Urban Sustainability Research Articles

Exploration on boosting bus ridership in the post-pandemic era referring to bus rider behavior changes

Boosting bus ridership after pandemics is critical to green city and urban sustainability, to curb the rapid increase of private cars and to provide alternative trip choice to the public. Referring to bus passengers’ changes in trip behaviors from the pandemics, we may better focus on the bus service attributes to which passengers are sensitive, and devote the limited resources to improve these attributes for bus ridership growth. The reason for not comparing passenger behaviors between the pre- and post-pandemics, lies in that bus service system has changed significantly during the pandemics, and that we have to continue that passed on from the pandemics. With questionnaire and statistical analyses, passengers in the post-pandemics are found to accept more transfers, traveling over a shorter distance with fewer accompanies and more urgent purposes. Targeting at the frequent passengers, the factors of accepting longer access distance, longer wait time, and independence from private cars during the pandemics contribute positively to the frequent bus usage in the post-pandemics, in addition to being aged 50 years old or more and being female. Suggestions follow to keep sufficient stop distance for in-vehicle time saving, and to promote reliable transfer, accurate bus arrival information, long route reformation, service enrichment, trip pleasure creation, extensive connection, and special attention for frequent passengers. This research contributes comprehensive understanding of bus trip patterns in the post-pandemics to guide the respondent agencies to reform bus service delivered from the pandemics, and to promote bus ridership increase.

A multi-scale user-friendliness evaluation approach on cycling network utilizing multi-source data

Cycling is a promising solution to transportation decarbonization and urban sustainability. To enhance the cycling environment, it's essential to assess the user-friendliness of cycling networks. Existing related studies lack large-scale quantitative assessments and typically focus on segments, which are cost-ineffective for extensive spatial coverage and disregard the comprehensive impact of the cycling network system. Therefore, we propose a multi-scale user-friendliness evaluation approach for cycling networks utilizing multi-source geospatial data. First, we construct the cycling network using bike-sharing trajectory data. Then, to assess the user-friendliness of individual cycling segments as transportation spaces, we derive and evaluate six fine-grained quantitative indicators of bike lanes (separation, shade, width, pavement, slope undulation, and connectivity) from streetview images, DEM, and the aforementioned cycling topological network. Thirdly, cycling communities, representing inhabitants' daily living spaces, are detected based on trajectory density that informs cycling patterns, and their user-friendliness is evaluated by combining the performance of cycling segments with the spatial distribution of POI facilities within each community. An empirical study in Xiamen, China demonstrates the effectiveness of the proposed method in pinpointing specific indicators at particular segments/underdeveloped communities, thus guiding investment and optimization of cycling infrastructure to promote greater adoption of cycling as a mode of transportation.

Exploring the cooling potential of green roofs for mitigating diurnal heat island intensity by utilizing Lidar and Artificial Neural Network

Urban areas frequently exhibit higher elevated temperatures than their rural counterparts due to the prevalence of structures over natural resources, a phenomenon known as daytime surface urban heat island (DSUHI). This study simulates the cooling effects of green roofs (GR) for mitigating DSUHI by utilizing 2D and 3D urban morphological parameters over downtown Austin, Texas, USA. We estimated spectral indices using Landsat 8, Sentinel-2A, and Lidar data to include built-up, vegetation, waterbodies, daytime land surface temperature (DLST), buildings (height volume and density), sky view factor (SVF), and solar radiation (SR). Finally, we integrated eleven different neural network algorithms for GR simulation, validation, and correlation between DLST and the above urban features- the strongest model generated an R2 of 0.783 and an RMSE of 0.925°F. We found converting 4.2% of the total rooftop area to GR resulted in an average DLST decrease of 2.80°F. The most significant cooling effects occurred with buildings heights between 13–28 meters, high SVFs, SR, and closer proximity to water bodies. Our findings amplify the strategic importance of GRs in urban morphology and planning, guiding green infrastructure development to mitigate and foster urban environment sustainability.

Evaluating the implementation of the Sustainable Sites Initiative (SITES) and its adaptation potential in China

The importance of green infrastructure for achieving urban sustainability is increasingly recognized globally. Originating in the United States, the Sustainable Sites Initiative (SITES) is an international green rating system designed to guide sustainable landscape development and evaluate sustainability throughout the entire lifecycle of landscapes and other outdoor spaces. Despite its international scope, the performance of SITES has not been quantified in diverse contexts. Understanding the performance of SITES is crucial for practitioners and regulatory authorities; however, quantitative analysis of previous projects is lacking. Furthermore, the adaptation of SITES in China requires further exploration. This study addresses these gaps by utilizing a dataset of SITES v2 certified projects to examine the achievement of sections and credits across various projects, aiming to understand performance patterns and identify barriers to certification. Performance was assessed using score ratios to measure how effectively projects met potential credits, and Association Rules Mining was employed to explore relationships between related credits. Furthermore, a detailed case study of Beijing Central Green Forest Park was conducted to assess the adaptability and applicability of SITES within the Chinese context. Our findings indicate significant variability in the difficulty of achieving certification across different sections. Among 1166 potential rules, 128 pairs of related credits were identified, suggesting substantive associations among the credits. The case study of Beijing Central Green Forest Park resulted in an overall score ratio of 82%, achieving 171 scores, which underscores the potential of SITES as a valuable sustainability tool in China. However, variability in section performance, particularly lower scores in the Materials Selection section, highlights the challenges faced and emphasizes the need for further adaptations of the SITES criteria to better align with local conditions.

Household, Sociodemographic, Building and Land Cover Factors Affecting Residential Summer Electricity Consumption: A Systematic Statistical Study in Phoenix, AZ

Understanding determinants of residential electricity consumption is crucial for urban sustainability efforts for planners and policy makers to develop targeted strategies to lower energy use, reduce greenhouse gas emissions, and to increase community resilience. This study presents a systematic approach to build an interpretable multivariate linear model, addressing challenges like outlier detection, multicollinearity, non-normality, and heteroscedasticity. Using 2019 summer residential electricity data for 426 census tracts in Phoenix and 30 variables, the approach involves (1) addressing multicollinearity and regression outliers through Variance Inflation Factor and studentized residual analysis, (2) comparing an automatic variable selection method with Ridge, Lasso, and Elastic Net regression, (3) evaluating the final model, and (4) interpreting variable effects. Critical findings reveal multicollinearity in land cover and racial variables, while 21 census tracts on the urban periphery exhibit outliers with unique features. Variable selection demonstrates the significance of household and building information in influencing residential electricity consumption. Household variables alone account for 84% of electricity usage variation. Incorporating building information and land cover variables reduces errors by 35% and 26% respectively, emphasizing the significance of including household characteristics as predictors or control variables when modeling electricity consumption. A final model with 93% explanatory power enables precise predictions.

Effects of Public Investment on Sustainable Urban Development

Effects of Public Investment on Sustainable Urban Development

Global Urbanization and Habitat Quality: Interactive Coercive Relationships

Urbanization is inevitably accompanied by drastic changes in regional land use and therefore presents an evident influence on ecosystems. Habitat quality (HQ) reflects the ability of a habitat to provide suitable conditions for the survival of an individual or population, and clarifying the interrelationships between urbanization level (UL) and HQ provides insights into sustainable urbanzation and ecosystems conservation. Much attention has focused on how urbanization related to HQ at multi-scales, but few studies have analyzed the interactive coercive relationships between UL and HQ on the global county level. To address this gap, we adopted the bivariate spatial autocorrelation and coupling coordination degree (CCD) model to determine their interactive coercive relationships at the county level globally. Results showed that the global average UL was 0.0807, 0.0838, and 0.0857 in 2000, 2010, and 2020, respectively, with a continuously increasing trend. The global average HQ was 0.6186, 0.6133, and 0.6111 for 2000, 2010, and 2020, reflecting opposite declining trends. The Moran’s I of population urbanization and HQ in 2000, 2010, and 2020 globally remained negative but showed an increasing trend, with values of −0.189, −0.228, and −0.254, respectively, while those of economic urbanization and HQ and land urbanization and HQ also remained negative and exhibited a similar increasing trend. The spatial autocorrelations for UL and HQ in different dimensions indicated that deserts and plateaus functioned as catchment areas with low UL and HQ. The CCD between UL and HQ in each county globally ranged from 0 to 0.70, while CCD between land urbanization and HQ was the most optimal at 0–0.90. The CCD between population urbanization and HQ was similar to that of economic urbanization in terms of spatial distribution. Nevertheless, the CCD of land urbanization in HQ exhibited the greatest degree of coordination among the three urbanization dimensions during the study period. These findings provided important support for sustainable urbanization and ecosystem protection globally.

Urban forest and sustainability: the historical perspective of Limbe in the Southwest Region of Cameroon

ABSTRACT Projected rapid urbanisation and climate change will increase the risk of natural disasters in fragile coastal African cities. Strategically planning and managing the urban forest can help to build resilience. This study aimed to document urban forest development and sustainability in Cameroon using the city of Limbe as a baseline through the classification of urban forest elements; description of their developmental history and assessment of the level of sustainability to inform strategic planning. Data was collected using a forest ethnology approach. Two groups of stakeholders’ representatives of public institutions (Group 1), Non-Governmental Organisations and educated indigenes (Group 2) provided oral histories of tree planting. Face to face interviews with 15 stakeholders were conducted guided by semi-structured questionnaires. From these results, there are four types of urban forest. Limbe Botanical Garden (LBG) developed during the colonial era was the most prominent. Municipal authorities by law should create different types of urban forest but their actions have been limited to Roadside Trees (RT) planting. The absence of budget, policy and inventory was responsible for the low level of urban forest sustainability. Increasing awareness of the effectiveness of urban forest nature-based solutions in tackling deadly landslides and floods in Limbe can promote urban forest sustainability.

Impact of E-Commerce Growth on Urban Logistics in France

Purpose: The article aims to examine how e-commerce growth is affecting urban logistics, including traffic congestion, environmental impact, and warehouse demand. It explores challenges posed by increased last-mile deliveries and suggests ways cities can adapt for efficient, sustainable logistics. This analysis seeks to provide solutions for balancing consumer demand with urban sustainability. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The findings revealed that e-commerce growth intensifies urban congestion, increases emissions from delivery vehicles, and drives up demand for warehouse space near cities. These changes challenge urban infrastructure and logistics efficiency. Sustainable solutions, like green vehicles and optimized delivery networks, are essential to mitigate these impacts. Unique Contribution to Theory, Practice and Policy: Urban growth theory, resource-based view (RBV) & sustainable urban logistics theory may be used to anchor future studies on the impact of e-commerce growth on urban logistics. Practically, logistics companies and urban planners should implement region-specific solutions to manage the unique challenges of e-commerce growth. From a policy perspective, city governments should establish clear guidelines and incentives to encourage sustainable urban logistics practices.

Sustainable urban street: a bibliometric analysis

ABSTRACT This research aims to conduct a bibliometric analysis of various pieces of literature on the “sustainable urban street (SUS)” between 1992 and 2023. This review intends to provide an overall understanding of the evolution and progression of research on the SUS over the past three decades. Our study adheres to the theoretical framework of bibliometric research and uses datasets from the Web of Science and Scopus websites. The diverse literature on the SUS study was compiled using the terms “sustainable” or “green” and “urban street” or “urban road”. The datasets include journal articles including early access, proceedings paper articles, book chapters, and review papers with terms mentioned anywhere in their title, abstracts, keyword lists, or bodies. After assembling and analysing the datasets, we got the final dataset of 812 documents on SUS for further analysis. The utilization of the open-source and free statistical and visualization software tools- R and VOS Viewer primarily served the purpose of processing and analysing datasets. The analysis provides the total number of documents that were published per year and the top 10 authors, publications, journals, research organisations, and nations that contributed to publishing papers on this topic. The lexical network analysis reveals that SUS is strongly associated with the two frequently occurring terms “sustainable development” and “air quality”. China, the United States of America, and the United Kingdom have conducted most studies on SUS. The top three most influential journals for SUS are “Sustainability”, “Building and Environment”, and “Sustainable Cities and Society”. The main contribution of this bibliometric analysis is to identify future research needs for sustainable urban streets. This dataset, based on an evaluation of 30 years of study in this field, will prove helpful in the future for researchers and policymakers alike. This study also enhances the existing knowledge of sustainable urban streets by offering a comprehensive framework and a systematic understanding of the challenges associated with urban transformation and sustainability that have emerged in recent years.

Planning eco-sensitive zones for promoting tree diversity and ecosystem services in urban academic landscapes: a case study from the North-western Indo-Gangetic plain

ABSTRACT Urban academic greenspaces provide a protective environment for the conservation of plants and animals and ecological health benefits to human beings. Several anthropogenic activities and landscape practices lead to patches of natural and built-up areas; therefore, a better understanding of campus tree diversity and ecosystem services among natural and built-up areas may help in biodiversity management. Considering this, we assessed how landscape heterogenicity (i.e. the proportion of natural and built-up/paved surface area) and planning principles influence tree density and diversity patterns in three zones (buffer zone: natural/remnant woodland; transition zone: mixed open grassland and woody plantation; core zone: paved/built-up, grassland, and scattered woody plantation) in the Indian Institute of Science Education and Research (IISER) Mohali campus (Punjab) India. We found a total of 1,993 trees belonging to sixty-eight species, mostly native flora. We found that landscape planning principles significantly influenced tree diversity patterns, with the highest tree diversity in the B3 buffer zone (H: 2.9, PIE: 0.9), which integrated elements of the original landscape and new plantations to optimise ecological value and the lowest diversity in the B2 buffer zone (i.e. H: 0.4; PIE: 0.2) with a monoculture plantation of exotic agroforestry species. We also observed that tree density was negatively influenced by impervious surface area (r = -0.68, p < 0.05) in the core zones and by the plantation of exotic species in the natural areas. The campus diversity supports birds of different feeding guilds (insectivore: 41%, omnivore: 25%, carnivore: 25%, frugivore/nectarivore: 13%, graminivore: 4%, and piscivore: 2%). Additionally, academic buildings planned with high canopy cover and density (45% and 95 ha−1) had reduced the wall temperatures of academic buildings from 12°C to 6°C compared to neighbouring buildings without tree cover. This highlighted the importance of maintaining tree diversity in urban academic institutions to promote urban sustainability.

Assessment of Health-Oriented Layout and Perceived Density in High-Density Public Residential Areas: A Case Study of Shenzhen

Rapid urbanization has intensified public housing development and building density, posing significant challenges to residents’ well-being and urban sustainability. With the population of the Greater Bay Area on the rise, enhancing the spatial quality of public housing is now essential. The study proposed a quantitative framework to evaluate the relationship between the residential design elements and perceived density in high-density public housing neighborhoods. It employed a virtual reality perception experiment to analyze the relationship between significant spatial indicators and perceived density by investigating 16 high-density residential layout models in 3 configurations: Tower-Enclosed, Balanced Slab-Enclosed, and Staggered Slab-Enclosed. The results indicate that: (1) greater building height intensifies perceived density, leading to sensations of overcrowding and discomfort; (2) an increased sky ratio mitigates perceived density, fostering a more open and pleasant environment; (3) recessed residential facades enhance residents’ density perception; and (4) Staggered Slab-Enclosed Layout configurations receive the most favorable evaluations regarding perceived density. The authors attempt to go beyond current regulations to propose tailored solutions for Shenzhen’s high-density context, improving spatial efficiency and residential comfort in future public housing designs. The finding provides scientific evidence to support urban planners and policymakers in developing more resilient and sustainable high-density neighborhoods.

The Importance of Implementing Sustainable Practices in Municipalities of the Araguaia River Valley, Mato Grosso, Brazil

Objective: The present work aims to identify and analyze strategies for implementing the Sustainable Development Goals (SDGs) in municipalities in Vale do Araguaia in the state of Mato Grosso - Brazil, as well as their benefits and challenges, providing recommendations for promoting sustainable development at the local level. Theoretical Framework: The text addresses essential strategies for implementing sustainable practices in local communities, such as solid waste management, conservation of water resources, use of renewable energy and promotion of sustainable agriculture. Highlights include recycling and composting to reduce waste, water reuse systems and renewable energy, such as solar, for energy self-sufficiency. Sustainable agriculture strengthens the local economy, while conscious consumption is encouraged by educational campaigns. Community participation and government support are crucial for balancing economic development, social justice and environmental preservation. Method: This comparative case study analyzed the implementation of the Sustainable Development Goals (SDGs) in 25 municipalities in Mato Grosso, in the Araguaia river basin. Different municipal strategies and contexts were examined, using data from public sources such as the Water and Sanitation Institute and the Sustainable Cities Development Index – Brazil (IDSC-BR). The analysis was organized in a table and the average of the results made it possible to evaluate the level of sustainable development of each city. Results and Discussion: The research points out that, although the implementation of sustainable practices in municipalities is essential to achieve the Sustainable Development Goals (SDGs), the results are still below expectations. Only three municipalities achieved average rates, and none achieved satisfactory results. This reveals structural challenges, such as a lack of adequate public policies and budget limitations. The research highlights the need for greater coordination between government spheres, the private sector and civil society, in addition to more concrete and inclusive planning to promote sustainable development. Research Implications: The practical implications of this study suggest the need for greater integration between government spheres and the private sector to optimize the implementation of the SDGs in municipalities, highlighting the importance of effective public policies and investments in priority areas. Theoretically, the study offers insights into the structural challenges faced by cities, contributing to the debate on sustainable urban management and the development of policy models that can accelerate the achievement of global sustainability goals. Originality/Value: The originality of the study lies in its detailed analysis of the implementation of the SDGs in municipalities, revealing the specific structural challenges that limit their progress. The value lies in providing a practical overview of the gaps in public policies and local management, in addition to highlighting the need for greater intersectoral coordination to achieve sustainable development, offering valuable guidelines for future urban planning and sustainability policies.

Analysing trends of computational urban science and data science approaches for sustainable development

AbstractUrban computing with a data science approaches can play a pivotal role in understaning and analyzing the potential of these methods for strategic, short-term, and sustainable planning. The recent development in urban areas have progressed towards the data-driven smart sustainable approaches to resolve the complexities around urban areas. The urban system faces severe challenges and these are complicated to capture, predict, resolve and deliver. The current study advances an unconventional decision-support framework to integrate the complexities of science, urban sustainability theories, and data science, with a data-intensive science to incorporate grassroots initiatives for a top-down policies. This work will influence the urban data analytics to optimize the designs and solutions to enhance sustainability, efficiency, resilience, equity, and quality of life. This work emphasizes the significant trends of data-driven and model-driven decision support systems. This will help to address and create an optimal solution for multifaceted challenges of an urban setup within the analytical framework. The analytical investigations includes the research about land use prediction, environmental monitoring, transportation modelling, and social equity analysis. The fusion of urban computing, intelligence, and sustainability science is expected to resolve and contribute in shaping resilient, equitable, and future environmentally sensible eco-cities. It examines the emerging trends in the domain of computational urban science and data science approaches for sustainable development being utilized to address urban challenges including resource management, environmental impact, and social equity. The analysis of recent improvements and case studies highlights the potential of data-driven insights with computational models for promoting resilient sustainable urban environments, towards more effective and informed policy-making. Thus, this work explores the integration of computational urban science and data science methodologies to advance sustainable development.

Assessing Appropriation of Space in Urban Green Spaces: Three Case Studies in Downtown Shanghai

This study investigated patterns of activities in urban green spaces (UGSs) in downtown Shanghai. UGSs are essential public infrastructure, contributing to urban sustainability, quality of life, and social cohesion. Although widely studied, there is a gap in the literature regarding Chinese UGSs when the object of study is the nature the activities. In this sense, we aimed to investigate the activities from the perspective of appropriation of the space, considered here as different from the use of space. This study addressed this by analyzing user demographics, frequency, and spatial activity patterns to assess how these activities could be classified and scored according to a varying levels of appropriation. Through a mixed-methods design based on non-participant observation and behavior mapping, the study was conducted across three comprehensive parks in Shanghai, divided into nine observation zones. The data were analyzed through descriptive statistics, IBM SPSS, and qualitative coding, revealing, as the main findings, sixty distinct activity types, a soft to moderate level of appropriation, and notable variations in demographic presence and temporal trends. This research underscores the effectiveness of observational methods, validates appropriation as an analytical category, and emphasizes the importance of structured classification systems for improving the understanding of UGSs’ socio-spatial performance and their societal role.

Visualizing the Philippines’ SDG 11.3.1 Status and Progress Using Open-Source Geospatial Software and Earth Observation Datasets

Abstract. This paper presents the development and implementation of a web GIS-based application designed to visualize the status and progress of Sustainable Development Goals (SDGs). Focusing specifically on SDG 11.3.1, which pertains to sustainable urbanization, we propose a prototype (accessible at https://ccgeoinformatics.github.io/sdg1131ph/) that leverages open-source geospatial software and earth observation (EO) datasets. By following the "where-when-what-how" framework of SDG visualization system development, we address the need for effective visualization methods while showcasing the potential of accessible datasets and technology in supporting sustainable development monitoring efforts across different administrative levels. We demonstrate the feasibility of the system in aiding policymakers and stakeholders in their decision-making processes toward achieving the SDGs by 2030. Additionally, our work exemplified how open-source software and EO datasets can address the challenges associated with SDG reporting, particularly in areas where both data and visualization tools are scarce or inaccessible. This paper also contributes to the ongoing discourse on SDG visualization and underscores the importance of localized analysis and visualization in understanding and addressing the challenges of sustainable urbanization using the example of the Philippines.

Home Services Recommendation System using Machine Learning for Urban Areas

The Kotlin-based on-demand home service system streamlines household tasks by offering services such as repairs, cleaning, gardening, and plumbing. In today's fast-paced world, it provides quick solutions for maintaining a clean and hygienic living environment. The Housekeeper Finder app is a prominent player in this space, utilizing advanced machine learning algorithms to deliver personalized service recommendations based on user preferences. It includes essential services like cooking, laundry, and elder care, along with a robust feedback system to improve service quality. Users can easily book services from anywhere, leveraging GPS technology to connect them with nearby providers. The "Domestic Android Application for Home Services" further enhances accessibility through location-based service requests and mapping capabilities for a seamless user experience across platforms.A s cities transition into Smart Cities, integrated information systems open new avenues for urban service recommendations, fostering liveability and sustainability. Recommendation systems analyse user behaviour to provide tailored suggestions using techniques like collaborative filtering and hybrid methods. In response to the challenges of modern work culture, the e-commerce landscape now offers easy access to essential services via mobile applications, ultimately transforming how households address their needs and promoting safer, cleaner living space.

Assessment and Dynamic Prediction of Green Space Ecological Service Value in Guangzhou City, China

As an important part of the urban ecosystem, urban green space provides a variety of ecosystem services, including climate regulation, soil conservation, carbon sink and oxygen release, and biodiversity protection. However, existing remote sensing evaluation methods for ecological service value lack the evaluation indicators of ecosystem service value for Guangzhou, China, and the evaluation method depends on the land cover type. Based on remote sensing technology and random forest algorithm, this study addresses these gaps by integrating remote sensing technology with a random forest algorithm to enhance the accuracy and rationality of ESV assessments. Focusing on Guangzhou, China, we improved the ecological service value evaluation system and conducted dynamic predictions based on land-use change scenarios. Our results indicate that the total ESV of Guangzhou’s green space was USD 7.323 billion in 2020, with a projected decline to USD 6.496 billion by 2030, representing a 12.37% reduction due to urbanization-driven land-use changes. This research highlights the noticeable role of green spaces in urban sustainability and provides robust, data-driven insights for policymakers to design more effective green space protection and management strategies. The improved assessment framework offers a novel approach for accurately quantifying urban ecosystem services and predicting future trends.

A New Electricity Infrastructure for Fostering Urban Sustainability: Challenges and Emerging Trends

A New Electricity Infrastructure for Fostering Urban Sustainability: Challenges and Emerging Trends

Comprehensive walkability assessment of urban pedestrian environments using big data and deep learning techniques.

Assessing street walkability is a critical agenda in urban planning and multidisciplinary research, as it facilitates public health, community cohesion, and urban sustainability. Existing evaluation systems primarily focus on objective measurements, often neglecting subjective assessments and the diverse walking needs influenced by different urban spatial elements. This study addresses these gaps by constructing a comprehensive evaluation framework that integrates both subjective and objective dimensions, combining three neighbourhood indicators: Macro-Scale Index, Micro-Scale Index, and Street Walking Preferences Index. A normalization weighting method synthesizes these indicators into a comprehensive index. We applied this framework to assess the street environment within Beijing's Fifth Ring Road. The empirical results demonstrate that: (1) The framework reliably reflects the distribution of walkability. (2) The three indicators show both similarities and differences, underscoring the need to consider the distinct roles of community and street-level elements and the interaction between subjective and objective dimensions. (3) In high-density cities with ring-road development patterns, the Macro-Scale Index closely aligns with the Comprehensive Index, demonstrating its accuracy in reflecting walkability. The proposed framework and findings offer new insights for street walkability research and theoretical support for developing more inclusive, sustainable and walkable cities.