Using new technologies to maintain, construct, and reuse naturally created products like asphalt, soils, and water can reserve the environment (Baqersad et. al 2017, 2016). The objective of this study was to specify and model the behavior of households regarding the installation of water conservation technology and evaluate strategies that could potentially increase water conservation technology adoption at the household level. In particular, this study created an agent-based modeling framework in order to understand various factors and dynamic behaviors affecting the adoption of water conservation technology by households. The model captures various demographic characteristics, household attributes, social network influence, and pricing policies; and then evaluates their influence simultaneously on household decisions in adoption of water conservation technology. The application of the proposed simulation model was demonstrated in a case study of the City of Miami Beach. The simulation results identified the intersectional effects of various factors in household water conservation technology adoption and also investigated the scenario landscape of the adoptions that can inform policy formulation and planning.
To mitigate water scarcity, understanding why, and to what extent households adopt water conservation technology is crucial. Most of the recent literature on demand-side conservation management and technology adoption considered some of the following features: public opinion/acceptance, cost, education/awareness, demographics, conservation technology, and peer effect/social network influence. The current studies on water management include some factors that encompass water conservation technology adoption behaviors, but none of them considered to combine effects of multiple phenomena simultaneously. To address this limitation, this study proposes an agent-based modeling (ABM) simulation approach to abstract and model various factors and phenomena affecting households’ behaviors regarding water conservation technology adoption. Researchers have shown ABM to be a useful tool to explore behaviors and interactions of individuals in built environment and infrastructure systems (Azar and Menassa 2011; Mostafavi et al. 2015; Mostafavi et al. 2012). In agent-based modeling, decision makers are characterized as agents, each with a set of social capabilities and goals, values, and preferences. Agents exist in an environment defined by specific rules/micro-behaviors and can inform or evolve their goals or priorities over time (Gilbert 2008). Agent-based modeling can account for: (1) various rational and behavioral decision making rules for different agents; and (2) an agent’s reactions to other agents’ decisions.
Agent-based modeling has been incredibly successful in studying complex behaviors and policy analysis in infrastructure systems (Mostafavi et al. 2012; Mostafavi et al. 2015). Since agent-based modeling allows to look at the micro-behaviors within the system of water conservation and project future actions, it is the best tool for this study. In a real community, as people are connected through different ways such as family, work, neighborhoods and so forth, it is impossible to identify all the possible connection profiles based on the empirical data (Bandiera and Rasul 2006). With surveys and interviews, for example, the data received would not be on actual actions taken in water conservation technology adoption. The results would be more hypothetical with “what if” scenarios rather than a direct action taken. For other objective approaches, this forward-reaching simulation would not be possible. Additionally, surveys and other research tools can only reflect one particular population at a time, while agent-based modeling can replicate many different types of populations. Agent-based modeling has the capabilities to project diverse, tangible scenarios throughout future years (Mostafavi et al. 2013).
Agent-based modeling as a tool to analyze water management systems has been utilized and shown to be successful in the past (Kanta & Zechman 2014). One such study was conducted by Athanasiadis et al. (2005). In this study, the researchers explored the consumer effect on water-pricing policies using agent-based modeling. The research measured the impact of five different water price policies, and assessed its durability and influence with specific econometric and environmental data. They accounted for peer effect and the water suppliers on consumer-level agents. The results concluded which of the five pricing policies measured garnered the most and least residential water demand. This research showed the potential of agent-based modeling for water management. As sustaining water resources is so prevalent, being able to analyze water policy has growing importance (Athanasiadis et al. 2005). While this study was crucial in understanding the connection between econometric and water policy, it differs from the current research project in that it does not account for many sociodemographic components. Additionally, the focus of Athanasiadis et al.’s (2005) study was on water management policies developed by water agencies and political regulators, whereas the focus of this current research is on household conservation practices.
Another study that used agent-based modeling to simulate water use patterns focused on recreational home gardening (Syme et al, 2004). The researchers combined interview and external data to create a model that identifies the conservation possibilities of household gardens. Individual household gardeners were the agents, and they incorporated variables reflecting lifestyle, garden recreation and interest, conservation attitude, social desirability, and choice demographic factors including lawn size, income, and education. As a result of their research, it was found that the demographic characteristics had the most influence on external water use. The attitudinal parameters also related to external water use; however, the interaction between the parameters had minimal impact (Syme et al., 2004). This study was important because it tied together how water is used in social situations. While water is commonly perceived as a simple utility, it is also important to realize how water is used leisurely. Kanta and Zechman (2014) developed a model framework for assessing the consumer water demand behavior against different degrees of water supply and water supply systems. Their model incorporated both consumers and policy-makers as agents as they adapted their behaviors to different water supply systems and rainfall patterns. Studies such as these have set a precedent that agent-based modeling is a viable research tool for water use and management issues. Therefore, it will be the most effective approach for establishing which factors affect a household’s willingness to convert to water-saving technologies.
There have been many studies that analyze the influence of certain demographic, household, social, and external factors on water conservation technology adoption in isolation; however, theoretically, all of these attributes have the potential to influence an agent’s adoption utility simultaneously. In this study, the proposed agent-based modeling framework captures various demographic characteristics, household attributes, social network influence, and external policies; and then evaluates their influence simultaneously on household adoption of water conservation technology. The presented model assessed the probability of adoption of water conservation technology for each agent (household) based on a set of theoretical elements (e.g., innovation diffusion, peer effect, and affordability) and also empirical data from previous studies. In the agent-based modeling framework proposed in this study, the first step requires the abstraction of agents and their attributes. An agent is the main target of influence, and the model shows how the agents’ attributes and behaviors change over a designated period of time (20 years). Since this study focuses on information regarding water conservation technology adoption at the household level, each household equals one agent. Most of the characteristics and factors can change and are fluid over time, thus changing its influence on a household. The following sections explain the theoretical framework underlying the proposed agent-based modeling and show the computational implementation of the framework in a case study of the City of Miami Beach.