Willingness to accept energy-saving measures and adoption barriers in the residential sector: An empirical analysis in Beijing, China

Renewable and Sustainable Energy Reviews 95 (2018) 56–73

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Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser

Willingness to accept energy-saving measures and adoption barriers in the residential sector: An empirical analysis in Beijing, China

T

⁎

Jun-Jun Jiaa, Jin-Hua Xub, , Ying Fanc, Qiang Jib a

School of Economics, Hefei University of Technology, Hefei 230601, China Center for Energy and Environmental Policy Research, Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China c School of Economics & Management, Beihang University, Beijing 100191, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Residential sector Energy-saving measures Drivers Barriers Willingness to accept Preference heterogeneity

This paper investigates the public attitude toward and willingness to accept six energy-saving measures in the residential sector in Beijing, China. The qualitative results derived from questionnaires show that financial incentives and the desire to reduce energy consumption are the most significant factors driving the public to adopt the measures and that the unavailability of electric vehicle (EV) chargers is a crucial barrier to the promotion of EVs. The quantitative results indicate that socioeconomic variables have a greater impact on technical energysaving measures than behavioral ones. Renters or individuals with a high risk preference have a lower willingness to accept energy-efficient air conditioners. The price of gasoline, convenience of EV chargers, EV policy package, and environmental concern can significantly influence willingness to purchase an EV. With regard to behavioral energy-saving measures, control of the heating system and the influence of nearby people (spillover effect) significantly increase the preference for closing windows when the heating is on. The public transportation convenience, high gasoline prices and the congestion charge have significant effects on residents’ willingness to use public transportation. Infrastructure upgrades to heating and transportation would remove adoption barriers and facilitate energy conservation. Price policies and preferential policies related to EVs could have a significant impact on household choice of transportation mode. Finally, increasing the public energysaving awareness and environmental concern is recommended to facilitate the promotion of energy-saving options.

1. Introduction In 2015, China consumed 22.9% of global energy, equivalent to 3014.0 million tons of oil, and became the largest energy consumer in the world [1]. China's residential sector is the second-largest energy consumer after the industrial sector. Household energy use for daily life increased by an annual average rate of 5.95% in 1995–2014 while private transportation increased by 12.47% during 1997–2013, accounting for 11% and 4% of total energy consumption respectively [2,3]. Furthermore, residential energy consumption has enormous growth potential in China [4]. First, energy consumption is bound to increase with the improvement of residents’ quality of life; this has been spurred by rapid urbanization [5]. The deepening of industrialization will likely increase the amount and share of household energy use, as evidenced by the experience of industrialized economies. In the US in 1949–2013, total energy consumption increased from 744.4 to 2265.8

million tons of oil equivalent. Household energy use for daily life increased from 17.5% to 21.7%, transportation energy consumption rose from 25.0% to 27.7%, and industrial energy use decreased from 46.0% to 32.3% [6]. The surge in residents’ energy consumption has led to energy shortages, pollutant discharge, and the release of CO2; encouraging household energy conservation is an effective means of addressing these problems. China's residential sector exhibits tremendous energy-saving potential. It was estimated that the energy efficiency labeling system for refrigerators alone could save 588–1180 TWh of electricity, reduce CO2 by 629–1260 million tons, and decrease SOX by 4.00–8.04 million tons and NOX by 2.37–4.76 million tons from 2003 to 2023 in China [7]. Energy-efficiency technologies for passenger cars could reduce CO2 emissions by 2698.8 million tons from 2010 to 2030 [8]. Based on these figures, it is essential to promote energy-saving measures and implement energy-saving policies in the residential sector. An investigation of household willingness to accept typical

Abbreviations: EV, Electric vehicle; WTA, Willingness to accept; TWh, Terawatt hour ⁎ Corresponding author. E-mail address: [email protected] (J.-H. Xu). https://doi.org/10.1016/j.rser.2018.07.015 Received 1 September 2017; Received in revised form 2 July 2018; Accepted 6 July 2018 1364-0321/ © 2018 Elsevier Ltd. All rights reserved.

Renewable and Sustainable Energy Reviews 95 (2018) 56–73

J.-J. Jia et al.

public satisfaction, while energy-saving renovation had no influence on residents’ energy-saving behaviors. Yue et al. [29] studied household willingness to adopt energy-saving behaviors in Jiangsu Province and demonstrated that usage reduction behavior was adopted most frequently; socio-demographic variables had a significant impact on household energy-saving behaviors and situational factors had a positive moderating effect. Ding et al. [30] found that compared to rural residents, urban residents were more likely to participate in energysaving activities; the residents in central Jiangsu Province had the highest level of participation in energy-saving activities. Wang et al. [31] investigated rural areas and identified energy consumption levels and structures in eight typical counties of eight economic zones. This paper examines the adoption status quo of six typical energysaving measures and the determinants of household willingness to accept them in Beijing: 1) installing an energy-efficient heating system (replacing a heating radiator with floor heating), 2) purchasing an energy-efficient air conditioner, 3) closing the windows when the heating is on, 4) purchasing an energy-efficient fuel car, 5) purchasing an electric vehicle (EV), and 6) using public transportation. The reasons for selection are twofold. On the one hand, we choose the six measures based on the energy-saving measures classification in the literature [32,33], which classified them by home and transport measures from domain attribute, or by technical or behavioral measures from strategy attribute. On the other hand, these measures correspond to the main household energy consumption sources in Beijing, China. For example, indoor heating is a large part of household energy consumption, and electric vehicles are actively promoted by the Beijing government. The study proceeds as follows. First, firsthand data about household energy use are collected using a questionnaire. Using qualitative analysis, the status quo of the adoption of six household energy-saving measures, as well as the reasons for and barriers to their adoption, are examined. Second, policy factors are quantified to determine how relevant policies affect public preferences for energy-saving measures. Specifically, the underlying factors that influence household willingness to accept six energy-saving measures are constructed based on the questionnaire items. Using an ordinal logit model, the study quantitatively analyzes the determinants of household willingness to accept the measures. Third, combining qualitative and quantitative results, the paper examines the practicability and effectiveness of energy-saving policies in the residential sector in Beijing and provides a scientific basis for evaluating existing policies, formulating promising policies, and guiding household energy-saving behavior. The remainder of this paper is organized as follows: Section 2 introduces the data, Section 3 illustrates the model, Section 4 presents the qualitative survey results and quantitative empirical results, Section 5 discusses the findings, and Section 6 outlines the conclusions.

energy-saving measures, the drivers promoting these measures, and the barriers to adopting them will provide a scientific basis for this objective. Since the 1970s oil crisis, international scholars and policymakers have paid considerable attention to household energy consumption and relevant energy-saving policies. The research focuses on identifying and summarizing effective energy-saving measures, as well as analyzing the barriers to and drivers of promoting energy-saving technologies. Abrahamse et al. [9] concentrated on social and environmental psychology and reviewed the factors affecting household energy use. The results shows that commitment, goal setting, and providing frequent feedback can generate significant energy savings. Lindén et al. [10] conducted a survey of 600 households to identify energy-saving and non-energy-saving behaviors in Sweden and provided possible suggestions. Steg [11] categorized three strategies for energy conservation: the psychological strategy to alter individual knowledge, cognition, motivation, and norms; the structural strategy to change the decisionmaking setting; and the promotion strategy to encourage the use of energy-efficient appliances. Gyberg and Palm [12] summarized and classified suggestions and tips for improving household energy efficiency provided by energy companies and government agencies. In addition, based on an analysis of the electricity consumption of 124 households in Hangzhou, Ouyang and Hokao [13] found that 10% of electricity could be saved by adhering to energy-saving guidelines and suggestions. Costa and Kahn [14] concluded that it would be more effective to implement energy-saving measures in liberal communities because the amount of electricity saved in liberal households was two to three times more than that saved in conservative households. Moreover, a host of studies show energy conservation can be achieved by making energy use visible to residents through information and feedback [15–19]. Among studies focusing on promoting energy-efficient technologies conducted outside China, Jakobsson et al. [20] investigated public willingness to accept the congestion charge policy and found that income, perception of fairness, infringement on liberty, intention to reduce car usage, and expectation of reducing car usage among others were major determinants. Mills and Schleich [21] determined that in Germany, household characteristics had a minimal impact on the purchasing of A-class energy-efficient goods, but environmental taxes and an increase in the amount of information on energy efficiency labels allowed for easier promotion of energy-efficient appliances. In Arizona and California, Qiu et al. [22] showed the higher the risk preference of consumers, the less likely they were to buy energy-efficient appliances (with the exception of air conditioners). Moreover, the probability of moving out magnifies the negative impact of risk preference on energy efficiency improvement. In a study on Tunisian household preferences for adopting solar water heaters, energy-saving lamps, and energy-efficient refrigerators, Jridi et al. [23] demonstrated that electricity cost, energy-saving revenue, and the price of energy-efficient goods had a significant impact on the purchase of these goods. The results of Braun's [24] research on household choices of heating systems in Germany revealed that residence characteristics had a significant impact, while income had a minimal impact. Gamtessa [25] analyzed household decisions to participate in energy-saving retrofits in Canada and found that energy cost savings, economic incentives, and retrofit costs were significant factors influencing the decision. As for China, only recently have similar studies on residential energy conservation gained popularity. Shi et al. [26] revealed that the use of biofuel produced by garden waste biomass accounted for 20.7% of urban residential electricity consumption in 2008. Du et al. [27] analyzed the barriers to promoting building energy-saving technologies and showed that unprofitability constituted the largest barrier; major companies face operation barriers, while small companies face barriers related to policies, regulations, and information asymmetry. Liu et al. [28] reviewed three building retrofit programs in Beijing and found that household degree of participation had a significant impact on

2. The data 2.1. The survey data The data used in the paper were collected via questionnaires. A survey on household energy consumption, based on the Sino-China Joint Scientific Thematic Research Programme “Energy Efficiency of Households in Cities: A Multi-method Analysis,” was designed by a research team comprised of Chinese and Netherlandish scholars and the survey was performed by a professional survey company in April–June 2016. The study population, approximately 7,500,000, is composed of households in urban Beijing. The survey comprised eight parts: (1) socioeconomic variables, (2) residence characteristics and the availability of public transportation services, (3) household features, (4) car ownership and service condition, (5) knowledge of and response to transportation policies, (6) environmental concern, (7) willingness to accept six energy-saving measures, and (8) adoption status quo of commonly used technical and behavioral energy-saving measures. Based on the data from part 8, this paper qualitatively examines the 57

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Table 1 Constructed variables and descriptive statistics. No.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

26 27 28 29 30 31 32

Variable Socio-economic variables Sex Age Educational levela Having a job or not Income levela Residence characteristics variables Residence typea Building yeara Years of moving in Owned or rented Heating controlled by yourself or not Household features variables Family size With elders aged above 60 or not With children aged below 6 or not Car ownership variables Having a car or not Transportation infrastructure variables Satisfaction degree for public transportation nearby Satisfaction degree for traffic jam at peak hours Satisfaction degree for electric vehicle (EV) charger Transportation policies variables Concern level for gasoline price Concern level for congestion charge Concern level for public transportation price Impact level of EV policies Behavioral variables Concern level for the distance to work Willingness to reduce car usage Risk preferencea Spillover effect Spillover effect (Installing an energy-efficient heating system [replacing a heating radiator with floor heating]) Spillover effect (Purchasing an energy-efficient air conditioner) Spillover effect (Closing the windows when the heating is on) Spillover effect (Purchasing an energy-efficient fuel car) Spillover effect (Purchasing an EV) Spillover effect (Using public transport) Environmental concern Willingness-to-accept of energy-saving measures Installing an energy-efficient heating system (replacing a heating radiator with floor heating) Purchasing an energy-efficient air conditioner Closing the windows when the heating is on Purchasing an energy-efficient fuel car Purchasing an EV Using public transport

Min

Max

Mean

SD

0 21

1 61

0.534 31.7

0.500 8.17

0

1

0.930

0.251

0 0 0

42 1 1

6.82 0.736 0.617

6.61 0.441 0.487

1 0 0

10 1 1

3.52 0.486 0.441

1.29 0.500 0.497

0

1

0.823

0.382

1 1 1

5 5 5

2.82 2.74 2.32

0.833 1.14 1.03

1 1 3 6

3 3 6 30

1.71 2.19 4.04 24.1

1.11 1.14 0.692 3.77

2 0

5 10

3.54 6.46

1.24 3.16

0

1

0.424

0.495

0 0 0 0 0 23

1 1 1 1 1 59

0.424 0.450 0.428 0.412 0.392 46.4

0.495 0.498 0.496 0.493 0.489 6.23

1

5

3.78

0.824

1 2 1 1 2

5 5 5 5 5

3.88 4.42 3.69 3.52 4.16

0.699 0.753 0.809 0.876 0.696

Note: a There are three levels or more for educational level, income level, residence type, building year and risk preference. 0–1 variables are used to represent different levels of those variables and thus, there are no descriptive statistics for those five variables.

infrastructure variables, (6) transportation policies variables, and (7) behavioral variables. For each variable, Table A1 in the Appendix presents the name, questions, and how it is constructed. Table 1 provides descriptive statistics for all variables.

adoption status quo of six energy-saving measures and the reasons for and barriers to their adoption. Based on parts 1–7, we construct the underlying factors affecting household willingness to accept six energysaving measures and quantitatively analyze household preferences for energy-saving measures. We randomly selected 569 sample households and, after eliminating incomplete datasets and data with obvious logical errors, use 352 samples in the qualitative analysis and 311 samples in the quantitative analysis. The response rates are 61.9% and 54.7%, respectively. The study uses SPSS software for qualitative analysis of household energy-saving measures.

3. The model In the survey, respondents indicate their willingness to accept six energy-saving measures on a 5-point Likert scale, ranging from 1 (“totally unacceptable”) to 5 (“totally acceptable”). Empirically, discrete choice models are appropriate for analyzing the determinants of discontinuous dependent variables. When confronted with discontinuous dependent variables having numerous categories, multinomial logit models and ordinal logit models are commonly used. To fully consider the feature that the willingness to accept energy-saving measures in the questionnaire is presented in the form of ordinal numbers, the ordinal logit model is adopted to study the household

2.2. Construction of variables The variables that could affect household willingness to accept energy-saving measures are categorized into seven groups: (1) socioeconomic variables, (2) residence characteristics variables, (3) household features variables, (4) car ownership variables, (5) transportation 58

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According to McFadden [34,35], taking the first-order derivative for Eq. (5), we get the following:

preferences for the six energy-saving measures. The model is as follows:

yi =

⎧ 0 if yi* < k1 ⎪ j if kj ≤ yi* < kj + 1 ⎨ ⎪ J if yi* ≥ kJ ⎩

dLnL (β , k1, …, kJ ) =0 d (β , k1, …, kJ )

(1)

yi* = Xi β + εi

Then, the threshold values k2, …, k5 and the coefficients vector β can be estimated.

(2)

where yi indicates the willingness of respondent i to accept the measure. j = 1, …, 5; J = 6 . k1 and kJ are defined as − ∞ and + ∞, respectively, and k2, ... ,k5 are threshold values to be estimated. yi* is a latent variable. Xi is the characteristic vector of the respondent i , including seven categories of underlying determinants: (1) socioeconomic variables, (2) residence characteristics variables, (3) household features variables, (4) car ownership variables, (5) transportation infrastructure variables, (6) transportation policies variables, and (7) behavioral variables. β is the coefficient vector to be estimated. Each element of β corresponds to the coefficient of each explanation variable in Xi . The greater the value of the element in β , the higher the probability that the respondent accepts energy-saving measures with the increase of corresponding explanation variable in Xi . Assuming the distribution function of εi is the logistic distribution function F (.) to characterize the feature that the sampling results of willingness to accept energy-saving measures are distributed relatively uniformly on the 1–5 scale, the specific probability of the willingness of respondent i to accept a given energy-saving measure is as follows:

4. The results 4.1. Qualitative results 4.1.1. Average willingness to accept six energy-saving measures Overall, residents have a relatively high willingness to accept energy-saving measures; the willingness exceeds 3.5 for all six measures, as illustrated in Fig. 1. The willingness to close the windows when the heating is on and the willingness to use public transport are highest at 4.42 and 4.16, respectively, followed by the willingness to install an energy-efficient heating system (3.78) and to purchase an energy-efficient air conditioner (3.88). Purchasing an energy-efficient fuel car and purchasing an EV have the lowest figures at 3.69 and 3.52, respectively. 4.1.2. Technical energy-saving measures Fig. 2 presents the adoption status quo of four technical energysaving measures—installing an energy-efficient heating system, purchasing an energy-efficient air conditioner, purchasing an energy-efficient fuel car, and purchasing an EV—and the most significant reasons for and barriers to adopting these measures. The detailed sample information is presented in Tables A2–A4 in the Appendix. Home energy-saving measures (installing an energy-efficient heating system and purchasing an energy-efficient air conditioner) are more favored than transport-related energy-saving measures (purchasing an energy-efficient fuel car and purchasing an EV). The results show installing an energy-efficient heating system or energy-efficient air conditioner is popular; 73.8% of households have done so in the past. In contrast, approximately half the households have purchased an energy-efficient fuel car (55.0%) or an EV (51.9%). Among households that have not adopted these technical measures, 63.2%, 60.9%, and 49.7% of respondents would consider adopting the above three measures respectively (those who chose “Yes, I would like to consider and probably will adopt” or “Yes, I would like to consider but probably will not adopt”). Financial factors and energy and environmental concerns are the most important determinants for adoption, corresponding to three common reasons: “It saves money,” “It reduces energy consumption,” and “It helps reduce global warming and avoid a negative environmental impact.” In addition, consideration of comfort, moral factors, and the influence of others are also significant reasons for adopting

1 P (yi = 0) = P (yi* kJ ⎟⎞ = 1 − 1 exp( kJ + Xi β ) + − ⎠

(3)

For N samples of respondents, the likelihood function of an energysaving measure having the willingness to accept (WTA) of j is ⎛ ⎞ L ⎜β , k1, …, kJ ⎟ = ⎝

⎠

N

J

∏ ∏ P (yi = j) yij i=1 j=1 N

=

J

1 1 ⎞ − ⎟ 1 + exp( −kj + Xi β ) ⎠ ⎝ 1 + exp( −kj + 1 + Xi β )

∏ ∏ ⎛⎜ i=1 j=1

yij

(4),

where yij = 1 when the WTA of respondent i is j and 0 otherwise. Taking the log form of the likelihood function, we get the following:

⎝

⎠

N

J

1

∑ ∑ yij ⎜⎛ 1 + exp(−k i=1 j=1

j+1

⎝

+ Xi β )

−

1 ⎞ ⎟ 1 + exp( −kj + Xi β ) ⎠

(5)

4.42, Closing the windows when the heating is on Energy-saving measures

⎛ ⎞ LnL ⎜β , k1, …, kJ ⎟ =

4.16, Using public transport 3.88, Purchasing an energy-efficient air conditioner 3.78, Installing an energy-efficient heating system (replacing a heating radiator with floor heating) 3.69, Purchasing an energy-efficient fuel car 3.52, Purchasing an electric vehicle (EV) 3.0

3.5

(6)

4.0

4.5

5.0

Average willingness-to-accept Fig. 1. Average willingness-to-accept of six energy-saving measures. 59

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J.-J. Jia et al.

80

V VI VII

9.2

I

100

27.6

II

80

60 40

36.8

VIII

73.8

20 26.4

III

Percentage(%)

1.4 6.3 18.5

Percentage(%)

100

60

12.5

V

16.0

VI

40 16.7

VII

22.9

VIII

20

IV

0

10.9

I

12.0

II

14.7

III

18.5

IV

0

Have adopted or not

Will adopt or not

The barriers

The reasons

I

It reduces energy consumption.

No, I would not like to consider.

II

It helps reduce global warming and avoid negative environmental impact.

III

Yes, I would like to consider but probably will not adopt.

III

It saves money.

IV

Yes, I would like to consider and probably will adopt.

IV

It improves my comfort and living conditions.

V

I can not decide.

V

I am too busy.

VI

I do not know.

VI

It takes too much effort.

VII

No

VII

I cannot afford the investment cost.

VIII

Yes

VIII

I do not know how.

I

I do not know.

II

Purchasing an energy-efficient fuel car 3.1 6.0

V VI

8.0

I

100

35.9

VII

31.4

II

80

Percentage(%)

80 60 40

44.1

VIII

III

55.0

Percentage(%)

100

20

60 40

9.7 10.1

V

18.1

VII

23.6

VIII

VI

20 16.5

IV

0

9.8

I

12.2

II

17.1

III

18.0

IV

0

Have adopted or not I

I do not know.

Will adopt or not

The barriers

The reasons

I

Moral and social responsibility

II

No, I would not like to consider.

II

It reduces energy consumption.

III

Yes, I would like to consider but probably will not adopt.

III

It helps reduce global warming and avoid negative environmental impact.

IV

Yes, I would like to consider and probably will adopt.

IV

It saves money.

V

I can not decide.

V

It is not powerful enough.

VI

I do not know.

VI

I do not have a permission to buy a fuel car.

VII

No

VII

I do not need a car.

VIII

Yes

VIII

It is too expensive.

Purchasing an electric vehicle (EV) 2.8 4.6

V VI

15.7

I

VII

34.6

II

80 40.7

60 40 20

51.9

30.8

III

18.9

IV

VIII

100

Percentage(%)

Percentage(%)

100

0

80 60 40 20

12.3

V

12.3

VI

12.6

VII

14.9

VIII

0

Have adopted or not

Will adopt or not

The barriers

11.7

I

15.9

II

18.4

III

19.1

IV

The reasons

I

I do not know.

I

People I care about are doing it.

II

No, I would not like to consider.

II

It reduces energy consumption.

III

Yes, I would like to consider but probably will not adopt.

III

It saves money.

IV

Yes, I would like to consider and probably will adopt.

IV

It helps reduce global warming and avoid negative environmental impact.

V

I can not decide.

V

It is not powerful enough.

VI

I do not know.

VI

I do not need a car.

VII

No

VII

I am worried about the availability of EV charger.

VIII

Yes

VIII

It is too expensive.

Reasons and barriers

Adoption status quo

Fig. 2. Adoption status quo, reasons and barriers for four technical energy-saving measures.

60

Renewable and Sustainable Energy Reviews 95 (2018) 56–73

J.-J. Jia et al.

Percentage (%)

80 60

3.7 4.8 15.1

V IV III

28.5

II

2.8

VIII

100

30.2

VII

80

40

Percentage (%)

100

VI 67.0 I

47.9

20

60

II

12.2

V

17.3

VI

24.8

III

18.8

VII VIII

30.6

IV

19.8

20 0

Adoption frequency for now I

I

19.0

40

0

10.0

Adoption frequency in the future

The barriers

Always

I

Moral and social responsibility It saves money.

The reasons

II

Often

II

III

Occasionally

III

It helps reduce global warming and avoid negative environmental impact.

IV

Seldom

IV

It reduces energy consumption.

V

Never

V

I do not think it is important.

VI

Yes, more frequently than now.

VI

It is hard to remember.

VII

About the same as now.

VII

It takes too much effort.

VIII

No, less frequently than now.

VIII

It is not convenient.

Using public transport

III

22.2

80

Percentage (%)

V IV

2.3 5.7

3.4 23.1

VIII

100

VII

80

60 II

37.0

40

73.5

20

32.8

VI

Percentage (%)

100

60 40

8.5 9.5 10.1

V VI VII

27.1

VIII

5.5

I

22.3

II

23.8

III

24.9

IV

20

I

0

0

Adoption frequency Adoption frequency for now in the future

The barriers

The reasons

I

Always

I

Moral and social responsibility

II

Often

II

It saves money.

III

Occasionally

III

It helps reduce global warming and avoid negative environmental impact.

IV

Seldom

IV

It reduces energy consumption.

V

Never

V

I do not think it is important.

VI

Yes, more frequently than now.

VI

I am too busy.

VII

About the same as now.

VII

It takes too much effort.

VIII

No, less frequently than now.

VIII

It is not convenient.

Adoption status quo

Reasons and barriers

Fig. 3. Adoption status quo, reasons and barriers for two behavioral energy-saving measures.

these measures, with “It improves my comfort and living conditions,” “Moral and social responsibility,” and “People I care about are doing it” corresponding to installing an energy-efficient heating system or purchasing an energy-efficient air conditioner, purchasing an energy-efficient fuel car, and purchasing an EV, respectively. In addition to the common financial barriers (“It is too expensive” or “I cannot afford the investment cost”), lack of knowledge and time are major barriers to promoting home technical measures, while the physical performance of the vehicle and policy restraints on buying fuel cars are the main barriers to the promotion of technical transportation measures. “I do not know how,” “It takes too much effort,” and “I am too busy” are three barriers to installing an energy-efficient heating system or purchasing an energy-efficient air conditioner. Meanwhile, “I do not need a car,” “It is not powerful enough,” and “I do not have permission to buy a fuel car” are often mentioned as reasons to not purchase an energy-efficient fuel car. “I am worried about the availability of an EV charger” is a significant barrier to purchasing an EV.

4.1.3. Behavioral energy-saving measures Fig. 3 presents the adoption status quo of two behavioral energysaving measures (closing the windows when the heating is on and using public transport) and the four most important reasons for and barriers to adopting these measures. The detailed sample information is presented in Tables A5 and A6 in the Appendix. Behavioral energy-saving measures are popular among the public, and there are relatively consistent reasons for and barriers to adopting the two measures. Closing the windows when the heating is on and using public transportation have a relatively high popularity, with over 90% of households adopting both measures (for those who chose “Always,” “Often,” or “Occasionally,” the percentages are 47.9% + 28.5% + 15.1% and 32.8% + 37.0% + 22.2%, respectively). In addition, approximately 70% of residents stated they would adopt the measures more frequently in the following year. Moral factors, financial considerations, and energy and environmental concerns are the most important determinants for adoption.

61

0.278 0.005 0.168 − 0.189 0.168 0.007 − 0.042

Satisfaction degree for public transportation nearby Satisfaction degree for traffic congestion at peak hours Having a car or not (Yes=1) Having a car* Concern level for gasoline price Having a car* Concern level for congestion charge Having a job* Concern level for the distance to work Having no car* Satisfaction degree for EV charger Having no car* Impact level of EV policies Satisfaction degree for EV charger Impact level of EV policies Having a car* Willingness to reduce car usage Concern level for public transportation price

− 0.777***

0.289

− 0.949**

− 0.852** − 0.103 0.243 − 0.063 0.329 0.681*** − 0.012

0.016

1.443 0.935* 1.212** 0.762 − 0.184 − 0.007

− 0.110

0.477 0.255 0.592 0.399 − 0.088 0.016

0.516

62 0.047 0.124 − 0.264

0.017 0.046 − 0.170

Family size With elders aged above 60 or not (Yes=1) With children aged below 6 or not (Yes= 1) Heating controlled by yourself or not (Yes= 1) Risk preference (Base=low) Median High − 0.049 0.716

− 0.056 − 1.958**

0.614**

− 0.060

Owned or rented (Owned=1)

0.004

3.784** 0.007

− 0.202 0.001

After 2010 Years of moving in

0.843***

0.007

0.219

− 0.200 0.008

0.713

0.877*

0.211

2000–2010

0.369

0.733

1990s

1.003*

− 0.019

Building year (Base=before the 1980s) 1980s 1.046*

− 0.246

− 0.544

1.103**

Detached house

1.016*

− 0.211 − 0.055

0.179 − 0.244

0.580 1.137

**

− 0.035*

− 0.302*

0.087

− 0.063

− 0.028 0.155*

0.064

− 0.440**

0.358

− 0.662**

− 0.699**

0.051*

− 0.053

− 0.232*

− 0.241 0.256**

0.199**

1.497 0.977** 1.341*** 0.819 0.045 0.042** − 0.116

***

− 0.528

− 0.538*

− 0.106

0.045*

0.102*

0.153*

− 0.156 0.219**

− 0.123*

0.118 − 0.035 − 0.098 − 0.161 − 0.141 0.014 0.147*

0.191

− 0.074

− 0.286* − 0.020*

Using public transport

(continued on next page)

− 0.282 − 0.009

− 0.157 − 0.020**

0.156 0.027

0.542* 0.040**

0.200 0.008

High-rise apartment terraced house

Sex (Male=1) Age Educational level (Base=high school or below) junior college or undergraduate postgraduate or above Income level (Base=less than 20 k) 20–50 k 50–100 k 100–200 k more than 200 k Spillover effect Environmental concern Residence type (Base=bungalow) Multi-storey apartment

Purchasing an electric vehicle (EV)

Purchasing an energy-efficient fuel car

Closing the windows when the heating is on

Installing an energy-efficient heating system (replacing a heating radiator with floor heating)

Purchasing an energyefficient air conditioner

Table 2 The empirical results of determinants that affect the willingness-to-accept of six energy-saving measures.

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Renewable and Sustainable Energy Reviews 95 (2018) 56–73

Renewable and Sustainable Energy Reviews 95 (2018) 56–73

− 372.6 − 389.0 32.81** 0.100 − 353.1 − 365.4 24.67* 0.076

Underlying determinants affect the willingness to accept each of the six energy-saving measures. Based on the variables in Table 1, the willingness to accept each energy-saving measure was regressed against the underlying variables and their interaction terms. The empirical results are presented in Table 2. The chi-square statistics of likelihood ratio tests are significant under 0.1, with the exception of one measure, installing an energy-efficient heating system, indicating that the underlying variables are able to well explain the willingness to accept the other five energy-saving measures. The six energy-saving measures can be categorized into home energy-saving measures (installing an energy-efficient heating system, purchasing an energy-efficient air conditioner, and closing the windows when the heating is on) and transportation energy-saving measures (purchasing an energy-efficient fuel car, purchasing an EV, and using public transportation). The determinants that significantly affect the willingness to accept the three home energy-saving measures and three transportation energy-saving measures are illustrated in Fig. 4 and 5, respectively. The meaning of the coefficients in Table 2 and Figs. 4 and 5 are discussed in the following subsections. 4.2.1. Installing an energy-efficient heating system Only education level and residence type have a significant impact on the willingness to accept the installation of an energy-efficient heating system. Individuals with a higher education level demonstrate a lower willingness to accept this measure (junior college or undergraduate [− 0.699] and postgraduate or above [− 0.852]) compared with residents with an education level of high school or below. In addition, living in a detached house is the only factor that has a significant positive impact on the installation of an energy-efficient heating system. Income level has no significant impact.

Note: *; **; and *** indicate the significance level of 10%, 5% and 1% respectively. a It represents the value of Log likelihood when all regressors except the intercept are restricted to zero. b Likelihood ratio test for null hypothesis that all coefficients across the model are simultaneously zero. c It refers to Cox and Snell's Pseudo R-Square.

− 287.6 − 303.7 32.06* 0.098 − 287.2 − 309.2 44.03** 0.132 Log likelihood Log likelihood_restra Chi-Squareb Pseudo R-Squarec

− 349.8 − 360.4 21.28 0.066

Closing the windows when the heating is on

4.2. Empirical results

Purchasing an energyefficient air conditioner Installing an energy-efficient heating system (replacing a heating radiator with floor heating)

Table 2 (continued)

Consideration of convenience and lack of time and attention are the primary barriers to adoption, specifically, “It is not convenient,” “It takes too much effort,” and “I do not think it is important.” Moreover, “It is hard to remember” and “I am too busy” are significant barriers to closing the windows when the heating is on and using public transportation, respectively.

− 305.3 − 314.6 27.73* 0.085

Purchasing an electric vehicle (EV) Purchasing an energy-efficient fuel car

Using public transport

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4.2.2. Purchasing an energy-efficient air conditioner Socioeconomic variables, house characteristics, and attitude toward risk are the main factors affecting willingness to accept the purchase of an energy-efficient air conditioner. Male residents have a higher willingness to accept, as do older people. The higher the education level (junior college or undergraduate, or postgraduate or above), the lower an individual's willingness to accept is. Similarly, among households with an annual income ranging from 20 k to 200k yuan, the higher the income level, the higher the residents’ willingness to accept. A significant increase in the willingness to accept is found in people living in residences built after the 1980s, compared with those living in residences built earlier. Furthermore, people living in residences built after 2010 have the highest willingness to accept (3.784). Compared to people who rent, residents who own their houses have a higher willingness to accept this measure (0.614). Finally, a significant decline in the willingness to accept is seen in residents with a high risk preference (− 1.958). 4.2.3. Closing the windows when the heating is on The behavior of neighbors and the characteristics of house functionality have a significant impact on the willingness to accept closing the windows when the heating is on. Energy-saving behaviors of nearby people significantly enhance household willingness to accept this behavioral energy-saving measure (spillover effect, 0.681). Compared to people who are unable to adjust their heating system, residents with control of their systems have a higher willingness to adopt this behavior (0.843). 63

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4.00

XIII

3.50 3.00 2.50

Coefficients

2.00 V

1.50

VII

IX

X XI

VI

1.00

X

XII

I

0.50

II III IV

III IV

0.00

XV

VIII

XIV XVI

-0.50 -1.00

Installing an energy-efficient heating system Purchasing an energy-efficient air conditioner (replacing a heating radiator with floor heating)

Closing the windows when the heating is on

-1.50 -2.00

Sex (male=1)

II

Age

III

Educational level (junior college or undergraduate)

IV

Educational level (postgraduate or above)

VI

Income level (50-100k)

I

V

Income level (20-50k)

VII

Income level (100-200k)

IX

Residence type (detached house)

X

Building year (1980s)

XI

Building year (1990s)

XII

Building year (2000-2010)

XIII

Building year (after 2010)

XIV

Owned or rented (owned=1)

XV

Heating controlled by yourself or not (yes=1)

XVI

Risk preference (high)

VIII

Spillover effect

Fig. 4. Determinants of home energy-saving measures.

concern over the price of gasoline is, the higher the household willingness to accept the measure. For households that do not own a car, the higher people's satisfaction with EV charger availability and people's recognition of the EV policy package are, the lower their willingness to purchase an energy-efficient fuel car (− 0.302 and − 0.035).

4.2.4. Purchasing an energy-efficient fuel car Sociodemographic variables, concern about the price of gasoline, the physical characteristics of EVs, and preferential policies favoring the purchase of EVs are the main factors that influence the decision to purchase an energy-efficient fuel car. As people age, the willingness to accept this measure decreases, while an increase in education also negatively affects adoption. For households that own a car, the higher the 2.00 V

1.50

VII VI

Coefficients

1.00

0.50 X XI

XI XIVXV

II III IV

0.00

-0.50

Purchasing an energy-efficient fuel car

VIII

III

XII

XVI

IX I II

Purchasing an electric vehicle (EV)

X

XI XIIXIII

XVII

Using public transport

-1.00

Sex (male=1)

II

Age

III

Educational level (junior college or undergraduate)

IV

Educational level (postgraduate or above)

V

Income level (20-50k)

VI

Income level (50-100k)

VIII

Environmental concern

I

VII

Income level (100-200k)

IX

Satisfaction degree for public transportation nearby

X

Satisfaction degree for traffic congestion at peak hours

XI

Having a car* Concern level for gasoline price

XII

Having a car* Concern level for congestion charge

XIII

Having a job* Concern level for the distance to work

XIV

Having no car* Satisfaction degree for EV charger

XV

Having no car* Impact level of EV policies

XVI

Impact level of EV policies

XVII

Having a car* Willingness to reduce car usage Fig. 5. Determinants of transportation energy-saving measures.

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II

Sex(Male=1) V

Age I II

Junior college or undergraduate (Base=high school or below)

Postgraduate or above (Base=high school or below)

II

III V

IV II

III

I

Installing an energy-efficient heating system (replacing a heating radiator with floor heating)

II

Purchasing an energy-efficient air conditioner

III

Purchasing an energy-efficient fuel car

IV

Purchasing an electric vehicle (EV)

V

Using public transport

I III

II IV

20-50k(Base=less than 20k)

II IV

50-100k(Base=less than 20k)

II

100-200k(Base=less than 20k)

IV -1.5

-1

-0.5

0

0.5

1

1.5

Coefficient Fig. 6. The heterogeneity in the willingness-to-accept of energy-saving measures in terms of socioeconomic variables.

5. Discussion

4.2.5. Purchasing an electric vehicle Socioeconomic variables, public concern for the environment, the congestion charge, and preferential policies have a significant impact on household willingness to accept the purchase of an EV. Households with a median income of 20–200k yuan have a higher willingness to accept than households with an annual income of less than 20k yuan (1.497, 0.977, and 1.341). People with a higher concern for the environment are more willing to accept this measure (0.042). The more satisfied with traffic congestion at peak hours residents are, the higher their willingness to purchase an EV is (0.199). For households that own a car, the higher the residents’ level of concern about the price of gasoline is, the higher their willingness to accept (0.256); however, the higher their level of concern about the congestion charge is, the lower their willingness to accept (− 0.232). In addition, the greater the level of recognition of EV policies, the higher an individual's willingness to purchase an EV (0.051).

5.1. Heterogeneity of the willingness to accept The heterogeneity of the willingness to accept energy-saving measures among residents with different socioeconomic characteristics is shown in Fig. 6. Men have a higher willingness than women to use an energy-efficient air conditioner but a lower willingness to use public transport. As individuals age, willingness to purchase an energy-efficient air conditioner increases while their willingness to accept energysaving transportation measures (purchasing an energy-efficient fuel car and using public transport) decreases. The higher the education level, the lower an individual's willingness to accept energy-saving measures aimed at improving energy efficiency (installing an energy-efficient heating system, purchasing an energy-efficient air conditioner, purchasing an energy-efficient fuel car, and purchasing an EV). Of those with an annual income from 20 k to 200 k yuan, the higher the income level is, the higher the willingness to purchase an energy-efficient air conditioner and an EV. Considering the heterogeneity of the willingness of residents to accept energy-saving measures, creating and implementing differentiated energy-saving policies for various resident groups would result in a duplication of efforts.

4.2.6. Using public transport Sociodemographic variables, traffic conditions, the price of gasoline, and the convenience of traveling to work constitute the main factors affecting people's willingness to accept the use of public transportation. Men are less prone to accept the use of public transport (− 0.286), while the willingness to accept decreases as people get older. The higher residents’ degree of satisfaction with nearby public transportation, the higher the willingness to use it (0.147). The higher the degree of satisfaction with traffic congestion at peak hours is, the lower the willingness to accept (− 0.123). For households that own a car, the higher the residents’ level of concern regarding the price of gasoline and the congestion charge, and the higher their willingness to reduce car usage is, the higher their willingness to use public transport (0.219, 0.153, and 0.045). For those who are employed, the higher the level of concern regarding the distance to work, the higher their willingness to use public transport (0.102).

5.2. Determinants involving policies With regard to home energy-saving measures (installing an energyefficient heating system, purchasing an energy-efficient air conditioner, and closing the windows when the heating is on), only residence type (detached house) has a significant impact on the willingness to install floor heating; a significant increase in the willingness to purchase an energy-efficient air conditioner and to close the windows when the heating is on are found in those living in newly built residences. In addition, house ownership and risk preference have a significant impact on the willingness to purchase an energy-efficient air conditioner, while

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efforts of households in the same building to retrofit their heating systems and fostering the habit of using heat meters. This would generate considerable energy-saving revenue that could encourage the adoption of energy-efficient heating systems [28].

whether the heating system can be controlled by the residents themselves affects their decision to close the windows when the heating is on. In relation to transportation energy-saving measures (purchasing an energy-efficient fuel car, purchasing an EV, and using public transport), the higher the residents’ satisfaction with nearby public transportation and the lower their satisfaction with traffic congestion is, the higher the willingness to use public transportation and the lower the willingness to purchase an EV. For households that own a car, the higher the level of concern about the price of gasoline and the congestion charge is, the higher the willingness to use public transport. Furthermore, people who care more about the price of gasoline are more willing to purchase an energy-efficient fuel car or an EV, while those who care more about the congestion charge are less willing to purchase an EV. For congestion charge, households that do not own a car, the availability of an EV charger and the EV policy package can significantly augment willingness to purchase an EV and significantly decrease willingness to purchase a fuel car.

5.3.2. Promotion of energy-efficient appliances Price subsidies decrease the initial investment cost of energy-efficient appliances and lower the investment risk of moving out, thereby facilitating the diffusion of energy-efficient technologies. Approximately 7% of household energy consumption can be attributed to home appliances. The average willingness to accept purchasing an energy-efficient air conditioner, one type of home appliance, is low at 3.88 (see Table 1). Among residents who rent houses and who have a high risk preference, willingness significantly declines (see Table 2). One reason is because house renters must assume the initial investment cost, which is unlikely to be recouped due to the high probability of moving out in the near future. The house owners or subsequent renters would then enjoy the energy-saving revenue from the energy-efficient air conditioner installed by those tenants. Those with a high risk preference would prefer a lower initial investment cost to maintain the same future energy-saving revenue. Respondents mentioned, “It is too expensive” or “I cannot afford the investment cost” when asked about purchasing an energy-efficient air conditioner, which indicates the high purchase price is a significant barrier to promoting energy-efficient technologies. China has established minimum energy efficiency standards and an energy efficiency labeling system for most appliances. The minimum energy efficiency standard specifies that appliances must meet the threshold of energy efficiency, and energy efficiency labels indicating energy consumption should be posted on appliances to address consumers’ information asymmetry. Between June 2012 and May 2013 in Beijing, air conditioners and refrigerators with an energy efficiency level of II or above were promoted by indirect subsidy. As opposed to policies regulating suppliers, price subsidies targeting consumers could alleviate or eliminate the barriers to promoting energy-efficient appliances. First, they shorten the payback period, boosting the likelihood of renters purchasing energy-efficient air conditioners. Second, if future energy-saving revenues remain unchanged, a price decline would mean that the rate of return in the payback period would rise, which would increase the probability that residents with a high risk preference purchase energy-efficient air conditioners. Moreover, households with a median income level (20–200k yuan) and residents living in newly constructed buildings have a higher willingness to accept energy-efficient air conditioners. Consequently, a subsidy policy promoting energy-efficient policies could be directed toward these groups.

5.3. Energy-saving potential and reduction of barriers to adoption Combining the qualitative and quantitative results in Section 4, this section discusses the factors of promoting household energy conservation—in terms of central heating, the popularization of energyefficient appliances, the promotion of energy-efficient fuel cars and EVs, and the extensive use of public transport—the barriers to their adoption, and their corresponding influence mechanisms, in order to establish a solid scientific foundation for policymakers to increase residents’ willingness to conserve energy. 5.3.1. Retrofit of heating system It is essential to retrofit heating systems and extend the use of heat metering to charge heating fees to residents. The residential sector consumes approximately half of the total energy used for indoor heading [36]. Not only is the majority of China's existing heating infrastructure energy-inefficient [37], but around a quarter of heating is wasted because the windows are open when the heating is on [38]. In Beijing, central heating is the main form of heating, and radiators are the primary way heat is transferred. Replacing radiators with floor heating could result in energy savings of 30%. However, the results of this study show residents have a low willingness to install such an energy-efficient heating system (3.78; see Table 1) and that only residents living in detached houses have a significant positive preference for this measure (1.103; see Table 2). There are two barriers to promoting energy-efficient heating systems. Conventionally, residents are charged a yearly heating fee based on floor space, leading to a discrepancy between the fee and the amount of heat used. Although heat meters are available for most buildings, in 2015 only 15.8% of residential heating areas in Beijing had been charged a fee based on metering. As a result, residents lack the financial incentive to improve their existing heating system. Moreover, in multistory apartments, individual indoor heating systems comprise only a portion of the heating system of the building. It is unlikely these individual systems can be retrofitted unilaterally without the residents reaching a consensus with other households in the building. This is a likely reason why residents report they “do not know how” when they consider retrofitting their heating systems (see Fig. 2 and Table A2 in the Appendix). The retrofitting of heat meters and the reform of the heat-charging mechanism would help ensure energy-efficient buildings save energy [39]. In addition to promoting charging fees based on heat metering, the government should play a crucial role in coordinating the

5.3.3. The price of gasoline and the congestion charge Transport price policies could effectively influence household mode of transportation and preference for purchasing cars. Around 30% of household energy consumption is used for private transportation [36]. In terms of adjusting private traffic energy consumption, the gasoline price policy (such as fuel tax) not only increases residents’ willingness to accept the use of public transport but also their preference for energy-efficient fuel cars and EVs (see Table 2). The congestion charge policy requires road users to pay certain fees in regulated areas at peak hours. The policy is likely to be implemented in the near future, and the expected fee is 20–50 yuan per day. The quantitative results show that the congestion charge policy has a significant regulatory effect on purchasing cars and the choice of transportation mode, not only encouraging residents to use public transportation (0.153) but also decreasing their willingness to purchase private cars (− 0.232). The results provide an empirical foundation for

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have little time to consider improving the efficiency of energy-consuming products. In addition to education and income levels, renters or those with a high risk preference are less willing to accept energy-efficient air conditioners, compared to homeowners or those with a lower risk preference. Environmental concern only has a significant effect on the purchase of EVs. The price of gasoline, concern about EV chargers and EV-favoring policies have a regulatory impact on the choice between fuel cars and EVs. People who care more about the price of gasoline and are more satisfied with the convenience of EV chargers and the EV policy package are more likely to purchase EVs and to have a lower preference for fuel cars. Regarding behavioral energy-saving measures, the practicability of controlling a heating system and the behavior of nearby people significantly increase the willingness to close the windows when the heating is on. Public transport is more favored by individuals who are content with public transportation infrastructure and are more concerned about the price of gasoline and the congestion charge. Overall, financial factors (saving money) and a reduction of energy consumption are the primary reasons residents adopt energy-saving measures. The unavailability of EV chargers is a significant barrier to promoting EVs. Based on the reasons for and barriers to adopting energy-saving measures, we identify four policy recommendations with the potential to remove the aforementioned barriers and promote household energy conservation. First, the existing infrastructure for both heating and transportation needs to be updated to provide the necessary hardware support for household energy conservation. The installation or retrofitting of heat meters in existing buildings will allow for charging heating fees based on individual household use and subsequently generate impressive financial incentives to save energy. Installing heat meters will permit residents to control their own heating system, making them more likely to reduce the frequency of opening windows when the heating is on. In addition, enlarging transportation infrastructure will encourage residents to use public transportation. The increased availability of EV chargers will not only help eliminate household concern about the inconvenience of charging EVs but also decrease willingness to use fuel cars. Second, existing and proposed price policies can facilitate home energy conservation and adjust transportation energy use. For one thing, widespread heat metering can generate a financial incentive that prompts residents to augment the energy efficiency of their heating systems. Implementing a price subsidy on home appliances can decrease the risk of investing in energy-efficient appliances and shorten the payback period, expediting the diffusion of energy-efficient technologies. For another, the gasoline price policy not only affects the choice of transportation and encourages people to use public transport more frequently but also increases willingness to accept energy-efficient fuel cars and EVs. The proposed congestion charge could decrease household preference for private cars and increase the preference for public transportation. The purchase tax exemption and purchase subsidy policies could promote the use of EVs while also depressing the demand for fuel cars. Third, the policy of not restricting the purchase and driving of EVs in Beijing can not only increase the use of EVs but also lower household preference for fuel cars, in turn verifying the effectiveness of the EV policy package. However, the maturity of EV technology, especially breakthroughs in battery technology, is the key factor that affects EV prevalence. Technical innovation is able to eliminate public concern about the power of the EV. Based on advances in EV technology and the government's preferential policies, it is believed there will be an increase in household willingness to purchase EVs, which would likely lower the demand for fuel cars.

charging this fee. In addition, levying a fuel tax is the primary means of incentivizing residents to purchase energy-efficient fuel cars internationally. However, China has not imposed the tax yet. The implementation of this fuel tax is expected to have a considerable adjusting effect on household choice of transportation mode and the purchasing of private cars. 5.3.4. EV policy package The EV policy package has a facilitation effect on household preference for EVs and an inhibition effect on preference for fuel cars. Beijing has created a package of policies to promote EVs. First, while fuel cars must enter a license plate lottery (the lot-winning probability is approximately 0.14%), EV purchasers can directly apply for license plates. Second, EV buyers enjoy the dual preferential policies of purchase tax exemption (approximately 8.55% of the EV price) and purchase subsidy (35–60k yuan). Third, unlike a fuel car, there are no restrictions on driving an EV. These policies not only lead to a significant increase in residents’ willingness to purchase an EV (0.051) but also decrease their willingness to purchase a fuel car (− 0.035). In addition, improved access to EV chargers can partially eliminate reluctance to purchase an EV. Among the barriers to purchasing an EV, “I am worried about the availability of an EV charger” is frequently mentioned. In households that do not own a car, the more satisfied with EV charger availability the residents are, the lower their willingness to accept a fuel car (− 0.302). 5.3.5. Energy conservation publicity Energy conservation publicity helps residents understand the relationship between energy conservation and environmental protection, as well as the effects of using public transport rather than private cars. “It helps reduce global warming and avoid a negative environmental impact” is a crucial qualitative reason for adopting energy-saving measures. Based on the quantitative results, energy conservation publicity generates multiple benefits. It fosters public energy-saving habits that encourage neighbors to save energy; this spillover effect can be found in the measure of closing the windows when the heating is on (0.681). Energy conservation publicity also boosts environmental concern, which increases willingness to purchase an EV (0.042). Environmental concern creates a solid foundation for household energy conservation, and the resulting energy-saving behavior is persistent [40–42]. Finally, if people understand the benefits of using public transport on energy conservation and environmental protection, it can enhance their willingness to reduce the use of private cars, increasing the use of public transport (0.045). Pamphlets, community advertising, and public service TV commercials are commonly used forms of energy conservation publicity. These activities should be adhered to over the long term because the resulting shift in public attitudes and behaviors gradually manifests [43]. 6. Conclusions Household preferences for six typical technical and behavioral energy-saving measures are investigated qualitatively and quantitatively based on Beijing residents’ responses to questionnaires. The results show that for technical energy-saving measures, socioeconomic variables have more impact than behavioral measures. Specifically, people with a higher level of education tend to be less likely to invest in energy-efficient technologies, while people with higher incomes are more prone to purchasing energy-efficient air conditioners or EVs. One possible explanation is that educated individuals tend to be busier and

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Acknowledgement

Finally, straightforward and practicable energy conservation publicity can boost public awareness of energy saving and environmental concern, thus increasing household energy conservation. Residents with an awareness of the importance of saving energy could encourage more people to save energy. The energy savings resulting from this spillover effect would have a diffusing effect. Moreover, energy conservation publicity could reduce residents’ willingness to use private cars and encourage the use of public transportation. The increase in environmental concern could also enhance the promotion of EVs.

Supports from the National Key Research and Development Program of China under Grant No.2017YFE0101800 and National Natural Science Foundation of China under Grant No. 71673266, No. 71690245, No.71774152 and the External Cooperation Program of the Chinese Academy of Sciences (Grant No. GJHZ1513) and Dutch Research Council NOW (Grant No. 467-14-023) are acknowledged. The authors appreciate the weekly seminars at CEEP in CAS, from where the earlier draft of the paper was improved.

Appendix See Tables A1–A6 here.

Table A1 Constructed variables, questions and how to construct variables. No.

Variables

1 2 3

Socio-economic variables Sex Age Educational level

4 5

Having a job or not Income level

6

Residence characteristics variables Residence type

7

Building year

8 9 10

18

Years of moving in Owned or rented Heating controlled by yourself or not Household features variables Family size With elders aged above 60 or not With children aged below 6 or not Car ownership variables Having a car or not Transportation infrastructure variables Satisfaction degree for public transportation nearby Satisfaction degree for traffic congestion at peak hours Satisfaction degree for electric vehicle (EV) charger Transportation policies variables Concern level for gasoline price

19

Concern level for congestion charge

If the congestion charge is introduced in the CBD, would you drive less?

20

Concern level for public transportation price

Q1: What is your travel cost per month for public transportation?

11 12 13 14 15 16 17

Questions

Description and the way of construction

One 0–1 variable; 0: female, 1: male Obtained directly Two 0–1 variables; 0, 0: high school or below; 1, 0: junior college or undergraduate; 0, 1: postgraduate or above One 0–1 variable; 0: having no job; 1: having a job Four 0–1 variables; 0, 0, 0, 0: less than 20k; 1, 0, 0, 0: 20–50 k; 0, 1, 0, 0: 50–100 k; 0, 0, 1, 0: 100–200 k; 0, 0, 0, 1: more than 200 k Four 0–1 variables; 0, 0, 0, 0: bungalow; 1, 0, 0, 0: multistorey apartment; 0, 1, 0, 0: high-rise apartment; 0, 0, 1, 0: terraced house; 0, 0, 0, 1: detached house Four 0–1 variables; 0, 0, 0, 0: before the 1980s; 1, 0, 0, 0: 1980s; 0, 1, 0, 0: 1990s; 0, 0, 1, 0: 2000–2010; 0, 0, 0, 1: after 2010 Obtained directly One 0–1 variable; 0: rented, 1: owned One 0–1 variable; 0: no, 1: yes Obtained directly One 0–1 variable; 0: no, 1: yes One 0–1 variable; 0: no, 1: yes One 0–1 variable; 0: no, 1: yes How do you rate the availability of The bus/tram/metro services in your neighbourhood? How do you rate the congestion at peak hours in your neighbourhood? How do you agree with the statement that charging is inconvenient after buying an EV?

Likert 5-point scale; the higher the score is, the higher the satisfaction degree is. Likert 5-point scale; the higher the score is, the higher the satisfaction degree is. Likert 5-point scale; the higher the score is, the higher the satisfaction degree is.

If the fuel is 10% cheaper compared to the current one, would you drive more frequently?

The answer options are “no”, “I do not know.” and “Yes”, indicating “low”, “median” and “high” concern level for gasoline price respectively. “1”, “2” and “3” are used to represent the three concern levels respectively. The answer options are “no”, “I do not know.” and “Yes”, indicating “low”, “median” and “high” concern level for traffic jam fee respectively. “1”, “2” and “3” are used to represent the three concern levels respectively. For Q1, the answer options are “I do not know.”, “I know, but do not give the cost.” and “I know, and give the cost.”, indicating “low”, “median” and “high” concern level for public transportation price respectively. “1”, “2” and “3” are used to represent the three concern levels respectively. For Q2, the answer options are “No”, “I do not know.” and “Yes”, indicating “low”, “median” and “high” concern level for traffic jam fee respectively. “1”, “2” and “3” are used to represent the three concern levels respectively. Sum the scores of Q1 and Q2 and the higher the score is, the higher the concern level is.

Q2: If the public transport pricing is 10% cheaper compared to the current one, would you choose it more?

(continued on next page)

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Table A1 (continued) No.

Variables

Questions

Description and the way of construction

21

Impact level of EV policies

Q1: How do you agree with the statement that the policy of free purchase tax makes me to prefer to an EV? Q2: How do you agree with the statement that the policy of purchase subsidy (35k-60k) makes me to prefer to an EV? Q3: How do you agree with the statement that the licenseplate lottery policy before 2016 (the probability of EV lottery is high) makes me to prefer to an EV? Q4: How do you agree with the statement that the licenseplate lottery policy in 2016 (one can apply for the licenseplate instead of lottery) makes me to prefer to an EV? Q5: How do you agree with the statement that I support the new license-plate lottery policy for the EV (one can apply for the license-plate instead of lottery) in 2016? Q6: How do you agree with the statement that the fact that the EV is not restricting driving makes me to prefer to an EV?

Likert 5-point scale for Q1-Q6. Sum the scores of Q1-Q6 and the higher the score is, the higher the impact level is.

22

Behavioral variables Concern level for the distance to work

Likert 5-point scale for Q1-Q2. Sum the scores of Q1-Q2 and the higher the score is, the higher the concern level is.

23

Willingness to reduce car usage

24

Risk preference

Q1: When you decided to move to your current dwelling, what was the importance of the distance to work place? Q2: How do you agree with the statement that if my work place was located closer to my residence, I would use the car less? Q1: Would you like to use the car less? Q2: Would it be easy to use the car less in everyday life? Q1: During the past years, have you ever adopted some technical energy-saving measure to save energy?

Q2: If you had not adopted some technical energy-saving measure in the past years and would not like to adopt it in the following year, what are the reasons why you do not adopt?

25

26

Spillover effect Spillover effect (Installing an energyefficient heating system [replacing a heating radiator with floor heating]) Spillover effect (Purchasing an energyefficient air conditioner) Spillover effect (Closing the windows when the heating is on) Spillover effect (Purchasing an energyefficient fuel car) Spillover effect (Purchasing an EV) Spillover effect (Using public transport) Environmental concern

Q1: Have you ever adopted some energy-saving measure? Q2: If you have adopted some energy-saving measure, what are the reasons to adopt? Q3: Does environmental motivation play an important role when you decide to adopt some energy-saving measure? Q4: How do you agree with the statement that I believe I should do the same when people around start to protect environment?

The New Environmental Paradigm (NEP) Scale, see Dunlap and Van Liere [44]

30

Willingness-to-accept of energy-saving measures How Installing an energy-efficient heating system [replacing a heating radiator with floor heating] Purchasing an energy-efficient air How conditioner Closing the windows when the heating is How on Purchasing an energy-efficient fuel car How

31

Purchasing an EV

How do you accept the energy-saving measure?

32

Using public transport

How do you accept the energy-saving measure?

27

28 29

Likert 5-point scale for Q1-Q2. Sum the scores of Q1-Q2 and the higher the score is, the higher the willingness is. Risk preference refers to the preference for current investment and future revenue. For Q1, the answer is “Yes”, defined as “low risk preference”. For Q1, the answer is “No”, and for Q2, the reasons not to adopt include “I could afford it, but don’t want to spend the money.”, defined as “high risk preference”. For Q1, the answer is “No”, and for Q2, the reasons not to adopt do not include “I could afford it, but don’t want to spend the money.”, defined as “median risk preference”. Two 0–1 variables; 0, 0: low risk preference; 1, 0: median risk preference; 0, 1: high risk preference One 0–1 variable; 0: There is no “spillover effect”, indicating that the adoption of some energy-saving measure is not affected by people around. 1: There is “spillover effect”, indicating that the adoption of some energy-saving measure is affected by people around.

Likert 5-point scale for Q1-Q12 in the NEP Scale. Sum the scores of Q1-Q12 and the higher the score is, the higher the environmental concern is.

do you accept the energy-saving measure?

Likert 5-point scale; the higher the score is, the higher the willingness-to-accept is.

do you accept the energy-saving measure?

Likert 5-point scale; the willingness-to-accept is. Likert 5-point scale; the willingness-to-accept is. Likert 5-point scale; the willingness-to-accept is. Likert 5-point scale; the willingness-to-accept is. Likert 5-point scale; the willingness-to-accept is.

do you accept the energy-saving measure? do you accept the energy-saving measure?

69

higher the score is, the higher the higher the score is, the higher the higher the score is, the higher the higher the score is, the higher the higher the score is, the higher the

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Table A2 Installing an energy-efficient heating system (replacing a heating radiator with floor heating) or purchasing an energy-efficient air conditioner. Q1: In the past years, have you adopted energy-efficient heating system or energy-efficient air conditioner to conserve energy? Yes No I do not know. I cannot decide. Samples 259 65 22 5 Percentage (%) 73.8 18.5 6.3 1.4 Q2: For respondents that answer “No” or “I do not know.” for Q1, will you adopt the measure in the following year? Yes, I would like to consider Yes, I would like to consider but probably will not adopt. and probably will adopt. Samples 23 32 Percentage (%) 26.4 36.8 The answer is “No” for Q1; the Samples Percentage (%) The answer is “Yes” for Q1; or the answer is “Yes, I would like to barriers to adopt? consider and probably will adopt.”, or “Yes, I would like to consider but probably will not adopt.” for Q2, the reasons to consider or adopt? I do not know how. 33 22.9 It saves money. It takes too much effort. 23 16.0 It improves my comfort and living conditions. I am too busy. 18 12.5 It improves the value of my dwelling. I cannot afford the investment cost. 24 16.7 I have financial incentives. I could afford it, but do not want to 17 11.8 It helps reduce global warming and avoid negative environmental spend the money. impact. Someone else in my family would 10 6.9 Someone asked me to do. object. Fear of limited efficiency 6 4.2 Moral and social responsibility improvement I do not care energy consumption. 12 8.3 People I care about are doing it. I do not care the environment. 1 0.7 It makes me feel happy. It improves my green image. People approve when I do it. It reduces energy consumption. It improves the quality of my dwelling.

No, I would not like to consider. 24 27.6 Samples

I do not know.

139 175 89 79 113

14.7 18.5 9.4 8.4 12.0

50

5.3

48

5.1

56 34 12 17 103 29

5.9 3.6 1.3 1.8 10.9 3.1

8 9.2 Percentage (%)

Table A3 Purchasing an energy-efficient fuel car. Q1: In the past years, have you purchased an energy-efficient oil-fueled car to conserve energy? Yes No I do not know. I cannot decide. Samples 193 126 21 11 Percentage (%) 55.0 35.9 6.0 3.1 Q2: For respondents that answer “No” or “I do not know.” for Q1, will you adopt the measure in the following year? No, I would not like to consider. Yes, I would like to consider Yes, I would like to and probably will adopt. consider but probably will not adopt. Samples 31 83 59 Percentage (%) 16.5 44.1 31.4 The answer is “No” for Q1; the Samples Percentage (%) The answer is “Yes” for Q1; or the answer is “Yes, I would like to barriers to adopt? consider and probably will adopt.”, or “Yes, I would like to consider but probably will not adopt.” for Q2, the reasons to consider or adopt? I do not know how. 12 4.2 It saves money. It takes too much effort. 20 6.9 I have financial incentives. I am too busy. 16 5.6 It helps reduce global warming and avoid negative environmental impact. It is too expensive. 68 23.6 Someone asked me to do. I could afford it, but do not want to 18 6.3 Moral and social responsibility spend the money. Someone else in my family would 10 3.5 People I care about are doing it. object. Fear of limited efficiency 14 4.9 It makes me feel happy. improvement I do not care energy consumption. 9 3.1 It improves my green image. I do not care the environment. 1 0.3 People approve when I do it. I do not need a car. 52 18.1 It reduces energy consumption. It is not powerful enough. 28 9.7 It reduces fuel consumption. It is too small. 8 2.8 It is not safe. 3 1.0 I do not have a permission to buy a 29 10.1 fuel car.

70

I do not know.

15 8.0 Samples

Percentage (%)

112 60 106

18.0 9.7 17.1

36 61

5.8 9.8

51

8.2

25

4.0

28 19 76 47

4.5 3.1 12.2 7.6

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Table A4 Purchasing an electric vehicle (EV). Q1: In the past years, have you purchased an EV to conserve energy? Yes No I do not know. I cannot decide. Samples 182 143 16 10 Percentage (%) 51.9 40.7 4.6 2.8 Q2: For respondents that answer “No” or “I do not know.” for Q1, will you adopt the measure in the following year? Yes, I would like to consider Yes, I would like to consider but probably will not adopt. and probably will adopt. Samples 30 49 Percentage (%) 18.9 30.8 The answer is “No” for Q1; the Samples Percentage (%) The answer is “Yes” for Q1; or the answer is “Yes, I would like to barriers to adopt? consider and probably will adopt.”, or “Yes, I would like to consider but probably will not adopt.” for Q2, the reasons to consider or adopt? I do not know how. 12 3.5 It saves money. It takes too much effort. 22 6.4 I have financial incentives. I am too busy. 13 3.8 It helps reduce global warming and avoid negative environmental impact. It is too expensive. 51 14.9 Someone asked me to do. I could afford it, but do not want 14 4.1 Moral and social responsibility to spend the money. Someone else in my family would 10 2.9 People I care about are doing it. object. Fear of limited efficiency 22 6.4 It makes me feel happy. improvement I do not care energy 9 2.6 It improves my green image. consumption. I do not care the environment. 6 1.8 People approve when I do it. I do not need a car. 42 12.3 It reduces fuel consumption. It is not powerful enough. 42 12.3 I do not like its look. 8 2.3 I am worried about the resale 10 2.9 value. I am worried about the perceived 38 11.1 maintenance costs. I am worried about the 43 12.6 availability of EV charger.

No, I would not like to consider. 55 34.6 Samples

I do not know.

115 52 119

18.4 8.3 19.1

34 48

5.4 7.7

73

11.7

36

5.8

28

4.5

20 99

3.2 15.9

25 15.7 Percentage (%)

Table A5 Closing the windows when the heating is on. Q1: How often do you close the windows when the heating is on? Always Often Occasionally Seldom Never Samples 168 100 53 17 13 Percentage (%) 47.9 28.5 15.1 4.8 3.7 Q2: Would you consider closing the windows when the heating is on in the following year? Yes, more frequently than now. About the same as now. No, less frequently than now. Samples 235 106 10 Percentage (%) 67.0 30.2 2.8 Samples Percentage (%) The answer is “Always” or “Often” or “Occasionally” for Samples The answer is “Seldom” or “Never” for Q1; or the answer is Q1; or the answer is “Yes, more frequently than now.” “About the same as now.” or “No, less frequently than for Q2, the reasons to adopt? now.” for Q2, the barriers to adopt? I am too busy. 16 8.1 It saves money. 160 It is hard to remember. 34 17.3 It helps reduce global warming and avoid negative 209 environmental impact. It is not convenient. 39 19.8 It reduces energy consumption. 258 It takes too much effort. 37 18.8 Someone asked me to do. 44 I do not think it is important. 24 12.2 Moral and social responsibility 84 Someone else in my family would object. 17 8.6 People I care about are doing it. 39 I do not care energy consumption. 11 5.6 It makes me feel happy. 46 It would reduce my comfort. 19 9.6 People approve when I do it. 3

71

Percentage (%)

19.0 24.8 30.6 5.2 10.0 4.6 5.5 0.4

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Table A6 Using public transport. Q1: How often do you use public transport? Always Often Samples 115 130 Percentage (%) 32.8 37.0 Q2: Would you consider using public transport in the following Yes, more frequently than now. Samples 258 Percentage (%) 73.5 The answer is “Seldom” or “Never” for Q1; or the answer is “About the same as now.” or “No, less frequently than now.” for Q2, the barriers to adopt? I am too busy. It is hard to remember. It is not convenient. It takes too much effort. I do not think it is important. Someone else in my family would object. I do not care energy consumption. I do not care the environment. It would reduce my comfort. The distance is too far. It is too expensive. There is no stop near my home or destination. I consider public transportation is for poor people. I would not feel safe. It is not good to my health and physical condition.

Occasionally 78 22.2

Seldom 20 5.7

Never 8 2.3

year?

Samples

19 16 54 20 17 8 6 5 14 10 6 10 2 10 2

About the same as now. No, less frequently than now. 81 12 23.1 3.4 Samples Percentage (%) Percentage (%) The answer is “Always” or “Often” or “Occasionally” for Q1; or the answer is “Yes, more frequently than now.” for Q2, the reasons to adopt? 9.5 It saves money. 198 22.3 8.0 It helps reduce global warming and avoid negative 212 23.8 environmental impact. 27.1 It reduces energy consumption. 221 24.9 10.1 Someone asked me to do. 48 5.4 8.5 Moral and social responsibility 49 5.5 4.0 People I care about are doing it. 34 3.8 3.0 It makes me feel happy. 34 3.8 2.5 People approve when I do it. 10 1.1 7.0 It saves time. 38 4.3 5.0 It helps to get more exercise. 45 5.1 3.0 5.0 1.0 5.0 1.0

References

[18] Faruqui A, Sergici S, Sharif A. The impact of informational feedback on energy consumption-A survey of the experimental evidence. Soc Sci Electron Publ 2010;35(4):1598–608. [19] Hargreaves T, Nye M, Burgess J. Making energy visible: a qualitative field study of how householders interact with feedback from smart energy monitors. Energy Policy 2010;38(10):6111–9. [20] Jakobsson C, Fujii S, Gärling T. Determinants of private car users' acceptance of road pricing. Transp Policy 2007;7(2):153–8. [21] Mills B, Schleich J. What's driving energy efficient appliance label awareness and purchase propensity? Energy Policy 2010;38(2):814–25. [22] Qiu Y, Colson G, Grebitus C. Risk preferences and purchase of energy-efficient technologies in the residential sector. Ecol Econ 2014;107:216–29. [23] Jridi O, Bargaoui SA, Nouri FZ. Household preferences for energy saving measures: approach of discrete choice models. Energy Build 2015;103:38–47. [24] Braun FG. Determinants of households' space heating type: a discrete choice analysis for German households. Energy Policy 2010;38(10):5493–503. [25] Gamtessa SF. An explanation of residential energy-efficiency retrofit behavior in Canada. Energy Build 2013;57:155–64. [26] Shi Y, Ge Y, Chang J, Shao H, Tang Y. Garden waste biomass for renewable and sustainable energy production in China: potential, challenges and development. Renew Sustain Energy Rev 2013;22:432–7. [27] Du P, Zheng LQ, Xie BC, Mahalingam A. Barriers to the adoption of energy-saving technologies in the building sector: a survey study of Jing-jin-tang, China. Energy Policy 2014;75(75):206–16. [28] Liu W, Zhang J, Bluemling B, Mol APJ, Wang C. Public participation in energy saving retrofitting of residential buildings in China. Appl Energy 2015;147:287–96. [29] Yue T, Long R, Chen H. Factors influencing energy-saving behavior of urban households in Jiangsu Province. Energy Policy 2013;62(9):665–75. [30] Ding Z, Wang G, Liu Z, Long R. Research on differences in the factors influencing the energy-saving behavior of urban and rural residents in China-a case study of Jiangsu Province. Energy Policy 2017;100:252–9. [31] Xiaohua W, Kunquan L, Hua L, Di B, Jingru L. Research on China's rural household energy consumption–Household investigation of typical counties in 8 economic zones. Renew Sustain Energy Rev 2017;68:28–32. [32] Poortinga W, Steg L, Vlek C, Wiersma G. Household preferences for energy-saving measures: a conjoint analysis. J Econ Psychol 2003;24(1):49–64. [33] Jia JJ, Xu JH, Fan Y. Public acceptance of household energy-saving measures in Beijing: heterogeneous preferences and policy implications. Energy Policy 2018;113:487–99. [34] Mcfadden D. Econometric models of probabilistic choice. Struct Anal Discret Data Econ Appl 1981:198–272. [35] Mcfadden D. Regression-based specification tests for the multinomial logit model. J Econ 1987;34(1–2):63–82. [36] Zheng XY. Chinese household energy consumption report (in Chinese). Science Press; 2015. [37] Ding Y, Wu Y, Zhu N, Tian Z. Scheme analysis of heat metering and energy

[1] Petroleum B. BP statistical review of world energy. 2016. [URL 〈http://www.bp. com/en/global/corporate/energy-economics/statistical-review-of-world-energy. html〉]. [2] National Bureau of Statistics of the People’s Republic of China. China energy statistical yearbook. 2015. [URL 〈http://www.stats.gov.cn〉]. [3] Xu JH, Peng BB, Fan Y, Kemp R, Nie HG. Analyzing the contributing sources of rapid growth of passenger transport energy consumption in China using a hybrid decomposition model. Working paper; 2017. [4] Nejat P, Jomehzadeh F, Taheri MM, Gohari M, Majid MZA. A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries). Renew Sustain Energy Rev 2015;43:843–62. [5] National Bureau of Statistics of the People’s Republic of China. 〈http://www.stats. gov.cn〉. [Accessed 27 July 2017]. [6] Energy Information Administration. 〈http://www.eia.gov/totalenergy/data/ annual/index.cfm〉. [Accessed 26 December 2016]. [7] Tao J, Yu SJ. Implementation of energy efficiency standards of household refrigerator/freezer in China: potential environmental and economic impacts. Appl Energy 2011;88:1890–905. [8] Peng BB, Fan Y, Xu JH. Integrated assessment of energy efficiency technologies and CO2 abatement cost curves in China's road passenger car sector. Energy Convers Manag 2016;109:195–212. [9] Abrahamse W, Steg L, Vlek C, Rothengather T. A review of intervention studies aimed at household energy conservation. J Environ Psychol 2005;25(3):273–91. [10] Lindén AL, Carlsson-Kanyama A, Eriksson B. Efficient and inefficient aspects of residential energy behaviour: what are the policy instruments for change? Energy Policy 2006;34(14):1918–27. [11] Steg L. Promoting household energy conservation. Energy Policy 2008;36(12):4449–53. [12] Gyberg P, Palm J. Influencing households' energy behavior-how is this done and on what premises? Energy Policy 2009;37(7):2807–13. [13] Ouyang J, Hokao K. Energy-saving potential by improving occupants' behavior in urban residential sector in Hangzhou City, China. Energy Build 2009;41(7):711–20. [14] Costa DL, Kahn ME. Energy conservation “nudges” and environmentalist ideology: evidence from a randomized residential electricity field experiment. J Eur Econ Assoc 2013;11(3):680–702. [15] Darby S. The effectiveness of feedback on energy consumption: A review for DEFRA of the literature on Metering, Billing Direct Displays. 486; 2006. [16] Abrahamse W, Steg L, Vlek C, Rothengather T. The effect of tailored information, goal setting, and tailored feedback on household energy use, energy-related behaviors, and behavioral antecedents. J Environ Psychol 2007;27(4):265–76. [17] Burgess J, Nye M, Burgess J. Re-materialising energy use through transparent monitoring systems. Energy Policy 2008;36(12):4454–9.

72

Renewable and Sustainable Energy Reviews 95 (2018) 56–73

J.-J. Jia et al.

[41] Lindenberg S, Steg L. Normative, gain and hedonic goal-frames guiding environmental behavior. J Soc Issues 2007;63(1):117–37. [42] De Groot JIM, Steg L. Morality and prosocial behavior: the role of awareness, responsibility, and norms in the norm activation model. J Soc Psychol 2009;149(4):425–49. [43] Henryson J, Håkansson T, Pyrko J. Energy efficiency in buildings through information-Swedish perspective. Energy Policy 2000;28(3):169–80. [44] Dunlap RE, Van Liere KD. The new environmental paradigm: a proposed measuring instrument and preliminary results. J Environ Educ 1978;9:10–9.

efficiency retrofit for existing residential buildings in China's northern heating regions (in Chinese). HV&AC 2010;40(11):19–23. [38] Lv SL, Wu Y. Target-oriented obstacle analysis by PESTEL modelling of energy efficiency retrofit for existing residential buildings in China's northern heating region. Energy Policy 2009;37(20):98–101. [39] Liu Y, Guo W. Effects of energy conservation and emission reduction on energy efficiency retrofit for existing residence: a case from China. Energy Build 2013;61(6):61–72. [40] Steg L, Dreijerink L, Abrahamse W. Factors influencing the acceptability of energy policies: testing VBN theory. J Environ Psychol 2005;25(4):415–25.

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