How Will We Measure Climate Adaptation?
Last year, after COP26, I asked: can we measure successful climate adaptation? A better question is: how will we measure climate adaptation? Regardless of how well we can, we will.
The invective of measuring adaptation is being advanced by the global stock-take of the Paris Agreement, which will begin at next year’s COP28. The global stock-take includes a thrust known as the Global Goal on Adaptation (GGA). In this year’s COP, there was intense debate on creating a framework for systematically measuring adaptation under the GGA.
A good framework for measuring adaptation will be actionable and accurate. Yet, one of the most basic challenges of creating a framework that satisfies all 195 UN member countries is that it will almost inevitably be too vague to be directly implementable.
Translating the policy into specific metrics derived from specific data sources will be resolved through technical working groups, not at negotiating tables. The disconnect between negotiating policy and implementing that policy means it is possible that negotiators agree on a framework that is very challenging or impossible to implement with available data and methods.
To create an actionable and accurate framework, two questions to resolve are:
- What does it mean to observe adaptation? (And what types of adaptation can we successfully identify in monitoring data?)
- What are the right spatial and temporal scales over which to measure adaptation?
These questions are not new to COP. They are already being discussed in a different stream of work—though the National Adaptation Plan (NAP) program, which pre-dates the Paris Agreement and the GGA. (I discuss NAPs in my previous post.)
It is possible that the GGA’s framework could build on existing conversations of measuring successful adaptation outcomes from NAPs. The major benefit of doing so is that embedded in each NAP is an implicit view of how each country perceives what adaptation means for them. Since many aspects of adaptation are specific to local interactions between biophysical systems (e.g., water, forested land, and biodiversity) and social systems (e.g., governance and culture), the NAPs reveal nuanced, country-level views on adaptation.
There are several barriers to using NAPs to measure adaptation on a global scale, however. One barrier is that that NAPs are optional. As of 2022, forty countries have submitted NAPs. Most countries are not engaged in producing a NAP and likely never will. As a result, NAPs provide insight into adaptation in some countries, but not most of the world.
A second barrier is that most existing NAPs lack indicators for evaluating national-level adaptation. If the NAP includes monitoring and evaluation, it is most often focused on measuring success at the project-level, rather how the projects add up to systemic change at the national-level. As a result, most NAPs are not ready to directly contribute metrics or data into adaptation tracking.
A third barrier is that adaptation of some systems is not suited to be measured at the country-level. For example, as climate change causes ecosystems and species to migrate, they may shift over national borders. Adaptation of the ecosystem and species would be best evaluated across these borders, not within borders.
In contrast to measuring adaptation from the bottom-up, some researchers are investigating the possibility of global-scale adaptation tracking. For example, Proença et al. (2017), examine global biodiversity monitoring. Global-scale adaptation monitoring overcomes many of the limitations of using NAPs. At the same time, global measurements also have major drawbacks.
One substantial drawback is that global-scale metrics impose a singular method of measuring adaptation across all countries. This is likely to be inaccurate and most countries are averse to imposed adaptation indicators. Another is that modeling at the global scale lends toward tracking coarse biophysical metrics and neglecting social metrics, since biophysical data are easier to observe from global data products.
It will likely be necessary to blend bottom-up and top-down adaptation measurements and models. This approach is already widely being taken in tracking Nationally Determined Contributions, allowing coarser, global measures to be locally improved with higher resolution country-specific data. This approach is likely wise for measuring adaptation.
Yet, even with a top-down/bottom-up modeling approach, an outstanding question will be how to integrate data that cannot be easily quantified. Adaptative responses are often social and behavioral and can be difficult to observe in monitoring data.
More importantly, socially maladaptive responses can be mixed with visible, seemly adaptive responses, like breakdowns in community cohesion and trust after a planned coastal retreat that protects housing stock from flooding. An evaluation of satellite imagery might reveal a change in housing location (adaptation), but not the lost community (maladaptation). The success of many adaptation actions are better understood through qualitative evaluation.
The negotiated framework has the potential to positively or negatively contribute to global adaptation. A positive outcome could be that the framework spurs the collection of new data and development of new models that enhance our understanding of adaptation activities. A negative outcome could be that implementing the framework takes far longer than accounted for because of difficulties in translating the guiding policy into an actionable method of measurement and, in the end, it inaccurately assesses adaptation activities.
Over the next year, iteratively developing the framework alongside the real, technical constraints of data and models, while answering questions on identifying successful adaptation, will help ensure the GGA positively contributes to adaptation.