Eliza Drury worked with me over the summer of 2015 in the Climate and Development Lab at Brown University to analyze geographic analysis in national adaptation programmes of action and apply geographic vulnerability analysis to the test case of Malawi’s climate change adaptation plans. We wanted to test whether adaptation plans really prioritized the places where climate change vulnerability is greatest, as defined in the adaptation planning process itself. We found that very few countries used geographic vulnerability analysis, but that the method helped improve the quality of proposed adaptation projects (see diagrams below).
We reviewed methods and guidelines for national adaptation planning provided by developing countries by the UNFCCC and Least Developed Countries Expert Group (LEG), and found that geographic analysis was recommended, but guidance was out of date. In response to this, Eliza took on senior work in environmental studies with myself and Dr. Timmons Roberts to update guidance for developing countries to use geographic vulnerability analysis using Malawi as an example case. She presented this work at the climate change research conference in Bangladesh and fulfilled an invitation to write a summary of the work for the Dhaka Tribune.
Together with collaborator Javier Gonzales and undergraduate researchers Eliza Drury and Kailani Acosta, I published chapter 5 of the report, focusing on the perspective of countries receiving climate change adaptation funds. In particular, we examined the national adaptation programmes of action (NAPA) and the funding they received for urgent climate change adaptation through the Least Developed Countries Fund (LDCF). The LDCF: is a fund established by the UNFCCC at the 7th conference of the parties, in 2001 in Marrakesh, and managed by the Global Environment Facility).
We found the adaptation planning and funding process to be extremely cumbersome, bureaucratic, and opaque. Much like donor countries, recipient countries frequently re-branded mainstream development projects as climate change and failed to flesh out the relationship between their proposed projects and vulnerability to climate change. The relationships between country-defined adaptation priorities and funded projects through the LDCF are often impossible to discern because
of tile changes, project consolidation, and project segmenting. Just try yourself to match projects from the NAPA database to projects funded by the LDCF in the GEF Project Database!
Recipient country government transparency of adaptation financing within its own borders is also very opaque, making it difficult for citizens or donor countries to hold them accountable for results. However, a few recipient countries and multilateral banks have started using aid management platforms to make development activities public through on-line databases and maps. Some examples include Development Gateway projects, the Open Aid Partnership, and the World Bank’s Global Reach database. These aid management platforms need to include variables indicating climate change vulnerabilities and adaptation at the level of individual project activity locations in order to make international adaptation finance more transparent and accountable.
As a GIS Teaching Fellow at Middlebury, I developed a methods course using entirely free and open source GIS software and data around the theme of studying population and environmental change in developing countries. We used SQLite, GRASS and QGIS to diagnose and fix errors in downloaded data and to analyze and visualize population density and change. We used SAGA, Google Earth, and QGIS to classify land cover from Landsat images, assess accuracy, and detect change over time. The worked examples in the lab manual are for Tanzania.
Two lectures each week helped deliver technical knowledge and methods. During the third lecture each week, we read and discussed GIScience theory on spatial data infrastructure, social construction of GIS methods and data, challenges and ethics of using GIS in development, and open source GIS and its potential in developing countries.
The course ran in spring 2014 and spring 2015. In 2015 we used a GitHub page to troubleshoot problems and experiment with collaboratively editing and visualizing a GeoJSON of their favorite places in Vermont. Each student ultimately completed an independent project for a developing country of their choice, and many of them have gone on to use open source GIS in their research, jobs, and businesses.