Aplicação de nudges no consumo de energia elétrica: um estudo experimental

Abstract

The growing recognition of climate change and the pressing need to reduce greenhouse gás emissions underscore the importance of developing solutions for the efficient use of electricity. However, a notable gap remains in the integration of behavioral interventions, such as nudges, into Information Systems to optimize energy consumption. This study seeks to address this gap by proposing a system based on smart electricity meters that generates behavioral messages (nudges) using Generative AI via the OpenAI API to promote sustainable practices. The objective of this research is to design an Information System that utilizes real-time monitoring and personalized messages to enhance awareness of energy consumption. The study employs a Systematic Literature Mapping (SLM) to identify effective nudges in energy efficiency, the Münscher, Vetter & Scheuerle [2016] taxonomy to construct the Nudge Architecture, and Design Science Research (DSR) to guide technological development. Behavioral messages, developed using Prompt Engineering techniques, are categorized as positive, neutral, or negative and delivered to users via SMS in real time. To evaluate the system, a descriptive and exploratory study employing a quantitative approach was conducted through a questionnaire. Data analysis, performed using Stata 14.0 software, revealed that the sample of 104 respondents exhibited a high level of environmental awareness (15.88 points) and a 78.3% engagement rate with the nudge messages. The results further indicated that environmental awareness and education level are significant predictors of sustainable behavior. Respondents with higher environmental awareness were more engaged in adopting sustainable practices and demonstrated greater mindfulness of their electricity consumption when using Information Systems. The main contribution of this research lies in demonstrating that the integration of nudges with real-time electricity monitoring systems can effectively promote sustainable behaviors. This system expands the potential for automated interventions to enhance energy consumption efficiency, offering promising opportunities at the intersection of technology and sustainability.