استراتژی مداخله برای تحریک رفتار صرفه جویی در انرژی ساکنان محلی

This study investigates intervention strategy in stimulating energy-saving behavior to achieve energy neutral urban development. A tree structure overview of potential interventions classified into three categories is revealed. An integrated behaviour model is developed reflecting the relations between behaviour and influence factors. A latent class model is used to identify segments of local residents who differ regarding their preferences for interventions. Data are collected from a sample of residents in the Eindhoven region of the Netherlands in 2010. The results indicate that social-demographic characteristics, knowledge, motivation and context factors play important roles in energy-saving behaviour. Specifically, four segments of residents in the study area were identified that clearly differed in their preferences of interventions: cost driven residents, conscious residents, ease driven residents and environment minded residents. These findings emphasize that the intervention strategy should be focused on specific target groups to have the right mixture of interventions to achieve effective results on stimulating them to save energy.

Sustainable urban development has developed in the past a few decades in the Netherlands to a mature subject of policy, research and innovation with various titles, such as low carbon city, energy neutral city, etc. The strategy of the local government to realize the energy-neutral target is based on the Trias Energetica: reduce energy demand, use renewable energy resources and use fossil fuels efficiently. The first step in this approach is to reduce energy demand because energy-saving is one of the cheapest ways to reduce CO2 emission (IEA, 2008). More than 25% of residential energy use could be reduced using readily available technologies (Gardner and Stern, 2008). Despite all efforts currently being undertaken, the energy-saving rate of residents is still very low (Laitner et al., 2009). Therefore, it is important to investigate how residents can be encouraged to save energy. There are two different types of energy-saving behaviors: investment behavior and curtailment behavior. Investment behavior is about spending money on the improvement of energy efficiency, and consequently saving energy. Curtailment behavior is about reducing energy usage by behavioral changes, such as shortening shower duration, lowering room thermostat settings. Contextual factors, knowledge, motivations, abilities and socio-demographic variables may influence such energy-saving behavior (Lutzenliser, 1993). There are certain interventions that local government can apply to promote energy-saving behavior, such as providing information, demonstration, offering free products, commitment with goal setting, giving feedback, rewards, financial support and legislation. There are a few researches about behavior models with causal relations between influence factors and behavior. Olander and Thøgersen's (1995) developed Motivation-Opportunities-Abilities model (MOA-model) with the focus on behavior in general. Value-Belief-Norm model developed by Stern (2000) addressed the environmental behavior in particular. However, an integrated behavior model with the focus on interventions and energy-saving behavior of residents is still missing. There are also researches that investigate household preferences for energy-saving measures using conjoint analysis (Poortinga, 2003), the relative impacts of two social change paradigms on residential behavioral energy-savings using regression model (Tiedemann, 2009), and behavior patterns and household profiles related to energy spent on heating using factor analysis(Guerra Santin, 2011). However, as we believe that people are different, interventions aimed at residential energy-saving may have different influences for different people (Guns, 2007). The intervention strategy that recognizes and accommodates the ways in which people differ will be more effective. In this paper, we propose a latent class model to tackle this problem. This paper is structured as follows: in Section 2 energy use behavior and its influence factors are discussed; Section 3 describes interventions; in Section 4 theoretical model of latent class are proposed; Section 5 provides information of data collection; in Section 6 all the analysis results are reported; and its implications for policy making are discussed in Section 7; finally some conclusions are drawn in Section 8. The main objectives of this study were to: (1) provide a overview of potential interventions, (2) attempt to present an integrated energy-saving behavior model of casual relations, (3) apply a new approach to identify segments with preferred energy-saving interventions. The results of this paper can provide decision support for local government in their policy making to effectively stimulate residents to save energy.

The aim of this study was to investigate how the local government could use interventions effectively to stimulate local residents to save energy. Specifically, the relationship between the segments and preferred interventions and between current energy behavior of residents and their characteristics are explored. Our results revealed that the estimates of feedback, rewards and financial interventions are most significant and positive for almost all four segments, while information, demonstration, free products and commitment are not. In particular, commitment intervention is valued by one segment with positive preference and by the other segment with absolute negative effect. It also provided some evidence for the argumentation that antecedent interventions has less impact compare to consequence interventions, while structural (financial/legislation) interventions have the most effects in changing residents’ behavior (Steg and Vlek, 2009). This is an important finding from a policy perspective in the sense that policies aiming to reduce energy use may especially want to target high user of energy through these interventions, because of a higher energy-saving potential. Remarkable is that four different segments of residents were found in this research that is in demand for different interventions: cost focused residents, conscious residents, ease driven residents and environment minded residents. This finding is in line with early studies on energy use behavior of the residents (Groot et al., 2008). This contributes to the current literature that municipal intervention strategy should focus on specific target groups, and utilize the interventions accordingly that are appreciated and effective for each target group. The findings of studies such as this can inform local governments with information about the impact prospects of interventions for various segments of the local population, and provide the support of energy neutral development. The preferences of interventions can be useful in developing optimal and effective policy strategies. Adopting energy-saving behavior often simply requires that people change their minds (Doppelt, 2008). A right mixture of interventions could increase the level of engagement, empowerment and sense of efficacy of involved people. The level of success is foreseeable for such a strategy concerning behavior change interventions. As such, our study provide an case that is successful at mapping the diversity that exists across the local population and providing targeted and appreciated interventions that effectively build on those differences. Our study had some limitations that are worth noting. First, several aspects such as detailed technical characteristics of dwelling features and lifestyle of the residents which might affect energy-saving behavior were not included in the study. Second, the respondents’ rate is rather low and from only one local municipality. It would be interesting to investigate whether or not differences can be found between different municipalities. Municipalities in urban areas may need to focus their strategies differently comparing with municipalities in rural areas. Third, the actual effectiveness of different interventions has not been tested. It is better in the next step to conduct some cognitive interviews with experts to validate the findings. It is also advised to do the reweight sampling to map it with the local census data before deploy them in reality. In addition, future research could focus on develop new evaluation methods to measure behavior-related energy-savings as well as evaluate the contexts and circumstances in which these energy-saving behavior are most likely to persist over time.