Rapid urbanization in Bangladesh has exponentially exacerbated environmental stressors, most notably in Dhaka and Rajshahi, where climate-related concerns are becoming more prevalent. This study adopted geographic information system (GIS) and remote sensing techniques to delineate and assess climate risk zones in Dhaka City Corporation (DCC) and Rajshahi City Corporation (RCC) in 2020 and 2024. The evaluation involved the incorporation of land use/land cover (LULC), land surface temperature (LST), and air pollution indicators. Sentinel-2A multispectral imager (MSI) was used to calculate LULC, Landsat-8 optical land imager (OLI) for LST, and Sentinel-5P for atmospheric pollutants, such as NO₂, SO₂, CO, and PM_2.5. The analysis revealed that the built-up land in Dhaka was expanded by 4.38% whereas in Rajshahi, it was 8.91%. Rajshahi recorded a maximum LST of 46.7°C in 2024, when compared to 37.6°C in Dhaka. The level of air pollution was consistently high in Dhaka, with an average concentration of NO₂ reaching 36.4 µmol/m², almost quadrupled the 9.81 µmol/m² in Rajshahi. Weighted overlay analysis demonstrated that 5.38% and 1.63% of the areas in Dhaka and Rajshahi, respectively, were categorized as very high-risk zones in 2024. The very low-risk zones accounted for less than 1.5% in both cities. These findings suggested significant regional differences in urban climate risk as Dhaka was experiencing more severe circumstances, due to dense urbanization and rising pollution levels. The study unraveled the potential of GIS and remote sensing-based multi-parameter integration for urban climate risk zoning, as well as the establishment of city-specific adaptation and mitigation measures in Bangladesh.

The Nusantara Capital City (IKN) in Indonesia has undergone rapid urbanization which threatens sustainable forest management and the well-being of the indigenous community, leading to conflicts over land rights and resources. This study explored trust, norms, social networks, proactive action, and care between indigenous and migrant forest communities in IKN to support collaborative governance. It contributed to forest governance research by applying social capital theory to a protected urban area under state-led development. The research explained differences in household participation and offered a framework connecting bonding and bridging ties to co-management. Surveys based on the Social Capital Assessment Tool (SCAT) and the Social Capital Integrated Questionnaire (SC-IQ) were conducted with 90 households (45 indigenous and 45 migrant) across six villages in Penajam Paser Utara and Kutai Kartanegara districts from March to September 2024. Spearman’s rank tests was employed to analyze relationships between traits and social capital. The analysis results indicated that both communities possessed strong social capital, particularly trust in leaders (scores 3.49–3.56) and norms (scores 3.53), yet demonstrated moderate trust in government and environmental commitment. Migrants generally have higher education, income, land ownership, and bridging social capital, whereas indigenous groups maintain strong bonding capital rooted in tradition and legitimacy of local leaders. Traits significantly correlated with social capital (indigenous: r = 0.756, p < 0.001; migrants: r = 0.823, p < 0.001). Overall speaking, effective forest city development depended on government policies, local leadership, environmental awareness, transparency, and acknowledgment of customary governance, as these elements could foster community-based forest management and equitable urban development in tropical forest areas.

This paper advanced a new methodological framework for understanding and classifying the urban–rural continuum in post-2000 South Africa. The abolition of administrative distinctions through the Municipal Systems Act in 2000 rendered traditional definitions of “urban” and “rural” obsolete, thus creating a conceptual gap in spatial classification. Drawing on the international frameworks, the study proposed a functionally grounded approach that transcends the urban–rural binary. Using a positivist design and quantitative spatial modelling, the research introduced the Dominant Impact Factor (DIF), a composite indicator integrating population size, participation of labour force, and economic production, to assess relative municipal dominance. Municipalities were subsequently categorized through a quartile-based classification into urban, mixed, and rural types, and further refined using geo-referencing and a spatial grid for fine-scale spatial differentiation. Findings revealed pronounced demographic and economic concentration in a small number of highly urbanized municipalities, contrasted with extensive and sparsely populated rural territories. The framework reconceptualized settlement systems as dynamic, relational, and functionally interlinked rather than dichotomous. The study aligns the spatial classification practice in South Africa with globally methodological standards, to offer a robust, transparent, and scalable tool for evidence-based planning, governance, and formulation of policy.