Determinants of energy savings in Indonesia: The case of LED lighting in Bogor

Accepted Manuscript Title: Determinants of Energy Savings in Indonesia: the case of LED lighting in Bogor Authors: Ryoko Nakano, Eric Zusman, Sudarmanto Nugroho, R.L. Kaswanto, Nurhayati Arifin, Aris Munandar, Hadi Susilo Arifin, Muchamad Muchtar, Kei Gomi, Tsuyoshi Fujita PII: DOI: Reference:

S2210-6707(16)30728-4 https://doi.org/10.1016/j.scs.2018.06.025 SCS 1159

To appear in: Received date: Revised date: Accepted date:

15-12-2016 26-5-2018 19-6-2018

Please cite this article as: Nakano R, Zusman E, Nugroho S, Kaswanto RL, Arifin N, Munandar A, Arifin HS, Muchtar M, Gomi K, Fujita T, Determinants of Energy Savings in Indonesia: the case of LED lighting in Bogor, Sustainable Cities and Society (2018), https://doi.org/10.1016/j.scs.2018.06.025 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Determinants of Energy Savings in Indonesia: the case of LED lighting in Bogor

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Ryoko Nakano1, Eric Zusman2, Sudarmanto Nugroho3, R.L. Kaswanto4, Nurhayati Arifin5, Aris Munandar6, Hadi Susilo Arifin7, Muchamad Muchtar8, Kei Gomi9, Tsuyoshi Fujita10

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Corresponding Author: Institute for Global Environmental Strategies (IGES), Integrated Policies for Sustainable Societies Area (IPSS), 2108-11 Kamiyamaguchi, Hayama, Kanagawa 240-0115 Japan; Tel: 046-855-3869; Fax: 046-855-3809; E-mail: [email protected] 1 Institute for Global Environmental Strategies (IGES); [email protected] 1 Institute for Global Environmental Strategies (IGES); [email protected] Bogor University of Agriculture (IPB), Faculty of Agriculture, Department of Landscape Architecture; [email protected]

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Bogor University of Agriculture (IPB), Faculty of Agriculture, Department of Landscape Architecture; [email protected] Bogor University of Agriculture (IPB), Faculty of Agriculture, Department of Landscape Architecture; [email protected] 1 Bogor University of Agriculture (IPB), Faculty of Agriculture, Department of Landscape Architecture; [email protected] 1 Wahana Usaha Universal PT; [email protected] 1 National Institute for Environmental Studies (NIES); [email protected] National Institute for Environmental Studies (NIES); [email protected]

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Abstract

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Electricity conservation could diverge from a fossil-fuel dependent path cost-effectively This was tested in Bogor City, Indonesia. An ordinal logit regression was used to analyze data from 600 respondents in Bogor. Respondents with relevant information were more likely to invest in electricity savings Participation in environmental activities could partially compensate for lack of information

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Highlights

This article analyzes which factors influenced willingness to purchase residential electricity savings

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technologies in Bogor, Indonesia. Survey data collected from 600 households between October and

November 2015 was used to test hypotheses on demographic, informational, and participatory determinants of willingness to invest in energy efficient lighting. Results of an ordinal logit regression on 600 respondents in Bogor show willingness to purchase these technologies were positively correlated with information and participation variables. The estimates from these fuller models suggest that information of both relevant policies and training in the workplace were more likely to purchase energy saving lightings relative to those with less information. The national energy efficiency labelling program are shown to have a positive effect on purchasing LED lightings. Moreover, information should be as specific to the local and personal context as possible. A lack of information could to some degree be compensated by engaging in

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environmentally-related activities—with regular participants more likely to change their willingness to

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change purchasing behavior than less frequent participants.

Key words: Residential Electricity Use; Determinants of Energy Savings Behaviors; Electricity Policy in

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Indonesia; Ordinal logit model; Prepaid metering

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Introduction

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A growing body of literature holds that lifestyle and consumption choices strongly influence residential energy consumption. Yet realizing energy savings in households is not easy. For many countries, it requires breaking path-dependencies that lock-in energy-intensive technologies and behaviors (Unruh et al 2002, 2006). Countries such as Indonesia are at stages in their development where they could avoid such a lock-in. Indonesia could provide information that may motivate consumers to invest in energy savings technologies before their behaviors and surrounding infrastructure close off more sustainable development paths. The provision of information may open these paths in Indonesia and elsewhere because it can induce behavioral changes from raise awareness of energy savings options and their benefits. These behavioral changes can further be achieved at a fraction of the cost of capital-intensive energy savings projects. This should make an approach focused on information particularly appealing to resource-constrained countries. For more than two decades, studies have examined the effects of information (and other determinants) on energy use in Europe, North America, Australia, and Japan; recent studies have looked at a comparable set of issues in East and South Asia. Studies of similar themes in Southeast Asia tend to use macroeconomic models to estimate the costs and impacts of energy savings scenarios (Kumar 2016, Tongsopit et al 2016). Limited work has investigated whether information or other socioeconomic variables make these scenarios more or less feasible in Southeast Asia. Given the region’s high rates of growth and fast changing consumption patterns, this represents a significant hole in the literature. This study aims to fill that gap by analyzing which factors influenced willingness to purchase light emitting diodes (LED) in Bogor, Indonesia. Survey data collected from 600 households during October and November 2015 was used to test hypotheses on demographic and socioeconomic, informational, and participatory determinants of willingness to purchase LED. Results of an ordinal logit regression show willingness to purchase LED were positively correlated with some measures of youth and ownership of appliances. They were also positively related to some forms of information (from labelling and knowledge of policies) and participation in environmental activities. Last but not least, the results suggest 2

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that participating in environmental activities may compensate for low levels of information. These findings are particularly relevant to Bogor since Indonesia has begun to phase in an energy efficiency labeling program and a host of other energy savings measures in recent years. The results to this research suggest both the kinds of information and the channels through which it disseminated could strengthen these measures. The remainder of the paper is divided into seven sections. The next section reviews literature on possible determinants of energy savings. Section 3 presents hypotheses that will be tested later in the paper. Section 4 offers a description of Bogor, Indonesia—the city where the data was gathered. Section 5 provides an explanation on the data sourcing and methodology, while section 6 offers an analysis of the variables. Section 7 gives the results. Section 8 discusses policy implications as well as areas for future research. Literature Review

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Reducing residential energy consumption can be achieved by increasing energy efficiency in electric appliances. It can also occur from switching traditional fossil fuel to cooking fuels with better thermal efficiency.11 Retrofitting buildings with fabric that reduce the need for heating, cooling and lighting is another common energy savings alternative. A fourth set of options involves introducing programs that encourage people to change behavior or consumption patterns. These four options are not necessarily mutually exclusive. The decision to purchase a new appliance, switch fuels, or retrofit a building requires changing a behavior. Moreover, as is the case in this article, some people may be more predisposed to make these changes based on their socioeconomic background or the level of knowledge about the costs and benefits of various energy savings options. The main purpose of this article is to identify factors influencing the decision to purchase efficient lighting. This is an important case because it is relatively low cost behavioral change. As such, even small changes to some of these factors may yield significant reductions in energy. In higher cost areas—for example, reducing the number of trips with personalized motor vehicles—changes to these factors may be more difficult (Abrahamse et al 2007; Urban & Scasny, 2012). Studies on the linkage between potentially influential factors and energy savings behavioral changes cover a wide range of possible causal relationships. These relationships can be divided into those involving 1) demographic and socioeconomic variables; and 2) informational and participatory variables (Martinsson et al, 2011; Sperling et al 2016). The next section reviews findings from literature for studies divided into these categories, beginning with possible links to age.

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2.1 Demographic and Socioeconomic Variables

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Several studies have looked at the relationship between energy savings and age. In terms of age, a cross-national study (Australia, Italy, South Korea, Norway, Canada, Czech Republic, France, Netherlands, and Sweden) found that older people tend to reduce energy use more than younger people (Urban, 2012). Other studies in India conclude younger people demonstrate a stronger willingness to curb energy consumption because they are interested in environmental issues and more willing to forgo the use of energy in an effort to combat climate change and preserve the local environment (Sperling et al 2016). A related branch of literature suggests one reason for these potentially different results is that younger people tend to save energy more for environmental reasons while older people tend to do so for financial reasons (Schleich and Mills 2012). Another important factor influencing energy consumption is gender. There are two contrasting views concerning the relationship between gender and energy consumption. One view, found in Sweden, is that women tend to be moderately more likely to save energy (Carlsson-Kanyama and Linden, 2007; Carlsson and Johansson-Stenman, 2000). This may be because men are frequently more engaged in economic 11 It is important to keep in mind a rebound effect wherein consumers increase the number energy efficient appliances, effectively offsetting

reductions from a single efficiency improvement.

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activities that are perceived as being incompatible with environmental protection and saving energy (Van Liere and Dunlap, 1980; Blocker and Eckberg, 1997). Another perspective suggests that men tend to be more cognizant of environmental issues because they are generally more educated and more socially connected; more education and connections helps raise awareness of energy savings options and benefits (Van Liere and Dunlap, 1980). Other studies point to the effects of income or socioeconomic status on energy savings. In terms of income, wealthier people may be more inclined to invest in energy saving technology as can be seen in Sweden and India (Zarnikau 2003; Sperling et al 2016; Urban 2012). At the same time, wealthier individuals may also be less responsive to price incentives to alter daily consumption patterns (e.g. lower thermostat, switch off lights) —their demand for energy may be more inelastic when facing, for instance, a price increase in electricity tariffs from a carbon tax (Aziz et al, 2013). Another possibly influential factor is the number of consumer electronics in a household. A greater number of appliances could lead to higher energy use, especially in developing countries. This is because house size, the number of appliances and demands for other modern energy comforts also rise as economies develop (World Business Council for Sustainable Development, 2009). Education is a final possible contributing factor. In many developed countries, education levels contribute to a greater willingness to invest in environmental conservation (Torgler and García-Valinas 2007) or energy savings (Zarnikau 2003; Scott 1997; Brechling and Smith 1994; Hirst and Goeltz 1982). But studies do not always show a strong correlation between higher levels of education and behavioral changes (Ferguson 1993; Despiri et al, 2014; Jridi, 2015). This may be due to the fact that education is indirectly linked to changes in energy consumption: it may be that more targeted information is needed to alter preferences for saving energy, leading to the discussion in the next section.

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2.2 Informational and Participatory Variables

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Just as there is a broad literature on demographic and socioeconomic determinants, there is an equally sizable literature on how more information and levels of participation in social activities influence energy savings. Part of this literature is similar to the studies in the previous section in that it aims to identify determinants of energy savings. A complementary set of policy-related studies concentrates on how governments and businesses can employ informational instruments to encourage consumers to alter their lifestyle and behavioral choices. In terms of work on determinants, some studies suggest environmental beliefs influence willingness to save energy. Those with more deeply held environmental beliefs are more inclined to save household energy (Brandon and Lewis, 1999; Abrahamse and Steg, 2011). Other observers similarly note that beliefs and knowledge contribute to differences in household energy consumption or energy savings measures (e.g. Viklund 2004, Sjoberg and Engelberg 2005, OECD 2008, Di Maria, Ferreira, and Lazarova 2010). Proenvironmental attitudes have also been found to have a positive effect on energy savings in yet other studies (Lutzenhiser 1992, 1993, Weber and Perrels 2000). More specifically, household information about potential energy savings has been associated with the purchase and use of energy saving technologies (Scott 1997). In terms of informational instruments, there is growing recognition that energy savings behavior can be influenced by the understanding that investing in energy savings technologies can save money over the lifetime of a purchase. The provision of information can also raise awareness of the options available to capture these savings. For example, international think tanks, such as the Alliance to Save Energy, offer programs in which they train school staff, and offer input on energy use changing behavior (Alliance to Save Energy, 2012). These findings are in line with work showing informational campaigns, repeated messages, customized information targeted at individual goal setting, and tailored feedback can encourage energy savings (Abrahamse et al 2005; 2007; Henryson et al 2000; Darby 2006). There is also some evidence that the impacts of information can spread if it is shared strategically among groups. To illustrate, educational programs that offer energy savings tips can have a multiplier effect on many family members when information is shared at home (Dias et al, 2003.) Arguably the most visible efforts to use information to achieve some of the above results are labelling schemes that offer consumers product information on energy savings (Sutherland 1991, Howarth et al. 2000; Sanchez et al., 2008; Lane et al., 2007; Banerjee and Solomon, 2003; Schiellerup, 2002; Bertoldi, 4

ecoEnergy Efficiency for Vehicles Opower

US

eCompass

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Sweden

Energy guzzlers website Mandatory eco-driving for Driving License Me and my car Drive smart and save fuel

Brochures; online portal; training on fuel-saving driving techniques.

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Smart Metering Program Your energy savings.gov.au

Leaflet on housing smart meters. A website with practical information on how to save energy, PR on smart energy use lifestyles

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Germany

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Australia Japan

China

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-Smart-life energy program -Eco-driving -Electricity Savings Action Enhanced efficiency monitoring and auditing

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ecoEnergy Efficiency for Housings

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Canada

Notes Offers consumers information on consumption patterns of consumers with similar lifestyles, house size, climate etc. A rating system that allows users to compare the energy performance of one house against another. Fuel-efficient driver training for commercial and institutional fleets. Offers customized information using internet portals, text messages, email and in-home energy displays. An easy to use tool for consumers to compare appliances for energy efficiency A website with internet-based CO2 calculator for electric appliances Eco-driving is included as part of the driving license test.

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Actions Empowering

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Country EU

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Table 1: Electricity Saving Training / Tailored Information Services

Positively affect driving habits. Post East Japan Earthquake, PR campaigns, advisory projects, workshop on energy saving Tailored advice to boost the capability of provincial efficiency monitoring centers and principal energyconsuming industries

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1999; Waide, 2001; Waide, 1998). Further, this kind of approach is becoming more common across a range of countries. Table 1 offers some examples for electricity saving programs in a wide number of countries. A third branch of literature holds that there is a process wherein different forms of information have different effects: the accumulation of sporadic or non-targeted information from everyday practices have a priming effect that make consumers more receptive to more tailored advice (Darby 2006.). For instance, initially “tacit information” for individuals such as electricity bills deliver easy to understand energy saving tips (Henrysson, 2000). This would, in turn, open one to seeking feedback and identify energy savings options based on that feedback. The EU has, to cite a relevant case, introduced its “Empowering” project to achieve the EU 20% energy efficiency improvement target for 2030. The program has developed a new type of information service related to the energy bill in which consumers are not only offered feedback on their own electricity consumption but also of other consumers who have similar lifestyles or reside in similar neighborhoods and buildings. Meanwhile, some companies have begun to apply findings related to tailoring information to different needs to help reduce energy consumption. For example, the company Opower (a subsidiary of Oracle) offers over 60 million company and consumer end-users customized information through internet portals, text messages, email and in-home energy displays. (Opower 2018).

Country EU

Actions Empowering

Mexico

Training programmes for professionals

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Energy saving information

Notes Offers consumers information on consumption patterns of consumers with similar lifestyles, house size, climate etc. A programme to train specialists in Electric Energy Savings Leaflet on smart energy use

Note: Compiled by the authors

Table 2: Summary of relevant literature Variable Age,

Urban & Scasny, 2012; Zarnikau 2003

Income

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Carlsson-Kanyama and Linden 2007; Carlsson and JohanssonStenman 2000 Barr et al 2005

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Linden 2006; Hedberg ad Holmberg 2005, Despiri et al, 2014 Abrahamse et al 2005; 2007 ; Henryson et al 2000; Darby 2006, Scott 1997 Dias et al, 2003, Zarnikau 2003; Scott 1997; Brechling and Smith 1994; Hirst and Goesltz 1982 Sutherland 1991; Howarth et al 2000; Sanchez et al 2008; Lane et al 2007; Banerjee and Solomon 2003; Schiellerup 2002; Bertoldi 1999; Waide

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Country India, Australia, Italy, South Korea, Norway, Canada, Czech Republic, France, Netherlands, and Sweden Australia, Italy, South Korea, Norway, Canada, Czech Republic, France, Netherlands, Sweden, US Sweden

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Author Sperling et al, 2016; Urban & Scasny, 2012;

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A fourth relevant branch of literature concentrates on the channel through which information is transmitted and acquired. One of the key channels highlighted in this work are community activities. The group dynamics via an environmental campaign or a competition in which a couple of households form a group can be particularly influential. For instance, some studies point to a deliberative process where people exchange information on energy savings, receive feedback for reducing consumption levels, and are rewarded upon achieving their specific goals (Darby 2006, McMichael 2013). To foreshadow a conclusion of this article, it may also be possible that participating in a process related to energy savings or environmental protection compensates for a lack of information or enhances the effect of information. Table 2 presents an overview of the relevant literature. It demonstrates that the majority of relevant studies on determinants of energy savings are in developed countries. Some recent research has also been conducted on cases in East Asia and South Asia. But there have been limited studies on Southeast Asia. The remainder of the article begins to test some of the hypotheses drawn from studies outside of Southeast Asia in Indonesia.

Family size

UK

House structure

Sweden, Greece

Information

Netherlands, Sweden, UK, Ireland

Information/ Educational program

Brazil, UK, US,

Information / Labelling

US, UK, Australia, Austria, EU

Author 2001; Waide 1998

Variable

Despiri et al, 2014; Jridi, 2015; Henryson et al 2000 McMicheal, 2012; Sperling et al, 2016;

Information / Energy bills

Greece, Sweden

Participation / Relationship

UK

Tunisia,

The Hypotheses

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This study suggests that demographic and socioeconomic factors as well as information and participatory factors can have a positive effect on energy savings. It also examines whether there are possible correlations between different sets of independent variables; more specifically, that information combined with participation can influence each other, thereby giving rise to greater change than either variable alone in energy consumption habits. The case that it uses to test these hypotheses is the purchase of energy efficient LED (which has a lower cost compared to more expensive measures such as building renovation, and fuel switching). An important qualifier for the results from this research is that the literature shows some difference in reported energy saving behavior (i.e. self-reported behavior) and actual behavior change. Subjectively reported energy saving behavior may actually reflect what respondents would like to do rather than actual behavioral change. Survey results have been known to overestimate the absolute value of what will stimulate actual change in behavior. While understanding this limitation is important, the article moves ahead while acknowledging that it is important to follow up studies like this with energy monitoring (Nancarrow et al., 2001, Martinsson et al, 2011). The literature review leads to the hypotheses in Table 3. The hypotheses are based chiefly on insights from countries outside Southeast Asia (Sperling et al 2016). The paper uses six independent variables from Table 2 to test the hypotheses. The variable “family size” was replaced by the number of appliances owned per household, and house structure was removed from the analysis since the data collected was not adequate to make an assessment for this specific variable. Table 3: Hypotheses 1. 2.

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3.

Hypothesis People with higher incomes will indicate a greater willingness to purchase LED lightbulbs Younger people will indicate a greater willingness to purchase LED lightbulbs Men will indicate a greater willingness to purchase LED lightbulbs. People with larger number of appliances will indicate a greater willingness to purchase LED lightbulbs People with greater information of energy saving’s benefits will indicate greater willingness to LED lightbulbs. People with a greater inclination to participate in environmental (social activities) will indicate a greater willingness to purchase LED lightbulbs

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Attributes Demographic and socioeconomic

4.

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Information and participation

5. 6.

Ordinal logistic regression models and chi-square tests were used to test the hypotheses. Ordinal logistic regression allows for comparisons across more than two categories (in this case, three) of the dependent or outcome variable. In an ordinal logit model, the dependent variables also need to have a meaningful sequential order such that a value for one category can be meaningfully interpreted to be “higher” than the category below it. Ordinal logit depends on maximum likelihood estimation to evaluate

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the probability of being in a given category (Orme et al, 2001.) The dependent variable is the respondents’ response on investing in LED. 3

The Location: Bogor, Indonesia

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The hypotheses are tested in Bogor, Indonesia. Bogor is a fast-growing city located approximately an hour via train and car from Indonesia’s capital, Jakarta. Through the 1990s, Bogor’s population grew at an average 10.25 percent before holding at a more moderate 2.8 percent pace in recent decades. While many of Bogor’s people are concentrated in the densely populated center (12,000 persons/km2) (as dense as Tokyo’s metropolitan area), growing pockets of residents in the city’s six districts have created several city sub-centers. Bogor’s tropical climate (avg. temperature 33℃, avg. humidity 90%), appealing landscape, and proximity to Jakarta have led to not only population growth but significant lifestyle and purchasing pattern changes over the past 30 years. These shifts in livelihood and consumer preferences have, in turn, increased energy use.

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Figure 1: Map of Bogor

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At present, some constraints on Bogor’s growth are becoming evident due to the city’s rapid development and growing energy use. For instance, fluctuations in the voltage of consumer goods is reducing the lifetime of electrical appliances. Further, end-use electricity consumption seems likely to rise with the purchase of larger houses, air conditioners, computers and other energy-intensive appliances. The encouraging news is that different levels of government in Indonesia have already introduced many measures that can help conserve energy. These include several policies and measures intended to increase energy efficiency. For example, the revised National Energy Policy (KEN) (2014) sets energy efficiency targets for multiple sectors, including the residential sectors. KEN further mandates regional and local governments to develop energy conservation plans (RIKED) based on overarching central plans. In addition to the above West Java province, where Bogor is located, has drafted a RIKED to implement energy-saving audits, and improve energy efficiency by 25% by 2030.

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Meanwhile, the national government has sought to expand the development of energy efficiency standards and labelling for electric appliances with enforcement starting initially with ballasted lamps in 2015 followed by air conditioners in 2016 (IEA 2017); further plans for additional appliances (e.g. rice cookers, refrigerators and fans) are likely to follow. In parallel, the national government has developed an energy saving campaign with leaflets that identify energy intensive electric appliances; a checklist for auditing energy consumption; possible awareness programs within the workplace and the provision of other sources of information. Furthermore, the state owned utility, Perusahaan Listrik Negarathe (PLN) is replacing the post-paid metering system with a pre-paid version that requires consumers to purchase a token. The new system is designed to remind consumers of their energy use per purchase. The above collection of measures underline that energy savings is an increasingly important policy goal in Indonesia. They also demonstrate that Indonesia is aiming to use some of the informational 8

instruments discussed previously to achieve that goal in cities like Bogor. 4

Data sourcing and methodology

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To test the hypotheses in Table 3 a survey was conducted together with the Agriculture University of Bogor. The survey was administered through in-person interviews at the homes of respondents from October and November 2015. A pair of interviewers visited each respondent with the city government’s official letter of approval for the survey. A simple random stratified sample was developed where the number of respondents was proportional to the population in the city’s six districts and 68 villages. The response rate where relatively high at 81 percent. Respondents were asked the following question: are you willing to change your electrical appliances from a conventional to LED lightbulb? Your electricity cost for lighting could fall by approximately 50% with an initial cost of around 50 USD. This is an admittedly narrow measure of willingness to invest in energy savings technologies. However, as noted previously, it is well-aligned with the steps taken by the government to facilitate energy savings in the residential sector and the only labelling scheme being implemented at the time of the survey. Electricity tariffs for households at the time of the survey were divided into three classes in the residential sector depending on the voltage of the contract: (1) 450 VA – 2200 VA; (2) 3500 VA- 5500 VA; (3) 6500 VA and over. In West Java Province where Bogor is located, 99 percent of the electricity residential users were in the lowest class. Monthly electricity charges per customer (i.e. households) in West Java province for this class in 2014 was approximately 100,000 IDR or roughly USD 7.5. This survey shows an average household would have four family members and a monthly income of approximately 4,000,000 IDR (roughly USD 300); paying for electricity therefore could constitute a significant share of average incomes. The survey results were then analyzed using ordinal logistic regression models to test the hypotheses in Table 3. The equations for estimating are presented in the following notation:

The probability for j= 1 is:

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X1 ….Xk are k explanatory variables.

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Y is the dependent variable with C ordered categories, j = 1…C, and probabilities π (j) = P(Y=j)

exp [α (1) – (β1X1 + ---- + βkXk)]

1+ exp [α (1) – (β1X1 + ---- + βkXk)]

……. (1)

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P(Y=1) = γ (1) =

The probability for the j =2 up to C-1 is: P(Y=j) = γ (j) γ (j-1)

- exp [α (j-1) – (β1X1 + ---- + βkXk)]

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= exp [α (j) – (β1X1 + ---- + βkXk)]

1+ exp [α (j) – (β1X1 + ---- + βkXk)]

1+ exp [α (j-1) – (β1X1 + ---- + βkXk)] ……. (2)

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The probability for j=C is: P(Y=C) = 1- γ (C-1) =

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exp [α (C-1) – (β1X1 + ---- + βkXk)] 1+ exp [α (C-1) – (β1X1 + ---- + βkXk)]

……. (3)

The Data, Variable Construction, and Preliminary Tests

This equation was applied to a sample of 600 respondents. A few points pertaining to the description of the data, variable construction, and preliminary chi-square tests merit underlining. This section covers

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those points, starting with a description of the data. To ensure gender balance, 300 men and women were surveyed; the same proportion of men to women was preserved across Bogor’s six districts (Table 4) although statistical data shows there is an average of 2 percent more women than men in Bogor throughout the six districts (Bogor City Socioeconomic Data, 2014). The modal response for the age of respondents was in the 30s with the next highest concentration in the 40s (the mean age was 43). When it came to household size, the highest percentage of responses was for households with four people (26.2 percent), followed by three people (22.3 percent), and five people (19.3 percent). Few respondents lived in households with only one (3.9 percent) or two people (11 percent). The vast majority of respondents—over 85 percent—owned a home; only 13 percent rented their home. The majority of respondents were employed in the private sector (54.3 percent) with the next highest proportion indicating they were housewives (22.7 percent). Education levels followed an approximate normal distribution, with the plurality of responses belonging to the “entered high school but did not graduate” category (38.5 percent).

Household size

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Education level

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Home ownership Metering system

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50% 50% 0.33% 15.17% 25.67% 22.00% 16.00% 9.00%

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10.27% 22.27% 20.77% 17.78%

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Location

300 300 2 91 154 132 96 51 66 (103,719) 131 (224,963) 118 (209,737) 110 (179,615 ) 59 (100,517) 116 (191,468) 23 65 132 155 114 102

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Age

Male Female Below 19 20s 30s 40s 50s 60s + Central Bogor (Total population) West Bogor (Total population) Tanah Sareal (Total population) North Bogor (Total population) East Bogor (Total population) South Bogor (Total population) 1 person 2 people 3 people 4 people 5 people 6 people or more College graduate or above High school graduate or above Entered high school but have not graduated Did not reach high school Other________________ Own a home Renting a home Others Prepaid system Postpaid system N/A

Percentage

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Gender

Number

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18.96%

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Table 4: Demographic Characteristics

3.9% 11.0% 22.3% 26.2% 19.3% 17.3%

95

15.8%

155

25.8%

229

38.2%

117

19.5%

4 514 79 7 144 455 1

0.7% 85.7% 13.2% 1.2% 24.0% 75.8% 0.2%

Another feature of the sample involved the effect of urbanization on Bogor. During the period when the sample was collected the majority of the households owned basic appliances such as lights—and the number of lights generally increased with income (Table 5). The number of air conditioners were surprisingly very limited throughout all income levels, suggesting this could lead to a rise in energy consumption as Bogor’s economic growth continues. 10

Table 5: Ownership per Income Level Monthly income level (IDR) 2.6-5 5.1Above million million 7.5 7.6

Equipment

Units Owned

0-2.5

Lightbulb

1-5 6-10 11-15 150 1 2 3

35% 55% 5% 0% 97% 2% 0% 0%

16% 61% 16% 7% 85% 12% 2% 1%

20% 53% 20% 7% 87% 13% 0% 0%

6% 29% 47% 18% 71% 12% 12% 6%

96% 3% 0% 0%

79% 9% 1% 0%

67% 27% 7% 0%

35% 53% 6% 6%

Air conditioners

Cars

0 1 2 3

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Table 6: Knowledge of national and local policies LED No

0: No 1: No interest 2: Have heard 3: Yes aware

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10.4% 26.7% 19.8% 43.1%

0: No 1: No interest 2: Probably yes 3: Definitely yes

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12.6% 10.4% 48.0% 29.0%

47.1% 26.5% 19.1% 7.4%

0: No 1: No interest 2: Have heard 3: Yes aware

* *

5.9% 16.8% 27.5% 49.8%

11.0% 27.2% 39.0% 22.8%

0: No 1: No interest 2: Have heard 3: Yes aware

* *

10.4% 32.2% 10.6% 46.8%

28.7% 43.4% 8.1% 19.9%

0: No 1: No interest 2: Have heard 3: Yes aware

* *

12.4% 28.5% 12.4% 46.8%

29.4% 42.6% 13.2% 14.7%

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LED Yes

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Local Policy

Are you aware that Indonesia has pledged to reduce emissions by 26% to 41% through cooperation with other countries? An energy efficiency labelling program exists for electric appliances. Do you select appliances based on this program? Bogor promotes the use of biofuel for public transportation by recycling cooking oil from the city’s shopping centers and eateries. Are you aware of this policy? Bogor promotes a “Climate Village Programme” for integrated waste management to reduce floods, food security. Are you aware of this policy? Bogor government replaces street lightbulb to energy efficient LED. Are you aware of this policy?

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National policy

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Questions

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Questions about awareness of five national and local climate and energy policies were used to assess levels of information. One of the survey questions focused on whether respondents referred to the national policy of energy efficiency labels when deciding on which electric appliance to purchase (Table 6). A second set of questions looked at information that was tailored to an individual’s background and needs. It asked respondents whether they checked their individual energy bills on a regular basis. A third set of questions involved whether an individual had access to information on energy savings at the workplace or other locations. It is important to note that information levels were generally high. Almost 50 percent of respondents indicated they were aware of existing climate and energy policies at the national and local levels.

24.3% 34.6% 22.1% 19.1%

Tailored info

Do you check your 1. Never * 25.6％ 26.9% monthly costs of 2. Sometimes * 16.6% 31.3% 41.8% 57.8% energy for both 3. Yes electricity and fuel? Have you received 0: No * 92.3% 98.5% 7.7% 1.5% energy savings 1: Yes * training? The χ~2 distribution was compared for respondents willing to replace lights with LED versus those who are not. The asterisks * indicate that the χ~2 is statistically different from zero at the 1 percent significance level, ** at the 5 percent level.

Table 7: Prepaid payment versus conventional post-paid metering method Questions

Answers

χ 2

Post paid

Prepaid

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A majority of the respondents who were willing to switch to LED indicate they are either aware or have heard of most of the national and local policies (Table 6). In comparison, those unwilling to switch to LEDs show that over a majority are either not interested or not aware of most of those policies.

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Would you replace your 1: Yes * 72.4% 82.9% 27.6% 17.1% conventional lights with LEDs 2: No in your house to reduce electricity bill if your cost for lighting is decreased by 50% with an initial cost of around 50 USD? The χ~2 distribution was compared for respondents with post-payment systems versus those pre-paid systems. The asterisks * indicate that the χ~2 is statistically different from zero at the 5 percent significance level.

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Another finding is the results from comparing respondents who were adopting the pre-paid PLN payment method versus the conventional post-paid metering method. As shown in Table 9, regardless of the payment method respondents favored switching to LED. According to the chi-square test (χ2 test), the pre-paid system, which reminds the respondent of the electricity costs incurred, could encourage a switch to LEDs.

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The respondents were also questioned about their tendency to participate in social activities to understand whether this would affect their willingness to purchase LEDs. One set of questions focused on if respondents were engaged in environmental activities. A second set of questions on participation was also included for multiple forms of social activities. Levels of participation in this wider set of activities were measured by identifying frequency of attendance at neighborhood associations; social activities; and sports activities. The answers for the latter three options were then summed together to develop a single variable labeled “social activities”. It resulted with two variables in the regression. Here also a chi-square test (χ2 test) examined the possible difference in distribution of answers between respondents who were willing to purchase energy efficient LED lightbulbs for their existing lightbulbs versus those who were not. The test showed a difference in participation in environmental activities and sports activities, while there was none found in terms of their participation in neighborhood and social activities. Table 8: Participation in environmental campaigns and social activities Questions

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Do you participate in environmental activities? Do you participate in neighborhood activities? Do you participate in social activities? Do you participate in sports activities?

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Answers

0. Never 1. Sometimes 2. Routinely 0. Never 1. Sometimes 2. Routinely 0. Never 1. Sometimes 2. Routinely 0. Never 1. Sometimes 2. Routinely

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LED Yes

LED No

*

41.9% 36.7% 21.3%

54.8% 34.9% 15.6%

The χ~2 distribution was compared for respondents willing to replace lights with LED versus those that are not. The asterisks * indicate that the χ~2 is statistically different from zero at the 5 percent significance level, ** at the 1 percent level.

21.3% 30.0% 48.6%

14.7% 33.1% 52.2%

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26.3% 38.0% 35.7%

31.6% 37.5% 30.9%

51.7% 30.7% 17.6%

69.1% 15.4% 15.4%

* *

Results

The number of respondents who were willing to switch to LEDs was high. As demonstrated in Figure 2, 67 percent indicated a willingness to switch; 23 percent were not willing to switch; and 10 percent were unable

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to answer the question. The next question was what does the model suggest about the relationship between the independent and dependent variables.

Figure 2: Willingness to switch to LED

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To understand these relationships, the ordinal logistical regression was run on two models (Table 10). The difference between the two models is the latter version includes an interaction term between the information and participation variables. The interpretation of that term and the differences between the two models are discussed in greater detail at the end of this section.

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Table9: Regression results

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The asterisks * indicate that the coefficients are statistically different from zero at the 5 percent level.

Demographic and socio-economic variables To appreciate the influence of specific variables, it is possible to examine the magnitude and significance of the variables across both models. The predicted probabilities for the “age” variable indicates younger people were, on average, more inclined to invest in LED, supporting hypothesis 2 in Table 3. For gender, in contrast to findings suggesting males would show a higher level of willingness, there was no significant differences across the sexes. Similarly, and slightly more surprisingly, the effects of the income levels do not demonstrate impacts on the decision to purchase LED specifically (Table 9). In contrast to the hypothesis based on the Swedish and Indian cases, wealthier individuals’ demand for energy remain inelastic to income levels in both models. This might require revisiting in future research. Meanwhile, supporting the hypothesis on the number of appliances, the number of appliances had a discernible impact on the predicted probability of choosing LED than those with no appliances.

Demographic and socioeconomic

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Gender Income Education

Information

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Intercept No | Not sure Intercept Not sure | Yes Age

Participation

Interactions

Ownership of appliances Information of policies Information of labelling program Information from energy bills Training in workplace Environmental activities Social activities (neighborhood, social, sports related) Information on policies x Environmental activities Residual deviance AIC N

Model 1

Model 2

1.8

324*

2.5

3.1*

-0.02 * (0.008) 0.08 (0.19) 0.06 (0.13) -0.1 (0.12) 0.13 * (0.06) 0.21 * (0.03) 0.87* (0.10)

-0.02 * (0.008) 0.12 (0.20) 0.06 (0.13) -0.14 (0.12) 0.12 * (0.07) 0.27 * (0.05) 0.85* (0.11)

0.09 (0.12)

0.10 (0.11)

0.49 (0.50) 0.26 (0.15) -0.08 (0.07)

0.83* (0.5) 0.98 * (0.44) -0.08 (0.06) -0.07 * (0.04)

818

815

844 600

843 600

Environmental Campaign

Policy.Knowledge*Environmental.Campaign effect plot Yes Not sure No

Willingness.to.switch.to.LED (probability) to switch to LED (probability) Willingness

0

1

4

Environmental.Campaign = 0

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Environmental.Campaign = 1

Environmental.Campaign = 2

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Policy knowledge levels

Yes Not sure No

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Figure 3: Environmental Campaign Participation and Policy Information Interaction Effects

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6.2 Information and participation The next set of questions focused how the information and participation variables influenced the decision to purchase LEDs. The results generally support the claim that more information about energy savings (and its benefits) increases the likelihood to purchase LEDs. However, this relationship did not hold for the all of the information variables. Information about policies and energy efficient labelling for appliances had a substantively and statistically significant effect on the respondents’ willingness to invest in LEDs across both models. Training in the workplace had a significant effect in the second model. Information from energy bills did not register a discernible effect. The results for the participation variables also conformed to some but not all expectations. Participation in “environmental campaigns" had the predicted positive effect on the willingness to purchase LEDs. In contrast, participation in the more general category of “social activities” had a negative sign and was not statistically different from zero. Perhaps most interestingly is the possible interaction between information and participation variables. To examine the interaction, a comparison between model 1 and 2 is helpful. One of the ways of assessing the performance of the two models is to review the relative “fit” of the data by looking at the akaike information criterion (AIC). The smaller the AIC, the better the fit of the model for the dataset in question. In this case, Model 2, the model with the interaction term, has a slightly smaller AIC and the better fit of the two models. The results to Model 2 also shows that the interaction term itself is significant. The next logical question is how to interpret the results from the model with the interaction term. One way of making the interpretation more visual is to use the estimated coefficients to plot the possible values of the dependent variable across the range of possible values for the information and participation variables (holding the other variables constant at their mean). The results of this plotting exercise can be seen in Figure 3. Figure 3 shows the frequency of participating in environmental campaigns ranging from “zero” in the diagram on the far left, “sometimes” in the center, and “regularly” in the far right; information levels range from “5” to “20” in each of these figures. The figure shows that the effects of information or participation are not sizable when the values for both information and participation are high. People with high levels of information do not become significantly more likely to purchase LEDs when they participate actively in environmental campaigns. However, the information and participation variables seemed to compensate for each other. The predicted probability of respondents with regular participation is higher 14

when the respondent has low levels of information. A lack of information could, to some degree, be compensated by engaging in environmentally-related activities—with regular participants more likely to change their willingness to alter purchasing behavior than less frequent participants.

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Policy Implications and Future Research

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This article’s main objective was to examine determinants of purchases of LED in a fast growing city in Southeast Asia. Toward that end, the article looked at the possible relationship between the willingness to purchase LEDs and a series of individual/socioeconomic as well as information/participation variables in Bogor, Indonesia. It found support for the link between most of the hypothesized sets of variables and willingness to purchase LED in Bogor. The income and education variables were possible exceptions. The results also showed that participation in environmental activities might compensate for low levels of information about the environment and energy savings. This compensatory effect may be particularly important because it suggests several influential paths to raising awareness. There are many policy implications that follow from these findings. The first set of implications involve the energy efficiency labelling policies that are initially concentrating on lightbulbs in Indonesia. The results of the modelling suggest that this kind of labelling is effective and should continue for LEDs. Plans to expand the scope of this labelling to other appliances would seem to be a reasonable way forward. It should nonetheless be borne in mind that this same approach may not be as effective for larger and more expensive appliances. A difference in the willingness to invest between LED and refrigerators may be likely if the initial costs of an energy savings refrigerator is significantly higher than a less efficient model. If this is the case, the government may need to invest in rebate programs for lower income households that are willing to spend a little more for long-term savings on these higher cost items. Alternatively, the government may want to focus more on low cost solutions for all consumers. A second set of implications involves the kinds of information that are likely to generate the most significant impacts. The data analysis shows that respondents do not choose appliances by looking at the information in their electricity bills. This implies that creating awareness of one’s energy consumption may be only the first step in a longer process toward more sustainable behavior and consumption. Concrete tips and advice on how to save energy could would be needed follow-up steps. Another set of similarly motivated follow-up options would draw upon the example of Opower in the U.S. and Empowering of the EU wherein the initiatives provide an impersonal account of the amount of electricity consumed for the similar households to motivate consumers to reduce consumption and possibly do more than their neighbors. The results also suggest that transferring information through bills is not the only way to promote LED. It may also be possible to share if information at events and gatherings during which there is sustained interaction among community members. Looking at the preliminary chi-square test results, a sporting event at which the community gathers could be a good opportunity to disseminate information and discuss the benefits of energy savings. This may be especially important now as Indonesia’s national government is placing a growing emphasis on energy savings. Local governments like Bogor could take an active role by offering opportunities for such community gatherings. Another set of option that follow the conclusions of the article involve integrating lessons on energy savings into the national school curriculum. Disseminating information early has significant potential since the results show younger citizens were more receptive to the idea of energy savings. It may also be worthwhile to target young students in elementary schools because this is when children become not only aware but also capable of understanding the need to improve society. Both of the two previous findings—on social activities and education--may be relevant for policies such as the West Java provincial energy plan (Rencana Umum Energi Daerah, RUED) wherein there is scope for promoting activities that help raise awareness of the benefits of energy savings. The main findings from this study opens up the possibility of several potentially valuable areas for future research. One such channel would be to look more closely at what kind of information matters under what kinds of conditions. For example, one could conduct follow-up research where there is a clear 15

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Acknowledgements

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effort to distinguish between the indirect and direct dissemination of relevant information. One could similarly look at the circumstances during which that information is provided. There may, to provide a relevant example, be an effort to share information in the context of a social gathering that brings together likeminded people in contrast to a setting with unfamiliar attendees. This kind of research design might help shed light on whether people are more receptive to energy savings behavior when they know that others will be considering the same kinds of actions. Another promising area of research would begin to look more closely at the revealed preference to save energy and the actual behaviors associated with that preference. This could be done, for example, with the installation of energy meters and survey designs that measure energy use behaviors before and after the provision of information or engagement in an activity where energy savings is discussed. This kind of approach may also look at whether and to what extent energy savings behaviors remain sustainable over time (Abrahamse et al 2005; 2007; Henryson et al 2000; Darby 2006). In so doing, it would have important linkages with work on sustainable energy transitions (Birol and Keppler, 2000; Geels, 2005; Loo and Loorback, 2012). Yet another potentially illuminating area of research would involve employing the same survey in another city in Indonesia or other rapidly developing countries. To illustrate, it would be useful to know whether and to what extent do similar findings hold for other parts of the world since the results from Bogor did not fully satisfy the hypothesis set up using the case study of India. It may also be useful to gather survey data and run similar tests over time in Bogor. This will further help to demonstrate the specificity or generalizability of the findings from this research.

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This research was conducted with the generous funding for the project “Innovative Modelling and Monitoring Research towards Low Carbon Society and Eco-Cities and Regions from the Ministry of Environment Japan. Special appreciation goes to the students of the Bogor University of Agriculture, Landscape Management Laboratory. Without their dedication to the social survey this paper would not have been possible. References

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