Climate

16 pages, 744 KB

Open AccessStudy Protocol

Warning System for Extreme Weather Events, Awareness Technology for Healthcare, Equitable Delivery, and Resilience (WEATHER) Project: A Mixed Methods Research Study Protocol

by Mary Lynch, Fiona Harris, Michelle Ierna, Ozayr Mahomed, Fiona Henriquez-Mui, Michael Gebreslasie, David Ndzi, Serestina Viriri, Muhammad Zeeshan Shakir, Natalie Dickinson, Caroline Miller, Andrew Hursthouse, Nisha Nadesan-Reddy, Fikile Nkwanyana, Llinos Haf Spencer and Saloshni Naidoo

Climate 2025, 13(8), 170; https://doi.org/10.3390/cli13080170 - 21 Aug 2025

Viewed by 217

Abstract

This study aims to develop, implement, and evaluate an Early Warning System (EWS) to alert communities and government agencies in KwaZulu-Natal, South Africa, about extreme weather events (EWEs) and related disease outbreaks. The project focuses on eThekwini and Ugu municipalities, using a participatory, [...] Read more.

This study aims to develop, implement, and evaluate an Early Warning System (EWS) to alert communities and government agencies in KwaZulu-Natal, South Africa, about extreme weather events (EWEs) and related disease outbreaks. The project focuses on eThekwini and Ugu municipalities, using a participatory, co-creation approach with communities and health providers. A systematic review will be undertaken to understand the impact of climate change on disease outbreaks and design an EWS that integrates data from rural and urban healthcare and environmental contexts. It will assess disease burden at primary healthcare clinics, examine health needs and community experiences during EWEs, and evaluate health system resilience. The project will also evaluate the design, development, and performance of the EWS intervention, including its implementation costs. Ethical approval will be sought, and informed consent obtained from participants. Based on the findings, recommendations will be made to the Department of Health to enhance early warning systems and health system resilience in response to EWEs and disease outbreaks. Full article

(This article belongs to the Special Issue Climate Change Responses and Mitigation at Technical, Economic, and Social Interfaces)

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17 pages, 261 KB

Open AccessArticle

Climate Change and Health: Impacts Across Social Determinants in Kenyan Agrarian Communities

by Elizabeth M. Allen, Leso Munala, Andrew J. Frederick, Cristhy Quito, Artam Enayat and Anne S. W. Ngunjiri

Climate 2025, 13(8), 169; https://doi.org/10.3390/cli13080169 - 15 Aug 2025

Viewed by 460

Abstract

Climate change is a global crisis that disproportionately affects vulnerable agrarian communities, exacerbating food insecurity and health risks. This qualitative study explored the relationship between climate change and health in the following two rural sub-counties of Kilifi County, Kenya: Ganze and Magarini. In [...] Read more.

Climate change is a global crisis that disproportionately affects vulnerable agrarian communities, exacerbating food insecurity and health risks. This qualitative study explored the relationship between climate change and health in the following two rural sub-counties of Kilifi County, Kenya: Ganze and Magarini. In fall 2023, we conducted 16 focus group discussions with adolescent girls (14–17), young adults (18–30), and older adults (31+). Thematic analysis revealed that climate change adversely affects health through key social determinants, including economic instability, environmental degradation, limited healthcare access, food insecurity, and disrupted education. Participants reported increased food scarcity, disease outbreaks, and reduced access to medical care due to droughts and floods. Economic hardship contributed to harmful survival strategies, including transactional sex and school dropout among adolescent girls. Mental health concerns, such as stress, substance use, and suicidal ideation, were prevalent. These findings highlight the wide-ranging health impacts of climate change in agrarian settings and the urgent need for comprehensive, community-informed interventions. Priorities should include improving nutrition, reproductive and mental health services, infectious disease prevention, and healthcare access. Full article

(This article belongs to the Special Issue Climate Impact on Human Health)

21 pages, 971 KB

Open AccessArticle

Lightning Nowcasting Using Dual-Polarization Weather Radar and Machine Learning Approaches: Evaluation of Feature Engineering Strategies and Operational Integration

by Marcos Antonio Alves, Rosana Alves Molina, Bruno Alberto Soares Oliveira, Daniel Calvo, Marcos Cesar Andrade Araujo Filho, Douglas Batista da Silva Ferreira, Ana Paula Paes Santos, Ivan Saraiva, Osmar Pinto, Jr. and Eugenio Lopes Daher

Climate 2025, 13(8), 168; https://doi.org/10.3390/cli13080168 - 14 Aug 2025

Viewed by 414

Abstract

Lightning nowcasting is crucial for ensuring safety and operational continuity in weather-exposed industries such as mining. This study evaluates three machine learning (ML)-based approaches for predicting lightning using dual-polarimetric weather radar data collected in the eastern Amazon, Brazil. The strategies propose advances in [...] Read more.

Lightning nowcasting is crucial for ensuring safety and operational continuity in weather-exposed industries such as mining. This study evaluates three machine learning (ML)-based approaches for predicting lightning using dual-polarimetric weather radar data collected in the eastern Amazon, Brazil. The strategies propose advances in literature in three ways by involving (i) grouping radar variables by temperature layers, (ii) statistical summaries at key altitudes, and (iii) analyzing all the 18 levels of reflectivity data combined with Principal Component Analysis (PCA) dimensionality reduction and ensemble models. For each approach, models such as Random Forest, Support Vector Machines, and XGBoost were trained and tested using data from 2021–2022 with class balancing and feature engineering techniques. Among the approaches, the PCA-based ensemble achieved the best generalization (recall = 0.89, F1 = 0.77), while the layer-based method had the highest recall (0.97), and the altitude-based strategy offered a computationally efficient alternative with competitive results. These findings confirm the predictive value of radar-derived features and emphasize the role of feature representation in model performance. Additionally, the best model was integrated into the operational LEWAIS alert system, and four integration strategies were tested. The strategy that combined alerts from both ML and LEWAIS systems reduced the failure-to-warn rate to 0.0531 and increased the lead time to 10.18 min, making it ideal for safety-critical applications. Overall, the results show that ML models based solely on radar inputs can achieve robust lightning nowcasting, supporting both scientific advancement and industrial risk mitigation. Full article

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27 pages, 1684 KB

Open AccessArticle

Comparative Study of Machine Learning-Based Rainfall Prediction in Tropical and Temperate Climates

by Ogochukwu Ejike, David Ndzi and Muhammad Zeeshan Shakir

Climate 2025, 13(8), 167; https://doi.org/10.3390/cli13080167 - 7 Aug 2025

Viewed by 682

Abstract

Reliable rainfall prediction is essential for effective climate adaptation yet remains challenging due to complex atmospheric interactions that vary across regions. This study investigates next-day rainfall predictability in tropical and temperate climates using daily atmospheric data—including pressure, temperature, dew point, relative humidity, wind [...] Read more.

Reliable rainfall prediction is essential for effective climate adaptation yet remains challenging due to complex atmospheric interactions that vary across regions. This study investigates next-day rainfall predictability in tropical and temperate climates using daily atmospheric data—including pressure, temperature, dew point, relative humidity, wind speed, and wind direction—collected from topographically similar sites in Alor Setar (tropical) and Vercelli, Williams, and Ashburton (temperate) between 2012 and 2015. Logistic regression and random forest models were used to predict rainfall occurrence as a binary outcome. Key variables were identified using Wald’s statistics and p-values in the logistic regression models, while the random forest models relied on mean decrease accuracy for ranking variable importance. The results reveal that rainfall in temperate climates is significantly more predictable than in tropical regions, with the Williams model demonstrating the highest accuracy. Atmospheric pressure consistently emerged as the dominant predictor in temperate regions but was not significant in the tropical model, reflecting the greater atmospheric variability and complexity in tropical rainfall mechanisms. Crucially, the study highlights that as global warming continues to alter temperate climate patterns—bringing increased variability and more convective rainfall—these regions may experience the same predictive uncertainties currently observed in tropical climates. These findings underscore the urgency of developing robust, climate-specific rainfall prediction models that account for changing atmospheric dynamics, with critical implications for weather forecasting, disaster preparedness, and climate resilience planning. Full article

(This article belongs to the Section Climate Dynamics and Modelling)

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14 pages, 5448 KB

Open AccessArticle

A Study of Climate-Sensitive Diseases in Climate-Stressed Areas of Bangladesh

by Ahammadul Kabir, Shahidul Alam, Nusrat Jahan Tarin, Shila Sarkar, Anthony Eshofonie, Mohammad Ferdous Rahman Sarker, Abul Kashem Shafiqur Rahman and Tahmina Shirin

Climate 2025, 13(8), 166; https://doi.org/10.3390/cli13080166 - 5 Aug 2025

Viewed by 889

Abstract

The National Adaptation Plan of Bangladesh identifies eleven climate-stressed zones, placing nearly 100 million people at high risk of climate-related hazards. Vulnerable groups such as the poor, floating populations, daily laborers, and slum dwellers are particularly affected. However, there is a lack of [...] Read more.

The National Adaptation Plan of Bangladesh identifies eleven climate-stressed zones, placing nearly 100 million people at high risk of climate-related hazards. Vulnerable groups such as the poor, floating populations, daily laborers, and slum dwellers are particularly affected. However, there is a lack of data on climate-sensitive diseases and related hospital visits in these areas. This study explored the prevalence of such diseases using the Delphi method through focus group discussions with 493 healthcare professionals from 153 hospitals in 156 upazilas across 21 districts and ten zones. Participants were selected by district Civil Surgeons. Key climate-sensitive diseases identified included malnutrition, diarrhea, pneumonia, respiratory infections, typhoid, skin diseases, hypertension, cholera, mental health disorders, hepatitis, heat stroke, and dengue. Seasonal surges in hospital visits were noted, influenced by factors like extreme heat, air pollution, floods, water contamination, poor sanitation, salinity, and disease vectors. Some diseases were zone-specific, while others were widespread. Regions with fewer hospital visits often had higher disease burdens, indicating under-reporting or lack of access. The findings highlight the need for area-specific adaptation strategies and updates to the Health National Adaptation Plan. Strengthening resilience through targeted investment and preventive measures is crucial to reducing health risks from climate change. Full article

(This article belongs to the Section Climate and Environment)

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25 pages, 5531 KB

Open AccessArticle

Transitions of Carbon Dioxide Emissions in China: K-Means Clustering and Discrete Endogenous Markov Chain Approach

by Shangyu Chen, Xiaoyu Kang and Sung Y. Park

Climate 2025, 13(8), 165; https://doi.org/10.3390/cli13080165 - 3 Aug 2025

Viewed by 472

Abstract

This study employs k-means clustering to group 30 Chinese provinces into four CO₂ emission patterns, characterized by increasing emission levels and distinct energy consumption structures, and captures their dynamic evolution from 2000 to 2021 using a discrete endogenous Markov chain approach. While [...] Read more.

This study employs k-means clustering to group 30 Chinese provinces into four CO₂ emission patterns, characterized by increasing emission levels and distinct energy consumption structures, and captures their dynamic evolution from 2000 to 2021 using a discrete endogenous Markov chain approach. While Shanghai, Jiangxi, and Hebei retained their original classifications, provinces such as Beijing, Fujian, Tianjin, and Anhui transitioned from higher to lower emission patterns, indicating notable reversals in emission trajectories. To identify the determinants of these transitions, GDP growth rate, population growth rate, and energy investment are incorporated as time varying covariates. The empirical findings demonstrate that GDP growth substantially increases interpattern mobility, thereby weakening state persistence, whereas population growth and energy investment tend to reinforce emission pattern stability. These results imply that policy responses must be tailored to regional dynamics. In rapidly growing regions, fiscal incentives and technological upgrading may facilitate downward transitions in emission states, whereas in provinces where emissions remain persistent due to demographic or investment related rigidity, structural adjustments and long term mitigation frameworks are essential. The study underscores the importance of integrating economic, demographic, and investment characteristics into carbon reduction strategies through a region specific and data informed approach. Full article

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20 pages, 7986 KB

Open AccessArticle

Investigating the Gender-Climate Nexus: Strengthening Women’s Roles in Adaptation and Mitigation in the Sidi Bouzid Region

by Houda Mazhoud, Arij Boucif, Abir Ouhibi, Lobna Hajji-Hedfi and Fraj Chemak

Climate 2025, 13(8), 164; https://doi.org/10.3390/cli13080164 - 1 Aug 2025

Viewed by 727

Abstract

Tunisia faces significant challenges related to climate change, which deeply affect its natural and agricultural resources. This reality threatens not only food security but also the economic stability of rural communities and mainly rural women. This research aims to assess the impact of [...] Read more.

Tunisia faces significant challenges related to climate change, which deeply affect its natural and agricultural resources. This reality threatens not only food security but also the economic stability of rural communities and mainly rural women. This research aims to assess the impact of climate change on rural women in the agricultural development group in Sidi Bouzid, focusing on the strategies adopted and the support provided by various stakeholders to mitigate this impact. To achieve this, we developed a rigorous methodology that includes structured questionnaires, focus group discussions, and topological analysis through Multiple Correspondence Analysis (MCA). The results revealed that rural women were categorized into three groups based on their vulnerability to climate change: severely vulnerable, vulnerable, and adaptive. The findings highlighted the significant impact of climate change on water resources, which has increased family tensions and reduced agricultural incomes, making daily life more challenging for rural women. Furthermore, a deeper analysis of interactions with external stakeholders emphasized the important role of civil society, public organizations, and research institutions in strengthening the climate resilience of rural women. Given these findings, strategic recommendations aim to enhance stakeholder coordination, expand partnerships, and improve access to essential technologies and resources for women in agricultural development groups. Full article

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20 pages, 621 KB

Open AccessArticle

Support Needs of Agrarian Women to Build Household Livelihood Resilience: A Case Study of the Mekong River Delta, Vietnam

by Tran T. N. Tran, Tanh T. N. Nguyen, Elizabeth C. Ashton and Sharon M. Aka

Climate 2025, 13(8), 163; https://doi.org/10.3390/cli13080163 - 1 Aug 2025

Viewed by 562

Abstract

Agrarian women are at the forefront of rural livelihoods increasingly affected by the frequency and severity of climate change impacts. However, their household livelihood resilience (HLR) remains limited due to gender-blind policies, scarce sex-disaggregated data, and inadequate consideration of gender-specific needs in resilience-building [...] Read more.

Agrarian women are at the forefront of rural livelihoods increasingly affected by the frequency and severity of climate change impacts. However, their household livelihood resilience (HLR) remains limited due to gender-blind policies, scarce sex-disaggregated data, and inadequate consideration of gender-specific needs in resilience-building efforts. Grounded in participatory feminist research, this study employed a multi-method qualitative approach, including semi-structured interviews and oral history narratives, with 60 women in two climate-vulnerable provinces. Data were analyzed through thematic coding, CATWOE (Customers, Actors, Transformation, Worldview, Owners, Environmental Constraints) analysis, and descriptive statistics. The findings identify nine major climate-related events disrupting livelihoods and reveal a limited understanding of HLR as a long-term, transformative concept. Adaptation strategies remain short-term and focused on immediate survival. Barriers to HLR include financial constraints, limited access to agricultural resources and technology, and entrenched gender norms restricting women’s leadership and decision-making. While local governments, women’s associations, and community networks provide some support, gaps in accessibility and adequacy persist. Participants expressed the need for financial assistance, vocational training, agricultural technologies, and stronger peer networks. Strengthening HLR among agrarian women requires gender-sensitive policies, investment in local support systems, and community-led initiatives. Empowering agrarian women as agents of change is critical for fostering resilient rural livelihoods and achieving inclusive, sustainable development. Full article

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11 pages, 985 KB

Open AccessArticle

Strengthening Western North Pacific High in a Warmer Environment

by Sanghyeon Yun and Namyoung Kang

Climate 2025, 13(8), 162; https://doi.org/10.3390/cli13080162 - 1 Aug 2025

Viewed by 396

Abstract

The geographical response of western North Pacific subtropical high (SH) to environmental conditions such as the El Niño-Southern Oscillation (ENSO) and global warming has been one of the main concerns with respect to extreme events induced by tropical convections. By considering observed outgoing [...] Read more.

The geographical response of western North Pacific subtropical high (SH) to environmental conditions such as the El Niño-Southern Oscillation (ENSO) and global warming has been one of the main concerns with respect to extreme events induced by tropical convections. By considering observed outgoing longwave radiation (OLR) as the strength of subtropical high, this study attempts to further understand the geographical response of SH strength to ENSO and global warming. Here, “SH strength” is defined as the inhibition of regional convections under SH environment. A meridional seesaw pattern among SH strength anomalies is found at 130°–175° E. In addition, the La Niña environment with weaker convections at lower latitudes is characterized by farther westward expansion of SH but with a weaker strength. Conversely, the El Niño environment with stronger convections at lower latitudes leads to shrunken SH but with a greater strength. The influence of the seesaw mechanism appears to be modulated by global warming. The western North Pacific subtropical high strengthens overall under warming in both the La Niña and El Niño environments. This suggests that the weakening effect by drier tropics is largely offset by anomalous highs induced by a warming atmosphere. It is most remarkable that the highest SH strengths appear in a warmer El Niño environment. The finding implies that every new El Niño environment may experience the driest atmosphere ever in the subtropics under global warming. The value of this study lies in the fact that OLR effectively illustrates how the ENSO variation and global warming bring the zonally undulating strength of boreal-summer SH. Full article

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23 pages, 2122 KB

Open AccessArticle

Climate Change of Near-Surface Temperature in South Africa Based on Weather Station Data, ERA5 Reanalysis, and CMIP6 Models

by Ilya Serykh, Svetlana Krasheninnikova, Tatiana Gorbunova, Roman Gorbunov, Joseph Akpan, Oluyomi Ajayi, Maliga Reddy, Paul Musonge, Felix Mora-Camino and Oludolapo Akanni Olanrewaju

Climate 2025, 13(8), 161; https://doi.org/10.3390/cli13080161 - 1 Aug 2025

Viewed by 723

Abstract

This study investigates changes in Near-Surface Air Temperature (NSAT) over the South African region using weather station data, reanalysis products, and Coupled Model Intercomparison Project Phase 6 (CMIP6) model outputs. It is shown that, based on ERA5 reanalysis, the average NSAT increase in [...] Read more.

This study investigates changes in Near-Surface Air Temperature (NSAT) over the South African region using weather station data, reanalysis products, and Coupled Model Intercomparison Project Phase 6 (CMIP6) model outputs. It is shown that, based on ERA5 reanalysis, the average NSAT increase in the region (45–10° S, 0–50° E) for the period 1940–2023 was 0.11 ± 0.04 °C. Weak multi-decadal changes in NSAT were observed from 1940 to the mid-1970s, followed by a rapid warming trend starting in the mid-1970s. Weather station data generally confirm these results, although they exhibit considerable inter-station variability. An ensemble of 33 CMIP6 models also reproduces these multi-decadal NSAT change characteristics. Specifically, the average model-simulated NSAT values for the region increased by 0.63 ± 0.12 °C between the periods 1940–1969 and 1994–2023. Based on the results of the comparison between weather station observations, reanalysis, and models, we utilize projections of NSAT changes from the analyzed ensemble of 33 CMIP6 models until the end of the 21st century under various Shared Socioeconomic Pathway (SSP) scenarios. These projections indicate that the average NSAT of the South African region will increase between 1994–2023 and 2070–2099 by 0.92 ± 0.36 °C under the SSP1-2.6 scenario, by 1.73 ± 0.44 °C under SSP2-4.5, by 2.52 ± 0.50 °C under SSP3-7.0, and by 3.17 ± 0.68 °C under SSP5-8.5. Between 1994–2023 and 2025–2054, the increase in average NSAT for the studied region, considering inter-model spread, will be 0.49–1.15 °C, depending on the SSP scenario. Furthermore, climate warming in South Africa, both in the next 30 years and by the end of the 21st century, is projected to occur according to all 33 CMIP6 models under all considered SSP scenarios. The main spatial feature of this warming is a more significant increase in NSAT over the landmass of the studied region compared to its surrounding waters, due to the stabilizing role of the ocean. Full article

(This article belongs to the Topic Climate Change Impacts and Adaptation: Interdisciplinary Perspectives, 2nd Edition)

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35 pages, 1524 KB

Open AccessArticle

Unveiling the Interplay of Climate Vulnerability and Social Capital: Insights from West Bengal, India

by Sayari Misra, Md Saidul Islam and Suchismita Roy

Climate 2025, 13(8), 160; https://doi.org/10.3390/cli13080160 - 26 Jul 2025

Viewed by 1137

Abstract

This study explores the interplay of climate vulnerability and social capital in two rural communities: Brajaballavpur, a high-climate-prone village in the Indian Sundarbans characterized by high ecological fragility, recurrent cyclones, and saline water intrusion affecting water access, livelihoods, and infrastructure; and Jemua, a [...] Read more.

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

(This article belongs to the Special Issue Sustainable Development Pathways and Climate Actions)

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16 pages, 421 KB

Open AccessReview

Applications of Machine Learning Methods in Sustainable Forest Management

by Rogério Pinto Espíndola, Mayara Moledo Picanço, Lucio Pereira de Andrade and Nelson Francisco Favilla Ebecken

Climate 2025, 13(8), 159; https://doi.org/10.3390/cli13080159 - 25 Jul 2025

Viewed by 819

Abstract

Machine learning (ML) has established itself as an innovative tool in sustainable forest management, essential for tackling critical challenges such as deforestation, biodiversity loss, and climate change. Through the analysis of large volumes of data from satellites, drones, and sensors, machine learning facilitates [...] Read more.

Machine learning (ML) has established itself as an innovative tool in sustainable forest management, essential for tackling critical challenges such as deforestation, biodiversity loss, and climate change. Through the analysis of large volumes of data from satellites, drones, and sensors, machine learning facilitates everything from precise forest health assessments and real-time deforestation detection to wildfire prevention and habitat mapping. Other significant advancements include species identification via computer vision and predictive modeling to optimize reforestation and carbon sequestration. Projects like SILVANUS serve as practical examples of this approach’s success in combating wildfires and restoring ecosystems. However, for these technologies to reach their full potential, obstacles like data quality, ethical issues, and a lack of collaboration between different fields must be overcome. The solution lies in integrating the power of machine learning with ecological expertise and local community engagement. This partnership is the path forward to preserve biodiversity, combat climate change, and ensure a sustainable future for our forests. Full article

(This article belongs to the Topic Disaster Risk Management and Resilience)

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24 pages, 319 KB

Open AccessArticle

Indigenous Contestations of Carbon Markets, Carbon Colonialism, and Power Dynamics in International Climate Negotiations

by Zeynep Durmaz and Heike Schroeder

Climate 2025, 13(8), 158; https://doi.org/10.3390/cli13080158 - 24 Jul 2025

Viewed by 913

Abstract

This paper examines the intersection of global climate governance, carbon markets, and Indigenous Peoples’ rights under the United Nations Framework Convention on Climate Change. It critically analyses how Indigenous Peoples have contested the Article 6 market mechanisms of the Paris Agreement at the [...] Read more.

This paper examines the intersection of global climate governance, carbon markets, and Indigenous Peoples’ rights under the United Nations Framework Convention on Climate Change. It critically analyses how Indigenous Peoples have contested the Article 6 market mechanisms of the Paris Agreement at the height of their negotiation during COP25 and COP26 by drawing attention to their role in perpetuating “carbon colonialism,” thereby revealing deeper power dynamics in global climate governance. Utilising a political ecology framework, this study explores these power dynamics at play during the climate negotiations, focusing on the instrumental, structural, and discursive forms of power that enable or limit Indigenous participation. Through a qualitative case study approach, the research reveals that while Indigenous Peoples have successfully used discursive strategies to challenge market-based solutions, their influence remains limited due to entrenched structural and instrumental power imbalances within the UNFCCC process. This study highlights the need for equitable policies that integrate human rights safeguards and prioritise Indigenous-led, non-market-based approaches to ecological restoration. Full article

18 pages, 411 KB

Open AccessArticle

Differences in Perceived Future Impacts of Climate Change on the Workforce Among Residents of British Columbia

by Andreea Bratu, Aayush Sharma, Carmen H. Logie, Gina Martin, Kalysha Closson, Maya K. Gislason, Robert S. Hogg, Tim Takaro and Kiffer G. Card

Climate 2025, 13(8), 157; https://doi.org/10.3390/cli13080157 - 24 Jul 2025

Viewed by 523

Abstract

Certain industries will bear a disproportionate share of the burden of climate change. Climate change risk perceptions can impact workers’ mental health and well-being; increased climate change risk perceptions are also associated with more favourable adaptive attitudes. It is, therefore, important to understand [...] Read more.

Certain industries will bear a disproportionate share of the burden of climate change. Climate change risk perceptions can impact workers’ mental health and well-being; increased climate change risk perceptions are also associated with more favourable adaptive attitudes. It is, therefore, important to understand whether climate risk perceptions differ across workers between industries. We conducted an online survey of British Columbians (16+) in 2021 using social media advertisements. Participants rated how likely they believed their industry (Natural Resources, Science, Art and Recreation, Education/Law/Government, Health, Management/Business, Manufacturing, Sales, Trades) would be affected by climate change (on a scale from “Very Unlikely” to “Very Likely”). Ordinal logistic regression examined the association between occupational category and perceived industry vulnerability, adjusting for socio-demographic factors. Among 877 participants, 66.1% of Natural Resources workers perceived it was very/somewhat likely that climate change would impact their industry; only those in Science (78.3%) and Art and Recreation (71.4%) occupations had higher percentages. In the adjusted model, compared to Natural Resources workers, respondents in other occupations, including those in Art and Recreation, Education/Law/Government, Management/Business, Manufacturing, Sales, and Trades, perceived significantly lower risk of climate change-related industry impacts. Industry-specific interventions are needed to increase awareness of and readiness for climate adaptation. Policymakers and industry leaders should prioritize sectoral differences when designing interventions to support climate resilience in the workforce. Full article

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19 pages, 2340 KB

Open AccessArticle

Analysis of Olive Tree Flowering Behavior Based on Thermal Requirements: A Case Study from the Northern Mediterranean Region

by Maja Podgornik, Jakob Fantinič, Tjaša Pogačar and Vesna Zupanc

Climate 2025, 13(8), 156; https://doi.org/10.3390/cli13080156 - 23 Jul 2025

Viewed by 811

Abstract

In recent years, early olive fruit drop has been observed in the northern Mediterranean regions, causing significant economic losses, although the exact cause remains unknown. Recent studies have identified several possible causes; however, our understanding of how olive trees respond to these environmental [...] Read more.

In recent years, early olive fruit drop has been observed in the northern Mediterranean regions, causing significant economic losses, although the exact cause remains unknown. Recent studies have identified several possible causes; however, our understanding of how olive trees respond to these environmental stresses remains limited. This study includes an analysis of selected meteorological and flowering data for Olea europaea L. “Istrska belica” to evaluate the use of a chilling and forcing model for a better understanding of flowering time dynamics under a changing climate. The flowering process is influenced by high diurnal temperature ranges (DTRs) during the pre-flowering period, resulting in earlier flowering. Despite annual fluctuations due to various climatic factors, an increase in DTRs has been observed in recent decades, although the mechanisms by which olive trees respond to high DTRs remain unclear. The chilling requirements are still well met in the region (1500 ± 250 chilling units), although their total has declined over the years. According to the Chilling Hours Model, chilling units—referred to as chilling hours—represent the number of hours with temperatures between 0 and 7.2 °C, accumulated throughout the winter season. Growing degree hours (GDHs) are strongly correlated with the onset of flowering. These results suggest that global warming is already affecting the synchrony between olive tree phenology and environmental conditions in the northern Mediterranean and may be one of the reason for the green drop. Full article

(This article belongs to the Section Climate Adaptation and Mitigation)

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30 pages, 459 KB

Open AccessReview

Recent Advances in Long-Term Wind-Speed and -Power Forecasting: A Review

by Jacqueline Muthoni Mbugua and Yusuke Hiraga

Climate 2025, 13(8), 155; https://doi.org/10.3390/cli13080155 - 23 Jul 2025

Viewed by 946

Abstract

This review examines advancements and methodologies in long-term wind-speed and -power forecasting. It emphasizes the importance of these techniques in integrating wind energy into power systems. Covering a range of forecasting timeframes from monthly to multiyear projections, this paper highlights the diversity of [...] Read more.

This review examines advancements and methodologies in long-term wind-speed and -power forecasting. It emphasizes the importance of these techniques in integrating wind energy into power systems. Covering a range of forecasting timeframes from monthly to multiyear projections, this paper highlights the diversity of applications and approaches. These applications and approaches are essential for managing the inherent variability and unpredictability of wind energy. Various forecasting methods, including statistical models, machine-learning techniques, and hybrid models, are discussed in detail. The review demonstrates how these methods improve forecast accuracy and reliability across different temporal and geographical scales. It also identifies significant challenges such as model complexity, data limitations, and the need to accommodate regional variations. Future improvements in wind forecasting include enhancing model integration, employing higher resolution data, and fostering collaborative research to further refine forecasting methodologies. This comprehensive analysis aims to advance knowledge on wind forecasting, facilitate the efficient integration of wind power into global energy systems, and contribute to sustainable energy development goals. Full article

(This article belongs to the Special Issue Wind‑Speed Variability from Tropopause to Surface)

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21 pages, 991 KB

Open AccessArticle

Strengthening Agricultural Drought Resilience of Commercial Livestock Farmers in South Africa: An Assessment of Factors Influencing Decisions

by Yonas T. Bahta, Frikkie Maré and Ezael Moshugi

Climate 2025, 13(8), 154; https://doi.org/10.3390/cli13080154 - 22 Jul 2025

Viewed by 495

Abstract

In order to fulfil SDG 13—taking urgent action to combat climate change and its impact—SDG 2—ending hunger and poverty—and the African Union CAADP Strategy and Action Plan: 2026–2035, which’s goal is ending hunger and intensifying sustainable food production, agro-industrialisation, and trade, the resilience [...] Read more.

In order to fulfil SDG 13—taking urgent action to combat climate change and its impact—SDG 2—ending hunger and poverty—and the African Union CAADP Strategy and Action Plan: 2026–2035, which’s goal is ending hunger and intensifying sustainable food production, agro-industrialisation, and trade, the resilience of commercial livestock farmers to agricultural droughts needs to be enhanced. Agricultural drought has affected the economies of many sub-Saharan African countries, including South Africa, and still poses a challenge to commercial livestock farming. This study identifies and determines the factors affecting commercial livestock farmers’ level of resilience to agricultural drought. Primary data from 123 commercial livestock farmers was used in a principal component analysis to estimate the agricultural drought resilience index as an outcome variable, and the probit model was used to determine the factors influencing the resilience of commercial livestock farmers in the Northern Cape Province of South Africa. This study provides a valuable contribution towards resilience-building strategies that are critical for sustaining commercial livestock farming in arid regions by developing a formula for calculating the Agricultural Drought Resilience Index for commercial livestock farmers, significantly contributing to the pool of knowledge. The results showed that 67% of commercial livestock farming households were not resilient to agricultural drought, while 33% were resilient. Reliance on sustainable natural water resources, participation in social networks, education, relative support, increasing livestock numbers, and income stability influence the resilience of commercial livestock farmers. It underscores the importance of multidimensional policy interventions to enhance farmer drought resilience through education and livelihood diversification. Full article

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Climate, Volume 13, Issue 8 (August 2025) – 17 articles

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