The understanding of weather and climate extremes provides academics, decision makers, international development agencies, nongovernmental organizations and civil society the necessary information for monitoring and giving early warning to prevent or minimize the risks associated with weather related hazards. Different researches were carried out to provide vital information that will further enhance the assessment of vulnerability and its impacts. Lack of proper understanding of weather and climate extremes was realized to be responsible for the huge and devastating losses that could have being averted or minimized over the past decades. Different countries and institutions have put in place a number of ways to increase sensitization and awareness of weather extremes. This became necessary in order to reduce the losses associated with these extremes both on local and regional scales.

In Eucalyptus plantations, coppice rotations often yield less than initial rotations. The TECHS project (Tolerance of Eucalyptus Clones to Hydric, Thermal and Biotic Stresses) studied short rotation coppicing across a 3,000 km gradient. The main objective of this work was to compare the survival, sprouting and initial growth of Eucalyptus clones managed, and to examine factors that might influence the productivity of the coppice rotation: climate, genotypes and stocking. Eight of the TECHS sites spread from latitudes 6o S to 30o S were included in the coppice study, with 17 genotypes at each site. The initial rotation had been planted at a 3 x 3 m spacing, and also in a spacing trial at densities from 450 to 14,000 trees ha-1. Six months after harvesting the initial Eucalyptus rotation, average survival was 88%, with tropical clones showing over twice the sprouting biomass (6.7 vs 2.9 Mg ha-1) and four times the woody biomass compared to subtropical clones (4.7 vs 1.1 Mg ha-1). Greater initial water deficits had stronger sprouting and growth. Clones with higher belowground carbon allocation in the initial rotation performed better in coppicing, and precipitation became more influential after 12 months. Drought and spacing trial significantly affected growth.

With the rapid development of artificial intelligence, machine learning is gradually becoming popular in predictions in all walks of life. In meteorology, It is gradually competing with traditional climate predictions dominated by physical models. This survey aims to consolidate the current understanding of Machine Learning (ML) applications in weather and climate prediction—a field of growing importance across multiple sectors including agriculture and disaster management. Building upon an exhaustive review of more than 20 methods highlighted in existing literature, this survey pinpointed eight techniques that show particular promise for improving the accuracy of both short-term weather and medium-to-long-term climate forecasts. According to the survey, while ML demonstrates significant capabilities in short-term weather prediction, its application in medium-to-long-term climate forecasting remains limited, constrained by factors such as intricate climate variables and data limitations. Current literature tends to focus narrowly on either short-term weather or medium-to-long-term climate forecasting, often neglecting the relationship between the two, as well as general neglect of modelling structure and recent advances. By providing an integrated analysis of models spanning different time scales, this survey aims to bridge these gaps, thereby serving as a meaningful guide for future interdisciplinary research in this rapidly evolving field.

This paper studies pandemic viruses that spread during the period (1759–2020) according to solar activity cycles. Our findings and results include the following: (1) The severity of a pandemic correlates negatively with the strength of solar activity; (2) Pandemic viruses are classified into three types based on their compatibility with solar activity associations. Most of them spread through the quiet Sun, where viruses survive better in cold and rainy weather, and in stable geomagnetic fields without strong disturbances; (3) The emergence of new strains of influenza viruses was manifested in two ways. First, the annual epidemics due to antigenic drift. Second, pandemics recur every 1–12 solar cycles (about 11–120 years) due to viral reassortment of new subtypes, which results in antigenic shifts; (4) Pandemic viruses have two groups according to their recurring period: first, recurring in nine solar cycles; second, recurring in twelve solar cycles. Furthermore, we reassort pandemic viruses from their previous spread in the same periodic classification. Moreover, we derive a periodicity formula for each subtype of the pandemic virus as a spread date.

Herein, we examined the effects of weather parameters and individual clinicodemographic risk factors on hemorrhagic stroke (HS) incidence, severity on admission, and disability at discharge in a harsh desert climate. In a retrospective design we studied patients admitted to a stroke unit in Arab Emirates in 2016-2019. With a distributed lag nonlinear model we explored immediate and delayed effects of weather on stroke incidence. Supervised machine learning was used to build models predictive of scores in NIHSS and mRS scales. We assessed model performance by calculating ROC AUC, F1 scores, specificity, and sensitivity. HS risk increased after a significant change in any weather parameter. The risk was reduced below baseline after 1-week adjustment to environmental changes. Climate input from the previous 3 days yielded reliable classification models. Humidex was a better predictor of severity than air temperature, whereas body mass index, age, and time of day at stroke onset were the strongest clinical predictors. Weather parameters before admission were stronger predictors of disease severity than individual clinical factors. We addressed limitations of previous studies by analyzing a full set of climate parameters: ambient temperature, relative humidity, humidex, atmospheric pressure, and wind speed. Predictive models may help to optimize patient management and develop preventative strategies.

In this paper major machine learning (ML) tools and the most important applications developed elsewhere for numerical weather and climate modeling systems (NWCMS) are reviewed. NWCMSs are briefly introduced. The most important papers published in this field in recent years are reviewed. The advantages and limitations of the ML approach in applications to NWCMS are briefly discussed. Currently, this field is experiencing explosive growth. Several important papers are published every week. Thus, this paper should be considered a simple introduction to the problem.

Background: The current coronavirus disease 2019 (COVID-19) is spreading globally at an accelerated rate. There is some previous evidence that weather may influence the incidence of COVID-19 infection. We assessed the role of meteorological factors including temperature (T) and relative humidity (RH) considering the concentrations of two air pollutants, inhalable coarse particles (PM10) and nitrogen dioxide (NO2) in the incidence of COVID-19 infections in Finland, located in arctic-subarctic climatic zone. Methods: We retrieved daily counts of COVID-19 in Finland from Jan 1 to May 31, 2020, nationwide and separately for all 21 hospital districts across the country. The meteorological and air quality data were from the monitoring stations nearest to the central district hospital. A quasi-Poisson generalized additional model (GAM) was fitted to estimate the associations between district-specific meteorological factors and the daily counts of COVID-19 during the study period. Sensitivity analyses were conducted to test the robustness of the results. Results: The incidence rate of COVID-19 gradually increased until a peak around April 6 and then decreased. There were no associations between daily temperature and incidence rate of COVID-19. Daily average RH was negatively associated with daily incidence rate of COVID-19 in two hospital districts located inland. No such association was found nationwide. The sensitivity analyses indicate the results are robust. Conclusions: Weather conditions, such as air temperature and relative humidity, may not be important factors affecting the COVID-19 incidence in the arctic and subarctic winter and spring. More evidence is needed on the associations between weather and COVID-19 during different seasons.

Having sustained, over the course of more than two decades, record-breaking natural catastrophe losses, American insurers and reinsurers are justifiably questioning the potential linkage between anthropogenic climate change and extreme weather. Here, we explore issues pertaining to this linkage, looking at both the likely short-term implications for the insurance industry, as well as potential longer-term impacts on financial performance and corporate resilience. We begin our discussion with an overview of the implications that climate change is likely to have on the industry, especially as it relates to how catastrophic risks are construed, assessed, and managed. We then present the rudiments of an econometric analysis that explores the financial resilience of the property/casualty (P/C) industry in the face of both natural and man-made catastrophes. In this analysis, we explore the profitability consequences of several illustrative scenarios involving large-scale losses from extreme weather—specifically, a sequence of storms like those striking the U.S. in 2004—and a scenario that explores the prospect of a Katrina-scale storm in combination with a mass terror attack on the scale of 9/11. At systemic levels of aggregation, our analysis suggests a high degree of macro-resilience for the insurance industry. Moreover, we find that insurer resilience is higher for larger impacts, considering both the speed of recovery, as well as the inverse of the area under the unaffected system profile. We conclude with a summary of our findings and a closing commentary that explores the potential implications of these results for P/C insurers moving forward.

Oil palm is one of the most important crops in Malaysia. Lately, the production of oil palm has been reduced due to a variety of factors, including the weather and climate. Temperature, wind speed, relative humidity, sunshine, and rainfall distribution all have an impact on palm tree growth and development, which in turn has an impact on oil palm production. This paper aims to investigate the effects of some weather elements (temperature, wind speed, relative humidity, sunshine, and rainfall) on oil palm production in Peninsular Malaysia. Data were analyzed using the Statistical Package for Social Sciences (SPSS 20.0 version), with descriptive statistics, and multiple linear regression (MLR). The MLR model determined the strength of the relationship between oil palm yield (dependent variable) and the changing variables of temperature, sunshine, wind speed, relative humidity, and rainfall (independent variables). The findings revealed that temperature, wind speed, relative humidity, sunshine, and rainfall have a low impact on oil palm production and yield turnover. The R2 value of 0.202 shows that the independent variables explained only 20.2% of the fluctuation in palm oil production. The study recommends working within an integrated approach involving scientific research, planting, improving variety, improving regional academic leadership, and engaging private and public stakeholders, emphasized collaborative efforts with researchers in consumer countries, and strengthening the capacity of growers to best agroecological practices.

Recently, NOAA has included GNSS (Global Navigation Satellite System) Radio Occultation (RO) data as one of the crucial long-term observables for weather and climate applications. To include more GNSS RO data in the numerical weather prediction system, the NOAA Commercial Weather Data Pilot program (CWDP) started to explore the commercial RO data available on the market. After two rounds of pilot studies, the CDWP decided to award the first Indefinite Delivery Indefinite Quantity (IDIQ) contract to GeoOptics and Spire Incs. in 2020. This study examines the quality of Spire data products for weather and climate applications. Spire RO data are collected from commercial CubeSats through careful comparison with the data from Formosa Satellite Mission 7–Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2), ERA-5, and high-quality radiosonde data. The results demonstrated that although with lower Signal-Noise-Ratio (SNR) in general, the pattern of the lowest penetration height for Spire is similar to those for COSMIC-2. The Spire and COSMIC-2 penetrate heights are between 0.6 and 0.8 km altitude at the tropical oceans. Although using different GNSS RO receivers, the precision of Spire STRASP receivers is of the same quality as those of COSMIC-2 Global Positioning System - GPS, GALILEO, and GLObal NAvigation Satellite System – GLONASS (TGRS) receivers. The retrieval accuracy from Spire is very compatible with those from COSMIC-2. We validated Spire temperature and water vapor profiles by comparing them with collocated radiosonde data. Generally, over the height region between 8 km and 16.5 km, the Spire temperature profiles match those from RS41 RAOB very well with temperature biases < 0.02 K. Over the height range from 17.8 to 26.4 km, the temperature differences are ~-0.034 K with RS41 RAOB being warmer. We also estimated the error covariance matrix for Spire, COSMIC-2, and KOMPSAT-5. Results showed that the COSMIC-2 estimated error covariance values are slightly more significant over the oceans at the mid-latitudes (45oN-30oN and 30oS-45oS), which may also be owing to COSMIC-2 SNR being lower at those latitudinal zones.

Background: Aedes albopictus, the Asian Tiger Mosquito, has become a prevalent pest in Italy, causing severe nuisance and posing a threat of transmission of arboviruses introduced by infected travellers. In this study, we investigated the influence of weather parameters on the seasonal population density of Aedes albopictus. Methods: A Bayesian approach was employed to identify the best meteorological predictors of species trend, using the eggs collected monthly from 2010 to 2022 by the Emilia-Romagna regional monitoring network. Results: The findings show that the winter-spring period (January to May) plays a crucial role in the size of the first generation and seasonal development of the species. Conclusions: A temperate winter and a dry and cold March, followed by a rainy and hot spring and a rainy July, seem to favour the seasonal development of Ae. albopictus.

In this study we investigated the association between weather types (WTs) and the Urban Heat Island Intensity (UHII) in the region of Attica (Greece). The application of the methodology results in ten WTs over Attica region. The UHII was calculated for every hour of the day from 2008 to 2017, using a new air temperature dataset produced by Copernicus Climate Change Service. To have more clear results concerning the association between WTs and UHII, we have used also the upper 5% of UHII (Urban Overheating-UO). The UO have been estimated for two-time intervals (daytime and nighttime) and for the warm period (June-September). The UHII frequency distribution as well as the spatial characteristics of the UO were also investigated. It was found that UO was amplified under WT2 during the night while, WT10 was mainly responsible for exacerbated UO magnitude at daytime, in all months. Furthermore, analysis results revealed that the UO effect is more pronounced in Athens during the night, especially at Athens center. The daytime hot-spots identified mainly in sub-urban and rural areas. Therefore, this methodology may help for heat mitigation strategies and climate adaptation measures, in urban environments.

There is high confidence that climate change has increased the probability of concurrent temperature-precipitation extremes, changed their spatial-temporal variations, and affected the relationships between drivers of such natural hazards. However, the extent of such changes has been less investigated in Australia. Daily weather data (131 years, 1889-2019) at 700 grid cells (1◦ × 1◦) across Australia was obtained to calculate annual and seasonal mean daily maximum temperature (MMT) and total precipitation (TPR). A nonparametric multivariate copula framework was adopted to estimate the return period of compound hot-dry (CHD) events based on an ‘And’ hazard scenario (hotter than a threshold ‘And’ drier than a threshold). CHD extremes were defined as years with joint return periods of larger than 25 years. Mann-Kendall nonparametric tests was used to analyse trends in MMT and TPR as well as in the frequency of univariate and CHD extremes. A general cooling-wetting trend was observed over 1889-1989. Significant increasing trends were detected over 1990-2019 in the frequency and severity of hot extremes across the country while trends in dry extremes were mostly insignificant (and decreasing). Results showed a significant increase in the association between temperature and precipitation at various temporal scales. The frequency of CHD extremes was mostly stable over 1889-1989, but significantly increased between 1990 and 2019 at 44% of studied grid cells, mostly located in the north, south-east and south-west. Spatial homogeneity (i.e. connectedness) and propagation of extreme events from one grid cell to its neighbouring cells was investigated across Australia. It can be concluded that this connectedness has not significantly changed since 1889.

The rescue, digitisation, quality control, preservation and utilization of long and high quality meteorological and climate records, particularly related to historical marine data, are crucial for advancing our understanding of the Earth's climate system. In combination with land and air measurements, the historical marine records serve as foundational pillars in linking present and past weather and climate information, offering essential insights into natural climate variability, extreme events in marine areas, baseline data for assessing current changes, and inputs for en-hancing predictive climate models and reanalyses. This paper provides an overview of marine data rescue activities covering the past centuries, presents and highlights several ongoing projects across the world and how the data are used in an integrative and international framework. Current and future continuous efforts in data rescue, digitisation, quality control and the de-velopment of temporally high resolution meteorological and climatological observations from the Sea, will greatly help to further complete our understanding and knowledge of the Earth system including extremes, as well as improving the quality of reanalysis.

During the last decade, Bangladesh has been experienced a significant climatic anomalies which lead to an increase in magnitude and frequency of various climate relate extreme events. Climate scientists substantiate that global temperature and precipitation pattern is expected to change, which may result in consequential impact on agriculture, livelihood, and ecosystem. Bangladesh is therefore likely to face substantial challenges in the coming decades. In order to adequately understand this complex, dynamic phenomena, Analyzing historical Climate change scenarios as well as projecting its future trends is a great concern for researcher. This study aims to analyzes/ historical climatic data from (1901-2020), and predict future temperature and precipitation patterns in Bangladesh using CMIP6 data. Data used in this study (Observed data is from CRU TS 4.05 and future data is from CMIP6) have been acquired from WorldClim v2.1. Various methods including correlation, regression, standard deviation, correlation matrix, percentiles, cell statistics, and IDW interpolation were performed to analyze the trends, variability and spatial patterns of temperature and rainfall. This study revealed that Over the historical study period (1901-2020) Bangladesh has been experienced a significant warming trend with an average increase in temperature 2°C and with annual decline of the total precipitation 607.26 mm along with a shift towards drier conditions despite weak correlation with hotter years. Projected climate models represents that Bangladesh minimum temperature is expected to increase from 1°C to 4.4°C as well as maximum temperatures from 1°C to 4.1°C by 2100. Moreover, projected precipitation is expected to increase by 480.38 mm, with the biggest rises during monsoon months. Regional variations in temperature and precipitation are again expected, with the Southeast (SE) likely experiencing the most significant warming and the Northeast (NE) seeing the most substantial increase in precipitation.

Climate change presents an urgent global challenge, manifesting in rising temperatures and extreme weather events with severe societal impacts. The Eastern Mediterranean, warming faster than the global average, faces immediate repercussions. Climate literacy emerges as pivotal, empowering individuals to comprehend climate science and act accordingly. This study delves into climate literacy, attitudes, and 'greening' behaviours in the Eastern Mediterranean hotspot of Greece, based on a survey of 1,962 citizens. Findings indicate high climate literacy but lower adoption of 'greening' behaviours, especially those involving financial costs. Regression analyses highlight the significant role of climate literacy, concerns about personal impacts, coping appraisal and trust in institutions in promoting 'greening' behaviours. The study underscores the need for multifaceted strategies emphasizing financial motivation, trust-building, and societal norm shifts. Socio-demographic disparities, including gender and occupation, highlight areas for targeted interventions. The emphasis on the mental health impacts of climate-related events underscores the need for holistic disaster management. Policy implications are discussed, highlighting the potential of expanded climate literacy to catalyze collective action toward sustainability.

In the last decades farm animals kept in confined and mechanically ventilated livestock buildings are increasingly confronted with heat stress (HS) due to global warming. These adverse conditions cause a depression of animal health and welfare and a reduction of the performance up to an increase of the mortality. To facilitate sound management decisions, livestock farmers need relevant arguments, which quantify the expected economic risk and the corresponding uncertainty. The economic risk was determined for the pig fattening sector based on the probability of HS and the calculated decrease in the gross margin. The model calculation for confined livestock buildings showed, that HS indices calculated by easily available meteorological parameters can be used for assessment quantification of indoor HS, which is so far difficult to determine. These weather-related HS indices can be applied not only for an economic risk assessment but also for a weather-index based insurance for livestock farms. Based on the temporal trend between 1981 and 2017, a simple model was derived to assess the likelihood of HS for 2020 and 2030. Due to global warming, the return period for a 90-percentile HS index is reduced from 10 years in 2020 to 3-4 years in 2030. The economic impact of HS on livestock farms was calculated by the relationship between an HS index based on the temperature-humidity index (THI) and the reduction of the gross margin. From the likelihood of the HS and this economic impact function, the probability of the economic risk could be determined. The reduction of the gross margin for a 10 year return period was determined for 1980 with 0.27 € per year and animal place and increased by the 20-fold to 5.13 € per year and animal place in 2030.

Xinjiang, as a climate-sensitive region in northwest China, holds significant importance in studying extreme weather events for agricultural production and socioeconomic development. Based on data from 52 meteorological stations and 23 extreme climate indicators in Xinjiang from 1960 to 2021, the 5a moving average, linear trend estimation, climate tendency rate, and GIS spatial analysis methods are used to analyze the distribution and change characteristics of extreme climate. The results revealed that from 1960 to 2021, TXx, TNx, TXn, TNn, FD, ID, TN10p, TX10p, and WSDI showed a significant upward trend, while SU, TR, TN90p, TX90p, and CSDI showed a significant downward trend. What is more, the range of changes in the extreme temperature indexes from 1990 to 2021 is higher than that of 1960 to 1989. The high-value areas of extreme indexes are primarily concentrated in the northern part, while the high-value areas of cold event indexes are mainly distributed in the southern part. The upward/downward trends all accounting for over 80.00% of the entire region. The Rx1day, Rx5day, PRCPTOT, R95p, R99p, R10, R20, and R25 showed a significant growth trend from 1960 to 2021, and the range of change in the extreme precipitation indexes from 1990 to 2021 was lower than that from 1960 to 1989. Moreover, their high-value areas and high climate tendency areas are mainly concentrated in the northern and western parts of Xinjiang, with upward trends all accounting for over 71.00% of the entire region. The research results provide theoretical foundations for formulating climate risk strategies in the northwest region of China.

Global warming is increasing the frequency and intensity of compound drought-heat events (CDHEs), potentially leading to larger and more extreme fire seasons in mesic forests. Wildfire activity in subtropical China, under the influence of monsoonal rainfall, was historically limited to dry winters and rare in rainy summers. Here we seek to test that this area is on the brink of a major change in its fire regime characterized by larger fire seasons, extending into the summer, leading to increases in burned area. We analyze fire activity in Chongqing Municipality (46,890 km2), an important area in subtropical China hosting the Three Gorges Reservoir Area. We observed significant increases in summer forest fires under anomalous dry-hot summer conditions, where total burned area was 3-6 higher than the historical annual mean (largely confined to the winter season). Vapor pressure deficit, an indicator of hot and dry conditions, was a strong predictor of fire activity, with major wildfires occurring on days where VPD was higher than 3.5kPa. Results indicate that major wildfire activity may occur in the area as a result of climate change, unless strong fire prevention policies are implemented.

This literature review paper delves into the synergies between Climate-Smart Agriculture (CSA) and the effective management of food losses. With the escalating challenges posed by climate change and the pressing need to address food security concerns, the intersection of these two critical domains becomes paramount for sustainable agricultural practices. The review explores existing research on the implementation of climate-smart agricultural techniques and their impact on mitigating food losses throughout the agricultural value chain. The paper synthesizes findings related to climate-resilient crop varieties, precision farming, water-use efficiency, and sustainable soil management, highlighting their contributions to reducing post-harvest losses and enhancing overall food security. In addition to summarizing current knowledge, this paper identifies gaps in the literature and proposes avenues for future research. This review serves as a foundation for researchers, policymakers, and practitioners seeking to contribute to the advancement of climate-smart agriculture and the reduction of food losses, paving the way for a more sustainable and resilient global food system.

The effects of climate change health impacts on urban health care systems and vulnerable populations are a researched topic with foundations on urban risk management, urban health methodologies, and sustainable urbanization. As climate change is a dynamic and transformative global issue the gap between adaptation and mitigation efforts and urban planning relies, among other things, in the limitations of previous studies of risk management structures tailored to the existing strategy to calculate the uncertainties and predict their influence on urban systems and subsystems. Moreover, as climate and health is a well-researched issue, it is also complex as the methodologies and strategic approach to minimize health risks of climate change is intrinsically contingent to risk methodologies and policy-making processes that are efficient with support of knowledge-based information. This paper emphasis the need for focusing on urban health risk management methods that serves to identify potential opportunities, and then manage and take action to prevent adverse health effects. It also emphasizes the probability of events and their consequences, which are measurable both qualitatively and quantitatively.

Drought monitoring and early detection have improved greatly in recent decades through the development and refinement of numerous indices and indicators. However, a lack of guidance, based on user experience, exists as to which drought monitoring tools are most appropriate in a given location. This review paper summarizes the results of targeted user engagement and the published literature to improve the understanding of drought across North America, and to enhance the utility of drought monitoring tools. Workshops and surveys were used to assess and make general conclusions about the perceived performance of drought indicators, indices and impacts information used for monitoring drought in the five main Köppen climate types (Tropical, Temperate, Continental, Polar Tundra, Dry) found across Canada, Mexico, and the United States. In Tropical, humid Temperate, and southerly Continental climates, droughts are perceived to be more short-term (less than 6 months) in duration rather than long-term (more than 6 months). In Polar Tundra climates, Dry climates, Temperate climates with dry warm seasons, and northerly Continental climates, droughts are perceived to be more long-term than short-term. In general, agricultural and hydrological droughts were considered to be the most important drought types. Drought impacts related to agriculture, water supply, ecosystem, and human health were rated to be of greatest importance. Users identified the most effective indices and indicators for monitoring drought across North America to be the U.S. Drought Monitor (USDM) and Standardized Precipitation Index (SPI) (or another measure of precipitation anomaly), followed by the Normalized Difference Vegetation Index (NDVI) (or another satellite-observed vegetation index), temperature anomalies, crop status, soil moisture, streamflow, reservoir storage, water use (demand), and reported drought impacts. Users also noted the importance of indices that measure evapotranspiration, evaporative demand, and snow water content. Drought indices and indicators were generally thought to perform equally well across seasons in Tropical and the colder Continental climates, but their performance was perceived to vary seasonally in Dry, Temperate, Polar Tundra, and the warmer Continental climates, with improved performance during warm and wet times of the year. The drought indices and indicators, in general, were not perceived to perform equally well across geographies. This review paper provides guidance on when (time of year) and where (climate zone) the more popular drought indices and indicators should be used. The paper concludes by noting the importance of understanding how drought, its impacts, and indicators are changing over time as the climate warms, and by recommending ways to strengthen the use of indices and indicators in drought decision-making.

Food insecurity remains a significant developmental challenge, particularly in rural areas, despite ongoing efforts to mitigate it. To better understand the persistence of this issue, this study investigates the causes of household food insecurity and the coping strategies employed by households in Zorkor Goo, Zorkor Kanga, and Zorkor Kadaare communities within the Bongo District of Ghana’s Upper East Region. A descriptive cross-sectional survey was conducted, sampling 196 households systematically from these communities. Data were collected through a structured questionnaire covering socio-demographic factors, causes of food insecurity, household coping strategies, and dietary intake. The analysis was performed using SPSS (Version 21.0), with food security status and dietary intake assessed via the FANTA Household Food Insecurity Access Scale (HFIAS) and Food Consumption Score, respectively. The study identified key factors contributing to food insecurity, including soil infertility, poor rainfall patterns, food spoilage, extravagant funerals, large family size, high unemployment, and lack of education. Major coping strategies among households included meal skipping, reducing meal size and frequency, migration, asset sales, and basket weaving. The findings revealed that 96.4% of households were severely food insecure, and over two-thirds (67.3%) exhibited poor dietary consumption. Notably, household employment levels, education, and food consumption scores were significantly associated with food insecurity (P<0.001). These findings underscore the need for targeted interventions addressing the root causes of food insecurity and enhancing household resilience in these communities.

With the development of the energy sector based on renewable primary sources, structural changes are emerging for the entire national energy system. Initially, it was estimated that energy generation based on fossil fuels would decrease until its disappearance. However, the evolution of CO2 capture capacity leads to a possible coexistence for a certain period with the renewable energy sector. The paper develops this concept of coexistence of the two systems, with the positioning of green hydrogen not only within the renewable energy sector but also as a transformation vector for carbon dioxide captured in the form of synthetic fuels such as CH4 and CH3OH. The paper also analyzes the economic aspects related to price differences for energy produced in the two sectors, as well as their interconnection. The technical aspect, as well as the economic one, for storage through various other solutions besides hydrogen, has been highlighted. The development of the renewable energy sector and its demarcation from the fossil fuel energy sector, even with the transcendent vector represented by green hydrogen, leads to the deepening of dispersion aspects between the electricity sector and the thermal energy sector, a less mentioned aspect in current works but of great importance.

Climate change is challenging the agricultural sector worldwide. The pressure exerted by the altered thermal conditions drives the zonal shift for various cultivations. This study presents the multiyear analysis of the Growing Degree Days (GDD) index over the Northern Mediterranean Basin (NMB), focused on the high-value vine cultivation derived from the E-OBS dataset. To handle this extensive environmental dataset, R-language scripts and packages were applied, along with the capabilities of the GIS software. The investigated time period spans from 1969 to 2018, and the performed analysis indicates a broad shift/expansion in areas of GDD exceeding 2000 heat units. The extracted results specify the viticultural areas where the GDD index surpasses the suitable values for vine cultivation, owing to the increase of the thermal stress risk. In addition to mapping areas with GDD ≥ 2000, spatial calculations of the surface of areas meeting this criterion per country in the study area were made. In the northern parts of the NMB, the rise of the GDD index values indicates the establishment of vine plantations in the future.

Background: Escalating climate change concerns have prompted the healthcare sector, particularly anaesthesia, to reassess its practices to align with environmental sustainability. Anesthetic gases like desflurane, sevoflurane, and isoflurane significantly impact the environment due to their high Global Warming Potentials (GWPs) and prolonged atmospheric lifetimes. This manuscript investigates these impacts and advocates for strategies to reduce the carbon footprint of anesthetic care while maintaining patient safety. Methods: We reviewed the environmental impacts of anesthetic gases and explored various strategies for sustainable anaesthesia. These strategies include adopting anesthetics with lower GWPs, enhancing recycling and waste reduction techniques, transitioning to intravenous anaesthesia, and implementing low-flow anaesthesia methods. We also examined barriers to these strategies, such as educational gaps, technological challenges, resistance to change, and policy limitations. Results: Adopting anesthetics with lower GWPs, such as sevoflurane over desflurane, and implementing low-flow anaesthesia can significantly reduce greenhouse gas emissions. Recycling and waste reduction techniques, though challenging, offer additional environmental benefits. Transitioning to intravenous anaesthesia can eliminate the direct GWP contributions from volatile anesthetic gases. However, these strategies require comprehensive education, advocacy for research and innovation, strategic change management, and supportive policies. Conclusions: Continuous monitoring and evaluation are crucial to ensure the effectiveness of sustainable practices in anaesthesia. Establishing robust Key Performance Indicators (KPIs) and utilizing advanced analytical tools will enable the anaesthesia community to adapt and refine practices. Collaborative efforts among clinicians, policymakers, and stakeholders are essential to significantly reduce the environmental impact of anaesthesia and promote ecological responsibility within healthcare.

Museums are called to respond to contemporary challenges and contribute to the formation of more progressive societies. One of the major challenges of our time is climate change and museums could construct cocreated narratives around climate and implement targeted actions for the audiences through their sensorial and affective experiences. More specifically, Geology and Palaeontology museums are repositories of knowledge on geoheritage and geoconservation, recognized for their scientific, pedagogical, cultural and recreational values. The educational potential of geological and paleontological exhibits could be used in informal education activities regarding the climate emergency. Taking the above into consideration, this study focuses on both the creation and evaluation of an educational program for the Natural History Museum of Arsakeio Campus at Psychicko.

Although the influence of climate on the fire regime is unanimously recognized, most publications are studies on this influence are at the global scale. Therefore, this study aims to demonstrate the role of climate in the incidence of wildfires at the country and regional scale using statistical analysis and machine learning methods. Mainland Portugal was chosen as a case study due to its climate and because it is the European region most affected by wildfires. Results demonstrate the climate signature in the spatial and temporal distribution of the wildfire incidence. The conclusions of the study include (i) the existence of two pyro-regions, with different types of climate (Csb and Csa) composed of NUTS II regions: the northern region composed of the Norte and Centro regions and the southern region composed of Alentejo and Algarve; (ii) the intra-annual variability in the incidence of wildfires, characterized by two peaks, one in spring and the other in summer, are a consequence of the country's type of climate; and, (iii) how the annual cycle of wildfire incidence varies over the years depends on the weather conditions throughout each year. These results are of fundamental importance for wildfire managers, especially in the context of climate change.

Polar stratospheric clouds (PSCs) form in polar regions typically between 15 and 25 km, when the local temperature is sufficiently low. PSCs play an important role in the ozone chemistry and the dehydration and denitrification of the stratosphere. Lidars with a depolarization channel may be used to detect and classify different classes of PSCs. The main PSC classes are water ice, nitric acid trihydrate (NAT) and supercooled ternary solutions (STS), the latter being liquid droplets consisting of water, nitric acid and sulphuric acid. PSCs have been observed at the lidar observatory at Concordia Station from 2014 on. The harsh environmental conditions at Concordia during winter render successful lidar operation difficult. In order to facilitate the operation of the observatory several measures have been put in place to achieve an almost complete remote control of the system. PSC occurrence is strongly correlated with local temperatures and is affected by dynamics, as the PSC coverage during the observation season shows.

Groundwater is becoming increasingly important in the Pacific Northwest of the USA due to declining snowpack volumes and shifts in precipitation type and timing, all connected with climate change. The Upper Williamson Basin of the Klamath Watershed is a groundwater dominated watershed with massive fluctuations in year-to-year streamflow volumes over the past four decades, including the complete absence of any live flow for several years. The precise relationship between groundwater and streamflow in the basin has been difficult to assess due to a limited number of monitoring wells and significant gaps in the water level time history. To address this challenge, we use a novel imputation technique that leverages Earth observations and machine learning to impute gaps in water level records. We use these more complete datasets to compute a groundwater storage change time series for the basin. We show that groundwater storage is highly correlated to streamflow and that groundwater storage is correlated to rainfall with a three- to four-year delay that appears variable depending on groundwater storage volumes. The tools and relationships we present make it possible for water managers to estimate when streamflows will return to the basin and be more informed to support better management.

Large-scale hydrological modeling in China is challenging given the sparse meteorological stations and large uncertainties associated with atmospheric forcing data.Here we introduce the development and use of the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) in the Heihe River Basin(HRB) for improving hydrologic modeling, by leveraging the datasets from the China Meteorological Administration Land Data Assimilation System (CLDAS)(including climate data from nearly 40000 area encryption stations, 2700 national automatic weather stations, FengYun (FY) 2 satellite and radar stations). CMADS uses the Space Time Multiscale Analysis System (STMAS) to fuse data based on ECWMF ambient field and ensure data accuracy. In addition, compared with CLDAS, CMADS includes relative humidity and climate data of varied resolutions to drive hydrological models such as the Soil and Water Assessment Tool (SWAT) model. Here, we compared climate data from CMADS, Climate Forecast System Reanalysis (CFSR) and traditional weather station (TWS) climate forcing data and evaluatedtheir applicability for driving large scale hydrologic modeling with SWAT. In general, CMADS has higher accuracy than CFRS when evaluated against observations at TWS; CMADS also provides spatially continuous climate field to drive distributed hydrologic models, which is an important advantage over TWS climate data, particular in regions with sparse weather stations. Therefore, SWAT model simulations driven with CMADS and TWS achieved similar performances in terms of monthly and daily stream flow simulations, and both of them outperformed CFRS. For example, for the three hydrological stations (Ying Luoxia, Qilian Mountain, and ZhaMasheke) in the HRB at the monthly and daily Nash-Sutcliffe efficiency ranges of 0.75-0.95 and 0.58-0.78, respectively, which are much higher than corresponding efficiency statistics achieved with CFSR (monthly: 0.32-0.49 and daily: 0.26 – 0.45). The CMADS dataset is available free of charge and is expected to a valuable addition to the existing climate reanalysis datasets for deriving distributed hydrologic modeling in China and other countries in East Asia.

CO2 emission activity of xylotrophic fungi response to an increase in temperature in the range of 10-30 °C on the example of pure dikaryotic cultures of Fomes fomentarius s. str., F. inzengae, Fomitopsis betulina, F. pinicola, Phellinus igniarius was analyzed. Emission activity was assessed by the difference in CO2 concentration in 0.5-liter exposure chambers with Petri dishes with mycelium growing on agar at the beginning of exposure and an hour later using a Gasmet DX-4030 FTIR spectrometer (Gasmet Technologies Oy, Finland), error measurements ±50 ppm. Specific (μg CO2/cm2/h) and total (μg CO2/h) emission activity and its relationship with temperature and size (area) of the mycelium were assessed. It has been shown that in the range of 10-30 °C, the specific and total CO2 emission activity of the mycelium is closely and positively related to temperature. Specific emission, which is an indicator of the respiratory activity of the mycelium, does not depend on its size; its only driver is temperature, the relationship with which is linear: an increase in temperature by 10 °C causes an increase in the specific emission activity of the mycelium by 1.7 times. The total CO2 emission activity, which is an indicator of the total amount of CO2 emitted, is directly proportional to the specific emission activity and the size of the mycelium. In the range of 10-30 °C, an increase in temperature causes an almost equal increase in both the specific emission activity of the mycelium (Q10 1.7) and its growth (Q10 1.5) and causes an exponential increase in the total emission of CO2. This must be taken into account when predicting CO2 emissions from woody debris under climate change, as it could potentially contribute to accelerating climate change.

Long term Precipitation and temperature variations are one of the main determinants of climate variability of one’s area. The aim of this study is to determine trends variation in climatic elements of temperature and precipitation in the southern zone of Tigray regional state, Ethiopia. The station is assumed for the study of climatic records over southern zone of the region in detection for probable trends. The daily, monthly and annual precipitation totals and temperature observed at korem meteorological station were used for the period of 1981-2010 for Precipitation and 1985 – 2010 for minimum and maximum temperature. Summary of descriptive statistics and Mann Kendall test methods were employed for the observed data analysis to demonstrate any existence of possible trends. The main findings of the study indicated that the mean and maximum temperature had a general increasing trend; however, minimum temperature showed decreasing trend. In general annual temperature from 1985 – 2010 of the area showed a warming trend. Moreover analysis of the 30 years (1981-2010) annual precipitation showed a coefficient of variation ranging from 33.77 – 233 %. It indicated that the precipitation dissemination is not normal with large year to year variances.

This article examines the impact of climate change on the hydrometeorological state of some lakes and reservoirs in the Akmola and North Kazakhstan regions. Time series with values of various climate parameters for the period 1986-2023 are used for the climate analysis; the reliability of the results was assessed using the Fisher criteria. Hydrometric indicators were studied using the ArcGIG program. Using GIS to spatially analyze water climate data, 8-channel satellite images from 1995 to 2023 were used. During the study, it was found that the area of the main lakes has undergone changes.

Subnational governments play a key role responding to climate change risks in terms of policy strategies and instruments. This article analyzes how Brazilian municipal and state governments have developed and implemented public policies to mitigate and to adapt to climate change risks. We surveyed all cities’ and states’ climate policies within the country. The methodological approach includes five main points of analysis: 1. mitigation targets and intentions; 2. adaptation actions; 3. stakeholders’ participation; 4. policy implementation; 5. participation in networks related to climate change. Our results suggest that even though subnational climate policies in Brazil are isolated initiatives within the national context, they play an important role responding to climate change risks in different scales and levels. The strongest Brazilian policies with both mitigation and adaptation actions counted on previous mobilization for the climate issue involving different stakeholders from several segments of the society. These governments have also participated in transnational cooperation networks related to climate change.

Oedaleus decorus asiaticus, Calliptamus abbreviatus, Angaracris rhodopa, and Myrmeleotettix palpalis are the main grasshoppers that harm the natural grassland in the Hexi Corridor. In this study, the MaxEnt model was employed to identify the key environmental factors affecting the distribution of the four grasshoppers habitats and to assess their distribution under current and future climate change conditions. The aim was to provide a basis for grasshopper monitoring, prediction, and precise control. In this study, 61 occurrences of O. decorus asiaticus, 68 occurrences of A. rhodopa, 58 occurrences of C. abbreviatus, and 92 occurrences of M. palpalis were recorded by GPS, and the distribution of suitable habitats for O. decorus asiaticus, C. abbreviatus, A. rhodopa, and M. palpalis were predicted under current and future climatic scenarios using a MaxEnt model. The average AUC and TSS values of the four grasshoppers were greater than 0.9, and the simulation results were excellent and highly reliable. The mean annual precipitation was the main factor limiting the current range of suitable areas for these four species. Under the current climate, O. asiaticus, C. abbreviatus, and A. rhodopa were mainly distributed in the central and eastern parts of the Hexi Corridor, and M. palpalis was distributed throughout the Hexi Corridor, with a potential distribution area of 1.44×104, 1.43×104, 1.29×104, and 2.12×104 km2, accounting for 15.3, 15.2, 13.7, and 22.5% of the total area of the grasslands in the Hexi Corridor, respectively. The highly suitable areas of O. asiaticus, C. abbreviatus, and A. rhodopa were mainly distributed in the east-central part of Zhangye City, the western part of Wuwei City, and the western and southern part of Jinchang City, with areas of 0.35×104, 0.29×104, and 0.20×104 km2, accounting for 3.7, 3, and 2.2% of the grassland area, respectively. The high habitat of M. palpalis was mainly distributed in the southeast of Jiuquan City, the west, middle, and east of Zhangye City, the west of Wuwei City, and the west and south of Jinchang City, with an area of 0.32×104 km2, accounting for 3.4% of the grassland area. In the 2030s, the range of O. asiaticus, C. abbreviatus, A. rhodopa, and M. palpalis was predicted to decrease.

Against the backdrop of the European Union's commitment to achieve climate neutrality by 2050, efforts to improve energy efficiency are being intensified. The manufacturing industry is a key focal point of these endeavors due to its high final electrical energy demand, while simultaneously facing a growing shortage of skilled workers crucial for meeting established goals. Expert systems (ESs) offer the chance to overcome this challenge by automatically identifying potential energy efficiency improvements and thereby playing a significant role in reducing electricity consumption. This paper systematically reviews state-of-the-art approaches of ESs aimed at improving energy efficiency in industry, with a focus on manufacturing. The literature search yields 1668 results, of which 62 articles published between 1987 and 2024 are analyzed in depth. These publications are classified according to the system boundary, manufacturing type, application perspective, application purpose, ES type, and industry. Furthermore, we examine the structure, implementation, utilization, and development of ESs in this context. Through this analysis, the review reveals research gaps, pointing toward promising topics for future research.

The human landscape transforming activity contributes to the global change of vegetation in different forms. Hunting is one of the most ancient human landscape-shaping activities. Feeders for hunting are particularly disruptive to vegetation. In the present study, we conducted a vegetation survey in these highly disturbed places. We are investigating the vegetation dynamics over several years in in the turkey oak–sessile oak zone, in two areas with different moisture and shade conditions (forest and clearing). One important background factor is changes in precipitation and temperature. The novelty of our present study lies in the fact that the observation takes place with continuous disturbance, where the human influence is very large. Our results confirm that the weed infestation be detectable at bait sites over a long period, and the interannual changes were typical in every year, according to the fact that in August, more weed species are usually present with a greater cover at the bait sites. The main meteorological factors played a role in the degree of weed infestation in each year. There was a higher proportion of vegetation regeneration in wetter years, while dry periods do not favor successional processes. With the drying of the climate, the disturbed areas are constantly losing their natural value, but wetter weather is not an automatic solution either. A more precise understanding of vulnerable systems and careful planning of treatment interventions is necessary.

Assessment of extreme events and climate change on reservoir inflow is important for water and power stressed countries. Projected climate is subject to uncertainties related to climate change scenarios and Global Circulation Models (GCMs’). Extreme climatic events will increase with the rise in temperature as mentioned in the AR5 of the IPCC. This paper discusses the consequences of climate change that include extreme events on discharge. Historical climatic and gauging data were collected from different stations within a watershed. The observed flow data was used for calibration and validation of SWAT model. Downscaling was performed on future GCMs’ temperature and precipitation data, and plausible extreme events were generated. Corrected climatic data was applied to project the influence of climate change. Results showed a large uncertainty in discharge using different GCMs’ and different emissions scenarios. The annual tendency of the GCMs’ is bi-vocal: six GCMs’ projected a rise in annual flow, while one GCM projected a decrease in flow. The change in average seasonal flow is more as compared to annual variations. Changes in winter and spring discharge are mostly positive, even with the decrease in precipitation. The changes in flows are generally negative for summer and autumn due to early snowmelt from an increase in temperature. The change in average seasonal flows under RCPs’ 4.5 and 8.5 are projected to vary from -29.1 to 130.7% and -49.4 to 171%, respectively. In the medium range (RCP 4.5) impact scenario, the uncertainty range of average runoff is relatively low. While in the high range (RCP 8.5) impact scenario, this range is significantly larger. RCP 8.5 covered a wide range of uncertainties, while RCP 4.5 covered a short range of possibilities. These outcomes suggest that it is important to consider the influence of climate change on water resources to frame appropriate guidelines for planning and management.

Unquestionably the rapidly changing climate and, therefore, alterations in the associated bioclimate, constitute an alarming reality with implications in daily practice and natural capital management. In this research the present and projected bioclimate evolution over the phytogeographical regions of Greece is displayed. For this purpose, ultrahigh-resolution computation results on the spatial distribution of the Emberger index’s Q2 classes of bioclimatic characterisation are analysed and illustrated for the first time. The assessments are performed over the reference period (1970–2000) and two future time frames (2021–2040; 2041–2060) under the RCP4.5 and RCP8.5 emission scenarios. By 2060 and under the extreme RCP8.5, intense xerothermic trends are demonstrated owing to the resulting significant spatial evolution mainly of the Arid-Hot, Semi-Arid-Very Hot, Semi-Arid-Hot and Semi-Arid-Temperate Q2 classes, respectively, over the phytogeographical regions of Kiklades (up to 29% occupation), Kriti and Karpathos (up to 30%), West Aegean Islands (up to 26%), North East (up to 56%)–North Central (up to 31%). The RCP8.5 long-term period exhibits the strongest impacts over approximately the right half of the Greek territory the bioclimate of which appears as more dry-thermal in the future.

Chronic stress, culminating in burnout, evolves gradually and is notoriously difficult to detect due to vague symptoms and individuals' hesitance to acknowledge their struggles. To expedite the identification and recognition of this condition, enabling timely psychological intervention, the integration of Artificial Intelligence (AI) is increasingly considered. This research does not assert the feasibility of an AI system managing all aspects of chronic stress's impact. However, it highlights the capability of current technology to detect stress indicators based on quantifiable data like Heart Rate (HR) and organizational climate dimensions. Although other physiological markers such as oximetry, skin galvanic response, and EKG have been explored, they have not shown reliable differentiation between stress and joy. Focused on the unique context of school principals in the Bedouin regions of Israel, this study investigates the interplay between HR, organizational climate, and stress levels. It introduces a novel application of a fuzzy logic tool that combines HR and organizational climate metrics to aid in stress diagnosis. This tool incorporates the psychologist's expertise to provide real-time data crucial for developing effective coping strategies. While the AI-supported fuzzy system does not replace professional psychological intervention, it significantly enhances the speed of condition identification and intervention planning, thus shortening the response time to stress-related issues in educational leadership within culturally specific settings. The application of such AI-driven tools is pivotal for sustaining the well-being and effectiveness of educational leaders, thereby supporting the broader goal of educational sustainability.

Achieving and sustaining stability for economic growth remain the greatest and most immediate development challenge for Ethiopia. For natural resource-based economies especially maintaining stability and growth depends fundamentally upon climate change adaptation and mitigation. The close links between climate and Ethiopia’s economy are reflected by the strong relationship between GDP growth rate and rainfall variability. A study by the World Bank projects that unless steps to build resilience are effective, climate change will reduce Ethiopia’s GDP growth by between 0.5 and 2.5% each year. Along with the challenges posed by climate change, a number of development opportunities are emerging in response to climate change which includes access to international climate finance. The international response to climate change in the form of external development finance plays a key role to support developing countries in their transition to a low-carbon, climate-resilient and sustainable development pathway. Therefore, this study was conducted to assess the flow and the overall contribution of climate finance to sustainable development in Ethiopia. Specifically, focused on outlining how climate finance is currently reconciled in the existing Ethiopian climate change governance and its contribution to sustainable development. In order to achieve these objectives, data were collected from different sources. The Rio Marker methodology applied to review climate financial flow over the 5 year period. The result reveals that, climate change is central to development agendas despite its recent emergence in the mainstream, with various initiatives under way to combat or reduce its impacts in Ethiopia. In addition, the amount of climate finance from the developed countries to Ethiopia shows some fluctuation for the past five years. In general, the overall flow of climate finance mostly targeted climate adaptation actions which spur and enable the transition towards climate-resilient growth and sustainable development.

This review examines methodologies studying soil carbon in urban green spaces, a field requiring higher attention as their ecosystem services are valued and urban mitigation plans against climate change are developed. In the growing global urbanization, urban green spaces play a crucial role as nature-based solutions to address climate challenges, demanding efficient carbon accounting and reduction strategies. In this research, a systematic review was conducted assessing publications from 2021 to 2023 in the databases Web of Science and Scopus. Findings expose the lack of standardization in the methodologies used to study soil carbon in urban green spaces, evidencing in situ measurements at different sampling depths and studies without longitudinal monitoring over time. In addition, studies originated primarily in universities from China, United States and Spain, which focused on carbon in the form of Soil Organic Carbon and Total Carbon. Based on these findings, recommendations are made advocating for the combination and standardizing methodologies. This review offers valuable insights that shed light on an ignore and often overseen issue. Further studies and standardized methodology are needed, to help cities efforts in their pursuit of carbon neutrality by acknowledging the influential role of urban soils, especially green spaces.

Despite energy-related financial concerns and the growing demand for sustainability, many energy efficiency measures are not being implemented in industrial practice. There are a number of reasons for this, including a lack of knowledge about energy efficiency potentials and the assessment of energy savings as well as the high workloads of employees. This article describes the systematic development of an expert system, which offers a chance to overcome these obstacles and contribute significantly to increasing the energy efficiency of production machines. The system employs data-driven regression models to identify inefficient parameter settings, calculate achievable energy savings, and prioritize actions based on a fuzzy rule base. Proposed measures are first applied to an analytical real-time simulation model of a production machine to verify that constraints required for the specified product quality are met. This provides the machine operator with the expert means to apply proposed energy efficiency measures to the physical entity. We demonstrate the development and application of the system for a throughput parts cleaning machine in the metalworking industry.

The World Meteorological Organization (WMO) recommends that the most recent 30-year period, i.e. 1991-2020, shall be used to compute climate normals of geophysical variables. A unique aspect this recent 30-year period is that the satellite-based observations of many different essential climate variables are available during this period, thus opening up new possibilities to provide a robust, global basis for the 30-year reference period in order to allow climate monitoring and climate change studies. Here, using the satellite-based climate data record of cloud and radiation properties, CLARA-A3, for the month of January between 1981 to 2020, we illustrate the difference between climate normal, as defined by guidelines from WMO on calculations of 30-yr climate normals, and climatology. It is shown that this difference is strongly dependent on the climate variable in question. We discuss the impacts of the nature and availability of satellite observations, variable definition, retrieval algorithm and programmatic configuration. It is shown that the satellite-based climate data records show enormous promise in providing climate normal for the recent 30-year period (1991-2020) globally. We finally argue that the holistic perspectives from the global satellite community should be increasingly considered while formulating the future WMO guidelines on computing climate normals.

Our study aimed to assess sprinkler irrigation's impact on the activity of selected soil enzymes concerning nitrogen metabolism and oxidation-reduction processes in the soil with different doses of inorganic nitrogen fertilizers. The soil Alfisol was sampled from the experimental field of spring barley in the University Research Center in the central part of Poland in the moderate transitory climate during the growing seasons 2015–2017. The index resistance (RS) was derived to recognise the resistance enzymes of drought. In the maturity phase, nitrate reductase activity was at an 18% higher level in irrigated soils and the activity of other enzymes was higher in no-irrigated treatments by 25% in dehydrogenase, 22% in peroxydase 33% in catalase and 17% in urease case. The development stage of barley influenced nitrate reductase activity. Enzymatic activity has been changing in the examined years of the research, depending on the weather conditions. During the maturity stage, the soil's lower ammonium nitrogen content resulted from higher spring barley uptake due to drought stress. Irrigation probably contributes to increased leaching of nitrate in soil. The highest index of resilience was presented in the soil catalase activity.

The present study aims at investigating the potential impact of agricultural policies on GHG emissions from agriculture across the European Union. The study begins with a brief analysis of the context of implementation of the 2014-2022 Common Agricultural Policy (CAP), within which many interventions were designed to improve sustainability and increase competitiveness. It follows a description of the structural changes the European agriculture is witnessing, that are both influenced and contribute influencing policies. The method of Qualitative Comparative Analysis (QCA) is used to investigate possible causalities and to cluster observations based on similar combinations of conditions (i.e., drivers) and outcomes (i.e., positive or negative variations in GHG emissions from agriculture between the end and the begin of the CAP programming period). The results reveal that the increase in GHG emissions from agriculture over the study period is mainly attributable to the low share of agricultural land under management contracts targeting climate change mitigation and carbon sequestration through the CAP. CAP payments coupled with the production is found to contribute to further increasing GHG emission from agriculture in some eastern and northern EU countries. Livestock concentration, income support payments and the high price of agricultural land drive the increase in GHG emissions for other central and eastern EU countries. The paper concludes addressing existing shortcomings due to conflicting interventions in the current CAP Strategic Plans.

This research aimed to investigate the relationship between climate policy uncertainty (CPU), clean energy (ENERGY), carbon emission allowance prices (CARBON), and Bitcoin returns (BTC) for the period from August 2012 to August 2022. The empirical analysis strategies utilized in this study included the Fourier Bootstrap ARDL long-term coefficient estimator, Fourier Granger Causality, and Fourier Toda Yamamoto Causality methods. Following the confirmation of cointegration among the variables, we observed a positive relationship between BTC and CARBON, a positive relationship between BTC and CPU, and a negative relationship between BTC and ENERGY. In terms of causal associations, we identified one-way causality running from CARBON to BTC, BTC to CPU, and BTC to the ENERGY variable. The study underscores the potential benefits and revenue opportunities for investors seeking diversified investment strategies in light of climate change concerns. Furthermore, it suggests actionable strategies for policymakers, such as implementing carbon taxes and educational campaigns, to foster a transition towards clean energy sources within the cryptocurrency mining sector and thereby mitigate environmental impacts.

In this paper, we studied the problem of early detection of natural phenomena associated with the formation of severe floods and the possibility of constant monitoring of their occurrence. The test object for the study was severe floods on the Crimean Peninsula, which occurred due to heavy rainfall in the summer of 2021. Based on the selected satellite and other data related to this period, an analysis carried out and key atmospheric objects identified, according to the proposed algorithm for their identification and grouping. Based on the results of the work, a positive conclusion made on the issue of their use to identify the likelihood of natural disasters that could lead to floods. In addition, the authors proposed to develop a portal for collecting and processing a large amount of satellite data related to flood prevention and monitoring the development of such processes, and stressed the need to create it.

The Shule River Basin (SRB) is a typical arid area in northwest China with a fragile ecology. Un-derstanding vegetation dynamics and its response to climate change and human activities provides essential information for ecological and environmental resource management. This study extracted fractional vegetation coverage (FVC) data from 2000 to 2019 using the Google Earth Engine plat-form and Landsat satellite images, employing trend analysis and other methods to examine spati-otemporal changes in vegetation in the SRB. Additionally, partial correlation and residual analysis were utilized to explore the response of FVC to climate change and human activities. The main results were as follows: (1) The regional average FVC in the SRB showed a significant upward trend from 2000 to 2019, increasing by 1.3 × 10−3 y–1. The area within 1 km of roads experienced a higher increase of 3 × 10−3 y–1, while the roadless areas experienced a lower increase of 1.1 × 10−3 y–1. Road construction from 2004 to 2006 significantly reduced the FVC within 200 meters of the roads. FVC spatial heterogeneity in the SRB is significant. (2) Partial correlation analysis shows that FVC has a strong positive correlation with precipitation and surface water area, with correlation coefficients of 0.575 and 0.744, respectively. There is a weak negative correlation between FVC and land surface temperature (LST). FVC changes are more influenced by precipitation than by LST. (3) Compared to climate factors, human activities have a greater impact on vegetation dynamics in the SRB. The contributions of climate change to vegetation recovery are increasing. (4) The area changes of dif-ferent land types are closely related to climate factors and human activities, reflecting their impact on vegetation. These results provide valuable information for ecosystem restoration and environ-mental protection in the SRB.

This study analyzes future hydrological droughts in LCKW using the SWAT model and data from 2010-2021, evaluating the Streamflow Drought Index (SDI) for 5, 10, 25, and 50-year return periods (RP) for 2029 and 2039 under RCP4.5 and RCP8.5 scenarios. The model shows high accuracy (R² = 0.82, NSE = 0.78). Under RCP4.5, streamflow may increase by 34.74% and 18.74%, while RCP8.5 predicts significant decreases of 37.06% and 55.84%. Historical data reveal frequent short-term droughts during SDI-3, notably in 2014–2016 and 2020–2021, with SDI-6 indicating less frequent but severe droughts, especially in 2015 and 2020. Future projections under RCP8.5 suggest wors-ening drought conditions in 2029 and 2039, with critical drought periods from January to April and November. Wavelet analysis indicates a high risk of severe hydrological drought in LCKW due to low annual recurrence rates, with the highest drought severity over a 50-year RP. The study high-lights an increasing likelihood of frequent and severe droughts, particularly under high emission scenarios such as RCP8.5, which could result in significant water shortages and ecosystem disrup-tions. It emphasizes the significance of adaptive water resource management strategies in mitigating negative impacts on water security and ecosystem health.

Matters relating to preservation of environmental resources and climate change, social inclusion and diversity, and open and sound governance have rapidly gained global attention. However, little is known about the construction industry in meeting the environmental, social and governance (ESG) criteria towards sustainable development. In this study, the purpose is to identify the key ESG indicators for construction development. A systematic literature review was undertaken using data from Scopus, PubMed, Google Scholar, JSTOR, ProQuest, and Web of Science. The results show some of the key ESG indicators as climate resilience and green construction activities (at the project level), and active stakeholder engagement and effective project governance (at the firm level) and meeting environmental and social regulation requirements (industry/national level). The limitation of the article is in the sample size of twenty-six journal articles with future expectation of increment as ESG gain more acceptance in the construction industry. This study will be significant for the design of practice framework and policies on ESG in the construction industry. Comprehensively, this study presents relevant ESG indicators for construction management and presents essential information for future research.

This study assessed stakeholders’ perception on the prospects and challenges of practising Climate–Smart Agroforestry in the Asunafo North Municipal Assembly in the Ahafo Region in Ghana. Interviewer administered questionnaires were used to collect cross-sectional data from 250 cocoa, food and cash crop farmers drawn from four (4) communities using simple random sampling. And nine (9) in-depth interviews were also conducted to elicit key stakeholder perspectives. Stakeholders also held the view that Climate-Smart Agroforestry encourages the use of stress-tolerant crop varieties, increase income, improve soil productivity, diversify farm produce, urges the reduction of input supply and recommends the timely usage of the inputs, ensures better market system for farm produce, cost of production decreases during the practice of Agroforestry, agro-ecological functions and Climate-Smart Agroforestry can best help adapt to the threats of climate change in the agriculture sector. The challenges included insecure tree ownership right, fast-declining soil fertility and long maturity period of Agroforestry trees, land tenure issues, illegal tree logging (chainsaw operators) on farms, lack of practical understanding of the approach, inadequate knowledge and information and inadequate supportive facilities in the study area. The study recommends further studies on the existing forest policy and legislations and its implications on the practice and adoption of Climate-Smart Agroforestry in respond to climate change in the area.

Climate simulations in West Africa have been attributed with large uncertainties. Global climate projections are not consistent with changes in observations at the regional or local level of the Niger basin, making management of hydrological projects in the basin uncertain. This study evaluates the potential of using the quantile mapping bias correction to improve the Coupled Model Intercomparison Project (CMIP5) outputs for use in hydrology impact studies. Rainfall and temperature projections from 8 CMIP5 Global Climate Models (GCM) were bias corrected using the quantile mapping approach. Impacts of climate change was evaluated with bias corrected rainfall, temperature and potential evapotranspiration (PET). The IHACRES hydrological model was adapted to the Niger basin and used to simulate impacts of climate change on discharge under present and future conditions. Bias correction significantly improved the accuracy of rainfall and temperature simulations compared to observations. Nash coefficient (NSE) for monthly rainfall comparisons of 8 GCMs to the observed was improved by bias correction from 0.69 to 0.84. The standard deviations among the 8 GCM rainfall data were significantly reduced from 0.13 to 0.03. Increasing rainfall, temperature, PET and river discharge were projected for all GCMs used in this study under the RCP8.5 scenario. These results will help improving projections and contribute to the development of sustainable climate change adaptation strategies.

Nanotechnology has become a popular topic in agricultural and animal research, generating sig-nificant global interest. Nanoparticles (NPs)-enabled agriculture and animal husbandry (AHs) products have three main principles: crop production, protection, and nutrient enrichment. Re-search on this topic has grown exponentially in recent years, with an increasing number of peer-reviewed literature and patents on NPs-enabled agriculture and AH products. This study examines and collects data on the various applications of NPs-enabled agricultural products for crop production, protection, and livestock improvement from the core nanotechnological database. The search returned several types of NPs and products used in agriculture and AH, countries that utilize these products at a commercial level, and analysis of these data by phyton, R-statistical software. Our findings show remarkable growth of nanotechnology in agriculture, AH, and re-search fields for future sustainability goals in the era of climate change

The publication presents an innovative tower cultivation device designed to significantly increase vertical farming efficiency. The device divides the cultivation system into separate chambers. One division corresponds to the different growth phases of the plants, while another reflects the daily variation in conditions. Each chamber presents slightly different conditions and cultivation patterns from the others. For the early stages, crops are grown horizontally in trays once they mature, transplanted into mobile cultivation towers. The closed circulation of ventilation and irrigation reduces water consumption by up to 95%. A unique separate day-night division optimises light, temperature and humidity conditions, mimicking natural growth patterns. This approach not only saves water and energy but also optimises cultivation in a three-dimensional space. The presented solution focuses on the often overlooked aspects of cultivating in vertical farms and makes this method of growing much more cost-effective and feasible to implement on a large scale.

Flooding, exacerbated by climate change, poses a significant threat to certain areas, increasing in frequency and severity. In response, the construction of supplementary dams has emerged as a reliable solution for flood management. This study employs a Remote Sensing (RS) approach integrated with GIS to assess the feasibility of constructing a supplementary dam near Linville, Brisbane, Australia, with the aim of mit-igating floods and preventing overtopping failure at the Wivenhoe Dam. Using QGIS software and a 25-meter resolution DEM from the Queensland Spatial Catalogue ‘QSpatial’ website, four potential dam sites were analysed, considering cross-sections, watershed characteristics, and water volume calculations. Systematic selection criteria were applied to identify the most optimal option based on dam wall di-mensions, volume-to-area, and volume-to-cost ratios. The selected option was further assessed against predefined criteria, yielding one optimal choice. The study provides insights into the feasibility and ef-fectiveness of supplementary dam construction for flood mitigation in the region, with recommendations for future research and implementation.

The temperature variations caused by the climate changes taking place in the Basilicata region, southern Italy - Ionian Seaside of the Gulf of Taranto - on the territory of the DOP Matera vineyard, were studied by means of an analysis of temperature trends considering both the period of the vegetative-productive season of the vineyards and the whole year. Two datasets were analyzed for this study: data from ERA5-Land of the C3S Climate Data Store -relative to the 1981-2022 window- and data from 5 weather stations belonging to the agrometeorological network of the Lucanian Agency for Development and Innovation in Agriculture (SAL-ALSIA), relative to the 2000-2023 window. From the results of this study, it can be deduced that, for the historical period analyzed, the Matera DOP area showed a clear and worrying trend of increasing temperatures and, in par-ticular, minimum temperatures. This increase, moreover, is more evident at higher elevations than at lower elevations close to the sea. At lower altitudes, in fact, temperatures are strongly “buffered” by the thermoregulatory activity of the bordering Ionian Sea. It follows that the DOP Matera viticultural areas, and in particular the areas furthest from the coastal strip, will already have to adapt to severe climatic conditions that will undoubtedly affect the quality and typicality of the wines.

As a precious natural resource, forests are being destroyed. In previous studies, there is a lack of interactive assessment of its distribution that comprehensively considers multiple external disturbances. This paper takes the Barcelona Metropolitan Region as an example. Based on remote sensing, it analyzes the development process of the forest from 2006 to 2018 through multiple landscape indicators, and OLS models were established to analyze variables that have direct and indirect effects on forest distribution. In addition, the ecological structure of the forest was analyzed based on NDVI. It was found that the forest area is the largest but has been decreasing, becoming more complex in distribution structure. Much of the forest was converted to agricultural land and grassland. The green quality of forests was increasing, and the broad-leaved forest, the second largest area, contributes the most. NDVI is the most important positively correlated variable, and daytime surface temperature is an important inverse factor related to NDVI. In addition, NDBI is also a negative condition that inhibits forest development. In conclusion: The BMR forest area is decreasing and becoming more fragmented. NDVI and daytime LST are the two most significant factors. Climate warming may lead to worse forest development.

Aim of the study was to determine the influence of climatic and hydrological changes on the growth of Scotch pine (Pinus sylvestris L.) and Black alder (Alnus glutinosa (L.) Gaertn) in the Narew National Park over past 50 years. The researches were based on tree increment cores extracted from 42 pine trees and 57 alder trees ranging in age from 25 to 88 years and climatic (temperature and precipitation for Bialystok) and hydrological (water level of the Narew River) data. Standardized data (5-year index) were analysed by species, generation (Y - 21-40 years, M - 41-60, O - 61-90) and decade using correlation analysis. The results of the study show that pine responded more strongly to changes in air temperature and changes in precipitation totals, while alder growth was more strongly related to fluctuations in the water level of the Narew River. The most variable climatic and hydrological conditions and the most difficult conditions for tree growth prevailed in the first and last decades (1971-1980, 2011-2020). The decrease in the water level of the Narew River that occurred over the last 50 years did not adversely affect the condition of the trees. Analysis of DBH growth rates showed that the younger generations (Y, M) grow faster than the older generation (O). One of the reasons for the accelerated tree growth and increased resistance to the decline in the water level of the Narew River may be the increase in CO2 concentration in the atmosphere. Accelerated tree growth and increased resistance to drought may have certain consequences like increased damage from wind as a result of the decrease in wood density or decline in the sensitivity of trees to climate and hydrological changes. However, less sensitivity to changes in climatic conditions may only apply to older trees.

A superior variant of dried jujube in China is ‘Huizao’ (HZ) jujube. Nonetheless, detailed evaluations of the texture quality of HZ fruits have been the subject of few studies. Texture is a significant indicator of the sensory and processed quality of a fruit. Here, we differentiate and characterize the texture quality of HZ fruits from the four primary producing regions in southern Xinjiang, as well as develop a system for assessing the texture quality of HZ fruits. Furthermore, five texture indices of HZ fruits from 109 plantations were exhaustively evaluated utilizing principal component analysis, Pearson correlation analysis, and hierarchical clustering analysis. Consequently, by the 10th, 30th, 70th, and 90th percentiles, each index was classified into five grades—lower, medium, high, and higher. Correlation investigation indicated strong relationships between the springiness, chewiness, cohesiveness, gumminess, and hardness of HZ fruits. Springiness, hardness, and adhesiveness are essential indicators that influence the texture of HZ fruits. We used clustering analysis to separate the six texture indices into three major categories (hard factor, stick-elastic factor, and adhesive factor), with indices representing hardness, springiness, and adhesiveness, respectively. Furthermore, according to the factor molecules, the texture quality of HZ fruits in the four production areas was evaluated as Bazhou (1.24) > Hotan (0.773) > Kashi (-0.577) > Aksu (-0.852). RDA analysis of six texture quality parameters and 24 climate conditions identified higher mean temperature (TEM) and lower relative humidity (RHU) as the primary factors contributing to the improved texture quality of HZ fruits in Xinjiang. This study identified the texture quality characteristics of HZ fruits in four major producing areas of Southern Xinjiang, and it offered a theoretical foundation for optimizing the dominant producing areas and regional production of HZ varieties in Xinjiang.

This bibliometric analysis delves into the realm of botanical gardens through the process of mapping literature and discerning research frontiers, underlying themes, and interconnections within the extant body of knowledge. The study uses a dataset of 1340 publications and employs methodologies such as authors' keywords co-occurrence data to examine the discipline's trends, patterns, and knowledge gaps. The study finds significant growth in literature from 1960 to 2023, emphasizing botanical gardens, conservation, taxonomy, biodiversity, ex-situ conservation, and diversity. Key influencers such as Sanja Kovačić, Elaissi, and Chemli have steered research directions towards ex-situ conservation and essential oils, reflecting the field's interdisciplinary nature. The study shows global institutional variation, with entities like the Chinese Academy of Sciences, the Royal Botanic Gardens UK, and the New York Botanical Garden US leading the discourse. Thematic analyses have identified core themes and emerging research topics, with ecological restoration, essential oils, invasive species, and climate change mirroring the field's expanding scope. The consistent prevalence of themes such as 'botanical gardens' and 'conservation' over time underscores their enduring significance. Emerging topics like climate change and biodiversity are gaining prominence, signaling a shift in research priorities. The expanding array of subjects, including 'invasive species' and 'genetic diversity,' indicates an amplified scope and growing complexity in botanical garden research. This study aims to bridge a crucial gap by outlining the developmental trajectory of botanical gardens and offering guidance for future research directions. Diverse research domains within botanical garden studies, including biodiversity, essential oils, taxonomy, and ex-situ conservation, present extensive avenues for exploration. Future research endeavors should delve into these impactful themes to better understand botanical ecosystems and their conservation. The study emphasizes the importance of nurturing interdisciplinary collaborations and multidisciplinary projects, advocating for a comprehensive and collaborative approach to future research in botanical garden studies.

This study examines the Soil Organic Carbon (SOC) within Greek croplands, offering a comprehensive understanding of its dynamics. SOC, a cornerstone in soil health, nutrient cycling, and global carbon dynamics, assumes critical significance in sustainable agriculture and climate change mitigation. Drawing on diverse soil properties, including pH, soil texture as well as different drainage and slope categories, the research explores the nuanced relationships shaping SOC dynamics in the diverse agroecological landscape of Greece. The investigation transcends local boundaries, emphasizing SOC's global role in climate change mitigation by sequestering carbon dioxide. Two maps were used as data sources: 1) SOC stock baseline map (2010) by JRC, 2) SOC stock map (2021) by the Institute of Soil and Water Resources, Hellenic Agricultural Organization – DIMITRA in collaboration with FAO. Greek croplands emerge as a mosaic of agroecological diversity, where anthropogenic activities wield transformative influences on SOC stock, demanding a delicate balance between agricultural productivity and soil health. The study unveils the influence of Soil Order, weaving a tapestry of SOC variability. Microscopic factors, from soil texture to cation exchange capacity, further shape SOC dynamics, emphasizing the role of clayey soils and coarse materials in carbon retention. Although soil organic carbon decreased from 2010 to 2021 the degree of carbon loss varied. This scientific endeavor synthesizes existing knowledge and unveils novel insights, offering a narrative woven from diverse soil properties. The findings contribute valuable insights for sustainable land management practices and climate change mitigation strategies, underscoring the importance of region-specific approaches in addressing global challenges.

We revisit the notion of climate, along with its historical evolution, tracing the origin of the modern concerns about climate. The notion (and the scientific term) of climate has been established during the Greek antiquity in a geographical context and it acquired its statistical content (average weather) in modern times, after meteorological measurements had become common. Yet the modern definitions of climate are seriously affected by the wrong perception of the previous two centuries that climate should regularly be constant, unless an external agent acted. Therefore, we attempt to give a more rigorous definition of climate, consistent with the modern body of stochastics. We illustrate the definition by real-world data, which also exemplify the large climatic variability. Given this variability, the term “climate change” turns out to be scientifically unjustified. Specifically, it is a pleonasm as climate, like weather, has been ever changing. Indeed, a historical investigation reveals that the aim in using that term is not scientific but political. Within the political aims, water issues have been greatly promoted by projecting future catastrophes while reversing the true roles and causality directions. For this reason, we provide arguments that water is the main element that drives climate and not the opposite.

Though Bangladesh is often labelled as "climate change ground zero," the Chittagong Hill Tracts (CHT) were never considered a significant climate change hotspot until heavy rainfall and consequent landslides in 2017. This shift in problem narrative seemingly overlooks myriad environmentally unsustainable 'development' initiatives along with (il)legal deforestation. This paper examines climate change trends and impacts on the lives and livelihoods of diverse local groups in CHT. By analysing changing patterns in meteorological data and qualitative information on changes and challenges in everyday life and the livelihoods of local people, we show a shortfall of captured climate data on CHT to make a strong case for climate change. Yet, this study finds that this region is exposed to a substantial threat of land degradation, flash floods, water scarcity, landslides, and other slow and quick onsets due to a combined effect of deforestation, neoliberalised development, and slight changes in temperature and precipitation patterns. This paper calls for proactive measures to address climate change impacts by endorsing sustainable development strategies prioritizing environmental stewardship, social justice, and economic prosperity.

Oncomelania Hupensis is the exclusive intermediary host of Schistosoma japonicum in China. The alteration of its habitat and population distribution directly affects the safety of millions of individuals residing in the Yangtze River Economic Belt (YREB), and the ecological stability. Therefore, it is crucial to analyse the influence of climate change on the distribution of O. Hupensis in order to achieve accurate control over its population. This study utilised the MaxENT model to forecast possible snail habitats by utilising snail distribution data obtained from historical literature. The subsequent outcomes were achieved: The primary ecological factors influencing the distribution of O. Hupensis are elevation, minimum temperature of coldest month, and precipitation of wettest month; Furthermore, future climate scenarios indicate a decrease in the distribution area and a northward shift of the distribution centre for O. hupensis; Specifically, those in the upstream will move northeast, while those in the midstream and downstream will move northwest; These changes in suitable habitat area, the average migration distance of distribution centers across different climate scenarios, time periods, and sub-basins within the YREB, resulting in uncertainty. This study offers theoretical justification for the prevention and control of O. hupensis along the YREB.

Artificial intelligence (AI) has emerged as a powerful tool for addressing the challenges posed by climate change and their impact on public health. By leveraging its capacity to analyze and predict climatic patterns, AI offers opportunities to enhance resource management and develop effective strategies for climate change mitigation. Moreover, AI can contribute to generating sustainable solutions that address the complex and interconnected nature of climate change. For example, AI can enable the optimization of energy consumption and facilitate the integration of renewable energy sources into existing systems. It can also support the development of climate models that provide timely and accurate predictions, enabling policymakers to implement proactive measures for disaster preparedness and response. Furthermore, AI-powered disease surveillance and mitigation techniques can improve public health outcomes by identifying patterns and trends in the spread of diseases in relation to climatic factors.However, the widespread adoption of AI-based solutions is not without challenges. Ethical concerns surrounding privacy and data ownership must be addressed, as the use of AI requires access to large datasets, raising potential privacy risks. Technical constraints, such as limited computational power and the need for sophisticated algorithms, also pose obstacles to the implementation of AI in climate change mitigation strategies. Furthermore, issues related to the accessibility and affordability of AI technologies must be resolved to ensure equitable distribution and maximize its potential impact on public health.To fully harness the power of AI in addressing climate change and improving public health outcomes, it is crucial to promote innovation, multidisciplinary collaboration, and open data science. Innovation can drive the development of new AI algorithms and technologies specifically tailored to address climate change challenges. Multidisciplinary approaches that bring together experts from diverse fields, including climate science, public health, and computer science, can foster a holistic understanding of complex systems and enable the design of comprehensive solutions. Finally, open data science practices, such as sharing data and algorithms, can facilitate collaboration and accelerate progress in mitigating climate change and its public health impacts.In conclusion, AI offers promising opportunities for effectively addressing climate change and its impact on public health. However, to fully realize its potential, it is essential to tackle ethical and privacy concerns, overcome technical constraints, and ensure accessibility and affordability. By promoting innovation, multidisciplinary collaboration, and open data science, we can unlock the transformative power of AI in mitigating climate change and improving public health outcomes.

The Vu Gia - Thu Bon River Basin (VG-TB) is confronting numerous challenges related to water security (WS), particularly in the face of the impacts of climate change. These challenges manifest in salinity intrusion, altered rainfall patterns, and reduced water supply in downstream areas. To assess the current state of water security in the basin, the study employed statistical analysis methods, Process Analysis Method (PAM), SMART, and Analytic Hierarchy Process (AHP) to develop a comprehensive Assessment Framework for Water Security (AFWS) in the VG-TB. The AFWS encompasses five key dimensions, 17 indicators, and 28 variables, aligning with the UN-Water definition of water security and addressing the Sustainable Development Goal 6 (SDG6) criteria for global water sustainability. It also adheres to the Asian Development Bank's (ADB) approach to water security assessment as outlined in the Asian Water Development Outlook (AWDO) reports. This allows for the computation of a comprehensive Water Security Index (WSI) for specific regions, serving as a foundation for decision-making and policy formulation aimed at enhancing water security in the context of climate change and facilitating sustainable basin development in the future.

Despite increased protection globally, climate change and other anthropogenic factors have caused a significant decline in seagrass cover. Identifying the specific causes of this decline is paramount if they are to be addressed. Therefore, we identified the causes of long-term change in seagrass/submerged aquatic vegetation (SAV) percentage cover and extent in the Bluefields Bay Special Fish Conservation Area (BBSFCA), a marine protected area on Jamaica’s southern coast. We used two random forest regression (RFr) models that included 2013 hydroacoustic survey SAV percentage cover data (dependent variable), and auxiliary data, reflectance data from level 2 processed 2013 Landsat 7 and 8 images and image band texture statistics maps (mean and variance) as predictors to generate 24 SAV percentage cover maps for the period 1984 – 2021 (37 years) using data from Landsat 4-5, 7 and 8 images., from which benthic features maps (SAV present, absent and coral reef) were created. Rainfall and map data were used to determine if SAV extent/area (km2) and average percentage cover and annual rainfall changed significantly over time and to evaluate the influence of rainfall. Rainfall impact on the overall spatial patterns of SAV loss, gain, and percentage cover change was assessed using a pixel-based regression. Finally, the most important spatial pattern predictors (two rainfall proxies (distance and direction from river mouth), benthic topography, depth, and hurricane exposure (a measure of hurricane disturbance)) of SAV loss, gain, and percentage cover change during 23 successive 1-to-4-year periods were identified using spatial Bayesian INLA generalized linear mixed models. SAV area/extent was largely stable with > 70% mean percentage cover for multiple years. However, Hurricane Ivan (in 2004) caused a significant decline in SAV area/extent (by 1.62 km2, or a 13%) during 2002 – 2006 and a second hurricane (Dean) in 2007 delayed recovery until 2015. Additionally, rainfall declined significantly by >1000 mm since 1901, and mean monthly rainfall positively influenced SAV percentage cover change. The pixel-based regression highlighted areas where mean monthly rainfall had a positive overall effect on SAV cover percentage change (across the entire bay) and gain (closer to the mouth of a river). The most frequently selected important spatial pattern predictors were the two rainfall proxies (areas closer to the river mouth were more likely to experience SAV loss and gain) and depth, with shallow areas generally having a higher probability of SAV loss and gain. Three hurricanes had significant but different impacts depending on their distance from the southern coastline. Hurricane Gilbert, which made landfall in 1988, resulted in higher SAV percentage cover loss in 1987 - 1988. Benthic locations with a northwestern/northern facing aspect (the predominant direction of Ivan’s leading edge wind bands) experienced higher SAV losses during 2002 – 2006. Although some locations impacted by Ivan and not by Dean recovered during 2006 – 2008, exposure to Ivan explained percentage cover loss during 2006 – 2008 and average exposure to (the cumulative impact of) Ivan and Dean (both with tracks close to the southern coastline) explained SAV loss during 2013 – 2015. Therefore, despite historic lows in annual rainfall, overall, higher rainfall was beneficial, multiple hurricanes impacted the site, and despite two hurricanes in three years SAV recovered within a decade. Hurricanes and a further reduction in rainfall may pose a serious threat to SAV persistence in the future.

This bibliographic study addresses key aspects related to fishing, product safety, and climate change in the Adriatic Sea region. The examination of product safety focuses on the assessment of contaminants originating from human activities such as industry, mining, agriculture, and household waste disposal. The contamination of the aquatic environment has emerged as a pressing global concern, extending to the Adriatic subbasin. Aquatic organisms, including fish, are prone to accumulating pollutants directly from polluted water sources and indirectly through the food web. The bio-accumulation of potentially hazardous substances, particularly heavy metals, pesticides, PCBs, PAHs, and antibiotic resistance in aquatic organisms, poses a significant threat to human health. Climate change effects will deplete our seafood supply in terms of quantity and safety owing to negative consequences such as higher levels of pollution, parasites, viruses, infections, acidification, and toxicities such as shellfish poisoning. Global food safety strategies should be developed to reduce greenhouse gas emissions and promote environmentally friendly technology, which indirectly affects seafood quality and microbiological safety, especially for Adriatic Sea, which is part of the Mediterranean Sea, characterized by the most polluted waters in the world.

Abstract: Rice is one of the most important food crops in sub-Saharan Africa. Climate change, variability, and economic globalization threaten to disrupt rice value chains across the subcontinent, undermining their important role in economic development, food security, and poverty reduction. This paper maps existing research on the vulnerability of rice value chains, synthesizes the evidence and the risks posed by climate change and economic globalization, and discusses agriculture and rural development policies and their relevance for the vulnerability of rice value chains in sub-Saharan Africa. Important avenues for future research are identified. These include the impacts of multiple, simultaneous pressures on rice value chains, the effects of climate change and variability on parts of the value chain other than production, and the forms and extent to which different development policies hinder or enhance the resilience of rice value chains in the face of climatic and other pressures.

In the context of global warming and increasing scarcity of fresh water resources, it becomes significant to evaluate the contribution and evolution of non-rainfall waters such as dew. This study therefore aims to evaluate the relative dew and rain contributions in three sites of south-western of Madagascar (Ifaty, Toliara and Andremba), a semi-arid region which suffers from a strong water deficit. The studied period is 1/1991 – 7/2023, with extrapolation to 8/2033. Dew is calculated from meteo data by using a well-established energy model. The extrapolation of dew and rain follows an artificial neural network approach. It is found that dew forms regularly (2-3 days in average between events), in contrast to rain (10-15 days). The evolutions of dew and rain are similar, with an increase from 1991 to 2000, a decrease up to 2020 and a further increase until 2033. These oscillations follow the Indian Ocean dipole variations and should be influenced by the climate change. Dew contribution to the water balance remains modest on a yearly basis (3-4%) but is important during the dry season (Apr.-Oct.), up to 30%. Dew therefore appears to be a reliable and sustainable resource for plants, small animals and population, especially during droughts.

Storm Ophelia made landfall over Ireland as an extra-tropical storm on the morning of the 16th of October 2017. The storm caused major power outages, lifted roofs, caused coastal flooding in Ireland, and resulted in the loss of three lives. A model’s capability to forecast extreme weather events such as Storm Ophelia is of utmost importance and now with a changing climate, it becomes more important to improve and enhance model forecasting capability. The Weather Research and Forecasting (WRF) model V3.9 has been configured for the Irish domain and this study presents a preliminary evaluation of the Model during Storm Ophelia. Simulated wind speed and direction were compared with hourly remote sensing (lidar) and in-situ (wind speed and wind direction at 10m) observations at the coastal site of Mace Head Atmospheric Research Station on the West coast of Ireland (53.33◦ N, 9.90 49 ◦ W). The model simulation has generally small biases in the simulated wind speed and wind direction during this case study. The model also realistically simulated the magnitude and geographical distribution of the wind speed and wind direction observed during Ophelia.

Wetland ecosystems in the Qinghai-Tibet Plateau are pivotal for global ecology and regional sustainability, contributing significantly to terrestrial ecosystems by regulating runoff, mitigating floods, and enhancing water quality. This study investigates the dynamic changes in wetland ecosystems within the Chaidamu Basin and their response to drought, aiming to foster sustainable wetland utilization in the Qinghai-Tibet Plateau. Using Landsat TM/ETM/OLI data on the Google Earth Engine platform, we employed a random forest method for annual long-term land cover classification. Meteorological drought conditions were assessed using SPEI3, SPEI6, SPEI9, and SPEI12, derived from monthly precipitation and evapotranspiration data. Pearson correlation analysis examined the relationship between wetland changes and various SPEI scales. The BFASAT method evaluated the impact of SPEI12 trends on wetlands, while cross-wavelet analysis explored teleconnections between SPEI12 and atmospheric circulation factors. Our findings revealed that the land cover dataset of the Chaidamu Basin (1990-2020) exhibited diverse categories with high classification accuracy (OA: 90.27%, Kappa: 88.34%). Wetlands, including lake, glacier, and marsh types, exhibited a noticeable increasing trend. Wetland expansion occurred during specific periods (1990-1997, 1998-2007, 2008-2020), featuring extensive conversions between wetland and other types, notably from other types to wetlands. Spatially, lake and marsh wetlands predominated in the low-latitude basin, while glacier wetlands were situated at higher altitudes. The study identified significant negative correlations between SPEI at various scales and total wetland area and types, with SPEI12 exhibiting the most substantial effect between September and December (r <-0.75) on wetlands. SPEI12 displayed a decreasing trend with non-stationarity and distinct breakpoints in 1996, 2002, and 2011, indicating heightened drought severity. Atmospheric circulation indices (ENSO, NAO, PDO, AO, WP) exhibited varying resonance with SPEI12, with NAO, PDO, AO, and WP demonstrating longer resonance times and pronounced responses.The continuous growth of wetlands amidst increasing aridification emphasizes the need for thoughtful wetland development to establish a sustainable "forest-lake-grass-field-river" ecological community. These findings underscore the significance of comprehending wetland changes and drought dynamics for effective ecological management in the Chaidamu Basin of the Qinghai-Tibet Plateau.

Agriculture contributes towards South African economy, but this sector is highly vulnerable to risks imposed by climate change. Smallholder maize farmers are specifically susceptible to climate change impacts. Maize crop plays a crucial role in the country's food security as it is considered a stable food for households and source of feed for livestock. The study aimed to analyze the socio-economic factors influencing smallholder maize farmers’ willingness to adopt climate-smart agriculture. It was conducted in three different areas due to their specific agro-ecological zones. Multipurpose research design was used to gather data, and multistage random sampling to choose the study areas. Subsequently, 209 purposively selected farmers were interviewed face-to-face using structured questionnaires and focus discussion groups. Descriptive statistics and Double-hurdle regression model were used to analyze the data. Descriptive results indicated that 81%, 67%, and 63% farmers were willing to adopt CSA in Ga-Makanye, Gabaza, and Giyani, respectively. Empirical results from the first and second hurdles of the regression analysis showed that farmers’ education, crop diversification, information about CSA positively influenced the decision to adopt CSA while household size and agricultural experience negatively influenced the decision to adopt CSA. It is recommended that the Department of Agriculture, Land Reform, and Rural Development should provide CSA workshops and educational programs to farmers to enhance their knowledge and decision-making processes regarding adaptation strategies.

Climate disruption has serious impact on the entire agricultural production system at global scale. Research and development efforts as well as innovations may be necessary to offset the negative impact of climate change. Adaptation to climate change has remained a major socio-ecological issue in the Northern Region of Cameroon for three decades. Several agricultural innovations have been promoted in this region by international organizations and NGO, within the framework of different projects and programs, with a strong potential for adaptation to climate change. However, little is still known about agricultural innovations that have evolved over time in response to climatic factors. Therefore, research aiming at identifying adaptation strategies and practices at the local level is still limited. Also, rural communities and households do not always have access to these agricultural innovations because of lack of efficient innovation support service for agriculture. Through surveys and focus group discussions carried out in several villages in the Northern Cameroon Region, this study provides empirical data on emerging agricultural innovations in contrasting socio-economic, agricultural and ecological contexts. The study demonstrated that the process of adaptation at the village level using agricultural innovations is the result of various forms of support provided by a diversity of actors. However, heads of household (83%) are more involved in innovative initiatives to the detriment of other social strata, resulting in an imbalance in access and proximity to agricultural innovations. Also, support for agricultural innovations is segmented over time and weakens the sustainable transformation of adaptation due to the lack of coordination and the very low visibility of permanent structures dedicated to supporting agricultural innovations.

The main objective of this paper is to assess pro-environmental behavior, climate change perception, anxiety, hope and despair in different political orientations. Specific aims included to validate all the instruments; to assess whether the factor structure of the scales were valid across political orientations; to evaluate their reliability; to assess differences concerning age, gender and political orientation; to know the variables that explain pro-environment behavior; and to evaluate the moderating role of climate change perception, despair and hope in the relationship between climate change anxiety and pro-environmental behavior. Confirmatory factor analysis (CFAs), multi-group CFAs (to measurement invariance), multiple linear regressions and moderations were performed. Results showed that pro-environment behavior and climate change hope achieved the four levels of invariance across different political orientations; climate change anxiety achieved the three first levels of invariance; climate change perception and climate change despair achieved configural invariance. Climate change anxiety personal experience, climate change perception total, reality and consequences present higher values in left political orientation that in right or center. Climate change anxiety variables contribute most to explaining pro-environmental behaviors. Hope, despair and climate change perception consequences moderate de relationship between climate change anxiety and pro-environment behavior. These results open up new avenues of investigation, namely, to understand why high levels of anxiety lead to more pro-environment al behaviors.

Ultramafic rocks become promising candidates for carbon sequestration by enhanced carbon dioxide (CO2) mineralization strategies due to their highly CO2-reactive mineral composition and its abundant availability. This study reports a mineralogy and microtextures of a representative ultramafic rock from the Ma-Hin Creek in northern Thailand and observes evidence of CO2 mineralization occurring through the interaction between CO2 and the rock with the existence of water under ambient conditions. After sample collection, rock description was determined by optical petrographic analysis. The rock petrography reveals a cumulated wehrlite comprising over 50% olivine and minor amounts of clinopyroxene, plagioclase, and chromian spinel. Approximately 25% of the wehrlite has altered to serpentine and chlorite. A series of CO2 batch experiments were conducted on six different rock sizes at a temperature of 40°C and pressure of 1 atm over five consecutive days. The post-experimental products were dried, weighed, and geochemically analyzed to detect changes in mineral species. Experimental results showed that product weight and the presence of calcite increased with reducing grain size. Additionally, the modal mineralogy of the wehrlite theoretically suggests a potential CO2 uptake of up to 53%, which is higher than the average uptake values of mafic rocks. These findings support the suitable rock investigation approach and the preliminary assessment of carbon mineralization potential, contributing to enhanced rock weathering techniques for CO2 removal that could be adopted by mining and rock supplier industries.

In recent years, long periods of drought and heat waves have become increasingly frequent, causing forest dieback phenomena that make stands more sensitive to biotic stressors. How trees may respond to extreme climatic events and which metabolites are involved under stress conditions is still not clear. In this study, we analysed by SPME how dieback (D) and non-dieback (ND) Hungarian oak trees from the San Paolo Albanese site respond to these climatic dynamics, in terms of volatile organic compounds (VOCs). For each group of trees, three wood samples were taken, each divided into four sub-samples with 5 growth rings, subjected to SPME and increase in basal area (BAI) analysis of the last 20 years. Dieback trees had a lower amount of leaves, and this condition may translate into less photosynthesis, less organic matter production and lower reserves of carbohydrates available for growth. Indeed, D trees showed lower radial increases and a lower content of aldehydes, terpenes, and fatty acids than ND trees, indicating a better health of ND trees compared to D trees. While D trees showed a reduction in terpenes, such as α-pinene, γ-eudesmol, and cyperene (with significant insecticidal activity), a reduction in aromatic aldehydes, such as furfural and 5-methylfurfural, and an increase in silanols (with antimicrobial function). Considering the different compounds’ contents between D and ND trees, our study could be useful for detecting bio-indicators to identify an early warning signal of dieback phenomena.

Wildfires have short- and long-term impacts on the geoenvironment, including the changes to biogeochemical and mechanical properties of soils, landfill stability, surface- and groundwater, air pollution, and vegetation. Climate change has increased the extent and severity of wildfires across the world. Simultaneously, anthropogenic activities—through the expansion of urban areas into wildlands, abandonment of rural practices, and accidental or intentional fire-inception activities—are also responsible for a majority of fires. This paper provides an overall review and critical appraisal of existing knowledge about processes induced by wildfires and their impact on the geoenvironment. Burning of vegetation leads to loss of root reinforcement and changes in soil hydromechanical properties. Also, depending on the fire temperature, soil can be rendered hydrophobic or hydrophilic and compromise soil nutrition levels, hinder revegetation, and, in turn, increase post-fire erosion and debris flow susceptibility of hillslopes. In addition to direct hazards, wildfires pollute air and soil with smoke and fire suppression agents releasing toxic, persistent, and relatively mobile contaminants into the geoenvironment. Nevertheless, the mitigation of wildfires’ geoenvironmental impacts does not fit within the scope of this paper. In the end, in no exhaustive way, some areas of future need for research are highlighted.

The once near-extinct Russell’s viper has recently come back to the area beyond its historical range. A couple of decades ago, its distribution was confined to 11 districts of Bangladesh. Hence, the study aims to update the distributional map of Russell’s Viper (Daboia russelii) across the country and find out the surges' causal factors. Both primary and secondary data were utilized in this study. It revealed that the distributional range of this snake was expanded from 22 to 33 districts in 2024. The respondents noted that climatic stressors like high humidity accompanied by hot weather, increased salinity, and increased frequency of flooding facilitated the spreading of the viper across the country. The cropping pattern and crop diversification were another major contributing factor. The shifting of the single-cropped area to a multi-cropped area in the Barind tract increased its main prey rats. Contrarily, the killing of natural predators such as foxes, monitor lizards and mongoose led to the rise in the snake population. The destruction of vegetation coverage in the upper part of the Padma Basin facilitated the movement of Russell's vipers downstream.

The viewpoint and reaction of a country towards climate change are shaped by its political, cultural, and scientific backgrounds, in addition to the distinct characteristics of its evolving climate and the anticipated and actual consequences of the phenomenon in the times ahead. A region's climate has a significant impact on how water is managed and used, mostly in the primary sector (agroforestry systems, livestock, and agriculture), and both the distribution of ecosystem types and the amount and spreading of species on Earth. As a result, the environment and agricultural practices are affected by climate, so evaluating both distribution and evolution is extremely pertinent. Towards this aim, the climate distribution and evolution in the S. Francisco River basin (SFRB) is assessed in three periods (1970−2000, 1981−2022) in the past and 20412060 in the future from an ensemble of GCMs under two SSPs (Shared Socioeconomic Pathways), SSP2-4.5 and SSP5-8.5. The Köppen-Geiger (KG) climate classification system is analyzed, and climate change impacts are inferred for this watershed located in central-eastern Brazil, covering an area equivalent to 8% of the country. Results predict the disappearance of the hot-summer (Csa) and warm-summer (Csb) Mediterranean climates, and a reduction/increase in the tropical savanna with dry winter (Aw)/dry summer (As). A striking increase in the semi-arid hot (BSh-steppe) climate is predicted with a higher percentage (10%) under SSP5-8.5. The Source and the mouth of SFRB are projected to endure the major impacts of climate change that is followed by a predicted increase/decrease in temperature/precipitation. Future freshwater resource availability, and quality for human use will all be impacted. Consequences on ecosystems, agricultural and for the socioeconomic sectors within the SFRB might deepen the current contrasts between regions, urban and rural areas, and even between population groups, thus translating to a larger extent, the inequality that still characterizes Brazilian society. Maps depicting land use and cover changes in the São Francisco River basin from 1985 to 2022 highlight tendencies such as urbanization, agricultural expansion, deforestation, and changes in shrubland and water bodies. Urban areas fluctuated slightly, while cropland significantly increased from 33.57% to 45.45%, and forest areas decreased from 3.88% to 3.50%. Socioeconomic data reveals disparities among municipalities: 74.46% with medium Human Development Index (HDI), 0.59% with very high HDI, and 9.11% with low HDI. Most municipalities have a Gross Domestic Product (GDP) per capita below US$ 6,000. Population distribution maps show a predominance of small to medium-sized urban and rural communities, reflecting the basin's dispersed demographic and economic profile. To build routes towards a shared sustainable goal—namely, to adapt and reduce the expected climate change impacts in SFRB—it is imperative that integrated measures be conducted with the cooperation of stakeholders, local population, and decision-makers.

Precipitation patterns are critical for understanding the hydrological and climatological dynamics of any region. Sicily, the largest island of the Mediterranean sea, with its diverse topography and climatic conditions, serves as an ideal case study for analyzing precipitation data to gain insights into regional water resources, agricultural productivity, and climate change impacts. This paper employs advanced statistical physics methods, particularly Tsallis q-statistics, to analyze sub-hourly precipitation data from 2002 to 2023, provided by the Sicilian Agrometeorological Informative System (SIAS). We investigate several critical variables related to rainfall events, including duration, depth, maximum record, and inter-event time. The study spans two decades (2002-2012 and 2013-2023), analyzing the distributions of relevant variables. Additionally, we examine the simple returns of these variables to identify significant temporal changes, fitting these returns with q-Gaussian distributions. Our findings reveal the scale-invariant nature of precipitation events, the presence of long-range interactions and memory effects, characteristic of complex environmental processes.

The climate change constitutes nowadays an enormous global threat for human health and environmental sustainability. The expanding world population and the increased global needs for food production have an important negative impact upon environment. Diet can link human health with environmental sustainability. Food production systems are closely related to anthropogenic greenhouse gas emissions and the aggravation of climate change, and current western type, animal-based, dietary patterns may lead to adverse environmental footprints. In the present narrative review, we address the interconnection of the Mediterranean diet (MD) with climate change and sustainability. MD is a highly recommended dietary intervention for the prevention and management of various endocrine and cardiometabolic diseases. Beyond its evidence-based, health-promoting effects, it also has beneficial environmental impact, reducing greenhouse gas emissions and enhancing biodiversity, food security and sustainability. Based on the evidence reviewed herein, MD should be incorporated within the framework of a “One Health” model, which involves the improvement not only of human health but also of planetary health and food system sustainability. Our review aims to provide a stimulus for health pro-fessionals to strongly recommend the implementation of MD under the current pressure of climate change, despite all barriers, targeting both human health preservation and the planetary well-being.

The German Constitutional Court’s climate verdict provides a re-interpretation of liberal- democratic core concepts and is highly relevant for liberal constitutional law in general – including EU and international law. The verdict accepts human rights as intertemporal and globally applicable; it applies the precautionary principle to these rights and frees them from the misleading causality debate. However, the court fails to address the most important violations of human rights, and it categorised climate policy as a greater threat to freedom than climate change. And the court does not make it clear that the Paris 1.5-degree limit implies a radically smaller carbon budget. Furthermore, little attention has so far been paid to the fact that the ruling implies an obligation for more EU climate protection, especially since most emissions are regulated supranationally. To be effective, the EU emissions trading system demands a reform, which should go well beyond the existing EU proposals to enable societal transformation to sustainability.

Abstract: Climate change is one of the main barriers for agricultural production and productivity globally. Hence, understanding farmers’ adoption level of CSA practices and determinants is of highly important for policy decisions. Consequently, the study's purpose was to assess adoption level of multiple CSA practices and determinants of climate-smart agriculture practices in the study area. The study was conducted in Welmera Woreda, Oromia Region, Ethiopia. From the Woreda, three kebeles were identified, and 306 respondent farmers were selected. A cross-sectional household survey, focus group discussion, and key informant interviews were used. Multivariate probit model was used to examine adoption determinants of multiple climate-smart agriculture practices. According to the result, Conservation agriculture, integrated soil fertility management, and crop diversification are the most commonly practiced CSA techniques in the study area. Demographic factors result indicated that being male as compared with female farmers, has positive and significant effect on crop diversification and improved livestock feed and feeding practices. Age of farmers significantly and negatively affecting the probability of adoption of improved soil fertility management practices and crop diversification. However, it affects the adoption of agroforestry practices positively and significantly. Based on the result of economic factors, having relatively large farm land size significantly increases the adoption of conservation agriculture, improved soil fertility management, crop diversification, improved livestock feed and feeding practices, and postharvest technology practice. Better farm income increases the likelihood of uptake of improved livestock feed and feeding. Having a large number of livestock positively influences adoption of conservation agriculture and having access to credit services positively influences the implementation of agroforestry, crop diversification, and postharvest technology. In addition, institutional factors such as access to agricultural extension service and training found that significantly and positively influences the adoption of crop diversification; access to participation on farmers’ field day similarly significantly and positively influence the adoption of both conservation agriculture and improved soil fertility management practice. Awareness creation for farmers and experts about climate change and including location specific CSA practices in to agricultural program is crucial.

In this study, an attempt was made to investigate the Kenya case of multivariate causality of carbon dioxide emissions by employing a time series data spanning from 1961-2011 using the ARDL method of cointegration analysis. The long-run elasticities show that, a 1% increase in financial development increases carbon dioxide emissions by 0.28%, a 1% increase in GDP per capita increases carbon dioxide emissions by 1.32% and a 1% increase in urbanization decreases carbon dioxide emissions by 1.14%. There was a unidirectional causality running from financial development, food production index, GDP per capita, industrialization and urbanization to carbon dioxide emissions. The innovation accounting shows that 20% of future shocks in carbon dioxide emissions are due to fluctuations in financial development, 9% of future shocks in financial development are due to fluctuations urbanization and 22% of future shocks in food production index are due to fluctuations in carbon dioxide emissions.

Human induced climate change led to the recognition of Community-Based Adaptation (CBA) as one of the remedies in building adaptive capacity and resilience of communities. It recognizes the resources and the skills as well as the agency of communities in shaping the optimal strategy. CBA interventions are vital in addressing community needs and contextual specificities, enable participatory and inclusive engagement and thereby facilitate more profound adaptation impacts. Agent-based modelling (ABM) harbors the potential to be the ideal tool to support developing of tailored adaptation strategies while considering capacities, resources, skills, priorities, needs and the cultural context of local communities. ABM allows to capture the complex relationships among various components within community decision making on CBA actions, the CBA actions themselves and the environment. There exist plenty of tools, however they provide for varying degrees of freedom in agent definition, sensitivity analysis, scalability, experiment design and output analysis to name a few. A set of required key criteria for CBA modelling is developed and all available ABM modelling and simulation tools are assessed for their suitability for CBA. NetLogo emerges as the most fitting tool for incorporating and handling the special features of CBA. It is closely followed by GAMA and Envision. The article provides insights and guidance to researchers and practitioners in selecting ABM tools aligned with the specific requirements of their CBA simulations and projects.

In the recent century, the tourism industry and within it the tourism economy are one of the most important and fundamental sectors of engaged business. E-tourism can be used as a dynamic tool in up to date areas of informative information and tourism marketing will be considered as a suitable field for the tourism industry. The aim of this study was to investigate the relationship between climate change and the amount of revenues from the tourism industry relying on a tool called e-tourism, and informing and providing services through this way so that Iran can achieve a greater share of export of a single-product oil economy combined with economic growth and sustainable development goals. The method of this research is descriptive-analytical.

The silver fir (Abies alba Mill.) is among the most valuable conifers in Europe for ecological and economic reasons. In the course of history, primarily in the 20th century, its share in stands has been declining due to ill-suited management practices, especially clear-cut management, air pollution, and wildlife-induced damage. Based on recent knowledge of fir ecology and population dynamics, small-scale shelterwood and selection management have been introduced in fir stands, which have also stabilized them. Fir is an essential species for maintaining high stability and biodiversity, especially on planosols and in waterlogged habitats. Due to its shade tolerance and environmental flexibility, particularly at higher altitudes, it can coexist very well with many tree species in mixtures, which can increase the productive potential of stands. It can form stands of heterogeneous structure, ranging from single to multi-layer to selection. For its successful natural regeneration, it is essential to reduce cloven-hoofed game and thus prevent bud browsing damage. On the other hand, fir is a species relatively resistant to bark stripping and the spread of secondary rot compared to Norway spruce (Picea abies [L.] Karst.). During global climate change, fir is expected to shift to higher elevations with sufficient precipitation, while in the southern part of its natural range or at lower elevations, outside water-influenced habitats, it is likely to decline. This paper essentially reviews the description and distribution of the species, its ecological requirements, threats and diseases, habitat and stand conditions, and close-to-nature forest management practices with emphasis on ongoing climate change.

The paper provides a methodological framework based on Multiple Criteria Decision Aid methods for determining relative regional vulnerabilities associated with forest fires that occurred during a given period. Different notions of ex-post vulnerability are defined based on the forest area burned and the frequency of forest fires, and their relation to the regions’ area and several socioeconomic characteristics. The methodology is applied to Greece and its administrative regions for the period 2000–2022. Linking the forest fire events with summer and spring temperatures and precipitation and using econometric count data analysis, the paper explores the climatic drivers of forest fire occurrence and provides projections of the expected change in the frequency of fires of specific size classes conditional on the evolution of mean seasonal regional temperature and precipitation according to IPCC scenarios. The results regarding relative regional vulnerabilities and expected forest fires could be useful in designing policies for preserving forests as natural capital and promoting sustainability.

This study investigates the adoption of ecological innovations and management of occupational health hazards among Nigerian paddy rice farmers in response to field contamination and climate risks. Conducted across four major rice-producing states—Ekiti, Kwara, Edo, and Ogun—the research employed a semi-structured questionnaire developed from a comprehensive literature review and validated through peer review. Data were collected from 192 farmers through face-to-face interviews and guided dialogues, focusing on ecological innovations, vulnerability assessments, satisfaction with implemented methods, occupational health hazards, and perceptions of sustainable rice cultivation. Findings reveal that only 29.69% of farmers have adopted new measures to address contamination and climate risks, while 70.31% have not. Work-related injuries, such as cuts and abrasions, affected 28% of farmers, with minimal participation (3.1%) in safety training and support from agricultural or government agencies (1%). Correlation analysis demonstrated weak relationships among variables such as farmers' education, adoption of ecological innovations, satisfaction with results, and use of personal protective equipment (PPE). Importantly, farmers who adopted ecological innovations expressed significantly higher satisfaction (odds ratio = 11.613), as did those with prior exposure to pesticides and chemicals (odds ratio = 4.084) regarding safety practices. The study emphasizes the critical need for increased awareness, training, and support to promote sustainable farming practices and improve occupational health outcomes among paddy rice farmers in Nigeria.

This academic paper explores the synergistic potential of Agroforestry systems and Climate-Smart Agriculture (CSA) in enhancing climate change adaptation strategies. Focusing on Australia and Pacific Island Countries, the study delves into the pivotal role of these approaches in mitigating the impacts of changing climate variables on agricultural production. By analysing important climate variables, their potential alterations, and their subsequent effects on crop production, the paper advocates for a holistic and forward-thinking approach to sustainable agriculture in the face of climate change.

In the decarbonization of the steel, cement, and chemical industry in Germany, green hydrogen is expected to play a crucial role. The utilization of green hydrogen in the production processes of said industries requires organizations to modify their business model, requiring sustainable business model innovation (SBMI). Numerous tools and frameworks that support organizations in the process of SBMI have been proposed in the literature in recent years. However, the applicability of these tools and frameworks for steel, cement, and chemical companies that intend to utilize green hydrogen to produce their goods remains unexplored. By conducting a systematic literature review on tools and frameworks for SBMI, a literature and practice review to identify and analyze existing green hydrogen projects of steel, cement, and chemical companies, and an evaluation of the identified tools and frameworks in an evaluation matrix, this paper aims to assess the suitability of SBMI tools and frameworks for steel, cement, and chemical companies that plan to use green hydrogen to produce their goods. Based on the evaluation, the Cambridge Business Model Innovation Process (CBMIP) was identified as the most suitable SBMI framework.

In Bangladesh, climate change is a major concern because of its geophysical location and climate dependent agriculture. As sessile organisms, crops plants have to face difficulties often in this environmentally vulnerable country. Therefore, this study examines the seasonal trend of two climatic parameters viz. temperature (maximum and minimum) and rainfall over a period of 1983 to 2013. Besides, this study provides insight into the relationship between climatic parameters and crop yield of two major crops viz. rice and wheat during 1997-2013. To assess the relationship of climatic parameters with time and yield using Pearson correlation analysis, time series data used at an aggregate level. SPSS software utilized for this analysis. The cropping seasons such as rice growing seasons Aus (summer rice), Aman (autumn rice) and Boro (winter rice) exhibited a significant increase in maximum and minimum temperature. Rainfall found to have a decreasing trend for all the seasons. This study also revealed that the climatic parameters had significant effects on rice yield, but these results varied among three rice crops. Maximum temperature had positive effects on all rice yields, especially on Aus and Aman. Minimum temperature had a negative effect on Aman rice yield but a positive effect on Aus rice yield. Wheat yield negatively associated with temperature. Rainfall exhibited negative relation with both rice and wheat yield.

Climate change mainly affects production and consumption systems, such as: food, livelihoods, production (e.g., reduced milk production), water, and land use. The role of local knowledge has been recognized as important for decision-making under changing circumstances. This study was conducted in the northern part of the Ecuadorian Andes using a sample of 170 dairy-cattle-producing households. The objectives were: i) to characterize the rural livelihoods of dairy cattle farmers, ii) to evaluate access to climate information and perceptions of climate change, and iii) to determine the relationship between livelihoods and perceptions of climate change. Significant differences were identified between the groups evaluated in relation to the dairy farmers’ livelihoods. In addition, 85.29% of the respondents mentioned that climate information is important, but 67.83% do not trust the sources of information. It was found that there is a significant relationship between the level of education and age with the variables of climate change perceptions. This combined knowledge allows people to promote agri-environmental and educational policies to achieve climate literacy at a rural level.

The paper presents the open source tool climdex-kit which includes utilities to compute, analyze and visualize climate indices based on the input data, target domain and temporal extent defined by the user. It is intended to support researchers as well as practitioners and decision makers to derive, handle and interpret meaningful information for climate change studies and sectoral applications. It currently includes the computation of 30 indices based on temperature and precipitation, describing both mean and extreme climate conditions, and it is designed to work with climate model projections. The tool is written in Python and integrates utilities from the well-established Climate Data Operators (CDO) and NetCDF Operators (NCO) libraries. The specific utilities for selecting, aggregating and visualizing data are thought to help users to produce tailored results improving understanding and communication of future climate change. In order to show the functionalities of the package as well as its potential integration in regional climate services, climdex-kit was applied to an ensemble of downscaled climate model projections up to 2100 for the region Trentino – South Tyrol (north-eastern Italian Alps). The projections for a selection of indices accounting for extreme temperature and precipitation conditions were derived and different visualization choices discussed. The package climdex-kit is developed in a way that allows users to implement additional routines for calculating other indices as well as for easily adapting the routines to handle with different data types and spatio-temporal targets defined by the specific application. Effective climate services can in fact be developed only if flexible tools and customizable climate information are integrated with a clear understanding of data features and limitations.

40 years of cloud observations are available globally from satellites allowing derivation of climate data records (CDRs) for climate change studies. The aim of this study is to investigate how stable these cloud CDRs are and whether they qualify stability requirements recommended by the WMO’s Global Climate Observing System (GCOS). We also investigate robust trends in global total cloud amount (CA) and cloud top temperature (CTT) that are significant and common across all CDRs. The latest versions of four global cloud CDRs, namely CLARA-A3, ESA Cloud CCI, PATMOS-x and ISCCP-HGM, are analysed. This assessment finds that all three AVHRR based cloud CDRs satisfy even the strictest GCOS stability requirements for CA and CTT when averaged globally. While CLARA-A3 is most stable in global averages, PATMOS-x offers the most stable CDR spatially. While we find these results highly encouraging, there however remain large spatial differences in the stability of and across the CDRs. All four CDRs continue to agree on the statistically significant decrease in global cloud amount over the last four decades, although this decrease is now weaker compared to the previous assessments. This decreasing trend is stabilizing or even reversing in the last two decades; the latter is seen also in MODIS-Aqua and CALIPSO GEWEX datasets. Statistically significant trends in CTT are observed in global averages in the AVHRR-based CDRs, but the spatial agreement in the sign and the magnitude of the trends is weaker compared to those in CA. We also present maps of Common Stability Coverage and Common Trend Coverage that could provide a valuable metric to carry out an ensemble-based analysis of the CDRs.

Cambodia is a predominantly agrarian society, with a majority of the population residing in rural areas and relying on agriculture for food and income. The primary sources of food for the Khmer society are rice and fish. However, the country faces vulnerabilities due to climate change, natural disasters, impacts of hydropower and infrastructure development, and land grabs, which disrupt food sources and the livelihoods of local communities. Since 2015, the Royal Government of Cambodia (RGC) has been actively promoting rice production for export, resulting in increased rice farming seasons and annual rice production. Despite these efforts, a significant portion of the population still experiences food insecurity. Therefore, this study aims to explore the reasons why food security issues persist despite the government program to increase rice production. The study involves a review of existing literature and an examination of food security in three provinces in the Cambodian Mekong Delta. It concludes that, on the one hand, the increased rice production has increased food availability, but access to and utilization of food have been challenged by indebtedness, increased cost of production and migration, and on the other hand, the rights to foods have been challenged by land crabs, water conflicts, and lack of sectoral integrations of food systems; and compounded by climate change and environmental degradation, which all contribute to food security.

Arctic feedbacks will accelerate climate change and could jeopardise mitigation efforts. The permafrost carbon feedback releases carbon to the atmosphere from thawing permafrost and the sea ice albedo feedback increases solar absorption in the Arctic Ocean. A constant positive albedo feedback and zero permafrost feedback have been used in nearly all climate policy studies to date, while observations and models show that the permafrost feedback is significant and that both feedbacks are nonlinear. Using novel dynamic emulators in the integrated assessment model PAGE-ICE, we investigate nonlinear interactions of the two feedbacks with the climate and economy under a range of climate scenarios consistent with the Paris Agreement. The permafrost feedback interacts with the land and ocean carbon uptake processes, and the albedo feedback evolves through a sequence of nonlinear transitions associated with the loss of Arctic sea ice in different months of the year. The US’s withdrawal from the current national pledges could increase the total discounted economic impact of the two Arctic feedbacks until 2300 by $25 trillion, reaching nearly $120 trillion, while meeting the 1.5 °C and 2 °C targets will reduce the impact by an order of magnitude.