Professor Wu teaches three courses in the Environmental Studies program on spatial and quantitative methods and climate change mitigation strategies.
- Typically offered Winter quarter
- This course is in high demand and fills up in pass 1
- All those on the waitlist will be sent an email with a link to a google form to fill out. Please do not email Prof Wu about getting into the course.
- See a prior syllabus here.
Geographic information systems (GIS) are computer systems designed to capture, manage, analyze and share spatial data. GIS are increasingly important tools across a variety of environmental applications. Whether tracking the spread of invasive species, coordinating wildfire responses or planning new marine protected areas, GIS plays an integral role in both research and management.
ENVS154 will introduce you to both the theory and application of GIS. Emphasis will be placed on the practical application of GIS to environmental questions. The primary goal of the class is to give you the knowledge needed to generate clear spatial questions, and the skills necessary to answer those questions and communicate your findings using compelling maps.
ENVS154 is an introductory class and doesn’t require any previous experience with GIS. However, the class needs to move at a fast pace to give you a working knowledge of GIS in a single quarter. If you’ve never worked with GIS, be prepared to invest a significant amount of time to keep up. The class may be redundant for students that have completed previous GIS classes.
- Typically offered Winter quarter
- Upper division standing only
- See prior syllabus here
The goal of the course is to provide an overview of cross-sector strategies that reduce GHG emissions and sequester carbon by comparing their climate change mitigation potential, challenges in implementation, costs, and co-benefits. The course will help students understand and implement common analytical frameworks based on carbon balance for estimating costs and tradeoffs that are used to develop suites of climate change mitigation strategies. Students would gain the analytical tools and topical knowledge to quantitatively assess and compare mitigation technologies and other climate solutions in terms of their carbon benefits and costs. This course will prepare students for employment opportunities as analysts in the private sector, NGOs, or government agencies working on climate solutions.
Prerequisites: Upper-division standing; Environmental Studies 2; and Mathematics 2B, or 3B, or 34B, or Mathematics 34A and Environmental Studies 25. ENVS 50, ENVS 105, or ENVS 117 are strongly encouraged.
- Typically offered in the Spring quarter
- Environmental Studies students are encouraged to take this in their first and second years to prepare them for upper division courses requiring quantitative skills.
- See a prior syllabus here
This class is designed to familiarize ES first or second year students with quantitative, mathematical, and analytical concepts commonly used when working on environmental issues. As members of the ES community it will often fall on you to interpret science to non-scientists, promote policy based on this science and argue effectively and based on the merits and inferences made by data. Successful interpretation of this data will often require familiarity with commonly used mathematical and statistical concepts. The goal of this course is to prepare you for engaging with quantitative data and data visualizations in other courses or in a job/internship.
Topics will include back-of-the-envelope calculations, exponential and logarithmic functions, (frequentist) statistics, and calculus, with a strong emphasis on applying these tools and concepts in environmental problem solving and interpretation of data presented. Labs will rely on a combination of Excel and R (optionally). No prior coding experience is required.
By the end of this course you should be able to:
- Understand the fundamentals of quantitative and data analysis
- Generate clear visual representations of that analysis
- Have a working understanding of how to use common spreadsheet software and a statistical programming language for data analysis and visualization