Managing artisanal and small-scale mining in forest areas: perspectives from a poststructural political ecology

Artisanal and small-scale mining (ASM) is an activity intimately associated with social deprivation and environmental degradation, including deforestation. This paper examines ASM and deforestation using a broadly poststructural political ecology framework. Hegemonic discourses are shown to consistently influence policy direction, particularly in emerging approaches such as Corporate Social Responsibility and the Forest Stewardship Council. A review of alternative discourses reveals that the poststructural method is useful for critiquing the international policy arena but does not inform new approaches. Synthesis of the analysis leads to conclusions that echo a growing body of literature advocating for policies to become increasingly sensitive to local contexts, synergistic between actors at difference scales, and to be integrated across sectors.

The economics of enhanced landfill mining: private and societal performance drivers

This paper addresses the economics of Enhanced Landfill Mining (ELFM) both from a private point of view as well as from a society perspective. The private potential is assessed using a case study for which an investment model is developed to identify the impact of a broad range of parameters on the profitability of ELFM. We found that especially variations in Waste-to-Energy (WtE efficiency, electricity price, CO2-price, WtE investment and operational costs) and ELFM support explain the variation in economic profitability measured by the Internal Rate of Return. To overcome site-specific parameters we also evaluated the regional ELFM potential for the densely populated and industrial region of Flanders (north of Belgium). The total number of potential ELFM sites was estimated using a 5-step procedure and a simulation tool was developed to trade-off private costs and benefits. The analysis shows that there is a substantial economic potential for ELFM projects on the wider regional level. Furthermore, this paper also reviews the costs and benefits from a broader perspective. The carbon footprint of the case study was mapped in order to assess the project’s net impact in terms of greenhouse gas emissions. Also the impacts of nature restoration, soil remediation, resource scarcity and reduced import dependence were valued so that they can be used in future social cost-benefit analysis. Given the complex trade-off between economic, social and environmental issues of ELFM projects, we conclude that further refinement of the methodological framework and the development of the integrated decision tools supporting private and public actors, are necessary.

Towards a parallel computationally efficient approach to scaling up data stream classification

Advances in hardware technologies allow to capture and process data in real-time and the resulting high throughput data streams require novel data mining approaches. The research area of Data Stream Mining (DSM) is developing data mining algorithms that allow us to analyse these continuous streams of data in real-time. The creation and real-time adaption of classification models from data streams is one of the most challenging DSM tasks. Current classifiers for streaming data address this problem by using incremental learning algorithms. However, even so these algorithms are fast, they are challenged by high velocity data streams, where data instances are incoming at a fast rate. This is problematic if the applications desire that there is no or only a very little delay between changes in the patterns of the stream and absorption of these patterns by the classifier. Problems of scalability to Big Data of traditional data mining algorithms for static (non streaming) datasets have been addressed through the development of parallel classifiers. However, there is very little work on the parallelisation of data stream classification techniques. In this paper we investigate K-Nearest Neighbours (KNN) as the basis for a real-time adaptive and parallel methodology for scalable data stream classification tasks.

Data mining for monitoring and managing systems and networks

Guest Editorial

A survey of data mining techniques for social media analysis

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors.

Phosphorus fertilisation and large legume species affect jarrah forest restoration after bauxite mining

Re-establishing nutrient-cycling is often a key goal of mine-site restoration. This goal can be achieved by applying fertilisers (particularly P) in combination with seeding N-fixing legumes. However, the effect of this strategy on other key restoration goals such as the establishment and growth of non-leguminous species has received little attention. We investigated the effects of P-application rates either singly, or in combination with seeding seven large understorey legume species, on jarrah forest restoration after bauxite mining. Five years after P application and seeding, legume species richness, density and cover were higher in the legume-seeded treatment. However, the increased establishment of legumes did not lead to increased soil N. Increasing P-application rates from 0 to 80 kg P ha−1 did not affect legume species richness, but significantly reduced legume density and increased legume cover: cover was maximal (∼50%) where 80 kg P ha−1 had been applied with large legume seeds. Increasing P-application had no effect on species richness of non-legume species, but increased the density of weeds and native ephemerals. Cover of non-legume species decreased with increasing P-application rates and was lower in plots where large legumes had been seeded compared with non-seeded plots. There was a significant legume × P interaction on weed and ephemeral density: at 80 kg P ha−1 the decline in density of these groups was greatest where legumes were seeded. In addition, the decline in cover for non-legume species with increasing P was greatest when legumes were seeded. Applying 20 kg P ha−1 significantly increased tree growth compared with tree growth in unfertilised plots, but growth was not increased further at 80 kg ha−1 and tree growth was not affected by seeding large legumes. Taken together, these data indicate that 80 kg ha−1 P-fertiliser in combination with (seeding) large legumes maximised vegetation cover at five years but could be suboptimal for re-establishing a jarrah forest community that, like unmined forest, contains a diverse community of slow-growing re-sprouter species. The species richness and cover of non-legume understorey species, especially the resprouters, was highest in plots that received either 0 or 20 kg ha−1 P and where large legumes had not been seeded. Therefore, our findings suggest that moderation of P-fertiliser and legumes could be the best strategy to fulfil the multiple restoration goals of establishing vegetation cover, while at the same time maximising tree growth and species richness of restored forest.

Change escalation processes and complex adaptive systems: from incremental reconfigurations to discontinuous restructuring

This study examines when “incremental” change is likely to trigger “discontinuous” change, using the lens of complex adaptive systems theory. Going beyond the simulations and case studies through which complex adaptive systems have been approached so far, we study the relationship between incremental organizational reconfigurations and discontinuous organizational restructurings using a large-scale database of U.S. Fortune 50 industrial corporations. We develop two types of escalation process in organizations: accumulation and perturbation. Under ordinary conditions, it is perturbation rather than the accumulation that is more likely to trigger subsequent discontinuous change. Consistent with complex adaptive systems theory, organizations are more sensitive to both accumulation and perturbation in conditions of heightened disequilibrium. Contrary to expectations, highly interconnected organizations are not more liable to discontinuous change. We conclude with implications for further research, especially the need to attend to the potential role of managerial design and coping when transferring complex adaptive systems theory from natural systems to organizational systems.