How do natural resources and economic growth impact load capacity factor in selected Next-11 countries? Assessing the role of digitalization and government stability.

With growing environmental concerns, everyone's attention has shifted to how we use our limited materials supplies. Rapid economic expansion is dependent on heavy resource use, decreasing biodiversity and raising the ecological footprints (EF), resulting in a reduction in the load capacity factor (LCF). Because of this, scholars and policymakers are actively looking for approaches to improve the LCF without hindering economic growth (GDP). For similar reasons, this research aims at how the selected next eleven economies improved their LCF from 1990 to 2018 by analyzing the effect of digitalization (DIG), natural resources (NAT), GDP, globalization, and governance. To account for dependence across sections and slope variation, the cross-sectional augmented ARDL model is used in this research. The long-term findings indicate that LCF was diminished by dependence on NAT, globalization, and economic growth and was bolstered by DIG and sound governance. The work recommends that financial and policy support is needed for initiatives such as zero-emission vehicle production and energy-efficient building construction. By offering a line of credit at low interest rates, renewable energy projects can attract domestic and private investors.

Understanding Collective Human Mobility Spatiotemporal Patterns on Weekdays from Taxi Origin-Destination Point Data.

With the availability of large geospatial datasets, the study of collective human mobility spatiotemporal patterns provides a new way to explore urban spatial environments from the perspective of residents. In this paper, we constructed a classification model for mobility patterns that is suitable for taxi OD (Origin-Destination) point data, and it is comprised of three parts. First, a new aggregate unit, which uses a road intersection as the constraint condition, is designed for the analysis of the taxi OD point data. Second, the time series similarity measurement is improved by adding a normalization procedure and time windows to address the particular characteristics of the taxi time series data. Finally, the DBSCAN algorithm is used to classify the time series into different mobility patterns based on a proximity index that is calculated using the improved similarity measurement. In addition, we used the random forest algorithm to establish a correlation model between the mobility patterns and the regional functional characteristics. Based on the taxi OD point data from Nanjing, we delimited seven mobility patterns and illustrated that the regional functions have obvious driving effects on these mobility patterns. These findings are applicable to urban planning, traffic management and planning, and land use analyses in the future.