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Informal settlements' geographic and demographic mapping is essential for evaluating human-centric sustainable development in cities, thus fostering the road to Sustainable Development Goal 11. However, fine-grained informal settlements' geographic and demographic information is not well available. To fill the gap, this study proposes an effective framework for both fine-grained geographic and demographic characterisation of informal settlements by integrating openly available remote sensing imagery, points-of-interest (POI), and demographic data. Pixel-level informal settlement is firstly mapped by a hierarchical recognition method with satellite imagery and POI. The patch-scale and city-scale geographic patterns of informal settlements are further analysed with landscape metrics. Spatial-demographic profiles are depicted by linking with the open WorldPop dataset to reveal the demographic pattern. Taking the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China as the study area, the experiment demonstrates the effectiveness of informal settlement mapping, with an overall accuracy of 91.82%. The aggregated data and code are released (https://github.com/DongshengChen9/IF4SDG11). The demographic patterns of the informal settlements reveal that Guangzhou and Shenzhen, the two core cities in the GBA, concentrate more on young people living in the informal settlements. While the rapid-developing city Shenzhen shows a more significant trend of gender imbalance in the informal settlements. These findings provide valuable insights into monitoring informal settlements in the urban agglomeration and human-centric urban sustainable development, as well as SDG 11.1.1. [ABSTRACT FROM AUTHOR]

In 2015, the United Nations (UN) established 17 Sustainable Development Goals (SDGs) to combat poverty, inequality, and climate change. Recently, integrating these goals into higher education curricula has emerged as essential for fostering positive environmental and civic engagement. Challenges persist, particularly the prioritization of social and economic concerns over eco-centered principles and ecopedagogy. Education must emphasize the politics of environmental issues, advocating sustainable practices that benefit both humans and nonhuman species. This article proposes ten simple rules for incorporating the SDGs into undergraduate courses in ecology, evolution, and environmental science. We conducted a literature review, analyzing 940 publications from 2014 to 2024 using ISI Web of Science and Google Scholar. We focused on fields like Green Sustainable Science and Technology and Environmental Education to identify the best practices for integrating SDGs. Our findings link pedagogical successes to the SDGs, facilitating effective educational strategies. For practical sustainability education, students must grasp the interplay between the environment and societal elements such as diversity, justice, and resilience. Connecting course topics to the SDGs offers a powerful framework for teaching undergraduates about complex environmental challenges. Our research highlights a novel approach to embedding SDGs in environmental education, promoting critical thinking and literacy across various settings. [ABSTRACT FROM AUTHOR]

The Food and Agriculture Organization (FAO) reports that Latin America and the Caribbean experienced the most rapid rise in food insecurity, with approximately 47.7 million individuals in the region affected by hunger in 2022. In Chile, almost three million people, 15.6% of the country's population, do not have regular access to sufficient nutritious food and suffer from some form of food insecurity. Moreover, Chile is particularly susceptible to the impacts of climate change and contends with the depletion of several critical natural resources, notably water, stemming from severe and prolonged drought conditions. This article aims to comprehensively evaluate food loss and waste (FLW) in Chile and analyzes the implications of FLW on the sustainable development goal (SDG). This will be achieved by utilizing a top-down mass balance methodology that integrates various data sources and an in-depth analysis of the main food categories at different stages of the food supply chain (FSC). In 2021, Chile generated 5.18 million tons of FLW, with fruit being the largest contributor at 2.5 million tons (48% of total FLW). Vegetables accounted for 0.8 million tons (16%). Other food groups each contributed 1% to 10% of the total FLW. Per capita FLW was 295 kg. FLW varied by food group and FSC stage. Fruits, vegetables, starchy roots, and pulses had the most FLW early in the FSC, while cereals had it later. Comparing FLW with the domestic supply quantity of food, it is observed that 68% of the fruit available for the population was discarded. Vegetables and starchy roots also showed significant volumes of discarded food, with 48% and 29% of the availability of these products in Chile. Furthermore, we explore the implications of FLW on realizing SDG 2—zero hunger, particularly emphasizing its correlation with target 12.3. The research underscores the potential of its findings to significantly shape public policies and strategies concerning FLW and their alignment with the associated SDGs, making a tangible impact on the lives of millions. [ABSTRACT FROM AUTHOR]

In response to the escalating challenges posed by climate change, the United Nations (UN) proactively formulated the Sendai Framework for Disaster Risk Reduction (SFDRR-2015- 2030) as a comprehensive strategy for disaster risk management (DRM). This framework delineates four priority action objectives, seven global targets, and a set of guiding principles aimed at mitigating the impact of disasters. Notably, each of the seven SFDRR targets is intricately linked with the Sustainable Development Goals (SDG-2030). Given that natural disasters, such as floods, continue to hinder a country's social and economic progress, achieving long-term development becomes challenging. Both frameworks prioritize initiating and investing in innovation, involving all of society's stakeholders, to build a risk-informed and people-centered disaster-resilient society. However, a research gap exists regarding the relationship between Social Innovation (SI) and disaster risk preparedness. To address this, the present study conducts an in-depth literature review and content analysis focused on SI and flood preparedness. The study also aims to offer strategic recommendations for adopting SI to improve preparedness against dam-related flood risks, aligning with SFDRR targets and SDGs-2030. The study's findings are valuable for academia, policymakers, flood risk management agencies, at-risk communities, and stakeholders. By shedding light on the role of SI in flood risk preparedness, the research contributes to existing knowledge and enhances understanding of SI in the context of disaster risk management. [ABSTRACT FROM AUTHOR]

The ambition of Sustainable Development Goal (SDG) target 6.3 is to improve global water quality by 2030. SDG indicator 6.3.2 monitors progress towards this target by assessing water bodies against ‘good’ ambient water quality criteria, with nutrients (nitrogen and phosphorus) as part of the key metrics. However, large data gaps present a fundamental challenge, especially for Africa on how to assess the progress being made with respect to both the current and desired future situations. Here, a continental water quality model for Africa is presented to simulate river sediment load, Total Nitrogen (TN) and Total Phosphorus (TP) loads and concentrations. Furthermore, critical areas and hotspots of TN and TP pollution are mapped for the period 2017 – 2019, in relation to the United Nations Environment Programme (UNEP) target thresholds used for the assessment of SDG indicator 6.3.2. Utilizing the UNEP’s criteria, which designates a water body as having “good ambient water quality” if 80% or more of its monitored values meet their targets, it is estimated that 44 % and 15 % of African rivers fail to meet the set water quality thresholds for simulated TP and TN, respectively. When synthesizing data for both TP and TN, 34 % of the rivers do not qualify as having “good ambient water quality”. Geographically, the most pronounced nutrient pollution hotspots were in North Africa, Niger River Delta, Nile River basin, Congo River basin and specific zones in Southern Africa. These areas correlate with regions characterized by high inputs of fertilizers, manure and wastewater discharge.

This study investigates the application of Machine Learning (ML) algorithms to classify human physical activities and detect different types of falls using smartphone sensor data. Smartphones equipped with various sensors offer new opportunities for healthcare and human activity monitoring, which are crucial for health and well-being, aligning with the United Nations' sustainable development goals (SDG). The study used the "Sensor Data Collector" Android application to collect smartphone sensor data. The data collection experiments involve ten healthy participants engaged in diverse physical activities and different fall events, utilizing accelerometers and gyroscope sensors. Different types of fall detection include forward, backward, left lateral, and right lateral fall, which are important from a seriousness of health point of view. Participants placed their smartphones in the front pocket of their trousers to ensure a fixed position during the data collection process. The collected datasets are preprocessed using a moving average filter to denoise and smoothen the data. Different statistical features and extracted and ML algorithms, namely Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), and K-Nearest Neighbors (KNN), are employed to create robust classifiers. SVM and RF performed exceptionally well in classifying normal physical activities, achieving a 99.09% accuracy. SVM excelled in daily walking activity classification with a 99.59% accuracy, while the K-NN model performed best for crucial different types of fall classification, achieving an accuracy of 85.71%. This research emphasizes the potential of ML models for accurate physical activity and various types of fall classification, expanding the horizons of smartphone-based healthcare applications and aligning with sustainable development goals. [ABSTRACT FROM AUTHOR]

Efficient implementation of clean energy technologies is paramount, with mobile solar PV systems on trailers (MSPTs) emerging as pivotal solutions, particularly in regions with limited power grid access. This endeavour is vital for meeting escalating electricity demands and aligning with the UN Sustainable Development Goal (SDG), aimed at ensuring dependable and sustainable energy provision in developing countries. This study investigates the aerodynamic behaviour of a designed MSPT using numerical simulation and experimental methods, thereby offering optimization potential for MSPT design and enhancing overall performance and reliability. Specifically, the study focuses on the effects of wind velocity and tilt angles on the drag and lift forces, as well as drag and lift coefficients on the panel used in the MSPT system. The overall wind force on the entire MSPT, including nine large solar PV panels, is scrutinised, considering combined wind flow and system geometry effects. The numerical investigations were conducted using ANSYS-Fluent software (version 2022/R2) and experimental testing was performed within the C15-10 Wind Tunnel, utilizing scaled-down models to validate the accuracy of the simulation. The findings from the numerical investigations showed an increased turbulence caused by gaps between panels, resulting in almost 62% higher suction flow velocity and 22% higher suction pressure compared to a single panel. Drag and lift forces on the entire MSPT were approximately 6.7 and 7.8 times greater than those on a single panel with the same 30-degree tilt angle, respectively. The findings revealed that scaling forces on a single panel is insufficient for accurately predicting the aerodynamic forces on the entire MSPT. The insights and the knowledge from this study pave the way for further improvements in mobile solar PV technology. [ABSTRACT FROM AUTHOR]

Abstract For more than one billion people living in coastal regions, coastal aquifers provide a water resource. In coastal regions, monitoring water quality is an important issue for policymakers. Many studies mentioned that most of the conventional models were not accurate for predicting total dissolved solids (TDS) and electrical conductivity (EC) in coastal aquifers. Therefore, it is crucial to develop an accurate model for forecasting TDS and EC as two main parameters for water quality. Hence, in this study, a new hybrid deep learning model is presented based on Convolutional Neural Networks (CNNE), Long Short-Term Memory Neural Networks (LOST), and Gaussian Process Regression (GPRE) models. The objective of this study will contribute to the sustainable development goal (SDG) 6 of the united nation program which aims to guarantee universal access to clean water and proper sanitation. The new model can obtain point and interval predictions simultaneously. Additionally, features of data points can be extracted automatically. In the first step, the CNNE model automatically extracted features. Afterward, the outputs of CNNE were flattened. The LOST used flattened arrays for the point prediction. Finally, the outputs of the GPRE model receives the outputs of the LOST model to obtain the interval prediction. The model parameters were adjusted using the rat swarm optimization algorithm (ROSA). This study used PH, Ca + + , Mg2 + , Na + , K + , HCO3, SO4, and Cl− to predict EC and TDS in a coastal aquifer. For predicting EC, the CNNE-LOST-GPRE, LOST-GPRE, CNNE-GPRE, CNNE-LOST, LOST, and CNNE models achieved NSE values of 0.96, 0.95, 0.92, 0.91, 0.90, and 0.87, respectively. Sodium adsorption ratio, EC, magnesium hazard ratio, sodium percentage, and total hardness indices were used to evaluate the quality of GWL. These indices indicated poor groundwater quality in the aquifer. This study shows that the CNNE-LOST-GPRE is a reliable model for predicting complex phenomena. Therefore, the current developed hybrid model could be used by private and public water sectors for predicting TDS and EC for enhancing water quality in coastal aquifers.

For more than one billion people living in coastal regions, coastal aquifers provide a water resource. In coastal regions, monitoring water quality is an important issue for policymakers. Many studies mentioned that most of the conventional models were not accurate for predicting total dissolved solids (TDS) and electrical conductivity (EC) in coastal aquifers. Therefore, it is crucial to develop an accurate model for forecasting TDS and EC as two main parameters for water quality. Hence, in this study, a new hybrid deep learning model is presented based on Convolutional Neural Networks (CNNE), Long Short-Term Memory Neural Networks (LOST), and Gaussian Process Regression (GPRE) models. The objective of this study will contribute to the sustainable development goal (SDG) 6 of the united nation program which aims to guarantee universal access to clean water and proper sanitation. The new model can obtain point and interval predictions simultaneously. Additionally, features of data points can be extracted automatically. In the first step, the CNNE model automatically extracted features. Afterward, the outputs of CNNE were flattened. The LOST used flattened arrays for the point prediction. Finally, the outputs of the GPRE model receives the outputs of the LOST model to obtain the interval prediction. The model parameters were adjusted using the rat swarm optimization algorithm (ROSA). This study used PH, Ca + + , Mg2 + , Na + , K + , HCO3, SO4, and Cl− to predict EC and TDS in a coastal aquifer. For predicting EC, the CNNE-LOST-GPRE, LOST-GPRE, CNNE-GPRE, CNNE-LOST, LOST, and CNNE models achieved NSE values of 0.96, 0.95, 0.92, 0.91, 0.90, and 0.87, respectively. Sodium adsorption ratio, EC, magnesium hazard ratio, sodium percentage, and total hardness indices were used to evaluate the quality of GWL. These indices indicated poor groundwater quality in the aquifer. This study shows that the CNNE-LOST-GPRE is a reliable model for predicting complex phenomena. Therefore, the current developed hybrid model could be used by private and public water sectors for predicting TDS and EC for enhancing water quality in coastal aquifers. [ABSTRACT FROM AUTHOR]

India's North-Eastern region, known for its cultural diversity and scenic beauty, has confronted distinctive developmental challenges. This diversity has given rise to unique cultural practices, music, dance, and crafts that are renowned across the world. Despite this richness, the region has grappled with developmental challenges such as lack of infrastructure, poor connectivity, poverty, hunger and limited access to healthcare and education. The adoption of the United Nations' Sustainable Development Goals (SDGs) by India in 2015 provides a framework to address these challenges and promote sustainable development in the region. This paper illustrates the progress made in achieving the SDGs in terms of SDG Index released by NITI Aayog and the various initiatives taken by the Government of India in the process. [ABSTRACT FROM AUTHOR]

The rapid development of artificial intelligence (AI) has affected higher education. Students now receive new tools that optimize the performance of current tasks. Universities have also begun implementing AI technologies to help university teachers and improve the quality of educational services and solve the Sustainable Development Goal 4. Hypothetically, it is possible to replace university teachers by using AI technologies. This is a hidden conflict of Sustainable Development Goal 4 and Sustainable Development Goal 8. This research aimed to examine the perceptions of Eastern European students about the possibility of replacing university teachers through AI technologies. The authors used an information study with a bibliometric analysis of 2000 sources, planning the experiments and compiling the questionnaire, surveying 599 students using an electronic questionnaire and cloud technologies, statistical processing questionnaires using Excel tables, and verifying statistical hypotheses. Verification of statistical hypotheses for replies of 599 respondents showed that more than 10% of the surveyed students from Eastern European universities are confident that AI will replace university teachers in five years. It was shown that the opinions of students in the 1st stage (undergraduate study) from the countries of the European Union and countries outside the European Union have significant differences. The obtained results were proven using one-sided testing and standard hypothesis testing level, α = 0.05. The article was completed with multilevel managerial and pedagogical recommendations. These recommendations are designed to increase higher education's sustainability in AI implementation. [ABSTRACT FROM AUTHOR]

Sustainability research is vast and complex, making it difficult to grasp the big picture. To help researchers understand the field better, we created a comprehensive knowledge map of sustainability in management literature by utilising citation‐based clustering and text mining techniques to analyse 25,737 sustainability‐related papers across 22 academic disciplines, as categorised by the Chartered Association of Business Schools (ABS)'s Academic Journal Guide. We identify 23 research streams, with the top three being corporate social responsibility, operations & supply chain and tourism. Each research stream has varying degrees of definition coverage, levels of analysis, levels of interdisciplinarity and connections to the 17 United Nations' Sustainable Development Goals (SDGs). Goals #9, #10, #12 and #13 receive more attention than Goals #3, #14 and #15. Our findings suggest a need for more interdisciplinary collaboration between researchers, and we propose four research directions to achieve this. [ABSTRACT FROM AUTHOR]

The Food and Agriculture Organization (FAO) reports that Latin America and the Caribbean experienced the most rapid rise in food insecurity, with approximately 47.7 million individuals in the region affected by hunger in 2022. In Chile, almost three million people, 15.6% of the country’s population, do not have regular access to sufficient nutritious food and suffer from some form of food insecurity. Moreover, Chile is particularly susceptible to the impacts of climate change and contends with the depletion of several critical natural resources, notably water, stemming from severe and prolonged drought conditions. This article aims to comprehensively evaluate food loss and waste (FLW) in Chile and analyzes the implications of FLW on the sustainable development goal (SDG). This will be achieved by utilizing a top-down mass balance methodology that integrates various data sources and an in-depth analysis of the main food categories at different stages of the food supply chain (FSC). In 2021, Chile generated 5.18 million tons of FLW, with fruit being the largest contributor at 2.5 million tons (48% of total FLW). Vegetables accounted for 0.8 million tons (16%). Other food groups each contributed 1% to 10% of the total FLW. Per capita FLW was 295 kg. FLW varied by food group and FSC stage. Fruits, vegetables, starchy roots, and pulses had the most FLW early in the FSC, while cereals had it later. Comparing FLW with the domestic supply quantity of food, it is observed that 68% of the fruit available for the population was discarded. Vegetables and starchy roots also showed significant volumes of discarded food, with 48% and 29% of the availability of these products in Chile. Furthermore, we explore the implications of FLW on realizing SDG 2—zero hunger, particularly emphasizing its correlation with target 12.3. The research underscores the potential of its findings to significantly shape public policies and strategies concerning FLW and their alignment with the associated SDGs, making a tangible impact on the lives of millions.

In the light of China's synergistic need to achieve Sustainable Development Goals (SDG) 10 and 13, it is necessary to consider the potential nexus between common prosperity and carbon reduction. Therefore, this work proposes a new comprehensive measure to assess the degree of common prosperity in 30 Chinese provinces from 2006 to 2019. Furthermore, we examine the impact of common prosperity on carbon emissions applying the system-generalized method of moments (SYS-GMM) approach. All major conclusions are as follows: (1) the level of common prosperity has continued to increase between 2006 and 2019; (2) common prosperity and carbon reduction are compatible with each other, i.e., common prosperity significantly reduces carbon emissions; (3) common prosperity reduces carbon emissions indirectly by improving labor productivity and upgrading industrial structure; (4) carbon emissions are indirectly increased by common prosperity through the expansion of gross fixed capital formation and higher levels of urbanization, and (5) in provinces with higher levels of common prosperity and lower levels of carbon emissions, common prosperity has a considerably greater influence on carbon emissions. Finally, we suggest several policy implications for promoting China's sustainable development. [ABSTRACT FROM AUTHOR]

Within the framework of a circular economy, we aim to efficiently use raw materials and reduce waste generation. In this context, the longevity of biomimetic material systems can significantly contribute by providing robustness and resilience of system functionality inspired by biological models. The aim of this review is to outline various principles that can lead to an increase in robustness (e.g., safety factor, gradients, reactions to environmental changes) and resilience (e.g., redundancy, self-repair) and to illustrate the principles with meaningful examples. The study focuses on plant material systems with a high potential for transfer to biomimetic applications and on existing biomimetic material systems. Our fundamental concept is based on the functionality of the entire system as a function of time. We use functionality as a dimensionless measure of robustness and resilience to quantify the system function, allowing comparison within biological material systems and biomimetic material systems, but also between them. Together with the enclosed glossary of key terms, the review provides a comprehensive toolbox for interdisciplinary teams. Thus, allowing teams to communicate unambiguously and to draw inspiration from plant models when developing biomimetic material systems with great longevity potential. [ABSTRACT FROM AUTHOR]

Food loss and waste are major issues in the food industry, and they affect all stages of the food supply chain (FSC). Food loss and waste are linked to environmental deterioration, economic loss, and an increase in hunger. Therefore, the food industry requires sustainable consumption and production (SCP) to reduce losses and waste. The circular economy (CE) concept has become a popular strategy for reducing food waste and boosting sustainability. Therefore, with efficient reverse logistics in the FSC, food producers can help achieve sustainable development goals (SDGs) like SCP and zero hunger. In literature, minimal research has been available in identifying the CE adoption challenges in FSC. This research identifies 15 critical challenges from the literature and discussion with the panel of experts. The relationship between the challenges has been established through an interpretive structural modeling (ISM) technique. The challenges were characterized in cause–effect according to their relational intensity obtained using the grey decision‐making trial and evaluation laboratory (DEMATEL) technique. Grey's relational theory is applied in DEMATEL to minimize uncertainty and vagueness of the expert judgment. The findings of this study suggest that creating policy from the government, providing incentives, and strictly enforcing environmental regulations are the most critical challenge. Hence, by focusing on the above, the effective adoption of the CE principle is achieved. This result also suggests that by addressing the challenges of CE, corporate social responsibility (CSR) can be performed. This study provides some recommendations for the practitioners to adopt CE towards sustainable development targets. [ABSTRACT FROM AUTHOR]

This paper outlines ideas typically covered in an investments course from a faith-based perspective. Topics include the time value of money, bonds, markets, taxes, risk, and portfolios. We provide example resources and suggestions of faith integration accomplished through engaging multiple types of pedagogies and assignments. A sample final project enables students to demonstrate how the entire investment process discussed throughout the term comes together at the end to achieve an integrated portfolio. Biblically responsible investing, environmental-social-governance investing, and sustainable development goals are also considered as practical ways of integrating faith and investing. [ABSTRACT FROM AUTHOR]

This article investigates the drilling behavior of hemp fiber-reinforced epoxy composites, both unfilled and fly ash-filled. Drilling performance tests were conducted at varying feed rates (8, 16, and 24 mm min ^−1 ) and spindle speeds (700, 1400, and 2100 RPM) using three drill geometries: parabolic, 8-facet, and dagger. The fly ash-filled hemp/epoxy composite showed significant reductions in maximum thrust force (25.09%, 19.92%, and 21.43%) and torque (80%, 79.87%, and 59.25%) when drilled with parabolic, 8-facet, and dagger drill bits, respectively, compared to the unfilled composite. The maximum drilling temperature reached 90.6 °C during cutting and decreased to 71.7 °C post-drilling. Overall, the fly ash-filled composites demonstrated superior performance in terms of drilling-induced stresses and damage.

Sustainable Development Goals (SDG) are at the forefront of government initiatives across the world. The SDGs are primarily concerned with promoting sustainable growth via ensuring wellbeing, economic growth, environmental legislation, and academic advancement. One of the most prominent goals of the SDG is to provide learners with high-quality education (SDG 4). This paper aims to look at the perspectives of the Sustainable Development Goals improvised to provide quality education. We also analyze the existing state of multiple initiatives implemented by the Indian government in the pathway to achieving objectives of quality education (SDG 4). Additionally, a case study is considered for understanding the association among the observed indicators of SDG4. For this purpose, exploratory data analysis, and numerical association rule mining in combination with QuantMiner genetic algorithm approaches have been applied. The outcomes reveal the presence of a significant degree of association among these parameters pointing out the fact that understanding the impact of one (or more) indicator on other related indicators is critical for achieving SDG 4 goals (or factors). These findings will assist governing bodies in taking preventive measures while modifying existing policies and ensuring the effective enactment of SDG 4 goals, which also will subsequently aid in the resolution of issues related to other SDGs. [ABSTRACT FROM AUTHOR]

• Limited African water quality data hinders SDG target 6.3 assessment. • Developed a continental water quality (sediment load, TN and TP) model for Africa. • 44% and 15% of rivers fail to meet the thresholds set for TP and TN, respectively. • Polluted rivers linked with high fertilizer, manure, and wastewater inputs. • Targeted strategies are crucial to reduce nutrient pollution. The ambition of Sustainable Development Goal (SDG) target 6.3 is to improve global water quality by 2030. SDG indicator 6.3.2 monitors progress towards this target by assessing water bodies against 'good' ambient water quality criteria, with nutrients (nitrogen and phosphorus) as part of the key metrics. However, large data gaps present a fundamental challenge, especially for Africa on how to assess the progress being made with respect to both the current and desired future situations. Here, a continental water quality model for Africa is presented to simulate river sediment load, Total Nitrogen (TN) and Total Phosphorus (TP) loads and concentrations. Furthermore, critical areas and hotspots of TN and TP pollution are mapped for the period 2017 – 2019, in relation to the United Nations Environment Programme (UNEP) target thresholds used for the assessment of SDG indicator 6.3.2. Utilizing the UNEP's criteria, which designates a water body as having "good ambient water quality" if 80% or more of its monitored values meet their targets, it is estimated that 44 % and 15 % of African rivers fail to meet the set water quality thresholds for simulated TP and TN, respectively. When synthesizing data for both TP and TN, 34 % of the rivers do not qualify as having "good ambient water quality". Geographically, the most pronounced nutrient pollution hotspots were in North Africa, Niger River Delta, Nile River basin, Congo River basin and specific zones in Southern Africa. These areas correlate with regions characterized by high inputs of fertilizers, manure and wastewater discharge. [ABSTRACT FROM AUTHOR]

The development of solutions to manage or mitigate climate change impacts is very challenging, given the complexity and dynamicity of the socio-environmental and socio-ecological systems that have to be modeled and analyzed, and the need to include qualitative variables that are not easily quantifiable. The existence of qualitative, interoperable and well-interlinked data is considered a requirement rather than a desire in order to support this objective, since scientists from different disciplines will have no option but to collaborate and co-design solutions, overcoming barriers related to the semantic misalignment of the plethora of available data, the existence of multiple data silos that cannot be easily and jointly processed, and the lack of data quality in many of the produced datasets. In the current work, we present the SustainGraph, as a Knowledge Graph that is developed to track information related to the progress towards the achievement of targets defined in the United Nations Sustainable Development Goals (SDGs) at national and regional levels. The SustainGraph aims to act as a unified source of knowledge around information related to the SDGs, by taking advantage of the power provided by the development of graph databases and the exploitation of Machine Learning (ML) techniques for data population, knowledge production and analysis. The main concepts represented in the SustainGraph are detailed, while indicative usage scenarios are provided. A set of opportunities to take advantage of the SustainGraph and open research areas are identified and presented.

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ANNOTATION. The issue of the relationship between trade and food security has not lost its relevance for decades, and the situation in the world caused by the spread of the COVID-19 pandemic has further highlighted in regulatory regime at the multilateral level, including the sustainable development goal (SDG) No. 2 on the elimination of hunger. This article analyses the theoretical and scientific aspects of the country's security and the impact of the coronavirus pandemic on the economy of Uzbekistan, as well as on the development of the country, detailed description and analysis of multilateral rules in the field of trade in agricultural products to meet current requirements for responding to food security, as well as the development of recommendations for adaptation of trade rules for these goods for Uzbekistan. The article reveals the economic essence of the global food problem in the context of sustainable development. It describes the approaches to determining the countries' food supply levels. The main determinants of international trade integration and its place in international cooperation to ensure food stability have been identified. Much attention has been paid to the institutional mechanisms to solve the global food problem and the exclusive role of the World Trade Organization (WTO), the Food and Agriculture Organization of the United Nations (FAO), and informal coalitions and associations in it. The Coronavirus pandemic that shook the world affects not only Uzbekistan but also the economies of developed countries. The current situation in this area reduces Uzbekistan's business activity and investment attractiveness, seriously undermining the country's reputation in the international arena. Agriculture is one of the leading sectors of the economy of Uzbekistan, and the share of one in the gross domestic product of Uzbekistan amounted to 28.8%. After gaining independence, Uzbekistan took measures to ensure food security in two areas: expansion of rural areas and the allocation of new ones, and revision of the composition of crops. Identified that 53.2% of agricultural products were crop products, and 46.8% were livestock products; 3.7 million people were employed in agriculture, 27.2% of the rural population, and half of the country's population lives in rural areas. The main goal of the reform of the agrarian sector of each country is to achieve independence in the supply of food, which is of great socio-economic importance. The need for self-sufficiency in food is one of the conditions for maintaining independence, economic security, and social stability in the country, further ensuring the country's food security, filling the market with high-quality, safe, and affordable food products, strengthening the purchasing power of the population, liberalizing foreign economic activity and developing a healthy competitive environment, as well as existing systemic problems in this area. [ABSTRACT FROM AUTHOR]

As an island nation, the topic of training and sending out seafarers is a present challenge in Japan. Awareness activities for elementary, junior high, and senior high school students are essential for securing future mariner job candidates. In this study, we conducted maritime and marine educational methods using various procedures to raise awareness about previous maritime industries. We also brought in maritime specialists for junior high school students who will be continuing to maritime educational institutions, and conducted a comparative study of the results. In this study, we compared three types of educational content: lectures on leadership techniques, observational field trips, and hands-on lessons. The results showed that students had the highest interest in the hands-on lessons, followed by observational field trips, then finally the lectures. These results demonstrated that when creating class content for maritime and marine education, this should be adapted to the identity of the young people who are to receive that education.

Biodiversity information and precise knowledge are critical for its conservation and management for sustainable development. With the advancement of information technology, various efforts have been made by the scientific and internet community to digitize the biodiversity information and put it on an open-source platform equipped with structured information, compiled knowledge, mapping, and spatial analysis tools. These portals assist researchers and conservation managers in a variety of ways, including providing open access bio-resource-related data and analysis tools, which has accelerated biodiversity and conservation research and management practices in recent years. The dissemination of knowledge about various aspect of these portals will expedite the current scenario. This paper is intended to highlight the various aspects of these portals. In this paper, globally recognized portals with rich biodiversity information, global coverage, and powerful spatial analysis tools are reviewed to characterize their vocabulary differences, strengths, and limitations. In addition to the global database, some country-specific initiatives that use biodiversity data in a geospatial context are included. It is observed that many portals have been solely developed for the purpose of creating an open access digital database of bio-resources, with the geographical aspect being ignored. Some of the global and regional initiatives such as Atlas of Living Australia (ALA), Global Biodiversity Information Facility (GBIF), etc. have attempted to fill this void by developing a variety of tools and integrating biodiversity data with geographic locations and other location-based parameters. Despite significant efforts, some issues remain unaddressed, viz., digitization of georeferenced information from natural history from across the world, the inclusion of more advanced web-GIS based tools for spatial analysis, development of agreed data standard, and so on, which could be the primary set of actions for future portals. The effort to address these issues is underway, and we anticipate that these will be resolved in the near future. [ABSTRACT FROM AUTHOR]

The United Nations Sustainable Development Goals (SDGs) are a unifying call for change - guiding global actions at multiple levels of governance for a better planet and better lives. Consequently, achieving the “future we want” may be hindered by overlooking valuable natural resources and services that are not explicitly included in the SDGs. Not recognizing the direct, intrinsic value of some natural resources may threaten the sustainability of the services they provide and their contributions to the SDGs. Here, we use inland aquatic ecosystems, and the fish and fisheries therein, as an example to explore opportunities for recognition and inclusion of other natural resources that are missing from the SDGs. Key resources absent from the SDGs are less likely to be incorporated in global, national, and regional objectives, dialogues, and policies. We outline multiple potential pathways for better inclusion and capitalization of contributions from these overlooked natural resources during the operationalization of the SDGs and other global instruments.

The majority of the world's population is still facing difficulties in getting access to primary healthcare facilities. Universal health coverage (UHC) proposes access to high-quality, affordable primary healthcare for all. The 17 UN sustainable development goals (SDGs) are expected to be executed and achieved by all the 193 countries through national sustainable development strategies and multi-stakeholder partnerships. This article addresses SDG 3.8—access to good quality and affordable healthcare and two subindicators related to societal impact (SDG 3.8.1 and 3.8.2) through two objectives. The first objective is to determine whether health expenditure indicators (HEIs) drive UHC, and the second objective is to analyze the importance of key determinants and their interactions with UHC in three economic blocks: emerging Gulf Cooperation Council (GCC); developing Brazil, Russia, India, China, and South Africa (BRICS) vis-à-vis the developed Australia, UK, and USA (AUKUS). We use the WHO Global Health Indicator database and UHC periodical surveys to evaluate the hypotheses. We apply state-of-the-art machine learning (ML) models and ordinary least square (traditional—OLS regression) methods to see the superiority of artificial intelligence (AI) over traditional ones. The ML Random Forest Tree method is found to be superior to the OLS model in terms of lower root mean square error (RMSE). The ML results indicate that domestic private health expenditure (PVT-D), out-of-pocket expenditure (OOPS) per Capita in US dollars, and voluntary health insurance (VHI) as a percentage of current health expenditure (CHE) are the key factors influencing UHC across the three economic blocks. Our findings have implications for drafting health and finance sector public policies, such as providing affordable social health insurance to the weaker sections of the population, making insurance premiums less expensive and affordable for the masses, and designing healthcare financing policies that are beneficial to the masses. UHC is an important determinant of health for all and requires an in-depth analysis of related factors. Policymakers are often faced with the challenge of prioritizing the economic needs of sectors such as education and food safety, making it difficult for healthcare to receive its due share. In this context, this article attempts to identify the key components that may influence the attainment of UHC and enable policy changes to address them more effectively and efficiently.

Energy has been the cornerstone for all the technological advancements that are happening in the world. The need for energy at present is great than ever before and it is foreseen to rise in the future, as a result, it triggered us to look into zero-emission alternatives i.e. renewables to meet our energy demand. Energy demand surge is not the only cause, rising fossil fuel prices and global warming are among the major factor for such a verdict. Governments of nearly all the nation are taking the necessary measures that will result in a greater good. Carbon neutralization is a feat that every nation must attain to ensure sustainable global development, renewables prove to be the best tool to achieve it. The scope of the study is to analyse both the drivers and de-motivators for renewable energy development for the study nations including India, China, Iceland, Sweden, and the US are taken into account, each country has its unique strongholds in renewable energy genesis. This work will give a descriptive overview of the country’s renewable assets and its green future by means of a SWOT analysis where each country will be assessed based on the four parameters namely Strength, Weakness, Opportunities and Threats for renewable resources. This study has been carried out to access each country potential for renewable energy generation. The motive for selecting these countries is that, these countries show promising outcomes to the leap towards green energy generation and utilization. Their positive results are due to various factors that will be analysed in this work. Additionally an arithmetic based approach was formulated to identify which of the countries will be able to attain Sustainable Development Goal 7 (SDG 7).

Fei Qi, Qi Wang Law School, Hainan University, Haikou, Hainan Province, People’s Republic of ChinaCorrespondence: Qi Wang Email hainan-equal-rights-research-group@outlook.comAbstract: The question of how to guarantee the health rights of people with disabilities, and their health equity in particular, is frequently neglected in infectious disease pandemics. The international response to the ongoing COVID-19 pandemic is no exception in this regard. This neglect is related to other forms of marginalization and exclusion, as people with disabilities are generally poorer and more vulnerable than their non-disabled counterparts. Sustainable Development Goal 3 lacks an appropriate human rights language that enshrines equality and inclusivity in pandemic prevention work and related policies and legislation; and, as a result, it does not sufficiently guarantee the health rights of people with disabilities. This paper draws on China’s pandemic prevention work to extract relevant lessons, and seeks to explain how decision-making systems and resource allocation mechanisms impact on the health rights of people with disabilities. It discusses the unique roles of justice and legislation in helping to guarantee the health rights of people with disabilities in an infectious disease pandemic, and concludes that future research should more closely consider how Sustainable Development Goal 16 can support Sustainable Development Goal 3.Keywords: COVID-19, health equity, laws, infectious disease pandemic, IDP, judicature, people with disabilities, sustainable development goal, SDG

This document is an editorial titled "One ocean, one climate, one future: environmental economics" published in the journal Frontiers in Environmental Economics. The editorial discusses the urgent need for sustainable management and preservation of marine and coastal ecosystems. It highlights five research articles that address various aspects of this complex challenge, including sustainable ocean-based industries, economic valuation of ecosystems, climate change solutions, coral reef conservation, and marine pollution reduction. The articles emphasize the importance of balancing economic growth with environmental sustainability and valuing ecosystem services for informed policymaking. The editorial calls for coordinated efforts to safeguard marine ecosystems for future generations in line with the global pursuit of sustainable development. [Extracted from the article]

The Sustainable Development Goals (SDGs) of the United Nations represent a crucial worldwide initiative aimed at advancing development and fostering a more favorable future. However, the advancement made towards achieving these objectives has been insufficient, and the allocation of research endeavors has been uneven. As a way to address this concern, the present study aimed to conduct an evaluation of the SDGs through examining their research-friendly characteristics. By conducting a systematic review of scholarly works and employing diverse evaluation criteria, the research employed a multi-criteria decision-making approach to rank the SDGs. The results of the study indicate that SDG 3, which pertains to health and well-being, was rated the most research-friendly, whereas SDG 4, which emphasizes quality education, and SDG 16, which concerns peace, justice, and strong institutions, received the lowest ratings. Besides, some of the research obstacles linked with every SDG have been outlined by this paper. The present investigation underscores the research-oriented character of the SDGs and emphasizes the significance of comprehending research agendas to formulate efficacious and sustainable resolutions to worldwide predicaments. The outcomes of this study offer a significant asset for individuals responsible for making decisions, the academic community, and other interested parties who are involved in the allocation of resources towards the SDGs. The current investigation provides an important contribution to the scholarly dialogue by creating a framework that can direct expected research efforts and aid policymakers in modifying existing or creating new goals that are more favorable to research and development. [ABSTRACT FROM AUTHOR]

Organizations across the globe are devising novel approaches to strive for carbon neutrality. Global institutions have manifested the critical need to develop reasonable strategies in every sector to mitigate the impending issues of excessive anthropogenic carbon emission and, in consequence, climate change. World‑leading economies have initiated significant steps by developing zero‑carbon emission policies to monitor the escalating carbon emissions to curb global warming. The clothing industry has a substantial carbon footprint while causing environmental pollution. Based on transition management theory, this study aims to explore and evaluate the critical determinants that can assist in pursuing carbon neutrality in the clothing industry. A decision support system comprising an integrated voting analytical hierarchy process (VAHP) and Bayesian network (BN) method fulfills our purpose. Pertinent literature is reviewed to determine the critical determinants for carbon neutrality (CDs-CN). After that, the VAHP method is employed to prioritize the CDs-CN. Further, the influence of CDs-CN on achieving carbon neutrality is modeled using a BN, predicting the carbon neutrality index (CNI) for the clothing industry. The findings reveal that professional expertise, laws and certifications, technological acceptance, availability of decarbonizing methods, and adequate carbon offsetting are the essential CDs-CN. This research extends the existing knowledge on integrating MCDM-ML techniques to address predictive modelling-based problems involving complex structures. Simultaneously, the present study helps practitioners and policymakers understand the key CDs-CN to successfully build and manage a carbon-neutral clothing industry by adopting the suggested strategies. Finally, recommendations concerning sustainable development goals (SDGs) are provided to achieve carbon-neutral manufacturing supply chains. • Evaluated 14 critical determinants (CDs) to achieve carbon neutrality (CN) • Integrated MCDM and machine learning approach is developed using VAHP-BN methods. • Clothing industry sector is considered for the evaluation of carbon neutrality. • Influence of CDs-CN on CN is measured to predict the carbon neutrality index (CNI). • Recommendations related to SDGs are provided for carbon-neutrality achievement. [ABSTRACT FROM AUTHOR]

The practice of raw material extraction has a high impact on the environment and represents a potential threat to the health and thriving of local communities. The concept of Extractive Essential Variables (EEVs) are explored in order to propose variables that can be used to quantify the environmental footprint of mineral extraction. Considering the interdependence of mining activities with social, economic and environmental issues, the variables target the development of monitoring tools for the implementation of the Sustainable Development Goals (SDGs). The identification of EEVs is based on the use of Earth Observation products in the field of mineral resources exploitation. A list of variables is proposed based on three classes of Essential Variables (EVs): installation and exploration phase, mineral extraction, and ore processing. These variables take into account the impacts of mining on the hydrology, land, water resources and the atmosphere of the area subjected to mineral exploitation. One of the variables is implemented as an operational workflow addressing SDG15, “life on land”. The workflow is intended to assess the area of forest ecosystem lost due to the presence of a mining site. Geospatial data on the extent of mining concessions and forest cover are combined using ArcGISTM. The workflow is successively translated into a Unix script to automatize the process of data treatment. The script is developed using the Geospatial Data Abstraction Library (GDAL). The use of a Virtual Laboratory Platform (VLab), a web-service-based access platform, increases the accessibility of data and resources and the re-use of the script. This work is a first attempt to propose a framework of EEVs, derived data workflows, while the underlying methodology, partially based on scientific publications and on personal reasoning, still needs to be tested and, improved based on expertise in the sector.

Ensuring healthy lives and promoting well-being for all ages is the 3rd Sustainable Development Goal (SDG). Inequality in access to health care remains one of the primary challenges in achieving the goal. With the ever-increasing expansion of urban areas and population growth, there is a need to regularly examine the pattern of accessibility of basic amenities across regions, States and urban areas. This study examined geographic access to Primary Health Care Facilities (PHCF) in Nigeria using the combination of open data and geospatial analysis techniques. Thus, showcasing an approach can be replicated across different regions in Sub-Saharan Africa due to issues of information gap. Data on elevation, location of health care facilities, population and network data were utilised. The result shows that PHCF aggregate at certain locations, e.g. major urban agglomerations, and transit route leading to these places. High concentrations are found in the capital city. The average travel time to the nearest PHCF is about 14 min (Standard Deviation ±13.30 min) while the maximum is about 2 hours. Pockets of low accessibility areas exist across the Akwa Ibom State in the Niger Delta region of Nigeria. There is an indication that most places have good geographic access. Across the 1787 settlements identified in our dataset, 98.3% are with good access (60 min). Geographic access is not the main limiting factor to health care access in the region. Therefore, computation of access to health care should take into consideration other dimensions of accessibility, to create a robust measure which will support effective and efficient health care planning and delivery.

In this paper legitimation of targets for state-owned enterprises (SOEs) is studied. In particular the study is focused on how conflicting targets are legitimized in consolidated government accounts before and after Agenda 2030 was signed. At a UN summit in 2015, representatives of most countries signed an agreement to adopt 17 global goals on sustainability. This is to be realized by 2030, which explains the term Agenda 2030. SOEs are hybrid organizations which are guided by combinations of different principles i. e. from private companies and the public sector. Their operations are monitored and their performance is associated with the government which also serves as the active owner. A discourse legitimation approach is used (van Leeuwen 2007, 2008) to study Swedish consolidated government accounts. The report is considered exemplary and the goals are ambitious. Since the introduction of Agenda 2030, the ultimate goals of SOEs have been rephrased and a configuration of the targets developed. The consolidated accounts become more political while other targets than economic ones gain prominence. This shift is legitimized by references to international corporations and commitments that are expected to serve as role models, implying that they are morally good companies contributing to a better world. The Swedish government is constructed as the responsible parent who ensures progress. Hence, the global goals of Agenda 2030 are legitimized, while they in turn legitimize state ownership with the government as an active owner of companies. [ABSTRACT FROM AUTHOR]

For sustainable development of any country, long-term economic development is essential. Developed countries over the world exhibits gradual economic development from decade to decade. This paper aims to group thirty-seven Asian countries based on long-term homogeneity of HDI (Human Development Index) trend from 1990 to 2018. The source of data is United Nations Development Program (UNDP). Out of forty-seven United Nations' (UN) listed Asian countries, thirty-seven Asian countries were selected due to missing data of the remaining ten countries. Hierarchical cluster analysis was conducted to visualize existing homogeneous groups and K-means cluster analysis was used to determine optimum number of clusters. The result of cluster analysis determines three distinguish clusters of Asian countries. Three clusters labeled as high HDI cluster (11 countries), medium HDI cluster (17 countries) and low HDI cluster (9 countries). Countries within clusters are found with similar economic development status and also geographically adjacent to each other. However, one-way ANOVA also indicates that, the average long-run HDI of three clusters were significantly different. [ABSTRACT FROM AUTHOR]