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Virtual Worlds explores the role of digital technologies in managing climate change and biodiversity loss. The program reviews how technology can improve environmental governance and include local knowledge for sustainable development.

This research explores how methane from radioactive waste moves through groundwater and whether it reaches the atmosphere or converts to carbon dioxide—key knowledge for risk assessment.

The project develops new methods to improve the validation of greenhouse gas models and assessments. The focus is on creating cost-effective, mobile, and high-resolution methods for measuring greenhouse gas fluxes and land use.

Littoral zones influence carbon sequestration and greenhouse gas emissions but are often overlooked. This project integrates them into lake carbon budgets using advanced measurement and modelling techniques.

Projektet utvecklar nya metoder för storskaliga växthusgasflödesmätningar med drönare, för att noggrant kvantifiera flöden av metan, lustgas och koldioxid. Detta möjliggör bättre bedömningar och reglering av klimatkänsliga utsläpp från landskap.

The project examines how declining sulfate deposition affects methane cycling in lakes, focusing on how sulfate reduction shifts microbial communities and impacts methane emissions through methanogenesis and methane oxidation.

In this project, Swedish aid is examined as a tool to integrate cross-cutting challenges such as environmental, climate, gender, and conflict perspectives into climate adaptation efforts.

This project aims to improve access to water and sanitation for all by working in close cooperation with inhabitants of informal settlements to implement innovative grey-water reticulation and treatment systems.

The management of species-rich grassland and deciduous forests have changed drastically over the last 80 years. Many species have become rare and endangered as the resources and habitat decreased. We study what is needed to preserve diversity.

Our research uses mathematical, computational, and statistical modeling techniques to integrate knowledge from large datasets across a variety of specialities.