The land-use mix index is a way to quantify the mixture of land-use patterns. Due to practical limitations, few studies have highlighted the validity of land-use mix indices. This paper aims to explore the potential characteristics of land-use mix indices using a three-step screening method. The data precision of indices was concluded after the first-step screening. A total of 10 virtual blocks and 217 blocks in Melbourne city center served as a case study and reflected the various land-use structures. The randomized controlled comparative trial was incorporated into the second- and third-screening to indicate the applicable condition and validity. The results illustrate that the value Herfindahl–Hirschman index related to the diversity of land-use types. The results also confirmed that Dissimilarity index-I was significantly associated with the balance status of the land-use mix. Entropy index reflects the evenness but did not correlate to the diversity or balance of the land-use mix. In addition, the study also provides a set of general recommendations for the application conditions of land-use mix indices.

Previous studies have mostly examined how sustainable cities try to promote non-motorized travel by creating a walking-friendly environment. Such existing studies provide little data that identifies how the built environment affects pedestrian volume in high-density areas. This paper presents a methodology that combines person correlation analysis, stepwise regression, and principal component analysis for exploring the internal correlation and potential impact of built environment variables. To study this relationship, cross-sectional data in the Melbourne central business district were selected. Pearson’s correlation coefficient confirmed that visible green ratio and intersection density were not correlated to pedestrian volume. The results from stepwise regression showed that land-use mix degree, public transit stop density, and employment density could be associated with pedestrian volume. Moreover, two principal components were extracted by factor analysis. The result of the first component yielded an internal correlation where land-use and amenities components were positively associated with the pedestrian volume. Component 2 presents parking facilities density, which negatively relates to the pedestrian volume. Based on the results, existing street problems and policy recommendations were put forward to suggest diversifying community service within walking distance, improving the service level of the public transit system, and restricting on-street parking in Melbourne.

Previous studies have mostly examined how sustainable cities try to promote non-motorized travel by creating a walking-friendly environment. Such existing studies provide little research that identifies how the built environment affects pedestrian volume in high-density areas. This paper presents a methodology that combines person correlation analysis, stepwise regression, and principal component analysis for exploring the internal correlation and potential impact of built environment variables. To study this relationship, cross-sectional data in the Melbourne central business district were selected. Pearson’s correlation coefficient confirmed that visible green index and intersection density were not correlated to pedestrian volume. The results from stepwise regression showed that land-use mix degree, public transit stop density, and employment density could be associated with pedestrian volume. Moreover, two principal components were extracted by factor analysis. The result of the first component yielded an internal correlation where land-use and amenities components were positively associated with the pedestrian volume. Component 2 presents parking facilities density, which negatively relates to the pedestrian volume. Based on the results, existing street problems and policy recommendations were put forward to suggest diversifying community service within walking distance, improving the service level of the public transit system, and restricting on-street parking in Melbourne.

In tropical island cities, the combined pressures of rapid high-density urbanization and year-round hot-humid climates make the pedestrian-level wind environment a critical determinant of outdoor thermal comfort and cooling performance. Focusing on Haikou, a tropical island city, this study optimizes building layouts on commercial plots under constant development intensity. A Pedestrian-level Cooling Performance Index (PLCPI) was constructed, prioritizing summer cooling and winter wind control through an AHP-EWM combined weighting method. The index integrates maximum pedestrian-level wind speeds and amplification factors to evaluate 65 layout configurations, including detached, row, perimeter, and courtyard types. The results reveal a nonlinear relationship between building count and cooling performance. Single-building layouts achieve the highest mean PLCPI (2.367), three-building layouts the lowest (1.825), prone to ventilation stagnation, and four-building layouts show a performance rebound (2.271) with stable efficiency. Crucially, spatial enclosure form is the decisive determinant under a constant building count: the optimal two-building layout B-8 (PLCPI=2.456) surpasses the best single-building layout A-2 (2.419), demonstrating that well-designed dispersed layouts can outperform centralized ones. This study proposes a dual-season adaptive evaluation framework for tropical commercial plots and reveals the nonlinear mechanism between building quantity and cooling performance, providing a scientific basis for fine-grained urban design in tropical island climates.

Road traffic has become one of the major sources of carbon dioxide emissions. However, there remains a lack of consensus on how urban land use influences these emissions. This study investigates the spatial distribution of municipal-level road traffic carbon dioxide emissions and their land use drivers in Japan’s Kanto region. Ordinary least squares (OLS), geographically weighted regression (GWR), and multi-scale geographically weighted regression (MGWR) models are employed to analyze the impacts of residential, industrial, and commercial land use on emissions, as well as to examine spatial heterogeneity in model residuals. The results reveal a clear spatial clustering pattern, with higher emissions in central Tokyo and lower emissions in peripheral areas. The MGWR model demonstrates the best fit and shows no spatial autocorrelation in residuals, highlighting the necessity of incorporating spatial heterogeneity. The MGWR results indicate that all three land use types significantly affect emissions, but their impacts vary spatially. Residential land use has the largest and most spatially heterogeneous effect, making it the most decisive factor. The findings underscore that effective low-carbon transportation policies cannot adopt a one-size-fits-all approach but must be tailored to locally dominant land-use–emission relationships. This study deepens the understanding of the spatial relationship between urban land use structure and road traffic carbon emissions and provides empirical support for sustainable urban development and low-carbon transportation policies.