ENV SUST INDICATORS: CONSTRUCTION & USE

This course will present students with the architecture, data, methods, and use cases of environmental indicators, from national-level indices to spatial indices. The course will draw on the instructor’s experience in developing environmental sustainability, vulnerability and risk indicators for the Yale/Columbia EPI as well as for a diverse range of clients including the Global Environmental Facility, UN Environment, and the US Agency for International Development. Guest lecturers will provide exposure to Lamont experience in monitoring the ecological and health impacts of environmental pollution and the use of environmental indicators in New York City government. Beyond lecture and discussion, classroom activities will include learning games, role play and case study methods.

The course will explore alternative framings of sustainability, vulnerability and performance, as well as design approaches and aggregation techniques for creating composite indicators (e.g., hierarchical approaches vs. data reduction methods such as principal components analysis). The course will examine data sources from both in-situ monitoring and satellite remote sensing, and issues with their evaluation and appropriateness for use cases and end users. In lab sessions, the students will use pre-packaged data and basic statistical packages to understand the factors that influence index and ranking results, and will construct their own simple comparative index for a thematic area and region or country of their choice. They will learn to critically assess existing indicators and indices, and to construct their own. In addition, students will assess the impacts of environmental performance in several developing and developed countries using available data (e.g., pollutant levels in soils and air in Beijing and NYC), and project future changes based on the trends they see in their assessments. The course will also examine theories that describe the role of scientific information in decision-making processes, and factors that influence the uptake of information in those processes. The course will present best practices for designing effective indicators that can drive policy decisions.

Advising Note: Students are required to have had prior coursework in descriptive and inferential statistics.