Landfill mining (LFM) is an alternative strategy to manage landfills that integrates remediation with secondary resource recovery. At present, LFM remains as an emerging concept with a few pilot-scale project implementations, which presents challenges when assessing its economic performance. These challenges include large knowledge deficits about the individual processes along the LFM process chain, lack of know-how in terms of project implementation and economic drivers, and limited applicability of results to specific case studies. Based on how these challenges were addressed, this thesis aims to analyze the usefulness and validity of different economic assessments of LFM towards the provision of better support for decision-making and in-depth learning for the development of cost-efficient projects. Different studies were analyzed including the previous studies through a systematic literature review and the factor-based method that is developed in this thesis. Four categories of economic assessment approaches were derived in terms of the study object that is about either an individual LFM project (case-study specific) or multiple LFM projects in a region (generic); and in terms of the extent of analysis that is about either the identification of the net economic potential (decision-oriented) or extended towards an in-depth learning of what builds up such result (learning-oriented). Across the different approaches, most of the previous studies have questionable usefulness and validity. The unaddressed parametric uncertainties exclude the influence of using inherently uncertain input data due to large knowledge deficits. While the narrowly accounted scenario uncertainties limits the fact that LFM can be done in various ways and settings in terms of site selection, project set-up and regulatory and market conditions. In essence, these uncertainties propagate from case-study specific to generic study object. From decision-oriented to learning-oriented studies, the identification of what builds up the result are unsystematically determined that raises issues on their subsequent recommendations for improvement based on superficially derived economic drivers. The factor-based method, with exploratory scenario development and global sensitivity analysis, is presented as an approach to performing generic and learning-oriented studies. As for general recommendations, applied research is needed to aid large knowledge deficits, methodological rigor is needed to account for uncertainties and systematically identify economic drivers, and learningoriented assessment is needed to facilitate future development of LFM. This thesis highlights the important role of economic assessments, which is not only limited for the assessment of economic potential but also for learning and guiding the development of emerging concepts such as LFM.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-165391 |
| Date | January 2020 |
| Creators | Esguerra, John Laurence |
| Publisher | Linköpings universitet, Industriell miljöteknik, Linköpings universitet, Tekniska fakulteten, Linköping |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Licentiate thesis, comprehensive summary, info:eu-repo/semantics/masterThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | Linköping Studies in Science and Technology. Licentiate Thesis, 0280-7971 ; 1876 |
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