Urbanization has adverse environmental effects, such as rising surface temperatures. This study analyzes the relationship between the urban heat island (UHI) intensity and Tianjin city’s land cover characteristics. The land use cover change (LUCC) effects on the green areas and the land surface temperature (LST) were also studied. The land cover characteristics were divided into five categories: a built-up area, an agricultural area, a bare area, a forest, and water. The LST was calculated using the thermal bands of spatial images taken from 2005 to 2020. The increase in the built-up area was mainly caused by the agricultural area decreasing by 11.90%. The average land surface temperature of the study area increased from 23.50 to 36.51 °C, and the region moved to a high temperature that the built-up area’s temperature increased by 1.5%. Still, the increase in vegetation cover was negative. From 2020 to 2050, the land surface temperature is expected to increase by 9.5 °C. The high-temperature areas moved into an aerial distribution, and the direction of urbanization determined their path. Urban heat island mitigation is best achieved through forests and water, and managers of urban areas should avoid developing bare land since they may suffer from degradation. The increase in the land surface temperature caused by the land cover change proves that the site is becoming more urbanized. The findings of this study provide valuable information on the various aspects of urbanization in Tianjin and other regions. In addition, future research should look into the public health issues associated with rapid urbanization.

Gastronomic tourism is becoming an essential consideration among tourists when choosing a tourist destination. One of the main reasons for visiting a specific destination for almost 15% of tourists is “gastronomy”. This has led to the development of a new kind of tourism called “Gastronomic Tourism”. However, there has been minimal research on gastronomy tourism, specifically in Pakistan. The primary purpose of this study is to measure the level of satisfaction in a tourist destination and furthermore consider gastronomy as a component of visitor motivation. A survey of 307 tourists who had recently visited Pakistan’s northern areas was undertaken to conduct the study. This has enabled us to better understand the variables that influence the behaviors and attitudes of tourists toward this popular tourist attraction. Gastronomy motivation impacts tourism location selection, and gastronomic experience influences satisfaction, according to the research. Specifically, tourists show a keen interest in gastronomic experiences after feeling satisfied with the destination and local foods, developing loyalty toward the destination.

The rapid infrastructure development in densely populated areas has had several negative impacts. Increases in urbanization have led to increased LST, and urban ecological systems have been negatively affected. Urban heat islands (UHIs) can be mitigated by understanding how current and future LST phenomena are linked to changes in landscape composition and land use cover (LUC). This study investigated the multi-scale spatial analysis of LUC and LST in Tianjin using remote sensing and GIS data. We used Landsat data from 2005 to 2020 to examine the effects of LUC on LST in urban agglomeration. According to the Urban Thermal Field Variance Index (UTFVI), the city’s ecological evaluation was carried out. Results show that changes in LUC and other anthropogenic activities affect the spatial distribution of LST. For the study years (2004–2009), the estimated mean LST in Tianjin was 25.32 °C, 26.73 °C, 27.62 °C, and 27.93 °C. Between LST and urban areas with other infrastructures, and NDBI, significant positive correlation values were found about 0.53, 0.48, and 0.76 (p < 0.05), respectively. Temperatures would almost certainly increase by 3.87 °C to 7.26 °C as a result of decreased plant cover and increased settlements. These findings strongly imply a correlation between LST and the vegetation index. Between 2005 and 2020, the anticipated increase in LST of 3.39 °C is expected to harm urban environmental health. This study demonstrates how Tianjin and other cities can achieve ecological sustainability.

Exposure to green spaces (GSs) has been perceived as a natural and sustainable solution to urban challenges, playing a vital role in rapid urbanization. Previous studies, due to their lack of direct spatial alignment and attention to a human-scale perspective, struggled to comprehensively measure urban GS exposure. To address this gap, our study introduces a novel GS exposure assessment framework, employing machine learning and street view images. We conducted a large-scale, fine-grained empirical study focused on downtown Shanghai. Our findings indicate a pronounced hierarchical structure in the distribution of GS exposure, which initially increases and subsequently decreases as one moves outward from the city center. Further, from both the micro and macro perspectives, we employed structural equation modeling and Geodetector to investigate the impact of the urban built environment on GS exposure. Our results highlight that maintaining an appropriate level of architectural density, enhancing the combination of sidewalks with GSs, emphasizing the diversity of regional characteristics, and avoiding excessive concentration of functions are effective approaches for increasing urban GS exposure and promoting human wellbeing. Our study offers scientific insights for urban planners and administrators, holding significant implications for achieving sustainable urban development.

In the recent years, the health and wellness tourism industry has become one of the new emerging industries in Pakistan. In terms of the sharing economy, the driving mechanism of the health and wellness tourism industry has undergone significant changes. Based on a combination of theoretical and practical research, the paper explores the dynamic system and mechanism of innovation development of health and wellness in the tourism industry in Pakistan. Firstly, it theoretically constructs the driving factors in four aspects, which include market demand, industrial supply, infrastructure, and external environment. Secondly, taking Khyber Pakhtunkhwa province (KPK) as an example, it uses the entropy method and Grey relational analysis to test the driving factors system. The empirical results show that four aspects jointly influence the innovation of the health and wellness tourism industry in Pakistan. Finally, this study discusses several practical implications for the development of the health and wellness tourism industry of KPK for academics, policymakers, business owners, and scholars, with recommendations for future research.

Liriodendron chinense (Hemsl.) Sarg. (Magnoliales: Magnoliaceae), valued for its medicinal properties and timber and as an ornamental plant, is now classified as an endangered species. Investigating how future climate-change scenarios might affect the potential geographic distribution of L. chinense will provide a crucial scientific basis for its protection and management strategies. The MaxEnt model was calibrated using the ENMeval optimization package, and then it was coupled with ArcGIS 10.8 to forecast the possible distribution areas of L. chinense in China, utilizing elevation data, bioclimatic factors, and human footprint as environmental variables. The results indicate: (1) The optimal model parameters were set as follows: FC = LQ, RM = 0.5, the MaxEnt model demonstrated high predictive accuracy and minimal overfitting; (2) The total suitable habitat area for the potential geographical distribution of L. chinense during the current period is estimated at 151.55 × 10⁴ km², predominantly located in central, eastern, and southwestern regions of China; (3) The minimum temperature of the coldest month (bio6), precipitation of the driest month (bio14), precipitation of the driest quarter (bio17), precipitation of the warmest quarter (bio18), elevation (alt), and human footprint (hf) are the main environmental variables determining the suitable habitat distribution of L. chinense; (4) During the period from 2041 to 2060, under the carbon emission scenarios of SSP126, SSP245, and SSP370, the suitable habitat for L. chinense shows varying degrees of increase compared to the current period. However, under the highest concentration scenario of SSP585, the suitable habitat area decreases to some extent; (5) The distribution of L. chinense is likely to move towards higher latitudes and elevations in the future due to changes in the climate. This research provides a comprehensive analysis of the potential impacts of climate change on L. chinense, offering valuable information for its protection and management under future climatic conditions.