Lori Mae Hunter

This study investigated perceptions of climate change among residents of 12 villages in Bushbuckridge, Mpumalanga, South Africa, through household surveys and focus group discussions. These perceptions were compared with climatic trends derived from Skukuza meteorological station data (1981–2023), focusing on summer seasonal temperature and rainfall patterns. The analysis of meteorological station data revealed significant increases in seasonal maximum temperatures, a marked decline in the number of wet days, and high interannual variability in total seasonal rainfall. While local perceptions aligned with recorded trends of rising temperatures, delayed onset of the rainy season, and more erratic rainfall patterns, discrepancies emerged regarding total rainfall amounts; participants perceived an overall decline, whereas meteorological data showed no statistically significant long-term trend in seasonal rainfall totals. These findings highlight the value of integrating local knowledge into climate change research. Local perceptions provide critical insights into micro-level climate impacts, such as changes in rainfall frequency and intensity, that may not be evident in observational data. Combining these perspectives with scientific measurements can enhance the design of culturally relevant and effective climate adaptation strategies. This approach fosters stronger community engagement and ensures that policies address both the observed and perceived impacts of climate change, thereby supporting more sustainable and inclusive responses at local and national scales.

As with all social processes, human migration is a dynamic process that requires regular theoretical reflection. This article offers such reflection as related to the role of the natural environment in contemporary migration research and theory. A growing body of evidence suggests that environmental contexts, as shifting social and ecological realities, are consequential to migration theory. In this article, we review some of this evidence, providing migration research examples that integrate environmental context and are applicable to core migration theories, including neoclassical economic and migration systems perspectives, the “push-pull” framework, and the new economics of labor migration. We suggest that neglecting consideration of the natural environment may yield misspecified migration models that attribute migration too heavily to social and economic factors, particularly in the context of contemporary climate change. On the other hand, we suggest, failure to consider migration theory in climate scenarios may lead to simplistic projections and understandings, as in the case of “climate refugees.” We conclude that migration researchers have an obligation to accurately reflect the complexity of migration's drivers, including the environment, within migration scholarship, especially in the context of global climate change.

Rural-urban classifications are essential for analyzing geographic, demographic, environmental, and social processes across the rural-urban continuum. Most existing classifications are, however, only available at relatively aggregated spatial scales, such as at the county scale in the United States. The absence of rurality or urbanness measures at high spatial resolution poses significant problems when the process of interest is highly localized, as with the incorporation of rural towns and villages into encroaching metropolitan areas. Moreover, existing rural-urban classifications are often inconsistent over time, or require complex, multi-source input data (e.g., remote sensing observations or road network data), thus, prohibiting the longitudinal analysis of rural-urban dynamics. Here, we develop a set of distance- and spatial-network-based methods for consistently estimating the remoteness and rurality of places at fine spatial resolution, over long periods of time. We demonstrate the utility of our approach by constructing indices of urbanness for 30,000 places in the United States from 1930 to 2018 and further test the plausibility of our results against a variety of evaluation datasets. We call these indices the place-level urban-rural index (PLURAL) and make the resulting datasets publicly available (https://doi.org/10.3886/E162941) so that other researchers can conduct long-term, fine-grained analyses of urban and rural change. In addition, due to the simplistic nature of the input data, these methods can be generalized to other time periods or regions of the world, particularly to data-scarce environments.