Akiyuki Kawasaki

Economic well-being is essential for assessing sustainability of human settlement in urbanizing regions; however, the geographic factors linking settlement characteristics to residents’ well-being remain underexplored, particularly in counties in China undergoing urban–rural transformation. In this study, six representative Chinese counties (Yanshou, Wafangdian, Bazhou, Yugan, Yongsheng, and Raoping) with varying urbanization levels are investigated to establish a multidimensional evaluation framework and reveal the geographic factors underlying economic well-being. Through original household surveys conducted across these six geographically and economically diverse counties, we collected primary data from 1659 households; these data provide unique insights into residents’ lived experiences. By integrating these original survey data with objective indicators from statistical yearbooks and geographic features from multisource spatial data, key drivers were identified using Pearson correlation and random forest models. The results show the following trends: (1) significant county-level variation in subjective well-being, with Wafangdian ranking the highest and Bazhou ranking the lowest, while well-being aligned more closely with economic development levels; (2) income and happiness were the dominant determinants of subjective well-being, with work-related factors also contributing substantially, whereas nighttime light intensity, building density, and construction land area drove fusion well-being; and (3) multifactor modeling demonstrated strong explanatory power for fusion well-being (training set R² = 0.8313; validation set R² = 0.7531), indicating generalizability. The primary data collection across varied settlement settings provides strong empirical grounding. The findings reveal the spatial differentiation of economic well-being in urbanizing settlements, offering empirical support for targeted settlement planning and urban governance policies to improve sustainability and residents’ well-being in developing countries.

Flood disasters increasingly shape rural livelihoods in Southeast Asia, not only through crop losses but also through long-term socioeconomic effects. This study develops a stylized agent-based framework (ABMAAF) to analyze flood–poverty dynamics in Candaba, Philippines. The model integrates spatially heterogeneous flood risk, endogenous rice market responses, and household wealth trajectories. Flood sequences were generated stochastically to highlight structural mechanisms rather than reproduce historical events. Results reveal that medium floods often generate larger inequality effects than rare large floods, as localized impacts create sharp disparities. Price elasticity of demand is critical: with low elasticity, price surges partly offset losses from large floods but amplify inequality during medium floods; with high elasticity, large floods dominate. Regression analysis further reveals that sequential shocks matter: medium floods following a large flood significantly increase inequality, as demonstrated by counterfactual comparisons and the interaction term in the regression model. These findings highlight that flood impacts depend not only on magnitude, but also on market functioning and event sequence. Methodologically, the ABMAAF links hydrological shocks with inequality trajectories, offering a framework for exploring disaster–poverty traps. Policy implications include strengthening safety nets during recovery and designing food market interventions that mitigate distributional risks under recurrent floods.

Building damage is the primary component of economic damage resulting from flood disasters. Understanding flood damage enables effective disaster risk reduction strategies and community resilience planning. In this study, a comprehensive framework for quantifying flood-induced damage to individual building properties (structural and content) is developed. This methodology combines geospatial data with machine learning and hydrodynamic modeling, as demonstrated through the 2023 flood event in the Dongdian flood storage and detention area (FSDA), Hebei Province, China. The main findings are as follows: (1) building-type classification using random forest algorithms achieved 98.4% accuracy in distinguishing residential, commercial, and industrial structures; (2) two-dimensional hydrodynamic simulations revealed maximum inundation depths predominantly ranging from 1.5 to 2.5 m, with structural damage ratios of 0.2–0.3 and interior property damage ratios of 0.9–1.0; (3) total direct economic damage to building properties in the Dongdian FSDA reached CNY 10.00–11.91 billion (approximately USD 1.42–1.69 billion), with industrial buildings accounting for 68.74% of damage, representing the dominant damage category. This framework delivers a precise flood damage assessment of building properties, transcends traditional survey limitations and offers a globally transferable approach for enhancing disaster resilience and reducing property risks in flood-vulnerable regions, subject to appropriate data availability and parameter adaptation.

Water-related disasters not only directly lead to loss of life and property, but also entrench poverty, widen disparities, and hinder the accumulation of human capital such as education and health, posing a long-term threat to people's livelihoods. The impact is not uniform: the more vulnerable the group, the greater the damage and the slower the recovery. Those lacking assets and social capital, in particular, have been found to recover more slowly, even from disasters of the same scale. Ultimately, this leads to increased poverty and inequality.In recent years, the number of water-related disasters worldwide has increased due to the impacts of climate change and other factors, accompanied by growing economic losses. Against this backdrop, there has been a growing trend to shift the focus of assessments from the traditional emphasis on 'costs of damage and loss' to 'restoring livelihood opportunities and socio-economic activities'. Therefore, it is essential to consider not only the direct damage and loss caused by water-related disasters, such as housing destruction, road inundation, farmland damage and asset loss, but also their medium- to long-term socioeconomic effects, such as widening inequality and the intergenerational entrenchment of poverty. New measurement and evaluation methodologies must also be pioneered.We argue that 'water-related disasters cause direct damage and loss and can also contribute to the widening of socioeconomic inequality, particularly in lower-middle- and low-income countries'. Based on the hypothesis that 'appropriate flood protection investments as climate adaptation measures can contribute to mitigating damage and loss, as well as reducing disparities and strengthening social resilience', we have conducted extensive research and development. This presentation introduces the following: (1) an empirical analysis of the impact of floods on poverty and economic inequality; and (2) an evaluation of climate adaptation measures that enhance social resilience, with a focus on the long-term socioeconomic spillover effects of flood protection investments.

Typhoon-induced flood events result in enormous property losses and lives, thereby jeopardizing the prospects of sustainable societal growth. Scientific, objective, and accurate information regarding the spatial pattern of their direct economic impacts is of great importance to flood emergency response planning, disaster-related insurance settlements, and reconstruction after such events. Systematic reviews on the direct economic estimation of typhoon flood events do not currently exist. This paper describes basic framework for estimating typhoon flood economic losses, explains data utilized in remote sensing on such economic losses, summarizes remote sensing simulation technologies on direct economic losses due to typhoon flood, and includes the most recent advances and demands for building digital twin watersheds to recommend core research technologies focusing on direct economic losses of Typhoon flood: (1) High-precision remote sensing recognition of principal factors leading to Typhoon flood and subsequent damage; (2) Property value and loss reconstruction and measurement with digital twin techniques; (3) Intelligent assessment and precision service technologies for direct economic loss estimation of typhoon flood.

Education is a powerful tool against poverty and inequality, yet its potential is undermined by floods. While floods do not affect the population equally, previous studies have ignored the chronic impact of recurrent flooding on education across different economic strata, thereby overlooking the effect of inequality. Our study investigates this dynamic along Thailand’s Chao Phraya River, exploring a novel research topic: “How recurrent floods create inequality in livelihood improvement through education”. Using qualitative methods, we conducted in-depth household interviews lasting 1.5 to 2 h per household. These interviews revealed stark differences in educational experiences between rich and poor, whether these people were flood-affected or not. Even when exposed to the same flood conditions, wealthier households tend to maintain stability while poorer families face severe disruptions that limit their ability to support their children’s education, thereby perpetuating the poverty cycle. Non-flooded poor often exhibit optimism and focus on livelihood upward growth, whereas the flooded poor may face a daily struggle to survive, leaving little time and opportunity for future planning. Meanwhile, affluent families, regardless of flooding, continue to accumulate wealth and pass advantages to their children, further widening economic inequality. Our findings highlight how recurrent flooding not only entrenches poverty but also exacerbates disparities by disrupting education. This underscores the important, and new, notion that to elevate poverty and reduce inequality in flood-prone developing countries, education alone is insufficient.