Locating carbon neutral mobility hubs using artificial intelligence techniques.

This research proposes a novel, three-tier AI-based scheme for the allocation of carbon-neutral mobility hubs. Initially, it identified optimal sites using a genetic algorithm, which optimized travel times and achieved a high fitness value of 77,000,000. Second, it involved an Ensemble-based suitability analysis of the pinpointed locations, using factors such as land use mix, densities of population and employment, and proximities of parking, biking, and transit. Each factor is weighted by its carbon emissions contribution, then incorporated into a suitability analysis model, generating scores that guide the final selection of the most suitable mobility hub sites. The final step employs a traffic assignment model to evaluate these sites' environmental and economic impacts. This includes measuring reductions in vehicle kilometers traveled and calculating other cost savings. Focusing on addressing sustainable development goals 11 and 9, this study leverages advanced techniques to enhance transportation planning policies. The Ensemble model demonstrated strong predictive accuracy, achieving an R-squared of 95% in training and 53% in testing. The identified hubs' sites reduced daily vehicle travel by 771,074 km, leading to annual savings of 225.5 million USD. This comprehensive approach integrates carbon-focused analyses and post-assessment evaluations, thereby offering a comprehensive framework for sustainable mobility hub planning.

Municipal solid waste management for low-carbon transition: A systematic review of artificial neural network applications for trend prediction.

Improper municipal solid waste (MSW) management contributes to greenhouse gas emissions, necessitating emissions reduction strategies such as waste reduction, recycling, and composting to move towards a more sustainable, low-carbon future. Machine learning models are applied for MSW-related trend prediction to provide insights on future waste generation or carbon emissions trends and assist the formulation of effective low-carbon policies. Yet, the existing machine learning models are diverse and scattered. This inconsistency poses challenges for researchers in the MSW domain who seek to identify and optimize the machine learning techniques and configurations for their applications. This systematic review focuses on MSW-related trend prediction using the most frequently applied machine learning model, artificial neural network (ANN), while addressing potential methodological improvements for reducing prediction uncertainty. Thirty-two papers published from 2013 to 2023 are included in this review, all applying ANN for MSW-related trend prediction. Observing a decrease in the size of data samples used in studies from daily to annual timescales, the summarized statistics suggest that well-performing ANN models can still be developed with approximately 33 annual data samples. This indicates promising opportunities for modeling macroscale greenhouse gas emissions in future works. Existing literature commonly used the grid search (manual) technique for hyperparameter (e.g., learning rate, number of neurons) optimization and should explore more time-efficient automated optimization techniques. Since there are no one-size-fits-all performance indicators, it is crucial to report the model's predictive performance based on more than one performance indicator and examine its uncertainty. The predictive performance of newly-developed integrated models should also be benchmarked to show performance improvement clearly and promote similar applications in future works. The review analyzed the shortcomings, best practices, and prospects of ANNs for MSW-related trend predictions, supporting the realization of practical applications of ANNs to enhance waste management practices and reduce carbon emissions.

Uncovering environmental performance patterns of plastic packaging waste in high recovery rate countries: An example of EU-27.

Plastic consumption and its end-of-life management pose a significant environmental footprint and are energy intensive. Waste-to-resources and prevention strategies have been promoted widely in Europe as countermeasures; however, their effectiveness remains uncertain. This study aims to uncover the environmental footprint patterns of the plastics value chain in the European Union Member States (EU-27) through exploratory data analysis with dimension reduction and grouping. Nine variables are assessed, ranging from socioeconomic and demographic to environmental impacts. Three clusters are formed according to the similarity of a range of characteristics (nine), with environmental impacts being identified as the primary influencing variable in determining the clusters. Most countries belong to Cluster 0, consisting of 17 countries in 2014 and 18 countries in 2019. They represent clusters with a relatively low global warming potential (GWP), with an average value of 2.64 t CO2eq/cap in 2014 and 4.01 t CO2eq/cap in 2019. Among all the assessed countries, Denmark showed a significant change when assessed within the traits of EU-27, categorised from Cluster 1 (high GWP) in 2014 to Cluster 0 (low GWP) in 2019. The analysis of plastic packaging waste statistics in 2019 (data released in 2022) shows that, despite an increase in the recovery rate within the EU-27, the GWP has not reduced, suggesting a rebound effect. The GWP tends to increase in correlation with the higher plastic waste amount. In contrast, other environmental impacts, like eutrophication, abiotic and acidification potential, are identified to be mitigated effectively via recovery, suppressing the adverse effects of an increase in plastic waste generation. The five-year interval data analysis identified distinct clusters within a set of patterns, categorising them based on their similarities. The categorisation and managerial insights serve as a foundation for devising a focused mitigation strategy.

Post COVID-19 ENERGY sustainability and carbon emissions neutrality.

This review covers the recent advancements in selected emerging energy sectors, emphasising carbon emission neutrality and energy sustainability in the post-COVID-19 era. It benefited from the latest development reported in the Virtual Special Issue of ENERGY dedicated to the 6th International Conference on Low Carbon Asia and Beyond (ICLCA'20) and the 4th Sustainable Process Integration Laboratory Scientific Conference (SPIL'20). As nations bind together to tackle global climate change, one of the urgent needs is the energy sector's transition from fossil-fuel reliant to a more sustainable carbon-free solution. Recent progress shows that advancement in energy efficiency modelling of components and energy systems has greatly facilitated the development of more complex and efficient energy systems. The scope of energy system modelling can be based on temporal, spatial and technical resolutions. The emergence of novel materials such as MXene, metal-organic framework and flexible phase change materials have shown promising energy conversion efficiency. The integration of the internet of things (IoT) with an energy storage system and renewable energy supplies has led to the development of a smart energy system that effectively connects the power producer and end-users, thereby allowing more efficient management of energy flow and consumption. The future smart energy system has been redefined to include all energy sectors via a cross-sectoral integration approach, paving the way for the greater utilization of renewable energy. This review highlights that energy system efficiency and sustainability can be improved via innovations in smart energy systems, novel energy materials and low carbon technologies. Their impacts on the environment, resource availability and social well-being need to be holistically considered and supported by diverse solutions, in alignment with the sustainable development goal of Affordable and Clean Energy (SDG 7) and other related SDGs (1, 8, 9, 11,13,15 and 17), as put forth by the United Nations.

Household waste management in Singapore and Shanghai: Experiences, challenges and opportunities from the perspective of emerging megacities.

Due to rapid economic development and urbanisation, emerging megacities with dense populations have witnessed a significant increase in waste generation. Megacities face challenges in developing sustainable waste management systems. Considerable heterogeneity exists across megacities in management strategies. The two selected emerging megacities, Singapore (a city-state) and Shanghai, have similar developmental characteristics, but their waste management modes differ strikingly. This study assessed the two modes in terms of management strategies, environmental effects, economic costs, and social outcomes. Environmental footprint analysis and cost quantification were employed for the assessment based on public data. The research results would permit a deeper understanding of the long-term sustainability of each mode while considering the feasibility of implementation across different contexts. It was found that the waste management system in Singapore had a relatively lower environmental impact than Shanghai before Shanghai's new waste segregation and recycling policy in 2019. However, when the effect of fossil fuel substitution is taken into account, the environmental burden in Shanghai can be lowered more substantially than the one in Singapore. Although Shanghai had more economic burden for the waste segregation at source, it tended to implement the circular economy principles (e.g., reduce, reuse, and recycling) better and improve its sense of community significantly. Based on the practical experiences from the two representative megacities, suggestions for better waste management practices were provided for Singapore, Shanghai, and other emerging megacities with similar circumstances. In addition, challenges and opportunities related to household waste segregation and recycling were identified to guide future practices in emerging megacities.

Forecasting plastic waste generation and interventions for environmental hazard mitigation.

Plastic waste and its environmental hazards have been attracting public attention as a global sustainability issue. This study builds a neural network model to forecast plastic waste generation of the EU-27 in 2030 and evaluates how the interventions could mitigate the adverse impact of plastic waste on the environment. The black-box model is interpreted using SHapley Additive exPlanations (SHAP) for managerial insights. The dependence on predictors (i.e., energy consumption, circular material use rate, economic complexity index, population, and real gross domestic product) and their interactions are discussed. The projected plastic waste generation of the EU-27 is estimated to reach 17 Mt/y in 2030. With an EU targeted recycling rate (55%) in 2030, the environmental impacts would still be higher than in 2018, especially global warming potential and plastic marine pollution. This result highlights the importance of plastic waste reduction, especially for the clustering algorithm-based grouped countries with a high amount of untreated plastic waste per capita. Compared to the other assessed scenarios, Scenario 4 with waste reduction (50% recycling, 47.6% energy recovery, 2.4% landfill) shows the lowest impact in acidification, eutrophication, marine aquatic toxicity, plastic marine pollution, and abiotic depletion. However, the global warming potential (8.78 Gt CO2eq) is higher than that in 2018, while Scenario 3 (55% recycling, 42.6% energy recovery, 2.4% landfill) is better in this aspect than Scenario 4. This comprehensive analysis provides pertinent insights into policy interventions towards environmental hazard mitigation.

Energy, environmental, economic and social equity (4E) pressures of COVID-19 vaccination mismanagement: A global perspective.

Vaccination now offers a way to resolve the COVID-19 pandemic. However, it is critical to recognise the full energy, environmental, economic and social equity (4E) impacts of the vaccination life cycle. The full 4E impacts include the design and trials, order management, material preparation, manufacturing, cold chain logistics, low-temperature storage, crowd management and end-of-life waste management. A life cycle perspective is necessary for sustainable vaccination management because a prolonged immunisation campaign for COVID-19 is likely. The impacts are geographically dispersed across sectors and regions, creating real and virtual 4E footprints that occur at different timescales. Decision-makers in industry and governments have to act, unify, resolve, and work together to implement more sustainable COVID-19 vaccination management globally and locally to minimise the 4E footprints. Potential practices include using renewable energy in production, storage, transportation and waste treatment, using better product design for packaging, using the Internet of Things (IoT) and big data analytics for better logistics, using real-time database management for better tracking of deliveries and public vaccination programmes, and using coordination platforms for more equitable vaccine access. These practices raise global challenges but suggest solutions with a 4E perspective, which could mitigate the impacts of global vaccination campaigns and prepare sustainably for future pandemics and global warming.

COVID-19 pandemics Stage II - Energy and environmental impacts of vaccination.

The COVID-19 pandemic developed the severest public health event in recent history. The first stage for defence has already been documented. This paper moves forward to contribute to the second stage for offensive by assessing the energy and environmental impacts related to vaccination. The vaccination campaign is a multidisciplinary topic incorporating policies, population behaviour, planning, manufacturing, materials supporting, cold-chain logistics and waste treatment. The vaccination for pandemic control in the current phase is prioritised over other decisions, including energy and environmental issues. This study documents that vaccination should be implemented in maximum sustainable ways. The energy and related emissions of a single vaccination are not massive; however, the vast numbers related to the worldwide production, logistics, disinfection, implementation and waste treatment are reaching significant figures. The preliminary assessment indicates that the energy is at the scale of ~1.08 × 1010 kWh and related emissions of ~5.13 × 1012 gCO2eq when embedding for the envisaged 1.56 × 1010 vaccine doses. The cold supply chain is estimated to constitute 69.8% of energy consumption of the vaccination life cycle, with an interval of 26-99% depending on haul distance. A sustainable supply chain model that responds to an emergency arrangement, considering equality as well, should be emphasised to mitigate vaccination's environmental footprint. This effort plays a critical role in preparing for future pandemics, both environmentally and socially. Research in exploring sustainable single-use or reusable materials is also suggested to be a part of the plans. Diversified options could offer higher flexibility in mitigating environmental footprint even during the emergency and minimise the potential impact of material disruption or dependency.

Impacts of COVID-19 on energy demand and consumption: Challenges, lessons and emerging opportunities.

COVID-19 has caused great challenges to the energy industry. Potential new practices and social forms being facilitated by the pandemics are having impacts on energy demand and consumption. Spatial and temporal heterogeneities of impacts appear gradually due to the dynamics of pandemics and mitigation measures. This paper overviews the impacts and challenges of COVID-19 pandemics on energy demand and consumption and highlights energy-related lessons and emerging opportunities. The discussion on energy-related issues is divided into four main sections: emergency situation and its impacts, environmental impacts and stabilising energy demand, recovering energy demand, and lessons and emerging opportunities. The changes in energy requirements are compared and analysed from multiple perspectives according to available data and information. In general, although the overall energy demand declines, the spatial and temporal variations are complicated. The energy intensity has presented apparent changes, the extra energy for COVID-19 fighting is non-negligible for stabilising energy demand, and the energy recovery in different regions presents significant differences. A crucial issue has been to allocate and find energy-related emerging opportunities for the post pandemics. This study could offer a direction in opening new avenues for increasing energy efficiency and promoting energy saving.

More Is Not Enough: A Deeper Understanding of the COVID-19 Impacts on Healthcare, Energy and Environment Is Crucial.

The coronavirus disease 2019 (COVID-19) pandemic has magnified the insufficient readiness of humans in dealing with such an unexpected occurrence. During the pandemic, sustainable development goals have been hindered severely. Various observations and lessons have been highlighted to emphasise local impacts on a single region or single sector, whilst the holistic and coupling impacts are rarely investigated. This study overviews the structural changes and spatial heterogeneities of changes in healthcare, energy and environment, and offers perspectives for the in-depth understanding of the COVID-19 impacts on the three sectors, in particular the cross-sections of them. Practical observations are summarised through the broad overview. A novel concept of the healthcare-energy-environment nexus under climate change constraints is proposed and discussed, to illustrate the relationships amongst the three sectors and further analyse the dynamics of the attention to healthcare, energy and environment in view of decision-makers. The society is still on the way to understanding the impacts of the whole episode of COVID-19 on healthcare, energy, environment and beyond. The raised nexus thinking could contribute to understanding the complicated COVID-19 impacts and guiding sustainable future planning.

Urban and industrial symbiosis for circular economy: Total EcoSite Integration.

The paper presents an extension of Pinch Analysis and namely, Total Site Process Integration. It benefits from up to date developments and introduction of Total EcoSite Integration for urban and industrial symbiosis. An important development is Pinch Analysis for Solid Waste Integration which is a crucial step for the symbiosis in a circular economy. As the potential EcoSites are usually extensive and cover various units, a methodology based on clusters has been used. The solution has been supported by graphical tools using the analogy with already implemented extensions of Pinch Analysis. The results of a demonstration case study revealed the potential of the novel approach. The identified integrated design increased the energy recovered from the solid waste by 11.39 MWh/d and diverted 2 t/d of the waste from the landfill, benefiting both the urban and industrial site. The proposed approach is also capable of minimising the requirement of energy-intensive thermal drying for waste whenever the process allowed, subsequently offer a solution with lower environmental footprint and cost. For future work, a even more comprehensive case study can be conducted by considering the other forms of the waste, recovery process and drying approaches.

Data analytics of social media publicity to enhance household waste management.

Household waste segregation and recycling is ranked at a high priority of the waste management hierarchy. Its management remains a great challenge due to the high dependency on social behaviours. The integration of Internet of Things (IoT) and subscription accounts on social media platforms related to household waste management could be an effective and environmentally friendly publicity approach than traditional publicity via posters and newspapers. However, there is a paucity of literature on measuring social media publicity in household waste management, which brings challenges for practitioners to characterise and improve this publicity pathway. In this study, under an integrated framework, data mining approaches are employed or extended for multidimensional publicity analytics using the data of online footprints of propagandist and users. A real-world case study based on a subscription account on the WeChat platform, Shanghai Green Account, is analysed to reveal useful insights for personalised improvements of household waste management. This study suggests that the current publicity related to household waste management leans towards propagandist-centred in both timing and topic dimensions. The identified timing, which has high user engagement, is 12:00-13:00 and 21:00-22:00 on Thursday. The overall relative publicity quality of historical posts is calculated as 0.95. Average user engagement under the macro policy in Shanghai was elevated by 138.5% from 2018 to 2019, during which the collections of biodegradable food waste and recyclable waste were elevated by 88.8% and 431.8%. Intelligent decision support by publicity analytics could enhance household waste management through effective communication.

An update of COVID-19 influence on waste management.

COVID-19 has been sweeping the world. The overall number of infected persons has been increased from 5 M in March 2020 to over 22 M in August 2020 and growing, which seems not to get its peak at the current stage. This has contributed to waste generation and different phases of challenges in waste management practices. The impacts including change in waste amount, composition, timing/frequency (temporal), distribution (spatial) and risk, which affects the handling and treatment practices. Recent impacts, challenges and developments on waste management in the response of COVID-19 have been assessed in this update. Singapore, the cities of Shanghai in China and Brno in the Czech Republic (a member state of the European Union), representing different pandemic development situation and also various cultural attitudes, are specifically analysed and discussed with current data. However, it should be noted that it is still fast developing. A varying trend in term of the waste amount is identified. Shanghai is showing a ~23% decline in household waste amount; however, Singapore is showing a ~3% increase, and Brno is showing a ~1% increase in household waste amount but ~40% decline in business and industrial waste. Manual sorting and recycling have been reported as restricted due to safety precaution. This is supported by the interview communication with ZEVO SAKO (the largest incineration plant in the Czech Republic). This study highlighted that the practices or measures at each place could serve as a guideline and reference. However, adaption is required according to the geographical and socioeconomic factors.

Spatial-temporal potential exposure risk analytics and urban sustainability impacts related to COVID-19 mitigation: A perspective from car mobility behaviour.

Coronavirus disease-2019 (COVID-19) poses a significant threat to the population and urban sustainability worldwide. The surge mitigation is complicated and associates many factors, including the pandemic status, policy, socioeconomics and resident behaviours. Modelling and analytics with spatial-temporal big urban data are required to assist the mitigation of the pandemic. This study proposes a novel perspective to analyse the spatial-temporal potential exposure risk of residents by capturing human behaviours based on spatial-temporal car park availability data. Near real-time data from 1,904 residential car parks in Singapore, a classical megacity, are collected to analyse car mobility and its spatial-temporal heat map. The implementation of the circuit breaker, a COVID-19 measure, in Singapore has reduced the mobility and heat (daily frequency of mobility) significantly at about 30.0%. It contributes to a 44.3%-55.4% reduction in the transportation-related air emissions under two scenarios of travelling distance reductions. Urban sustainability impacts in both environment and economy are discussed. The spatial-temporal potential exposure risk mapping with space-time interactions is further investigated via an extended Bayesian spatial-temporal regression model. The maximal reduction rate of the defined potential exposure risk lowers to 37.6% by comparison with its peak value. The big data analytics of changes in car mobility behaviour and the resultant potential exposure risks can provide insights to assist in (a) designing a flexible circuit breaker exit strategy, (b) precise management via identifying and tracing hotspots on the mobility heat map, and (c) making timely decisions by fitting curves dynamically in different phases of COVID-19 mitigation. The proposed method has the potential to be used by decision-makers worldwide with available data to make flexible regulations and planning.

The energy and environmental footprints of COVID-19 fighting measures - PPE, disinfection, supply chains.

The still escalating COVID-19 pandemic also has a substantial impact on energy structure, requirements and related emissions. The consumption is unavoidable and receives a lower priority in the critical situation. However, as the pandemic continues, the impacts on energy and environment should be assessed and possibly reduced. This study aims to provide an overview of invested energy sources and environmental footprints in fighting the COVID-19. The required energy and resources consumption of Personal Protection Equipment (PPE) and testing kits have been discussed. The protecting efficiency returned on environmental footprint invested for masks has been further explored. The main observation pinpointed is that with a proper design standard, material selection and user guideline, reusable PPE could be an effective option with lower energy consumption/environmental footprint. Additional escalated energy consumption for aseptic and disinfection has been assessed. This includes the energy stemming from emergency and later managed supply chains. The outcomes emphasised that diversifying solutions to achieve the needed objective is a vital strategy to improve the susceptibility and provide higher flexibility in minimising the environmental footprints. However, more comprehensive research proof for the alternative solution (e.g. reusable option) towards low energy consumption without compromise on the effectiveness should be offered and advocated.

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.

Minimising the present and future plastic waste, energy and environmental footprints related to COVID-19.

The COVID-19 pandemic has had growing environmental consequences related to plastic use and follow-up waste, but more urgent health issues have far overshadowed the potential impacts. This paper gives a prospective outlook on how the disruption caused by COVID-19 can act as a catalyst for short-term and long-term changes in plastic waste management practices throughout the world. The impact of the pandemic and epidemic following through the life cycles of various plastic products, particularly those needed for personal protection and healthcare, is assessed. The energy and environmental footprints of these product systems have increased rapidly in response to the surge in the number of COVID-19 cases worldwide, while critical hazardous waste management issues are emerging due to the need to ensure destruction of residual pathogens in household and medical waste. The concept of Plastic Waste Footprint (PWF) is proposed to capture the environmental footprint of a plastic product throughout its entire life cycle. Emerging challenges in waste management during and after the pandemic are discussed from the perspective of novel research and environmental policies. The sudden shift in waste composition and quantity highlights the need for a dynamically reponsive waste management system. Six future research directions are suggested to mitigate the potential impacts of the pandemic on waste management systems.

Implementing Circular Economy in municipal solid waste treatment system using P-graph.

Municipal solid waste (MSW) is one of the issues associated with the growth of economic and urban population. The aim of this study is to develop an integrated design of waste management systems in support of a Circular Economy by P-graph (a bipartite graphical optimisation tool) as an effective decision support tool. The case study considers four MSW compositions based on different country income levels. Solving the P-graph model identifies the most suitable treatment approaches, considering the economic balance between the main operating cost, type, yield, quality of products, as well as the GHG emission (externality cost). The identification of near-optimal solutions by P-graph is useful in dealing with the trade-offs between conflicting objectives, e.g. local policy and practical implementation, that are difficult to monetise. For a lower-income country, the optimal solution includes a combination of at source separation, recycling, incineration (heat, electricity), anaerobic digestion (biofuel, digestate) and the landfill. It avoids an estimated 411 kg CO2eq/t of processed MSW and achieves a potential profit of 42 €/t of processed MSW. The optimisation generally favours mechanical biological treatment as the country income level rises, which affects the composition of the MSW. The relative prices of biofuel, electricity and heat (>20%) cause a significant impact on the highest-ranking treatment structure and overall profit. This study shows that the developed framework by P-graph is an effective tool for MSW systems planning. For future study, localised data inputs can be fed into the proposed framework for a customised solution and economic feasibility assessment.