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This study develops an Adaboost-GIS model for flood susceptibility mapping and evaluates its relative performance by undertaking a comparative assessment of the machine learning model with Multi-Criteria Decision Making (MCDM) and soft computing models integrated with GIS. An Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), Fuzzy-AHP, Fuzzy-ANP and AdaBoost machine learning models were developed and integrated with GIS to classify the susceptibility of the study area. Out of 70 sample validation locations, Adaboost’s performance was the best with a 95.72% similarity match with very high and high susceptibility locations followed by F-ANP, ANP, F-AHP and AHP with 95.65%, 92.75%, 81.42% and 77.14% similarity matches, respectively. It also had the highest AUC (0.864). Thus, the Adaboost machine learning, Fuzzy computing and conventional MCDM models can be adopted by stakeholders for accurately assessing flood susceptibility, thereby fostering safe and resilient cities. [ABSTRACT FROM AUTHOR]

In this study, we conducted a holistic evaluation of current and future trend in coastal sea level at the 21 stations along Malaysia's coastline. For sea level prediction, univariate and 3 scenarios of multivariate Long Short Term Memory (LSTM) neural networks were trained with absolute sea level data and ocean-atmospheric variables. The result from the four scenario predictive models revealed that multivariate LSTM neural network trained with combined ocean-atmospheric variables performed best for modelling sea level variation, giving a mean RMSE and R accuracy of 0.060 and 0.861, respectively. The national sea level rise estimated from the average of sea level trend at all stations is 3.72 mm/yr for relative sea level and 3.68 mm/yr for absolute sea level. The 2050 and 2100 projections indicate that sea level will continue to rise but at a very slow rate with no acceleration. [ABSTRACT FROM AUTHOR]

In sub-Saharan Africa, mass rural-urban migration negatively affectthe agriculture sector that accounts for about 23% of the GDP and employs over 60% of the population. Together with a rapidly changing climate, unplanned urbanization poses serious threats to Africa's agriculture sector with the risk of chronic food shortages in the future. To stem this tide, it is imperative to systematically assess the unplanned urbanization trend from a socio-economic perspective and distill the broader implication for sustainable urban farming within the context of climate change in the region. The potentials of digitalization as a tool for transformative adaptation to climate change and enabler of sustainable development in different domains, including agriculture, are beginning to emerge. However, most studies are based on data from Asia, Europe, North America, and Oceania. There is minimal documentation of current applications and prospects of digitalization for sustainable agricultural practices in Africa, particularly in an increasingly urbanized era. Thus, this study addresses this need by evaluating the potentials of digitalization to enable sustainable farming in the face of unprecedented climate change constraints in Africa and minimize the negative impacts of urbanization on agriculture. Through a desk research approach, the present study explores the challenges to digital farming in Africa despite its successful implementation in the global North. Drawing lessons from successful case-studies worldwide, we suggest possible pathways to overcome the challenges and implement localized digitalization approaches to strengthen preventive action against climate risks, enhance disaster preparedness, and aid effective planning and management of agriculture practices. Integrating agriculture into the city via digital urban farming is crucial for long-term food security and creating appealing clean-tech jobs for a large number of new immigrants, thereby supporting African cities' resilience and sustainable development. [ABSTRACT FROM AUTHOR]

Global Land Cover (GLC) maps have been extensively used as background data for numerous applications such as land-use planning, climate change monitoring, and urban planning. While countries in Europe and North America have greatly developed land cover maps, several developing countries in Asia and Africa lack such vital data sources. GlobeLand30's 30 m resolution dataset has the potential to meet the mapping needs of these developing regions. However, differences in accuracy of the dataset between countries can be significant making it unsuitable to adopt regional accuracy directly for assessing individual country's accuracy level. Therefore, it is critical to develop reliable localized assessments. This research assesses the accuracy of GlobeLand30 for Malaysia using multiple evaluation approaches. Seven classes of land cover data were evaluated using 197 samples in the country. An overall accuracy of 63.45% and 65.70% was obtained from the Error Matrix using sample counts and the new unbiased area estimator respectively. We conclude that the overall accuracy of GlobeLand30 in tropical developing countries like Malaysia is generally lower than the accuracy in developed countries and arid developing countries thereby requiring significant improvement for optimal use. However, despite the errors due largely to misclassification, the GlobeLand 30 data has the potential to complement other LULC data to estimate and monitor vital land-use changes in Malaysia and the ASEAN region in general for sustainable development. [ABSTRACT FROM AUTHOR]

The visual similarity of oil slick and other elements, known as look-alike, affects the reliability of synthetic aperture radar (SAR) images for marine oil spill detection. So far, detection and discrimination of oil spill and look-alike are still limited to the use of traditional machine learning algorithms and semantic segmentation deep learning models with limited accuracy. Thus, this study developed a novel deep learning oil spill detection model using computer vision instance segmentation Mask-Region-based Convolutional Neural Network (Mask R-CNN) model. The model training was conducted using transfer learning on the ResNet 101 on COCO as backbone in combination with Feature Pyramid Network (FPN) architecture for feature extraction at 30 epochs with 0.001 learning rate. Testing of the model was conducted using the least training and validation loss value on the withheld testing images. The model's performance was evaluated using precision, recall, specificity, IoU, F1-measure and overall accuracy values. Ship detection and segmentation had the highest performance with overall accuracy of 98.3%. The model equally showed a higher accuracy for oil spill and look-alike detection and segmentation although oil spill detection outperformed look-alike with overall accuracy values of 96.6% and 91.0% respectively. The study concluded that the deep learning instance segmentation model performs better than conventional machine learning models and deep learning semantic segmentation models in detection and segmentation. [ABSTRACT FROM AUTHOR]

Many cities across the world are facing many problems climate change poses to their populations, communities and infrastructure. These vary from increased exposures to floods, to discomfort due to urban heat, depending on their geographical locations and settings. However, even though some cities have a greater ability to cope with climate change challenges, many struggle to do so, particularly in cities in developing countries. In addition, there is a shortage of international studies which examine the links between climate change adaptation and cities, and which at the same time draw some successful examples of good practice, which may assist future efforts. This paper is an attempt to address this information need. The aim of this paper is to analyse the extent to which cities in a sample of developing countries are attempting to pursue climate change adaptation and the problems which hinder this process. Its goal is to showcase examples of initiatives and good practice in transformative adaptation, which may be replicable elsewhere. To this purpose, the paper describes some trends related to climate change in a set of cities in developing countries across different continents, including one of the smallest capital cities (Georgetown, Guyana) and Shanghai, one the world's most populous cities. In particular, it analyses their degree of vulnerability, how they manage to cope with climate change impacts, and the policies being implemented to aid adaptation. It also suggests the use of transformative approaches which may be adopted, in order to assist them in their efforts towards investments in low-carbon and climate-resilient infrastructure, thereby maximizing investments in urban areas and trying to address their related poverty issues. This paper addresses a gap in the international literature on the problems many cities in developing countries face, in trying to adapt to a changing climate. Unlabelled Image • Studies on adaptation challenges in developing cities are scarce. • Ageing and failing infrastructure limit cities' resilience. • There is a positive relationship between cities' income level and adaptation capacity. • Large population is not a barrier per se to a city's adaptation capacity. • Most cities lack local-level climate policies and programmes. [ABSTRACT FROM AUTHOR]

Proper pipeline route selection is an integral component of a typical oil exploration and transportation project. Improperly selected routes could have severe consequences including pipe failures, oil spillage, and environmental disasters. Consequently, technologies like geographic information systems (GIS) are increasingly being used to facilitate the oil pipeline route selection procedure—especially for onshore routing projects. Surprisingly, not much has been documented on the application of GIS to offshore pipeline routing. With recent discoveries on the merits of offshore oil exploration, it is necessary to extend the analytical capabilities of GIS to the unique offshore domain. However, concerns have been raised regarding the limitations of GIS in accurately prioritizing diverse selection criteria in typical multi-criteria decision-making (MCDM) problems like route selection. Consequently, this paper addresses the offshore/subsea pipeline routing constraint using a hybrid decision support system (DSS), which integrates a GIS and fuzzy logic-based approximate reasoning (AR) models for optimal performance. The resultant spatial decision support system (SDSS) was successfully applied to a case study in Malaysia. The AR algorithm calculated the significance level of the multiple criteria using various fuzzy linguistic variables and membership functions. The aggregated priority ranking from different pipeline routing experts showed that the overall influence of the environmental criteria (61.4%) significantly exceeded that of other equally important criteria in the study area. These rankings were inputted into the SDSS to simulate various probable routes. Final results accurately highlighted an optimal route, which places a premium on the protection of environmental features in the subsea study area—in alignment with the preferences of majority of the experts. [ABSTRACT FROM AUTHOR]

Climate change is generally known to impact ozone concentration globally. However, the intensity varies across regions and countries. Therefore, local studies are essential to accurately assess the correlation of climate change and ozone concentration in different countries. This study investigates the effects of climatic variables on ozone concentration in Malaysia in order to understand the nexus between climate change and ozone concentration. The selected data was obtained from ten (10) air monitoring stations strategically mounted in urban-industrial and residential areas with significant emissions of pollutants. Correlation analysis and four machine learning algorithms (random forest, decision tree regression, linear regression, and support vector regression) were used to analyze ozone and meteorological dataset in the study area. The analysis was carried out during the southwest monsoon due to the rise of ozone in the dry season. The results show a very strong correlation between temperature and ozone. Wind speed also exhibits a moderate to strong correlation with ozone, while relative humidity is negatively correlated. The highest correlation values were obtained at Bukit Rambai, Nilai, Jaya II Perai, Ipoh, Klang and Petaling Jaya. These locations have high industries and are well urbanized. The four machine learning algorithms exhibit high predictive performances, generally ascertaining the predictive accuracy of the climatic variables. The random forest outperformed other algorithms with a very high R2 of 0.970, low RMSE of 2.737 and MAE of 1.824, followed by linear regression, support vector regression and decision tree regression, respectively. This study's outcome indicates a linkage between temperature and wind speed with ozone concentration in the study area. An increase of these variables will likely increase the ozone concentration posing threats to lives and the environment. Therefore, this study provides data-driven insights for decision-makers and other stakeholders in ensuring good air quality for sustainable cities and communities. It also serves as a guide for the government for necessary climate actions to reduce the effect of climate change on air pollution and enabling sustainable cities in accordance with the UN's SDGs 13 and 11, respectively. • Climate change has the potential to influence air pollution. • Temperature is an essential factor for ozone pollution. • Wind serves as a medium for transboundary pollution. • Random forest outperformed the other machine learning algorithms for surface ozone prediction. • Industrialized and urbanized areas are hotspots for bad air quality. [ABSTRACT FROM AUTHOR]

Rising sea level is generally assumed and widely reported to be the significant driver of coastal erosion of most low-lying sandy beaches globally. However, there is limited data-driven evidence of this relationship due to the challenges in quantifying shoreline dynamics at the same temporal scale as sea-level records. Using a Google Earth Engine (GEE)-enabled Python toolkit, this study conducted shoreline dynamic analysis using high-frequency data sampling to analyze the impact of sea-level rise on the Malaysian coastline between 1993 and 2019. Instantaneous shorelines were extracted from a test site on Teluk Nipah Island and 21 tide gauge sites from the combined Landsat 5–8 and Sentinel 2 images using an automated shoreline-detection method, which was based on supervised image classification and sub-pixel border segmentation. The results indicated that rising sea level is contributing to shoreline erosion in the study area, but is not the only driver of shoreline displacement. The impacts of high population density, anthropogenic activities, and longshore sediment transportation on shoreline displacement were observed in some of the beaches. The conclusions of this study highlight that the synergistic use of multi-sensor remote-sensing data improves temporal resolution of shoreline detection, removes short-term variability, and reduces uncertainties in satellite-derived shoreline analysis compared to the low-frequency sampling approach. [ABSTRACT FROM AUTHOR]

Although advancements in remote sensing technology have facilitated quick capture and identification of the source and location of oil spills in water bodies, the presence of other biogenic elements (lookalikes) with similar visual attributes hinder rapid detection and prompt decision making for emergency response. To date, different methods have been applied to distinguish oil spills from lookalikes with limited success. In addition, accurately modeling the trajectory of oil spills remains a challenge. Thus, we aim to provide further insights on the multi-faceted problem by undertaking a holistic review of past and current approaches to marine oil spill disaster reduction as well as explore the potentials of emerging digital trends in minimizing oil spill hazards. The scope of previous reviews is extended by covering the inter-related dimensions of detection, discrimination, and trajectory prediction of oil spills for vulnerability assessment. Findings show that both optical and microwave airborne and satellite remote sensors are used for oil spill monitoring with microwave sensors being more widely used due to their ability to operate under any weather condition. However, the accuracy of both sensors is affected by the presence of biogenic elements, leading to false positive depiction of oil spills. Statistical image segmentation has been widely used to discriminate lookalikes from oil spills with varying levels of accuracy but the emergence of digitalization technologies in the fourth industrial revolution (IR 4.0) is enabling the use of Machine learning (ML) and deep learning (DL) models, which are more promising than the statistical methods. The Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the most used machine learning algorithms for oil spill detection, although the restriction of ML models to feed forward image classification without support for the end-to-end trainable framework limits its accuracy. On the other hand, deep learning models' strong feature extraction and autonomous learning capability enhance their detection accuracy. Also, mathematical models based on lagrangian method have improved oil spill trajectory prediction with higher real time accuracy than the conventional worst case, average and survey-based approaches. However, these newer models are unable to quantify oil droplets and uncertainty in vulnerability prediction. Considering that there is yet no single best remote sensing technique for unambiguous detection and discrimination of oil spills and lookalikes, it is imperative to advance research in the field in order to improve existing technology and develop specialized sensors for accurate oil spill detection and enhanced classification, leveraging emerging geospatial computer vision initiatives. [ABSTRACT FROM AUTHOR]

Oil spills are a global phenomenon with impacts that cut across socio-economic, health, and environmental dimensions of the coastal ecosystem. However, comprehensive assessment of oil spill impacts and selection of appropriate remediation approaches have been restricted due to reliance on laboratory experiments which offer limited area coverage and classification accuracy. Thus, this study utilizes multispectral Landsat 8-OLI remote sensing imagery and machine learning models to assess the impacts of oil spills on coastal vegetation and wetland and monitor the recovery pattern of polluted vegetation and wetland in a coastal city. The spatial extent of polluted areas was also precisely quantified for effective management of the coastal ecosystem. Using Johor, a coastal city in Malaysia as a case study, a total of 49 oil spill (ground truth) locations, 54 non-oil-spill locations and Landsat 8-OLI data were utilized for the study. The ground truth points were divided into 70% training and 30% validation parts for the classification of polluted vegetation and wetland. Sixteen different indices that have been used to monitor vegetation and wetland stress in literature were adopted for impact and recovery analysis. To eliminate similarities in spectral appearance of oil-spill-affected vegetation, wetland and other elements like burnt and dead vegetation, Support Vector Machine (SVM) and Random Forest (RF) machine learning models were used for the classification of polluted and nonpolluted vegetation and wetlands. Model optimization was performed using a random search method to improve the models' performance, and accuracy assessments confirmed the effectiveness of the two machine learning models to identify, classify and quantify the area extent of oil pollution on coastal vegetation and wetland. Considering the harmonic mean (F1), overall accuracy (OA), User's accuracy (UA), and producers' accuracy (PA), both models have high accuracies. However, the RF outperformed the SVM with F1, OA, PA and UA values of 95.32%, 96.80%, 98.82% and 95.11%, respectively, while the SVM recorded accuracy values of F1 (80.83%), OA (92.87%), PA (95.18%) and UA (93.81%), respectively, highlighting 1205.98 hectares of polluted vegetation and 1205.98 hectares of polluted wetland. Analysis of the vegetation indices revealed that spilled oil had a significant impact on the vegetation and wetland, although steady recovery was observed between 2015-2018. This study concludes that Chlorophyll Vegetation Index, Modified Difference Water Index, Normalized Difference Vegetation Index and Green Chlorophyll Index vegetation indices are more sensitive for impact and recovery assessment of both vegetation and wetland, in addition to Modified Normalized Difference Vegetation Index for wetlands. Thus, remote sensing and Machine Learning models are essential tools capable of providing accurate information for coastal oil spill impact assessment and recovery analysis for appropriate remediation initiatives. [ABSTRACT FROM AUTHOR]

This article explores using satellite images to monitor spatiotemporal variations in temperature related to urban form. Land surface temperatures (LST) were estimated from Landsat images (1986–2016) and the land cover and urban form LST were extracted by using samples representing different urban forms/cover types. A transect of 20 km was taken across the city to derive the LST across the different land cover types. Urban heat island index and statistical analysis were carried out to understand the influence of urban form and cover on changes in surface temperature. The results are compared with temperature regimes of an industrial city (Yanbu) to depict differences in the two cities. The analysis of variance (ANOVA) shows variations, at 0.01 level of significance, in the LST values of the city centre, high-rise, low-density, vegetation, desert and industrial land-use types. The outcome of the study is valuable for decision-makers in achieving sustainable urban development. [ABSTRACT FROM AUTHOR]

Climate change is perceived as a major challenge of modern times. Apart from mitigation measures, such as greenhouse gas emission reductions, a further means to tackle it is via the deployment of adaptation policies and responses, which may also help to address the natural hazards associated with it. Despite the importance of adaptation measures, their effectiveness is often limited by a failure to integrate actions addressing both the causes and symptoms of vulnerability. There is evidence that adaptation processes are largely fragmented, incremental, with limited capacity for transformational change. This paper suggests measures through which transformative adaptation may be further deployed to tackle climate challenges in conjunction with efforts that address poverty alleviation. The implications of this paper are two-fold. Firstly, it offers a comprehensive review of the literature on transformative climate change adaptation, outlining its nature and special features. Secondly, it contains a unique set of cases from 20 countries, predominantly from the Global South, mostly affected by climate change, and is one of the largest studies on the topic ever undertaken. The experiences from this paper will support attempts to sustain transformative adaptation and natural hazards control, which are relevant to the many countries suffering from climatic variations. • Examines the concept of transformative adaptation and its adoption in facing climate change adaptation challenges. • Presents cases of climate change adaptation strategies from 20 countries. • Highlights the context-specific nature of climate change adaptation challenges. • Six countries adopted mainly incremental adaptation approach while 14 countries implemented transformative adaptation methods. • Highlights measures for improving the implementation of transformative adaptation to tackle challenges of climate change. [ABSTRACT FROM AUTHOR]

Abstract Plastic debris is a worldwide problem. This is particularly acute in the Pacific region, where its scale is a reason for serious concerns. There is an obvious need for studies to assess the extent to which plastic debris affects the Pacific. Therefore, this research aims to address this need by undertaking a systematic assessment of the ecological and health impacts of plastic debris on Pacific islands. Using pertinent historical qualitative and quantitative data of the distribution of plastic debris in the region, this study identified pollution and contamination trends and risks to ecosystems, and suggests some measures which may be deployed to address the identified problems. The study illustrates the fact that Pacific Island States are being disproportionately affected by plastic, and reiterates that further studies and integrated strategies are needed, involving public education and empowerment, governmental action, as well as ecologically sustainable industry leadership. It is also clear that more research is needed in respect of developing alternatives to conventional plastic, by the production of bio-plastic, i.e. plastic which is produced from natural (e.g. non-fossil fuel-based sources) materials, and which can be fully biodegradable. Graphical abstract Unlabelled Image Highlights • Substantial amounts of plastic are found in the Great Pacific Garbage Patch. • Littering and solid wastes are major sources of land plastic debris in the Pacific. • Commercial fishing and shipping contribute to the problem. • Plastic debris negatively impact the environment, economy and human health. • Political, economic and scientific interventions are needed to solve the problem. [ABSTRACT FROM AUTHOR]

Summary: Motivation: Communities across the global south use their rich indigenous and local knowledge (ILK) to predict weather events and climate hazards. ILK may assist efforts to address climate change challenges in Africa and make subsequent decisions regarding climate adaptation. Purpose: The article documents evidence of the ILK's potential in reducing vulnerability to climate change and/or improving the resilience of communities. The study also reflects on major barriers that hinder the improved mainstreaming of ILK into adaptation strategies. Methods and approach: The present study uses two main methods: a literature review and a presentation of case studies from a sample of African countries where ILK informs adaptation options, including indigenous land‐tenure practices and weather prediction. The selected case studies highlight the historical legacy of ILK and its effectiveness in reducing vulnerability and the impacts of climate change. Findings The results indicate that, despite being acknowledged as a valuable resource for climate adaptation, current national adaptation policies on the African continent still show serious gaps in effectively integrating ILK systems within the legal frameworks to reduce vulnerability. Policy implications: ILK should be better integrated with modern climate change adaptation strategies to anticipate more effective responses. Both rural communities and relevant government agencies should complement the use of ILK with climate change strategies, so as to maximize its contribution to the effective implementation of climate change policies. [ABSTRACT FROM AUTHOR]

Piping erosion is one of the water erosions that cause significant changes in the landscape, leading to environmental degradation. To prevent losses resulting from tube growth and enable sustainable development, developing high-precision predictive algorithms for piping erosion is essential. Boosting is a classic algorithm that has been successfully applied to diverse computer vision tasks. Therefore, this work investigated the predictive performance of the Boosted Linear Model (BLM), Boosted Regression Tree (BRT), Boosted Generalized Linear Model (Boost GLM), and Deep Boosting models for piping erosion susceptibility mapping in Zarandieh Watershed located in the Markazi province of Iran. A piping inventory map including 152 piping erosion locations was prepared for algorithm training and testing. 18 initial predisposing factors (altitude, slope, plan curvature, profile curvature, distance from river, drainage density, distance from road, rainfall, land use, soil type, bulk density, CEC, pH, clay, silt, sand, topographical position index (TPI), topographic wetness index (TWI)) was derived from multiple remote sensing (RS) sources to determine the piping erosion prone areas. The most significant predisposing factors were selected using multi-collinearity analysis which indicates linear correlations between predisposing factors. Finally, the results were evaluated for Sensitivity, Specificity, Positive predictive values (PPV) and Negative predictive value (NPV), and Receiver Operation characteristic (ROC) curve. The best Sensitivity (0.80), Specificity (0.84), PPV (0.85), NPV (0.79), and ROC (0.93), were obtained by Deep Boosting model. The results of the piping erosion susceptibility study in agricultural land use showed that 41% of agricultural lands are very sensitive to piping erosion. This outcome will enable natural resource managers and local planners to assess and take effective decisions to minimize damages to agricultural land use by accurately identifying the most vulnerable areas. Hence, this research proved Deep Boosting model's ability for piping erosion susceptibility mapping in comparison to other popular methods such as BLM, BRT, and Boost GLM. [ABSTRACT FROM AUTHOR]

Background: "The impacts of the Coronavirus Disease 2019 (COVID-19) pandemic and the shutdown it triggered at universities across the world, led to a great degree of social isolation among university staff and students. The aim of this study was to identify the perceived consequences of this on staff and their work and on students and their studies at universities.Method: The study used a variety of methods, which involved an on-line survey on the influences of social isolation using a non-probability sampling. More specifically, two techniques were used, namely a convenience sampling (i.e. involving members of the academic community, which are easy to reach by the study team), supported by a snow ball sampling (recruiting respondents among acquaintances of the participants). A total of 711 questionnaires from 41 countries were received. Descriptive statistics were deployed to analyse trends and to identify socio-demographic differences. Inferential statistics were used to assess significant differences among the geographical regions, work areas and other socio-demographic factors related to impacts of social isolation of university staff and students.Results: The study reveals that 90% of the respondents have been affected by the shutdown and unable to perform normal work or studies at their institution for between 1 week to 2 months. While 70% of the respondents perceive negative impacts of COVID 19 on their work or studies, more than 60% of them value the additional time that they have had indoors with families and others. .Conclusions: While the majority of the respondents agree that they suffered from the lack of social interaction and communication during the social distancing/isolation, there were significant differences in the reactions to the lockdowns between academic staff and students. There are also differences in the degree of influence of some of the problems, when compared across geographical regions. In addition to policy actions that may be deployed, further research on innovative methods of teaching and communication with students is needed in order to allow staff and students to better cope with social isolation in cases of new or recurring pandemics. [ABSTRACT FROM AUTHOR]

Selection of pipeline routes is a geospatial multi-criteria evaluation problem since it involves the assessment of technical, environmental and safety-related factors that influence pipeline system operations. This study determines the optimal pipeline route for transmission of oil and gas products using the Dangote Refinery Project in Nigeria as a case study. Pertinent route selection factors were identified and structured into the expert feedback model of the Analytical Network Process (ANP) for accurate prioritization. LANDSAT 8 imageries of the study area were processed and classified into various land use and land cover types, which were further modelled in ArcMap 10.2 GIS software for routing analysis. Findings reveal that the most highly prioritized factors for proper route selection were the environmental and geological factors, while the least prioritized was the economic factor. In the end, alternative Route 2 was identified as the optimal route among the four evaluated alternative routes because of its minimum route length, minimum passage length through sensitive settlement areas, and less number of river, stream and existing platforms' crossings. [ABSTRACT FROM AUTHOR]

In 2017, over 320 million tons of polymers, excluding fibers, were manufactured across the globe. The excessive amounts of plastics produced today pose a threat to both man and nature. Urgent approaches towards reducing plastic consumption and increasing its recyclability are needed. This paper discusses the matter of accelerating the circularity of plastic-based material systems. It investigates a sample of plastics-converting companies in Emilia Romagna region (Italy), which stand out for radical innovations in business models, with the aim to catalyse changes in current manufacturing practices. The findings provide empirical support for a positive relationship between business strategies and the use of non-virgin plastic materials. The innovative value of this paper relies on the fact that it elaborates on the vision established within the European Strategy for Plastics in a Circular Economy that sets ambitious targets about achieving high plastic recycling targets by 2025. Legislative, economic, technological and social barriers can only be tackled by radically revising the current philosophy of designing, producing, distributing and consuming plastic as part of goods and services. A systemic thinking perspective is an essential building block in this context: moving from a micro to a meso-scale analysis can represent a useful means supporting the creation of viable management approaches towards achieving environmental and economic gains, especially in European plastic conversion industry, where SMEs are the majority (about 50000). Thanks to its analysis and scope, this paper provides useful insights to the plastic industry: it shows that this substantial improvements in this sector will require innovative solutions and major efforts by key decision makers, producers, recyclers, manufacturer, retailers as well as consumers. • Plastics pose a major pressure to the natural and human environment. • The use of non-virgin plastic materials should be reduced. • Integrated plastic value chain leads to environmental and economic gains. • Innovative solutions are needed to better integrate plastics as part of the circular economy. [ABSTRACT FROM AUTHOR]

Abstract The textile/apparel industry is of great importance to the economy in terms of trade, employment, investment and revenue all over the world. This sector is however characterised by substantial losses, due to production excesses on the one hand, and the "throw away" culture on the other. This state of affairs suggests that textile recycling is needed. Apart from the environmental and ecological advantages which can be brought about by the re-use and recycling of textiles, there are many benefits of a socio-economic nature. This paper analyses current trends on textile recycling, the barriers impeding greater recycling, and outlines the socio-economic advantages of moving towards a circular economy. Some examples of on-going initiatives are discussed, together with some of the measures that may be used to further reduce the current waste deriving from the apparel sector. Graphical abstract Image 1084 Highlights • The apparel industry is economically important all over the world. • Production excesses lead to losses, so textile recycling is needed. • There are socio-economic, environmental and ecological advantages brought about by recycling of textiles. • There are measures that may be used to reduce the current waste deriving from the apparel sector. [ABSTRACT FROM AUTHOR]

The urban environment is characterised by many pressures caused by population growth, transport (and its related emissions), and the damage to green areas. Yet, there is a variety of ecosystem services available in urban areas, which may be deployed to ameliorate the current problems and foster their sustainability. This paper reviews the role of ecosystem services as tools for sustainability, based on an urban setting. It also describes a series of multi-country case studies, where an assessment of their functions using a set of benefits valuation approaches such as health benefits, economic benefits, social benefits and benefits to climate resilience, are provided, along with an appraisal of their role in up-keeping the overall quality of the urban environment in the studied areas. Policy recommendations aimed at enhancing the role of ecosystem services, and fostering sustainability in the sampled sites -and beyond-are provided. [ABSTRACT FROM AUTHOR]

The potential for energy efficiency and for the deployment of renewable energy at universities is substantial, and they represent promising ways to meet an institution's energy needs on the one hand, without a large climate burden on the other. However, in order to achieve successful investment and results, it is imperative to understand the level of current commitment to energy actions. Therefore, this paper investigates the level of engagement in energy efficiency measures of a sample of 50 higher education institutions from round the world, and identifies which types of renewable energy are being used to date. Results show that in more than half of the universities only a small portion of energy consumption comes from renewable sources (1%–20%) and solar/photovoltaics is the most used source (70%). Investment in energy efficiency is more common in the sample, with 54% of the universities reporting higher levels of engagement, mainly in buildings (78%) and equipment/machineries (56%). Departing from the assumption that sustainable energy use is a pre-condition for campuses to pursue sustainability, the paper identifies current deficiencies and discusses improvements in this key area, with experiences which may be replicable elsewhere. • Renewable energy accounts as minor part of the energy usage in most universities. • Solar/photovoltaic energy applications are the most common modalities of renewable energy used at HEIs. • Buildings and equipment have been identified as the primary areas where energy efficiency improvements are needed. •. A positive correlation between engagement in energy efficiency and investments in renewable energy was observed. • There is much room for improvements in what universities can do to enhance their energy efficiency. [ABSTRACT FROM AUTHOR]