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1. An Agent-Based Model for Adoption of Clean Technology Using the Theory of Planned Behavior

   https://doi.org/10.1115/1.4047901  

   Pakravan, Mohammad H.; MacCarty, Nordica (February 2021, Journal of Mechanical Design)

   null (Ed.)

   Abstract Technology adoption in low-income regions is among the key challenges facing international development projects. Nearly 40% of the world's population relies on open fires and rudimentary cooking devices exacerbating health outcomes, deforestation, and climatic impacts of inefficient biomass burning. Clean technology alternatives such as clean cookstoves are among the most challenging technologies to approach their target goals through sustainable adoption due to a lack of systematic market-driven design for adoption. Thus, a method is needed to provide insight regarding how target customers evaluate and perceive causes for adopting a clean technology. The holistic approach of this study captures technology adoption through lenses of social networks, individual and society scale beliefs, and rational decision-making behavior. Based on the data collected in the Apac region in Northern Uganda, an agent-based model is developed to simulate emerging adoption behavior in a community. Then, four different scenarios investigate how adoption patterns change due to the potential changes in technology or intervention strategy. These scenarios include influence of stove malfunctions, price elasticity, information campaigns, and strength of a social network. Results suggest that higher adoption rates are achievable if designed technologies are more durable, information campaigns provide realistic expectations for users, policymakers, and education programs work toward women's empowerment, and communal social ties are recognized for influence maximization. The application of this study provides insight for technology designers, project implementers, and policymakers to update their practices for achieving sustainable and to the scale clean technology adoption rates. 

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2. What Motivates Behavior Change? Analyzing User Intentions to Adopt Clean Technologies in Low-Resource Settings Using the Theory of Planned Behavior

   https://doi.org/10.3390/en13113021  

   Pakravan, Mohammad H.; MacCarty, Nordica (June 2020, Energies)

   null (Ed.)

   Understanding and integrating the user’s decision-making process into product design and distribution strategies is likely to lead to higher adoption rates and ultimately increased impacts, particularly for those products that require a change in habit or behavior such as clean energy technologies. This study applies the Theory of Planned Behavior (TPB) in design for global development, where understanding the tendency to adopt beneficial technologies based on parsimonious approaches is critical to programmatic impact. To investigate robustness and applicability of behavioral models in a data scarce setting, this study applies TPB to the adoption of biomass cookstoves in a sample size of two remote communities in Honduras and Uganda before and after a trial period. Using multiple ordinal logistic regressions, the intention to adopt the technology was modeled. Results quantify the influence of these factors on households’ intentions to cook their main meals with improved cookstoves. For example, the intention of participants with slightly stronger beliefs regarding the importance of reducing smoke emissions was 3.3 times higher than average to cook more main meals with clean cookstoves. The quantitative method of this study enables technology designers to design and develop clean technologies that better suit user behavior, needs, and priorities. In addition, the data driven approach of this study provides insights for policy makers to design policies such as subsidies, information campaigns, and supply chains that reflect behavioral attributes for culturally tailored clean technology adoption initiatives. Furthermore, this work discusses potential sources of bias and statistical challenges in data-scarce regions, and outlines methods to address them. 

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3. Behavioural frameworks to understand public perceptions of and risk response to carbon dioxide removal

   https://doi.org/10.1098/rsfs.2020.0002  

   Shrum, Trisha R.; Markowitz, Ezra; Buck, Holly; Gregory, Robin; van der Linden, Sander; Attari, Shahzeen Z.; Van Boven, Leaf (October 2020, Interface Focus)

   The adoption of carbon dioxide removal (CDR) technologies at a scale sufficient to draw down carbon emissions will require both individual and collective decisions that happen over time in different locations to enable a massive scale-up. Members of the public and other decision-makers have not yet formed strong attitudes, beliefs and preferences about most of the individual CDR technologies or taken positions on policy mechanisms and tax-payer support for CDR. Much of the current discourse among scientists, policy analysts and policy-makers about CDR implicitly assumes that decision-makers will exhibit unbiased, rational behaviour that weighs the costs and benefits of CDR. In this paper, we review behavioural decision theory and discuss how public reactions to CDR will be different from and more complex than that implied by rational choice theory. Given that people do not form attitudes and opinions in a vacuum, we outline how fundamental social normative principles shape important intergroup, intragroup and social network processes that influence support for or opposition to CDR technologies. We also point to key insights that may help stakeholders craft public outreach strategies that anticipate the nuances of how people evaluate the risks and benefits of CDR approaches. Finally, we outline critical research questions to understand the behavioural components of CDR to plan for an emerging public response. 

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4. How Modeling the Adoption of Household Clean Energy Technologies can Inform Stakeholder Decisions

   https://doi.org/10.1109/GHTC46280.2020.9342936  

   Peiffer, Erin; MacCarty, Nordica (October 2020, 2020 IEEE Global Humanitarian Technology Conference (GHTC))

   null (Ed.)

   Increasing the adoption of household clean energy technologies is important to achieving sustainable development and to improving the environmental, economic, and social impacts of these technology interventions. While much work has been done to understand the many factors driving successful interventions, little research has been done to quantify and then model the adoption of these technologies. Current optimization models to maximize impact rely on the effective prediction of adoption, yet this piece remains the least understood component. The purpose of this paper is to outline the various ways in which being able to model the adoption of household clean energy technologies would be beneficial for designers, implementation organizations, and policymakers to aid in their design and decision-making processes. We provide a brief review of the literature and current challenges to adoption, examples of current methods and modeling tools that can be used to optimize sustainable impacts, and how these tools could be improved through adoption modeling. We discuss the benefits of being able to model adoption for various stakeholders in the clean energy sector along with proposing some methodologies that can be used to accomplish this goal. 

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5. Social Network Influence on Residential Solar Photovoltaic Adoption in an Agent-Based Electricity System Model

   https://doi.org/10.1115/DETC2024-140793  

   Dello_Russo, Gina; Wu, Lei; Lytle, Ashley; Odonkor, Philip (August 2024, American Society of Mechanical Engineers)

   Abstract As the United States phases out traditional fossil fuels in favor of renewable energy sources, it is important to capitalize on all available avenues to increase renewable penetration. In the last decade, the costs associated with residential solar photovoltaic (PV) installations have decreased significantly, providing more homeowners with the opportunity to generate their own clean electricity. Research has found that the decision to invest in a residential solar PV system is guided by economic, social, and personal factors. Accounting for such complexities, the joint power of agent-based modeling and social network analysis is leveraged in this study to evaluate the effect of social influence on solar PV adoption. Featuring residential consumer agents with data-driven attributes, a logistic regression function to predict solar adoption, and random and small-world social network implementations, this work simulates residential solar PV adoption in New Jersey. Results indicate that including social influence in an agent-based electricity system model leads to increased installed residential solar capacity, but not necessarily higher adoption rates. These findings suggest that, with an understanding of the intricacies of consumer social networks, there are potential opportunities to bolster residential solar installations through low-cost social campaigns that motivate individuals to adopt home solar through their social ties. 

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