The need to address climate change and establish sustainable urban environments has driven increased efforts in Europe to develop climate-neutral cities. This study highlights the importance of integrating solar energy technologies and nature-based solutions as key strategies to achieve climate neutrality. By examining current practices, emerging trends, and case examples, it explores the benefits, challenges, and prospects associated with the integration of solar energy and nature-based solutions in urban contexts. The study presents a pioneering approach to assess the urban heat and climate change mitigation benefits of combining building-integrated photovoltaics and nature-based solutions specifically within the European context. The results emphasize the synergistic relationship between nature-based components and solar conversion technology, identifying effective combinations for different climatic zones. In warmer regions of Southern Europe, strategies like rooftop photovoltaics on cool roofs, photovoltaics shadings, green walls, and urban trees have demonstrated effectiveness. Conversely, mid- and high-latitude European cities have seen positive impacts through the integration of rooftop photovoltaics and photovoltaics facades with green roofs and green spaces. As solar cell conversion efficiency improves, the environmental impact of photovoltaics is expected to decrease, facilitating their integration into urban environments. The study emphasizes the importance of incorporating water bodies, cool pavements, spaces with high sky-view factors, and effective planning in urban design to maximize resilience benefits. It also highlights the significance of prioritizing mitigation actions in low-income regions and engaging citizens in the development of social photovoltaics positive energy houses, resilient neighbourhoods, and green spaces. By adopting these recommendations, European cities can lead the way in creating climate-neutral urban environments that prioritize clean energy, nature-based solutions, and the overall well-being of residents. The findings underscore the need for a multidisciplinary approach that combines technological innovation, urban planning strategies, and policy frameworks to effectively achieve climate neutrality in European cities.

The Global natural and manmade events are exposing the fragility of the tourism industry and its impact on the global economy. Prior to the COVID-19 pandemic, tourism contributed 10.3% to the global GDP and employed 333 million people but saw a significant decline due to the pandemic. Sustainable and smart tourism requires collaboration from all stakeholders and a comprehensive understanding of global and local issues to drive responsible and innovative growth in the sector. This paper presents an approach for leveraging big data and deep learning to dis-cover holistic, multi-perspective (e.g., local, cultural, national, and international) and objective information on a subject. Specifically, we develop a machine learning pipeline to extract parameters from academic literature and public opinions on Twitter, providing a unique and comprehensive view of the industry from both academic and public perspectives. The academic-view dataset was created from the Scopus database and contains 156,759 research articles from 2000 to 2022, which were modelled to identify 33 distinct parameters in 4 categories: Tourism Types, Planning, Challenges, and Media & Technologies. A Twitter dataset of 485,813 tweets was collected over 18 months starting March 2021 to August 2022 to showcase public perception of tourism in Saudi Arabia, which was modelled to reveal 13 parameters categorized into two broader sets: Tourist Attractions and Tourism Services. Discovering system parameters are re-quired to embed autonomous capabilities in systems and for decision-making and problem-solving during system design and operations. The proposed approach improves AI-based information discovery by extending the use of scientific literature, Twitter, and other sources for autonomous, dynamic optimizations of systems, promoting novel research in the tourism sector and contributing to the development of smart and sustainable societies. The paper also presents a comprehensive knowledge structure and literature review of the tourism sector based on over 250 research articles.

In the event of an abrupt sunlight reduction scenario there is a time window that occurs between when food stores would likely run out for many countries (~6 months or less) and ~1 year when resilient foods are scaled up. A promising temporary resilient food is leaf protein concentrate (LPC). Although it is possible to extract LPC from tree biomass (e.g. leaves and needles), neither the yields nor the toxicity of the protein concentrates for humans from the most common tree species has been widely investigated. To help fill this knowledge gap, this study uses high-resolution mass spectrometry and an open source toolchain for non-targeted screening of toxins on five common North American coniferous species: Western Cedar, Douglas Fir, Ponderosa Pine, Western Hemlock, and Lodgepole Pine. The yields for LPC extraction from the conifers ranged from 1% to 7.5%. The toxicity screenings confirm that these trees may contain toxins that can be consumed in small amounts and additional studies including measuring the quantity of each toxin are needed. The results indicate that LPC is a promising candidate to be used as resilient food, but future work is needed before LPCs from conifers can be used as a wide-scale human food.

Sanitation, water supply and their governance remain major challenges in many Pacific Island Countries. National sustainable development strategies (NSDSs) are promoted throughout the Pacific as overarching improved governance instruments to identify priorities, plan solutions and fulfill commitments to sustainable development. Their relevance to local village-level development priorities is uncertain. The Kingdom of Tonga provides opportunities to compare both. Tonga’s Strategic Development Frameworks (TSDFI 2011-2014 and TSDFII 2015-2025) were developed to focus government and its agencies on national outcomes. From 2007 to 2016, 136 villages throughout Tonga’s five Island Divisions (IDs) formulated Community Development Plans (CDPs) involving separately 80% of women, youth and men in each village. Censuses in 2006 and 2016 show linked improvements in water supply and sanitation systems but reveal IDs with continuing challenges. Sanitation and water are prominent in TSDFI but absent from the current TSDFII. In contrast, CDPs show in one ID, 53% of villages ranked sanitation as a priority with marked differences between IDs and between women, youth and men. CDPs’ sanitation priorities in IDs mostly correspond to sanitation and water metrics in the Censuses, but some reflect impacts of natural disasters. Explanations for differences in sanitation priorities between national and local development plans, as well as suggestions for improving NSDS processes in island countries, are advanced.

In this paper, the overall performance of Cu-(In,Ga)-Se₂ (CIGSe) solar cells was improved in the ultraviolet (UV)–visible wavelength region by two-segment process. In which, adjusting DC sputtering powers (20–40 W) for absorbers (segment I), and metal-alloy compositions (CdS, ZnO/CdS, ZnMgO/CdS and ZnMgO) for buffers (segment II) were explored and characterized. Upon choosing the optimal-30-W CIGSe-absorber (with 0.95-CGI ratio) sputtered by the segment-I process, the Cu-rich film can distinctly boost grain growth, thus reducing the trap state density. After segment-II process as the toxic-CdS alternative, the optimal Zn_0.9Mg_0.1O-alloy buffer can reach the best conversion efficiency (η = 8.70%) was attained for the environmental protection. Meanwhile, and the overall internal/external quantum efficiencies (IQE/EQE) were improved by 13.15%, respectively for the 2.48–3.62-eV bandgap (short wavelength) range. The developed photovoltaic (PV) module (with 9 optimum-CIGSe cells) exhibits acceptable stability with a variance within ±5% over the 60-day experiment. This discovery in PV-device research contributes to a new scientific understanding of renewable energy. Furthermore, this study undeniably enhances the progress of practical applications for PV-modules in alignment with sustainable development goals. It also actively supports the development of eco-friendly communities.

With the trend of public infrastructure becoming more sustainable, large multi-purpose sports venues (LMPSVs) have become one of the world's most complex architectural projects and one of the landmark buildings of an international city. LMPSVs will lead the world in sustainable design, construction and operational innovations, and play a crucial role in raising awareness of sustainability around the world. System Thinking (ST), System Dynamics (SD) and Sustainable System Development (SSD) theories are three interrelated concepts that together form a methodological framework for solving complex system problems and driving strategies. The purpose of this study is to examine how LMPSVs can implement environmental, social, governance, and innovation (ESGI) enterprise transformation strategy (ETS) based on a sustainable systems development (SSD) framework. In view of the current global environmental challenges and social responsibility requirements, the motivation of this study is to provide an integrated transformation pathway for LMPSVs to achieve the goal of sustainable development and to contribute to the field of related disciplines. The methodology of this research adopts a combination of literature review and case study analysis, and the theoretical basis is mainly drawn from cutting-edge research in the fields of sustainable development, corporate social responsibility (CSR) and environmental management. Then, by analyzing successful transformation cases in China, we extract data and information to explore the application and effectiveness of these strategies in practice. The results of the study show that by implementing ESGI-based transformation strategy, LMPSVs can not only make significant progress in terms of environmental protection, social responsibility, and governance structure, but also drive long-term corporate growth and competitiveness through innovation. Specifically, these results are realized in terms of increased efficiency in energy use, improved waste management, community involvement and increased stakeholder satisfaction. The study concludes by emphasizing that the SSD-based ESGI Transformation Strategic framework provides a sustainable pathway for LMPSVs. In the future, the extension of this transformation strategy will help a wider range of industry sectors to achieve sustainable development goals, and has wide application potential and far-reaching social significance.

In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policy-makers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing upon established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale but also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.

Red plum apricot is the best apricot over the world. Since having been introduced into Guyuan county in 1987 from Shaanxi province, red plum apricot grows well and then has been selected as good varieties to popularize in semirid loess hilly regions because the color of red plum apricot is beautiful, aroma fragrant , and taste is the mix of sour and sweet and nutrition is rich. Since 1995, the yield, benefits and planting area of red plum apricot increase doubly, and the distribution range of red plum apricot increased from Guyuan to the whole Ningxia, and then to Gansu province and so on in the water-limited regions, China. However, there are still some problems in the sustainable produce of red plum apricot. In order to promote the development of red plum apricot and meet people's increasing demand, we should do intensive study of the relationship between red plum apricot and environment and the comparison of possible methods to reduce the bad influence of drought, low temperature and frost on quality and production of red plum apricot, and determine the soil water resources use limit by red plum apricot and the suitable leaf amount of red plum apricot when the planting density equal soil water carrying capacity for red plum apricot and select the best method to reduce or evade the bad influence of soil drought, low temperature, frost and soil drought on red plum apricot to ensure the sustainable produce of red plum apricot in Sustainable Produce of red plum apricot in water-limited regions.

This research aimed to determine the most viable locations for establishing solar energy facilities in Yemen through the utilization of the Analytical Hierarchy Process (AHP), Best-Worst Method (BWM), and Geographic Information System (GIS). A total of twelve key criteria were meticulously selected and evaluated based on their impact on the appropriateness of sites for solar power plants. These criteria were amalgamated using a weighted overlay tool, resulting in a map that classified regions as either optimal, highly suitable, or suitable. The findings of the study indicated that the eastern, southern, and northern parts of Yemen are prime candidates for solar energy installations due to their moderate climate and flat topography. On the other hand, the western regions were deemed less favorable. The breakdown of site classifications revealed that 38% of areas were optimal, 61% were highly suitable, and only 1% were considered suitable, based on the assigned weights from the AHP and BWM methodologies. Moreover, a sensitivity analysis was carried out to evaluate the impact of adjusting the weights of Global Horizontal Irradiance (GHI) and slope on site classifications. A minor increase in the weights of GHI (from 30% to 31%) and slope (from 10% to 11%) resulted in a significant alteration in the suitability categories, highlighting the importance of weight adjustments in multi-criteria decision-making processes. In conclusion, the study emphasizes the effectiveness of AHP, BWM, and GIS in assessing site suitability for solar energy facilities, as well as the significance of sensitivity analyses. These approaches provide valuable insights for decision-makers, aiding in strategic planning and fostering sustainable energy development in Yemen.

This review paper delves into the multifaceted aspects of pineapple cultivation, focusing on morphology studies, harvesting challenges, and mechanization efforts. Pineapple, renowned for its commercial value and unique flavor, holds a prominent position in global agriculture, with India alone producing 19.64 lakh tonnes annually. However, the hilly terrains of regions like northeastern India pose significant hurdles to traditional harvesting methods, necessitating innovative solutions. Understanding the morphology of pineapple plants is crucial for optimizing cultivation practices and designing efficient harvesting equipment. Insights from various studies shed light on leaf structure, fruit maturity, and growth patterns, providing valuable information for engineering tailored solutions. Mechanization endeavors, including semi-mechanized harvesting machines and semi-manual harvesters, demonstrate promising results in improving efficiency and reducing labor-intensive practices. Despite challenges such as increased crop losses and operational costs, continued research and development in mechanization hold the key to sustainable pineapple cultivation. This paper underscores the importance of integrating morphology studies and mechanization efforts to address the unique challenges faced by pineapple farmers, particularly in hilly terrains, and highlights the need for ongoing innovation to ensure the long-term sustainability of pineapple cultivation.

The integration of Artificial Intelligence (AI) in dairy farm management through biometric facial recognition for cows is a significant stride in livestock management. This review critically evaluates the evolution, applications, and challenges of AI-driven biometric facial recognition in dairy farming. It emphasizes the role of this technology in enhancing individual monitoring of dairy cows, providing accurate health, behavior, and productivity tracking. Originally derived from human facial recognition systems, this approach utilizes distinctive bovine facial features for essential, non-invasive, real-time monitoring in large-scale operations. The progression of AI from elementary pattern recognition to advanced Convolutional Neural Networks (CNNs) and deep learning models marks a shift toward data-driven farming. This study addresses key challenges such as environmental variability, data collection hurdles, ethical concerns, and technological limitations. It also contrasts various AI models, spotlighting their unique strengths and practical utility in dairy farming scenarios. Despite these challenges, facial recognition technology holds promise for improving farm efficiency, animal welfare, and sustainable practices, highlighting the need for continuous research and development. The review concludes by advocating for future research focused on environmental adaptability, ethical AI application, cross-breed compatibility, and integration with other farming technologies. Ultimately, it underscores AI's transformative potential in modernizing dairy farming towards a more data-oriented, responsible agricultural future.

This paper explores the challenges faced by blue-collared international migrants in adapting to new and unfamiliar climates while pursuing job opportunities, with a focus on poverty alleviation. Drawing on existing research and empirical evidence, the study investigates the trials encountered by these migrants as they navigate climate shocks and assesses the potential impacts on poverty reduction efforts. The findings underscore the significant difficulties blue-collared migrants experience when confronted with abrupt changes in climate conditions, adversely affecting their physical and mental well-being, as well as their employment prospects. The process of adjustment involves not only acclimating to new climate patterns but also navigating cultural nuances and social dynamics, exacerbating stress and feelings of isolation. Consequently, these challenges can perpetuate poverty by disrupting employment stability and limiting opportunities for career advancement. To address these issues, the paper proposes comprehensive support measures, including targeted pre-departure training programs, access to retraining opportunities, and assistance in securing sustainable livelihoods. By prioritizing climate resilience and empowering blue-collared migrants to adapt effectively to new climates, it becomes possible to enhance their livelihood prospects, reduce poverty levels, and foster sustainable development. The paper concludes by highlighting the significance of integrating climate adaptation strategies with poverty alleviation efforts to create a more inclusive and resilient future for blue-collared migrants in their pursuit of sustainable livelihoods.

This paper proposes and demonstrates, in full scale, a novel type of energy geostructure (“the Climate Road”) that combines a ground source heat pump (GSHP) with a sustainable urban drainage system (SUDS) by utilizing the gravel roadbed simultaneously as energy source and rainwater retarding basin. The Climate Road measures 50m x 8m x 1m (length, width, depth) and has 800 m of geothermal piping embedded in the roadbed, serving as the heat collector for a GSHP that supplies a nearby kindergarten with domestic hot water and space heating. Model analysis of operational data from 2018-2021 indicates sustainable annual heat production levels around 0.6 MWh per meter road, with a COP of 2.9-3.1. The continued infiltration of rainwater to the roadbed increases the amount of extractable heat by an estimated 17% compared to the case of zero infiltration. Using the developed model for scenario analysis we find that draining rainwater from three single family houses and storing 30% of the annual heating consumption in the roadbed, increases the predicted extractable energy by 56% compared to zero infiltration with no seasonal energy storage. The Climate Road is capable of supplying three single family houses with heating, cooling and rainwater management year-round.

The goods and services produced by forest vegetation ecosystem are the driving force for the rapid, high-quality and sustainable development of human society. In history, with the increase of population and economic development, there is an increasing demand for the quantity and variety of forest vegetation ecosystem products and services in a country or a region. To meet the demands, most of the original forest has become farmland, plantation and grass and crops land. As a result, the plant water relationship changed from equilibrium to non-equilibrium, which easily led to soil drought, soil degradation and vegetation decline in dry years or waste of soil water in rainy years in most of water-limited regions. In order to solve the question and realize the sustainable utilization of soil water resources and the high-quality sustainable development of forest, it is necessary to apply the limit theory of plant utilization of soil water resources and the vegetation carrying capacity theory in the critical period of plant-water relationship regulation to adjust the relationship between plant growth and soil moisture to obtain the maximum yield and benefit and realize high-quality and sustainable development.

Technological advancements and innovations have profoundly changed the lives of people giving rise to smart environments, cities, and societies. As homes are the building block of cities and societies, smart homes are critical to establishing smart living and are expected to play a key role in enabling smart cities and societies. The current academic literature and commercial advancements on smart homes have mainly focused on developing and providing smart functions for homes to provide security management and facilitate the residents in their various activities such as ambiance management. Homes are much more than physical structures, buildings, appliances, operational machines, and systems. Homes are composed of families and are inherently complex phenomena underlined by humans and their relationships with each other, subject to individual, intragroup, intergroup, and intercommunity goals. There is a clear need to understand, define, consolidate existing research, and actualize the overarching roles of smart homes, the roles of smart homes that would serve the needs of future smart cities and societies. This paper introduces our data-driven parameter discovery methodology and uses it to provide, for the first time, an extensive, rather fairly comprehensive, analysis of the families and homes landscape seen through the eyes of academics and the public using over a hundred thousand research papers and nearly a million tweets. We develop a methodology using deep learning, natural language processing (NLP), and big data analytics methods and apply it to automatically discover parameters that capture a comprehensive knowledge and design space of smart families and homes comprising social, political, economic, environmental, and other dimensions. The 66 discovered parameters and the knowledge space comprising 100s of dimensions are explained by reviewing and referencing over 300 articles from the academic literature and tweets. The knowledge and parameters discovered in this paper can be used to develop a holistic understanding of matters related to families and homes facilitating the development of better, community-specific, policies, technologies, solutions, and industries for families and homes, leading to strengthening families and homes, and in turn, empowering sustainable societies across the globe.

There is a balanced plant-water relationship in the primary vegetation of desert area. With the increase of population and social development in desert areas, people’s need for forest vegetation ecosystem’s goods and service have been changed. To meet the growing demand for plant community goods and services, more original vegetation has been changed into non-native vegetation such as in China loess plateau. However, with the plant growth, sometime soil drying happens and then becomes gradually serious with times in most of desert regions. Serious drying of soil eventually result in soil degradation, vegetation decline and agriculture failure，which influence the produce and supply of forest vegetation goods and service in market in dry year or waste of soil water resources in wet year, which wastes precious nature resources. In order to solve these problems, the soil water resources have to be used in sustainable way and plant-water relationship have to be regulated on Carrying Capacity of Soil Water for Vegetation in the key period of plant water relationship regulation, to carry out sustainable use of nature resources, high-quality and sustainable development of forest and grass or high-quality produce of fruit and crop in desert re-gions.

Background: This study examines the relationship between energy usage, health issues, and pro-environmental behavior (PEB) in Kyrgyzstan, amidst the country's commitment to transition from coal-based energy to renewable sources in line with the Paris Agreement. The purpose is to investigate citizens' attitudes towards PEB and their intentions to engage in environmentally friendly actions, focusing on gas, electricity, and coal. Methods: Drawing upon the Theory of Planned Behavior (TPB) framework, a survey was conducted among 1,455 respondents to explore attitudes towards PEB and energy sources' impact on health issues. Results: Decarbonization efforts in Kyrgyzstan and Central Asia are in their early stages, with coal remaining a primary energy source. The study emphasizes the importance of governmental policies and citizen action in achieving decarbonization goals. Rising electricity costs outweigh the increase in indirect energy costs for food, posing challenges for households in adapting to changing energy dynamics. Conclusions: Targeted interventions and communication strategies are crucial to promote pro-environmental behavior and facilitate the transition to sustainable energy sources. Understanding the relationships between health concerns, air pollution awareness, PEB, and energy source choices can inform policymakers and organizations in their efforts to ensure a sustainable and healthy future for Kyrgyzstan and other Central Asian countries.

This study advances the field of solar irradiance nowcasting by introducing a discrete-level classification approach, diverging from traditional continuous measurement methods. Grounded in a need for cost-effective and modular solutions, the research employs high-resolution computer vision coupled with a deep learning framework to predict Direct Normal Irradiance (DNI). By harnessing the capabilities of a Logitech C900 1080p HD camera and an NVIDIA Jetson module, the study achieves real-time data processing, pivotal for CSP systems' operational efficiency. The core of the methodology is the ResNet-50 convolutional neural network, refined via transfer learning on a bespoke dataset, culminating in a predictive accuracy of 85.78%. This discrete classification model contrasts with conventional, costly instruments like the MS-57, offering a novel and accessible alternative for DNI estimation. Such innovation not only demonstrates high predictive accuracy but also signifies a shift towards less resource-intensive and more adaptable solar energy forecasting tools, contributing a significant leap towards optimizing renewable energy systems.