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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 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.

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.

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.

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.

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.

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

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.

This paper reviews the key role that Earth Observations (EO) play in achieving the Sustainable Development Goals (SDGs) as articulated in the 2030 Agenda document and in monitoring, measuring, and reporting on progress towards the associated targets. This paper also highlights how the Group on Earth Observations (GEO) would contribute to ensure the actual use of EO in support of the 2030 Agenda; and how the Global Earth Observations System of Systems meets requirements for efficient investments in science and technology and a good return on investment, which is elaborated in the Addis Ababa Action Agenda on development financing. Through a number of examples, we first discuss how extensive EO use would: provide a substantial contribution to the achievements of the SDGs by enabling informed decision-making and by allowing monitoring of the expected results; improve national statistics for greater accuracy, by ensuring that the data are “spatially-explicit” and directly contribute to calculate the agreed SDG Targets and Indicators support the fostering of synergy between the SDGs and multilateral environmental agreements by addressing cross-cutting themes such as climate and energy; and facilitate countries’ approaches for working across different development sectors, which is, according to the special adviser on the 2030 Agenda, a key challenge to achieve the SDGs. We then focus on the role that GEO could play in enabling actual use of EO in support of the 2030 Agenda by directly addressing the Strategic Development Goal 17 on partnerships.

In the late 1960s, the Islamic Republic of Pakistan’s capital shifted from Karachi to Islamabad, officially named Islamabad Capital Territory (ICT). In this aspect, the ICT is a young city, but undergoing rapid expansion and urbanization, especially in the last two decades. This study reports the measurement and characterization of ICT land cover change dynamics using Landsat satellite imagery for the years 1976, 1990, 2000, 2010, and 2016. Annual rate of change, landscape metrics, and urban forest fragmentation spatiotemporal analyses have been carried out, along with the calculation of the United Nations Sustainable Development Goal (SDG) indicator 11.3.1 Land Consumption Rate to the Population Growth Rate (LCRPGR). The results show consistent increase in the settlement class, with highest annual rate of 8.79% during 2000–2010. Tree cover >40% and 500 acres’ class decreased from 392 km2 (65.41%) to 241 km2 (55%), and ‘patch forest’ class increased from 15 km2 (2.46%) to 20 km2 (4.54%), from 1976 to 2016. The LCRPGR ratio was 0.62 from 1976 to 2000, increasing to 1.36 from 2000 to 2016.

Large-scale 2nd generation bioenergy deployment is a key element of 1.5 °C and 2 °C transformation pathways. However, large-scale bioenergy production might have negative sustainability implications and thus may conflict with the Sustainable Development Goal (SDG) agenda. Here, we carry out a multi-criteria sustainability assessment of large-scale bioenergy crop production throughout the 21st century (300 EJ in 2100) using a global land-use model. Our analysis indicates that large-scale bioenergy production without complementary measures results in negative effects on the following sustainability indicators: deforestation, CO _2 emissions from land-use change, nitrogen losses, unsustainable water withdrawals and food prices. One of our main findings is that single-sector environmental protection measures next to large-scale bioenergy production are prone to involve trade-offs among these sustainability indicators—at least in the absence of more efficient land or water resource use. For instance, if bioenergy production is accompanied by forest protection, deforestation and associated emissions (SDGs 13 and 15) decline substantially whereas food prices (SDG 2) increase. However, our study also shows that this trade-off strongly depends on the development of future food demand. In contrast to environmental protection measures, we find that agricultural intensification lowers some side-effects of bioenergy production substantially (SDGs 13 and 15) without generating new trade-offs—at least among the sustainability indicators considered here. Moreover, our results indicate that a combination of forest and water protection schemes, improved fertilization efficiency, and agricultural intensification would reduce the side-effects of bioenergy production most comprehensively. However, although our study includes more sustainability indicators than previous studies on bioenergy side-effects, our study represents only a small subset of all indicators relevant for the SDG agenda. Based on this, we argue that the development of policies for regulating externalities of large-scale bioenergy production should rely on broad sustainability assessments to discover potential trade-offs with the SDG agenda before implementation.