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Why learning is critical for climate change investments



By Nathan Fiala, Assistant Professor, Department of Agricultural and Resource Economics, University of Connecticut

The World Economic Forum estimates that $5.7 trillion needs to be invested annually in green infrastructure for adaptation to climate change. Much of this is for developing countries. However, the Climate Policy Initiative, a think-tank, estimates that the current figure for private and public climate investments is only around $360 billion annually.

Irrespective of the actual amounts, it is clear that currently there is a shortfall in the investment needed to fund this fight. It is also clear that dollars need to be spent wisely.

The one way to make optimal use of climate finance is to ensure organizations that implement social and development programmes are learning organizations – organizations capable of delivering programmes that learn as they go along. This requires flexibility in climate investments.

Climate change is a completely new phenomenon for us. In combatting it, we will inevitably come across challenges that we could never have predicted or previously experienced.

To plough on ahead with a project as if it doesn’t have challenges is a sure way to arrive at a result far removed from the one intended. But with flexibility, it is possible to correct as we go along. It is possible to make changes en route and arrive at our preferred destination.

Some international organizations have made learning and flexibility a key part of their programming. For instance, David Miliband, CEO, and Ravi Gurumurthy, Vice President for Strategy and Innovation, of the International Rescue Committee (IRC), recently made the argument for finding more evidence of the effect of programmes.

This learning must happen continuously. Indeed, waiting until the end of a programme to learn if it works can be a significant waste of resources.

Consider, for example, improved or “clean”, cookstoves. Engineers have developed a range of products that governments and NGOs distribute with the promise of clean, cheap, efficient cooking. Langbein, Peters and Vance examined cookstoves in their famous study and concluded that they deliver less than they originally promised. Jetter and co-authors examined 22 cookstove models under laboratory-controlled operating conditions and classified them according to their ability to reduce emissions. Langbein, Peters and Vance then pointed out that laboratory-based trials did not provide a good representation of how people actually cook. Developing high-quality cookstoves may be well-intentioned, but they are likely to be of little value if we don’t know how people really use them. Learning how people use technologies needs to be done early, during the development stage.

Some of the 22 cookstoves examined in the study by Jetter et al.

Learning also needs to occur at multiple stages during a programme. A recent example of this comes from Evidence Action, a poverty-reduction focused CSO. Evidence Action recently scaled up an intervention in Bangladesh that provided small subsidies to poor, rural workers so they could avoid seasonal poverty by migrating to urban areas to find short-term work between planting and harvesting seasons. The initiative showed promise in early piloting. Then the CSO tested the impacts of the programme at scale. They found no impacts. As a result of this work, the programme is being halted. While the results were disappointing, Evidence Action is receiving high praise – including from donors – for being honest about their programming.

Indeed incorporating learning into programme design and implementation is the best way to ensure that limited funds are used optimally.