduction in residential energy demand.
estimation results reported in column (B) are similar.
Table 4: Estimation results of DID
The percentage change is calculated by using 100[e-1], where α is the coefficient of the policy variable.
Average energy consumption between two groups (in Mtce)
Provinces treated with HMEER policy
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Log Household Size
Log Heating Degree
Log Cooling Degree
Provincial Fixed Effects
Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1
The coefficients reported in Table 4 provide information on the impact of
different variables on residential energy demand. To note, that in a log-log
functional form, the coefficients can be interpreted as elasticities. As expected
the price effect is negative statistically significant. However, the value of the price
elasticity is very small. This result may be due to the fact that the level of
aggregation of the price index used in this study is relatively high or due to the
low variation of the energy prices due to the price regulation in China, or a bit of
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The income elasticity is around 1.39-1.51 in the models, and both are
highly significant at the 1% level. This indicates that a 1% increase in household
income will lead to more than 1% increase in energy demand for households.
Therefore, the income elasticity of energy demand is quite high, as is expected
for emerging countries.
The coefficients of the three demographic variables, household size,
population, and urbanization rate, provide an interesting picture for the
emerging economy. As expected, population has positive and significant effect on
energy demand, whereas the average household size has negative and significant
impact on energy demand due to economies of scale. The coefficient of
urbanization rate is positive and insignificant.
Both heating degree days (HDD) and cooling degree days (CDD) affect the
demand positively. The effects of CDD are significant at the 5% level. This may
imply that as global warming continues, households demand more energy for
cooling services while the variation of HDD becomes less significant.
The econometric results reported in Table 4 can be used to calculate the
reduction of CO2 emissions attributed to the introduction of the HMEER policy.
In order to obtain this reduction, we multiplied the average energy saving
obtained in each province after the introduction of the policy by the national CO2
The direct impact of the HMEER policy on energy saving
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.
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for the policy period (2007-2010) is about 80 Mtce, whereas the reduction of CO2
emissions (2007-2010) is around 200 Mt CO2 equivalent.
As discussed previously, the main goal of China’s 11th Five-Year Plan was
to reduce the level of energy intensity. Using the results of the empirical analysis,
it is also possible to provide a rough approximation of the impact of the HMEER
policy on decreasing the energy intensity of the residential sector during the five-
year plan period. In fact, between 2005 and 2010, the level of energy intensity
measured as the level of energy consumption divided by GDP in the Chinese
residential sector decreased by 13%.
Part of this reduction, approximately 43%,
was due to the introduction of the HMEER policy.
Since energy efficiency has received much attention and a series of
measures have been implemented for China’s development strategy, rigorous ex-
post evaluation of these policies is vital for understanding and, hence, improving
the contributions of such policies to improvement in energy efficiency. In this
study, we integrate the energy demand model with the difference-in-differences
approach for an ex-post evaluation of the impacts of the HMEER policy on
residential energy consumption at the provincial level.
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.
We use the values of total energy consumption and GDP reported in the statistics (NBS(a) 2006, 2011; LBNL 2012).
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Our analysis confirms that the HMEER policy contributes to a reduction
in residential energy consumption. We find quantitatively that the average
impact of the policy is a 10% reduction in energy demand for the treated
provinces. This result provides empirical support for the continuation of the
HMEER policy in the future. As discussed in Bao et al. (2012), the HMEER policy
in the 11th FYP promoted the retrofit of only 4.6% of the total building stock that
needs retrofitting. The energy saving potential can be very promising if the
HMEER policy could be implemented on a large scale.
Of course, we should recognize that a limitation of our analysis is that we
considered only the impact of the policy on energy consumption and did not
account for the costs of implementing the HMEER policy. Therefore, we were not
able to estimate the cost of reducing CO2 emissions through the HMEER policy.
Moreover, a complete cost-benefit analysis of the HMEER policy would also
require estimating the benefits of the improved environment and air quality.
Further, a limitation of this study stems from the data used. The sample
considered in the empirical analysis was relatively small and based on aggregate
data at the provincial level. For future research, it would be interesting to
perform a similar analysis using energy consumption data at the building level.
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