Assessing the Regional Vulnerability of Large Scale Mining in Ghana: An Application of Multi Criteria Analysis

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1 Assessing the Regional Vulnerability of Large Scale Mining in Ghana: An Application of Multi Criteria Analysis Mukesh Subedee, GISDE 14 Arun Poojary, ES&P 13 Clark University April 29, 2013

Ghana: A case of Resource Curse Rich in gold, bauxite,

Africa Expanding since 1990s Contributes 6.

population Mining in areas close to urban centers, in

2 Ghana: A case of Resource Curse Rich in gold, bauxite, diamonds, and manganese Second largest producer of Gold in Africa Expanding since 1990s Contributes 6.74% of national GDP Employs only 1 percent of the population Mining in areas close to urban centers, in forest rich areas and cocoa cultivation areas Ecological and social vulnerability of region

Vulnerability of Mining Impacts Vulnerability function

land degradation, pollution of river systems,

indigenous community Vulnerability variables Forest

lease area and Concession area Population density

3 Vulnerability of Mining Impacts Vulnerability function of exposure to risk and susceptibility Deforestation, land degradation, pollution of river systems, health-related issues and endangering the habitat of indigenous community Vulnerability variables Forest cover Agriculture area River toxicity exposure Mining lease area and Concession area Population density Vulnerable age groups Rural population Unemployment Illiteracy rate IMR

4 Study Area 170 districts- 10 regions 12 large scale mining companies Many small scale and illegal mining areas Current mining areas are within the forest rich regions Major rivers carry effluences of mining Assessment of both current lease areas and prospective mine areas

5 Research Objectives to explore different factors that influence the vulnerability of geographical region due to mining operations in the region to derive vulnerability index of each districts in Ghana due to mining to see significant clusters and hotspots of higher and lower areas of vulnerability

6 Data Sources Name Source Type Description Mines WBI/Clark University Vector Point Location of mines Mining_Leases WBI/Clark University Vector Polygon Areas where mining is permitted Concession areas WBI/ Clark University Vector Polygon Areas where mining concession is identified Rivers UN-FAO Vector Line These river come from a continental dataset from UN-FAO Population Ghana Statistical Service Excel File Shows population density by region, rural and urban distribution, and critical age group population. DEM Clark University Raster Shows the elevation of Ghana GHA_admn GADM/Clark University Vector Polygon Administrative Boundaries Land use UN-FAO, IIASA Vector Polygon Land use data for Ghana, will be used for extracting agriculture land Forest cover GADM/Clark Unviersity Polygon Areas of forest cover in Ghana Education and Unemployment Ghana Statistical Service Excel File This data provides literacy rateand unemployment rate of each district of Ghana Infant Mortality Rate Ghana Statistical Service PDF This data provides literacy rate of each district of Ghana

7 Data Treatment Cleaning data sources Rectifying the number of districts Refining Excel data of social-economic indicators Projection

GIS Workflow Variable workflows District Forest

Land use Project Reclassify Agri Intersect Dissolve

Project Intersect Dissolve Area Pct_Min Pop_Den

8 GIS Workflow Variable workflows District Forest Project Intersect Dissolve Area Pct_Forest District Land use Project Reclassify Agri Intersect Dissolve Area Pct_Agri District Lease/ Concession area Project Intersect Dissolve Area Pct_Min Pop_Den Excel File District Join Pct_Unemp Pct_Illit Pct_vuln Pct_rur IMR

9 GIS Workflow. Hydrologic Modeling

10 Workflow Chart: Vulnerability Index Pct_Forest Pct_Agri Pop_Den Pct_Unemp Pct_Illit Pct_vuln OWA in Excel Vulnerability _Index Cluster and Hotspot Analysis Pct_rur IMR Pct_ (lease/pros) LeasePros_ Toxicsum

11 Vulnerability Index- MCE approach Selection of Indicators Standardization of data- rank order Weighting AHP weights Aggregation WOWA aggregation Identification of vulnerable districts

12 Geographic Indicators

13 Socio-Economic Indicators

14 Vulnerability Map

15 Clustering of Vulnerability

16 Hotspot Analysis of Vulnerability

17 Conclusions Vulnerability is weighted with exposure and susceptibility factors Toxicity, Mine, IMR are prominent Western, central and Ashanti region has more statistically significant HH clusters of vulnerable districts Extension of mining areas to concession area lead to increase in average vulnerability from to and maximum index from 0.43 to More data (IMR, toxic exposure) and application of sensitivity analysis would give more robust results

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