Gendered Impacts of Climate Change: Empirical Evidence from Asia

Authored by: Sara Duerto-Valero and Sneha Kaul

Categories: Humanitarian Emergencies
Sub-Categories: Climate and Environment, Disaster Risk Reduction and Resilience (DRRR)
Region: East Asia and the Pacific
Year: 2023
Citation: Duerto-Valero, Sara, and Sneha Kaul. Gendered Impacts of Climate Change: Empirical Evidence from Asia. UN Women, 2023.

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Executive Summary

The effects of climate change, such as changes in temperatures, precipitation and biodiversity loss, are affecting human health, food security and livelihoods, as well as the quality and availability of land, water and other natural resources. As individuals are intrinsically linked to their environment, climate change poses a serious threat to every aspect of human life. It has been long recognized that the consequences of climate change are not experienced evenly, and women – who are less likely than men to own productive assets and are more dependent on natural resources for their livelihoods – are likely to be disproportionately affected. Social norms often put women in charge of gathering food, collecting water and fetching fuel for cooking and heating – chores that are increasingly time-consuming as climate change affects the availability and quality of these resources. Women’s capacity to cope with the effects of climate change is also hindered by their overall disadvantage: they are overrepresented among the poor, face barriers to decision-making, experience disproportionate mobility challenges and face unequal access to resources.

To put in place inclusive strategies that increase the resilience of women and men in all their diversity, there is an urgent need to better understand the gendered effects of climate change across countries. To achieve this, this paper explores the connections between phenomena related to climate change and gender related outcomes in Bangladesh, Cambodia, Nepal, the Philippines and Timor-Leste. In particular, it tests these associations by utilizing random forest machine learning techniques and binary logistic regression analysis, on a data set that integrates data from Demographic and Health Surveys (DHS) and geographical information systems (GIS).