This paper employs satellite imagery and machine learning approach to analyze regional disparities in human development (HDI) and multidimensional poverty (MPI). Concentrating on 213 subnational regions worldwide, this study strategically selects countries based on the Human Development Index (HDI), Gini coefficient, and the Theil-T index as measures of deprivation and inequality. The utilization of Google Static Map API and Convolutional Neural Networks (CNNs), facilitates the comprehensive analysis of these indicators. The findings challenge uniform development assumptions in nations, providing refined insights for targeted policy interventions. This quantitative approach, merging satellite technology and artificial intelligence, offers a significant contribution to understanding sustainable development.

This research is centered on the development of a BERT-based metric for greenwashing, designed to address the existing limitations inherent in ESG assessment methodologies. Unlike standard assessments, ESG-BERT considers real-time policy details and reduces the risk of inaccurate evaluations and greenwashing. We employed ESG-BERT along with financial and environmental data to predict greenwashing among Korean KOSPI200 companies. By using advanced machine learning models like ANN, LR, RF, and XGB, the study found that XGB performs best in predicting greenwashing. Furthermore, the study compares greenwashing predictions between companies with top5 and bottom5 ESG ratings. The results showed better performance for the top 5 companies compared to the bottom 5 companies.

Measuring the impact of tourism on society is important for efficient tourism planning and budgeting. Considerable amount of financial resources has been allocated for tourism promotional efforts. Therefore, it is important to understand how well promotional efforts have stimulated tourism social impacts. This study aims to predict the UNWTO promotional video views and its resulting impact on society. A CNN model with VGG16 was used to classify videos and images into clusters and RGB models. Quantitative analysis was conducted to predict video views and annual GDP as a societal impact. Our model performance for predicting official video views reveals strong relationship between image characteristics and promotional video views. Most notably, the strong correlation between image/video data and countries’ GDP growth rate highlighted by the model’s performance illuminates the potential of image data as a predictive tool in understanding the economic impacts of tourism particularly in rural areas.