This study aimed to identify suitable sites for tea cultivation using both random forest and logistic regression models. The study utilized 2770 sample points to map the tea plantation suitability zones (TPSZs), considering 12 important conditioning factors, such as temperature, rainfall, elevation, slope, soil depth, soil drainability, soil electrical conductivity, base saturation, soil texture, soil pH, the normalized difference vegetation index (NDVI), and land use land cover (LULC). The data were normalized using ArcGIS 10.2 and the models were calibrated using 70% of the total data, while the remaining 30% of the data were used for validation. The final TPSZ map was classified into four different categories: highly suitable zones, moderately suitable zones, marginally suitable zones, and not-suitable zones. The study revealed that the random forest (RF) model was more precise than the logistic regression model, with areas under the curve (AUCs) of 85.2% and 83.3%, respectively. The results indicated that well-drained soil with a pH range between 5.6 and 6.0 is ideal for tea farming, highlighting the importance of climate and soil properties in tea cultivation. Furthermore, the study emphasized the need to balance economic and environmental considerations when considering tea plantation expansion. The findings of this study provide important insights into tea cultivation site selection and can aid tea farmers, policymakers, and other stakeholders in making informed decisions regarding tea plantation expansion.

Globally, there has been a lot of focus on climate variability, especially variability in annual precipitation and temperatures. Depending on the area, different climate variables have different degrees of variation. Therefore, analyzing the temporal and spatial changes or dynamics of meteorological or climatic variables in light of climate change is crucial to identifying the changes induced by climate and providing workable adaptation solutions. This study examined how climate variability affects tea production in Darjeeling, West Bengal, India. It also looked at trends in temperature and rainfall between 1991 and 2023. In order to identify significant trends in these climatic factors and their relationship to tea productivity, the study used a variety of statistical tests, including the Sen’s Slope Estimator test, the Mann–Kendall’s test, and regression tests. The study revealed a positive growth trend in rainfall (Sen’s slope = 0.25, p = 0.001, R² = 0.032), maximum temperature (Sen’s slope = 1.02, p = 0.026, R² = 0.095), and minimum temperature (Sen’s slope = 4.38, p = 0.006, R² = 0.556). Even with the rise in rainfall, there has been a decline in tea productivity, as seen by the sharp decline in both the tea cultivated area and the production of tea. The results obtained from the regression analysis showed an inverse relationship between temperature anomalies and tea yield (R = −0.45, p = 0.02, R² = 0.49), indicating that the growing temperatures were not favorable for the production of tea. Rainfall anomalies, on the other hand, positively correlated with tea yield (R = 0.56, p = 0.01, R² = 0.68), demonstrating that fluctuations in rainfall have the potential to affect production but not enough to offset the detrimental effects of rising temperatures. These results underline how susceptible the tea sector in Darjeeling is to climate change adversities and the necessity of adopting adaptive methods to lessen these negative consequences. The results carry significance not only for regional stakeholders but also for the global tea industry, which encounters comparable obstacles in other areas.

This article investigates whether increasing economic status through adopting digital payment capabilities in Delhi fosters economic and financial inclusion among urban women street vendors in Mahila Haat. Digital freedom is a new step forward in technology for everyone. Still, a woman not only balances the social responsibilities of childbearing, caring for her children and family, and struggling with economic issues, health issues, and undernourishment, but can also balance the household job of street vending to increase self-esteem and financial independence. This research work conducted a sampling survey and applied the Kruskal–Wallis H-test with a p-value (0.05) significance level by evaluating 11 variables to investigate the relationship between the digital capabilities and economic independence of street vendors in Mahila Haat (a women’s market where the vendors are all women) in the Red Fort area of New Delhi. UPI systems were created using measurements based on a five-point Likert scale to analyze different levels of satisfaction in clusters of digital capabilities on digital platforms. Further, the ordinary least squares (OLS) method was used to estimate quality of life and social happiness in the context of digital empowerment. Digital payment systems positively influence women’s empowerment. Women vendors can adopt digital payment methods, making them economically independent. The positive relationship between women vendors and customer satisfaction before UPI use and after UPI use is also analyzed. This research will be helpful for both government and non-government organizations to provide financial assistance, informational awareness, skill development training, and advocacy for gender equality to increase women’s empowerment.

The Ganga River is one of the major rivers in South Asia, sustaining nearly 600 million people and playing key roles in the region’s water supply, agriculture, ecology, and cultural life. Annual floods in the river trigger heavy riverbank erosion, which reshapes floodplains and affects the livelihoods of riparian communities. This calls for intensive geomorphological investigations to support regional sustainability. The study examined the long-term lateral migration of the Ganga River along with meander dynamics and associated riverbank erosion-deposition dynamics in Bihar, India, from 1972 to 2020. The NDWI and MNDWI indices were applied to delineate the river channel skeletons, and the Fluvial Corridor and Channel Migration Toolbox was employed to assess channel migration patterns. Erosion hotspot analysis was employed using the Getis-Ord Gi* statistic, in combination with Moran’s I index. The results reveal significant channel variations, with an average length migration rate of 20.33 m/year and a shape area migration rate of 9.73 km²/year. The total erosion and deposition exhibit a near balance, covering 1,137.46 km² and 1,107.66 km², respectively. Moran’s I index indicates a clustered pattern (I = 0.91) with erosion hotspots and coldspots measuring 7,584.20 km² and 5,276.48 km², respectively. The analysis reveals extensive erosion along the left bank, particularly impacting north Bihar. This has significant economic and food security implications for the society. This study thus furnishes evidence of active channel migration and associated erosion-deposition dynamics in the Ganga plains. The study offers critical insights for policymakers to mitigate environmental consequences through specific mitigation strategies.