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Data Science for Environmental Policy
Last Updated: 2026-06-03 00:07:20
Abstract
This course examines how algorithms and data infrastructures shape environmental decision making and governance. Students work with real-world datasets and algorithms, assess environmental and social impacts of digital systems, and critically evaluate when data-driven approaches can support, or hinder, sustainable and equitable decision-making.
Objective
By the end of the course, students will be able to: • Explain how algorithms and data infrastructures influence environmental governance processes. • Apply computational methods to analyse environmental datasets (e.g. spatial, tabular, text) in policy-relevant contexts. • Analyse how algorithmic systems can reproduce or amplify inequalities, biases, and power asymmetries. • Assess the environmental impacts of digital infrastructures and their alignment with sustainability goals. • Interpret patterns in environmental discourse using computational text analysis and relate them to governance debates. • Critically evaluate the appropriateness of algorithmic tools and justify when their use should be limited or refused.
Content
This course provides an interdisciplinary, project-based introduction to algorithmic environmental governance. It examines how data infrastructures and algorithmic tools increasingly shape environmental decision-making, from biodiversity monitoring to regulatory enforcement.The course focuses on both the potential and limitations of data-driven governance. Students analyse how algorithmic systems influence knowledge production, decision authority, and representation, and when they may reinforce inequalities or environmental injustices. The course is structured into four modules: 1. Politics of Planetary Data Infrastructure – design, governance, and data justice 2. Decision-Making Technologies – algorithms for resource allocation and enforcement 3. Environmental Sustainability of Compute – ecological impacts of digital infrastructures 4. Technology Refusal – limits, resistance, and alternatives to computational systems Students work with real-world datasets using methods such as spatial analysis, optimisation, and text analysis. The course follows a reversed classroom and project-based format, with a strong emphasis on collaborative learning and critical reflection.
General Information
- Language
- English
- Levels
- MSC
- Frequency
- Yearly recurring
Examination
- Type
- ungraded semester performance
Registration & Places
- Max Places
- 48
- Signup End
- 20.09.2026
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Data Science for Environmental Policy | No time listed | 4 h weekly |
Offered In
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Theoretical Foundations for Environmental Policy (One of the two courses either 701-1563-00 Climate Policy or 701-1651-00 Environmental Governance is compulsory)
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Modeling and Statistical Analysis (Tthe course 701-1565-00 Quantitative Policy Analysis and Modeling is compulsory)
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