Better Questions Lead to Better Investments

Clean energy is increasingly positioned as an investment strategy for funders focused on women’s economic empowerment. A new synthesis of more than 115 studies, produced by Caribou delivered in partnership with Value for Women, with support from the Shell Foundation, FCDO, and Gates Foundation, shows that, across asset types, geographies, and women’s diverse social and economic roles, clean energy assets consistently generate measurable returns for women.

These findings should shape how investors prioritize capital in the growing gender–clean energy nexus. This is the third blog in a series exploring the findings of this research. 
Read blog one: Assumptions VS Realities in the Gender–Clean Energy Nexus.
Read blog two: Asset Deployment VS Systems Design.

Financial models for clean energy in emerging markets tend to answer one question: Does this investment pay off? 

The answer is often yes. Solar irrigation lifts farm revenues by 30%, an improved cookstove saves up to US$480 in fuel annually, and a cold storage facility cuts post-harvest loss by 13 percentage points. These numbers are real, and assets often pay for themselves within a few years. But “Does the asset pay off?” is not the question that determines whether a clean energy investment delivers for a low-income woman in rural Kenya or Nigeria. Instead, other questions determine benefits: does it pay off for her, at her income level, under the financing terms she can actually access, and given the conditions that actually exist in her market?

As part of Caribou’s latest research into the gender–clean energy nexus, we created evidence-backed interactive financial models that identified the gap between a positive return on paper and a viable investment in practice. Caribou developed models for fifteen clean energy products across five technology categories: solar irrigation, milk chilling, cold storage, solar refrigeration, and clean cooking. These models draw on impact evaluation evidence, practitioner reports, and market data. They are analytical tools, designed to enable investors to test assumptions and risks as part of wider investment decision-making processes.

The models show that positive financial returns for women are achievable, though whether they materialize depends less on the technology than on the conditions surrounding it: who the buyer is, what she already earns, how the asset is financed, and whether local context supports sustained use.(1)

In modeling solar fridges for micro-entrepreneurs, we found that returns from the same technology scale according to the business a woman already runs. Micro-enterprises with larger existing revenue bases see outsized returns. This difference matters because in the past, investors who are targeting the smallest micro-enterprises may actually be modeling using data from businesses with a higher turnover. To reflect this, our models allow for adjusting to real operator scenarios rather than relying on averaged assumptions.

The technology, financing terms, and percentage income uplift are identical. Baseline revenue is the only variable, determining whether the investment builds wealth or creates financial risk.

Financing can determine whether returns materialize at all. A model that tests the viability of a technology without testing financing terms is testing a scenario that most women buyers will never face. For many low-income households, the available financial services that enable the purchase of a clean energy asset often eliminate the economic case for adoption. Across all but the cheapest assets we modeled (improved cookstoves), realistic financing terms frequently flip positive returns into negative ones. For example, a solar irrigation system that can generate a 15% annual return when purchased up front can incur losses when financed through a pay-as-you-go (PAYGo) arrangement or a consumer loan carrying an annual percentage rate of 25% to 35%. Users may pay more in financing costs than they earn in additional revenue.

Premiums on PAYGo products reflect the cost of serving households with volatile incomes, maintenance, default risk, and collection infrastructure. Programs that want to lower those premiums need to absorb the risk elsewhere, through guarantees, first-loss capital, or extending the loan period.

Alongside returns and financing, our models also assess affordability for low-income buyers. A technology can show a positive ROI and carry manageable financing terms, and still be out of reach if monthly payments consume too large a share of volatile household income. Without an affordability check, a model can recommend an investment that looks sound on paper but creates debt in practice.

For financing to be sustainable, repayments should settle at or below 10% of monthly household income. For households with volatile incomes that usually earn less than US$100 per month, even modest payments quickly exceed that threshold.

Our models’ affordability checks flag this consistently (solar irrigation model, improved cookstove model). Improved cookstoves and Liquified Petroleum Gas stoves are affordable across all income segments because of their low price points. In contrast, solar cooking kits strain low-income household resources. Milk chillers are unaffordable for low-income households, even at the lowest capacity, with payment burdens exceeding 25% of monthly income. Larger solar irrigation systems and cold storage push well beyond 50%.

For capital-intensive assets like milk chillers, larger irrigation systems, and cold storage solutions,  individual ownership usually remains structurally unaffordable. Shared ownership through cooperatives or savings groups, fee-for-service models, and community-scale facilities can spread both capital costs and under-utilization risk. A milk chiller that would create unsustainable debt for a single farmer earning US$130 per month from dairy can generate viable returns when split across three or four farmers using 80% of the chiller’s capacity.

Our models take standard feasibility assumptions such as access to financial services, supply chain availability, and reasonable governance as given. The conditions that determine viability in our modeling are more granular: how much a vendor already earns, whether evening milk collection exists in the area beyond morning collection alone, whether households continue to use old stoves alongside improved ones, and whether foot traffic supports premium pricing. 

Other financial models for clean energy may establish such conditions as assumptions and solve for returns. We treat conditions as variables and ask: what has to hold for a positive return to materialize? Verifying conditions before modeling them changes what the model recommends.

Across the five technology categories, several patterns emerge:

Let’s return to Amina’s kiosk. Her US$800 solar refrigerator, under interest-free financing, reaches break-even at month 35. Add a realistic PAYGo premium and her cumulative returns may never turn positive. A program that reported a 30% revenue uplift from this technology would be telling the truth. It would also be asking the wrong question.

Financial models like these are not new, and many investors and program designers already use them. But how they are used also matters. Financial modeling can show funders what to ask before investing: What does this woman already earn? What will she actually pay for the asset? Do the conditions exist for the technology to deliver? Specifying models under realistic assumptions raises these questions before funds are committed rather than after, when costs may fall on households least able to absorb them.

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Read the full report, produced by Caribou with Value for Women and supported by Shell Foundation and FCDO. Caribou works with funders, investors, and program designers on exactly these questions. If you are designing or evaluating clean energy investments for women, we would like to hear from you. Get in touch with Marius.

(1) Each model is grounded in a synthesis of impact evaluation evidence, practitioner reports, and market data across lower-income countries. Users can adjust parameters like household income, financing terms, technology costs, and local market conditions to investigate how returns shift under different assumptions. The models focus on financial outcomes only, including ROI, payback periods, and affordability relative to income. They do not capture gains in health, reduced drudgery, or time for rest and family, dimensions that may justify investment even when the financial case is marginal or nonexistent. Outputs are illustrative and reflect modeled scenarios, not forecasts for any specific geography or population.