The authors focus on internally displaced climate migrants and settlement dwellers enduring urban climate vulnerabilities in Bangladesh, and combine their ethnographic observations with policy content analysis to examine how structural inequities persist through policies in Bangladesh’s climate adaptation context.
This study used China’s provincial panel dataset during period 2003–2015 and employed the “Threshold-STIRPAT” model to investigate the impact of urbanization on carbon emissions under different urbanization thresholds, with the intermediate effect model combined.
Using panel data from 1996 to 2019, this paper explores the heterogeneous effect of urbanization and nonrenewable energy consumption on the environment in 54 African nations.
The authors combine data from household surveys, key informant interviews, and secondary sources related to regional disaster, demographic, resource, and economic trends between 1990 and 2020 from north central and central dryland Namibia to investigate (i) human migration flows and the influence of climate hazards on these flows and (ii) the benefits and dis-benefits of migration in supporting climate change adaptation, from the perspective of migrants (personal factors and intervening obstacles), areas of origin, and areas of destination.
The authors conducted a household survey in Nuevo Leon, Mexico, to understand the extent to which climate change perceptions and the sociodemographic characteristics of households influence their reported mitigation performances and their perceived mitigation efforts.
The authors expand the STIRPAT model to empirically analyse the effects of China’s urbanization and urban sprawl on CO2 emissions from 1997 to 2018 in order to identify policy recommendations for addressing both how to reduce China’s carbon and how to encourage future low-carbon development.
In this paper, the authors investigate how more stringent traffic regulations implemented in Central London from late 2015 affected pollution levels and school absences.
In this research, the authors propose a supervised machine learning approach, Random Forest, for population density appraisal in a large and dense developing city.
This paper provides an original conceptual framework examining whether and how natural disasters exert any influence on nuptiality by drawing on socio-psychology and economics literature contributions.
The authors analyzed census data to examine the spatial heterogeneity in drinking water sources and neighborhood income in the Greater Accra Metropolitan Area (GAMA), the largest urban agglomeration in Ghana using the Shannon Entropy Index and the Index of Concentration at the Extremes at the enumeration area level.
