Above: Kai and F&ES classmate Andrew Moffat at the Renewable Energy Industries Forum 2015 in Washington, D.C.
Hailing from Harbin (哈尔滨市), the capital of China’s Heilongjiang (黑龙江) province – known as the “Ice City” for its extremely cold winters – Xu Kaiyang (Kai) (徐开阳) moved to Columbus, Ohio at age 18 to study at The Ohio State University (OSU). In 2014, Kai earned a Bachelors of Science in Biological Sciences and Bachelors of Arts in Economics from OSU and enrolled in Yale’s School of Forestry & Environmental Studies (F&ES) to continue his research at the graduate level. While at F&ES, Kai’s interests shifted to energy systems and data analysis, and, under the mentorship of Dr. Angel Hsu, Data-Driven Yale’s director, he honed his analytical skills in R, the statistical programming language. He brings his expert analytical ability, native Mandarin Chinese skills, and a passion for complex problem-solving to the Data-Driven Yale team with the aim to move the world towards a cleaner and sustainable future.
Q: Welcome to Data-Driven Yale! Can you tell us a bit about the projects you’ll be working on in your new role?
I will be Data-Driven Yale’s main data analyst, working on almost all of the group’s projects. I will lead the quantitative work underpinning the team’s research on the Non-State Actor Zone for Climate Action (NAZCA) platform and our collaboration with The French Environment and Energy Management Agency (ADEME). I am helping lead a project examining China’s three pollution action plans – one each for air, soil, and water – trying to find funding for these programs. And our team is researching renewable energy and energy efficiency programs in the developing world for a major report that we will produce this fall in conjunction with UNEP, the government of Norway, and The NewClimate Initiative. I will be doing data collection and analysis for all of these projects. As of July I will be the only native Mandarin speaker on the Data-Driven Yale team, meaning that I will also have a central communications role on any project dealing with China.
Q: Tell us a bit about your research interests and how they have changed over time. What drew you to environmental data and policy?
My research interests center on using data to analyze energy production and consumption, with a focus on energy systems and economics in the U.S. and China. As an undergrad I worked as a research assistant and field technician in an ecology lab, learning about forest system dynamics. This experience led me to Yale’s School of Forestry & Environmental Studies (F&ES) to pursue a Masters of Environmental Science (MESC). While at Yale, I became very interested in data analysis and energy systems. Data analysis is a potent problem-solving strategy that allows us to understand complex problems and model solutions into an uncertain future. Big data science could provide answers to human civilization’s most pressing questions, describing the world in greater detail than ever before.
Kai enjoying pig brain in Beijing.
Q: How have your masters studies and experiences outside of school contributed to your current work?
I received invaluable training while studying at F&ES, learning to capture the synergies of group work and mastering advanced techniques in data analysis. F&ES encourages students to work on projects in groups so that every student learns how to best contribute their expertise. Developing efficient, cooperative work plans with teammates is a valuable skill that I have honed at F&ES and employ in my current research. I now do large-scale data management and analysis for Data-Driven Yale. I developed a mastery of the programming language R in the graduate level Statistical Case Study and Regression Modeling course. I bring these skills as well as abilities with other data science methods to Data-Driven Yale’s team.
Q: Why, in your estimation, is data collection and analysis so important to good governance?
Data analysis has become indispensable to sound research. The most respected organizations, including multinational institutions like the World Bank and NGOs like the World Resource Institute (WRI), collect huge stores of data and build their own databases to support their research. Data is crucial for policymaking because governments need quantitative evidence to support their policies and actions, especially when it comes to dealing with environmental problems. I believe that collecting and analyzing data is now integral to sound policymaking, and this belief has become common among policy experts.
Data transparency and accuracy are also important factors that contribute to good governance, and these issues are especially problematic for developing countries. Countries that use open, transparent data sources receive input from a range of sources, which helps fill information gaps. Improving data accuracy, meanwhile, illuminates problems and solutions for countries that remain in the dark about their own environments.
Q: As a recent graduate from the Yale School of Forestry & Environmental Studies, what advice would you give to students interested in environmental policy?
To incoming students, I recommend to find what you are deeply interested in, develop your technical skills (data analysis, spreadsheet modeling, Life Cycle Assessment etc.), and connect your work to the real world. F&ES offers capstone classes in business, environmental policy and industrial ecology. These practical classes give you an opportunity to solve real world problems for clients in the private sector and in government. The courses have you work with professionals in the field allowing you to gain an understanding of the challenges that they face and use your knowledge and skills to help them solve problems. These sorts of projects teach students what skills they need to learn, how to bring a project to fruition, and how to communicate with professionals from different walks of life.
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