Search Results (2,541)

This paper presents an ethnographically informed analysis of research in Cairo and Sharm El-Sheikh (Egypt) surrounding the 2022 United Nations Framework Convention on Climate Change (UNFCCC) Conference of Parties (COP27) summit. I discuss the geopolitics and geopolitical disruptions of researching activism and activist lives in politically sensitive environments. As shown here, developing new methodological interventions plays a crucial role in understanding contextual methodological limitations, dealing with logistical challenges, and building authentic relationships with research participants. Here, I introduce counter-interviews as a methodological strategy to build trust and invest in researcher–participant relationships. This article draws on participant observation, conversations with environmental and climate activists and non-activists in Cairo prior to and after COP27 and in Sharm El-Sheikh during the second week of the summit, reflective field notes, and 20 semi-structured interviews conducted online between February and August 2023. Here, I use the term “environmental non-activism” to draw attention to the sensitivity, complexity, and fragility of political or apolitical environmental and climate action in an authoritarian context where any form of collective action is highly monitored, regulated, and sometimes criminalized by the state. The main argument of this paper is that examining interlocking power dynamics that shape and reshape the activist space in relation to the state is a requirement for understanding and researching the complexities and specificities of climate activism and non-activism in authoritarian contexts. Along with this argument, this paper invites climate education researchers to reevaluate what non-movements and non-activists in the Global South offer to their analyses of possible alternatives, socio-political change, and politics of hope (and to the broader field of activism in educational research, where commitment to disruption, refusal, and subversion play a key role. Full article

In this study, we focus on solving the nonlinear time-fractional Hirota–Satsuma coupled Korteweg–de Vries (KdV) and modified Korteweg–de Vries (MKdV) equations, using the Yang transform iterative method (YTIM). This method combines the Yang transform with a new iterative scheme to construct reliable and efficient solutions. Readers can understand the procedures clearly, since the implementation of Yang transform directly transforms fractional derivative sections into algebraic terms in the given problems. The new iterative scheme is applied to generate series solutions for the provided problems. The fractional derivatives are considered in the Caputo sense. To validate the proposed approach, two numerical examples are analysed and compared with exact solutions, as well as with the results obtained from the fractional reduced differential transform method (FRDTM) and the q-homotopy analysis transform method (q-HATM). The comparisons, presented through both tables and graphical illustrations, confirm the enhanced accuracy and reliability of the proposed method. Moreover, the effect of varying the fractional order is explored, demonstrating convergence of the solution as the order approaches an integer value. Importantly, the time-fractional Hirota–Satsuma coupled KdV and modified Korteweg–de Vries (MKdV) equations investigated in this work are not only of theoretical and computational interest but also possess significant implications for achieving global sustainability goals. Specifically, these equations contribute to the Sustainable Development Goal (SDG) “Life Below Water” by offering advanced modelling capabilities for understanding wave propagation and ocean dynamics, thus supporting marine ecosystem research and management. It is also relevant to SDG “Climate Action” as it aids in the simulation of environmental phenomena crucial to climate change analysis and mitigation. Additionally, the development and application of innovative mathematical modelling techniques align with “Industry, Innovation, and Infrastructure” promoting advanced computational tools for use in ocean engineering, environmental monitoring, and other infrastructure-related domains. Therefore, the proposed method not only advances mathematical and numerical analysis but also fosters interdisciplinary contributions toward sustainable development. Full article

Traditional Chinese villages, which have long supported villagers’ comfort level of daily activities, are increasingly affected by global climate change and rural reconstruction, prompting growing research interest in their outdoor microclimate design. This systematic review aims to synthesize and evaluate the outdoor microclimate spatial design mechanism studies in traditional Chinese villages noted for their uniqueness and complexity. Following the PRISMA method, this study was carried out on November 27, 2024, by retrieving studies from the Scopus and CNKI databases and applying predefined inclusion and exclusion criteria; 42 empirical studies were systematically reviewed. It identifies current research trends, summarizes concepts, frameworks, indicators, and methodologies with a focus on the design mechanisms considering scales, contexts, and user groups, and outlines directions for future research. The findings reveal a growing number of publications, with case studies predominantly concentrated on three concepts: physical microclimates, human comfort, and behavioral responses, characterized as distributed in south-east areas. Based on these concepts and their correlations, this study proposes a classification framework based on multiple scales, contexts, and user groups. Within this framework, the study found that relative humidity and PET (physiological equivalent temperature) emerge as the most commonly used indicators, while field measurements, simulations, surveys, and observations are identified as the primary methods. The review further reveals that unique outdoor spatial design characteristics shape physical microclimates, human comfort, and behavior indicators influenced by contexts and users from the macro to the micro scale. Future research should advance existing studies by enriching the current contextual framework and explore more microclimatic factors. This review offers a comprehensive overview and actionable insights for outdoor microclimate design, policymaking, and the promotion of climate adaptation and villagers’ public health in different traditional rural settings. Full article

The aim of this research is to identify policy and practice interventions that support a just transition towards resilient practices for resource-dependent communities. We focus on Thai Nguyen and Phu Tho, two provinces in the Northern Midlands and Mountains of Vietnam. The region is reliant on agriculture but is assessed as highly vulnerable to climate change. We surveyed 105 farming households. A Likert-type questionnaire asked respondents to self-assess their experiences of weather extremes and of changes they had made to their farming practices. Our results show that for both Thai Nguyen and Phu Tho, farmers see the effects of climate change on their crops. Respondents in Thai Nguyen were more likely to report technically driven adaptation and engagement with extension services. Respondents in Pho Tho were more likely to continue traditional practices. For both, use of traditional knowledge and practices was related to taking measures to adapt to climate change. Our main conclusion is that at least three actions could support a just transition to resilient livelihoods. First is incorporating natural science and traditional knowledge into decision-making for just transitions. Second is considering long-term implications of interventions that appear to support livelihoods in the short term. Third is tailoring messaging and engagement strategies to the requirements of the most vulnerable people. The main message of this study is that a just transition for resource-dependent communities will inevitably be context-specific. Even in centralized and authoritarian contexts, flexibility to adapt top-down policies to locals’ own experiences of changing climates is needed. Full article

Solid biomass (agro-residue) is the most important source of renewable energy. The accelerating impacts of climate change and global population growth contribute to air pollution through the use of fossil fuels. These processes increase the demand for energy. The European Union has adopted a climate action plan to address the above challenges. The main aim of this study was to assess the economic performance of the producers of biomass for energy generation in Poland. The detailed objectives were to determine land resources in the studied agricultural farms and to determine the value of fixed and current assets in the analyzed farms. We used questionnaires as the main method to collect data. Purposive sampling was used to choose the farms. We conducted various tests to analyze the revenues from biomass sales and their normality, such as the Dornik–Hansen test, the Shapiro–Wilk test, the Liliefors test, and the Jargue–Berra statistical test. Moreover, we conducted regression analysis to find factors that are the basis for the economic performance (incomes) of farms that sell biomass. Results: This study demonstrated that biomass sales had a minor impact on the performance of agricultural farms, but they enabled farmers to maintain their position on the market. The economic analysis was carried out on a representative group of Polish agricultural farms, taking into account fixed and current assets, land use, production structure, and employment. The findings indicate that a higher income from biomass sales was generally associated with better economic results per farm and per employee, although not always per hectare of land. This suggests that capital intensity and strategic resource management play a crucial role in the profitability of bioenergy-oriented agricultural production. Conclusions: We concluded that biomass sales had a negligible influence on farm income. But a small income from biomass sales could affect a farm’s economic viability. Full article

The Latin American and Caribbean (LAC) region faces persistent challenges of inequality, climate change vulnerability, and deteriorating air quality. The Aburrá Valley, where Medellín is located, is a narrow tropical valley with complex topography, strong thermal inversions, and unstable atmospheric conditions, all of which exacerbate the accumulation of pollutants. In Medellín, ${\mathrm{NO}}_2$ concentrations have remained nearly unchanged over the past eight years, consistently approaching critical thresholds, despite the implementation of air quality control strategies. These persistent high concentrations are closely linked to the variability of the atmospheric boundary layer (ABL) and are often intensified by prolonged dry periods. This study focuses on a representative street canyon in Medellín that has undergone recent urban interventions, including the construction of new public spaces and pedestrian areas, without explicitly considering their impact on ${\mathrm{NO}}_x$ dispersion. Using Computational Fluid Dynamics (CFD) simulations, this work evaluates the influence of urban morphology on ${\mathrm{NO}}_x$ accumulation. The results reveal that areas with high Aspect Ratios (AR > 0.65) and dense vegetation exhibit reduced wind speeds at the pedestrian level—up to 40% lower compared to open zones—and higher ${\mathrm{NO}}_2$ concentrations, with maximum simulated values exceeding 50 μg/m³. This study demonstrates that the design of pedestrian corridors in complex urban environments like Medellín can unintentionally create pollutant accumulation zones, underscoring the importance of integrating air quality considerations into urban planning. The findings provide actionable insights for policymakers, emphasizing the need for comprehensive modeling and field validation to ensure healthier urban spaces in cities affected by persistent air quality issues. Full article

The integration of artificial intelligence into sustainable agriculture holds significant potential to transform traditional agricultural practices. This transformation of agricultural practices through AI directly intersects with several critical sustainable development goals, such as Climate Action (SDG13), Life Below Water (SDG 14), and Life on Land (SDG 15). However, such implementations are fraught with multifaceted challenges. This study explores the technological, organizational, and environmental challenges confronting top management in the agricultural sector utilizing the technological–organizational–environmental framework. As interest in AI-enabled sustainable initiatives continues to rise globally, this exploration is timely and relevant. The study employs an interpretive case study approach, drawing insights from a carbon sequestration project within the agricultural sector where AI technologies have been integrated to support sustainability goals. The findings reveal six key challenges: sustainable policy inconsistency, AI experts lacking farming knowledge, farmers’ resistance to change, limited knowledge and expertise to deploy AI, missing links in the existing system, and transition costs, which often hinder the achievement of long-term sustainability outcomes. This study emphasizes the importance of field realities and cross-disciplinary collaboration to optimize the role of AI in sustainability efforts. Full article

Urban areas are at the forefront in addressing global challenges such as climate change and biodiversity loss. Among the key responses are nature-based solutions, which are increasingly being integrated into policy frameworks but which require strong community engagement for their effective implementation. This paper presents the findings of surveys conducted within the Place Lab in Katowice, Poland, an initiative developed as part of an international project and used as a participatory tool for co-creating and implementing green infrastructure. The project applies both place-based and people-centred approaches to support European cities in their transition towards regenerative urbanism. Place Lab activities encourage collaboration between local authorities and residents, enhancing awareness and fostering participation in environmental initiatives. The survey data collected during the project allowed for the evaluation of changes in public attitudes and levels of engagement and for the identification of broader societal phenomena that may influence the implementation of nature-based solutions. The findings revealed, for instance, that more women were interested in supporting the project, that residents tended to be sceptical of governmental actions on climate change, and that views were divided on the trade-off between urban infrastructure such as parking and roads and the presence of green areas. Furthermore, questions of responsibility, awareness, and long-term commitment were frequently raised. Building on the survey results and the existing literature, the study proposes a set of indicators to assess the contribution of citizen participation to the adoption of nature-based solutions. While the effectiveness of nature-based solutions in mitigating climate change impacts can be assessed relatively directly, evaluating civic engagement is more complex. Nevertheless, when conducted transparently and interpreted by experts, indicator-based assessment can offer valuable insights. This study introduces a novel perspective by considering not only drivers of engagement but also the obstacles. The proposed indicators provide a foundation for evaluating community readiness and commitment to nature-based approaches and may be adapted for application in other urban settings and in future research on climate resilience strategies. Full article

Background: Human activity is recognised as a major contributor to changes in Earth’s climate, land surface, oceans, ecosystems, and biodiversity. These alterations are largely due to greenhouse gas emissions, deforestation, mass pollution, and land degradation. In light of these environmental challenges, examining the psychological determinants of pro-environmental behaviour has become increasingly important. Study’s Aim: To provide a comprehensive model evaluating the structural relationships among biospheric values, green self-identity, green self-efficacy, and eco-anxiety to investigate the underlying mechanisms relating to the adoption of various pro-environmental behaviours (PEBs). Methods: An online self-report questionnaire was completed by 510 Italian participants (aged 18–55, M = 35.18, SD = 12.58) between November and December 2023. Data analysis was performed using R statistical software, employing Structural Equation Modelling. Results: The results indicate that eco-anxiety, green self-efficacy, and green self-identity are significant positive predictors of PEBs. Furthermore, green self-identity significantly influences eco-anxiety and green self-efficacy, while biospheric values are a major trigger for both green self-efficacy and green self-identity, but not for eco-anxiety. Conclusions: These findings suggest that while eco-anxiety can be an adaptive motivator for PEBs, biospheric values foster a green self-identity and self-efficacy, which in turn drive pro-environmental actions. The study concludes that encouraging biospheric values and strong green self-identity is crucial for promoting sustainable behaviours. Full article

Heatwaves intensified by climate change increasingly threaten urban populations, especially the elderly. However, most existing studies have concentrated on short-term or single-scale analyses, lacking a comprehensive understanding of how land cover changes and urbanization affect the vulnerability of the elderly to extreme heat. This study aims to investigate the spatiotemporal distribution patterns of heat-related health risks among the elderly in Nanchang City and to identify their key driving factors within the context of rapid urbanization. This study employs Crichton’s risk triangle framework to the heat-related health risks for the elderly in Nanchang, China, from 2002 to 2020 by integrating meteorological records, land surface temperature, land cover data, and socioeconomic indicators. The model captures the spatiotemporal dynamics of heat hazards, exposure, and vulnerability and identifies the key drivers shaping these patterns. The results show that the heat health risk index has increased significantly over time, with notably higher levels in the urban core compared to those in suburban areas. A 1% rise in impervious surface area corresponds to a 0.31–1.19 increase in the risk index, while a 1% increase in green space leads to a 0.21–1.39 reduction. Vulnerability is particularly high in economically disadvantaged, medically under-served peripheral zones. These findings highlight the need to optimize the spatial distribution of urban green space and control the expansion of impervious surfaces to mitigate urban heat risks. In high-vulnerability areas, improving infrastructure, expanding medical resources, and establishing targeted heat health monitoring and early warning systems are essential to protecting elderly populations. Overall, this study provides a comprehensive framework for assessing urban heat health risks and offers actionable insights into enhancing climate resilience and health risk management in rapidly urbanizing regions. Full article

The worldwide agriculture industry is facing increasing problems due to rapid population increase and increasingly unfavorable weather patterns. In order to reach the projected food production targets, which are essential for guaranteeing global food security, innovative and sustainable agricultural methods must be adopted. Conventional approaches, including traditional breeding procedures, often cannot handle the complex and simultaneous effects of biotic pressures such as pest infestations, disease attacks, and nutritional imbalances, as well as abiotic stresses including heat, salt, drought, and heavy metal toxicity. Applying phytohormonal approaches, particularly those involving hormonal crosstalk, presents a viable way to increase crop resilience in this context. Abscisic acid (ABA), gibberellins (GAs), auxin, cytokinins, salicylic acid (SA), jasmonic acid (JA), ethylene, and GA are among the plant hormones that control plant stress responses. In order to precisely respond to a range of environmental stimuli, these hormones allow plants to control gene expression, signal transduction, and physiological adaptation through intricate networks of antagonistic and constructive interactions. This review focuses on how the principal hormonal signaling pathways (in particular, ABA-ET, ABA-JA, JA-SA, and ABA-auxin) intricately interact and how they affect the plant stress response. For example, ABA-driven drought tolerance controls immunological responses and stomatal behavior through antagonistic interactions with ET and SA, while using SnRK2 kinases to activate genes that react to stress. Similarly, the transcription factor MYC2 is an essential node in ABA–JA crosstalk and mediates the integration of defense and drought signals. Plants’ complex hormonal crosstalk networks are an example of a precisely calibrated regulatory system that strikes a balance between growth and abiotic stress adaptation. ABA, JA, SA, ethylene, auxin, cytokinin, GA, and BR are examples of central nodes that interact dynamically and context-specifically to modify signal transduction, rewire gene expression, and change physiological outcomes. To engineer stress-resilient crops in the face of shifting environmental challenges, a systems-level view of these pathways is provided by a combination of enrichment analyses and STRING-based interaction mapping. These hormonal interactions are directly related to the United Nations Sustainable Development Goals (SDGs), particularly SDGs 2 (Zero Hunger), 12 (Responsible Consumption and Production), and 13 (Climate Action). This review emphasizes the potential of biotechnologies to use hormone signaling to improve agricultural performance and sustainability by uncovering the molecular foundations of hormonal crosstalk. Increasing our understanding of these pathways presents a strategic opportunity to increase crop resilience, reduce environmental degradation, and secure food systems in the face of increasing climate unpredictability. Full article

This study explores the interplay of climate vulnerability and social capital in two rural communities: Brajaballavpur, a high-climate-prone village in the Indian Sundarbans characterized by high ecological fragility, recurrent cyclones, and saline water intrusion affecting water access, livelihoods, and infrastructure; and Jemua, a low-climate-prone village in the land-locked district of Paschim Bardhaman, West Bengal, India, with no extreme climate events. A total of 85 participants (44 in Brajaballavpur, 41 in Jemua) were selected through purposive sampling. Using a comparative qualitative research design grounded in ethnographic fieldwork, data were collected through household interviews, Participatory Rural Appraisals (PRAs), Focus Group Discussions (FGDs), and Key Informant Interviews (KIIs), and analyzed manually using inductive thematic analysis. Findings reveal that bonding and bridging social capital were more prominent in Brajaballavpur, where dense horizontal ties supported collective action during extreme weather events. Conversely, linking social capital was more visible in Jemua, where participants more frequently accessed formal institutions such as the Gram Panchayat, local NGOs, and government functionaries that facilitated grievance redressal and information access, but these networks were concentrated among more politically connected individuals. The study concludes that climate vulnerability shapes the type, strength, and strategic use of social capital in village communities. While bonding and bridging ties are crucial in high-risk contexts, linking capital plays a critical role in enabling long-term social structures in lower-risk settings. The study contributes to both academic literature and policy design by offering a relational and place-based understanding of climate vulnerability and social capital. Full article

The environmental detriments of the growing global production and overconsumption of beef, including greenhouse gas emissions, deforestation, and biodiversity loss, are well-documented. However, public awareness of how dietary choices affect the environment remains limited. This study examines sustainability views on beef consumption and the potential for behavioral change as a step toward more sustainable intake levels. An observational web-based survey was conducted (n = 1367) to assess respondents’ current beef intake frequency, views on beef consumption related to planetary health, tropical deforestation, greenhouse gas emissions, and climate change, and willingness to modify beef consumption behavior. Chi-square tests were used for group comparisons, and weighted average scores were applied to rank levels of resistance to reducing beef intake. Environmental concern related to beef consumption was associated with greater beef cutback intentions and lower long-term intake reduction resistance amongst beef eaters. Beef eaters who strongly agreed that global beef consumption negatively impacts the environment were considerably more likely to express intentions to reduce their long-term beef intake compared to those who strongly disagreed (94.4% vs. 19.6%). Overall, 76.6% of beef eaters indicated wanting to eat less beef or phase it out entirely (30.7% reduce, 29.4% minimize, 16.6% stop), with only 23.4% of them intending to keep their consumption unchanged. Compelling messages that help translate awareness into action, such as the #NoBeefWeek concept explored in this study, may support individuals in adopting more sustainable food choices. These cross-national findings provide evidence for a ‘knowledge–intent’ gap in sustainable diet research, with relevance for health communicators and policymakers. Future research could examine the factors and motivations influencing decisions to modify beef consumption, including the barriers to achieving sustainable consumption levels and the role of suitable alternatives in facilitating this transition. Full article

Muslims are religiously obligated to care for the Earth, yet little empirical research exists on how Muslim communities in the U.S. engage with climate change. This study used a mixed-methods approach to explore climate change knowledge, attitudes, and practices (KAP) among 82 Muslims in Knoxville, Tennessee, building on prior theoretical or internationally focused work. Results found that participants largely accepted anthropogenic climate change and were strongly willing to act, citing Islamic principles such as stewardship and divine accountability as key motivators. However, many felt underinformed and lacked clarity on how to take action. Religious texts, more than religious leaders, shaped environmental views, offering interpretations that both aligned with and diverged from scientific narratives. Education and personal experience were the most frequently cited sources of climate understanding. Religion emerged as an important source of climate knowledge and a filter through which scientific information was interpreted. The knowledge and environmental attitudes inspired by their religion guided many participants to mitigate climate impacts, although some expressed a more fatalistic view of climate change. These findings suggest that effective climate communication in Muslim communities should integrate faith-based teachings with scientific messaging and engage religious leaders as amplifiers. Expanding this research to include more diverse Muslim populations across the U.S. can provide deeper insight into how Islamic worldviews shape climate engagement and behavior. Full article

Understanding and forecasting energy consumption patterns is crucial for improving energy efficiency and human well-being, especially in diverse infrastructures like Spain. This research addresses a significant gap in energy demand forecasting across three building types by comparing six machine learning algorithms: Artificial Neural Networks, Random Forest, XGBoost, Radial Basis Function Network, Autoencoder, and Decision Trees. The primary aim is to identify the most effective model for predicting energy consumption based on historical data, contributing to the relationship between energy systems and urban well-being. The study emphasizes challenges in energy use and advocates for sustainable management practices. By forecasting energy demand over the next three years using linear regression, it provides actionable insights for energy providers, enhancing resilience in urban environments impacted by climate change. The findings deepen our understanding of energy dynamics across various building types and promote a sustainable energy future. Stakeholders will receive targeted recommendations for aligning energy production with consumption trends while meeting environmental responsibilities. Model performance is rigorously evaluated using metrics like Squared Mean Root Percentage Error (RMSPE) and Coefficient of Determination (R²), ensuring robust analysis. Training times for models in the LUCIA building ranged from 2 to 19 s, with the Decision Tree model showing the shortest times, highlighting the need to balance computational efficiency with model performance. Full article