Input-output models and waste management analysis: A critical review

Towa, Edgar; Zeller, Vanessa; Achten, Wouter

doi:10.1016/j.jclepro.2019.119359

Cited by 55 publications

(34 citation statements)

References 76 publications

(158 reference statements)

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“…It is worth noting that the approach has certain limitations in that the reviewers choose characteristics and features based on what they thought were essential for a study that focused on evaluating waste management strategies at the national level. A quantitative and analytical rating of IO models against a wider pool of parameters, for example, will enhance the review [30]. e future of waste management is a complex balancing act of diplomacy, technology, public access to information, and, most importantly, goodwill to improve the situation [31].…”

Section: Literature Reviewmentioning

confidence: 99%

[Retracted] Machine Learning and IoT‐Based Waste Management Model

Khan

Kumar

Srivastava³

et al. 2021

Computational Intelligence and Neuroscience

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A rapid rise in inhabitants across the globe has led to the inadmissible management of waste in various countries, giving rise to various health issues and environmental pollution. The waste-collecting trucks collect waste just once or twice in seven days. Due to improper waste collection practices, the waste in the dustbin is spread on the streets. Thus, to defeat this situation, an efficient solution for smart and effective waste management using machine learning (ML) and the Internet of Things (IoT) is proposed in this paper. In the proposed solution, the authors have used an Arduino UNO microcontroller, ultrasonic sensor, and moisture sensor. Using image processing, one can measure the waste index of a particular dumping ground. A hardware prototype is also developed for the proposed framework. Thus, the presented solution for the efficient management of waste accomplishes the aim of establishing clean and pollution-free cities.

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Section: Literature Reviewmentioning

confidence: 99%

[Retracted] Machine Learning and IoT‐Based Waste Management Model

Khan

Kumar

Srivastava³

et al. 2021

Computational Intelligence and Neuroscience

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“…However, except the hybrid (or mixed unit) MRIOT of EXIOBASE, most databases account for intersectoral flows of products in monetary units and environmental extensions in physical units. The disconnection between the monetary values of intersectoral flows and the physical environmental extensions may hamper the capacity for policy support for example for waste management and Circular Economy (CE), since the definition and achievement of quantitative targets within these policies could be facilitated when adequately formulated in physical terms (Aguilar-Hernandez et al, 2018;McCarthy et al, 2018;Towa et al, 2020a). In addition to the physical MRIOT, subnational MRIOT and related applications develop at slower pace than economic and national MRIOT.…”

Section: Introductionmentioning

confidence: 99%

Toward the development of subnational hybrid input–output tables in a multiregional framework

Towa

Zeller

Merciai

et al. 2020

J of Industrial Ecology

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Environmental input-output analyses (EIOA) can be a useful decision support tool at the subnational level, due to its ability to capture economic and environmental impacts at other geographical levels. Yet, such analyses are hindered by the lack of subnational IO tables. Furthermore, the lack of physical product and waste flows in what is known as a "hybrid" table prevents a range of consumption-based and circular economy type analyses. We demonstrate the development of a multiregional hybrid IOT (MRHIOT) along with environmental extensions at the subnational level and exemplify it for the case of Belgium. The development procedure discloses a novel approach of combining national hybrid tables, subnational monetary tables, and physical survey-based data. Such a combination builds upon a partial-survey approach that includes a range of techniques for initial estimation and reconciliation within a balancing procedure. For the validation of the approaches, we assessed the magnitude of deviations between the initial and final estimates and analysed the uncertainties inherent to each initial estimation procedure. Subsequently, we conducted a consumption-based analysis where we assessed the carbon footprint (CF) at the subnational level and highlighted the CF inherent to the interregional linkages. This study provides methodological and application-based contributions to the discussion on the relevance of hybrid subnational tables and analyses compared to national ones. The proposed approach could be replicable to some extent for further developing subnational

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“…In general terms, the model consists of a system of linear equations that describe purchases and sales between sectors of the economy [36]. The basic assumptions of the input-output analysis have been developed over time and adapted to many other aspects and areas, such as climate change [41], waste management [42], transportation [43], water management, resource management [44], and the bioeconomy [20,21]. Despite the well-known limitations of the model, such as fixed coefficients, linearity, and simplifications, it has been widely used in many countries.…”

Section: Input-output Model Basic Frameworkmentioning

confidence: 99%

Comparing Bioeconomy Potential at National vs. Regional Level Employing Input-Output Modeling

2021

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The support of economic sectors that exploit natural bio-based resources in a particular region is an opportunity to benefit from local potentials in terms of sustainability, employment, output, and household income. Hence a relevant question emerges, namely, how can bioeconomy sectors be adequately supported? Within this context, another issue is whether the bioeconomy development strategy at a national level should be the same as that at a regional level. To address these issues, in the current study a comparison was made between the bioeconomy sectors at the country level based on the case study of Poland and one of the poorest regions in the European Union—the Lubelskie Region. A regional input–output model was built for the regional economy and compared with the national model. The bioeconomy-oriented regional input–output table was built by applying a hybrid regionalization method, combining non-survey techniques and a questionnaire survey that was carried out in companies of mixed bio-based sectors. Sectoral linkages, such as multipliers and elasticities, indicate notable differences among the bio-based sectors’ potentials of the regional and national economies. Therefore, a bioeconomy development strategy should be seen to differ at national and regional levels.

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Input-output models and waste management analysis: A critical review

Cited by 55 publications

References 76 publications

[Retracted] Machine Learning and IoT‐Based Waste Management Model

[Retracted] Machine Learning and IoT‐Based Waste Management Model

Toward the development of subnational hybrid input–output tables in a multiregional framework

Comparing Bioeconomy Potential at National vs. Regional Level Employing Input-Output Modeling

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