Public Health Practice Spotlight: Ming Cheng Yap, MPH ’26 (Health Policy; Climate Change and Health)

I was a Leadership in Global Health Fellow at UNICEF over the summer. I focused on challenges to the development and implementation of climate-resilient sanitation.

New York.

I hope to use data‑driven methods to make a rigorous case for how better climate brings about better health, and how better health brings about better climate.

The Yale School of Public Health (YSPH) and the Yale Institute for Global Health (YIGH).

I was a Leadership in Global Health Fellow at UNICEF headquarters in New York over the summer. I focused on challenges to the development and implementation of climate-resilient sanitation. My work involved producing concept notes and strategic briefs on AI applications in Water, Sanitation and Hygiene (WASH) programming, helping countries articulate how their sanitation projects deliver climate mitigation and adaptation benefits to secure international climate funding. This culminated in my development of ACRES (AI for Climate-REsilient Sanitation), an AI tool (a custom GPT) that helps countries develop sanitation proposals to secure funding from the Green Climate Fund (GCF). I was most excited about the integration of climate data and projections to automate the generation of climate rationales for sanitation projects.

Additionally, I analyzed country reports to identify potential trends and developments in how climate resilience was included in sanitation programming. The process was fulfilling, as I learned of some of the more innovative approaches countries have taken, such as Bolivia's program to transform treated fecal sludge into compost for reforestation, and China's development of national standards and technology marketplaces for climate-resilient sanitation.

My time at UNICEF fundamentally shifted my understanding of how climate and sanitation intersect at the global policy level. I learned that the gap between technical solutions and policy implementation often comes down to how well countries can articulate climate benefits in funding proposals. The experience showed me that AI tools can democratize access to technical expertise, particularly for countries with limited resources to hire consultants for proposal development.

The most surprising and ultimately rewarding aspect was building my first custom GPT from scratch. I'd never worked with GPT builders or Application Programming Interface (API) integrations before, so developing ACRES was a steep learning curve. Using ChatGPT's no-code builder, I had to figure out how to structure prompts, integrate World Bank climate data APIs, and create a logical flow that could guide users through the complex GCF proposal process.

But this challenge revealed something profound: AI tools are becoming increasingly accessible to non-programmers. The fact that I could build a functional tool that automates complex climate rationale development without writing traditional code showed me how AI can democratize technical capabilities. What would typically require a team of developers and climate consultants could now be prototyped by someone with domain knowledge and persistence. Seeing ACRES successfully generate climate contexts for countries like Barbados, pulling real-time data and creating viable proposal frameworks, made me realize we're at an inflection point where AI can dramatically reduce technical barriers for global development work.

My coursework gave me the theoretical foundation to understand the intersection of the fields of climate and health, and a willingness to try new approaches. Health Policy trained me to translate technical findings into policy recommendations—a skill I used constantly when developing briefs at UNICEF. Perhaps most importantly, YSPH's emphasis on systems thinking helped me see how sanitation, climate, health, and finance are interconnected rather than siloed issues.

Be prepared to work outside your comfort zone—you'll likely be asked to do things you've never done before, like building AI tools or analyzing data types you haven't encountered. That's actually the point. My biggest growth came from having to figure out GPT builders and API integrations with zero prior experience. It was frustrating at times, but that's where learning happens.

Start playing with AI tools now, even if it's not required for your coursework. The ability to quickly prototype solutions using no-code platforms or GPT builders is becoming essential in global health work. You don't need to become a programmer, but you should understand how to leverage these tools.