Predictive modeling of the relationships among infrastructure, resource extraction, and environmental governance in Latin American forests

Collaborators

• Nick Cuba (Clark U -> Auburn U at Montgomery)

• Pilar Delpino Marimon (Clark University)

• Madelyn Rivera (Fundación PRISMA)

• Laura Sauls (Clark University; University of Sheffield)

Project Information

The loss of tropical forest plays a significant role in climate change, biodiversity loss and livelihood disruption. Large-scale infrastructure, often linked to extractive industries and agro-industrial expansion, may catalyze forest conversion in sensitive socio-ecological zones. Global financial flows, national political settlements, and local rights also influence patterns of forest conversion in the context of new infrastructure. Extensive data on these elements exist, but consistent and careful analysis across boundaries and data types is lacking. This pursuit will synthesize geospatial data and existing qualitative research to ask: (1) what are likely future scenarios for relationships among infrastructure investment, forest cover and communities; (2) how do different forms of environmental governance affect these scenarios; (3) how are these different forms of environmental governance explained: what factors bring them into being?

Other Resources

• https://www.extractivesatclark.org/

• https://www.prisma.org.sv/

• https://www.climateandlandusealliance.org/reports/impacts-of-extractive-industry-and-infrastructure-on-forests/

README.md

A README.md file is a very useful component of any project repository; it is the first file that unfamiliar users will open to learn about your project. If this course uses GitHub, you will also notice that the README.md is automatically rendered on GitHub as a simple "homepage" for your project. Instructions for creating your own GitHub repository from these files may be given during the course. The same instructions are also summarized in CONTRIBUTING.md.

Data

If this project does not contain a data folder, the way to access data for the worksheets depends on whether you are using RStudio Server and Jupyter hosted by SESYNC or your own compute resources.

To access the data from a SESYNC hosted environment, open RStudio and enter the following command at the > prompt.

file.symlink('/nfs/public-data/training', 'data')

Otherwise, download the "data.zip" folder from the course syllabus (if not currently there, it will be posted after the course), and unzip it to this "handouts" folder. The result should be a subdirectory called "data" within this project.