It is no mystery that climate change is a dire issue that plagues all of humanity. Therefore, much research has gone into understanding how it affects populations and human development, as well as forecasting climate-related disasters so that we can act accordingly. Today, we understand that not everybody is affected equally from climate change, and as a result, many different people’s needs must be met (8). To put it simply, we know that throughout the globe, climate change has negative effects on populations, despite heavy regional differences (1, 3, 6-8). In order to determine and deal with people’s numerous vulnerabilities to climate disasters, we have developed sophisticated means of forecasting and using geospatial population data in order to allow for appropriate warnings and action. Despite our understanding and technical prowess, we have not perfected our ability to evaluate vulnerability and make policies accordingly (1-3, 5-9). For one, due to the high amount of complexity in climate science, it is near-impossible to perfectly determine the effects of climate change (7). Additionally, only recently have we begun to understand the importance of considering the role of sex and gender in climate change vulnerability (8). As a result, much of the recent climate change literature fails to account for such (6).
Today, we have a vast understanding of the climate change effects that populations can suffer. Todd Pugatch in his World Development article, “Tropical Storms and Mortality Under Climate Change” addresses how climate change disasters adversely affect human health. With a case study in Mexico using storm wind speed data from the National Oceanic and Atmospheric Administration Tropical Prediction Center and mortality data from Mexican Vital Statistics records, Pugatch found that hurricanes and tropical storms caused by climate change result in an increased mortality rate (7). In order to increase the security of populations and mitigate mortality in the face of climate disasters, it is necessary to be prepared and evacuate (7). Pugatch also discusses how impoverished communities see higher mortality when struck by severe storms due to their lack of robust infrastructure, as well as that a higher amount of economic development helps decrease mortality from tropical storms (7). This connects to Amartya Sen’s idea of how socioeconomic freedoms (or lack thereof) act in a positive feedback mechanism.
We know that climate change disasters, especially in developing areas, result in reduced economic and food security. Carlo Azzarri and Sara Signorelli in their World Development article, “Climate and Poverty in Africa South of the Sahara” examine the ramifications of climate shocks on Sub-Saharan African (SSA) population welfare. In their study, they used household surveys from 24 different SSA countries, which provide information of household consumption expenditure and income per capita at the sub-national level (1). This allowed for them to account for heterogeneity within the countries, making the data more robust. In order to determine droughts, heatwaves and floods, they use data from the Standardized Precipitation Evapotranspiration Index (SPEI), which contains data on precipitation, temperature and evapotranspiration (1). By using Moran’s I test statistic and Lagrangian multiplier tests, followed by spatial regression analysis, they found that there is in fact a correlation between population welfare and the ramifications of climate change (1). The fact that the majority of the SSA food supply comes from agriculture means that its security can be easily compromised by disasters like flooding and temperature changes. As a result, many SSA societies are trapped in extreme poverty (1). Based on this, a sustainable development goal would be to implement policies that help improve food security (1), so that in the case of climate disasters, there would be a lesser rise in poverty rates, allowing for a better quality of life.
In modern times, it is understood that when evaluating climate change vulnerability, it is important to account for gender differences. Men and women generally suffer differently from climate disasters, despite heavy regional heterogeneity (3). Joshua Eastin in his World Development article, “Climate Change and Gender Equality in Developing States” discusses how women in such regions generally suffer worse than do men because of their comparative lack of ownership of assets including land, as well as climate change’s effects on their domestic lives and duties (3). They have a harder time adapting in the face of climate disasters and also bear greater domestic burdens, which results in less socioeconomic stability, security, and individual freedom. This results in a major downward socioeconomic spiral for women, because their inability to maintain a high socioeconomic status results in less employment opportunities and in turn less domestic bargaining power (3). Additionally, they are deincentivized from participating in policy-making decisions, facilitating systematic gender discrimination (3). In order to quantify women’s rights in developing areas, Eastin uses data from the Cingranelli-Richards Human Rights Dataset (3). He regresses these data with climate data using ordered-logistic regressions, followed by ordered-probit regressions and ordered-logistic regressions with random effects to verify the validity of his assessment (3). He found that increases in temperature do appear to negatively affect women’s rights, but that there was no statistically significant effect from precipitation (3).
In addition to the socioeconomic consequences for women resulting from climate disasters, it is known that men have their own issues regarding climate change vulnerability. Arja Rautio et al. in their article, “Climate Change in the Arctic–The Need for a Broader Gender Perspective in Data Collection” from the International Journal of Environmental Research and Public Health” discuss how in addition to understanding the vulnerabilities of women to climate change, it is also important not to ignore those of men (8). They start off by discussing how gender issues should not be viewed as interchangeable with women’s issues, since that not only discounts the vulnerabilities of men, but also ignores the vast heterogeneity that exists among women (8). Men’s issues being ignored like this is exemplified by Jacqueline D. Lau et al. in their Nature Climate Change article, “Gender Equality in Climate Policy and Practice Hindered by Assumptions,” in which they mention how in Nicaragua, because the government saw water scarcity as primarily affecting women, male widowers had much less water security than they should have (6). In order to examine the lack of study regarding gender in climate change, Rautio et al. searched literature from 2010 and 2019 with the keywords, “climate change, “human health,” “gender,” and “policies.” They found in both years that very few publications contained both the keywords “climate change” and “gender,” showing that there is much work to be done in incorporating gender in the study of climate change vulnerability (8). Additionally, they conducted a case study in the Russian Arctic zone where they gave out questionnaires and interviews to various inhabitants. With this, they found that women generally tended to feel a lesser ability to influence environmental policy (8). The authors conclude their paper by stating that men’s and women’s vulnerabilities need to be addressed independently going forward, and that policies should address such (8).
In order to determine the parts of populations that are vulnerable to climate disasters, we have numerous sophisticated ways to determine population dynamics and to forecast extreme climate-related events. Thanks to the prevalence and capabilities of mobile phones, they serve as an indispensable means of generating real-time data on the movement of people (2). Sébastien Dujardin et al. in their Sustainability article, “Mobile Phone Data for Urban Climate Change Adaptation: Reviewing Applications, Opportunities and Key Challenges,” discuss how Call Detail Records (CDRs) can be used to generate a high-spatio-temporal-resolution map of urban population dynamics, activities, and even land use. This allows for government agencies to accurately determine in real-time who is most at risk in the face of climate disasters, and they can act and issue warnings accordingly (2). Additionally, CDR’s can be used to localize victims of climate disasters, allowing for a much more efficient response and allocation of aid (2). These capabilities offered by CDRs help improve the safety and adaptive capacities of urban populations (2). Although CDRs do have their limitations, consolidating them with other expert knowledge types has proven invaluable for the resilience of urban communities to the ramifications of climate change (2).
In addition to being able to accurately determine who is vulnerable to climate disasters, in this day and age we have also vastly improved our ability to forecast disasters. Chris Huntingford et al. in their Environmental Research Letters article, “Machine Learning and Artificial Intelligence to Aid Climate Change Research and Preparedness,” discuss how Machine Learning (ML) and Artificial Intelligence (AI) have allowed for superior disaster forecasting and the allocation of aid (5). As stated previously, studying the effects of climate change has been difficult due to the high amount of complexity and uncertainty involved, but we’ve also had trouble with handling big Earth System model (ESM) data (5, 7). However, as a result of our improved computing power, ML is now able to detect correlations in big and complex ESM data which helps to determine climate trends and the imminence of disasters (5). This allows for forecasts of much greater accuracy compared to traditional methods, making it an invaluable supplement to the study of climate change (5). AI is able to use this new knowledge to issue timely warnings and determine for whom and when aid is needed, bolstering the adaptive capacities of human societies in the face of climate change (5).
In addition to using ML and AI, much of our modern climate disaster forecasting abilities come from the use of satellites. Particularly, Byron D. Tapley et al. in their Nature Climate Change article, “Contributions of GRACE to Understanding Climate Change,” discuss how the Gravity Recovery and Climate Experiment (GRACE) satellite program has allowed us to measure changes in the overall mass distribution of water and ice, which play a major role in numerous climate disasters like floods and droughts (9). The GRACE program lasted for 15 years from 2002 to 2017, and consisted of two satellites that orbited the Earth in tandem. Any perturbations in the distance between the satellites could be attributed to changes in Earth’s gravitational field caused by the mass distribution, allowing us to accurately measure it (9). In the past, quantifying the water cycle has been nearly impossible due to its constant evolution. However, thanks to the high temporal resolution of GRACE’s measurements, we were able to do so, and thus improve our forecasting of floods and droughts (9). Because of the success of the original GRACE mission, another improved version of the satellites (GRACE Follow-On) was launched, which continues to provide us with high-resolution data on the water cycle and potential climate disasters today (9). With a vast arsenal of tools to forecast climate disasters, we have readily available knowledge about the adaptive measures we will need to take.
Despite our vast knowledge and capabilities, as stated previously, we have just begun to understand the significance of gender in the study of climate change effects. Lau et al. in their article discuss how efforts to mitigate vulnerability are hampered as a result of gender assumptions, as well as that such assumptions are prevalent in much climate change literature (6). What makes gender assumptions problematic are the fact that they ignore the complexity and heterogeneity that exists within and between cultures, as well as the fact that they increase the prevalence of stereotypes, such as that women’s lives are more based on the natural environment, that they are more caring, et cetera (6). As a result, women often tend to be viewed as more susceptible to the ramifications of climate change, and the vulnerabilities of men are accordingly downplayed (6). Additionally, Lau et al. corroborate Rautio et al. regarding how it is not feasible to see women’s and gender issues as one and the same by discussing how gendered climate change policies focusing on women have proven ineffective (6). In order to properly address gender in climate change research and policy, it is important to scrutinize the work for gender assumptions, make sure that generalizations aren’t being made, and attain a deeper understanding of the issue of gender and its impacts (6).
Unfortunately, much of the literature examined in this review either fails to take gender into account, or does so in an unhelpful manner. For starters, although CDR’s are able to track the dynamics of populations, Dujardin et al. did not discuss their ability to be gender disaggregated and how that could impact assessments (2). It has recently been shown by Rahul Goel et al. in their Plos One article, “Understanding Gender Segregation Through Call Data Records: An Estonian Case Study,” that CDR’s can in fact be used to study the dynamics of males and females separately (4), showing that Dujardin et al. missed a major improvement to CDR-based vulnerability measurements. Additionally, Eastin’s article is the quintessential embodiment of the issues described by Lau et al. and Rautio et al. He focuses almost exclusively on women in developing countries and their vulnerabilities, which according to Rautio et al. homogenizes women and is ignorant to the vulnerabilities of men (8). Additionally, one of Eastin’s main arguments is that women are more vulnerable to the effects of climate change than are men because their main responsibilities revolve around gathering resources from the environment (3). Not only does this generalize the role of women in developing societies, but also reinforces the stereotype mentioned by Lau et al. that women are more dependent on the environment (6), making Eastin’s article inviable for better understanding gender and climate change. Because of its basis on gender assumptions and overemphasis on the vulnerabilities of women, Eastin’s article hampers progress on gender equality in climate change understanding and policy.
We have only recently acknowledged the importance of taking into account sex and gender differences in the study of climate change as well as our issues with handling gender in studying vulnerability. As a result, much of the literature is either incomplete or fundamentally flawed. Despite our technical and forecasting prowess, our understanding of climate change vulnerability has much room for improvement. According to Lau et al., one of the biggest issues plaguing climate change research and progress in the modern day and age is the unavailability of viable gender-disaggregated data (6). In order to rectify this issue, the collection and use of gender-disaggregated data needs to become more commonplace (6). Taking advantage of our ability to use CDR’s to evaluate population dynamics and gender differences, I will apply CDR-based, gender-disaggregated measurements of population dynamics to assessments of climate change vulnerability. With this, we would be able to better our understanding of gender disparity in climate disaster vulnerability and therefore be able to better enact climate policies that facilitate gender equality.