Government agencies and foundations have committed sizeable climate adaptation investments to advance climate resilience—broadly considered to be the ability to withstand or flourish amid current and future climate risks. As these efforts expand, so too does the need to rigorously assess their impact and measure return on investment to encourage continued adaptation financing. However, there remains work to be done to align the climate and evidence communities on how to define and measure climate resilience. This is a necessary first step to effectively design policies and programs that help communities and systems thrive in our changing climate and to determine which adaptation investments are successful.
Mathematica has recently been exploring the concept of climate resilience measurement as learning partners for AGRA (Alliance for a Green Revolution in Africa), seeking to improve farmer resilience to climate change. Our efforts to bring forth the best evidence in this role guide our broader interest in solving measurement challenges to enhance the impact of climate action on well-being. To further inform our climate resilience measurement work, we joined other climate adaptation and resilience measurement experts at the Resilience Evidence Forum, hosted by the U.S. Agency for International Development (USAID) and the Global Resilience Partnership in Cape Town, South Africa in late June. This three-day convening examined the latest resilience practices and their larger implications for related programming and policy development.
I appreciated the opportunity to speak on two panels on evidence needs for locally led climate adaptation. I also found the chance to have conversations with donors, evidence generators, and evidence users particularly illuminating to advance our work. Here are my six main takeaways as I reflect on the fruitful discussions from this forum surrounding the current gaps in measuring climate resilience and the way forward:
- We are more aligned than we may think when defining climate resilience.
There was broad agreement on the definition of resilience in general: USAID defined it as the ability to adapt and manage through change and adversity without compromising future well-being, and the Global Resilience Partnership defined it as having the capacities to live and develop with change and uncertainty. An emerging consensus also surfaced when defining climate resilience specifically. Attendees generally consider climate resilience to be the ability to maintain and improve current and future well-being in the face of climate change-related stressors and shocks. The climate and evidence communities will need to hone our focus to align with this more precise definition as we approach the measurement challenges and opportunities related to climate change.
- Time horizons are key to defining and measuring climate resilience.
Maintaining a big-picture view of climate change over time and taking into consideration both near-term shocks, such as extreme weather events, as well as long-term stressors, such as changing weather patterns and sea-level rise, is essential to resilience measurement. This is because some climate adaptation efforts with positive short-term impacts might damage the prospects for achieving future resilience. For example, access to irrigation could shift production systems to high-value water-intensive crops, which may not be sustainable in the long run.
Because the dynamics and severity of climate shocks change over time, and climate stressors affect long-run trends, present conditions and future possibilities must be considered when defining and measuring climate resilience. This approach also makes the concept of recovering above a certain level of well-being particularly important. It is not enough to maintain welfare after climate shocks; instead, today’s climate adaptation efforts should strive to improve future well-being and ensure that it exceeds a desired threshold, such as the poverty line.
- Scenario analysis and predictive analytics are needed to shed light on these time horizons.
Accounting for time horizons means that scenario analysis—or planning for potential climate change-related risks—is critical to assessing how today’s adaptation interventions will position communities or systems to withstand risk over time. Predictive analytics that incorporate long-run climate forecasts into evaluations of adaptation interventions improve understanding of the uncertainties and long-standing impacts of those interventions. Uncertainty in climate forecasts should be modeled and accounted for to support decision making for sustainable growth.
Scenario analyses can provide estimates of expected rates of return on investment, which climate financiers need to inform their investment decisions. Ongoing measurement of an adaptation intervention’s outcomes can help determine if any adjustments to its design are needed. It is also helpful to consider how long we should expect to wait before seeing impact from a particular intervention or updating an intervention’s performance predictions.
- Evidence needs for designing climate adaptation interventions are different from evidence needs for measuring impact.
Donors and investors want to know how the specific interventions they fund are designed and implemented and how their investments move the needle toward improving climate resilience. As such, the evidence and measurement needs that arise when designing those interventions differ from those that arise when measuring the impact of those interventions.
In the design phase, providing information on the likelihood of an intervention’s success requires exploring how a changing household’s, community’s, geography’s, or system’s unique absorptive, adaptive, and transformative capacities can affect its climate resilience to ensure the design will enhance those capacities. When measuring impact, the evaluations of climate adaptation interventions should focus on measuring the capacities a particular program seeks to modify, and the well-being outcomes the program targets. But there remains the challenge of needing a common metric across various programs that focus on improving different well-being outcomes.
- Measuring the impact of climate adaptation interventions requires flexibility to account for implementation context.
The specific well-being outcomes we choose to measure when assessing an intervention’s impact on resilience will change depending on implementation context. This requires flexibility to use the most appropriate well-being outcomes (for example, food security, income) or their proximate outcomes (for example, crop yields, livestock holdings). When tracking well-being outcomes over time and across varying climate shocks and stressors, we can measure climate resilience by the extent to which a particular outcome is maintained across areas that received climate adaptation interventions compared with areas that did not receive intervention.
Accounting for context is also critical for determining the frequency and type of data collection. For instance, it might be important to collect data quickly following a climate shock to avoid missing measurement opportunities if the impact of the shock is short-lived. High-frequency collection could make this type of fast turnaround more feasible, lower the chances of failing to obtain potentially valuable data, and enable examination of a greater number of data points at once—all of which can help reveal patterns in resilience. However, high-frequency data collection can be resource intensive and potentially burdensome in some contexts, so it should only be used if there are factors that we would not be able to accurately measure otherwise.
- Together, we can forge a path forward.
My conversations with donors and practitioners at the Resilience Evidence Forum sent the compelling message that now more than ever, we need rigorous evidence on climate adaptation interventions. I am also convinced that the climate research community has a great deal in common in terms of how we think about the topics discussed, despite our varied approaches to adaptation and resilience programming and measurement. As we continue to move toward a common vision, the climate and evidence communities must work together to build an evidence base that meets diverse needs and informs the scaling up of effective interventions for climate adaptation. We at Mathematica look forward to continuing to help this vital work take shape.