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This paper presents a bibliometric analysis within the research domain dedicated to the utilization of agent-based modeling (ABM) in the field of transportation. By employing specific keywords related to both agent-based modeling and transportation, we have identified and extracted 1016 scholarly papers from the ISI Web of Science database, spanning the period from 2002 to 2023. Through the application of bibliometric methods, we have systematically examined key contributors, affiliations of academic institutions, influential publications, and renowned journals within this domain. Our analysis reveals a consistent and robust growth in scholarly interest pertaining to agent-based modeling in the field of transportation throughout the considered period. Notably, within approximately four decades of ABM's application in transportation, a distinct upward trajectory began in 2008, culminating in the year 2021. The entire considered period witnessed a remarkable surge in paper production, characterized by an annual growth rate of 21.67%. Furthermore, employing an n-gram analysis, we have delineated and discussed the principal areas within transportation that have progressively benefited from the advancements in agent-based modeling. Prominently, the domains of air transport and road transport have exhibited substantial development over time, while the implications of climate change have emerged as a persistent concern throughout the entire study period. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have already been acknowledged to be the most viable solution to the climate change that the entire globe has long been combating. Along the same line, it is a salient subject to expand the availability of EV charging infrastructure, which quintessentially necessitates the optimization of the charger's locations. This paper proposes to formulate the optimal EV charger location problem into a facility location problem (FLP). As an effort to find an efficient method to solve the well-known nonpolynomial deterministic (NP) hard problem, we present a comparative quantification among several representative solving techniques. This paper features two comprehensive case studies representing regions with an average and a high density of EVs. As such, this paper shows that the proposed framework can lead to successful location optimization with adequate refinement of solving techniques. [ABSTRACT FROM AUTHOR]

Featured Application: This work helps to realise improvements in the cultivation of corn and corn–nixtamalisation–dough–tortillas production, which could mean more environmentally friendly food production. The research on the environmental impacts of corn-derived products has been mainly on cultivation techniques and the production of biofuels, so there is limited information on the impacts produced by the transformation of corn for human consumption. The tortilla is a millennial product derived from corn of which consumption is increasing in North America. The aim of this study is to identify the environmental hotspots of the tortilla using a life cycle assessment (LCA) approach. The process studied included only the corn–nixtamalisation–dough–tortillas production. The functional unit is one kg of tortillas packed in kraft paper. The impacts of the tortilla production process were evaluated using SimaPro 8.5.0 software, considering ReCiPe Midpoint. The production has the greatest impact in 15 of the 18 impact categories. The normalisation reveals that the most significant impacts concentrate in the categories terrestrial acidification (TA), particulate matter formation (PMF), marine ecotoxicity (MET) and fossil fuel depletion (FD). Improvements in the cultivation could mean more environmentally friendly tortilla production. [ABSTRACT FROM AUTHOR]

Due to an urgent need to reduce the fast-progressing climate changes, a rapid, standardized and replicable solution for the infrastructure restructuring of the heat demand coverage of buildings at the district and city levels must be developed. As more and more communities in Europe, especially in Germany, are obligated to have plans for coverage of the heat demand, city planners and energy agencies seek a tool that will help them to design the first drafts of heating network routes, which can supply whole districts and cities with renewable energy. ArcGIS Pro Tools like Least-Cost-Path Analysis (LCPA) and Closest Facility (CF) allow us to find the shortest and "cheapest" way between the heat source and heat consumers in the analyzed areas. Starting from the community level, through the district, county, voivodeship and whole-country levels, the replication of the methods for generating district heating (DH) network infrastructure can be achieved. The proposed LCPA- and CF-based methods help to design the most suitable and efficient DH networks in the analyzed areas. As only a few open-source data inputs, like street networks and building footprints, are needed, the methods can be implemented in all communities in the country of Poland. In this paper, one example of one community in Wroclaw county, called Siechnice, and its surroundings is presented. [ABSTRACT FROM AUTHOR]

This paper follows a bibliometric assessment methodology to provide a systematic review of studies devoted to cultural heritage (CH) buildings that simultaneously refer to hygrothermal conditions and/or behavior and climate change (CC) scenarios. Moreover, this paper aims at identifying the main research trend and research gaps in this combined research field, and to provide a critical discussion of previous works relating to CH, hygrothermal behavior, and CC. Scopus database was used for data gathering and export, followed by VOSviewer for network visualization. The advanced search on the database showed that only 36 documents have considered the study of these three subjects together, as of 2022. The majority of research has been carried out in Italy (27% of the relevant documents). This paper further describes some funded research projects on the topic of CH, hygrothermal behavior, and CC. It was concluded that the investigation on this combined research topic is very recent and scarcely unexplored, and further research is required to assess how future CC scenarios will affect the hygrothermal behavior of CH buildings. These new understandings are fundamental for all stakeholders involved in topics related to heritage conservation and assessment of the hygrothermal behavior of buildings. [ABSTRACT FROM AUTHOR]

The improvement of efficiency in new and existing buildings is one of the key aspects in achieving the climate change targets promoted by international regulatory and technical bodies, and among the measures that deserve renewed attention is the balancing of hydronic systems. However, the balancing procedures currently applied have not been updated for decades and are still largely unimplemented, as they are mainly based on cumbersome and iterative procedures. This paper deals with the proposal and advanced adaptation of a non-iterative balancing method previously developed for air systems, known as the progressive flow method (PFM). The application to water systems of the PFM's concepts includes some aspects of an existing empirical method called the compensated method (CM) and overcomes its main limitations; moreover, the original PFM has been radically rethought in its implementation aspects, taking advantage of the tightness of water distribution systems, minimising instrumentation and the number of measurement operations, to definitively overcome the iterative nature of the currently applied methods. Experimental validation was carried out. Compared with a standard method, the enhanced PFM reduced the number of measurements by 48% and the number of balancing operations by 41%, achieving final flow rates within tolerances and the same configuration of balancing devices. [ABSTRACT FROM AUTHOR]

Featured Application: This review subscribes to the MiDiNA project as a part of the European Union-African Union partnership for renewable energy, 'LEAP-RE'. This review will shed light on the energy transition in North African countries and the resilient controls for improving power availability in microgrids. It seeks to draw attention to the advantages and drawbacks of each type of control as well as discuss the specifications for resilient controls that are necessary for microgrids in the North African environment. The ambition of making North Africa a hub for renewable energies and green hydrogen has prompted local governments and the private sector to work together towards boosting the growth of locally available, sustainable energy resources. Numerous climate and energy challenges can be addressed by microgrid technologies, which enable cost-effective incorporation of renewable energy resources and energy storage systems through smart management and control infrastructures. This paper discusses the ongoing energy transition in the countries of North Africa, highlighting the potential for renewable energy sources as well as regional obstacles and challenges. Additionally, it explores how robust and stable controls and advanced management strategies can improve microgrids' performances. Special attention is given to assessing the advantages and disadvantages of conventional and advanced controllers, with an emphasis on resilience needed within the harsh North African environment. [ABSTRACT FROM AUTHOR]

Flood control is a global problem; increasing number of flooding disasters occur annually induced by global climate change and extreme weather events. Flood studies are important knowledge sources for flood risk reduction and have been recorded in the academic literature. The main objective of this paper was to acquire flood control knowledge from long-tail data of the literature by using deep learning techniques. Screening was conducted to obtain 4742 flood-related academic documents from past two decades. Machine learning was conducted to parse the documents, and 347 sample data points from different years were collected for sentence segmentation (approximately 61,000 sentences) and manual annotation. Traditional machine learning (NB, LR, SVM, and RF) and artificial neural network-based deep learning algorithms (Bert, Bert-CNN, Bert-RNN, and ERNIE) were implemented for model training, and complete sentence-level knowledge extraction was conducted in batches. The results revealed that artificial neural network-based deep learning methods exhibit better performance than traditional machine learning methods in terms of accuracy, but their training time is much longer. Based on comprehensive feature extraction capability and computational efficiency, the performances of deep learning methods were ranked as: ERNIE > Bert-CNN > Bert > Bert-RNN. When using Bert as the benchmark model, several deformation models showed applicable characteristics. Bert, Bert-CNN, and Bert-RNN were good at acquiring global features, local features, and processing variable-length inputs, respectively. ERNIE showed improved masking mechanism and corpus and therefore exhibited better performance. Finally, 124,196 usage method and 8935 quotation method sentences were obtained in batches. The proportions of method sentence in the literature showed increasing trends over the last 20 years. Thus, as literature with more method sentences accumulates, this study lays a foundation for knowledge extraction in the future. [ABSTRACT FROM AUTHOR]

Featured Application: The integrated analysis method for understanding the main driving factors shaping forested areas proposed in this paper can provide helpful insight into sustainable and long-term land use systems in Senegal. Understanding the factors influencing the vulnerability of forested areas is crucial for human well-being and effective governance of ecosystem supply and demand. Based on remote sensing data, this study also considered ten natural and human variables as indexes to explore the main influencing factors that may impact the vulnerability of the Thies region's forested areas. The 2005, 2010, 2015, and 2020 satellite image data were processed using ArcGIS 10.6 and ENVI 5.1 software. The methodology includes using the transfer matrix approach and calculating the geographic landscape index to describe the dominant morphology of forested areas. Furthermore, a mixed linear regression model was built to establish the connection between forested areas and the potential contributing components. Our study revealed that the forested areas led to relative fragmentation, with an average of 88 patches for Aggregation Index (AI), 3.25 for Largest Patch Index (LPI), 2.50 for Patch Density (PD), and 112 for Landscape Shape Index (LSI) between 2005 and 2020. In addition, the transfer matrix indicated that the loss of forestry areas was about −78.8 km2 for agricultural land, −127.8 km2 for bare land, and −65.3 km2 for artificial surfaces. The most critical factors that influenced forested areas were agricultural and manufactural added value, rainfall (p < 0.05), slope, distance to the road, and agricultural sown area (p < 0.001). Overall, this investigation has revealed that the effective management of forested areas in the Thies region requires an understandable assessment. It was observed that both human anthropogenic and natural factors significantly contribute to the decline in forested areas. [ABSTRACT FROM AUTHOR]

Peatlands can become valuable resources and greenhouse gas sinks through the use of different management practices. Peatlands provide carbon sequestration; however, they are also among the greatest greenhouse gas emissions sources. The estimated annual carbon dioxide equivalent emissions from peat worldwide are 220 million tons. Novel strategies, methods, and technologies must be developed to enhance the sustainable use of peatlands and achieve climate targets by 2050, as set forth by the European Commission. There is no consensus in the scientific literature on which strategies included in the policy documents are more fruitful for reducing emissions. There are uncertainties and knowledge gaps in the literature that summarise the cons and benefits of each strategy regarding the potential of GHG emission reduction. Currently, peat is undervalued as a resource in the bioeconomy and innovation—a way that could save costs in peatland management. This review paper aims to analyse existing and potential strategies to minimise greenhouse gas emissions from peatlands. Studies show significant debates in the literature on whether the rewetting of peatlands and afforestation of previously drained peatlands can be defined as restoration. A more effective management of peatland restoration should involve combining restoration methods. The rewetting of peatlands should be realised in combination with top-soil removal to minimise methane emissions. The rewetting of peatlands should be used only in combination with revegetation after rewetting. One of the promising solutions for methane emission reduction could be paludiculture using sphagnum species. Products from paludiculture biomass can reduce GHG emissions and store long-term emissions in products. Paludiculture can also be the solution for further income for landowners and innovative products using the biomass of harvested paludiculture plants. [ABSTRACT FROM AUTHOR]

With the warming and humidification process in the Northwest Arid Zone over the past 30 years, the runoff of a vast majority of rivers has been affected to different degrees. In this paper, the runoff from the Weigan River, a typical inland river in the arid zone of Northwest China, is taken as an example, and seven types of CMIP6 data are selected with the help of a SWAT model to predict the runoff volume of the Weigan River in the next 30 years under climate change. The results show that (1) the SWAT model can simulate the runoff from the Weigan River well and has good applicability in this study area. (2) With an increase in radiative forcing, the temperature, precipitation and runoff in the study area show an increasing trend. (3) Under the four radiative forcing scenarios in 2030 and 2050, the runoff from the Weigan River out of the mountain is predicted to be maintained at 25.68 to 30.89 × 108 m3, which is an increase of 1.35% to 21.91% compared with the current runoff, of which the contribution to the increase in future runoff caused by the changes in temperature and precipitation is 68.71% and 27.24%, respectively. It is important to explore the impact of climate change on the runoff from the Weigan River to understand the impact of climate change on the Northwest Arid Region scientifically and rationally, and to provide a scientific basis for evaluating the risk of climate change and formulating policies to deal with it. [ABSTRACT FROM AUTHOR]

As a consequence of climate change impact, a significant variation in terms of temperature, atmospheric humidity, and carbon dioxide concentration levels is happening. This condition leads to several negative effects on the safety and the life cycle of existing concrete structures, such as the increase in the rate of material degradation, due to corrosion phenomena. In fact, the presence of carbonation and corrosion phenomena significantly influence the load-bearing capacity of existing reinforced concrete (RC) structures, under both static and dynamic loads. Among the wide range of existing RC constructions, bridges stand out for their importance. Furthermore, as structures directly exposed to the weather effects, they are more susceptible to these phenomena. In this paper, the influence of corrosion on existing RC motorway viaducts' seismic behavior, considering the impact of climate change, is investigated, by means of an efficient procedure based on the implementation of 3D simplified finite element models and the use of analytical relations to obtain the amount of reduction in the steel reinforcement area as a function of the age of the bridge and of the different corrosion scenarios analyzed. Several scenarios for the expected variations in CO2 concentrations, temperature, and relative humidity are evaluated, considering that most of the viaducts present in the Italian motorway network were built between the 1960s and the 1970s. The results obtained using the projection of climate change impacts are compared with those calculated considering the corrosion scenarios resulting from the DuraCrete research project, to understand if the evolution of climate change leads to worse scenarios than those previously assessed. [ABSTRACT FROM AUTHOR]

Water temperature in lakes is their basic property that determines many processes. Changes in average values are thoroughly investigated using a long-term approach. Knowledge of extreme situations such as temperature minimums is still insufficient. This paper analyses changes in the minimum temperature in 10 lakes in Poland in the period 1972–2021. The obtained results show variability over the course of the parameter, both at the annual and monthly scale. In the first case, half of the analysed set showed statistically significant increasing trends (on average 0.10 °C per decade). In the latter case, the greatest changes occurred in the months of the warm half-year (on average 0.57 °C per decade). The reported situation is caused by the individual conditions of particular lakes, i.e., their location, morphometric parameters, or the dynamics of the occurrence of ice phenomena. A successive increase in the minimum temperature in lakes has its consequences for biotic and abiotic processes. Exceeding specific thresholds results in the evident transformation of these ecosystems. [ABSTRACT FROM AUTHOR]

This paper offers a comprehensive overview of machine learning (ML) methodologies and algorithms, highlighting their practical applications in the critical domain of water resource management. Environmental issues, such as climate change and ecosystem destruction, pose significant threats to humanity and the planet. Addressing these challenges necessitates sustainable resource management and increased efficiency. Artificial intelligence (AI) and ML technologies present promising solutions in this regard. By harnessing AI and ML, we can collect and analyze vast amounts of data from diverse sources, such as remote sensing, smart sensors, and social media. This enables real-time monitoring and decision making in water resource management. AI applications, including irrigation optimization, water quality monitoring, flood forecasting, and water demand forecasting, enhance agricultural practices, water distribution models, and decision making in desalination plants. Furthermore, AI facilitates data integration, supports decision-making processes, and enhances overall water management sustainability. However, the wider adoption of AI in water resource management faces challenges, such as data heterogeneity, stakeholder education, and high costs. To provide an overview of ML applications in water resource management, this research focuses on core fundamentals, major applications (prediction, clustering, and reinforcement learning), and ongoing issues to offer new insights. More specifically, after the in-depth illustration of the ML algorithmic taxonomy, we provide a comparative mapping of all ML methodologies to specific water management tasks. At the same time, we include a tabulation of such research works along with some concrete, yet compact, descriptions of their objectives at hand. By leveraging ML tools, we can develop sustainable water resource management plans and address the world's water supply concerns effectively. [ABSTRACT FROM AUTHOR]

Featured Application: The featured application of this paper focuses on the impact of climate change on ecosystem services provided by the Phyllognathus excavatus beetle, specifically its role in recycling organic matter. The study highlights that dung beetles, including Phyllognathus excavatus, play a crucial ecological role in nutrient cycling, waste decomposition, and soil improvement. These beetles facilitate the breakdown of organic matter, such as dung, and contribute to the recycling of nutrients back into the ecosystem. However, climate change can disrupt the life cycles and habitat suitability of these beetles, affecting their ability to perform their recycling function effectively. Changes in temperature and precipitation patterns can alter the availability of fresh dung, which is essential for successful reproduction and larval development. Shifts in rainfall patterns and increased temperatures can also impact the timing of dung decomposition and alter the composition of microbial communities associated with dung, indirectly affecting the survival and fitness of dung beetles. Determining the resilience of ecosystem services, such as recycling, and creating effective conservation strategies to mitigate the negative effects of climate change on these significant ecological processes depend on our ability to understand the potential effects of climate change on Phyllognathus excavatus and other dung beetles. Climate change poses a significant threat to ecosystems, food security, and human well-being. This study focuses on the Phyllognathus excavatus beetle, an important insect species in the Mediterranean region with ecological importance in nature recycling of organic wastes. The aim of this study is to assess its current habitat suitability and predict its distribution under future climate scenarios. The beetle's occurrence records were gathered and climate information, including 19 bioclimatic variables, was retrieved from the Global Biodiversity Informatic Facility (GBIF) and WorldClim depository, respectively. The MaxEnt algorithm was used to calculate habitat appropriateness using geographic information systems (GISs) and species distribution modeling (SDM) with an accuracy of 0.907 using the AUC test. The findings show that the annual mean temperature is the most important factor, with the beetle flourishing in temperatures between 13.9 and 19.1 °C. The distribution is greatly impacted by the mean temperature of the warmest quarter. Future projections using different climate scenarios suggest potential changes in the beetle's distribution. By integrating climate data and occurrence records, this study provides insights into the vulnerability of Phyllognathus excavatus to climate change and identifies regions where its habitat may be at risk as 81% of its current habitat will be lost. The research helps to prioritize efforts to reduce the harmful effects of climate change on insect biodiversity and to design effective conservation strategies. Overall, this study advances our knowledge of the Phyllognathus excavatus beetle's present and projected distribution patterns in the Mediterranean region under the influence of climate change. It illustrates the significance of taking into account how climate change would affect insect populations and the use of SDM and GIS tools for researching and protecting insect biodiversity. [ABSTRACT FROM AUTHOR]

To cope with climate change and other environmental problems, countries and regions around the world have begun to pay attention to the development of renewable energy under the drive of achieving the global carbon emission peak and carbon neutrality goal. The distributed photovoltaic (PV) power grid is an effective solution that can utilize solar energy resources to provide clean a energy supply. However, with the continuous grid connection of distributed energy, it poses great challenges to the power supply stability and security of the grid. Therefore, it is particularly important to promote the local consumption of distributed energy and the construction of the energy internet. This paper aims to study the cooperative operation and energy optimization scheduling problem among distributed PV power grids, and proposes a new scheme to reduce the electricity cost under the constraint of power supply and demand balance. The optimization problem is modeled as a Markov decision process (MDP), and a Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is used to solve the MDP problem. Simulation results show that the proposed algorithm outperforms other benchmark algorithms in terms of reducing electricity cost, convergence and stability, and verifies its effectiveness. [ABSTRACT FROM AUTHOR]

According to the climate change scenario, climate change in the Korean Peninsula is expected to worsen due to extreme temperatures, with effects such as rising average temperatures, heat waves, and droughts. In Republic of Korea, which relies on foreign countries for the supply of forage crops, a decrease in the productivity of forage crops is expected to cause increased damage to the domestic livestock industry. In this paper, to solve the issue of climate vulnerability for forage crops, we performed a study to predict the productivity of forage crops in relation to climate change. We surveyed and compiled not only forage crop production data from various regions, but also experimental cultivation production data over several years from reports of the Korea Institute of Animal Science and Technology. Then, we crawled related climate data from the Korea Meteorological Administration. Therefore, we were able to construct a basic database for forage crop production data and related climate data. Using the database, a production prediction model was implemented, applying a multivariate regression analysis and deep learning regression. The key factors were determined as a result of analyzing the changes in forage crop production due to climate change. Using the prediction model, it could be possible to forecast the shifting locations of suitable cultivation areas. As a result of our study, we were able to construct electromagnetic climate maps for forage crops in Republic of Korea. It can be used to present region-specific agricultural insights and guidelines for cultivation technology for forage crops against climate change. [ABSTRACT FROM AUTHOR]

The past decade has witnessed a growing demand for drone-based fire detection systems, driven by escalating concerns about wildfires exacerbated by climate change, as corroborated by environmental studies. However, deploying existing drone-based fire detection systems in real-world operational conditions poses practical challenges, notably the intricate and unstructured environments and the dynamic nature of UAV-mounted cameras, often leading to false alarms and inaccurate detections. In this paper, we describe a two-stage framework for fire detection and geo-localization. The key features of the proposed work included the compilation of a large dataset from several sources to capture various visual contexts related to fire scenes. The bounding boxes of the regions of interest were labeled using three target levels, namely fire, non-fire, and smoke. The second feature was the investigation of YOLO models to undertake the detection and localization tasks. YOLO-NAS was retained as the best performing model using the compiled dataset with an average mAP50 of 0.71 and an F1_score of 0.68. Additionally, a fire localization scheme based on stereo vision was introduced, and the hardware implementation was executed on a drone equipped with a Pixhawk microcontroller. The test results were very promising and showed the ability of the proposed approach to contribute to a comprehensive and effective fire detection system. [ABSTRACT FROM AUTHOR]

The generation and propagation of internal waves in the ocean are a common phenomenon that plays a pivotal role in the transport of mass, momentum, and energy, as well as in global climate change. Internal waves serve as a critical component of oceanic processes, contributing to the redistribution of heat and nutrients in the ocean, which, in turn, has implications for global climate regulation. However, the automatic identification of internal waves in oceanic regions from remote sensing images has presented a significant challenge. In this research paper, we address this challenge by designing a data augmentation approach grounded in a modified deep convolutional generative adversarial network (DCGAN) to enrich MODIS remote sensing image data for the automated detection of internal waves in the ocean. Utilizing t-distributed stochastic neighbor embedding (t-SNE) technology, we demonstrate that the feature distribution of the images produced by the modified DCGAN closely resembles that of the original images. By using t-SNE dimensionality reduction technology to map high-dimensional remote sensing data into a two-dimensional space, we can better understand, visualize, and analyze the quality of data generated by the modified DCGAN. The images generated by the modified DCGAN not only expand the dataset's size but also exhibit diverse characteristics, enhancing the model's generalization performance. Furthermore, we have developed a deep neural network named "WaveNet," which incorporates a channel-wise attention mechanism to effectively handle complex remote sensing images, resulting in high classification accuracy and robustness. It is important to note that this study has limitations, such as the reliance on specific remote sensing data sources and the need for further validation across various oceanic regions. These limitations are essential to consider in the broader context of oceanic research and remote sensing applications. We initially pre-train WaveNet using the EuroSAT remote sensing dataset and subsequently employ it to identify internal waves in MODIS remote sensing images. Experiments show the highest average recognition accuracy achieved is an impressive 98.625%. When compared to traditional data augmentation training sets, utilizing the training set generated by the modified DCGAN leads to a 5.437% enhancement in WaveNet's recognition rate. [ABSTRACT FROM AUTHOR]

Featured Application: We provide a case study for the Wildfire Response, Risk Mitigation and Recovery and a methodology for research-to-commercialization (R2C) for analytics of value using solutions-oriented science. Global climate change and associated environmental extremes present a pressing need to understand and predict social–environmental impacts while identifying opportunities for mitigation and adaptation. In support of informing a more resilient future, emerging data analytics technologies can leverage the growing availability of Earth observations from diverse data sources ranging from satellites to sensors to social media. Yet, there remains a need to transition from research for knowledge gain to sustained operational deployment. In this paper, we present a research-to-commercialization (R2C) model and conduct a case study using it to address the wicked wildfire problem through an industry–university partnership. We systematically evaluated 39 different user stories across eight user personas and identified information gaps in public perception and dynamic risk. We discuss utility and challenges in deploying such a model as well as the relevance of the findings from this use case. We find that research-to-commercialization is non-trivial and that academic–industry partnerships can facilitate this process provided there is a clear delineation of (i) intellectual property rights; (ii) technical deliverables that help overcome cultural differences in working styles and reward systems; and (iii) a method to both satisfy open science and protect proprietary information and strategy. The R2C model presented provides a basis for directing solutions-oriented science in support of value-added analytics that can inform a more resilient future. [ABSTRACT FROM AUTHOR]

Featured Application: The Intergovernmental Panel on Climate Change (IPCC)'s Sixth Assessment Report (AR6) recognizes the physico-chemical process known as carbonation. The present paper utilizes a way of calculating carbon dioxide uptake quickly and easily following the simplified methodology known as Tier 1 applied to the largest cement consumer in South America. The worldwide cement industry plays an important role in addressing the climate change challenge. Brazil's cement industry currently has 91 cement plants with an installed production capacity of 94 million tons per year and has started to calculate the net CO2 emissions to achieve a carbon-neutral cement sector by 2050. Accordingly, the carbon dioxide uptake due to mortar and concrete carbonation is subtracted from the carbon dioxide emitted by the chemical reaction for the calcination of lime, i.e., the calcination process performed during clinker production. Now-adays, the Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas (GHG) Inventories to report the GHG emissions do not include any calculation procedure to consider the mortar and concrete carbonation. However, the Intergovernmental Panel on Climate Change (IPCC)'s Sixth Assessment Report (AR6) recognizes the physico-chemical process known as carbonation. Brazilian net carbon dioxide emissions of cements produced from 1990 to 2019 are estimated considering the carbon dioxide uptake during the service-life and end-of-life and secondary usage stages (Tier 1). This is a fundamental scientific and technological novelty that changes the current approach to estimate the carbon dioxide emissions due to the Portland cement clinker production. Even considering the relative novelty of this approach, it should be promoted in the future and included in the national inventory report (NIR). The carbon dioxide uptake by mortar and concrete carbonation for 30 years is about 140 million tons. Within this thirty-year period about 483 million tons have been released due to the calcination process. [ABSTRACT FROM AUTHOR]

The continuous cumulative worsening impact of climate change on heritage sites represents a new challenge for most of the nonrenewable resources of heritage sites and buildings in general; this is especially true with respect to coastal heritage sites, which are facing a more dangerous situation as the climate becomes more extreme in coastal areas and sea levels rise, putting heritage sites at risk. A strict adaptation plan, usually made for reducing the impact of climate change, may not be the solution, as different heritage site locations, materials, and hazard types need tailored plans. Therefore, in this research paper, a resilience approach was introduced to help adapt the most problematic sites to the impacts of climate change, i.e., coastal heritage sites. To fulfill the objective of achieving adaptation in a resilient way that can easily be developed in relation to different types of sites, mixed research methods were used. First, the literature was reviewed using the Connected Papers tool. Then, machine learning methods were used to process and analyze the input data of the resilience adaptation plan for an Egyptian coastal heritage site case study, i.e., Alexandria. Next, the data were arranged and analyzed, highlighting the main classifying algorithms responsible for identifying the resilience range, using the machine learning software packages Infra Nodus and WEKA, according to the differences in the climate change impact at the heritage sites. The final outcome of this research is a resilience approach that can be adapted to rescue plans for coastal heritage sites via machine learning. [ABSTRACT FROM AUTHOR]

Several factors control the vulnerability of historic geostructures to climate change. These factors are both temporally and spatially variable depending on construction techniques and climatic conditions. This paper provides a review of both the historical construction practices on the UK transport network and recent developments in the understanding of climate change effects, allowing for an assessment of the impact of climate change on existing geostructures. Geostructures in the UK can be split between pre-regulation and post-regulation construction techniques. In general, highways were constructed after the implementation of modern regulations and are therefore less vulnerable to climate change due to formalisation of construction methods. In comparison, the performance of the railway network has shown to be inferior due to historic construction practices including poor or absent compaction, lack of consideration for foundations, or selection of fill materials. Recent findings have shown that the impacts of climate change are also a multiscale problem, influenced not only by regional geology but also the pore structure of soils and its evolution. While the research into these impacts is critical, the limitations of common methods employed to survey these structures and study the behaviour of their constituent materials requires consideration. In this paper, these aspects are examined in detail in a bid to integrate holistically the complexity of the systems involved. [ABSTRACT FROM AUTHOR]

Semiconductor and display industries in the Republic of Korea make up the global electronics market with some of the greatest potential for growth due to accelerated digital transformation. Greenhouse gases (GHGs) present in the Earth's atmosphere could trap heat and contribute to the greenhouse effect, leading to global warming and climate change, and it is important to note that while GHGs are naturally present in the atmosphere and play a crucial role in regulating the Earth's temperature, human activities have significantly increased their concentration, leading to accelerated global warming and climate change. Volatile fluorinated compounds (FCs), including perfluorocompounds (PFCs), hydrofluorocompounds (HFCs), NF3, and SF6, are potent long-standing greenhouse gases that are used and emitted by electronics during the manufacturing and display stages of semiconductors. In accordance with global climate change, GHG reduction has developed as a demand of the times, and the electronics industry has also made efforts to reduce GHG emissions in response. Until now, process emissions from the use of fluorinated greenhouse gases (F-GHGs) in various industries have been calculated according to the '06 IPCC G/L, and emission factors of '06 IPCC G/L have also been applied. However, the reduction and emission factors proposed in the IPCC G/L are values that do not reflect the latest and advanced reduction technologies in South Korean electronics, and national GHG emissions are overestimated. In this paper, by preparing accurate measurement methods for destruction removal efficiency (DRE), the use rate of gas (Ui), and b-product emission factors (Bby-product, i), which are characteristic parameters for estimating GHG Tier 3a emissions, we aim to increase the accuracy of GHG emissions by advancing emission factors that are unique to the semiconductor and display industries within the Republic of Korea. [ABSTRACT FROM AUTHOR]

Featured Application: The application of this research in agriculture is related to the rapid progression of climate change, which has drastic effects on crops. We can build an accurate and robust model that can help identify the optimum planting day to maximise the crop yield, based on the weather forecast and meteorological conditions of the region. This can support farmers in their agricultural activity planning and help minimise the impact of weather anomalies. Machine learning is often used to predict crop yield based on the sowing date and weather parameters in non-irrigated crops. In the context of climate change, regression algorithms can help identify correlations and plan agricultural activities to maximise production. In the case of sunflower crops, we identified datasets that are not very large and have many missing values, generating a low-performance regression model. In this paper, our aim is to study and compare several approaches for missing-value imputation in order to improve our regression model. In our experiments, we compare nine imputation methods, using mean values, similar values, interpolation (linear, spline, pad), and prediction (linear regression, random forest, extreme gradient boosting regressor, and histogram gradient boosting regression). We also employ four unsupervised outlier removal algorithms and their influence on the regression model: isolation forest, minimum covariance determinant, local outlier factor and OneClass-SVM. After preprocessing, the obtained datasets are used to build regression models using the extreme gradient boosting regressor and histogram gradient boosting regression, and their performance is compared. The evaluation of the models shows an increased R 2 from 0.723 when removing instances with missing data, to 0.938 for imputation using Random Forest prediction and OneClass-SVM-based outlier removal. [ABSTRACT FROM AUTHOR]

This paper presents a multidisciplinary approach to understand the impacts of temperature increase on the retention of particulate pollutants, such as heavy metals and microplastics, by the road pavement material. A soil with a particle size distribution similar to road dust was selected. A Wheel Tracking device was used to assess the permanent deformation behavior of prismatic specimens and the retention of the dust, at controlled temperatures of 40 °C and 60 °C, likely to occur on pavement in the future. The soil representing road pollutants was placed at the top of the slabs prior to the Wheel Tracking Test (WTT), based on the European Standard EN 12697-22:2020. After the WTT, two common methods were used, in order to evaluate the soil retention (pollution accumulation) on road pavement. The results confirm that the viscoelastic behavior of bituminous mixtures under increased temperatures can contribute to particle retention at the pavement. Future studies are needed to understand the phenomena, the retention characteristics by different bituminous mixtures, and the efficiency of pollutants capture. The work opens the opportunity to develop innovative road pavement bituminous mixtures that can reduce the discharge of road particulate pollutants, and have increased resilience and sustainability in extreme weather conditions. [ABSTRACT FROM AUTHOR]

Featured Application: Authors are encouraged to provide a concise description of the specific application or a potential application of the work. This section is not mandatory. Waste-to-energy (WtE) incineration is an important technique in waste management systems and waste hierarchy. It is used to treat approximately 63% of the waste in European countries. The flue gas volumetric rate and its composition are essential to determine and monitor the emissions from waste incineration plants. This paper presents two methodologies used to evaluate the emissions from incinerators during the design phase. The first consists of a set of equations applicable in Excel (calculation model), while the second is the built-in components in Ebsilon 13.2 software which simulates the emissions from a furnace. This paper also proposes a comprehensive flue gas cleaning system for a simulated waste incineration plant in Jordan. According to Ebsilon, the results showed that for a 25 kg/s loading rate, there was 258,514 mg/Nm3, 749.90 mg/Nm3, 890.20 mg/Nm3, and 717 mg/Nm3 of CO2, NO2, SO2, and HCL, respectively. It was noted that these values relate to 1.5 of excess air ratio, where the effect of excess air ratio as the main driver for any combustion process was examined. The calculation method (set of equations) evaluated the flue gas volumetric rate, the CO2 emissions, and N2O and SO2 levels. Ebsilon allows for simulation of the treatment stages and calculates the amount of materials required. Selective non-catalytic reduction (SNCR) (a built-in component in the Ebsilon library) was used to treat the NO2 emissions. For 1.5 of excess air ratio, those emissions were reduced from 749 mg/Nm3 to 180 mg/Nm3, while the Ca(OH)2 injector used to treat the SO2 and HCL emissions reduced emissions from 890.20 mg/Nm3 and 717 mg/Nm3 to 44 mg/Nm3 and 7.16 mg/Nm3, respectively. Regarding the reduction in CO2, the spherical carbon absorption concept was simulated using 9.4 kg/s of carbon which was adequate to verify a 91% reduction rate of CO2. Furthermore, the calculation model was validated and approved as a valuable model to predict the flue gas volume, the oxygen required, and flue gas emissions at the design stage. [ABSTRACT FROM AUTHOR]

Climate change is a global problem facing mankind, and achieving peak CO2 emissions and carbon neutrality is an important task for China to respond to global climate change. The quantitative evaluation of the trends of urban energy consumption and carbon emissions is a premise for achieving this goal. Therefore, from the perspective of urban expansion, this paper analyzes the complex relationship between the mutual interactions and feedback between urban population, land expansion, economic growth, energy structure and carbon emissions. STELLA simulation software is used to establish a system dynamics model of urban-level carbon emissions effects, and Changsha city is used for the case study. The simulated outputs of energy consumption and carbon emissions cover the period from 1949 to 2016. From 1949 to 2016, Changsha's total energy consumption and carbon emissions per capita have continuously grown. The total carbon emissions increased from 0.66 Mt-CO2 to 60.95 Mt-CO2, while the per capita carbon emissions increased from 1.73 t-CO2/10,000 people to 18.3 Mt-CO2/10,000 people. The analysis of the structure of carbon emissions shows that the industrial sector accounted for the largest proportion of emissions, but it had gradually dropped from between 60% and 70% to about 40%. The carbon emissions of residential and commercial services accounted for less than 25%, and the proportion of transportation carbon emissions fluctuated greatly in 2013 and 2016. From the perspective of carbon emissions effects, carbon emissions per unit of GDP had a clear downward trend, from 186.11 t-CO2/CNY104 to 1.33 t-CO2/CNY104, and carbon emissions per unit of land showed two inflection points: one in 1961 and the other in 1996. The general trend showed an increase first, followed by a decrease, then a stabilization. There is a certain linear correlation between the compactness of urban shape and the overall trend of carbon emissions intensity, while the urban shape index has no linear correlation with the growth rate of carbon emissions. The carbon emissions assessment model constructed in this paper can be used by other municipalities, and the assessment results can provide guidance for future energy planning and decision making. [ABSTRACT FROM AUTHOR]

Urban infrastructure (UI), subject to ever-increasing stresses from artificial activities of human beings and natural disasters due to climate change, assumes a key role in modern cities for maintaining their functional operations. Therefore, understanding UI resilience turns essential. Based on the Pressure-State-Response (PSR) model, this paper built a comprehensive evaluation index system for urban infrastructure resilience evaluation. Four municipalities, including Beijing, Tianjin, Shanghai, and Chongqing in China, were selected for the case study, given their specific significance in terms of geographical location and urban infrastructure scale. Temporal differences of UI resilience in those four cities during 2002–2018 were explored. The results showed that: (1) The various stages of PSR relative importance for the urban infrastructure resilience development in the four cities were different. The infrastructure status, primarily resource environmental benefit, had the most significant effect on urban infrastructure resilience, accounting for 38.73%. (2) While Shanghai ranked first, the levels of urban infrastructure resilience in four cities were generally poor in 2002–2018 with continuously low resilience. (3) Significant differences were found in the resilience levels associated with the three stages of pressure, state and response failing to form a positive development cycle, with the poorest pressure resilience. This paper puts forward some recommendations for providing scientific support for urban resilient infrastructure development in four municipalities in China. [ABSTRACT FROM AUTHOR]

Forecasting of PM2.5 concentration is a global concern. Evidence has shown that the ambient PM2.5 concentrations are harmful to human health, climate change, plant species mortality, etc. PM2.5 concentrations are caused by natural and anthropogenic activities, and it is challenging to predict them due to many uncertain factors. Current research has focused on developing a new model while overlooking the fact that every single model for PM2.5 prediction has its own strengths and weaknesses. This paper proposes an ensemble framework which combines four diverse learning models for PM2.5 forecasting in Puli, Taiwan. It explores the synergy between parametric and non-parametric learning, and short-term and long-term learning. The feature set covers periodic, meteorological, and autoregression variables which are selected by a spiral validation process. The experimental dataset, spanning from 1 January 2008 to 31 December 2019, from Puli Township in central Taiwan, is used in this study. The experimental results show the proposed multi-model framework can synergize the advantages of the embedded models and obtain an improved forecasting result. Further, the benefit obtained by blending short-term learning with long-term learning is validated, in surpassing the performance obtained by using just single type of learning. Our multi-model framework compares favorably with deep-learning models on Puli dataset. It also shows high adaptivity, such that our multi-model framework is comparable to the leading methods for PM2.5 forecasting in Delhi, India. [ABSTRACT FROM AUTHOR]

A render is the first protective layer of exterior walls against the outdoor climate, which, due to its constitution, aims to gradually slow down the liquid moisture penetration to prevent it from reaching the wall inner layers. Due to the future expected changes in the outdoor climate, today's exterior mortar might not be adequately designed to protect these walls. This paper aims to analyse the influence of mortars on the hygrothermal performance of solid brick walls under current and future climates. The study includes four types of assemblies and three types of mortars, and it was carried out for Lisbon and three other climates by using a computational simulation tool. Finally, the moisture gains and respective reach due to the future wind-driven rain (WDR) spells were assessed by means of using future weather files for Lisbon's climate. It was shown that the solid brick layer is influenced differently depending on the characteristics of the mortar layers and outdoor conditions. In terms of WDR spells, aside from the precipitation and the spell period, the distribution of the WDR events within the spell also conditions the dryness of the assembly. The depth that the outdoor moisture was able to reach varies between 94 and 200 mm. [ABSTRACT FROM AUTHOR]

Increased temperature due to global climate change is increasing the magnitude and frequency of extreme winds making billboard structures more vulnerable. This paper proposes a methodology to determine the structural safety of flexible outdoor single-post billboards. A CFD model of a flexible single-post billboard was performed as an example. Resultant wind forces were obtained for the previous model using different wind speeds. A mechanical numerical model of the billboard was realized, and this was subjected to the resultant wind forces. Internal forces for the most vulnerable places of the billboard were obtained for all different adopted wind speeds. Next, a reliability analysis of the billboard was performed considering several values for the bias factor and coefficient of variation for the internal forces caused by wind. Safety levels determined from the reliability analysis indicate that a billboard designed with a nominal wind speed of 180 km/h cannot achieve the target probability index of 3.2 for wind speed higher or equal to 200 km/h for any of the adopted probabilistic parameters. Significant differences in the found safety levels for the evaluated probability parameters indicate that billboard structures could undergo safety values below the target one with changes in the case where wind characteristics endanger this type of structure. [ABSTRACT FROM AUTHOR]

Potable water utilities are currently making great efforts to reduce leakage rates and assure long-term supply to the population due to the challenges of climate change, growing population and water shortage scenarios that have been on them over the last years. One of the most employed methods to reduce leakage includes the installation of pressurereducing valves along the water distribution network and the utilization of pressure management schemes. Pressure management includes different types of control models, which are applied according to the requirements of each site. The most advanced and sophisticated scheme is critical point control, which relies on a flow signal from a measuring device or online communication between the critical point and the valve. This paper proposes the utilization of a seasonal autoregressive integrated moving average, or the SARIMA model, to correlate pressure at the outlet of the valve and pressure on the critical point of the area supplied, aiming to set a fixed pressure in the critical point. The SARIMA model is developed according to historical data logged in the field and then validated. Later, the SARIMA model was tested on a real location in the village of Noja, Spain. The analysis of the field test results prove that the proposed model is feasible to be used since there is no significance difference between the target values set in the critical point and the real values measured in the field. The research proves that the SARIMA model can be used as an alternative for critical point control in water distribution networks when no flow signal is available or when communication between the critical point and the pressure reducing valve is not an option. [ABSTRACT FROM AUTHOR]

The world's population keeps growing together with the construction rate of buildings that need to reduce their environmental footprint in order to mitigate global warming. This paper analyses if the spread of green buildings can be used as an indicator of broader sustainable development. The study is carried out with data taken from Eurostat Database and green buildings directories for 27 EU countries in the 10-year period spanning from 2010 to 2019. The relationship between the indicators is examined through simple linear regressions, and the results confirm the Environmental Kuznets Curve hypothesis: in developed countries, a growing economy (more GDP) is related to an environmental improvement (fewer GHG emissions). In addition, this study proves that the variance of green buildings expresses with a consistent probability the variance of GDP per capita (p-value = 0.0004 and R2 = 0.8475) and the variance of GHG emissions (p-value = 0.0002 and R2 = 0.8825), meaning that green buildings are indeed an indicator of sustainable development. This is due to the upfront cost required to implement advanced construction technologies that ultimately cut GHG emissions during the building lifecycle. This also points out that policy makers should encourage green building implementation through tax relieves and grants. [ABSTRACT FROM AUTHOR]

Coastal regions in Southwest Europe have experienced major interventions and transformations of the territory with unprecedented urban development, primarily related to growing tourism activity. The coast is the place where marine and terrestrial processes converge, making it highly vulnerable to the effects of climate change. However, the lack of information on the frequency of these extreme weather events and their impacts on the coast hampers an accurate analysis of the consequences of global change. This paper provides a detailed analysis of the extreme weather events (EWE) that have affected the Atlantic and Mediterranean coasts of Southwest Europe during the period from 1 January 2009 to 28 February 2020, as well as a quantification of their impacts: fatalities, injuries and economic damage. Official sources from France, Portugal and Spain were consulted, along with technical reports, scientific articles, etc., to generate a unified database. A total of 95 significant extreme events have caused 168 fatalities, 137 injuries and almost €4000 M in direct economic losses. Cyclone Xynthia (February 2010) on the French Atlantic coast stands out, having caused 47 fatalities, 79 injuries and substantial economic losses valued at €3000 M. The study shows a slight upward trend in the number of events recorded, especially during the last three years of the analysis, as well as in human losses and damages. The results reveal a higher exposure of the Mediterranean coast of Southwest Europe when compared to the Atlantic, especially the Spanish Mediterranean coast, with 61% of the fatalities recorded there during the study period. This is primarily due to a model of exponential tourism growth on the Mediterranean coast, with an enormous urban and infrastructure development during the last decades. Traditionally, the Mediterranean coast is less prepared to reduce the effects of marine storms, extreme events that are becoming more frequent and virulent in the context of climate and global change. This work highlights the need to create a continuous monitoring system–at the European level–of the impacts of extreme weather events on the coast, where 40% of the European population is concentrated. This observatory should serve as a source of information for risk mitigation policies (predictive, preventive and corrective), as well as for emergency management during disasters. [ABSTRACT FROM AUTHOR]

In this paper, we critically reflect on the ability of spatial and sectoral planning approaches to deal with regional climate change, especially sea level rise, and analyze the potential of ecosystem service assessments in regional coastal climate change adaptation planning. We present two concrete but contrasting case studies that focus on coastal realignment and wetland restoration as well as seaside tourism and resort development on the German Baltic Sea coast. The ecosystem service assessments compare past, present and potential future local development scenarios. Complementary, we apply the InVest and the Matrix ecosystem service assessment approaches. Despite some shortcomings, the proactive German hierarchical planning system seems suitable to deal with climate change and provide a framework for adaptation measures in coastal zones. It largely prevents a shift of climate change risks between municipalities and stakeholders. Comparative ecosystem service assessments seem well suited to play a role in the planning process by raising awareness about problems, possible solutions and consequences. On a local level, they can provide comprehensive insight into consequences of alternative measures. Our examples show that, in principle, potential future situations can be addressed with comparative ecosystem service approaches. We discuss whether the results of hypothetical future scenarios, with inherent uncertainties, might be misleading and counterproductive. [ABSTRACT FROM AUTHOR]

The increasing presence of non-programmable renewable energy plants increases the intermittency of the electricity supply and thus threatens the adequacy of a power system. Hydropower can solve this problem due to its flexibility. This paper applies statistical approaches to assess water availability in the context of hydropower generation and adequacy analysis on a seasonal basis for one site in Sicily and the other in Sardinia, where major hydroelectric plants are present. First, an empirical relationship between soil moisture content (SMC) and potential evapotranspiration (ET0) is evaluated through linear regression analysis. Then, precipitation trends over the last twenty years are analyzed to determine any effects of global warming on water availability. Finally, Monte Carlo algorithms are used for the stochastic generation of hourly precipitation, direct runoff profiles, and daily SMC profiles. Strong positive and negative correlations between ET0 and SMC (p < 0.05), and R2 ≥ 0.5 are found for both sites, except for summer, and R2 ≥ 0.5 is obtained. The cumulative pH-historical precipitation shows changes in seasonal trends, with evidence of a decrease at the annual level. The algorithms used to synthetically generate hourly precipitation and direct runoff profiles, as well as daily SMC profiles, effectively simulate the statistical variability of the historical profiles of these physical quantities. [ABSTRACT FROM AUTHOR]

The diffusion of mechanical ventilation systems increased rapidly due to the climate changes in all parts of the world. The mechanical ventilation systems are mainly used in the summer for many difficulties to face very hot temperatures. One of the biggest problems considered if every residential unit is equipped with a mechanical ventilation system is the generation of noise by the rotating blades of the fan for refrigeration. This paper discusses the applications of metamaterials to create attenuation filters to be installed inside the encases of the mechanical ventilation systems in order to obtain sound attenuation. A three-dimensional reticular structure made with spheres has been studied in different configurations related to the numbers of layers employed. The sound attenuations were measured at some specific octaves, depending on the particular configurations. In general, the sound attenuation peaks have been measured between 4 kHz and 8 kHz; this is expected to mitigate the tonal noise component typical of fans based on different variables that compose the whole system (e.g., fan diameter, number of blades, fan speed). However, the outcomes shall be considered in terms of laboratory conditions since material properties of the enclosure and potential polarization effects due to reflection of sound waves at the boundaries may occur. [ABSTRACT FROM AUTHOR]

The observation of the sea through remote sensing technologies plays a fundamentalan role in understanding the state of health of marine fauna species and their behaviour. Mesoscale phenomena, such as upwelling, countercurrents, and filaments, are essential processes to be analysed because their occurrence involves, among other things, variations in the density of nutrients, which, in turn, influence the biological parameters of the habitat. Indeed, there is a connection between the biogeochemical and physical processes that occur within a biological system and the variations observed in its faunal populations. This paper concerns the proposal of an automatic classification system, namely the Mesoscale Events Classifier, dedicated to the recognition of marine mesoscale events. The proposed system is devoted to the study of these phenomena through the analysis of sea surface temperature images captured by satellite missions, such as EUMETSAT's Metop and NASA's Earth Observing System programmes. The classification of these images is obtained through (i) a preprocessing stage with the goal to provide a simultaneous representation of the spatial and temporal properties of the data and enhance the salient features of the sought phenomena, (ii) the extraction of temporal and spatial characteristics from the data and, finally, (iii) the application of a set of rules to discriminate between different observed scenarios. The results presented in this work were obtained by applying the proposed approach to images acquired in the southwestern region of the Iberian peninsula. [ABSTRACT FROM AUTHOR]

Due to the increasing global population and climate change, new sustainable food sources are being intensively sought to replace less favorable livestock production. Especially new protein sources and their food applications are being focused on. In this paper, several selected protein sources that may have potential application in future functional foods, such as fermented foods, were examined and compared. These sources include single cell protein (SCP), Arthrospira platensis (Algae), Acheta domesticus (edible insect), potato, and rice protein. The above sources were compared to whey proteins. The parameters studied were total nutritional value, amino acid profile, fatty acid profile, the content of some elements, and the presence of toxins. [ABSTRACT FROM AUTHOR]

Climate change is already happening. It is one of the significant challenges that the planet has to face. Asphalt road pavements cover a large area of the Earth's surface; consequently, climate change challenges can significantly affect their performance and serviceability. Thus, pavement solutions have been developed to address the problem. This paper aims to present an overview of those topics to increase awareness among transportation engineers and practitioners. First, the most significant aspects of road asphalt pavements' materials, design and condition are presented. Second, the most relevant climate change challenges for asphalt pavements are described. Then, different pavement solutions are presented. This overview concludes that there are pavement solutions able to address climate change. These depend on local climate conditions and should be incorporated into the decision-making process in planning, design and maintenance. [ABSTRACT FROM AUTHOR]

International pressures through global agreements such as the recent Paris agreement in 2015 have put stress on governments and industries to find lasting solutions for the built environment. The built environment was recognized as an important factor in reducing global emissions for the first time at the Conference of Parties (COP) 21 meeting in Paris through a dedicated 'Buildings Day.' The Global Alliance for Buildings and Construction (GlobalABC) was also launched at COP 21 as a network to globally support zero emission, efficient and resilient buildings and construction sector. The Paris Agreement brought all nations to collectively combat climate change with a view to limit temperature increases to no more than 2 degrees Celsius (°C). Nations agreed to report their efforts through the monitoring program. In most countries, residential and commercial buildings spend a large proportion of their energy in lighting, heating, ventilation, air conditioning and in various appliances requiring energy for operation. This paper takes a broad understanding of zero energy. Starting with buildings, the definitions also consider understanding zero energy and from a carbon perspective, considering going from beyond buildings to include precincts and cities. The paper brings an understanding of zero energy, its importance, and its urgency with respect to global commitments to reduce the impact of the building and construction sector and the role of governments and industries in supporting the lowering of emissions in the built environment now and in the future. [ABSTRACT FROM AUTHOR]

Featured Application: The presented results can be used to evaluate the energy-saving potential and ecological impact of electric motors of various energy efficiency classes in various applications of electric drive. The high energy intensity of the modern industry and the threat of climate change determine the high urgency of increasing the energy efficiency of electric motors. In this paper, energy consumption, energy costs, payback periods, and CO2 emissions of 75 kW, 4 pole induction motors with direct grid supply in a fixed-speed pump unit are evaluated. Motors of the IE2, IE3, and IE4 efficiency classes according to IEC 60034-30-1 standard are compared in terms of life-time energy savings, payback period, and CO2 emissions. To carry out the analysis, polynomial interpolation of the data from the available manufacturer datasheets of the motors is used. It concluded that even though the initial investment cost of the IE4-motor is higher than that of IE3-motor, the IE4-motor is more profitable if more than 3 years of operation are considered and also provides significant reductions of CO2 emissions. The paper presents a calculation method of the aforementioned indicators which can be useful for companies, researchers, and engineers for quick assessment and selection of technical solutions. [ABSTRACT FROM AUTHOR]

Energy retrofits can enhance the liveability and efficiency of historic buildings while preserving their historic and aesthetic values. However, measures like improved insulation and airtightness may increase their vulnerability to overheating and climate change may further worsen their performance in the future. This paper investigates indoor overheating risks brought by climate change in retrofitted historic buildings and proposes effective adaptation strategies. Firstly, local weather conditions are analysed to identify homogenous climatic zones. For each climatic zone, "a business-as-usual" emissions scenario is adopted, and most representative regional climate models are selected to obtain hourly output of future climate projection. A comparative study is adopted where typical alpine residential buildings, "Portici house", are simulated with regard to future energy use and indoor thermal state using the dynamic model in EnergyPlus. Energy use and indoor thermal conditions are compared before and after energy retrofit, as well as under present and future climate conditions. The results demonstrate that retrofit interventions could significantly improve energy efficiency of historic buildings in both present and future scenarios. A change in climate together with retrofit interventions will, however, result in higher risk of indoor overheating in South Tyrol. Potential negative side effects of energy retrofit could be controlled by adopting adequate shading and ventilation approaches that minimise, or eliminate, the risk of overheating during high temperature periods while optimising historic buildings' energy performance. [ABSTRACT FROM AUTHOR]

When climate change became a global concern in the 1980s, mitigation was considered the best strategy to address all challenges. For a long time, it was thought possible to stabilize atmospheric concentrations of greenhouse gases (GHGs), which, according to many experts, brought on an unfit adaptation. There are international agreements designed to significantly reduce CO2 emissions and achieve carbon neutrality by 2050, but the policy measures taken so far are insufficient to achieve this goal. In addition, the crisis caused by the COVID-19 pandemic highlighted the relevance of placing this issue at the core of international policies and the need for bottom-up measures and options. The purpose of this paper is to explore how collaborative planning can contribute to adapting the urban tourism sector to climate change in the Porto Metropolitan Area (PMA), located in the northern region of mainland Portugal. In this investigation, we used mixed methods based on the following: (1) the discussion of urban tourism's adaptation planning to climate change with undergraduate students; (2) the application of a modified Delphi questionnaire survey, to 47 international researchers and technicians in the first round and 35 international researchers and technicians in the second round, about the predictability of the adaptation measures; and (3) a theoretical-practical workshop aimed to discuss the main action intentions and ways of adaptation in the short and medium term. All empirical data were collected during the year of 2021. This research highlights the need for more detailed information, the weak interaction between stakeholders and the limitation of resources. Our research identifies the main impacts and local vulnerabilities and determines priorities for adaptation and implementation of actions, aimed at mitigating the effects of climate change and maintaining tourism attractiveness in urban areas. In addition, this investigation allowed the definition of a research agenda, which seeks to guide the area of tourism climatology regarding the new challenges imposed by the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

As the cement industry continues to address its role in the climate crisis, Portugal's cement industry has started to calculate its net CO2 emissions to become an entirely carbon neutral sector. These emissions are calculated by simply subtracting the total CO2 uptake due to mortar and concrete carbonation from the total CO2 that is emitted during the calcination process (clinker production). However, the procedures given in the Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas (GHG) Inventories to report GHG emissions do not contain any element that would grant this calculation method the status of an internationally recognized procedure. Therefore, some climate models are not accurate because they do not account for the carbon dioxide uptake due to concrete and mortar carbonation, as is evidenced in this paper. Climate models have improved since the IPCC's Fourth Assessment Report (AR4), but they can further improve by implementing carbon dioxide uptake by cement-based materials. In the present paper, a quick and easy method of evaluating net CO2 emissions is utilized (simplified method) along with an advanced method. Portuguese net CO2 emissions of the cement produced from 2005 to 2015 were calculated while taking carbon dioxide uptake during the service-life and end-of-life and secondary usage stages into account. Following the simplified method, 8.7 million tons of carbon dioxide were found to be uptake by mortars and concretes made with Portuguese cement over the ten-year period, in which 37.8 million tons were released due to the calcination process. In addition, an advanced method has been used to estimate the carbon dioxide uptake, which provided only slightly higher results than that of the simplified method (9.1 million tons). [ABSTRACT FROM AUTHOR]

In addition to the possible contributions of buildings to mitigating CO2 emissions, increased attention is being paid to the potential impacts of climate change on urban environments. According to the United Nations, about 54% of the planet's population currently lives in cities, but this percentage is expected to rise to 66% in 2050, which reveals the scale of this issue. This paper develops a reflection on the possible contributions of water-related building installations to mitigate emissions and increase urban area adaptation to the effects of climate change. One of the most promising solutions to facing climate change, which is analysed in detail in this paper, is combining rainwater harvesting systems with green roofs. However, in view of developing the necessary engineering projects, there are insufficient existing studies to estimate the parameters to be used in each location given their climate characteristics, particularly the monthly runoff coefficients, which constitute the key parameter for designing these installations in some regions. Some recent standards present generic theoretical values for designing these combined installations, but they are far from reality in some regions, such as the Mediterranean basin. Therefore, based on the data available in Portugal, this paper reports some of the results obtained from research on the values of the monthly runoff coefficients. [ABSTRACT FROM AUTHOR]

Extreme droughts have a strong impact on agricultural production. In France, the 2003 drought generated records of yield losses at a national scale for grassland (more than 30%) and for cereals (more than 10% for soft winter wheat and winter barley). These extreme events raise the question of farm resilience in the future. Studying them makes it possible to adapt risk management policy to climate change. Therefore, the objective of this paper was to analyze the frequency and the intensity of extreme drought in 2050 and their impact on crop yield losses (grassland and cereals) in France. We used the DOWKI (Drought and Overwhelmed Water Key Indicator) meteorological index based on a cumulative water anomaly, which can explain droughts and their consequences on agricultural yield losses at a departmental scale. Then, using the ARPEGE-Climat Model developed by Meteo-France, DOWKI was projected in 2050 and grassland, soft winter wheat, and winter barley yield losses were simulated. The results compare the frequency and intensity of extreme droughts between the climate in 2000 and 2050. Our results show that the frequency of extreme droughts (at least as intense as in 2003) doubled in 2050. In addition, the yield losses due to 10-year droughts increased by 35% for grassland and by more than 70% for cereals. [ABSTRACT FROM AUTHOR]

In the context of climate change and energy crisis around the world, an increasing amount of attention has been paid to developing clean energy and improving energy efficiency. The penetration of distributed generation (DG) is increasing rapidly on the user's side of an increasingly intelligent power system. This paper proposes an optimization method for industrial task-continuous load management in which distributed generation (including photovoltaic systems and wind generation) and energy storage devices are both considered. To begin with, a model of distributed generation and an energy storage device are built. Then, subject to various constraints, an operation optimization problem is formulated to maximize user profit, renewable energy efficiency, and the local consumption of distributed generation. Finally, the effectiveness of the method is verified by comparing user profit under different power modes. [ABSTRACT FROM AUTHOR]

Featured Application: We present an analytical model, using a queueing theory framework, that identifies periods of time for air traffic controllers when they can permit the vast majority of approaching aircrafts to land using Continuous Descent Approach, thereby reducing noise, fuel consumption, and pollution, while enhancing air transportation sustainability. Continuous Descent Approach (CDA) is the flight technique for aircraft to continuously descend from cruise altitude with an idle thrust setting and without level-offs, contrary to the staircase-like Step-down Descent Approach (SDA). Important for air transportation sustainability, using CDA reduces noise, fuel consumption, and pollution. Nevertheless, CDA has been limited to low traffic levels at airports, often at night, because it requires more separation distance between aircraft arrivals and, thus, could decrease throughput. Insufficient attention has been given to helping air traffic controllers decide when CDA may be used. In this paper, we calculate the probability that an aircraft arriving during a particular brief period of time (e.g., 15 min) will need to revert to SDA when the controller tentatively plans to permit CDA for all aircrafts arriving during that time period. If this probability is low enough, the controller may plan to permit CDA during that time period. We utilize an analytical approach and queueing theory framework that considers factors such traffic and weather conditions to estimate the probability. We also provide the number of aircrafts that can be accommodated within the airport's stacking space using CDA. This number provides insight into whether a particular aircraft may use CDA. [ABSTRACT FROM AUTHOR]