Field notes: Climate and clean energy

Max Glanville MPA/ID 2027

Climate analyst intern, Suyana

Tell us a bit about your background. What drew you to climate and clean energy before HKS?

I started my career in the financial markets group at the Reserve Bank of Australia (RBA). I then worked in the Australian Treasury’s macroeconomic analysis and policy department before returning to the RBA to join its note issue department.

I developed an interest in climate and clean energy while working as a policy analyst in the chief economist’s office at the Organisation for Economic Co-operation and Development (OECD). At the OECD, we talked a lot about the “four Ds” shaping the policy landscape: digitalization, demographics, deglobalization, and decarbonization. The fourth D—decarbonization—sparked my interest in climate work. 

The OECD was doing interesting research on the effectiveness of mitigation policies, the need for adaptation frameworks, the effects of climate change on productivity, and countries’ ability to meet decarbonization targets under different policy scenarios. This work was so fascinating that it inspired me to pursue work in climate and clean energy.   

Where did you spend your summer, and what was your role?

I spent the summer at Suyana, whose mission is to bridge the insurance gap in Latin America by providing coverage to people and businesses that lack access to traditional insurance or cannot afford it.

As a climate analyst intern, I worked on two work streams. First, I analyzed insurance products that protect Colombian rice farmers against excess or insufficient rainfall. Specifically, I investigated the basis risk associated with these products—the risk that an insurance product pays out when the insured has not experienced a loss, or fails to pay when a loss happens.

Second, I worked on business strategy, including Suyana’s application to obtain delegated underwriting authority from an insurance carrier—the right to bind clients to insurance products on the carrier’s behalf. 

What kind of work did you do? Tell us about a project you’re especially proud of. 

I led the development of a business strategy to support Suyana’s transformation from a calculating agent to a managing general agent. Suyana currently designs insurance products and licenses or sells them to carriers. Its next step is to become a managing general agent—a carrier gives Suyana authority to bind them to insurance contracts on the carrier’s behalf. 

This transformation would help Suyana stabilize its revenues and scale its business, giving more people and businesses across Latin America access to insurance. Working on the application made me feel that I was directly contributing to Suyana’s mission and driving it forward.

What was the biggest challenge—something you had to work through or rethink?

The biggest challenge was working through the statistical complexities of designing parametric insurance products. A central challenge in parametric insurance is basis risk—the difference between the losses estimated by an index and the losses actually experienced by the insured. 

Although solving basis risk was beyond the scope of my work, understanding its drivers was not. For example, in the case of Colombian rice farmers, we measured rainfall across 5-by-5-kilometer grid cells, but each farmer’s plot represented only a small portion of that area. When topography is complex and climate conditions vary within a grid cell, the average measurement can differ significantly from the rainfall at any one location.

These differences can be driven by localized weather patterns, altitude, proximity to waterways, wind patterns, and the technology used to measure rainfall. Understanding the issue was challenging, statistically and conceptually. 

Did anything from your HKS coursework click in an unexpected way?

Absolutely! ! I drew heavily on the concepts from Professor Dan Levy’s API-209: Advanced Quantitative Methods I: Description and Prediction course while working on a rainfall-related project.

For the Colombian rice farmers, we used CHIRPS, a dataset that measures aggregate rainfall across 5-by-5-kilometer grid cells. Because rainfall can vary within those cells, a farmer may experience more or less rain than the amount recorded for the cell as a whole.

We wanted to compare rainfall measurements at the grid-cell level with measurements from a weather station located at a specific point—for example, if a farmer lived next an existing in-situ weather station. We also calculated false-negative rates, drawing on the machine-learning classification concepts we studied in Professor Levy’s course.

The challenge was to balance false positives against false negatives. Farmers need to be properly insured, but the product also cannot pay out too much such that carriers don’t want it on their balance sheet. Thank you, Professor Levy! 

What was a highlight—something fun, surprising, or memorable—from your summer?

Suyana is a Spanish-speaking organization, and I am a native English speaker who speaks a woefully small amount of French after living in Paris for two years. So, I spent some of the summer trying to learn some Spanish. 

Although I would not say, “Hablo español,” I would say: “Soy Max. Hablo un poquito de español. Vamos!”