Decentralising natural resource governance in Ghana: critical reflections on the artisanal and small-scale mining sector

This paper critically examines the impact of decentralization on contemporary and future governance arrangements in Ghana’s artisanal and small-scale mining (ASM) sector. The sector, while providing valuable employment in rural areas, is beleaguered by environmental and social issues. Proponents of decentralization argue that re-distributing decision-making authority leads to more responsive, transparent and efficient natural resource management. The analysis presented here, however, demonstrates how weak decentralization has exacerbated the complex, conflictual and clandestine nature of local resource politics surrounding ASM. If future decentralization reforms are going to reverse this trend and improve the governance of ASM in Ghana, then facilitating the participation of traditional authorities is imperative. It is argued that doing so requires addressing the reticence regarding the role of chiefs in resource governance; simply ironing out existing technical issues with decentralization reforms is unlikely to improve the social and environmental performance of ASM in the country. In light of the chronic resource management deficiencies in Ghana, epitomized in the ASM sector, fostering frank political debates on resource governance is becoming urgent.

Efficiency of evolutionary algorithms in water network pipe sizing

The pipe sizing of water networks via evolutionary algorithms is of great interest because it allows the selection of alternative economical solutions that meet a set of design requirements. However, available evolutionary methods are numerous, and methodologies to compare the performance of these methods beyond obtaining a minimal solution for a given problem are currently lacking. A methodology to compare algorithms based on an efficiency rate (E) is presented here and applied to the pipe-sizing problem of four medium-sized benchmark networks (Hanoi, New York Tunnel, GoYang and R-9 Joao Pessoa). E numerically determines the performance of a given algorithm while also considering the quality of the obtained solution and the required computational effort. From the wide range of available evolutionary algorithms, four algorithms were selected to implement the methodology: a PseudoGenetic Algorithm (PGA), Particle Swarm Optimization (PSO), a Harmony Search and a modified Shuffled Frog Leaping Algorithm (SFLA). After more than 500,000 simulations, a statistical analysis was performed based on the specific parameters each algorithm requires to operate, and finally, E was analyzed for each network and algorithm. The efficiency measure indicated that PGA is the most efficient algorithm for problems of greater complexity and that HS is the most efficient algorithm for less complex problems. However, the main contribution of this work is that the proposed efficiency ratio provides a neutral strategy to compare optimization algorithms and may be useful in the future to select the most appropriate algorithm for different types of optimization problems.