Despite advances, spatial resilience planning remains constrained in its integration of complex system principles to address slow-variable disturbances. This study provided a methodological test of a novel multi-faceted and network-based hybrid resilience assessment that examined rural shrinkage in paired regions of Germany (Lüneburg) and Türkiye (Trakya). The method integrated Specified Resilience Assessment (SRA) and General Resilience Assessment (GRA) under Socio-Ecological Systems (SES) and Complex Adaptive Systems (CAS) lenses and operated through five steps. SRA employed (i) a multi-faceted survey to identify prioritized factors, solutions, and institutional roles/success and (ii) Relational Network Analysis (RNA) to assess complex factors and leverage points; GRA computed (iii) Spatial Network Analysis (SNA) to identify physical connectivity as hubs and sub-clusters; (iv) correlation analysis to determine significant variables among socio-demographic, land-use, facility, and network variables, and (v) k-means clustering to map shrinkage urgency levels. The synthesized outputs generated two operational strategies: strengthening sub-centers and connecting shrinking settlements to these hubs. While the strategies of Germany focused on the needs of the elderly and innovative digital solutions (wd ≈ 28), examples of Türkiye emphasized ecological concerns and the support of cooperatives as a leverage (wd = 54). GRA highlighted weighted degree (up to r = 0.79) and urban-industrial land cover (r ≈ 0.6) as critical drivers of stability; meanwhile, distance to the center (r ≈ -0.55) significantly correlated with shrinkage. Despite limitations of sample size and manual network construction, the study operationalized SES/CAS concepts for slow variables and integrated both qualitative and quantitative insights. It advances resilience research in sustainable spatial development by demonstrating a proof-of-concept and transferable decision-support workflow, while scaling and automation point to the directions for future research.

Climate change poses significant challenges for developing countries, where coastal communities are the most vulnerable. However, adaptation policies targeting coastal populations often remain ineffective because top-down governance structures fail to integrate social-ecological systems adequately. This study empirically examined an integrated Collaborative Governance-Social Ecological Systems (CG-SES) model encompassing principled engagement, shared capacity, joint action, multi-level fit, and community resilience. The analysis was based on the survey data collected through a structured questionnaire from 411 coastal residents in Padang City, Pesisir Selatan Regency, and the Mentawai Islands Regency, West Sumatra. Partial Least Squares-Structural Equation Modelling (PLS-SEM) was employed to assess relationships among various governance dimensions and resilience outcomes. The results indicated that principled engagement had a positive and significant effect on shared capacity (β = 0.936, p < 0.001) and community resilience (β = 0.651, p < 0.001). Shared capacity also positively influenced joint action (β = 0.472, p < 0.001) and community resilience (β = 0.342, p < 0.001). These relationships enhanced the predictive power of the model (R² = 0.926 for multi-level fit; R² = 0.941 for community resilience), indicating that governance variables explained a substantial proportion of variation in resilience outcomes. The findings further suggested that shared capacity functioned as a key mediating mechanism linking principled engagement to community resilience. At the same time, multi-level fit and joint action did not demonstrate direct or moderating effects. Overall, the results highlight the importance of co-produced knowledge, institutional trust, and collective capacity in shaping climate adaptation outcomes in the coastal regions of the Global South.

Quality of life (QoL) in the Arab world hinges on credible institutions and effective social spending; yet evidence linking governance, health budgets, and environmental pressures remains fragmented and seldom extends beyond 2020. This study clarified these links by assembling a balanced panel of 14 Arab countries from 2000–2020 to examine how institutional quality and public health expenditure shaped QoL, while accounting for carbon emissions, economic expansion, and education expenditures. QoL is proxied by life expectancy, while institutional quality is captured through a composite index constructed by applying Principal Component Analysis to the Worldwide Governance Indicators. The analysis employed country- and year-fixed effects, along with panel-corrected standard errors (PCSE), to address heteroskedasticity and cross-sectional dependence. Results indicated that institutional quality was the dominant driver as the composite index was strongly associated with higher QoL (β = 1.843, p < .01). Health expenditure was also crucial though the effect was economically small (β ≈ 0.0063, p < .05). Education expenditure was weakly negative (p < .10), thus reflecting quality and governance constraints in the education sector. Carbon emissions displayed a small positive coefficient (β ≈ 0.0949, p < .05), which likely implied policy and structural weaknesses rather than genuine welfare gains. Moreover, GDP per capita exhibited a statistically significant yet negligible and slightly negative elasticity (≈−0.000124), indicating rent-dependent growth that failed to translate into improved well-being. Collectively, the findings imply that governance reforms yield the greatest QoL, whereas spending without institutional credibility produces limited returns. Future work should test interaction effects, explore thresholds, and incorporate subjective QoL metrics to guide the sequencing of reforms across Gulf Cooperation Council (GCC) and non-GCC settings.

This study examined climate-related risks to public health, settlements and human security in Thailand, with a particular focus on vulnerable groups such as children and the elderly. Distinguishing itself from traditional assessments, this research innovatively integrated future climate projections from 2016–2035 under a high-emission scenario of RCP8.5 with data about current structural vulnerability, based on the Multidimensional Poverty Index (MPI) in 2024. This approach proactively identified “at-risk” areas where future environmental hazards might exacerbate existing social inequalities. The analysis on 76 provinces except Bangkok, utilized Bivariate Polygon Render to visualize risk-poverty intersections and Local Spatial Autocorrelation (Local Moran’s I) to rigorously detect statistically significant spatial clusters. Results indicated that the Northeastern and Western regions consistently faced elevated risks. Quantitative analysis confirmed critical “High-High” hotspots in the Northeast, specifically in Khon Kaen (LMI = 1.103, p = 0.004) and Buriram (LMI = 1.724, p = 0.008), where high climate exposure significantly overlapped with child multidimensional poverty. Conversely, Mae Hong Son emerged as a significantly “Low-High” spatial outlier (LMI = -0.634, p = 0.008), highlighting a region with concentrated elderly vulnerability despite lower relative climate risks. These findings underscored the utility of MPI over simple population counts for policy targeting. Ultimately, the study supports climate justice principles by providing spatially explicit evidence to guide interventions that address both local needs and structural inequalities.