​The following courses are pre-approved for Area 3:

  • EEEB GU4192 Introduction to Landscape Analysis (Area 2 or 3)

  • EEEB GU4195 Marine Conservation Ecology (Area 2 or 3)

  • EESC GR5400 Dynamics of Climate Variability and Change

  • EESC GR6926 Idealized Models of Climate Processes 

  • EHSC P6360 Analysis of Environmental Health Data (Area 2 or 3) *R-based

  • EHSC P8304 Public Health Impacts of Climate Change

  • EHSC P8332 Advanced Data Analytics

  • EPID P8432 Environmental Epidemiology (Area 2 or 3)

  • SUMA PS4030 Hungry City Workshop (Area 3 or 5)

  • SUMA PS4190 Economics of Sustainability Management

  • SUMA PS5020 Cost Benefit Analysis

  • SUMA PS5021 Theory and Practice of Life Cycle Assessment

  • SUMA PS5033 Decision Models and Management

  • SUMA PS5142 Sustainable Finance

  • SUMA PS5146 Water Systems Analysis

  • SUMA PS5170 Sustainable Operations

  • SUMA PS5195 Accounting, Finance & Modeling of Sustainability

  • SUMA PS5401 International Environmental Law

This page will be updated as more courses are approved for this area.


(9 Credits)

Courses in this area train students to analyze and model scientific data to understand current and future environments and their interactions with human systems. By learning analysis and modeling, students are better able to inform sustainability policy, management, and decision-making.

SUSC PS5010 Climate Science for Decision Makers: Modeling, Analysis, and Applications

Instructors: Dr. Michael Previdi and Dr. Yutian Wu


Both human and natural systems are growing more vulnerable to climate variability (e.g., the anomalous weather induced by the El Nino-Southern Oscillation, or the increase in hurricanes that occurs when ocean currents warm the Atlantic) and to human-induced climate change, which manifests itself primarily through increases in temperature, precipitation intensity, and sea level, but which can potentially affect all aspects of the global climate. This course will prepare you to estimate climate hazards in your field thereby accelerating the design and implementation of climate-smart, sustainable practices. Climate models are the primary tool for predicting global and regional climate variations, for assessing climate-related risks, and for guiding adaption to climate variability and change. Thus, a basic understanding of the strengths and limitations of such tools is necessary to decision makers and professionals in technical fields.

This course will provide a foundation in the dynamics of the physical climate system that underpin climate models and a full survey of what aspects of the climate system are well observed and understood and where quantitative uncertainties remain. Students will gain a fundamental understanding of the modeling design choices and approximations that distinguish Intergovernmental Panel on Climate Change (IPCC)-class climate models from weather forecasting models and that create a diversity of state-of-the-art climate models and climate projections.

This course will provide an overview of the ways in which climate model output and observations can be merged into statistical models to support applications such as seasonal and decadal projections of climate extremes, global and regional climate impacts, and decision-making. Students will develop the skills to visualize, analyze, validate, and interpret climate model output, calculate impact-relevant indices such as duration of heat waves, severity of droughts, or probability of inundation, and the strategies to characterize strengths and uncertainties in projections of future climate change using ensembles of climate models and different emission scenarios.

SUSC PS5080 Monitoring and Analysis of Marine and Estuary Systems

Instructor: Dr. Braddock Linsley


From a global perspective, many of the Earth’s most important environments and resources for global sustainability are located in marine and estuarine areas. These areas are also difficult to monitor for logistical and political reasons. A few examples include 1.) oceanic environments were incompletely understood processes regulate the exchange of heat, water and carbon dioxide gas with the atmosphere, 2.) the relationship between nutrients and primary production and fisheries in open ocean, estuarine and coral reef environments and climatic phenomenon such as El Nino South Oscillation (ENSO), the Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO).This class will explore the marine environment from a modern process perspective and evaluate what is known about interannual and decadal-scale variability of these environments, with respect to oceanic circulation, the flux of heat, gases and dust from the atmosphere and sediment from rivers. Throughout the class, we will explore marine and estuarine processes by studying regional and local responses to broader scale climatic forcing.

SUSC PS5050 Geographic Information Systems (GIS) for Sustainability Science

Instructor: Dr. Frank Nitsche


Many environmental and sustainability science issues have a spatial, location-based component. Increasingly available spatial data allow location-specific analysis and solutions to problems and understanding issues. As result, analyzing and identifying successful and sustainable solutions for these issues often requires the use of spatial analysis and tools. This course introduces common spatial data types and fundamental methods to organize, visualize and analyze those data using Geographic Information Systems (GIS). Through a combination of lectures and practical computer activities the students will learn and practice fundamental GIS and spatial analysis methods using typical sustainable science case studies and scenarios.

A key objective of this course is to provide students with essential GIS skills that will aid them in their professional career and to offer an overview of current GIS applications. In the first part, the course will cover basic spatial data types and GIS concepts. The students will apply those techniques by analyzing potential impacts of storms on New York City as part of a guided case study. A mid-term report describing this case study and the results is required. In the second part, building on the basic concepts introduced in the first part, students will be asked to identify a sustainable science question of their choice that they would like to address as a final project. Together with the instructor they will be developing a strategy of analyzing and presenting related spatial data. While the students are working on their projects additional GIS method and spatial analysis concepts will be covered in class. At the end of the course Students will briefly present their final project and submit a paper describing their project

SUSC PS5060 Statistics, Data Analysis and Coding for Sustainability Science

Instructors: Dr. James Davis and Dr. Michael Previdi


This course provides an overview of essential mathematical concepts, an introduction to new concepts in statistics and data analysis, and provides computer coding skills that will prepare students for coursework in the Master of Science in Sustainability Science program as well as to succeed in a career having a sustainability science component.  In addition to an overview of essential mathematical concepts, the skills gained in this course include statistics, and coding applied to data analysis in the Sustainability Sciences. Many of these skills are broadly applicable to science-related professions, and will be useful to those having careers involving interaction with scientists, managing projects utilizing scientific analysis, and developing science-based policy.


Students enrolled in this course will learn through lectures, class discussion, and hands-on exercises that address the following topics: Review of mathematical concepts in calculus, trigonometry, and linear algebra; Mathematical concepts related to working on a spherical coordinate system (such as that for the Earth); Probability and statistics, including use of probability density functions to calculate expectations, hypothesis testing, and the concept of experimental uncertainty; Concepts in data analysis, including linear least squares, time-series analysis, parameter uncertainties, and analysis of fit; Computer coding skills, including precision of variables, arrays and data structures, input/output, flow control, and subroutines, and coding tools to produce basic X-Y plots as well as images of data fields on a global map.