Can blue economy interventions mitigate rural poverty and outmigration in land-drying eastern Africa?

Introduction

Coastal and rural villages in Eastern Africa are more likely to have mitigated or even avoided poverty and outmigration during the last three decades of land desertification and natural disasters when developing a sustainable blue economy providing alternative resources and livelihoods.

Research Framework

To test this core hypothesis, we will rely on a long-term causal inference framework combining satellite imagery, artificial intelligence algorithms, and spatial matching statistical methods, but also multidisciplinary field surveys.

Objectives

More precisely, BLUE-AFRICA has the ambition to quantitatively and causally assess on the long term whether:

1. Maintaining productive coral reefs through the establishment of fisheries management.
2. Investing in mariculture.
3. Developing nature-based tourism.

These can be potential transformation pathways on the eastern African coast to improve wealth assets and prevent outmigration when arable land and terrestrial resources are vanishing under climate change.

Specific Goals

Four main objectives will be reached:

1. Mapping and characterizing all rural socio-ecological systems (RSES) on the coasts of Madagascar, Mozambique, and Tanzania, but also blue economy activities.
2. Building, testing, and validating artificial intelligence algorithms accurately predicting the level of human poverty and outmigration across space and time in Africa from satellite imagery and auxiliary covariates.
3. Testing the causal link between the establishment of a blue economy activity and the level of poverty and outmigration in the drying rural eastern Africa over a long time period (up to 30 years) using a spatial matching method between control and treated RSES and then fitting several response curve models.
4. Identifying, visiting, and understanding the ‘bright’ vs. the ‘dark’ spots, i.e., the RSES with the highest vs. lowest level of economic development and net migration using a deviance approach from model expectations, in-depth field surveys, and statistical comparisons.