An environmentally sustainable decision model for urban solid waste management

Costi, P; Minciardi, R.; Robba, Michela; Rovatti, Mauro; Sacile, Roberto

doi:10.1016/s0956-053x(03)00126-0

Cited by 140 publications

(60 citation statements)

References 13 publications

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“…It is estimated that a total of USD 34 millions could be generated as a result of the URS by Year 6. Figure 14 highlights the yearly and cumulative estimated Value Added Tax [76], income tax and trade fees revenue. Public revenue on the other hand is expected to witness an increase as more property space becomes available to host businesses.…”

Section: Financial Impactmentioning

confidence: 99%

Economically Incentivising Smart Urban Regeneration. Case Study of Port Louis, Mauritius

Allam

Newman

2018

Smart Cities

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Port Louis, the capital city of Mauritius, has been the preferred city for hosting the judicial, political and business activities of the country for the past two centuries. However, new policies have created nine new smart cities in greenfield locations within 10 km from Port Louis, so the capital city is facing economic decline as it is losing businesses, as well as administrative functions. This loss equates to an erosion in municipal revenue along with a reduced interest in contributing to the development of the city; all of which takes a toll on its urban economic landscape, as well as on the broader Mauritian economy. This paper builds from the findings of a focus group study to propose a smart urban regeneration model for the City of Port Louis, which could enable the old city to be restored and regenerated rather than redeveloped in modernist architecture, as has happened in the new smart cities model. A smart urban regeneration model is proposed backed by the pillars of smart infrastructure, culture, metabolism and governance. The proposed model is applied to the context of Port Louis to generate an urban regeneration scheme. The potential benefits in terms of financial outcomes, investment attraction and job creation are explored through a combined application of econometric forecasting models. The results support positive figures of both investment and job creation, and the findings of this study aim at informing and providing the governing bodies of Port Louis with a tangible solution for revamping the centuries-old capital city, as well as demonstrating to the world that smart cities can mean sensitive urban regeneration.Keywords: urban regeneration; economics; culture; port louis; smart cities; fiscal measures IntroductionNested in the North-West littoral side of the Island of Mauritius and bounded inland by a mountain range, Port Louis extends over an area of 46.7 km 2 . With a total population of 119,333 inhabitants as at December 2016, the capital city of Mauritius is the most densely populated geographical district of the island with 2954 P/km 2 [1]. Port Louis hosts the only trade port of the island and has been the main administrative centre from a judicial, political and business perspective for more than two centuries. However, within the last decade, there has been an emergence of Techno-parks and privately-owned smart cities across the island which opened a new competitive landscape for businesses and administrative functions [2]. There have been notable highlighted risks that the loss in business and administrative functions from both the state and privately-owned organisations will eventually lead to an erosion in municipal revenue [3]. Lesser financial input will trickle down to lesser budget availability not only for city development but also for its maintenance. This might lead to an increase in urban decay, as has been the case with Detroit in USA [4]. In fact, at the moment more than 95% of municipal revenue is used for administrative ends such as salaries for the employe...

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Section: Financial Impactmentioning

confidence: 99%

Economically Incentivising Smart Urban Regeneration. Case Study of Port Louis, Mauritius

Allam

Newman

2018

Smart Cities

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“…generation, source separation, processing, recycling and final disposal. Costi et al (2004) presented the structure and application of a decision support system (DSS) designed to help decision makers of a municipality in the development of incineration, disposal, treatment and recycling integrated programs. The main goal was to plan the MSW management, defining the refuse flows that have to be sent to recycling or to different treatment or disposal plants, and to suggest the optimal number, the types and location of such plants.…”

Section: Materials Flow Analysis In Waste Managementmentioning

confidence: 99%

A Goal Programming Based Multi-Time Step Optimal Material Flow Analysis Model for Integrated Computer Waste Management

Ahluwalia¹

2007

J ENV INFORM

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ABSTRACT. Management of computer waste is a growing concern and is more serious in developing countries where rudimentary methods of reuse, recovery and disposal are in frequent use which poses grave environmental and health hazards. Hence there is a clear reason to be concerned about the management scheme for computer waste which will be cost effective and also environmentally friendly. However, assessment of risk from the management of computer waste is a difficult task due to uncertainty in exact composition of toxic constituents and their release mechanism in the environment. The present study attempts to assess the risk associated with various computer waste management activities in relative terms and presents an integer linear goal programming based multi time step optimal material flow analysis model to achieve satisfaction of multiple objectives of economy and health and environmental risk. The model selects various treatment and disposal facilities from a given set and allocates optimum quantities of waste to them along chosen transportation routes, depending on different priorities to cost and risk. An illustrated hypothetical example of computer waste management is presented to demonstrate the usefulness of the proposed formulation. Uncertainty in the waste generation quantities has been addressed using Monte Carlo simulation.

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“…The strength of the integrated system approach to waste management has underlined and shown the usefulness of DSS in this area [10]. This approach has been applied to municipal solid waste management [11], [12], [13]. A study of DSS application to e-waste management has been done by Achillas et al [6] using ELECTRE III for optimizing the optimal location of electrical and electronic equipment in Greece.…”

Section: Introductionmentioning

confidence: 99%

Multicriteria decision analysis for optimizing site selection of electronic and electricity equipment waste dismantling and sorting facility (Case study: In Indonesia, using AHP)

Andarani

Budiawan

2015

2015 International Conference on Science in Information Technology (ICSITech)

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Abstract-E-waste management involves the selection of ewaste location of dismantling and sorting facility (DSF). Unfortunately, Indonesia, at present, has no disposal sites. Site selection decision complexity involves a lot of criteria to filter the various alternative locations. As the initial step, the aims of this study is to select the optimal location of DSF in Indonesia using multi-criteria decision analysis. In this case, Analytic Hierarchy Process (AHP) model was applied. Eight criteria were selected to choose the optimal location, e.g. local population (C 1 ), population served (C 2 ), percentage of monthly non-food expenditure (C 3 ), distance from existing e-waste dismantling and sorting facility (C 4 ), average house per unit (C 5 ), unemployed population (C 6 ), financial status of local population (C 7 ), and distance from the nearest port (C 8 ). Data of eight criteria then were ranked based on the objective function of each criterion, which were maximizing function (C 1 , C 2 , C 3 , C 4 , C 6 ) and minimize function (C 5 , C 7 , C 8 ). The results of those ranking then summed based on the weight resulted from AHP, and finally generated five main alternatives in selecting e-waste DSF. Based on this model, West Java was selected as the optimal location of e-waste DSF. West Java is one of the most populated provinces in Indonesia and located near the capital city of Indonesia.

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An environmentally sustainable decision model for urban solid waste management

Cited by 140 publications

References 13 publications

Economically Incentivising Smart Urban Regeneration. Case Study of Port Louis, Mauritius

Economically Incentivising Smart Urban Regeneration. Case Study of Port Louis, Mauritius

A Goal Programming Based Multi-Time Step Optimal Material Flow Analysis Model for Integrated Computer Waste Management

Multicriteria decision analysis for optimizing site selection of electronic and electricity equipment waste dismantling and sorting facility (Case study: In Indonesia, using AHP)

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