A Novel Feature Selection Technique to Better Predict Climate Change Stage of Change

Mindful Eco-Wellness: Steps Toward Personal and Planetary Health

Rising greenhouse gas levels heat the earth's surface and alter climate patterns, posing unprecedented threats to planetary ecology and human health. At the same time, obesity, diabetes, and cardiovascular disease have reached epidemic proportions across the globe, caused in part by decreases in physical activity and by over-consumption of carbon-intensive foods. Thus, interventions that support active transportation (walking or cycling rather than driving) and healthier food choices (eating plant-based rather than meat-based diets) would yield health and sustainability "co-benefits." Emerging research suggests that mindfulness-based practices might be effective means toward these ends. At the University of Wisconsin-Madison, we have developed a mindfulness-based group program, Mindful Eco-Wellness: Steps Toward Healthier Living. Loosely based on the Mindfulness-Based Stress Reduction course, our curriculum teaches mindfulness practices in tandem with sustainability principles, following weekly themes of Air, Water, Food, Energy, Transportation, Consumption, Nature Experience, and Ethics. For example, the "Air" class offers participants practice in guided breath meditations while they learn about the benefits of clean air. The theme of "Food" is presented through mindful eating, accompanied by educational videos highlighting the consequences of food production and consumption. "Transportation" includes walking/movement meditations and highlights the health benefits of physical activity and detriments of fossil-fueled transportation. Pedagogical lessons on energy, ecological sustainability, and the ethics of planetary health are intertwined with mindful nature experience and metta (loving-kindness) meditation. Curricular materials, including teaching videos, are freely available online. Pilot testing in community settings (n = 30) and in group medical visits (n = 34) has demonstrated feasibility; pilot data suggests potential effectiveness. Rigorous evaluation and testing are needed.

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters

Travel mode choice (TMC) prediction is crucial for transportation planning. Most previous studies have focused on TMC in adults, whereas predicting TMC in children has received less attention. On the other hand, previous children's TMC prediction studies have generally focused on home-to-school TMC. Hence, LIGHT GRADIENT BOOSTING MACHINE (LGBM), as a robust machine learning method, is applied to predict children's TMC and detect its determinants since it can present the relative influence of variables on children's TMC. Nonetheless, the use of machine learning introduces its own challenges. First, these methods and their performance are highly dependent on the choice of "hyperparameters". To solve this issue, a novel technique, called multi-objective hyperparameter tuning (MOHPT), is proposed to select hyperparameters using a multi-objective metaheuristic optimization framework. The performance of the proposed technique is compared with conventional hyperparameters tuning methods, including random search, grid search, and "Hyperopt". Second, machine learning methods are black-box tools and hard to interpret. To overcome this deficiency, the most influential parameters on children's TMC are determined by LGBM, and logistic regression is employed to investigate how these parameters influence children's TMC. The results suggest that MOHPT outperforms conventional methods in tuning hyperparameters on the basis of prediction accuracy and computational cost. Trip distance, "walkability" and "bikeability" of the origin location, age, and household income are principal determinants of child mode choice. Furthermore, older children, those who live in walkable and bikeable areas, those belonging low-income groups, and short-distance travelers are more likely to travel by sustainable transportation modes.

The cultural differences in the effects of carbon emissions - an EKC analysis

The postmodernism stance critiques the current discourse of modern consumption-based societies. These societies legitimize their embedded beliefs and actions based on their lifestyle reflected through their consumption patterns. Their consumption patterns relate to carbon footprints that are yet to be explored in the current body of knowledge at the macro level. The legitimacy of their lifestyle and consumption patterns rely on their conscious efforts in reducing carbon footprints. This study uses the environmental Kuznets curve model and explores the impact of household lifestyle and household consumption patterns on CO₂ emissions across different cultures and societies worldwide. 49 countries were taken to assess how different cultures define consumption patterns related to CO₂ emission. The results showed that the curvilinear relationship between consumption and CO₂ depends on the country's national cultural orientation.