Climate Change

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Overview

Climate change poses significant challenges to sustainable development, disproportionately affecting vulnerable populations through acute weather events and long-term environmental changes. Enhancing our understanding of how individuals, households, and communities are affected by climate change, how they respond or adapt to it, what drives their responses, and the effectiveness of these actions is essential for informing policies and interventions to build climate resilience.

This requires accurate and timely information to capture local conditions, which is generally obtained through the integration of different data sources. While household surveys provide insights into socioeconomic conditions, coping strategies, and adaptive behaviors, geospatial data offer information on climate-related risks and extreme events. Yet, these sources have limitations, in addition, evidence on the fit of survey-integrated geospatial data products to capture local events is insufficient. To address these gaps, our team has designed a climate change survey methods agenda.

Work Areas

1. Measuring Climate Adaptation in Household Surveys

Climate change adaptation is an important way individuals, households, and communities can become more resilient to the adverse impacts of climate shocks. Adaptation strategies can vary widely and span multiple livelihood sectors. Men, women, children, and the elderly experience the effects of climate change differently and may adopt distinct adaptation strategies.

Understanding these adaptive behaviors and their contributions to building climate resilience is essential for improving the targeting and effectiveness of interventions. Although household surveys have a comparative advantage over other data sources to inform this type of research, they often lack systematic, comprehensive, individually disaggregated data on climate change adaptation and its potential drivers.

To address this gap, the LSMS team is intensifying efforts to enhance data capture on climate adaptation, with an emphasis on behavioral aspects and how they differ for men and women. While previous research primarily focused on climate adaptation in agriculture, going forward, this line of work will extend to other household livelihood activities and sectors, including non-farm enterprises, employment, energy, and housing and cover individual-level elements, such as climate change knowledge and awareness, expectations, and perceived experiences.

2. Improving Climate Resilience Measurement Using Longitudinal Household Data

In a time of multiple crises, from pandemics to conflicts to climate extremes and natural disasters, resilience has emerged as a cornerstone in the development discourse. However, measuring resilience remains a complex task mainly due to its multidimensional and dynamic nature, which also entails extraordinary data requirements.

As a result, none of the most widely recognized resilience indicators has, to date, been established as a universal method for measuring resilience, so this methodological research field remains widely open, offering opportunities to improve resilience’s measurement. The LSMS team is currently collaborating with experts to develop and empirically validate innovative methods for a definition and sound estimation of climate resilience.

Under this line of work, both parametric and non-parametric approaches are explored, including the application of impact evaluation techniques and novel causal machine learning tools to assess data derived from multitopic longitudinal national household surveys. Our research will also determine the ability of newly proposed resilience indicators to accurately predict and forecast household resilience capacity — key to target climate adaptation interventions and development policies.

3. Improving Local Accuracy of Remote Sensing Climate Data

Accurate measurement of weather and climate is crucial for understanding household welfare, as these factors directly influence livelihoods, health, and economic stability. Reliable weather and climate data is also essential for precise local forecasting and improved planning and decision-making. To inform policies and interventions that are effective at building adaptation and resilience locally, reliable weather and climate information must be integrated with granular agricultural and socioeconomic data. This is done using a variety of methods and data products, but their suitability for micro-level analyses is not fully understood.

We aim to fill this knowledge gap by conducting methodological research to evaluate measurement error and develop approaches for increasing the reliability of survey-integrated weather and climate data products. Recent research undertaken by the LSMS team in collaboration with colleagues from the University of Arizona, highlights that survey-based estimates of agricultural productivity are significantly affected by the choice of remote sensing weather datasets.

However, further analysis is needed, particularly using a credible ground truth benchmark—an element often lacking in developing regions. To tackle this challenge, ongoing methodological work is testing and validating the deployment of in-situ weather sensors within survey communities. These efforts aim to establish reliable benchmarks in areas with sparse and inconsistent weather station coverage, thereby advancing this line of research.

4. Climate-Sensitive Sampling for Household Surveys

Nationally representative household surveys often fall short in providing rigorous estimates of the impacts of climatic shocks due to sampling designs that do not adequately cover affected areas, so our team is developing sampling methods that support robust climate resilience analyses. These methods include the use of spatial resources to identify areas prone to extreme weather events and oversample communities and households within those areas.

This new line of work will commence with an initial research phase focused on developing the methodology including the identification and selection of appropriate data sources and sampling techniques. The ultimate objective is to operationalize the approach, integrating it into forthcoming national data collection efforts under the new LSMS-led Resilient Futures initiative.