In recent years, there has been a notable shift in people's attitudes towards the environment, with an increasing emphasis on seeking out sustainable and eco-friendly destinations for their travel experiences. This evolving mindset has given rise to the pressing need for destinations to position themselves as a sustainable option. As the demand for services that prioritize sustainability continues to rise, the concept of a sustainable destination image has emerged as a pivotal marketing strategy to attract environmentally conscious tourists. This study employs the Theory of Planned Behavior (TPB) to delve into tourists' intentions toward sustainable tourism. investigate the sustainable intentions of tourists, a quantitative study has been conducted in Naples (Italy) with a sample size of 503 participants, including both Italian and foreign tourists. The SmartPLS tool was used to evaluate the measurement and structural models using the partial least squares structural equation modelling method (PLS-SEM). After evaluating the model, the research hypotheses are tested to measure the influence of Sustainable Tourism Intentions. This research makes a significant contribution to the Theory of Planned Behavior by introducing and integrating two innovative constructs: Environmental Awareness (EA) and Sustainable Destination Image (SDI). By incorporating these elements, this research sheds light on these factors' critical role in shaping sustainable tourism intentions. The study explores the relationship between sustainable tourism and tourist behaviors, advocating for a collaborative approach between governments and service providers to promote sustainability, with destination image playing a pivotal role in shaping tourist behavior. Governments can drive positive change through regulations and incentives for eco-friendly practices. Prioritizing a sustainable destination image is essential for attracting environmentally conscious tourists, who increasingly seek eco-friendly destinations.

Sustainable consumption is crucial for mitigating global sustainability challenges. Understanding consumer behaviors and motivations, particularly in developing regions, is essential for designing effective interventions. This study pioneers an innovative methodology integrating participatory visual methods (photovoice) and artificial intelligence analysis to investigate food waste perceptions in an emerging economy context. Twenty-six university students participated in the study, documenting their lived experiences and perspectives on household food waste through photographs and narratives. The key results included 32% of participants expressing shock at the extent of food waste in their daily lives, while 28% showed relative indifference. AI-powered (Artificial Intelligence) computer vision and natural language processing were used to efficiently analyze the large visual and textual dataset. The mixed methods approach generated nuanced, situated insights into consumer attitudes, behaviors, and socio-cultural drivers of wastage. The key themes included low waste consciousness, aesthetic and convenience motivations, social norms, and infrastructural limitations. The participatory process proved effective for raising critical consciousness and uncovering consumption practice dynamics. AI analysis enabled rapid knowledge discovery from the qualitative data while mitigating researcher bias. This innovative integration of participatory methodologies and computational analytics advances sustainable consumption research by empowering marginalized voices and generating contextual insights from unstructured data. With further development, such human-centered AI approaches can transform the study and governance of sustainable consumption.

Creativity and innovation are integral to progress, yet neurocognitive mechanisms underlying creative thought remain unclear. This EEG study examined gender differences in brain activation during verbal and figural creative thinking tasks in 20 adults (10M, 10F). Participants completed verbal (Ask-and-Guess, Just Suppose) and figural (Unusual Uses) divergent thinking tasks from the Abbreviated Torrance Test while EEG was recorded. Analyses examined default mode, executive control, and salience network activity across frequency bands. Results revealed subtle gender differences in network activations and interactions during creative ideation. Females exhibited greater frontoparietal coupling, indicating enhanced cognitive flexibility. Males displayed higher default mode synchronization, suggesting greater persistence within an imaginative space. Findings provide initial neural evidence for specialized neurocognitive profiles between genders, with female advantages in flexible divergent thinking and male strengths in focused conceptual exploration. Request access from your librarian to read this chapter's full text.

Forecasting future trends in tourism growth is imperative for sustainability planning, yet highly complex due to the sector's multifaceted nature. This study leverages machine learning techniques to develop an integrated model predicting foreign tourist arrivals to India. Utilizing 2000-2022 data encompassing tourist statistics alongside relevant socioeconomic indicators, advanced algorithms like XGBoost uncover key drivers and relationships to generate strategic long-range forecasts. The multilayered analysis reveals tourism infrastructure investments strongly stimulate arrivals, underscoring policy priorities. However, skills training expenditures exhibit a more nuanced linkage, indicating localized needs. Beyond forecasting accuracy, the research makes significant methodological contributions regarding multivariate input features and model robustness for tourism ecosystems. It advocates systems thinking-based approaches over reductionist modeling of isolated past arrivals, given tourism's interdependence with broader socioeconomics. Request access from your librarian to read this chapter's full text.

Eating disorders and food waste present urgent public health and environmental crises requiring innovative solutions. Advances in artificial intelligence (AI) and mobile technology enable new interventions to promote healthy, sustainable eating habits. This research conceptualizes an AI system delivered via smartphone to monitor individuals' food contexts and deliver real-time nudges during preparation and consumption. Qualitative methodology through interviews and focus groups will refine nudge timing, content, and delivery for optimal impact based on user experiences. While human behavior remains complex, this research explores AI's potential to scale personalized interventions targeting problematic habits. Even minor improvements nudged at the individual level could have multiplicative benefits for population health and sustainability. Next steps include evaluating nudging effectiveness and addressing technical challenges like privacy and sustained engagement. Transdisciplinary collaboration is critical to develop ethical, human-centered AI systems for positive social change.