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Forests, Resources and Poverty in Sub-Saharan Africa - Geospatial Analysis for Planning and Improved Decision-Making

CHALLENGE

Africa‚Äôs forests, landscapes, and ecosystems contribute to poverty alleviation and shared prosperity by providing goods and services that sustain the livelihoods of the rural poor and contribute raw materials to other economic sectors. In sub-Saharan Africa, investments in programs for reduced emissions from deforestation and forest degradation (REDD+), have catalyzed a vibrant and inclusive dialogue on opportunities and tradeoffs related to landscape and forest issues. Combined, these efforts are building consensus on the role of forests, how they can contribute to sustained economic growth, and the need to improve land use planning and governance to generate more investment in the sector.  Improved knowledge is needed to help countries move from conditions of degraded landscapes with low productivity and high poverty to a state with better productivity, sustainable livelihoods, and more equitable sharing of benefits.

APPROACH

This activity will conduct a geospatial analysis or forestry and poverty issues in sub-Saharan Africa, using the unique Hidden Dimensions of Poverty dataset (HDD), which contains district level data for all countries in Africa.  The data includes global environment and natural resource indicators, geographical variables, monetary and asset-based poverty measures, and measures closely correlated with poverty such as children‚Äôs health and GDP.

The detailed work program will entail:

Production of Country-level Poverty-Environment maps and correlation analysis. The first step will involve producing detailed maps, statistical correlation analysis, and country ‚Äúscorecards‚ÄĚ for 30 African countries, overlaying subnational poverty data with other relevant indicators.  The analysis will combine existing good and relatively fine grained data on forest cover and loss across Africa with increasingly available data on the distribution of people and poverty. It will include coincidence maps, scatter plots on correlations between poverty and natural resource indicators, and country scorecards. 

Country-Specific Review of Poverty-Environment Patterns and Hotspots. The second step will involve a round of consultation between the Hidden Dimensions team, World Bank staff and other experts familiar with each country being analyzed.  Discussions with country experts will help understand the local reality as reflected in the global dataset.

RESULTS

This activity is completed. Results from this activity have demonstrated the power of geospatial analysis as a useful tool for integrating an understanding of the distribution of poverty and environmental indicators. The HDD includes numerous environmental indicators at varying levels of spatial disaggregation, based on globally available, comparable data sets. This enables useful graphic representations in maps that combine biophysical and socioeconomic data in innovative ways to demonstrate trends and associations between worsening environmental outcomes and worsening poverty outcomes.

The mapping outputs from this activity served an important purpose in raising attention on forest-poverty interactions and trends and setting the stage for deeper questions that can be followed up. To complement that, these analyses showcased the availability and richness of the HDD as a resource for facilitating further, deeper analysis. However, this specific piece of analytical work cannot answer all demands for deeper follow-up that may arise in each country.

Within the resources available and prioritizing countries based on demand, the results of the spatial analysis have been integrated and used in the preparation of several analytical products, including Ghana (SCD and CEA), Liberia (SCD), the Central African Republic (SCD), Somalia (SCD), Kenya (CEA), Senegal (SCD), and Mozambique (SCD). Additional forest-poverty spatial overlays/ analysis was done for Tanzania (CEA), Morocco (SCD), and Malawi (CEA). The ongoing engagement has resulted in this data being utilized in planning for the Integrated Feeder Roads Development Project in Mozambique.

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Last Updated : 06-10-2019

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