The future of waste management in smart and sustainable cities: A review and concept paper

Data-driven analytical framework for waste-dumping behaviour analysis to facilitate policy regulations

Waste sorting at the source is a vital strategy of waste management and to improve urban sustainability. If the strategy is implemented by relying solely on publicity and civic awareness, the impact is less significant. Proactive measures, such as policy regulations, supervisory guidance, and stimulating incentives, play essential roles for better management. The unknown waste-dumping behaviour of residents is a great challenge for decision-makers to allocate resources for waste-collection operations and to refine regulations. Traditional behaviour analysis methods such as questionnaire surveys and simulation methods have limitations considering the population size and the complexity of individual behaviour. This study aims to design a data-driven analytical framework to analyse household waste-dumping behaviour and facilitate policy regulations by using the Internet of Things (IoT) and data mining technologies. The analytical framework is further developed into a four-step management cycle. A case study in Shanghai is employed to demonstrate the effectiveness of the analytical framework and management cycle. The results of behaviour analyses reveal that (1) waste-dumping frequency is high in the evening but negligible in the early afternoon; (2) compared to working days, peak-value time at weekends occurs later in the morning and earlier in the evening; (3) residents require longer waste-dumping time windows than those empirically recommended by administrators. Managerial insights and decision support based on these research results have been presented for decision-makers to guide operations management and facilitate policy regulations.

Characterizing the implications of waste dumping surrounding the Yangtze River economic belt in China

China has prohibited an extensive list of solid waste from abroad since 2017. While China seeks to move away from being the world's dumping ground, cleaning up its own backyard is proving to be a great challenge. China's Yangtze River economic zone, which covers 11 provinces and accounts for 40% of the country's Gross Domestic Product, has been found to be alarmingly polluted: 74 million metric tons of solid wastes, including industrial solid waste, construction debris, municipal solid waste, and hazardous waste, have been disposed of by dumping. In this study, the statistics and spatial patterns of waste dumping were determined and mapped, and then the subsequent environmental impacts on the local and downstream marine ecosystem were evaluated. The results indicated the largest dumped-waste volume was found in Sichuan province (industrial solid waste) and Hubei province (solid waste mixture). The potential environmental impacts aroused by waste dumping in Hubei, Jiangxi and Sichuan provinces were serious, while the impacts in Yunnan and Zhejiang were slight. It is imperative for the Yangtze River Economic Zone to develop stringent measures for curbing the dumping of solid waste, assessing the implications from existing dumping activities, and enhancing the capacity for responsible waste management.

Waste paper recycling decision system based on material flow analysis and life cycle assessment: A case study of waste paper recycling from China

China's paper industry development is rapid, but the recycling rate of China's waste paper has been low all the time. Meanwhile, material flow analysis can help determine the flow of waste paper, and life cycle assessment (LCA) is the methodological framework for quantifying greenhouse gas emissions. Therefore, present study integrates these two methods into the model construction of China's waste paper recycling decision system. Present study constructs a benchmark model of China's waste paper recycling decision system in 2017, focusing on the impact of nonstandard waste paper recycling on the economic and environmental benefits of China's domestic waste paper recycling system. This model construction is followed by sensitivity analysis of the relevant parameters affecting the efficiency of the waste paper recycling system. Finally, present study forecasts the system's economic benefits and greenhouse gas (GHG) emissions in the context of integrating and regulating nonstandard recycling vendors. The results show that the economic benefit of China's waste paper recycling in 2017 is approximately 458.3 yuan/t and that the GHG emissions are 901.1 kgCO₂eq. The standard recovery rate and nonstandard recovery acceptance rate will both have a significant impact on the system's economic benefits and improve the GHG emissions structure. In the context of integrating nonstandard recycling enterprises and individual recycling vendors, the economic benefits will rise to 3312.5 yuan/t in 2030, while GHG emissions will rise to 942.9 kgCO₂eq. Present study can play a certain guiding role for policy makers in formulating waste paper recycling industry specifications and formulating relevant policies.

Content analysis of literature on big data in smart cities

Purpose

The purpose of this paper is to provide a systematic literature review on the technological aspects of smart cities and to give insights about current trends, sources of research, contributing authors and countries. It is required to understand technical concepts like information technology, big data analytics, Internet of Things and blockchain needed to implement smart city models successfully.

Design/methodology/approach

The data were collected from the Scopus database, and analysis techniques like bibliometric analysis, network analysis and content analysis were used to obtain research trends, publications growth, top contributing authors and nations in the domain of smart cities. Also, these analytical techniques identified various fields within the literature on smart cities and supported to design a conceptual framework for Industry 4.0 adoption in a smart city.

Findings

The bibliometric analysis shows that research publications have increased significantly over the last couple of years. It has found that developing countries like China is leading the research on smart cities. The network analytics and article classification identified six domains within the literature on smart cities. A conceptual framework for the smart city has proposed for the successful implementation of Industry 4.0 technologies.

Originality/value

This paper explores the role of Industry 4.0 technologies in smart cities. The bibliometric data on publications from the year 2013 to 2018 were used and investigated by using advanced analytical techniques. The paper reviewS key technical concepts for the successful execution of a smart city model. It also gives an idea about various technical considerations required for the implementation of the smart city model through a conceptual framework.

“Smart Is Not Smart Enough!” Anticipating Critical Raw Material Use in Smart City Concepts: The Example of Smart Grids

Globally emerging smart city concepts aim to make resource production and allocation in urban areas more efficient, and thus more sustainable through new sociotechnical innovations such as smart grids, smart meters, or solar panels. While recent critiques of smart cities have focused on data security, surveillance, or the influence of corporations on urban development, especially with regard to intelligent communication technologies (ICT), issues related to the material basis of smart city technologies and the interlinked resource problems have largely been ignored in the scholarly literature and in urban planning. Such problems pertain to the provision and recovery of critical raw materials (CRM) from anthropogenic sources like scrap metal repositories, which have been intensely studied during the last few years. To address this gap in the urban planning literature, we link urban planning literatures on smart cities with literatures on CRM mining and recovery from scrap metals. We find that underestimating problems related to resource provision and recovery might lead to management and governance challenges in emerging smart cities, which also entail ethical issues. To illustrate these problems, we refer to the smart city energy domain and explore the smart city-CRM-energy nexus from the perspectives of the respective literatures. We show that CRMs are an important foundation for smart city energy applications such as energy production, energy distribution, and energy allocation. Given current trends in smart city emergence, smart city concepts may potentially foster primary extraction of CRMs, which is linked to considerable environmental and health issues. While the problems associated with primary mining have been well-explored in the literature, we also seek to shed light on the potential substitution and recovery of CRMs from anthropogenic raw material deposits as represented by installed digital smart city infrastructures. Our central finding is that the current smart city literature and contemporary urban planning do not address these issues. This leads to the paradox that smart city concepts are supporting the CRM dependencies that they should actually be seeking to overcome. Discussion on this emerging issue between academics and practitioners has nevertheless not taken place. We address these issues and make recommendations.

Digitalisation and intelligent robotics in value chain of circular economy oriented waste management - A review

The general aim of circular economy is the most efficient and comprehensive use of resources. In order to achieve this goal, new approaches of Industry 4.0 are being developed and implemented in the field of waste management. The innovative K-project: Recycling and Recovery of Waste 4.0 - "ReWaste4.0" deals with topics such as digitalisation and the use of robotic technologies in waste management. Here, a summary of the already published results in these areas, which were divided into the four focused topics, is given: Collection and Logistics, Machines and waste treatment plants, Business models and Data Tools. Presented are systems and methods already used in waste management, as well as technologies that have already been successfully applied in other industrial sectors and will also be relevant in the waste management sector for the future. The focus is set on systems that could be used in waste treatment plants or machines in the future in order to make treatment of waste more efficient. In particular, systems which carry out the sorting of (mixed) waste via robotic technologies are of interest. Furthermore "smart bins" with sensors for material detection or level measurement, methods for digital image analysis and new business models have already been developed. The technologies are often based on large amounts of data that can contribute to increase the efficiency within plants. In addition, the results of an online market survey of companies from the waste management industry on the subject of waste management 4.0 or "digital readiness" are summarized.

A Comparative Study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in Estimating the Heating Load of Buildings’ Energy Efficiency for Smart City Planning

Energy-efficiency is one of the critical issues in smart cities. It is an essential basis for optimizing smart cities planning. This study proposed four new artificial intelligence (AI) techniques for forecasting the heating load of buildings’ energy efficiency based on the potential of artificial neural network (ANN) and meta-heuristics algorithms, including artificial bee colony (ABC) optimization, particle swarm optimization (PSO), imperialist competitive algorithm (ICA), and genetic algorithm (GA). They were abbreviated as ABC-ANN, PSO-ANN, ICA-ANN, and GA-ANN models; 837 buildings were considered and analyzed based on the influential parameters, such as glazing area distribution (GLAD), glazing area (GLA), orientation (O), overall height (OH), roof area (RA), wall area (WA), surface area (SA), relative compactness (RC), for estimating heating load (HL). Three statistical criteria, such as root-mean-squared error (RMSE), coefficient determination (R2), and mean absolute error (MAE), were used to assess the potential of the aforementioned models. The results indicated that the GA-ANN model provided the highest performance in estimating the heating load of buildings’ energy efficiency, with an RMSE of 1.625, R2 of 0.980, and MAE of 0.798. The remaining models (i.e., PSO-ANN, ICA-ANN, ABC-ANN) yielded lower performance with RMSE of 1.932, 1.982, 1.878; R2 of 0.972, 0.970, 0.973; MAE of 1.027, 0.980, 0.957, respectively.