The paper presents the results of research on the influence of logistics customer service on sustainability-focused freight transport practices of enterprises. Additionally, the extended perspective of the key relation through the inclusion of the joint effect of selected organizational competencies of the companies and their competitiveness in interaction with logistics customer service was introduced. The adopted research procedure included the use of several different statistical methods with regard to data collected in 275 freight transport enterprises. First, the Kaiser-Meyer-Olkin test and the Bartlett Sphericity test were determined, then a factor analysis was carried out with the intention of performing a reliability analysis and discriminant validity assessment, and finally, correlations and hierarchical multiple regression were determined. The findings of the research suggest a primal concluding explication that sustainability-focused freight transport practices are conditioned by auxiliary logistics processes realized by the enterprise within logistics customer service, joint competencies within the organization’s management, as well as peripheral circumstances of competitiveness.

Environmental issues have remained one of the most challenging social-economic impacts on the world and most countries. Tackling these challenges has remained an underlying issue as a concise approach, method, and policy are yet to be globally made available. Machine learning (ML) with support from IoTs, big data, NLP, and cloud computing is radicalizing the development of a modern-day economy via human support systems. With technical devices, systems, and processes intricately oriented to human understanding. Little environmental needs have been developed to give humans a comfortable place. Even though sensors capture and satisfy human needs, global ecosystem barriers have weighed beyond. Following changes in the world today, automated restrictions and barriers have been seen limiting humans from enjoying opportunities offered by IoTs, big data, NLP, and cloud computing due to environmental impact. Machine learning with capabilities to help humans become more informed is insignificantly exploited on the environmental needs. To suggest an integrated system, method, and areas that IoTs, big data, NLP, and cloud computing should focus on to fight negative environmental impact as a major step to fight climate change. In the study, two research questions and a hypothesis were used. Daily data on emission accusations was collected and used to respond to research questions and hypotheses. In 30 minutes per day and within a month, 412 diesel cars emitted 54,384 g CO₂/km, 636 petrol cars emitted 76,320 g CO₂/km, and 157 LPG cars emitted 9,577 g CO₂/km. Predictions and forecasts were determined based on the data collected. Data accusations reveal they worsen the future impact as both hypotheses and research questions positively support findings that integration of sensors with machine learning can predict future climate situations. Improved gardens are needed, limit artificial items and diesel cars, and improved afforestation is needed in this city.

The natural resources that exist on this small island have the potential as a tourism destination. The purpose of this research is to be able to develop the potential of community-based tourism. The method used to collect data is observation and in-depth interviews with key figures, then the data is analyzed descriptively and analysis of component 4(A) namely attraction, accessibility, amenity, and ancillary of Tourist Attractions. The results identified are land use patterns, tourist attractions include beach tourism, mangrove tourism, underwater tourism (snorkelling), special interest tours for langurs, cycling tours (around the island), climbing tours, and religious tourism. Existing accessibility includes roads, boats, and piers. The amenities include places of worship, public toilets, food stalls, lodging, and snorkeling equipment rental services. Additional or ancillary facilities that exist are an information center located at the village office and a tour guide. The need for human resource training is also very much needed to shape community-based tourism to be much better.

The paper aims towards understanding the factors responsible for the adoption of green products by consumers in the Indian market. A well-structured survey questionnaire was developed and circulated to 327 participants. Various factors associated with the adoption of green products were taken into account. They were analyzed by using descriptive statistics and factor analysis. The paper concludes that 61.2% of the respondents were willing to adopt green products and the 77.1% of the respondents are aware of green products and various benefits associated with them. An effort was made to understand the influence of green labelling on the adoption of green products by consumers. From the survey, it was inferred that green labelling is an important tool used by consumers for verifying and procuring green products. It was also found that factors like “concerns about the environment” and “recommendations from family and friends” significantly influence consumer's purchase decisions about green products.

When tourists choose their accommodation space, they tend to prefer areas with beautiful natural landscapes, or close to scenic spots, or those that have local styles. Based on existing research results of tourist accommodation space, some scholars proposed to design smaller spaces for tourist accommodation, however, in terms of the spatial correlation with tourism resources, relevant analysis is insufficient and needs to be supplemented. This paper studied the distribution characteristics and spatial correlation of tourist accommodation spaces based on environment information. At first, the paper analyzed the correlation between tourism resources and tourist accommodation spaces, gave the route of spatial correlation analysis, and analyzed the distribution characteristics of tourist accommodation spaces; then, this paper gave the determination process of optimal spatial distribution pattern of tourist accommodation spaces based on environment information, and adopted the kernel density estimation and the spatial attributes of tourist accommodation space points to study the spatial distribution characteristics. At last, combining with experiment, the distribution characteristics of the tourist accommodation spaces in the study area were given and analyzed.