1

Does Information Feedback Help Reduce Residential

Electricity Consumption? Evidence from a Chinese

Houeshold Survey

WEI Chua , GUO Jina, DU Liminb,*

a Department of Energy Economics, Renmin University of China, Beijing

b China Academy of West Region Development, Zhejiang University, Hangzhou

Abstract: This paper investigates the impact of information feedback on residential

electricity conservation, based on a household survey dataset collected in 2012 that

covered 26 provinces in China. The results of the basic regression reveal a negative

price elasticity but a positive income elasticity. Urban households consume more

electricity than do rural households. The electricity consumption is positively

associated with family size, dwelling area, householder's years of schooling, and

duration of appliance operation. Further tests show that information feedback does have

effects on residential electricity consumption. There is reduced electricty consumption

when households obtain electricity consumption information through interaction with

meter readers, receive ex ante feedback (for households that use a prepaid metering

system), and receive explicit feedback by directly paying meter readers. However, the

energy conservation effects of increased frequency of information feedback and

installation of smart meters are not significant. The sensitivity analysis of quantile

regressions confirms the robustness of the results.

Keywords: Electricity Conservation; Information Feedback; Household Survey Data;

China

*Corresponding author. Tel: +0086 571 88273009

E-mail address: dlmzju@zju.edu.cn (Limin DU)

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1. Introduction

The residential electricity consumption of China has increased tremendously over

the past decades. In 1990, residential electricity consumption in China was only 48.1

TWh, but it increased to 621.9 TWh in 2012. The annual growth rate reaches about

12.3%. Moreover, the share of residential consumption in total electricity consumption

increased from 7.7% in 1990 to 12.5% in 2012. It is reasonable to expect that the

increasing trend will continue with further economic growth because electricity demand

and GDP exhibit a positive long-run causal relationship (Shiu and Lam, 2004; Yuan et

al., 2007). Thus, residential electricity conservation is extremely important for energy

saving and carbon abatement in China.

Traditional electricity policies focus on price-based interventions such as multi-part

tariff and peak-load pricing. However, most of the previous studies find that electricity

consumption usually exhibits low income and price elasticities (Reiss and White, 2005;

Shin, 1985; Zhou and Teng, 2013). Thus, price-based interventions are not very useful

for electricity conservation. More recently, policies have begun to pay attention to

information-based interventions. A large number of experiments reveal that residential

electricity consumption may not actually lack price elasticity, but only appear to be lack

of elasticity because they do not have full information on energy price and quantity.

Providing more information on electricity consumptions to the households may help

them to better control their energy consumption (Abrahamse et al., 2005; Darby, 2006;

Faruqui et al., 2010; Fischer, 2008).

Electricity consumption has specific features. The first is that consumption decisions

are made in real time whereas expenditure is only experienced intermittently (usually

monthly when the bill arrives). Such separate experiences of consuming a good and

spending money cause the problem of salience (Gilbert and Graff Zivin, 2014). The

second feature is that the price structure of electricity is usually complicated; consumers

probably do not fully understand the price structure and do not know how to optimize

their electricity consumption (Bushnell and Mansur, 2005; Ito, 2012)1. The consumer

may have no idea about when, how or by which device electric current is used.

Moreover, electricity consumption usually accounts for only a small share of household

budgets; therefore, households may not want to invest too much effort and time to get

information about price and quantity. If the household is provided simple and low-cost

information, such as installation of an in-home display smart meter, residential

electricity consumption may be reduced remarkably (Jessoe and Rapson, 2014).

1 Kempton and Layne (1994) have pointed out that "electricity consumption is like shopping in a grocery store in

which no individual item has a price marking and the consumer receives a monthly bill at an aggregate price for

food consumption".

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The Chinese government is already aware of the importance of information feedback

for residential electricity savings. A number of policies have been implemented to

improve information feedback to consumers, including in-house display of meters,

development of smart grids and smart meters, etc. Though economic theory predicts a

reduction of electricity consumption through improved information feedback, whether

it works in practice remains an empirical issue. Additionally, which type of information

feedback is most effective among various feedback types needs to be identified.

In this paper, we aim to investigate the energy conservation effect of information

feedback on residential electricity consumption, based on a household survey dataset

collected in 2012 that covered 26 provinces in China. To achieve this goal, we test a

series of hypotheses. The first is whether more information access can help consumers

reduce electricity consumption; the second is whether prepayment of electricity fees

leads to energy savings; the third is whether automatic payment of the bill through a

bank account is harmful to energy conservation; and the fourth and fifth hypotheses test

whether higher billing frequency and installation of smart meters, respectively, are

associated with lower electricity consumption.

The results show that residential electricity consumption is both price- and income-

inelastic. Urban households consume more electricity than do their rural counterparts.

Moreover, residential electricity demand is also positively associated with family size,

dwelling area, heating and cooling degree days, and householders’ years of schooling.

Our results also reveal that information feedback does affect residential electricity

consumption. First, households that obtain electricity consumption information through

interaction with meter readers are associated with lower electricity consumption.

Second, ex ante information feedback (households that use a prepaid metering system)

is associated with less electricity consumption. Third, explicit information feedback

(households that directly pay meter readers) is associated with less electricity

consumption. However, the energy conservation effects of information feedback

frequency and the smart meter program are not significant.

Our paper makes several contributions to the literature. First, we employ a newly

collected, large sample household survey dataset. The dataset not only covers the

majority of Chinese provinces but also includes both urban and rural households. Thus,

it is more representative and reliable. Second, we investigate the effects of a series of

information feedback types on residential electricity savings. To the best of our

knowledge, no previous studies have focused on this topic for the Chinese case.

The paper proceeds as follows: section 2 reviews the previous studies briefly; section

3 describes the dataset; section 4 raises hypotheses; section 5 is the empirical model;

section 6 presents and discusses the estimation results; and the last section concludes.

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2. Literature review

Early studies on the effect of information feedback on energy conservation, mainly

carried out by psychologists, can be traced back to the 1970s and 1980s, and the

feedback was mostly considered as an intervention (Seligman and Darley, 1977; Winett

et al., 1978; Winkler and Winett, 1982).

The first quantitative analysis of the effectiveness of information feedback on energy

conservation is provided by Sexton et al. (1989). Thereafter, a large number of field

experiments and studies have focused on quantifying the effectiveness of information

on household electricity demand (Darby, 2006; Faruqui et al., 2010; Fischer, 2008). The

literature can be classified into three main branches according to the feedback types.

The first type of information feedback is the energy audit, which is usually offered

for free by utilities. Hartman (1988) estimates the impact of the residential household

conservation programs run by Portland General Electric Company from July 1978

through December 1979. The results show that the amount reasonably attributable to

conservation programs is less than originally thought if self-selection bias is corrected.

The programs accounted for roughly 50% of the electricity savings of the households

and 45-60% of the savings of the higher-income, better-educated, younger home

oweners. Waldman and Ozog (1996) also argue that the incentive-induced energy

savings (including audits, rebates and loans) in the US are overstated. Their results

show that only 71% of total conservation is associated with the incentive program, and

the remaining 29% conservation would have happened regardless.

The second type of information feedback is more informative billing. Henryson et al.

(2000) find that electricity bills have a positive effect on energy savings. Gleerup (2010)

evaluates the effect of information feedback by sending text messages and emails to

consumers. In their randomized field experiment, 1452 households are allocated to

three treatment groups and two comparison groups randomly. Their results show that

text messages and emails reduce annual electricity consumption by about 3%. Allcott

(2011) evaluates a series of non-price energy conservation programs which send Home

Energy Report letters to residential electricity customers. The letters not only compare

the electricity consumptions of the consumers to that of their neighbors, but also

provide energy conservation tips. The results show that about 2% of energy

consumption are reduced because of the information provided by the letters, which is

equivalent to the effect of a 11-20% increase of electricity price. Mizobuchi and

Takeuchi (2013) also find that comparative feedback has a positive effect on energy

savings. Gilbert and Graff Zivin (2014) find that electricity bill can reduce households'

consumption by 0.6-1%.

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The third type of information feedback is in-home display (IHD) of energy usage,

which is also the most widely employed feedback method. Sexton et al. (1989) find that

the monitoring devices have a significant positive effect on the distribution of electricity

demand. It tends to shift load from the peak period to the off-peak period. Matsukawa

(2004) estimates the effects of information on residential electricity consumption based

on a Japanese experiment. The results find that monitoring device usage leads to energy

conservation. Ueno et al. (2006) find that installation of an on-line interactive energy

consumption information system leads to a 9% reduction in electricity usage. Gans et

al. (2013) find that if the consumer are allowed to track electricity usage in real time,

the electricity consumption in Northern Ireland will decline 11-17%. Carroll et al. (2014)

find that participation in a smart metering program with time-of-use tariff significantly

reduces electricity demand. Jessoe and Rapson (2014) test the effect of information

feedback on the price elasticity of residential electricity demand, based on a randomized

trial. They find that households with IHD treatment are more sensitive to price increases.

However, Hargreaves et al. (2013) find that smart energy monitors do not necessarily

encourage householders to reduce their consumption in the UK.

Very few studies have focused on China's residential electricity savings based on

survey data. Based on a dataset obtained from a questionnaire survey conducted in 13

cities in China, Murata et al. (2008) find that about a 28% reduction in urban households’

electricity usage could be achieved in the year 2020 by improving the efficiency of end-

use appliances. Feng et al. (2010) investigate the barriers to energy efficiency in the

residential sector based on a survey data covering about 600 households in Liaoning

province of China. Zhou and Teng (2013) use annual household survey data for Sichuan

Province from 2007-2009 to estimate the income elasticity and price elasticity of

residential electricity demand. They find that residential electricity demand is both

price- and income-inelastic. To the best of our knowledge, no studies have estimated

the effect of information feedback on residential electricity conservation in China. Our

work is to fill this gap.

3. Description of surveyed residential electricity behavior

Our household survey data is collected from the 2012 China Residential Energy

Consumption Survey (CRECS-2012). The questionnaire covers six parts: household

characteristics, dwelling characteristics, kitchen and home appliances, space heating

and cooling, residential transportation, and electricity billing, metering and pricing

options. The survey was administrated by the Department of Energy Economics at

Renmin University of China during 2012-2013. The sampling distribution among

provinces is based on household population distribution reported in the 6th National

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Population Census in 2010. A total of 1640 households in 26 provinces were initially

invited to take the face-to-face survey. Eventually, 1542 households were enrolled in

the study, with a high response rate of 94%. After validity and consistency checks, 1450

total observations were left for the final analysis2. The geographic distribution of our

sample at prefecture level is as shown in Figure 1.

2 The sample selection process is as follows. In the first stage, we recruited around 120 undergraduate and graduate

students from Renmin University in December 2012. The sampling size of each province depends on the household

population distribution reported in the 6th National Population Census which was implemented in 2010. This

procedure yields a geographic distribution of 38.3%, 43.2% and 18.5% for the eastern, middle and western areas,

respectively. The official population distribution in the eastern, middle and western areas is 40.6%, 31.7% and 27.7%,

respectively. In the second stage, all interviewers were required to attend a half-day training lecture, to assure that

all of them understand the underlying meaning of each question. They also learned the necessary knowledge and

skills regarding how to conduct interviews and how to use GPS services to locate household addresses. The survey

was implemented in the third stage in February to March 2013. The interviewers were required to collect very

detailed information for each electric appliance by checking their nameplate (e.g., power capacity) and asking the

householder their usage pattern of each device face-to-face (e.g., usage frequency and duration). On average, an

interview took 60-90 minutes. In order to avoid data bias and ensure the data quality, the interviewers were allowed

to randomly contact the families in their social networks. However, the invited households must met four criteria:

(1) live in present home for more than six months in 2012; (2) use energy only for consumption rather than production

purpose; (3) only one candidate household for each community; (4) can provide electricity bill record in 2012. Each

invited household got a mobile phone prepaid card worth 50 RMB as a remuneration as long as they finished the

survey. The interviewers were paid 50 RMB for each verified questionnaire. In the fourth stage, 10% questionnaires

for each interviewer were randomly selected to conduct a phone verification. Finally, we got 1450 valid observations.

For more details on this survey, please refer to Zheng et al. (2014).

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Fig.1 Geographic distribution of sample at prefecture level

This survey data shows a high degree of consistency with the official statistics of

the National Bureau of Statistics (NBS) of China (as is shown in Table 1). Taking the

comparable characteristics as an example, the CRECS-2012 shows that males account

for 48.5% in total respondents, for which the NBS number is 51.3%. The average

household size in CRECS-2012 is 2.65 persons in total, 2.57 persons in urban and 2.95

persons in rural areas, which are slightly smaller than the numbers of NBS, i.e. 3.02

persons, 2.86 persons and 3.88 persons, respectively. In addition, the dwelling

characteristics in CRECS-2012 are close to the official report, i.e., the average dwelling

area for urban and rural household in CRECS-2012 is 96 and 135 m2, quite close to the

NBS number of 94 and 143 m2, respectively. We also observe the similar patterns

regarding the ownership of durable goods, i.e., the penetration rate of refrigerator,

washing machine and air-conditioner for urban residences in CRECS-2012 is close to

the number of NBS. However, the ownership of air-conditioner for the rural household

in CRECS-2012 is higher than the NBS’s figure, which may result from the small rural

observations in CRECS-2012.

The survey collected in-depth information for most of electric appliances, including

refrigerator, washing machine, television, personal computer, light bulb, air-conditioner,

water heater and electric fan. The investigator was required to check the physical

nameplate for each device, i.e., the model, power, energy efficiency label, year of

purchase, etc. More importantly, data on usage behavior were also collected, i.e.,

operation duration, operation mode, usage frequency and other information. This

detailed information enables us to estimate the electricity consumption quantity.

Because we need to compare and regress against different end-use purposes, we mainly

rely on device-based estimated electricity consumption. It can be estimated based on

the power capacity, usage frequency, and durations of the devices 3. On average, our

surveyed households consumed 1792 kWh in year 2012, of which 23% was used for

cooking, 47% for home appliances, 12% for water heating, 13% for space cooling and

the remaining 5% for space heating.

3 We use the estimated electricity consumptions rather than the reported ones for two reasons. First, the device-

based estimation can be used to analyze the distribution among various end-use purpose. Second, most of

respondents cannot provide a whole year’s metered record. Instead, they provide an approximate estimation which

shows less variation among seasons. We run a t test to check if the means of the estimated household electricity

consumptions (1792 kWh) and the reported ones (1729 kWh) are the same. The result indicates that we can reject

the null hypothesis at the 10% significance level.

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Table 1 Comparison of CRECS-2012 with NBS

Index Unit CRECS-2012 NBS

Total Urban Rural Total Urban Rural

Number of observations - 1450 1167 283 - - -

Male percentage % 48.5 48.2 49.5 51.3

a

50.6 51.5

Average household size persons 2.65 2.57 2.95 3.02

a

2.86 3.88

Dwelling area m2 103.73 96.15 134.98 - 94.1 b 143.9

Number of refrigerator per

100 households

- 89 91 77 - 98.5 b 67.3

Number of washing machine

per 100 households

- 91 94 76 - 98 b 67.2

Number of air conditioner

per 100 households

- 113 127 51 - 126.8

b

25.4

Note:

a derived from China Population and Employment Statistical Yearbook (2012);

b derived from China Statistical Yearbook (2013).

The residential electricity consumption pattern shows great spatial heterogeneity.

Urban households used 1892 kWh on average in 2012, nearly 38% more than rural

households (1375 kWh). Figure 2 draws the box plot of the electricity consumptions

over location. The vertical axis of the figure represents electricity consumptions and the

horizontal axis represents locations of the households. The horizontal axis divides the

households into four groups, i.e. households in rural area of northern cities, households

in rural area of southern cities, households in urban area of northern cities, and

households in urban area of southern cities. From the figure, we can observe that urban

residents in the southern area (latitude below 33°) used more electricity than did

households in the northern area significantly. This may be due to the fact that the

northern cities are provided district heating while the southern cities are not4.

4 The cities in northern area of China are provided central heating by the government, while the southern

cities are not. The households in the southern area usually use heating units in the winter. For more details,

please refer to Guo et al. (2015).

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Fig.2 Household electricity consumption by city and geographic location

Notes: (1) The vertical axis measures electricity consumption and the horizontal axis indicates

location of the household.

(2) Rural: households in rural area; Urban: households in urban area; North: households in

northern area; South: households in southern area.

There are two consumption payment systems in use. One is pre-paid; the power

supply will stop if the pre-paid credits run out. Another method is to pay the bill after

usage. In total, 1379 respondents provided this information, of which around one-third

of households are using a pre-paid metering system and the remaining users are billed

after usage. As we will see, consumers receive information about usage and make

payments in different ways depending on which payment system is in use.

We now look at the different ways in which consumers receive electricity

information. In the questionnaire, the respondents were asked (1) whether they use a

smart-meter; (2) whether they know the quantity or cost of their electricity consumption;

and (3) how they access this information. We have 1389 responses, of which 41% of

households have installed a smart-meter, while the remaining households have not.

Among all the responding households, around 22% respondents do not know any

information about their monthly electricity usage. The remaining users access their

electricity information through four channels: “power grid (or bank) billing statement”

(43%); “informed by meter readers” (19%); “prepayment record” (10%); and other

methods (6%). We compared the electricity consumption for various information access

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types (as Figure 3 shows). From the figure, we can observe that the user who is informed

by the meter readers has a lower mean and a lower median amount of usage.

Fig. 3 Electricity consumption by various information access types

Notes: (1) The vertical axis measures electricity consumption and the horizontal axis indicates

how the households access the information of their electricity consumption.

(2) Don’t know: the respondents do not know any information about their electricity usage; meter

reader: informed by meter readers; bill: informed by billing statement; informed by prepayment

record: informed by prepayment record; others: informed by other ways.

Another relevant question is “how do you pay?”, which received 1380 responses.

Over half of the users pay the bill at the counter of the grid company (53%). There are

270 users, around 20% of respondents, who pay through an automatic deduction from

their bank account. About 16% of the respondents transfer the fee manually through

bank or internet transfers, and the remaining 12% pay the meter reader. As Figure 4

shows, if the respondents choose to pay the meter readers, they consume less electricity

on average and have less variation than the users of other payment types.

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Fig. 4 Electricity consumption by various payment types

Notes: (1) The vertical axis measures electricity consumption and the horizontal axis indicates

how the households pay the bill.

(2) Automatic deduction: pay the bill through an automatic deduction from their ban account; grid

counter: pay the bill at the counter of the grid company; transfer manually: pay the bill manually

through bank or internet transfer; meter reader: pay the bill through the meter reader.

Finally, there were 1320 responses on information feedback frequency. Around 64%,

21% and 15% of respondents pay the bill (or pre-pay the fee) every month, every 2-5

months and every 6+ months, respectively. As shown in Figure 5, the mean and median

of electricity consumption for respondents with different payment frequencies differ

only slightly, but the variation of electricity consumption for respondents who make

monthly payments is larger than the variation for the respondents with other payment

frequencies.

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Fig. 5 Electricity consumption by various payment frequencies

Notes: (1) The vertical axis measures electricity consumption and the horizontal axis indicates

how frequent the households pay the bill.

(2) Monthly: pay the bill monthly; quarterly: pay the bill every 2-5 months; half-yearly: pay the

bill every 6+ months.

4. Hypotheses

In the light of previous literature and observations from the survey, we propose the

following hypotheses that need to be empirically examined.

H1: the more information accessed, the less electricity consumed

As Wilhite and Ling (1995) explained, information feedback is put into effect

through the following transmission mechanism. First, the increased information

feedback promotes energy conservation awareness or knowledge; then, this information

enables people to teach themselves how to save energy, promoting and reinforcing self-

efficacy (Oltra et al., 2013); finally, these changes in energy consumption behavior lead

to a decrease in consumption. Thus, we hypothesize that the users who get feedback

about their electricity information are more likely to change their consumption behavior,

leading to lower electricity usage. In contrary, those households that do not access any

information feedback may lack the necessary knowledge and have less incentive to

change their behavior, resulting in higher consumption of electricity.

H2: ex ante information feedback is associated with less electricity compared to

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ex post feedback

Under the prepayment system, the consumer charges money to her electric meter

account before using power; once the credit points are used up, the power supply stops

until a new payment is made. Usually, the meter will remind the user to charge

immediately when the remaining points trigger a boundary value. In comparison, the

bill payment system allows the user to consume electricity before payment. The grid

company normally sets up a payment period (which could be monthly or less frequently)

when it signs the contract with the user. Even if the consumer does not pay immediately,

the power service will not be stopped, but it incurs an additional overdue fine.

Obviously, the prepayment system feeds back ex ante information to the user, while the

bill payment system provides ex post feedback. Households that receive ex ante

feedback face a more certain and tighter constraint than those that receive ex post

feedback. Similarly to Faruqui et al. (2010), who find that prepaid systems can boost

electricity conservation, we hypothesize that the household with ex ante feedback

consumes less electricity.

H3: explicit information feedback leads to lower electricity demand relative to

implicit feedback

We re-categorize the payment method into two groups. For the householder who

authorizes the bank to automatically deduct from his associated account, he may be

unconscious of the payment record in terms of quantity and price. Thus, we define this

kind of information as implicit and passively delivered to customers. Another category

is defined as explicit information feedback because the payment is implemented in an

active/interactive way, i.e., the householder has to know the consumption quantity and

cost expenditure when she pays the bill (or charges the account) through a grid counter,

bank or internet transfer or meter reader. We hypothesize that implicit information

feedback (i.e., automatic bank deduction) leads to higher electricity consumption.

H4: the more frequent feedback, the less electricity demand

A number of studies conclude that feedback frequency is a key factor in energy

savings (Fischer, 2008; Wood and Newborough, 2003). These studies divide feedback

frequency into three categories: continuous/in-time feedback, daily feedback and

weekly (or monthly) feedback. As Fischer (2008) suggested, quick feedback improves

the link between consumers’ actions and effects; consequently, it increases

consciousness about the action’s outcome. Accordingly, we hypothesize that high-

frequency information feedback leads to lower electricity demand.

H5: smart meter users have lower electricity consumption

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Recent studies have examined the effect of smart information feedback devices on

electricity demand. For example, Carroll et al. (2014) find supportive evidence that the

smart meter program significantly reduces electricity demand. However, some

empirical studies give opposite results. For example, Hargreaves et al. (2013) suggest

that the smart energy monitor device in the UK does increase householders’ knowledge,

but it does not necessarily motivate householders to reduce their electricity

consumption. We are also interested in the Chinese case. Here we hypothesize that

smart meter users will reduce their electricity demand.

5. Residential electricity demand function

To empirically check these hypotheses, we use a regression approach. The classical

electricity demand specification in log-log function form is given as follows (Alberini

and Filippini, 2011; Terza, 1986).

ln i i i iKWH feedback X

(1)

where dependent variable lnKWHi is the electricity consumption of the i-th household

measured in KWh. It is estimated according to the reported home appliance, capacity

power, usage frequency, energy efficiency label and other technology characteristics.

The detailed estimation procedure can be found in Zheng et al. (2014). Xi is a row vector

of exogenous variables which are related to residential electricity consumption. All

continuous independent variables take logarithmic form. The determinants of

residential electricity demand can be divided into two categories.

The first category is related to household characteristics. In the present paper, we

take the following variables into account. (1) Price (lnprice): Electricity price is a key

component of electricity demand. The literature assumes that people will respond to the

marginal price. However, China started to implement a tiered pricing policy, which

continued until the end of 2012. That means our surveyed household actually faced a

constant price. Following previous studies, we use the average price to proxy the price

heterogeneity across regions. Average price is defined as the annual electricity

expenditure over annual electricity consumption in logarithmic form. A negative sign

is expected. (2) Income (lnincome): This is the annual gross income measured in Yuan.

Because electricity is a normal good, an increase in income leads to an increase of

electricity demand. (3) Household size (lnsize): This is measured as the number of

permanent residents. Population and its structure are expected to influence energy

demand, especially the presence or absence of children in households. We expect that

more family members in the household will induce more electricity demand. (4)

Dwelling area (lnarea): This is expressed in the dwelling’s floor area. A larger space

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is normally associated with more lights and appliances. Therefore, a positive coefficient

is expected. (5) Householder’s education level (lneduyear): This is represented by the

householders’ years of schooling. It has two distinct effects on electricity demand. On

the one hand, households with highly-educated members tend to consume less

electricity because they have a stronger awareness of conservation and environmental

concerns. On the other hand, higher education is normally associated with higher

income, which tends to increase electricity usage. Therefore, the coefficient is uncertain.

(6) Urbanization (citytown): we use a dummy variable to differentiate urban and rural

households. It equals 1 for urban households and 0 for rural residents. A positive

relationship is expected between urbanization and consumption.

Another branch of literature explaining residential electricity demand is related to

household lifestyles and behaviors (Carlsson‐Kanyama et al., 2005; Wyatt, 2013;

Zhou and Teng, 2013), i.e., how the household behaves, how members use appliances,

and their attitude toward energy conservation and other environmental concerns. In the

present paper, we use two variables to reflect appliances usage. One is the heating

degree days (lnHDD). This measures the number of days when the household uses an

electrical device for the purpose of space heating. Similarly, lnCDD is the cooling

degree days. It measures the number of days when the household uses an air

conditioning device for space cooling. It is reasonable to expect that the coefficients of

these two terms are positive, which means that the greater the number of days heating

and cooling appliances are used, the more electricity will be consumed.

Feedback is represented by using five candidate variables according to our

hypotheses. (1) Whether and how the electricity information can be accessed

(H1_infor). It equals 0 if the household doesn’t know any information. A non-zero

value of 1, 2, 3 or 4 indicates the information source is from a meter reader, billing

statement, prepayment record, or other source, respectively. (2) The billing type

(H2_bill_type). It equals 1 if the household prepays the electricity fee. Otherwise, it

equals 2 for a billing statement user. (3) The payment approaches (H3_pay_way). It

equals 1, 2, 3 and 4 for households that pay the bill (or charge) through authorized

automatic deduction from a bank account, at the grid company counter, through a bank

or internet transfer or directly to the meter readers, respectively. (4) The payment

frequency (H4_freq). It is assigned value 1, 2 and 3 for payment every month, 2-5

months or 6 or more months, respectively. (5) The use of smart meter (H5_smart). It

equals 1 if the smart meter is used. Otherwise, it equals 0.

The survey data reflects household information in 2012 and offers cross-sectional

data for analysis. Descriptive statistics of all variables are listed in Table 2. On average,

a surveyed household in the year 2012 had 2.66 persons. The average household

consumed 1738.7 kWh based on device usage with an average cost of 0.53 Yuan per

16

kWh.

Table 2 The descriptive statistics for all variables

Variable obs Unit Mean S.D. Min. Max.

Estimated KWh KWH 1379 kWh 1738.66 1200.28 26.28 7620.68

Electricity price price 1379 Yuan/kWh 0.53 0.05 0.32 0.70

Household income income 1370 10,000 Yuan 9.68 15.51 0.50 350.00

Household size size 1375 person 2.66 1.06 1.00 8.00

Householder’s

schooling years eduyear 1276 year 11.31 3.93 0.00 22.00

Dwelling area area 1366 m2 104.30 49.00 21.00 250.00

Heating degree

days HDD 1379 day/year 27.10 40.51 0.00 195.00

Cooling degree

days CDD 1379 day/year 31.06 33.66 0.00 150.00

Urban or Rural citytown 1377 -- 0.80 0.40 0 1

Information access H1_infor 1379 -- 1.60 1.13 0 4

Billing type H2_bill_type 1369 -- 1.68 0.47 1 2

Payment type H3_pay_way 1370 -- 2.20 0.89 1 4

Payment frequency H4_freq 1310 -- 1.52 0.75 1 3

Smart meter H5_smart 1379 -- 0.41 0.49 0 1

6. Results and discussions

The regression results are listed in Table 35. We firstly regress residential electricity

consumption on the classical driving forces and present the results in column (1).

Considering the possible heterogeneity, an OLS estimation with Huber-White sandwich

standard errors is applied.

(1) Basic Model

Column (1) reports the results for the basic model. The price elasticity is -0.79 and

is significant at the 0.1% level. When the Feedback variables are taken into account,

the price elasticity ranges from -0.82 to -0.6. This finding is consistent with our

expectation and most of the literature. For example, Filippini and Pachauri (2004)

estimate price elasticity in India during 1993-1994, with results ranging from -0.42 to -

0.29; Zhou and Teng (2013) use urban household survey data for Sichuan Province in

China from 2007-2009 to estimate the price elasticity of urban residential electricity

5 We have checked the problem of multi-collinearity for all the regressions by Variance Inflation Factor (VIF), and

the results show that it is not a severe problem.

17

demand and get elasticity ranges from -0.50 to -0.35. The coefficient of household

income is also significant at the 0.1% level. Our estimation gives an income elasticity

of 0.1, which is very close to that found by Yoo et al. (2007), who report income

elasticity of 0.06-0.11 for Seoul’s residents in 2005. It also coincides with Zhou and

Teng (2013)’s finding that the income elasticity in urban China is 0.14-0.33.

As we expected, other explanatory variables positively and significantly contribute

to residential electricity consumption, i.e., the more permanent family members and the

larger the area, the more electricity consumed (Filippini and Pachauri, 2004; Yoo et al.,

2007). As for the householders’ years of schooling, we consider awareness of

conservation and the income effect at the same time. The coefficient of householders’

years of schooling is 0.168, which is significant at the 0.1% level; this indicates that the

income effect is dominant. For the urban dummy variable, the positive coefficient is

significant at the 1% level. It indicates that urban households generally use more

electricity than do rural households.

Regarding lifestyle, most previous studies proxy this dimension by using the number

of appliances, i.e., refrigerators, air conditioners, and computers. However, these

variables cannot capture people’s behavior about how they employ these devices, i.e.,

some appliances that are registered in the survey are not actually in use. Instead, our

data enables us to depict people’s preferences in a more accurate way. The remarkable

positive signs for the heating and cooling days across all columns show that the usage

of home appliances plays a key role. Ceteris paribus, the longer the duration of

operation for home appliances, the more electricity is consumed.

In general, the basic model in column (1) shows results consistent with previous

studies. It reveals a negative price elasticity and positive income elasticity for our

surveyed data. Urbanization does increase residential electricity demand. Moreover,

electricity demand is positively correlated with family size, dwelling area, years of

18

Table 3 Regression results

Variables Model (1) Model (2) Model (3) Model (4) Model (5) Model (6) Model (7)

Basic H1 H2 H3 H4 H5 Mixed

lnPrice -0.789*** -0.703*** -0.817*** -0.731*** -0.801*** -0.793*** -0.659**

(-4.42) (-3.76) (-4.48) (-4.06) (-4.13) (-4.44) (-3.26)

lnIncome 0.0986*** 0.101*** 0.0996*** 0.0955*** 0.0949*** 0.0994*** 0.0981***

(4.25) (4.35) (4.31) (4.09) (3.90) (4.26) (4.04)

x_lnsize 0.162*** 0.161*** 0.158*** 0.182*** 0.183*** 0.160*** 0.188***

(3.44) (3.43) (3.40) (3.85) (3.81) (3.40) (3.90)

x_lneduyear 0.158*** 0.149** 0.161*** 0.160*** 0.151** 0.159*** 0.143**

(3.32) (3.09) (3.39) (3.41) (2.93) (3.35) (2.80)

x_lnarea 0.105* 0.109** 0.104* 0.104* 0.0886* 0.108* 0.104*

(2.50) (2.59) (2.46) (2.45) (1.98) (2.55) (2.34)

x_lnHDD 0.0402*** 0.0431*** 0.0381*** 0.0418*** 0.0381*** 0.0404*** 0.0428***

(3.91) (4.20) (3.67) (4.05) (3.55) (3.93) (3.95)

x_lnCDD 0.0860*** 0.0838*** 0.0858*** 0.0869*** 0.0861*** 0.0866*** 0.0851***

(8.59) (8.34) (8.52) (8.66) (8.28) (8.64) (8.13)

x_citytown 0.142* 0.162** 0.143* 0.148** 0.143* 0.141* 0.156**

(2.49) (2.83) (2.51) (2.59) (2.41) (2.48) (2.63)

H1: Information source (baseline: no information feedback)

1 meter-reader -0.114* -0.152*

(-2.08) (-2.46)

19

Variables Model (1) Model (2) Model (3) Model (4) Model (5) Model (6) Model (7)

Basic H1 H2 H3 H4 H5 Mixed

2 bank billing statement 0.0439 -0.0182

(0.90) (-0.34)

3 prepaid record -0.0423 -0.0126

(-0.68) (-0.20)

4 others 0.0961 0.0821

(1.25) (1.04)

H2: Billtype (baseline: prepay system, or ex-ante feedback)

2 bill (ex-post feedback) 0.0854* 0.131**

(2.45) (2.97)

H3: Payment mode (baseline: bank automatic debit, or implicit information feedback)

2 grid counter -0.0105 -0.0128

(-0.22) (-0.27)

3 bank or internet transfer -0.0264 -0.0296

(-0.45) (-0.46)

4 meter reader -0.160** -0.171*

(-2.59) (-2.58)

H4: information feedback frequency (baseline: monthly pay)

2 quarterly 0.0302 0.0640

(0.67) (1.35)

3 more than half a year -0.0312 0.00457

(-0.60) (0.08)

20

Variables Model (1) Model (2) Model (3) Model (4) Model (5) Model (6) Model (7)

Basic H1 H2 H3 H4 H5 Mixed

H5: smart meter (baseline: no smart meter)

2 smart -0.0221 -0.0581

(-0.64) (-1.59)

Constant 5.202*** 5.244*** 5.129*** 5.248*** 5.272*** 5.193*** 5.278***

(20.54) (19.39) (20.03) (20.46) (19.76) (20.51) (18.64)

Observations 1243 1243 1234 1235 1182 1243 1176

R-squared 0.185 0.194 0.191 0.194 0.179 0.186 0.201

AIC 2265.1 2259.3 2241.4 2242.1 2164.4 2266.7 2142.3

BIC 2311.3 2325.9 2292.5 2303.5 2220.3 2318.0 2243.7

Log likelihood -1123.6 -1116.7 -1110.7 -1109.1 -1071.2 -1123.4 -1051.1

Note: t-values in parentheses; *** 0.1% significance level, ** 1% significance level, * 5% significance level.

21

schooling and duration of operation of home appliances.

(2) Hypotheses Test

We examine our hypotheses by gradually adding relevant Feedback variables in

columns (2)-(6). Finally, we include all variables in column (7).

First, we check whether information feedback changes people’s behavior and leads

to less electricity consumption. The households with the response that “they do not

know any electricity information” are treated as the base group. From column (2), we

find that information feedback does matter, depending on the information source. The

information feedback from bank billing statements, prepayment records and other

sources are not significant. In other words, these types of feedback have no statistical

impact on electricity consumption. However, the coefficient for “feedback from meter

readers” is negative at the 5% significance level. It indicates that households that obtain

usage information from meter readers use less electricity. Our results provide

supportive evidence for H1, which is consistent with the claim that information

feedback is one of the most successful strategies for residential energy conservation

(Abrahamse et al., 2005).

Understanding how meter readers work may help explain our findings. In most rural

areas and some urban areas, the grid companies employ a large number of meter readers

to periodically collect residential electricity consumption data. Each meter reader is

responsible for several communities. Their duties are to copy the meter number and

visit residents to inform them of their consumption quantity and expense. In most

villages, the consumers can directly pay the meter readers because they know each other.

In other cases, they can pay the bill at the counter of the grid company, through the bank

system or in other ways. This system, on the one hand, is inefficient compared with

modern remote automatic meter reading; on the other hand, it creates face-to-face

interaction between the electricity agency and power subscriber.

One reason why meter readers work well is that this kind of information feedback

may provide customers with more in-depth information (Wilhite and Ling, 1995). The

conversation not only delivers the consumption record for the last billing round, but

also may be related to the users' historical records and/or neighbors’ information. This

kind of information comparison will promote energy conservation awareness by

competition (Fischer, 2008; Mizobuchi and Takeuchi, 2013). Another reason is the

information feedback from meter-readers is more understandable and friendly. The

customers can also obtain advice and conservation tips, which enhance the users'

perceived information and self-learning capacity, and leads to saving energy more

effectively (Allcott, 2011).

Second, we look at hypothesis H2 on the effect of billing types. The households that

22

get ex ante information feedback, or use a prepaid system, are treated as a benchmark.

In column (3), the coefficient for the group that gets ex post feedback is significantly

positive at the 5% level. This indicates that, ceteris paribus, the households that pay the

bill after consumption consume more electricity than the households that prepay the

bill. This result confirms our hypothesis and is consistent with Faruqui et al. (2010),

who suggest that the prepaid system can reduce residential electricity consumption. The

reason is that households that get ex ante feedback have more certain and tighter

constraints, which may lead to far more proactive behavior changes. In comparison, the

households with ex post feedback take less account of the cost constraint for their

present consumption. Consequently, they may overuse electricity.

Third, we examine whether various payment types matter for hypothesis H3. In

column (4), the reference group is “implicit information feedback,” i.e., people who

pay through automatic bank deductions. We find that households that pay at the grid

company counter or via bank/internet transfer are not significantly different than the

benchmark group. However, consistent with H1, “paying through meter readers” can

significantly reduce residential electricity consumption. A possible explanation is that

households that pay through meter readers have an incentive not to be in arrears because

they have some kind of social connection with the meter readers. This strengthens the

cost constraint and creates a pressure to change consumption.

Fourth, hypothesis H4 on information feedback is not confirmed in our case. From

column (5) we find that, compared with the base group that pays every month, the

alternative groups are not significantly different. This indicates that the information

feedback frequency does not matter. This result is different from previous studies which

claim that quick feedback improves the link between action and effect (Dobson and

Griffin, 1992; Fischer, 2008; McCalley and Midden, 2002). In those studies, the effect

of consumption information feedback became less effective over a longer period

(Hargreaves et al., 2013; Van Dam et al., 2010). A possible reason is that those studies

usually examined real-time or continuous feedback, which is much shorter and faster

than our baseline monthly feedback cycle.

Finally, we find that the smart meter is not as important as we expected in hypothesis

H5. Our results, as listed in column (6), show that households with a smart meter

consume the same amount of electricity as their counterparts who do not use a smart

meter. This finding is different from some previous studies that suggest that the smart

meter program can significantly promote electricity conservation. However, other

empirical studies offer evidence similar to our estimation, For example, Hargreaves et

al. (2013) use UK trial data and argue that smart meters do not necessarily lead to lower

electricity consumption; their effect depends on some preconditions. For our case,

hypothesis H5 about the smart meter is not supported. A possible explanation is that a

23

majority of residents do not understand smart meters, so the meters cannot increase

their consciousness of electricity conservation or change their consumption behavior.

In column (7), we pool all information feedback variables together. The estimation

result is similar to the discussions above. We find positive evidence for H1, H2 and H3.

That is, information feedback through meter readers, ex ante information feedback (i.e.,

the prepaid system) and explicit information feedback through interactions with meter

readers are effective ways to promote electricity conservation in our surveyed

household data.

(3) Robustness Test

In this section, we offer a series of quantile regressions for robustness checks.

Compared with the regression curve for averages of distributions corresponding to

exogenous variables by OLS, the quantile regression allows us to compute several

different regression curves at various percentiles of the distribution. It offers detailed

information to show how some percentiles of electricity consumption may be more

affected than others by certain feedback information and other factors. The mixed

model (column 7 in Table 3) is duplicated with various quantile settings and the

regression results are listed in Table 4. The OLS result in column (1) is treated as the

reference group. Quantile regression results (QR hereafter) for 10th, 25th, 50th, 75th, and

90th are presented in column (2)-(6).

Similar to the result given by OLS, residential electricity demand has low price

elasticity in QR results, ranging from -0.827 to -0.452. In particular, the 10th quantile

of electricity consumption is not sensitive to price. The OLS regression seems to

overestimate price elasticity at the 10th quantile. The income elasticity is around 0.100

and significant across all columns. The OLS regression also underestimates income

elasticity at the 10th quantile. For other control variables, family size and lifestyle are

always significant factors in influencing electricity consumption for various quantile

groups. However, the householder’s education level, dwelling area, and location are not

significant for some percentiles.

Now let us look at information feedback. It seems that households reduce electricity

consumption if they get feedback from meter readers, except at the 10th percentile. The

majority of households tend to consume more if they receive ex post feedback, i.e., if

they pay the bill after consumption. Moreover, households that directly pay the

electricity fee to the meter reader tend to use less electricity. However, the OLS

24

Table 4 Robustness test

Variables Model (1) Model (2) Model (3) Model (4) Model (5) Model (6)

OLS QR 10th QR 25th QR 50th QR 75th QR 90th

lnPrice -0.659** -0.488 -0.827*** -0.607** -0.452* -0.737*

(-3.26) (-1.41) (-3.56) (-3.20) (-2.48) (-2.31)

lnIncome 0.0981*** 0.163*** 0.100*** 0.0956*** 0.111*** 0.0978*

(4.04) (4.33) (3.50) (3.98) (4.29) (2.17)

x_lnsize 0.188*** 0.165* 0.199*** 0.189*** 0.169*** 0.204*

(3.90) (2.43) (3.56) (3.92) (3.37) (2.22)

x_lneduyear 0.143** 0.145 0.154** 0.100 0.142** 0.0810

(2.80) (1.83) (2.58) (1.95) (2.83) (0.90)

x_lnarea 0.104* 0.0298 0.0515 0.0501 0.123** 0.114

(2.34) (0.42) (0.99) (1.15) (2.71) (1.37)

x_lnHDD 0.0428*** 0.0428** 0.0442*** 0.0406*** 0.0391*** 0.0760***

(3.95) (2.68) (3.69) (3.99) (3.65) (3.78)

x_lnCDD 0.0851*** 0.109*** 0.0905*** 0.0738*** 0.0752*** 0.0632**

(8.13) (6.67) (7.58) (7.08) (6.76) (3.03)

x_citytown 0.156** -0.0169 0.117 0.200*** 0.151* 0.350**

(2.63) (-0.19) (1.74) (3.50) (2.51) (3.07)

H1: Information source (baseline: no information feedback)

1 meter reader -0.152* -0.0888 -0.217** -0.161** -0.238*** -0.254*

(-2.46) (-0.93) (-3.12) (-2.62) (-3.65) (-2.16)

25

Variables Model (1) Model (2) Model (3) Model (4) Model (5) Model (6)

OLS QR 10th QR 25th QR 50th QR 75th QR 90th

2 bank billing statement -0.0182 -0.0714 -0.0447 0.0176 -0.0282 -0.0779

(-0.34) (-0.89) (-0.75) (0.34) (-0.52) (-0.80)

3 prepaid record -0.0126 -0.0643 -0.0920 -0.00908 -0.0572 -0.0596

(-0.20) (-0.60) (-1.19) (-0.14) (-0.83) (-0.47)

4 others 0.0821 0.0746 0.0557 0.0883 0.0684 0.0729

(1.04) (0.58) (0.61) (1.12) (0.82) (0.49)

H2: Billtype (baseline: prepay system, or ex-ante feedback)

2 bill (ex-post feedback) 0.131** -0.0194 0.0177 0.175*** 0.239*** 0.289**

(2.97) (-0.27) (0.35) (3.89) (4.95) (3.17)

H3: Payment mode (baseline: bank automatic debit, or implicit information feedback)

2 grid counter -0.0128 0.00955 0.0285 -0.0236 0.0252 -0.116

(-0.27) (0.13) (0.53) (-0.50) (0.53) (-1.32)

3 bank or internet transfer -0.0296 -0.116 -0.0450 0.0204 0.0551 -0.130

(-0.46) (-1.16) (-0.62) (0.32) (0.84) (-1.05)

4 meter-reader -0.171* -0.0758 -0.0411 -0.203** -0.174* -0.364**

(-2.58) (-0.68) (-0.52) (-2.97) (-2.47) (-2.69)

H4: information feedback frequency (baseline: monthly pay)

2 quarterly 0.0640 0.0486 0.0355 0.0524 0.0281 0.0179

(1.35) (0.63) (0.63) (1.08) (0.57) (0.20)

3 more than half a year 0.00457 0.0574 -0.0457 -0.0156 0.0243 0.0858

(0.08) (0.63) (-0.69) (-0.27) (0.39) (0.76)

26

Variables Model (1) Model (2) Model (3) Model (4) Model (5) Model (6)

OLS QR 10th QR 25th QR 50th QR 75th QR 90th

H5: smart meter (baseline: no smart meter)

2 smart -0.0581 -0.122* -0.0750 -0.0619 -0.0682 -0.0856

(-1.59) (-2.16) (-1.78) (-1.67) (-1.75) (-1.17)

Constant 5.278*** 5.084*** 5.102*** 5.652*** 5.659*** 5.903***

(18.64) (11.10) (15.49) (20.47) (19.79) (11.79)

Observations 1176 1176 1176 1176 1176 1176

R-squared 0.201

Pseudo R-squared 0.125 0.110 0.109 0.128 0.139

AIC 2142.3

BIC 2243.7

Log likelihood -1051.1

Note: t-values in parentheses; *** 0.1% significance level, ** 1% significance level, * 5% significance level.

27

regression overestimates these two effects at the 10th and 25th quantile groups.

Consistent with the OLS results, the frequency of feedback information has no impact

on electricity consumption for all columns. The effect of the smart meter is also not

significant, except at the 10th quantile.

7. Conclusion

In order to identify and examine the effect of information feedback on residential

electricity consumption, the present paper applies a log-log electricity demand function

on unique micro-level Chinese household survey data from 2012. We found that price

and income elasticity are significantly negative and positive, respectively. Urban

households consume more electricity than do rural residents. Moreover, residential

electricity demand is also positively associated with family size, dwelling area,

householders’ years of schooling, and heating and cooling days.

Beyond these control variables, we also empirically check five hypotheses regarding

information feedback. Our results reveal three points. First, information feedback

matters. The households that obtain electricity consumption information through

interacting with meter readers have lower electricity demand. Second, the ex ante

information feedback, i.e., households that use prepaid metering systems, are associated

with less electricity demand. Third, consumers who receive explicit information

feedback, i.e., households that directly pay meter readers, use less electricity. However,

we do not find supportive evidence for information feedback frequency or for the smart

meter program.

Our study suggests that residential electricity demand is partly determined by

household characteristics and consumers’ behavior and attitudes. Moreover,

information feedback, i.e., the interaction with meter readers and the prepaid system,

do affect residential electricity consumption. These findings do not suggest that the old-

fashioned meter reader system is more efficient in promoting residential energy

conservation. Instead, it reveals that residential energy demand management is

seriously absent in more modern systems for energy payment; either appropriate

measures targeting the household sector are lacking or the energy saving programs that

do exist, such as smart meters, are not effective. In other words, information from

sources other than meter readers does not deliver adequate information or does not

transform information into knowledge or action. Considering that a vast literature has

demonstrated the great potential in developed countries to save electricity through

information feedback, decision makers in China should be aware of the importance of

managing residential electricity demand and should put more attention into policy

design in the future.

28

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