Uncovering local climate solutions using non-traditional data


By Basma Albanna and Andreas Pawelke

Source: Yupachingping on iStock

The climate crisis is often viewed as a planetary-scale problem that requires international policy solutions and the mobilization of technical and political resources. However, with vulnerable communities at the frontline of its impacts, we also need to view the crisis through a local lens.

In practice, this involves action that is local and rooted in a sense of place and community making solutions more effective as they are tailored to the local context, ease uptake, and harness the inherent knowledge of the impacted communities. Yet, data shows that only 10% of climate finance is used for local-level climate action while 90% is spent on global responses.

While more and more governments and development partners have started to shift towards locally-shaped climate solutions, there are challenges impeding the scaling of such solutions. Among those challenges is the ability to pay attention to what is already happening, in other words, how to identify and scale existing local solutions.

This is where the Data-Powered Positive Deviance (DPPD) method comes into play. DPPD combines non-traditional data (e.g. earth observation) and traditional data (e.g. surveys) to identify outperforming individuals or communities in development-related challenges to help scale their uncommon but successful solutions. It presents a new method to leverage digital data to uncover local solutions in response to climate-related mitigation and adaptation challenges.

Why use DPPD to tackle climate-related challenges

Not every problem lends itself to the DPPD method. For instance, DPPD is not suitable when the problem requires a predominantly technical solution that doesn’t necessitate new knowledge or expertise, such as building a dam or installing wind turbines.

The method also relies heavily on the availability of reliable and accessible digital data that can capture outcomes directly related to the addressed development problem. Nonetheless, DPPD seems to fit well in addressing climate change challenges for the following reasons:

1. Digital data on climate-related outcomes is becoming increasingly available

Recent developments in the availability of digital data — remote sensing data, social media data, or data captured by wearable or other IoT devices, for example — relating to individuals, communities and spaces, provide opportunities for climate action.

Earth observation (EO) data, in particular, has proven to be one of the most viable sources of data for DPPD. Thanks to the recent advances in its temporal, spatial, and spectral resolution, EO data enables us to identify bright spots across natural and built environments when it comes to the adoption of climate change measures. At the same time, EO data makes it possible to control for contextual factors, such as: climate events, biophysical drivers, and land cover. These information-rich bright spots will then act as the starting point in the search for solutions by collecting and analyzing in-situ data.

Additionally, EO data can often be acquired without any administrative restrictions, making EO-derived performance measures ideal for applying DPPD and reducing the time and cost of data collection.

Examples of EO datasets that can be used to capture climate-related outcomes and the contextual realities of communities

2. Tackling the climate crisis requires a change in behavior

Climate change solutions often require behavioral change, which could be at the level of individuals, communities, or systems. This encourages a shift away from the need-based approaches to development, which involve top-down identification of needs and problems, and the external imposition of solutions to meet these needs. Instead, a move towards asset-based approaches, which capitalize on a community’s inherent knowledge, assets, and capabilities in solving their own problems, is needed. DPPD (building on the positive deviance approach) is one of such asset-based approaches that uncovers bottom-up solutions by searching for outliers — the ones that succeed against the odds — to replicate and scale their solutions.

3. Sustainable local solutions are needed

One of the biggest challenges facing local-level climate action is the lack of financial support and the absence of mechanisms enabling local actors to access global funds, which threatens the sustainability of local-level action.

DPPD implies that the solutions needed for development already exist within communities. By leveraging non-traditional data, DPPD can be used to identify communities with remarkably above-average outcomes to source their context-aware grassroots solutions. As these solutions are locally-sourced, they can be more affordable and scalable. And since they are not dependent on external financial aid and expertise, they often are more accessible, sustainable, and scalable in relevant environments.

Applying the DPPD method for local climate action

Pilot Projects

Over the past three years, together with the GIZ Data Lab, the Pulse Lab Jakarta, the UNDP Accelerator Labs Network, and the University of Manchester Centre for Digital Development, we have tested and refined the DPPD method in a series of pilot projects.

Among them were projects tackling climate change challenges. We learned from climate-resilient farmers in Niger who achieve higher-than-usual cereal crop productivity, cattle farmers in Ecuador who deforest less than other farmers, and pastoral communities in Somalia who manage to preserve their rangelands despite frequent droughts.

For these pilots, we used remote-sensing derived measures of performance to identify positively-deviant communities. We then engaged with individuals within these communities to discover their uncommon but successful practices and strategies for other community members to ultimately adopt.

Existence of Faidherbia Albida (“Gao tree”) in a positively-deviant farm near Bey Beyé, Niger, which helps with fertilizing the soil and increasing yields

DPPD Masterclass

Over the past two months, we have worked with the GIZ Data Lab to run a Masterclass for GIZ teams working on climate change mitigation and adaptation challenges.

We split the course into five live sessions, individual team mentoring sessions, and deep dives on technical topics for participants to not only understand the method as a novel way of using data but to also allow them to develop their own DPPD use cases.

During the Masterclass, we covered the process of working through the five stages of DPPD, explained how the method was applied in previous pilot projects, and trained the participants in using a variety of tools which can be found in the DPPD Handbook.

The Problem Definition tool used by the course participants in designing their DPPD use cases

We grouped the 14 participating teams from eight countries based on the development challenges the teams worked on during the course:

  • Climate resilience.
  • Nature conservation and restoration.
  • Climate-smart agriculture.
  • Pollution prevention.

The DPPD use cases included smallholder farmers in Brazil who deforest less than their peers, healthy community-managed wetlands in India, fisheries in the Philippines that achieve higher-than-expected yields, and climate-resilient farmers in Ghana.

Reach out

In 2023 we will continue applying the DPPD method for local climate action. It is time for climate mitigation and adaptation measures to take a bottom-up approach and leverage local solutions developed by those most affected.

Get in touch if you want to join us on that journey.



Data Powered Positive Deviance DPPD

We are an international collective that is dedicated to utilizing big data to find effective locally developed solutions to complex problems.