High-tech solutions for natural resource conservation (KB-38-001-028)

The aim of this project is to develop a dataspace that contributes to the broader data economy for the sustainable management of natural resources, through responsible co-design of data-driven and high-tech monitoring solutions and by understanding their social impact in a low-tech environment.

More and more high-tech solutions are being developed to address complex societal challenges such as food security, climate change, biodiversity loss, and social inequality. However, the applicability of these solutions often fails due to various barriers. These obstacles may be social, technical, economic, ecological, institutional, etc., and may occur at different “levels,” such as actual use, technological design, or system complexity. Examples include lack of access to high-tech solutions due to limited affordability, digital literacy, or other digital divides; or poor solution design resulting from inadequate interoperability or designer bias. They may also relate to the overarching digital ecosystem in which the solution operates, such as the development of dataspaces and appropriate business models needed to ensure that the high-tech solution remains relevant and functions within the broader data economy for sustainable natural resource management. Although research on these topics exists, it is often conducted in contexts aligned with high-tech solutions (i.e., contexts that already have the capacity to facilitate a new high-tech solution). However, little is known about the impact of high-tech solutions in low-tech environments.

In this project, using a Responsible Research and Innovation (RRI) approach, we will develop and test various high-tech solutions in three different low-tech environments, namely:

1. Digital tools for community-based forest monitoring (Peru);
2. Automatic cocoa fruit recognition from geo-tagged images using AI (West Africa); and
3. Offshore infrastructure biodiversity monitoring (the Netherlands).