Internet of things and Big Data as potential solutions to the problems in waste electrical and electronic equipment management: An exploratory study

Smart e-waste management: a revolutionary incentive-driven IoT solution with LPWAN and edge-AI integration for environmental sustainability

Managing e-waste involves collecting it, extracting valuable metals at low costs, and ensuring environmentally safe disposal. However, monitoring this process has become challenging due to e-waste expansion. With IoT technology like LoRa-LPWAN, pre-collection monitoring becomes more cost-effective. Our paper presents an e-waste collection and recovery system utilizing the LoRa-LPWAN standard, integrating intelligence at the edge and fog layers. The system incentivizes WEEE holders, encouraging participation in the innovative collection process. The city administration oversees this process using innovative trucks, GPS, LoRaWAN, RFID, and BLE technologies. Analysis of IoT performance factors and quantitative assessments (latency and collision probability on LoRa, Sigfox, and NB-IoT) demonstrate the effectiveness of our incentive-driven IoT solution, particularly with LoRa standard and Edge AI integration. Additionally, cost estimates show the advantage of LoRaWAN. Moreover, the proposed IoT-based e-waste management solution promises cost savings, stakeholder trust, and long-term effectiveness through streamlined processes and human resource training. Integration with government databases involves data standardization, API development, security measures, and functionality testing for efficient management.

Linking energy-based circularity with environment in high-income economies

A circular economy is a regenerative approach that emphasizes resource efficiency, waste reduction, and the reuse of materials for a sustainable world. By adopting circular practices, we can reduce the negative impact of traditional linear economic models on the environment. According to the International Renewable Energy Agency (IRENA), the world is generating only 26% of total energy production from circular practices, which positively impacts environmental health. Therefore, this study aims to evaluate the empirical estimation of circular practices regarding energy on the environment. The current study focuses on the association between the circular economic index, economic growth, trade, digitization, energy use, and the financial development index on the environment in 29 high-income countries from 1990 to 2019. The study employs the second-generation econometric technique Driscoll-Kraay to empirically estimate the association among the variables of interest after confirming cross-sectional dependency within the data set. The study findings reveal that circular practices improve high-income countries' environmental conditions. Furthermore, the study confirms the association between economic growth, financial development index, energy use, trade, and digitization on the environment, and it leads to a more sustainable situation. Policies are drawn based on findings for policymakers toward a sustainable world.

Facile "Synergistic Inner-Outer Activation" Strategy for Nano-Engineering of Nature-Skin-Derived Wearable Daytime Radiation Cooling Materials

Natural skin-derived products, as traditional wearable materials are widely used in people's daily life due to the products' excellent origins. Herein, a versatile daytime-radiation cooling wearable natural skin (RC-skin) consisting of the collagen micro-nano fibers with the on-demand double-layer radiation cooling structure is nano-engineered through the proposed facile "synergistic inner-outer activation" strategy. The bottom layer (inner strategy) of the RC-skin is fabricated by filling the skin with the Mg₁₁ (HPO₃ )₈ (OH)₆ nanoparticles by soaking. The superstratum (outer strategy) is constituted by a composite coating with an irregular microporous structure. The RC-skin harvests the inherent advantages of natural building blocks including sufficient hydrophobicity, excellent mechanical properties, and friction resistance. Owing to the subtle double-layer structure design, the solar reflectance and the average emissivity in the mid-infrared band of RC-skin are ≈92.7% and ≈95%, respectively. Therefore, the RC-skin's temperature in the sub-ambient is reduced by ≈7.5 °C. Various outdoor practical application experiments further substantiate that RC-skin has superior radiation cooling performances. Collectively, RC-skin has broad-application prospects for intelligent wearing, low-carbon travel, building materials, and intelligent thermoelectric power generation, and this study also provides novel strategies for developing natural-skin-derived functional materials.

Towards automatic waste containers management in cities via computer vision: containers localization and geo-positioning in city maps

This paper describes the scientific achievements of a collaboration between a research group and the waste management division of a company. While these results might be the basis for several practical or commercial developments, we here focus on a novel scientific contribution: a methodology to automatically generate geo-located waste container maps. It is based on the use of Computer Vision algorithms to detect waste containers and identify their geographic location and dimensions. Algorithms analyze a video sequence and provide an automatic discrimination between images with and without containers. More precisely, two state-of-the-art object detectors based on deep learning techniques have been selected for testing, according to their performance and to their adaptability to an on-board real-time environment: EfficientDet and YOLOv5. Experimental results indicate that the proposed visual model for waste container detection is able to effectively operate with consistent performance disregarding the container type (organic waste, plastic, glass and paper recycling,…) and the city layout, which has been assessed by evaluating it on eleven different Spanish cities that vary in terms of size, climate, urban layout and containers' appearance.

Performance evaluation of a recurrent deep neural network optimized by swarm intelligent techniques to model particulate matter

Atmospheric pollution refers to the presence of substances in the air such as particulate matter (PM) which has a negative impact in population ́s health exposed to it. This makes it a topic of current interest. Since the Metropolitan Zone of the Valley of Mexico's geographic characteristics do not allow proper ventilation and due to its population's density a significant quantity of poor air quality events are registered. This paper proposes a methodology to improve the forecasting of PM10 and PM2.5, in largely populated areas, using a recurrent long-term/short-term memory (LSTM) network optimized by the Ant Colony Optimization (ACO) algorithm. The experimental results show an improved performance in reducing the error by around 13.00% in RMSE and 14.82% in MAE using as reference the averaged results obtained by the LSTM deep neural network. Overall, the current study proposes a methodology to be studied in the future to improve different forecasting techniques in real-life applications where there is no need to respond in real time.Implications: This contribution presents a methodology to deal with the highly non-linear modeling of airborne particulate matter (both PM10 and PM2.5). Most linear approaches to this modeling problem are often not accurate enough when dealing with this type of data. In addition, most machine learning methods require extensive training or have problems when dealing with noise embedded in the time-series data. The proposed methodology deals with this data in three stages: preprocessing, modeling, and optimization. In the preprocessing stage, data is acquired and imputed any missing data. This ensures that the modeling process is robust even when there are errors in the acquired data and is invalid, or the data is missing. In the modeling stage, a recurrent deep neural network called LSTM (Long-Short Term Memory) is used, which shows that regardless of the monitoring station and the geographical characteristics of the site, the resulting model shows accurate and robust results. Furthermore, the optimization stage deals with enhancing the capability of the data modeling by using swarm intelligence algorithms (Ant Colony Optimization, in this case). The results presented in this study were compared with other works that presented traditional algorithms, such as multi-layer perceptron, traditional deep neural networks, and common spatiotemporal models, which show the feasibility of the methodology presented in this contribution. Lastly, the advantages of using this methodology are highlighted.

Building demolition waste management through smart BIM: A case study in Hong Kong

Hong Kong's construction industry, known for its massive building infrastructure, produces an enormous amount of waste every year, the vast majority of which is disposed for landfills. Therefore, some effective operational measures and waste management policies have been implemented. However, enormous waste remains a concern for stakeholders and exert pressure on the limited capacity of Hong Kong's landfills. Though previous research discusses Building Information Modelling (BIM) application for construction waste management enhancement, the BIM model has not been widely implemented for building demolition with waste management. Hence, as a response to the aforementioned shortcomings, this paper develops a conceptual framework that allows collecting, maintaining, and analyzing comprehensive information through Smart BIM that uses advanced technologies such as Internet of Things (IoT) and capable of reacting to user activities such as waste quantitative assessment, demolition process planning, optimal disposal route selection, and waste management strategy are executed. The advantages of the proposed framework are shown in a case study benefit-cost analysis based on three planned reuse and recycling-rate scenarios that explain on- and off-site recycling methods. The results show that the proposed framework will pave the way for generating sustainable waste disposal practices by providing technical and decision-making support functionalities to engineers and planners in the construction industry.

Modeling the Constraints to the Utilization of the Internet of Things in Managing Supply Chains of Off-Site Construction: An Approach toward Sustainable Construction

Despite persistent calls for cleaner production and improved automation of construction processes, the adoption of the Internet of Things (IoT) in managing the supply chains of off-site construction businesses has been discouraged due to various constraints. This paper methodically identifies and prioritizes the crucial factors that impede the application of the Internet of Things (IoT) in off-site construction. Content analysis and an expert-based evaluation strategy were used to identify and evaluate the constraints affecting Internet of Things adoption in off-site construction. The ISM, MICMAC, and DEMATEL techniques were used to analyze the data. This study identifies the “lack of clear strategy for governing IoT utilization in supply chain management” as the most significant factor that impedes the application of the Internet of Things (IoT) in off-site construction businesses. The outcomes also provide a rich source of insights into off-site construction businesses to clearly recognize the implications of utilizing IoT technologies in managing the supply chains of businesses and what to expect when applying IoT technologies and solutions. While this paper advocates for improved green construction practices, cleaner production, and automation in the construction industry, it has set the stage for integrating IoT technologies in the supply chain management of off-site construction businesses.

Chaotic simulated annealing multi-verse optimization enhanced kernel extreme learning machine for medical diagnosis

Classification models such as Multi-Verse Optimization (MVO) play a vital role in disease diagnosis. To improve the efficiency and accuracy of MVO, in this paper, the defects of MVO are mitigated and the improved MVO is combined with kernel extreme learning machine (KELM) for effective disease diagnosis. Although MVO obtains some relatively good results on some problems of interest, it suffers from slow convergence speed and local optima entrapment for some many-sided basins, especially multi-modal problems with high dimensions. To solve these shortcomings, in this study, a new chaotic simulated annealing overhaul of MVO (CSAMVO) is proposed. Based on MVO, two approaches are adopted to offer a relatively stable and efficient convergence speed. Specifically, a chaotic intensification mechanism (CIP) is applied to the optimal universe evaluation stage to increase the depth of the universe search. After obtaining relatively satisfactory results, the simulated annealing algorithm (SA) is employed to reinforce the capability of MVO to avoid local optima. To evaluate its performance, the proposed CSAMVO approach was compared with a wide range of classical algorithms on thirty-nine benchmark functions. The results show that the improved MVO outperforms the other algorithms in terms of solution quality and convergence speed. Furthermore, based on CSAMVO, a hybrid KELM model termed CSAMVO-KELM is established for disease diagnosis. To evaluate its effectiveness, the new hybrid system was compared with a multitude of competitive classifiers on two disease diagnosis problems. The results demonstrate that the proposed CSAMVO-assisted classifier can find solutions with better learning potential and higher predictive performance.

Waste Management 4.0: An Application of a Machine Learning Model to Identify and Measure Household Waste Contamination—A Case Study in Australia

Waste management directly and indirectly contributes to all sustainable development goals. Hence, the modernisation of the current ineffective management system through Industry 4.0-compatible technologies is urgently needed. Inspired by the fourth industrial revaluation, this study explores the potential application of waste management 4.0 in a local government area in Perth, Western Australia. The study considers a systematic literature review as part of an exploratory investigation of the current applications and practices of Industry 4.0 in the waste industry. Moreover, the study develops and tests a machine learning model to identify and measure household waste contamination as a waste management 4.0 case study application. The study reveals that waste management 4.0 offers various opportunities and sustainability benefits in reducing costs, improving efficiency in the supply chain and material flow, and reducing as well as eliminating waste by achieving holistic circular economy goals. The significant barriers and challenges involve initial investments in developing and maintaining waste management 4.0 technology, platform and data acquisition. The proof-of-concept case study on the machine learning model detects selected waste with considerable precision (over 70% for selected items). The number and quality of the labelled data significantly influences the model’s accuracy. The data on waste contamination are essential for local governments to explore household waste recycling practices besides developing effective waste education and communication methods. The study concludes that waste management 4.0 can be an effective tool for acquiring real-time data; however, overcoming the current limitations needs to be addressed before applying waste management 4.0 into practice.

Indicators and ICTs application for municipal waste management

The worldwide populace is rising steadily. Urbanization is likewise expanding quickly with the rising populace. Fast urbanization has considerably increased the generation of municipal solid waste (MSW). The MSW management issues have recently been analyzed through various assessment indicators and information and communication technologies (ICTs). This article provides an overview of applications of assessment indicators and ICTs for addressing the environmental issues of waste disposal and management in municipalities. The selection of indicators mainly depends on the stakeholders' specific requirements, such as waste management strategies, urban planning and development, human health, and energy generation. The literature analysis revealed that collection, sorting, recycling, cost efficiency, and environmental aspect were the leading indicators used in waste management studies. And these indicators reduce the complexity of systems and formulate evaluations easier for the decision-maker. Moreover, these are also helpful in assessing the improvement and reporting the waste condition to the expert. These analysis further revealed that information and communication technology is a requirement in the planning and managing of current solid waste disposal problems. The use of ICTs in waste management systems mitigates possible constraints regarding spot selection, inept waste disposal, waste collection monitoring, and proper recycling.

Ecotoxicological assessment of spent battery extract using zebrafish embryotoxicity test: A multi-biomarker approach

Water environmental pollution caused by spent batteries is a nonignorable environmental issue. In this study, the early life stage of zebrafish was employed to assess the environmental risk of spent batteries after exposure to 0, 1%, 2%, 5% and 10% spent battery extract for 120 h. Our results clearly indicated that spent battery extract can significantly decrease the survival rate, hatching rate and body length and increase heart rate. Moreover, spent battery extract exposure-induced zebrafish larvae generate oxidative stress and inhibit the mRNA transcriptional levels of heat shock protein (HSP70) and metallothionein (MT) genes. These results showed that the spent batteries not only affected the survival and development performance of zebrafish at an early life stage but also caused oxidative stress and interfered with the detoxification of zebrafish. This study provided novel insight into spent battery induced toxicity in the early life stage of fish.

Improving the sustainability of a reverse supply chain system under demand uncertainty by using postponement strategies

In recent decades, issues of resource depletion and waste piling have grown at an alarming rate, which are happening in the cases of product wastes with significant residual values, such as e-waste. To address these issues, stakeholders have focused to develop a reverse supply chain (RSC) system that can sustain profitable takeback, reuse, and recycling operations in the long-term. Such a system requires efficiency in handling complex operations involving various players while being responsive to demand uncertainty and changes. One way in realizing these capabilities is by incorporating postponement concepts to the integrated RSC network, allowing the delay of operations susceptible to demand uncertainty. This study pioneers the formulation of a two-stage stochastic mixed-integer model of a multi-player RSC with speculation-postponement strategies. The sample average approximation method is used to solve and verify the proposed model that has an uncertain demand. Various speculation-postponement strategies, namely, disassembly, reconditioning, and reassembly strategies are developed to configure forecast and demand-driven RSC operations, including the purchasing, product takeback, production planning, inventory, and item speculation decisions. Numerical examples of the notebook computer RSC demonstrate that utilizing the right operation postponement can increase the network's flexibility, allowing better economic performances even under high demand uncertainty risks and stricter environmental regulations. In various cases, the RSC performs better with speculation-postponement strategies than without postponement strategy, demonstrating the proposed model's superiority. This study can provide insight to decision-makers to improve RSC sustainability through postponement. Moreover, the model is generic and can be applied to other products as well.

Bio-inspired gelatin/single-walled carbon nanotube nanocomposite for transient electrochemical energy storage: An approach towards eco-friendly and sustainable energy system

Wide-scale production of non-biodegradable e-waste from electrical appliances are causing great harm to the environment. The use of bio-polymer based nanomaterials may offer a promising approach for the fabrication of eco-friendly sustainable devices. In this work, gelatin/single walled carbon nanotube (Gel/SWCNT) nanocomposites were prepared by a simple and economic aqueous casting method. The effect of SWCNT on the structural, surface-morphological, electrical, and electrochemical properties of the nanocomposite was studied. Fourier transform infrared spectroscopy (FTIR) and field emission scanning electron microscope (FESEM) showed an improved degree of interaction between the SWCNTs and Gel matrix. The surface wettability of the nanocomposites was found to be changed from hydrophilic to hydrophobic in nature due to the incorporation of SWCNTs into the Gel matrix. The incorporation of SWCNTs was also found to reduce the DC resistivity of the nanocomposite by 4 orders of magnitude. SWCNTs also increase the specific capacitance of the nanocomposite from 124 mF/g to 467 mF/g at a current density of 0.3 mA/g. The electrochemical impedance spectroscopy analysis revealed an increase of the pseudo-capacitance increased from 9.4 μF to 31 μF due to the incorporation of SWCNT. The Gel/SWCNT nanocomposite showed cyclic stability with capacitive retention of about 98% of its initial capacitance after completing 2000 charging/discharging cycles at a current density of 100 mA/g. The nanocomposite completely dissolves in water within 12 h, demonstrates it as a promising candidate for transient energy storage applications. The Gel/SWCNT nanocomposite may offer a new route for the synthesis of eco-friendly, biodegradable, and transient devices.

Big Data and the United Nations Sustainable Development Goals (UN SDGs) at a Glance

The launch of the United Nations (UN) 17 Sustainable Development Goals (SDGs) in 2015 was a historic event, uniting countries around the world around the shared agenda of sustainable development with a more balanced relationship between human beings and the planet. The SDGs affect or impact almost all aspects of life, as indeed does the technological revolution, empowered by Big Data and their related technologies. It is inevitable that these two significant domains and their integration will play central roles in achieving the 2030 Agenda. This research aims to provide a comprehensive overview of how these domains are currently interacting, by illustrating the impact of Big Data on sustainable development in the context of each of the 17 UN SDGs.

An evolutionary approach to the vehicle route planning in e-waste mobile collection on demand

The article discusses the utilitarian problem of the mobile collection of waste electrical and electronic equipment. Due to its $$\mathcal {NP}$$ NP -hard nature, implies the application of approximate methods to discover suboptimal solutions in an acceptable time. The paper presents the proposal of a novel method of designing the Evolutionary and Memetic Algorithms, which determine favorable route plans. The recommended methods are determined using quality evaluation indicators for the techniques applied herein, subject to the limits characterizing the given company. The proposed Memetic Algorithm with Tabu Search provides much better results than the metaheuristics described in the available literature.

Digital Transformation and Environmental Sustainability: A Review and Research Agenda

Digital transformation refers to the unprecedented disruptions in society, industry, and organizations stimulated by advances in digital technologies such as artificial intelligence, big data analytics, cloud computing, and the Internet of Things (IoT). Presently, there is a lack of studies to map digital transformation in the environmental sustainability domain. This paper identifies the disruptions driven by digital transformation in the environmental sustainability domain through a systematic literature review. The results present a framework that outlines the transformations in four key areas: pollution control, waste management, sustainable production, and urban sustainability. The transformations in each key area are divided into further sub-categories. This study proposes an agenda for future research in terms of organizational capabilities, performance, and digital transformation strategy regarding environmental sustainability.

Carbon emissions and environmental management based on Big Data and Streaming Data: A bibliometric analysis

Climate change and environmental management are issues of global concern. The advent of the era of Big Data has created a new research platform for the assessment of environmental governance and policies. However, little is known about Big Data application to climate change and environmental management research. This paper adopts bibliometric analysis in conjunction with network analysis to systematically evaluate the publications on carbon emissions and environmental management based on Big Data and Streaming Data using R package and VOSviewer software. The analysis involves 274 articles after rigorous screening and includes citation analysis, co-citation analysis, and co-word analysis. Main findings include (1) Carbon emissions and environmental management based on big data and streaming data is an emerging multidisciplinary research topic, which has been applied in the fields of computer science, supply chain design, transportation, carbon price assessment, environmental policy evaluation, and CO₂ emissions reduction. (2) This field has attracted the attention of nations which are major contributors to the world economy. In particular, European and American scholars have made the main contributions to this topic, and Chinese researchers have also had great impact. (3) The research content of this topic is primarily divided into four categories, including empirical studies of specific industries, air pollution governance, technological innovation, and low-carbon transportation. Our findings suggest that future research should bring greater depth of practical and modeling analysis to environmental policy assessment based on Big Data.

Sustainable financing for municipal solid waste management in Nepal

Financing municipal solid waste (MSW) services is one of the key challenges faced by cities in developing countries. This study used plastic waste, a constituent of MSW, to explore the possibility of generating revenue for financing MSW management in the municipalities of Nepal. The results of this study suggest that plastic material recovery could generate revenue, which is equivalent to 1.38 times of the plastic-waste-related management cost when collection efficiency reaches 66.7%. An increase in 1% of recovery rate and collection efficiency could cover an additional 4.64% and 2.06% of the costs of managing plastic waste, respectively. In addition, an increase in tax on imported plastic materials could also motivate recovery of plastic waste for recycle and reuse. An additional 1% tax on plastic imports would be sufficient to cover plastic-related waste management when plastic waste recovery and collection efficiency rates are low. This plastic recovery- revenue exercise could be expanded to other materials such as paper and metal to fully understand the possibility of sustainable financing of MSW management and reducing environmental harm in developing countries like Nepal.

Sustainable manufacturing and industry 4.0: what we know and what we don't

Purpose

The emergence the fourth industrial revolution, known as well as industry 4.0, and its applications in the manufacturing sector ushered a new era for the business entities. It not only promises enhancement in operational efficiency but also magnify sustainable operations practices. This current paper provides a thorough bibliometric and network analysis of more than 600 articles highlighting the benefits in favor of the sustainability dimension in the industry 4.0 paradigm.

Design/methodology/approach

The analysis begins by identifying over 1,000 published articles in Scopus, which were then refined to works of proven influence and those authored by influential researchers. Using rigorous bibliometric software, established and emergent research clusters were identified for intellectual network analysis, identification of key research topics, interrelations and collaboration patterns.

Findings

This bibliometric analysis of the field helps graphically to illustrate the publications evolution over time and identify areas of current research interests and potential directions for future research. The findings provide a robust roadmap for mapping the research territory in the field of industry 4.0 and sustainability.

Originality/value

As the literature on sustainability and industry 4.0 expands, reviews capable of systematizing the main trends and topics of this research field are relevant.

Environmental and economic performance of an integrated municipal solid waste treatment: A Chinese case study

The application of integrated municipal solid waste (MSW) management has become increasingly common for the mitigation of the ever-growing MSW stream. However, despite their popularity across the globe, little is known about the performance of integrated MSW management (MSWM) plants. This study quantitively investigates the environmental and economic performance of an integrated MSW treatment center in the city of Horqin Left Rear Banner, Inner Mongolia Province, China, using a combined life cycle assessment (LCA) and life cycle costing (LCC) methodology. Results indicate that the integrated MSWM plant is sustainable in both environmental and economic aspects, as the life cycle environmental impacts and economic costs can be offset by substituting virgin products with recycled counterparts. Amongst the included treatments, MSW separation, brick making and plastic recycling are the greatest contributors to the total environmental burdens and economic expenses. LCC results demonstrate that the equipment cost, tax and other asset costs are the greatest contributors to the total costs of the plant. Sensitivity analysis confirms that the increasing source separation ratio results in the reduction of environmental burdens and economic expenses via the usage of biogas and photovoltaic power. Furthermore, we offer recommendations for the promotion of the environmental and economic sustainability of integrated MSW treatment facilities.

The adoption of internet of things in a circular supply chain framework for the recovery of WEEE: the case of lithium-ion electric vehicle battery packs

The rapid growth in the sales of electric and electronic devices over recent decades is generating worldwide concern about the management of Waste Electrical and Electronic Equipment (WEEE). New methodologies to extend the useful life of products have long been sought, accelerating the shift from a linear to a Circular Economy (CE). When products reach the End-of-Life (EoL) stage, the Reverse Supply Chain (RSC) is responsible for managing operations, with greater efforts being needed to improve the associated information infrastructure. In fact, this has become increasingly feasible due to the emergence of a new digital revolution led by the Internet of Things (IoT). To shed light on this matter, we propose the Circular Supply Chain (CSC) framework for EoL management aimed at satisfying the information infrastructure requirements in a particular scenario for the recovery of Electric Vehicle Battery (EVB) packs. We present a qualitative evaluation of the CSC information requirements, and the capabilities of IoT to satisfy them. As a result, a heterogeneous IoT network deployment is proposed in pursuit of a digital CSC information infrastructure.

MIR spectral characterization of plastic to enable discrimination in an industrial recycling context: I. Specific case of styrenic polymers

One of the major limitations in polymer recycling is their sorting as they are collected in mixes. The majority of polymers are highly incompatible without compatibilizers. For sorting of polymers, high-speed online Near-Infrared (NIR) spectroscopy is nowadays relatively widespread. It is however limited by the use of carbon black as a pigment and UV-stabilizer, which strongly absorbs near-infrared signals. Mid-Infrared (MIR) hyperspectral cameras were recently put on the market. However, their wavelength ranges are smaller and their resolutions are poorer, in comparison with laboratory equipment based on Fourier-Transform Infrared (FTIR). The identification of specific signals of end-of-life polymers for recycling purposes is becoming an important stake since they are very diverse, highly formulated, and more and more used in copolymers and blends, leading to complex waste stocks mainly as WEEE (Waste Electrical and Electronic Equipment). Dark colored plastics are the major part of WEEE, which also contains mainly styrenics (ABS, HIPS and their blends). In addition, styrenics are especially concerned by the need of identification. In this framework, spectral characterizations of ten types of polymers were scrutinized through about eighty pristine and real waste samples. Polymer characteristic signals were aggregated in charts to help rapid and automatized distinction through specific signals, even in limited resolution and frequency ranges.

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.

Sustainable Change Management through Employee Readiness: Decision Support System Adoption in Technology-Intensive British E-Businesses

Technology brings green sustainable management practices to the workplace. It is important to ascertain the factors that enable or inhibit employees’ perceptions towards technology adoption. Corporate sustainability and sustainable management practices partially depend on employees for the successful implementation of technological changes in the workplace. This study aims at applying the technology acceptance model (TAM) from an employees’ user-perspective. It addresses those factors that form employee readiness for e-business and enable their intention to use e-business technologies such as decision support systems (DSS). It focuses on technology intensive firms while combining Davis’ technology acceptance model and Lai and Ong’s employee readiness for e-business (EREB) model. A survey questionnaire was used to collect the data for this cross-sectional study from 331 employees of 28 well-established small and medium-sized e-businesses located in the United Kingdom. The outcomes show that the four dimensions of EREB explain the 58.2% of variance in perceived ease of use and the 50.2% of variance in perceived usefulness. Together, perceived usefulness and perceived ease of use explain the 51.8% of variance in intention to use while fully mediating the relationship between higher order EREB construct and intention to use DSS.

Is Bicycle Sharing an Environmental Practice? Evidence from a Life Cycle Assessment Based on Behavioral Surveys

As an icon of sharing economy and product service systems, bicycle sharing is gaining an increasing global popularity, yet there is little knowledge about the environmental performance of this emerging traveling mode. To seek the answer to the question, the paper employs a survey-based method and a life cycle assessment (LCA) approach. We first conduct a questionnaire-based survey to identify the changes in traveling modes after the introduction of shared bicycles. The survey results show that the use of shared bicycles is more popular among young and low-income populations, and shared bicycles are predominantly used to replace walking and bus-taking. Based on the survey results, we model the environmental impacts of the changed traveling behaviors and the life cycle of shared bicycle with the aid of Gabi software. The LCA results shows that bicycle sharing is currently an environmentally friendly practice, as it brings environmental savings in all the indicators except metal consumption. Further, the results of sensitivity analysis show that aging, rising rental fees, and increasing volume of shared bicycles would impart negative impacts on the environmental performance of bicycle sharing. The findings of this work facilitate the management and development of bicycle sharing.

Big Data and Climate Change

Climate science as a data-intensive subject has overwhelmingly affected by the era of big data and relevant technological revolutions. The big successes of big data analytics in diverse areas over the past decade have also prompted the expectation of big data and its efficacy on the big problem—climate change. As an emerging topic, climate change has been at the forefront of the big climate data analytics implementations and exhaustive research have been carried out covering a variety of topics. This paper aims to present an outlook of big data in climate change studies over the recent years by investigating and summarising the current status of big data applications in climate change related studies. It is also expected to serve as a one-stop reference directory for researchers and stakeholders with an overview of this trending subject at a glance, which can be useful in guiding future research and improvements in the exploitation of big climate data.

Exploring "Internet+Recycling": Mass balance and life cycle assessment of a waste management system associated with a mobile application

Individual users cannot readily access the collection channels is a persistent problem in municipal solid waste (MSW) management, resulting in low MSW collection rates. A new waste management model, "Internet+Recycling", has come into being; this model enables individuals to arrange collection appointment through various online platforms, then the collectors pick up the waste on-site. It is believed that "Internet+Recycling" can be a solution to mitigate the collection barrier in MSW management, as it provides individuals a convenient access to formal waste management systems. However, whether this emerging MSW collection model would bring environmental benefits is yet unknown. We here quantitatively examine the mass balance and environmental performance of MSW recycling associated with the use of such a "Internet+Recycling" mobile application - Aibolv. All transactions occurred on the mobile application within a period of six monthare included, and all related activities are modeled using the methodology that combines material flow analysis (MFA) and life cycle assessment (LCA). According to the extant MSW management legislation in China, we classify the collected MSW into three categories, subsidized waste electric and electronic equipment (WEEE) like television and refrigerator - T1, unsubsidized WEEE like mobile phone - T2, and other recyclables like paper and fabric - T3. The MFA results show that plastics and common metals are the dominate secondary material streams, and glass, precious metals and battery metals are mainly recovered from WEEE. The LCA results indicate that the disposal of the T2 waste has the highest environmental savings, due to the recovery of precious metals. Increased remanufacturing rates impart negative impacts, while increments in the quantity of spent mobile phones could significantly improve overall environmental performance. Based on the acquired results, recommendations are provided for facilitating the future development of "Internet+Recycling", and limitations of this work are identified as well.

Value generation of remanufactured products: multi-case study of third-party companies

The importance of reverse logistics has increased owing to environmental factors and recent legislations. In this context, the market for remanufactured goods has become attractive. Manufacturers, retailers, and third-party companies have improved return programs and operations that add value to the return chain for electronic appliances, rather than treating it as a secondary process. The objective of this study is to identify the variables related to value generation in the reverse logistics of electronic products from the perspective of third-party companies. Reverse logistics of electronic products depends much on the context and local regulations; in addition, the fact that there are few studies on developing countries points to an important gap in extant research. This study presents the influence of quality and warranties, processing time, and partnerships between third-party companies, manufacturers, and retailers on the value generation from remanufactured products. These variables are related to optimal results and optimistic expectations for growth among third-party companies. These internal factors, together with an analysis of external factors and product portfolios, complement the scenario description for the cases studied. The main contribution of this study is to highlight the major factors, which are presented in the framework. The lessons learned can be used in other contexts involving third-party companies.

Toward sustainable and systematic recycling of spent rechargeable batteries

A comprehensive and novel view on battery recycling is provided in terms of the science and technology, engineering, and policy.