Micro-mobility has the potential to address first -mile challenges, improving transit accessibility and encouraging public transit usage. However, users’ acceptability of modal integration between various micro-mobility options and public transit remains largely unexplored in the literature. Our study investigates the user behavior for first-mile options, focusing on four alternatives: walking, bicycling, motorcycling, and bus, to access urban mass rapid transit (UMRT) in Hanoi, Vietnam. Based on data collected from 1380 individuals, a Nested Logit Model (NLM) was proposed to analyze the determinants of users’ acceptability under each access mode option as well as evaluate further impacts of shifts in access mode choice on vehicle-kilometer traveled and emissions. The analysis shows that the availability of access modes might increase UMRT use by 47.83%. While this increase further generates additional vehicle-kilometer traveled due to the increase in park-and-ride users, this is offset overall by the large number of motorcycle users shifting to UMRT. Under the most optimistic scenario, modal integration for transit-access trips leads to an average reduction of 17.7% in net vehicle-kilometer traveled or 14.5% in net CO₂ emissions or 10.9% in NO_x from private vehicles. Our findings also imply that the introduction of parking fees for bicycling- or motorcycling-access trips, while impactful, does not significantly change UMRT choice. Therefore, the pricing schemes should be a focus of parking planning surrounding stations. Finally, a number of policy suggestions for parking planning and first-mile vehicles are presented.

This research is based on the condition of the ever-rampant events of illegal logging perpetrated by companies in various areas in Indonesia and Malaysia. The issue of corporate illegal logging happened due to a concerning level of conflict of interest between companies, the government, and local societies due to economic motives. this paper aims to analyze the law enforcement on corporate illegal logging in Indonesia and Malaysia as well as the law enforcement on corporate illegal logging that is based on sustainable forestry. this research used the normative legal approach that was supported by secondary data in the forms of documents and cases of illegal logging that happened in Indonesia and Malaysia. this paper employed the qualitative analysis. Results showed that Indonesia had greater commitment and legal action than Malaysia because Indonesia processed more illegal logging cases compared to Malaysia. But mere commitment is not enough as the illegal logging ratio in Indonesia compared to timber production is 60%. meanwhile, in Malaysia, it is 35%. This shows that the ratio of law enforcement in Malaysia is more effective when comparing the rate of illegal logging and timber production. The phenomenon of forest destruction in Indonesia happened due to a disharmonic situation or an improper social relationship between society, the regional government, the forestry sector, business owners, and the law-enforcing apparatus. The sustainable forest-based law enforcement concept against corporate illegal logging is carried out through the integration approach that involves various parties in both countries.

To study the environment of the Kipushi mining locality (LMK), the evolution of its landscape was observed using Landsat images from 2000 to 2020. The evolution of the landscape was generally modified by the unplanned expansion of human settlements, agricultural areas, associated with the increase in firewood collection, carbonization, and exploitation of quarry materials. The problem is that this area has never benefited from change detection studies and the LMK area is very heterogeneous. The objective of the study is to evaluate the performance of classification algorithms and apply change detection to highlight the degradation of the LMK. The first approach concerned the classifications based on the stacking of the analyzed Landsat image bands of 2000 and 2020. And the second method performed the classifications on neo-images derived from concatenations of the spectral indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Normalized Difference Water Index (NDWI). In both cases, the study comparatively examined the performance of five variants of classification algorithms, namely, Maximum Likelihood (ML), Minimum Distance (MD), Neural Network (NN), Parallelepiped (Para) and Spectral Angle Mapper (SAM). The results of the controlled classifications on the stacking of Landsat image bands from 2000 and 2020 were less consistent than those obtained with the index concatenation approach. The Para and DM classification algorithms were less efficient. With their respective Kappa scores ranging from 0.27 (2000 image) to 0.43 (2020 image) for Para and from 0.64 (2000 image) to 0.84 (2020 image) for DM. The results of the SAM classifier were satisfactory for the Kappa score of 0.83 (2000) and 0.88 (2020). The ML and NN were more suitable for the study area. Their respective Kappa scores ranged between 0.91 (image 2000) and 0.99 (image 2020) for the LM algorithm and between 0.95 (image 2000) and 0.96 (image 2020) for the NN algorithm.

To achieve the electrification of private vehicles, it is urgent to develop public charging infrastructure. However, choosing the most beneficial type of public charging infrastructure for the development of a country or region remains challenging. The municipal decision’s implementation requires considering various perspectives. An important aspect of energy development involves effectively integrating and evaluating public charging infrastructure. While car charging facilities have been thoroughly studied, motorcycle charging facilities have been neglected despite motorcycles being a vital mode of transportation in many countries. The study created a hybrid decision-making model to evaluate electric motorcycle charging infrastructure. Firstly, a framework for evaluating electric motorcycle charging infrastructure was effectively constructed through a literature survey and expert experience. Secondly, decision-makers’ opinions were gathered and integrated using Bayesian BWM to reach a group consensus. Thirdly, the performance of the alternative solutions was evaluated by exploring the gaps between them and the aspiration level through modified VIKOR. An empirical analysis was conducted using examples of regions/countries with very high rates of motorcycle ownership worldwide. Finally, comparative and sensitivity analyses were conducted to demonstrate the practicality of the proposed model. The study’s findings will aid in addressing municipal issues and achieving low-carbon development objectives in the area.

In a time of a growingly age-diverse workforce, modern organizations are facing the challenge of simultaneously maintaining job satisfaction for both younger and older workers. In that regard, this study aims to analyse and further explore the difference in job expectations of employees from the IT industry who belong to different age groups. Based on the extant literature, an appropriate research model was designed, which was subsequently tested using the data gathered through the surveys conducted over the past fourteen years. The research results show that the main difference between younger and older employees within the IT industry is related to professional and personal growth. Specifically, younger employees primarily look for personal development and rapid professional advancement, which are of minor importance to their older counterparts. Intriguingly, the obtained results showed no difference between the younger and older employees regarding the work environment, including its competitiveness.