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There has been plenty of research on the influence of various socio-economic and demographic data on waste generation to develop effective and targeted waste reduction measures, including energy recovery. This study evaluates the relationship between the waste generation and Circular Material Use rate, Environmental Tax Revenue, and Global Innovation Index beyond the typical socio-economic factors (e.g., gross domestic product or population). Correlation analysis is conducted on the EU-27 datasets before the development of the predictive model. The correlation strength between the factors is discussed to identify the potential rebound effect from the central driver of economic growth and development. A positive correlation and partial rebound effect are identified in the data. The waste amount ending in disposal and energy recovery treatment increases with the Circular Material Use rate, suggesting that the expected gains from Circular Material Use rate are offset by other socio-economic factors such as increasing population or gross domestic product. However, a diminishing trend is observed in the rebound effect over the years. Multiple linear regression with validation is applied to identify the best fit model for predicting waste generation. Using population, gross domestic product, Circular Material Use rate, and Environmental Tax Revenues as independent variables, a model is generated with a mean absolute percentage error of 18.65% (7% lower than the benchmark) and R2 (coefficient of determination) of 0.995.

The plastics are becoming one of the most discussed topics in media and research. They have been branded sometimes as evil, which has to be replaced, in some cases at any cost. However, an equitabl...

This study intended to overview the e-waste or waste electrical and electronic equipment (WEEE) in the EU-27, including the roles in a Circular Economy. There is a strong linear correlation between WEEE and the Gross Domestic Product (GDP). However, there is no apparent trend when referring to the GDP per capita. A country with the highest GDP per capita (Luxembourg) is not the country with the highest WEEE (Sweden). Absolute GDP and Energy consumption are suggested as the suitable independent variables in estimating the amount of WEEE. Socioeconomic factors are deemed less relevant in determining treatment share, e.g. recycling rate, in the EU-27 countries. Although reuse and recycling are generally viewed as preferable in a circular economy, in some cases, replacing a new product could lead to a lower environmental footprint, whereas customised assessment is highly suggested. Extended producer responsibility targeting waste minimisation and improved recycling/mining approaches are the keys to mitigating the impact of WEEE in the EU-27 further.

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.

Environmental sustainability has gained increasing attention in recent years due to the evident impacts of rapid economic growth and urbanisation. Efficient resources and utility management is one of the vital strategies to minimise waste/losses, pollution and extraction of virgin resources. It facilitates the development of a circular economy. This review discusses the cross-disciplinary approaches devoted to clean technologies, process modelling, monitoring and management framework in the effort to mitigating GHG, air (causing, e.g. smog/haze) and water pollution. The review focuses on the roles of (a) Energy and water management, (b) Waste management and (c) Green policy and pollution minimisation strategies. Each section is divided into two sub-sections starting with the introduction on the assessed areas, followed by the sections based on the latest contribution of a Special Volume and the proposed direction for future research. This review suggests (i) More studies based on case studies in real life context and prototypes involving consensus building among the stakeholders, proven by viable economic feasibility analyses, are needed to enable environmental sustainability. (ii) Data availability, enabling policy and cost of investment is the common barrier to put the proposed solutions or methodologies into practice. (iii) Substantial improvement of environmental sustainability can be benefited through the waste (solid, water, heat) recovery.

Renewable energy should play a crucial role in increasing energy supplies and achieving the potential target of reducing 50% of CO2 emissions by 2050. The main objective of this study is to propose a neuro-fuzzy modelling entitled ensemble-Adaptive Neuro-Fuzzy Inference System (ANFIS) learning to predict and analyse the interrelationship between renewable energy consumption, economic growth, and CO2 emissions of G8+5 countries. This will help the governments and industry sectors to formulate energy policies and develop energy resources sustainably. The prediction method was constructed by extracting the fuzzy rules from the real-world dataset of World Development Indicators (WDI) and generalising the relationships of the inputs and output parameters for accurate prediction of CO2 emissions. The performance of the proposed method was evaluated, and the results show its efficiency in the prediction of CO2 emissions by incorporating the import indicators, including renewable energy consumption and economic growth. The U test of Sasabuchi–Lind–Mehlum (SLM) was conducted to identify the interrelationship results obtained from the ensemble ANFIS learning and the Environmental Kuznets Curve (EKC) hypothesis. The results of SLM test found an inverse U-shape condition among all countries except Brazil. The prediction of CO2 emissions trends using the soft computing approach (ensemble ANFIS) indicated that the consumption of renewable energy reduces CO2 emissions. The proposed soft computing method was found efficient in predicting CO2 emissions. It was in line with the foreseen targets of increasing the renewable energy generation and achieving the nationally determined contributions (NDCs) objectives.

The manufacturing sector has been one of the most significant energy consuming and CO2 emitting sectors over the past three decades. Therefore, it is important to have energy flow models of manufacturing processes to identify efficiency improvement and waste heat recovery opportunities with very specific targets. One of the best methods to pinpoint such locations is by referring to manufacturing process step models. Such models can be created on a large scale per industry and on small scale per process within an industry branch. Industry level modeling provides energy saving contingency by large magnitude where the impact on the economy and environment can be observed more visibly especially when sequential trends are identified. The methodology, reviewed in this paper, describes how to model energy flows on an industry scale by deriving the connection between key energy conversion activities on the one hand – including energy purchased/sold and onsite electricity generated, and the key end uses on the other hand. Industry-level energy flow model, referred to in this work, does not assume any specific system. That feature makes it scalable and allows analysis of manufacturing at various levels starting with single processes and finishing at sector and national economy levels. A combined analysis of the procedure is described in detail for creating process step models of manufacturing processes. The availability of U.S. national databases to calibrate these models is discussed and the challenges that arise in finding and using such databases are identified and analysed. In addition to addressing generic data issues associated with energy flow models, the paper also focuses on energy and material models and presents several examples of energy-intensive manufacturing processes, producing industrial gases, textiles, pulp and paper. Finally, importance of water use in onsite energy conversion and process end uses in various industries is addressed along with the sources of where this important data can be found to complete the energy flow modeling.

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.

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 % to 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., Graphical abstract Image 1, Highlights • Spatiotemporal potential exposure risks are analysed by capturing human behaviours. • Big urban data in Singapore (SG) are used to demonstrate the method's applicability. • Air emissions were reduced significantly after implementing the circuit breaker in SG. • Precise management via identifying and tracing hotspots on the mobility heat map. • Spatial potential exposure risks can guide a flexible local lockdown exit strategy.

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.

Water use within power supply chains has been frequently investigated. A unified framework to quantify the water use of power supply chains deserves more development. This article provides an overview of the water footprint and virtual water incorporated into power supply chains. A water-use mapping model of the power supply chain is proposed in order to map the analysed research works according to the considered aspects. The distribution of water footprint per power generation technology per region is illustrated, in which Asia is characterised by the largest variation of the water footprint in hydro-, solar, and wind power. A broader consensus on the system boundary for the water footprint evaluation is needed. The review also concludes that the water footprint of power estimated by a top-down approach is usually higher and more accurate. A consistent virtual water accounting framework for power supply chains is still lacking. Water scarcity risks could increase through domestic and global power trade. This review provides policymakers with insights on integrating water and energy resources in order to achieve sustainable development for power supply chains. For future work, it is essential to identify the responsibilities of both the supply and demand sides to alleviate the water stress.

The plastic, more correctly to be addressed as polymers, are becoming a hot topic in media and also research. The plastics have been branded sometimes as an evil, which has to be replaced, in some cases at any cost. However, a coldblooded analysis and assessment are needed comparing all pros and cons based on environmental footprints quantification with full life cycle assessment. This should include assessment of possible health risks, consumed energy, released emissions and effluents, as well as consumption of raw materials, water and dealing with the wastewater. The assessment of recyclability, reprocessing and environmental burden is needed. The work tends to contribute to the discussion of what recommendations should be developed to the industry and business to minimise the environmental impacts. A Plastic Footprint has been assessed, and a novel Plastic Waste Footprint introduced.

This study aims to analyse the environmental sustainability performance, focuses on carbon emission footprint, of bioenergy (power) under different background/reference system. The considered variation includes the biogenic factor (effected by rotation year of biomass and time horizons of global warming potential), utilisation of biochar (energy or carbon sequestration) and soil carbon dynamics. The identified carbon emission footprint is compared against the existing electricity mix in EU-27. The carbon emission footprint of bioenergy is higher than the electricity mixes in Slovakia, Finland, Latvia, Austria, France, Lithuania and Sweden, at biogenic factor = 0.05 (rotation year = 1). It suggests the importance of a throughout assessment towards a sustainable bioenergy transition, as there is hardly one-size-fits-all solution and bioenergy is not necessary a solution with lower carbon emission footprint.

Mitigation of greenhouse gases (GHG) emissions is desirable without compromising the economic growth. This paper reviews the recent trends to mitigate GHG emissions in the key sectors of energy and solid waste. The energy sector is the key admitter for global GHG emissions, and a range of optimisation and modelling tool has been developed to minimise the GHG emissions and overall cost, especially for the implementation of renewable energies such as biofuel and biogas. A few carbon sequestration technologies such as the carbon capture and storage (CCS) and biochar application have been reviewed. The review included the challenges and knowledge gaps regarding the utilisation of CCS, such as the storage capacity, long-term policy framework, high costs and the potential risk. Although solid waste contributes about 40%) in the municipal solid waste for many developing countries in Asia, composting has been proposed as a viable treatment technology to convert waste-to-wealth. A range of waste management tools, including scenario analyses on different waste technologies, optimisation of waste collection routes, multi-criteria decision tools, is reviewed to support the decision-making for solid waste management. A range of environmental management system (EMS) has been adopted by organisations to improve product quality, reducing production cost and improves reputation of firms. An environmental policy such as tax exemption could be helpful to promote the adoption of EMS that could be costly. CO2 and material flow footprint tools, such as water–energy–materials nexus, are applicable at a city and regional level. The tools are used to mitigate GHG emissions by developing the mechanisms with shared markets of virtual resource flows (carbon, water, food, energy) between the trading partners regionally and internationally.

Over 90% of economies in Asia have submitted the mitigation objectives in the Intended Nationally Determined Contribution (INDC) for reducing GHG emissions targets and communicating other relevant climate policy actions. It is crucial to continuously monitor the achievement of this GHG mitigation targets using monitoring tools and evidenced by scientific methods. This Special Volume (SV) is devoted to presenting feasible cleaner technologies supported by environmental assessment tool to enable the transition of Asia into a low-carbon-emission economy. A half of the Asia emissions reduction through 2050 is contributed by low-carbon-emission energy production and another third from energy efficiency measures, with the rest achieved by curtailing emissions from forest deforestation, land degradation, and other non-energy sources. The concept of enabling low carbon emissions is not just reducing the CO2 equivalent (eq.), it concerns on other negative impacts of the emission on the whole ecosystem such as particle matter and other emissions (NOx, O3, and SOx) that are threatening the air quality and premature. Carbon emissions from the major sectors of energy, transportation, and industrial waste with the focus of presenting feasible cleaner practices or technologies to enable the implementation of sustainable development in Asia are presented. Life cycle and environmental management study on the assessment methods for environmental impact prevention, control or minimisation. Waste to wealth presented the cleaner technologies developed to convert waste resources into valuable products to promote a green economy. Process system engineering and LCA tools are widely applied in the energy sector; they could be applied to other sectors as well as to evaluate the sustainability performance index. On-going efforts are essential to close the gaps among technology providers, government sectors and investor to enable a low-carbon emissions transition in Asia.

Resource recovery has an essential role in achieving sustainable development. This study aims to minimise the environmental footprint of biomass integration for energy recovery, where a wide range of biomass, including manure, residual and forest wood, are considered. The assessed case study is the Tomsk region in the Russian Federation, focusing on GHG (Greenhouse Gas) footprints (CO2, CH4, N2O). The adapted clustering-based method suggested that biomass integration, consisting of 16 districts, can be divided into three major clusters. The clusters could facilitate further resource planning, considering the land-use footprint of different energy generation. This study identified biomass integration design with minimum GHG footprint (origin of biomass and transportation) of - 217 kt CO2eq/y mainly contributed by the avoided methane from manure. Scenario 1, where the energy demand (3,723 TJ/y) is solely fulfilled by forest wood, contributes to 85.73 kt CO2eq/y. By limiting waste biomass (manure and residual) for local utilisation only (without integration), 60% higher GHG emission (- 90.2 kt CO2eq/y) is identified compared to the identified solution with a minimum GHG footprint. The environmental sustainability of bioenergy is highly dependent on the type of biomass, transporting activities, and heating value. It is crucial to evaluate case by case situation in substituting fossil-based energy or other renewable energy.

Waste segregation, recycling and reduction have been prioritised in the Circular Economy transition of household waste management to reduce environmental impacts. With digitalisation and innovation developments in waste management, residents become more active on waste management-related social media platforms. However, there is still needed a tangible analysis of resident engagement (e.g. user comments and interactions) and related sentiment changes on such platforms to enhance waste management and ease the environmental burden at source. This study develops an integrated solution to analyse resident engagement by leveraging statistical analysis and text-mining methods. Four interrelated components are incorporated in the solution: population behaviour quantification, sentiment analysis and dynamics, popular concerns and probability distribution fitting, and rule-based managerial insight identification. The novel solution is applied to a real-world case study on a subscription account related to waste management in Shanghai. This research produces several major observations based on the studied case: (i) The resident engagement Monday-to-Thursday was more active than Friday-to-Sunday. (ii) Compared to 2018, the resident engagement by commenting on online posts was elevated by 107.1% in 2019 when Shanghai introduced a new management policy. Meanwhile, the yearly resource-type waste collection was increased by 114.5% in 2019. (iii) It took approximately one year to recover positive sentiments in user comments after introducing the policy. However, the comments with negative sentiments assisted in improving waste management. (iv) The best-fitted negative binomial distribution of the number of votes for user comments could guarantee the managerial insight identification from the minority of comments with popular concerns.

Challenges of high energy efficiency, water saving, low-carbon emissions, waste management and food saving have been considerable pressures on both regional and global sustainable development. This paper reviews the water-energy extended nexuses (e.g. food, greenhouse gases, waste, pollution, land and others) from the perspective of relationship and practicability in relieving the challenges towards environmentally-related sustainable development goals. A specific issue identified is that with the popularity of “nexus” and extension beyond resources nexus, the positioning and identity of nexuses become ambiguous. In most cases, nexus is a more attractively sounding term to replace specific expressions such as multi-criteria optimisation, trade-offs, correlation/relationship, input-output assessment, material flow analysis, and integrated design. The methodologies (e.g. input-output analysis and life cycle assessment) entail needing to be adequately integrated or expanded for diverse water-energy extended nexuses. It is crucial for evaluating resource flows in multiple regions and sectors and assessing the associated environment-economic impacts. A standardised understanding like establishing life cycle assessment and even more quantified footprints could ensure that the nexus would not fall as the group of a buzzword and contribute to sustainable development goals. This study serves as a stepping stone to establish quantified characteristics or components as eligibility to classify as nexus approaches and studies.

The European Union (EU) has the world's most extensive regional power trade, making it essential for the EU environmental energy policies. Understanding the nexus amongst water, power and CO2 emissions is essential for sustainable water and energy resource management. Although a number of related studies on the nexus have been conducted, integrating virtual water and CO2 emission footprints for the power sector requires further attention. The current study analyses the virtual water and CO2 emissions embodied in the power supply, demand and regional trade. A network model is developed to track the virtual water and CO2 emissions of the inter-regional power trade for the case of EU-27 countries in 2017. The total virtual CO2 emissions and the embodied virtual water in the EU inter-regional power trade are estimated as 7.0 × 104 t CO2 and 5.6 × 108 m3, with a hydropower contribution of 37.8%. The largest virtual water and CO2 emissions exporters are identified by France (8.8 × 107 m3) and Germany (2 × 104 t), whilst the largest virtual CO2 importer is Austria (1 × 104 t). The identified synergy of climate mitigation and water scarcity provides a benchmark for policymakers to develop strategies for sustainable power development considering the virtual footprint in trade flow simultaneously.

Cyanobacterial blooms under global warming are increasing worldwide, producing emerging contaminants, which threaten the health of human beings and aquatic ecosystems. The health burdens warrant the development of a useful risk-assessment tool and a holistic preventive-control scheme to prevent cyanobacterial blooms. This paper aims to integrate cyanobacterial risk assessment and risk preventive control by investigating the relationships amongst cyanobacterial blooms and multi-dimensional influencing variables. Two challenges hinder such a task. First, the time-series variations in cyanobacteria and influencing variables are uncertain and nonlinear. Second, there rarely exists an explicit modelling framework for integrating cyanobacterial risk assessment and risk preventive control. This study builds an extended Bayesian network model and proposes an integrated framework with functions of assessment, inference, preventive control, and visualisation of the risk of cyanobacterial blooms. Field data from a tropical lake are used to evaluate the model and framework. The proposed model achieves better performance than the seven models in comparison. The cyanobacterial risk is anticipated to increase by 38.5% under global warming. On the contrary, guided by the model and framework, the risk could be reduced by about 60% by taking the identified risk preventive control scheme. The cyanobacterial risk prevention would reduce aquatic emerging contaminants in drinking and recreational water sources.

The building of new waste treatment plants has not been closely following the Circular Economy (CE) hierarchy, and the incurred cost and social acceptance have been challenging. Integrated regional waste management by waste trading can offer the sharing of resources to achieve a mutually beneficial system in CE transition. This study aims to minimise the environmental footprints, with the consideration of economic feasibility, in integrated regional waste management by developing an extended Pinch Analysis-based targeting method. The proposed graphical decision-support method is applied to a case study where the waste trading amongst Central Europe (Austria, the Czech Republic, Germany, Poland, and Slovakia) is considered. A monthly waste trading network is identified with Germany, and Austria serves as the country with a surplus of waste treatment capacity. The pessimistic estimation indicated that the GHG emission of the waste trading design is 0.311 t CO2eq/t of waste, where the GHG emission of 1 t waste is reduced by 20 kg – 30 kg compared to the baseline scenario. The assessment suggested that when the amount of waste to be transported by lorry is >5 t and the transport distance to the incineration plant compared to the landfill is less than ~500 – 940 km, waste trading for recovery offers a lower net GHG emission. However, to prevent pollution haven and to consider the economic feasibility, the landfill fees have to be increased significantly. The developed method can identify integrated waste management network for different levels of administrative division to minimise environmental footprints.

There are contrasting opinions on the utilisation of microbial inoculant (MI) as a bioremediation technology for composting. The effectiveness can be affected by a diversity of factors, and the reproducibility of results can be a great challenge. This study aims to review the effectiveness of MI, specified as isolated or cultured single and consortium micro-organisms, for composting, with the main component of lignocellulosic waste (LW) and the organic portion of municipal solid waste (OPMSW). The potential for the enhancing effect of each parameter, i.e. compost quality parameters: pH, germination index, nitrogen content, phosphorus content, potassium content, C/N ratio; composting parameters: temperature, odour, enzymatic activities, organic matter content, microbial population, volume reduction, humification, is discussed and evaluated. Based on 13 analysed parameters, the use of MI tends to exert a significant positive effect on the composting of LW compared to that of OPMSW. There has not been a full consensus on the efficiency of MI for composting as it varies on a case-by-case basis. A decision framework is required for the application of MI for different composting scenarios. This overview proposes MI technology, which has been generally less practical than optimising the physicochemical parameters of the composting due to the higher variation in the composting efficiency. Comprehensive comparison studies between the two approaches are needed for further verification which can contribute to the support of composting as a clean technology. The unnecessary uses of resources (e.g. MI, electric, fuel) can consequently be avoided for environmental footprint minimisation.

Climate change is global issues with significant economic, social and environmental implications. Climate change mitigation in Asia has large impacts on global CO2 emission reduction. CO2 emission is the largest source of greenhouse gas emission constitutes about 65% of the total emission. Low-carbon Society initiative is one of the various mechanisms that have been deployed to achieve green economic growth, societal well-being and development, environmental preservation and management in a holistic manner. Energy efficiency improvement in Asia will be a key factor to tackle the climate change issues. Water and energy conservation, green transportation and low emission technology are the key aspects to catalyse the shift towards climate-resilient economic growth. The latest developments in these aspects are reviewed in this special volume to sustain the development of low-carbon emission in Asian countries. The use of holistic management system to integrate these key areas for long-term sustainability goal is also highlighted.

Transportation is one of the significant contributors of GHG and air pollutants, from both the tank to well and well to wheel cycle. This study aims to introduce a graphical approach to mapping the circumstances where the energy transition offers a lower emission. The proposed method is demonstrated by a case study of the EU condition (EU-28, Latvia and Sweden). Assessed energy sources to power transportation are electricity, compressed natural gas, liquefied natural gas, diesel and biodiesel. This approach facilitates the transport mode selection (road, rail, sea) and the understanding of how electricity mix or renewable energies influence the energy transition. The environmental oriented approach can be improved in a future study by integrating the economic perspective.

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.

This contribution provides an overview of IoT embedded in the cities, its contributions and the challenges, from the environmental perspective. Energy, which is the essential lifeline of smart cities, can be a crucial sector benefiting from IoT implementation. However, there is still a research gap on the offset/saving versus the footprint of the IT sector. The overall reduction of energy consumptions through the integration of IoT is yet to be verified. The end of life management remains an issue. Continuous research and development, as well as assessment, could bring the sustainable smart concept into its real-life implementation in the foreseeable future. The presentation stems to provide a platform for the discussion of future research directions and priorities.

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.

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., Graphical abstract Unlabelled Image, Highlights • The influence of COVID-19 on waste management is summarised. • Manual sorting and recycling are restricted due to the potential risk of infection. • Shanghai shows a 23% reduction in MSW and 3% increase in Singapore. • Brno demonstrates 1% increase for MSW from household and small business, 40% decrease for business and industrial. • The impacts of COVID-19 can be diverse depending on the geographical and sociological factors.

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.

Highlights • Data mining approaches are applied to measure and improve social media publicity. • The current publicity in household waste management leans toward propagandist-centred. • The identified high user engagements are at 12:00–13:00 and 21:00–22:00 on thursday. • The macro policy in shanghai increases the recyclable waste collection by 431.8%. • A case study in shanghai presents personalised improvements in 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., Graphical abstract Image, graphical abstract

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., Graphical abstract Image 1, Highlights • COVID-19 pandemic impacts on energy structure, energy requirements and related emissions. • Overview of the invested energy sources and environmental footprints increased. • Required energy and resources consumption of Personal Protection Equipment (PPE) and testing kits. • An assessment of intensive energy consumption for aseptic and disinfection. • The outcomes emphasised that diversifying solutions are a vital strategy needed.

Reduction of landfills and dealing with the waste are among the key elements of the cleaner production and advanced environment policy. Composting represents one of the cleaner technologies in diverting organic waste from landfill. However, it has not been commonly practised in the developing countries due to low economic feasibility and environmental issues that arise from the abuse of compost quality. There are a lack of product standards and an indication of compost quality to govern its market and application. In order to foster public confidence in compost utilisation and as much as possible offset the operating cost, an economic assessment system is needed to benchmark the quality of the compost at a reasonable cost. In this study, a set of composting parameters and their analysis have been reviewed and ranked following their total scores in terms of relation to the agronomic value, technical complexity and analysis cost. The parameters were selected based on different assessment criteria. An assessment system developed on the base of the decision analysis comprises minimal analyses needed to assess the compost quality. The cost savings results from the presented work are illustrated by four scenarios: Scenario 1 served as the baseline to include all the necessary analyses and the remaining three (Scenarios 2, 3 and 4) had been evaluated by the developed assessment system. It is shown that the cost to assess the compost quality was reduced from 17 to 84 % depending on the type of input material and composting performance. The highest cost saving based on the analysis of compost quality reaching 84 % was achieved by Scenario 4 where composting was carried out using the segregated food and landscape waste. The assessment system could be very useful for improving the compost utilisation towards sustainable composting in the developing countries.

Energy plays a leading role in supporting the smart concepts, such as the Internet of Things and data storage, for the advanced development of cities and industries. The smart-concept makes use of the information and communication technologies (ICT) to supply information required for effective management. This Special Issue in Energy includes the latest developments in energy research presented at the 4th International Conference on Low Carbon Asia & Beyond (ICLCA′18) with the theme “Transition to Sustainable Energy System for Smart Cities and Industries”, held in Johor Bahru, Malaysia, on 24–26 October 2018. ICLCA hosted over 200 oral and 50 poster presentations. Thirty papers are included in this special issue which constitutes the latest developments in energy research. ICLCA serves as a yearly conference which provides a platform for the researchers and stakeholders from the academic community, industry sectors, and governments in Asia to share their experience, knowledge, and expertise in sustainability. The main purpose is to catalyse the transformation of low CO2 emissions development in the context of Asia and beyond. The paper covers a range of topics relevant to a sustainable energy system: (i) smart energy systems, (ii) low CO2 emissions technologies, (iii) biomass and pollution reduction, and (iv) renewable energy.

Energy is a fundamental element supporting societal development, particularly with the increasing dependency on the Internet of Things. It is also the main contributor to environmental impacts and subsequently, a potential sector for mitigation. Sustainable energy system design considers energy savings and energy efficiency, waste and consumption reduction, process efficiency enhancement, waste heat recovery, and integration of renewable energy. Emerging tools range from advanced Process Integration, modelling, simulation, and optimisation, to system analysis and assessment. This review covers selected emerging studies promoting sustainable system design, including the recent developments reported in the Special Issue (SI) of the 22nd Conference on Process Integration, Modelling and Optimisation for Energy Saving and Pollution Reduction (PRES&rsquo, 19). The primary emphasis was to enhance the economic and environmental performance. However, social factors were also highlighted as essential for future sustainable development. The discussion and analysis in this review focus on the most recent developments of (a) heat integration and heat transfer, (b) integrated and newly developed heat exchangers, (c) integration of renewables, and (d) roles in economic and environmental sustainability. The key results are highlighted, and future research ideas are suggested according to their links to a broader context.

This study introduces a new graphical decision-making tool to facilitate the rapid selection of transportation modes with minimum energy consumption or emissions, indicating the most sustainable transportation mode. Greenhouse gas (GHG) and air pollutants (NOx, PM and SO2), together with a composite price-weighted total environmental burden (TEB), are considered in the analysis. The graphical tool, which has a similar appearance to a phase diagram, presents a map of energy use (or emission) of different transportation modes based on the values of the ratio of the transportation distances (R) and the absolute load (L). A freight transportation case study (Rotterdam to Antwerp and Genova) demonstrates the construction and application of the graphical tool. For this case study, the electric train is the transport mode that offers the lowest energy consumption and minimum TEB. The graphical decision tool can also indicate the next-best solutions when one or more options (e.g. electric train) are not available. In this scenario, general cargo shipping achieves the lowest GHG emission, but heavy lorry imposes the lowest TEB. The developed tool further demonstrates the impacts of possible future fuels and technology developments on transportation selection, including renewable biodiesel and transport electrification under different grid mixes (e.g. Latvia, Sweden and EU-28). Further criteria, including economics, can be included for future study using the proposed tool as a foundation. The graphical approach transforms the transport selection problem into an easily understandable format from which arises sound solutions.