ArticlePDF Available

Abstract and Figures

China's 11 th 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.
Content may be subject to copyright.
Page 1 of 28
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.
Keywords: Chinese residential sector; difference-in-differences; energy
consumption; policy evaluation; heat metering and energy efficiency retrofit
Massimo Filippini
ETH Zurich and University of Lugano
Switzerland
E-mail: mfilippini@ethz.ch
Lin Zhang
City University of Hong Kong
Hong Kong, China
E-mail: l.zhang@cityu.edu.hk
Page 2 of 28
1 Introduction
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).
1
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
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.
Page 3 of 28
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.
2
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
2
The provinces covered by the HMEER policy are Beijing, Gansu, Hebei, Heilongjiang, Henan,
Inner Mongolia, Jilin, Liaoning, Ningxia, Qinghai, Shaanxi, Shandong, Shanxi, Tianjin,
Xinjiang.
Page 4 of 28
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
panel data.
3
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
climate variables.
3
See Imbens and Wooldridge (2009) for a general overview of the methods used in ex-post evaluation studies.
Page 5 of 28
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
Page 6 of 28
trials to evaluate energy-related programs.
4
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.
4
Recently, the University of CaliforniaBerkeley, 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
Page 7 of 28
They also noted that, on average, consumers spend four months making their
purchasing decisions.
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,
Page 8 of 28
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.
5
The residential
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)
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.
Page 9 of 28
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.
Page 10 of 28
By adopting a DID approach, the policy impact can be estimated using the
following regression:
it
it i dT t dPOL it it
E dT dPOL X
 
 
(1)
where Eit is the energy consumption for province i in time t,
dPOL
is a
dichotomous variable equal to 1 if the HMEER policy has been adopted by
province i in time t, and
it
u
is the error term. The province-specific fixed effects
i
allow us to control for time invariant unobserved heterogeneity.
6
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.
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. 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.
Page 11 of 28
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
provinces
7
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),
8
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.
7
Tibet, Hainan, Taiwan, Hong Kong, and Macau are excluded from this study, as some data information is missing in
the statistics.
8
The publication of statistical yearbooks in China has one-year delay, which means the yearbook in 2004 reports the
statistics of 2003.
Page 12 of 28
Table 1: Descriptive statistics of the dependent and independent variables
Variable
Obs
Mean
Std.
Dev.
Min
Max
Residential energy consumption
(Mtce)
261
11.11
6.70
1.40
40.49
Energy price index (Year
2003=100)
261
150.42
33.27
100
251.83
Real income (100 million yuan)
261
8043.03
6871.79
385.34
35261.60
Population (10,000)
261
4470.66
2587.82
534
10505
Average household size (persons)
261
3.20
0.31
2.39
4.4815
Heating degree days
261
547.97
678.11
0
2585.6
Cool degree days
261
97.55
112.43
0
401.70
Urbanization rate
261
0.48
0.15
0.25
0.89
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
Page 13 of 28
obtained using all provinces.
9
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
small.
Table 2: Provinces and the HMEER policy
Provinces that belong to
control group
Provinces that belong to treated
group
All provinces
Anhui, Chongqing, Fujian,
Guangdong, Guangxi,
Guizhou, Hubei, Hunan,
Jiangsu, Shanghai, Sichuan,
Yunnan, Zhejiang
Beijing, Gansu, Hebei,
Heilongjiang, Henan, Inner
Mongolia, Jilin, Liaoning,
Ningxia, Qinghai, Shaanxi,
Shandong, Shanxi, Tianjin,
Xinjiang
Provinces located
along the border
Anhui, Chongqing, Guizhou,
Hubei, Hunan, Jiangsu,
Shanghai, Sichuan, Zhejiang
Beijing, Gansu, Hebei, Henan,
Ningxia, Qinghai, Shaanxi,
Shandong, Shanxi, Tianjin
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.
Page 14 of 28
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.
Page 15 of 28
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.
10
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
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 Figure 1 refer to the consumption at the end of each year.
Page 16 of 28
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
provinces
Fig. 1: Average residential energy consumption of treated and control
groups over time
Page 17 of 28
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:
 

 

 
   (2)
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.
Page 18 of 28
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.
11
The
estimation results reported in column (B) are similar.
Table 4: Estimation results of DID
(A)
(B)
VARIABLES
Total Energy
Total Energy
11
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)
2006
2007
Difference
Control provinces
10.11
11.14
1.03
Provinces treated with HMEER policy
9.66
10.21
0.55
Difference
-0.45
-0.93
-0.48
Page 19 of 28
Policy
-0.111**
-0.126**
(0.044)
(0.054)
Log Price
-0.004***
-0.004**
(0.001)
(0.002)
Log Income
1.506***
1.390***
(0.262)
(0.519)
Log Household Size
-0.676*
-0.859*
(0.353)
(0.481)
Log Population
0.737**
1.363***
(0.367)
(0.414)
Log Heating Degree
Days
0.004
0.010
(0.014)
(0.015)
Log Cooling Degree
Days
0.029**
0.031*
(0.015)
(0.016)
Urbanization
1.296
3.624**
(1.165)
(1.500)
Constant
-36.580***
-45.083***
(11.030)
(16.024)
Observations
232
160
Provincial Fixed Effects
YES
YES
Time Dummies
YES
YES
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
both.
Page 20 of 28
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
emission coefficient.
12
The direct impact of the HMEER policy on energy saving
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.
Page 21 of 28
for the policy period (2007-2010) is about 80 Mtce, whereas the reduction of CO2
emissions (2007-2010) is around 200 Mt CO2 equivalent.
13
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%.
14
Part of this reduction, approximately 43%,
was due to the introduction of the HMEER policy.
5 Conclusion
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.
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
We use the values of total energy consumption and GDP reported in the statistics (NBS(a) 2006, 2011; LBNL 2012).
Page 22 of 28
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.
Page 23 of 28
References
Adan, H. and Fuerst, F. (2015). Do energy efficiency measures really reduce
household energy consumption? A difference-in-difference analysis.
Energy Efficiency, 9(5), 1207-1219.
Allcott, H., and Kessler, J. (2016). The Welfare Effects of Nudges: A Case Study
of Energy Use Social Comparisons. E2e Working Paper 023.
http://e2e.haas.berkeley.edu/pdf/workingpapers/WP023.pdf#page=1
Ameli, N., Pisu, M., Kammen, D. M. (2017). Can the US keep the PACE? A natural
experiment in accelerating the growth of solar electricity. Applied Energy,
191, 163-169.
Ashenfelter, O. and Card, D. (1985). Using the longitudinal structure of earnings
to estimate the effects of training programs. Review of Economics and
Studies, 67, 648-660.
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.
Boomhower, J. and Davis, L. (2016). Do Energy Efficiency Investments Deliver
at the Right Time? E2e Working Paper 023.
http://e2e.haas.berkeley.edu/pdf/workingpapers/WP023.pdf#page=1
Cai, W.G., Wu, Y., Zhong, Y., Ren, H., (2009). China building energy
consumption: situation, challenges and corresponding measures. Energy
policy 37 (6), 20542059.
Page 24 of 28
Chen, S., Li, N., Guan, J., Xie, Y., Sun, F., Ni, J. (2008zz). A statistical method
to investigate national energy consumption in the residential building
sector of China. Energy and Building 40, 654-665.
Colombi, R., Kumbhakar, S., Martini, G., & Vittadini, G. (2014). Closed-skew
normality in stocastic frontiers with individual effects and long/short-run
efficiency. Journal of Productivity Analysis, 42, 123-136.
Datta, S. and Filippini, M. (2016). Aanlysing the impact of ENERGY STAR rebate
policies in the US. Energy efficiency, 9(3), 677-698.
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.
Fowlie, M., Greenstone, M., Wolfram, C. (2015). Do Energy Efficiency
Investments Deliver? Evidence from the Weatherization Assistance
Program. NBER WORKING PAPER SERIES 21331.
Filippini, M. and Greene, W. (2015): Persistent and transient productive
inefficiency: A Maximum Simulated Likelihood Approach. Journal of
Productivity Analysis, 45, 187-196.
Filippini, M. and Hunt, L. (2012). US residential energy demand and energy
efficiency: A stochastic demand frontier approach, Energy Economics 34,
1484-1491.
Filippini, M. and Hunt, L. (2015): Measurement of Energy Efficiency Based on
Economic Foundations, Energy Economics, 52(S1), S5-S16.
Page 25 of 28
Filippini, M. and Zhang, L. (2016). Estimation of the energy efficiency in Chinese
provinces. Energy Efficiency, forthcoming.
http://link.springer.com/article/10.1007%2Fs12053-016-9425-z
Greene, W. (2005a): Reconsidering heterogeneity in panel data estimators of the
stochastic frontier model. Journal of Econometrics, 126, 269-303.
Greene, W. (2005b): Fixed and random effects in stochastic frontier models.
Journal of Productivity Analysis, 23, 7-32.
Gillingham, K., Harding, M., Rapson, D. (2012): Split Incentives in Residential
Energy Consumption. Energy Journal (2012), 33(2): 37-62
Horowitz, M. (2007). Changes in electricity demand in the United States from the
1970s to 2003. The Energy Journal, 28(3):93119.
Houde, S., Todd, A., Sudarshan, A., Flora, J.A., Armel, K.C. (2013): Real-time
feedback and electricity consumption: A field experiment assessing the
potential for savings and persistence. The Energy Journal, 34(1)
Imbens, G. W. and Wooldridge, J. M. (2009). Recent developments in the
econometrics of program evaluation. Journal of Economic Literature, 47(1),
5-86.
Jondrow, J., Lovell, C.A.K., Materov, I.S., and Schmidt, P. (1982): On the
estimation of technical efficiency in the stochastic frontier production
function model. Journal of Econometrics, 19, 233-238.
Kumbhahka, S.C., Lien, G., Hardaker, J.B. (2014): Technical efficiency in
competing panel data models: a study of Norwegian grain farming. Journal
of Productivity Analysis 41, 321-337.
Page 26 of 28
Levinson, A. (2016): How much energy do building energy codes save? Evidence
from California houses. Energy Policy, 106(10), 2867-2894.
LNBL (2012). China Energy Databook Version 8.0. Lawrence Berkeley National
Laboratory, USA.
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)95th).
http://www.mohurd.gov.cn/zcfg/jswj/jskj/200806/t20080613_171707.
htm.
NBS (a), 2004-2012, China Statistical Yearbooks. Beijing, China.
NBS (b), 2004-2012, China Urban Life and Price Yearbook. Beijing, China.
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.).
Sheer, J.m Clancy, M., Hogain, S.N. (2013). Quantification of energy savings
from Ireland’s home energy saving scheme: and ex post billing analysis.
Energy Efficiency 6, 35-48.
Sekitou, M., K. Tanaka, and S. Managi. 2018. Household Electricity Demand
after the Introduction of Solar Photovoltaic Systems. Economic Analysis
and Policy, 57, 102-110.
Page 27 of 28
Tanaka, K., M. Sekito, S. Managi, S. Kaneko, and V. Rai. 2017. Decision-Making
Governance for Purchases of Solar Photovoltaic Systems in Japan. Energy
Policy, 111, 75-84.
Tsinghua University, Building energy research center, (2007). 2007 Annual
Report on China Building Energy Efficiency. China Building Industry
Press, Beijing.
Xu, X., Andon, L.D., 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.
Zhang, L. (2013). Model projections and policy reviews for energy saving in
China’s service sector. Energy Policy, 59, 312-320.
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, 21242130.
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, 21062112.
Zhao, X., Li, N., Ma, C. (2012). Residential energy consumption in urban China:
A decomposition analysis. Energy Policy 41, 644-653.
Page 28 of 28
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.
Zhou, N., Michael A. McNeil, David Fridley, Jiang Lin, Lynn Price, Stephane de
la Rue du Can, Jayant Sathaye, and Mark Levine (2007). Energy use in
China: Sectoral trends and future outlook. LBNL-61904. Environmental
Energy Technologies Division, Lawrence Berkeley National Laboratory.
... Conversely, the difference-in-differences method can be used in macroeconomic analsyis (Giavazzi and Tabellini, 2005;Papaioannou and Siourounis, 2004;Persson, 2005;Persson and Tabellini, 2006;Rodrik and Wacziarg, 2005). This method has been used for assessing the impact of specific carbon and energy policies (Filippini and Zhang, 2019;Meng et al., 2017;Lin and Zhu, 2019). However, differences-in-differences assumes that the policy intervention may be represented as a binary variable (on/off), which is not the case here. ...
Article
Full-text available
Over the last two decades, the European Union and its Member States have introduced policies aimed at improving energy efficiency. The Energy Service Directives (ESD) introduced the concept of measurement of energy savings attributed to policies. Two different and complementary methodologies for the evaluation of energy savings have been developed under the ESD: the bottom-up (BU) approach, based on a technical analysis of each measure, and the top-down (TD) approach, based on the analysis of how energy intensity changes over time. BU methods can hardly take into account policy-induced behavioural changes, whereas TD methods have difficulties in disentangling policy-induced savings from other savings. Econometric models have been proposed as a viable alternative to deal with both drawbacks. The purpose of this article is to present an econometric model aimed at estimating the energy savings induced by energy efficiency policies in the EU Member States in the period 1990–2013. We introduce an explicit measure of Energy Policy Intensity based on the MURE database, which is used as explanatory variable in a dynamic panel model for 29 European countries. Our results suggest that energy consumption in 2013 in Europe would have been about 12% higher in the absence of energy efficiency policies.
Article
Full-text available
Background The residential sector releases around 17% of global greenhouse gas emissions and making residential buildings more energy efficient can help mitigate climate change. Engineering models are often used to predict the effects of residential energy efficiency interventions (REEI) on energy consumption, but empirical studies find that these models often over-estimate the actual impact of REEI installation. Different empirical studies often estimate different impacts for the same REEI, possibly due to variations in implementation, climate and population. Funding for this systematic review was provided by the evaluation function at the European Investment Bank Group. Objectives The review aims to assess the effectiveness of installing REEIs on the following primary outcomes: energy consumption, energy affordability, CO2 emissions and air quality indices and pollution levels. Search Methods We searched CAB Abst, Econlit, Greenfile, Repec, Academic Search Complete, WB e-lib, WoS (SCI and SSCI) and other 42 databases in November 2020. In addition, we searched for grey literature on websites, checked the reference lists of included studies and relevant reviews, used Google Scholar to identify studies citing included studies, and contacted the authors of studies for any ongoing and unpublished studies. We retrieved a total of 13,629 studies that we screened at title and abstract level, followed by full-text screening and data extraction. Selection Criteria We included randomised control trials, and quasi-experimental studies that evaluated the impact of installing REEIs anywhere in the world and with any comparison. Data Collection and Analysis Two independent reviewers screened studies for eligibility, extracted data and assessed risk of bias. When more than one included study examined the same installation of the same type of REEI for a similar outcome, we conducted a meta-analysis. We also performed subgroup analyses. Main Results A total of 16 studies were eligible and included in the review: two studies evaluated the installation of efficient lighting, three studies the installation of attic/loft insulation, two studies the installation of efficient heat pumps, eight studies the installation of a bundle of energy efficiency measures (EEMs), and one study evaluated other EEMs. Two studies, neither appraised as having a low risk of bias, find that lighting interventions lead to a significant reduction in electricity energy consumption (Hedges' g = −0.29; 95% confidence interval [CI]: −0.48, −0.10). All the other interventions involved heating or cooling, and effects were synthesizised by warmer or colder climate and then across climates. Four studies examined the impact of attic/loft insulation on energy consumption, and two of these studies were appraised as having a low risk of bias. Three studies took place in colder climates with gas consumption as an outcome, and one study took place in a warmer climate, with the electricity consumption (air conditioning) as the outcome. The average impact across all climates was small (Hedges' g = 0.04; 95% CI: −0.09, 0.01) and statistically insignificant. However, two of the studies appear to have evaluated the effect of installing small amounts (less than 75 mm) of insulation. The other two studies, one of which was appraised as low risk of bias and the other involving air conditioning, found significant reductions in consumption. Two studies examined the impact of installing electric heat pumps. The average impact across studies was not statistically significant (Hedges' g = −0.11; 95% CI: −0.41, 0.20). However, there was substantial variation between the two studies. Replacing older pumps with more efficient versions significantly reduced electricity consumption in a colder climate (Hedges' g = −0.36; 95% CI, −0.57, −0.14) in a high risk of bias study. However, a low risk of bias study found a significant increase in electricity consumption from installing new heat pumps (Hedges' g = 0.09; 95% CI, 0.06, 0.12). Supplemental analyses in the latter study indicate that households also used the heat pumps for cooling and that the installed heat pumps most likely reduced overall energy consumption across all sources—that is, households used more electricity but less gas, wood and coal. Seven studies examined bundled REEIs where the households chose which EEMs to install (in five studies the installation occurred after an energy audit that recommended which EEMs to install). Overall, the studies estimated that installing an REEI bundle is associated with a significant reduction in energy consumption (Hedges' g = −0.36; 95% CI, −0.52, −0.19). In the two low risk of bias studies, conducted with mostly low-income households, installed bundles reduced energy consumption by a statistically significant amount (Hedges' g = −0.16; 95% CI, −0.13, −0.18). Authors' Conclusions The 16 included studies indicate that installing REEIs can significantly reduce energy consumption. However, the same type of REEI installed in different studies caused different effects, indicating that effects are conditional on implementation and context. Exploring causes of this variation is usually not feasible because existing research often does not clearly report the features of installed interventions. Additional high quality impact evaluations should be commissioned in more diverse contexts (only one study was conducted in either Asia or Africa—both involved lighting interventions—and no studies were conducted in South America or Southern Europe).
Article
Energy retrofits are significant in improving the energy efficiency of existing residential buildings (ERB) and mitigating greenhouse gas emissions. While many countries have introduced relevant retrofit policy instruments (RPIs), retrofit rates are still relatively low due to various complications in practical implementation. This study aims to organize the scattered information related to RPIs and their implementation, success, and obstacles across different countries and provide valuable references for retrofit strategy development. This article examined various RPIs for ERB in 11 selected countries. The investigated RPIs were grouped into four categories: direction and command, assessment and disclosure, research and service, and financial incentives. The RPIs implementation approaches in the surveyed countries were summarized and compared. Furthermore, obstacles to the uptake of retrofit schemes were identified. Finally, policy recommendations to overcome the obstacles and improve the penetration of retrofit schemes were proposed. This study can assist policy makers and other stakeholders in gaining a holistic view of RPIs and their implementation in different countries, understanding the barriers to the uptake of retrofit schemes, and developing more efficient RPIs in the future.
Article
Full-text available
The total factor carbon emission performance has been largely used to investigate the effectiveness of climate policies and to support the design of carbon reduction strategies. Despite the important information that this indicator is providing in relation to historical and cross-country trends, no previous studies have been specifically devoted to analyse the persistent and the transient components of the total factor carbon emission performance. By disaggregating the time-variant and the time-invariant elements of the carbon dioxide emission changes, this paper adopts, for the first time, a new methodological approach to decompose the components of the total factor carbon emission performance indicator. Using panel data for selected 30 Chinese provinces for the time-period 1997-2017, this paper combines the environmental production technology, the Shephard distance function, and the stochastic frontier models to measure and investigate the spatio-temporal evolution of the total factor carbon emission performance and to evaluate the effectiveness of Chinese policies. By providing a better understanding of the main drivers of carbon dioxide emission changes, the proposed methodology, is suitable to be replicated across regions and countries, and provides an important opportunity for international comparisons and for the design of coordinated carbon reduction strategies.
Article
Full-text available
This paper measures the performance and efficiency dynamics of provincial water use by differentiating the long-run persistent efficiency from the short-run transient efficiency. We combine the econometric frontier approach with panel Markov-switching and Tobit estimations to investigate the macro level data over the period 2002-2016. The result reveals evidence of significant provincial and regional disparities, with a mean efficiency of 0.42 in terms of water use. We find that a large share of inefficiency is attributed to the long-term structural component, with persistency inefficiency estimated as 55%. We also find that the probability of maintaining efficient is less sustainable (about 48.5% by the sixth year) compared with 54.3% for staying inefficient. Thus, it is relatively difficult to transition out of the less efficient state. We thus suggest policy directions for sustainable and efficient management of water resource use.
Article
Green retrofit in existing buildings is of great significance in achieving a sustainable environment. Various policies have been introduced around the world to promote building green retrofit, whereas the uptake is rare for policy ineffectiveness. To improve policy effectiveness, it has been emphasized that a systematic review of existing retrofit policies has a key priority. In this study, a comprehensive review on retrofit policies in China was conducted because there are great application potential for green retrofit in China's existing buildings. This study examined the state-of-the-art development of retrofit policies during 1996–2019 and explored policy characteristics based on content analysis. The ambitious retrofit objectives and multiple retrofit instruments were identified. The relevant policy instruments can be grouped into six categories, including command and control, economic incentives, technology, information, certification, organization & professional. The most cited policy was the command and control instrument, followed by the certification-based instrument. Moreover, policy instruments related to economic incentives, technology and information were also valued by the China government, while the government attempted to reduce the adoption of economic incentives and technology measures since 2010. In addition, organization & professional instruments are largely ignored in China. In the light of these findings, a roadmap was proposed to enhance the effectiveness of building retrofit policies. This study sheds lights on the effectiveness of China's building policies to accelerate the popularity of green retrofit, and provides valuable references for other countries and regions to shape their own policy pathways towards the large-scale promotion of building green retrofit.
Article
Most analyses of energy efficiency investments ignore that the value of electricity varies widely across hours. We show how much timing matters. Using novel hourly consumption data from an air conditioner rebate program in California, we find that energy savings are concentrated in high-value hours. This significantly increases the value of these investments, especially after we account for the large capacity payments that electricity generators receive to guarantee supply in peak hours. We then use engineering predictions to calculate timing premiums for a wide range of energy efficiency investments, finding substantial variation in economic value across investments. (JEL L94, L98, Q41, Q48)
Article
"Nudge"-style interventions are often deemed successful if they generate large behavior change at low cost, but they are rarely subjected to full social welfare evaluations. We combine a field experiment with a simple theoretical framework to evaluate the welfare effects of one especially policy-relevant intervention, home energy social comparison reports. In our sample, the reports increase social welfare, although traditional evaluation approaches overstate gains because they ignore significant costs incurred by nudge recipients. Overall, home energy report welfare gains might be overstated by $620 million. We develop a prediction algorithm for optimal targeting; this approach would double the welfare gains.
Article
In this study, we analyze the factors that affecting purchasing decision time for solar photovoltaic (PV) s in Japan. Based on our survey, consumers spend about 4 months to make purchase decision. Also, our estimation results show that information and knowledge that consumers obtained from the neighborhood and elsewhere make consumers more careful in their decision-making and extend the purchase decision. On the other hand, experts on the advantages and disadvantages of installation shortened the decision time. The situation and environment of each household in terms of income, family size, and the way of purchase of new homes have influenced on the decision to purchase a PV system. In addition, the availability of feed in tariffs was highly correlated with purchasing motivation, but unexpectedly the capital subsidy programs have either little impacts or even delayed impacts on the purchasing timing.
Article
This study quantitatively evaluates the effect of solar photovoltaic system (PV system) installation on the actual amounts of electricity usage in Japanese households. Using consumer-level data, the effects of installing a PV system on the electricity demand are estimated in terms of the impact of the technological performance which was a direct contributor to a reduction in the electricity demand. Also, we confirm the effect of peoples’ electricity consumption behavior by installation of the PV system. As a result, we estimate that the technological performance of PV system had a major effect on the reduction of the electricity demand after the installation of a PV system. Furthermore, for each additional 1 kW increase in battery capacity, the average electricity fee savings per month are approximately 517 Japanese yen per month in the summer, 152 Japanese yen per month in the winter, and approximately 334 Japanese yen annually.
Article
In Switzerland, the annual circulation taxes on road vehicles are set by and paid to the cantons (not to the federal government). We exploit the 26 different circulation tax rules and their variation over time, which we interpret as a natural experiment, to see if linking them to a vehicle’s CO2 emissions rate has helped shift new car sales towards lower-emitting vehicles. We find that even when the penalty associated with a highly polluting vehicle is high, the effect is relatively small. For example, in canton Zurich, imposing a 50% “malus” on the annual registration fee for cars that emit 200 or more grams of CO2 per kilometer reduces the average CO2 emissions rate from new cars by only 0.46 gram per kilometer, bringing it to 158.11 grams per kilometer in 2011. A similar effect would be attained with a modest increase in fuel taxes. Bonus policies may trigger new car sales and engender a net increase in CO2 emissions.
Article
Conventional wisdom suggests that energy efficiency (EE) policies are beneficial because they induce investments that pay for themselves and lead to emissions reductions. However, this belief is primarily based on projections from engineering models. This paper reports on the results of an experimental evaluation of the nation’s largest residential EE program conducted on a sample of more than 30,000 households. The findings suggest that the upfront investment costs are about twice the actual energy savings. Further, the model-projected savings are roughly 2.5 times the actual savings. While this might be attributed to the “rebound” effect – when demand for energy end uses increases as a result of greater efficiency – the paper fails to find evidence of significantly higher indoor temperatures at weatherized homes. Even when accounting for the broader societal benefits of energy efficiency investments, the costs still substantially outweigh the benefits; the average rate of return is approximately -9.5% annually.
Article
Growing global awareness of climate change has ushered in a new era demanding policy, financial and behavioural innovations to accelerate the transition to a clean energy economy. Dramatic price decreases in solar photovoltaics (PV) and public policy have underwritten the expansion of solar power, now accounting for the largest share of renewable energy in California and rising fast in other countries, such as Germany and Italy. Governments’ efforts to expand solar generation base and integrate it into municipal, regional, and national energy systems, have spawned several programs that require rigorous policy evaluations to assess their effectiveness, costs and contribution to Paris Agreement’s goals. In this study, we exploit a natural experiment in northern California to test the capacity of Property Assessed Clean Energy (PACE) to promote PV investment. PACE has been highly cost effective by more than doubling residential PV installations.
Article
Regulations governing the energy efficiency of new buildings have become a cornerstone of US environmental policy. California enacted the first such codes in 1978 and has tightened them every few years since. I evaluate the resulting energy savings three ways: comparing energy used by houses constructed under different standards, controlling for building and occupant characteristics; examining how energy use varies with outdoor temperatures; and comparing energy used by houses of different vintages in California to that same difference in other states. All three approaches yield estimated energy savings significantly short of those projected when the regulations were enacted.