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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]

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]

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]

• 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 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 coexistence of agricultural land and solar photovoltaics (PV) can be named Agriphotovoltaics (APV). APV concept was developed two decades ago however its actual implementation is happening nowadays. APV directly solves SDGs 7, and 11 by generating benevolent renewable energy without damaging the land and keep producing food for people. In this work, a comprehensive review of the APV system is documented. Currently available software tools, field experiment results, and PV for APV are described in this work which identified that for forecasting APV, a more robust tool is required. Vertically placed Bifacial PV, transparent, and semitransparent tilted PVs can be suitable for shade-intolerant crops whereas opaque PVs are appropriate for shade-tolerant crops. The knowledge gap between various stakeholders such as solar PV researchers, agricultural researchers, and land users needs to be more rigorous. Economic and policymakers should share dialogue to improve the growth of APV which not only solves SDG 7, and 11 but also meets the target for SDG 5, 8, 9,12, and 15. [ABSTRACT FROM AUTHOR]

Landslide is a natural hazard that occurs all over the world and can cause enormous damage to the environment and human life. The developing countries are not spared from dealing with the issue of landslides where land use activities are recognised as one of the most triggering factors of landslide occurrences. In this study, investigation on spatial relationship was performed between landslide occurrences and land use activities at Langat River Basin. Landslide spatial distribution was determined using land use activities for the year 2021. The land use map was generated using supervised classification by Artificial Neural Network (ANN) approach. In addition, the accuracy assessment was applied using Kappa coefficient to validate the land use classification. A total of 70 historical landslide points was used to analyse the spatial relationship between landslide and land use activities. Based on the results, land use category at Langat River Basin was dominated by agriculture with 59.65%. The overall accuracy for land use classification was 90.81% and kappa coefficient was 0.88. Meanwhile, the spatial relationship between landslide occurrence and land use activities was higher in the built-up area with 48.57%. The growing population and economic sector in Langat River Basin were found to be contributing to the increase in built-up areas. Thus, these results are particularly useful in understanding the spatial relationship between landslide occurrence and land use activities where the development area is more vulnerable to facing landslide disasters. These findings will also help the government to achieve the agenda for Sustainable Development Goal (SDG) 2030 as highlighted to mitigate the risk of natural disasters. • Landslide is a natural phenomenon that occurs in most countries. • Change of land use activities become a major contributor to landslide occurrences. • Sustainable Development Goal highlighted to mitigate the risk of natural disaster. • Planning of land development activities is important avoid landslide occurrences. [ABSTRACT FROM AUTHOR]

This study analyses the sustainable entrepreneurship issues faced during the pre-seed and seed phases of the life cycle of entrepreneurial ventures, which are considered the protagonists of innovative changes towards a cleaner environment. Specifically, it investigates the role innovation plays in the life cycle of entrepreneurial ventures seeking to create sustainable business models and tools for measuring the social impact of such ventures in sustainability assessment systems designed to help achieve the Sustainable Development Goals (SDGs) adopted by the UN 2030 Agenda. The study conducts a rigorous literature review and bibliometric analysis of 166 English-language articles on entrepreneurship and sustainability published between 2001 and 2021, and formulates a map for the knowledge produced and disseminated in previous investigations. The results advance our understanding of how sustainability assessment can be integrated into the life cycle phases of innovative entrepreneurial ventures. This review can aid in the development of a comprehensive framework and of research propositions for sustainable entrepreneurship issues in the pre-seed and seed phases. The study also extends institutional and stakeholder theories to provide decision support for sustainable entrepreneurial ventures in their pre-seed and seed phases. It explains how stakeholders' participation and engagement with institutional structures can be invigorated and how this affects the decision consequences for sustainable entrepreneurs. Our results can help practitioners and researchers accurately interpret 'innovation' in the life cycle of entrepreneurial ventures and sustainability assessment systems, thus allowing them to formulate policies, strategies, and guidelines for promoting a sustainable development agenda. • This study investigates the role of innovation in the life cycle of entrepreneurial ventures for sustainable business models. • This study provides a retrospective review of published content in the field of entrepreneurial ventures and sustainability. • The findings highlight that business models are responsible for achieving the SDGs by integrating sustainability assessment into the life cycle phases of innovative entrepreneurial ventures. [ABSTRACT FROM AUTHOR]

The United Nation's Sustainable Development Goals (SDGs) want to have a peaceful world where human life will be in a safe, healthy, sustainable environment without any inequalities. However, the year 2020 experienced a global pandemic due to COVID-19. This COVID-19 created an adverse impact on human life, economic, environment, and energy and transport sector compared to the pre-COVID-19 scenario. These above-mentioned sectors are interrelated and thus lockdown strategy and stay at home rules to reduce the COVID-19 transmission had a drastic effect on them. With lockdown, all industry and transport sectors were closed, energy demand reduced greatly but the time shift of energy demand had a critical impact on grid and energy generation. Decreased energy demand caused a silver lining with an improved environment. However, drowned economy creating a negative impact on the human mind and financial condition, which at times led to life-ending decisions. Transport sector which faced a financial dip last year trying to coming out from the losses which are not feasible without government aid and a new customer-friendly policy. Sustainable transport and the electric vehicle should take high gear. While people are staying at home or using work from home scheme, building indoor environment must specially be taken care of as a compromised indoor environment affects and increases the risk of many diseases. Also, the energy-efficient building will play a key role to abate the enhanced building energy demand and more generation from renewable sources should be in priority. It is still too early to predict any forecast about the regain period of all those sectors but with vaccination now being introduced and implemented but still, it can be considered as an ongoing process as its final results are yet to be seen. As of now, COVID-19 still continue to grow in certain areas causing anxiety and destruction. With all these causes, effects, and restoration plans, still SDGs will be suffered in great order to attain their target by 2030 and collaborative support from all countries can only help in this time. [Display omitted] • Social distancing, lockdown, sewage management implemented to combat COVID-19. • Education sector affected in rural areas due to lack of internet during lockdown. • Psychological difficulties and negative behaviour during lockdown and isolation. • Energy demand in transport sector was low while revenue loss was prominent. • Residential sector consumed higher energy with growing demand of renewable energy. [ABSTRACT FROM AUTHOR]