Extreme Weather Events Research Articles

Climate change scenarios forecast increased drought exposure for terrestrial vertebrates in the contiguous United States

Anthropogenic climate change is altering patterns of extreme weather events across the planet, with far-ranging consequences for biodiversity. Increases in the frequency, duration, and severity of droughts are anticipated to substantially impact natural ecosystems. Here, we predicted the extent to which 1221 terrestrial vertebrates in the contiguous United States will be increasingly exposed to annual (12-month) and prolonged (36-month) drought, under three global climate models and two representative concentration pathways. On average, a 377% increase in annual drought exposure and a 579% increase in prolonged drought exposure are anticipated for the study area by 2050–2080, compared to 1950–2005. Species in the southwestern USA could see the largest increases in drought exposure, while this area also has the highest vertebrate diversity and an abundance of drought-threatened species. Our results aid in identifying vertebrates and ecoregions anticipated to see large increases in drought exposure, which can inform conservation strategies aimed at mitigating vertebrate extinction risks.

Using Explainable AI and Transfer Learning to Understand and Predict the Maintenance of Atlantic Blocking With Limited Observational Data

AbstractBlocking events are an important cause of extreme weather, especially long‐lasting blocking events that trap weather systems in place. The duration of blocking events is, however, underestimated in climate models. Explainable Artificial Intelligence are a class of data analysis methods that can help identify physical causes of prolonged blocking events and diagnose model deficiencies. We demonstrate this approach on an idealized quasigeostrophic (QG) model developed by Marshall and Molteni (1993), https://doi.org/10.1175/1520‐0469(1993)050<1792:taduop>2.0.co;2. We train a convolutional neural network (CNN), and subsequently, build a sparse predictive model for the persistence of Atlantic blocking, conditioned on an initial high‐pressure anomaly. Shapley Additive ExPlanation (SHAP) analysis reveals that high‐pressure anomalies in the American Southeast and North Atlantic, separated by a trough over Atlantic Canada, contribute significantly to prediction of sustained blocking events in the Atlantic region. This agrees with previous work that identified precursors in the same regions via wave train analysis. When we apply the same CNN to blockings in the ERA5 atmospheric reanalysis, there is insufficient data to accurately predict persistent blocks. We partially overcome this limitation by pre‐training the CNN on the plentiful data of the Marshall‐Molteni model, and then using Transfer learning (TL) to achieve better predictions than direct training. SHAP analysis before and after TL allows a comparison between the predictive features in the reanalysis and the QG model, quantifying dynamical biases in the idealized model. This work demonstrates the potential for machine learning methods to extract meaningful precursors of extreme weather events and achieve better prediction using limited observational data.

Climate Change and Urban Flooding: Assessing Remote Sensing Data and Flood Modeling Techniques: A Comprehensive Review

This review critically examines the current understanding of climate change impacts on urban flooding, synthesizing recent findings to provide a comprehensive overview of how rising temperatures, sea-level rise, and increased extreme weather events influence urban floods. Additionally, it discusses various flood modeling techniques, outlining their advantages and disadvantages. Covering a broad spectrum of studies published between 2007 and 2024, selected for their relevance and methodology, this review highlights several key findings. Climate change significantly exacerbates urban flood disasters, with remote sensing emerging as an indispensable tool for climate change and flood monitoring and prediction. Despite these advancements, notable gaps remain in the literature. There is a conspicuous lack of long-term impact studies and limited discourse on the efficacy of existing mitigation strategies. Additionally, the review underscores the deficiency of real-time flood monitoring systems specifically designed for urban areas. These gaps highlight an urgent need for targeted research and the development of adaptive management practices to enhance flood prediction and management in urban settings. Future research must focus on advancing real-time monitoring systems, integrating remote sensing technologies more effectively, and developing comprehensive flood models. The review also emphasizes the importance of interdisciplinary approaches that merge hydrology, climatology, urban planning, and technology. By consolidating existing research, this review serves as a valuable resource for researchers and policymakers. It underscores the critical need for innovative flood modeling techniques and adaptive management strategies to tackle the challenges posed by climate change. This synthesis not only enriches the current body of knowledge, but also provides clear directions for future investigations, emphasizing the imperative for improved prediction, preparedness, and response mechanisms in urban flood management.

Analysis of landslide deformation mechanisms and coupling effects under rainfall and reservoir water level effects

Changes in rainfall, groundwater levels, and reservoir water levels exacerbate the deformation of water-involved landslides, accelerating the transition from landslide evolution to extinction. Extracting the destruction patterns of landslides from extensive monitoring data, and understanding their overall deformation mechanisms are crucial for geological hazard prevention and control. Herein, we took the Jiuxianping landslide in the Three Gorges Reservoir area as an example and proposed a deformation mechanism analysis model for water-related landslides based on monitoring data mining techniques. Using Granger causality testing, the study analyzes the spatiotemporal characteristics of GPS displacement data from three different profiles, which confirms that Jiuxianping exhibits a traction destruction mode. By comparing GPS displacement data and their Granger causality relationships across different profiles, we reveal that segmented sliding features of the landslide's front, middle, and trailing during its evolution. Furthermore, the impact intensity of triggering factors (rainfall and reservoir water level changes) on landslide displacement was identified. Based on GPS displacement data from profiles II–II′, an empirical mode decomposition–long short-term memory-regression model (EMD-LSTM-regression) is developed for multisource prediction of landslide displacements. The Shapley additive explanations algorithm is used to analyze the influence of rainfall and reservoir water level changes on periodic displacements at different positions of the landslide. Owing to the large area of the Jiuxianping landslide, the response to triggering factors varies across different locations. In the context of global warming and frequent extreme weather events, these findings offer important insights for preventing and mitigating water-related landslides in the Three Gorges Reservoir area, while also providing new perspectives for the analysis of global water-involved landslide deformation.

A high‐resolution climatological study of explosive cyclones in the Mediterranean region: Frequency, intensity and synoptic drivers

AbstractIn the complex context of climate change and in a heavily populated area like the Mediterranean region, understanding and studying explosive cyclones (ECs) is crucial because of their potential to cause extreme weather events, including strong winds, heavy rainfall, and significant disruptions to human activities and infrastructure, as happened in 2018 with the Vaia storm. The genesis and persistence of ECs, characterized by a rapid drop in central mean sea level pressure exceeding 12 hPa in 12 hours, are influenced by various physical processes and while water vapour convergence in the mid‐low troposphere is crucial, mid‐upper tropospheric cyclonic vorticity advection and upper tropospheric divergence also play significant roles. In this study, we used ERA5 hourly data of mean sea level pressure from 1979 to 2020 to analyse EC occurrences in the Mediterranean. The identification of EC events followed the 12‐hr deepening‐rate criterion introduced by Zhang et al. (2017), as shorter deepening rates definitions proved unreliable. A total of 80 ECs were identified and classified based on their spatial occurrence and magnitude, 34 of which were concentrated in the northwest of the basin, likely due to orographic deformation caused by the Alps on baroclinic waves originating from the Atlantic. The study also identified recurring synoptic patterns associated with the persistence of ECs over the sea, including the presence of potential vorticity (PV) streamers and mid‐tropospheric dry‐air intrusions. Empirical Orthogonal Function (EOF) analysis highlighted the Scandinavian pattern as the primary driver, with blocking conditions over western Russia and Scandinavia, and composite analyses revealed the intrusion of PV to various atmospheric levels, extending up to 500 hPa. In summary, this study offers valuable insights into the complex mechanisms governing EC formation in the Mediterranean Basin, shedding light on the synoptic patterns, atmospheric dynamics, and potential impacts on regional weather systems.

Excess mortality related to high air temperature: Comparison of the periods including 1994 and 2018, the worst heat waves in the history of South Korea.

Climate change has caused extreme weather events, including frequent summer heat waves. We examined how the effects of high air temperatures on mortality have changed between the two study periods (1991-1995 and 2015-2019), including 1994 and 2018, the worst heat wave years in the meteorological history of South Korea. Temperature data from the Korea Meteorological Administration and mortality data from Statistics Korea were used in this study. We used distributed lag nonlinear models to estimate the cumulative relative risks (CRRs) to determine the association between daily maximum temperature in summer (June to September) and mortality. CRRs were estimated for each province and pooled using a random-effects meta-analysis for all provinces. Maximum temperature and annual average days in heat wave were 37.7°C and 11.8 in 1991-1995 and 38.3°C and 18.8 in 2015-2019. The slope of the CRR for mortality increases with increasing temperature and has been steeper in the past than in recent years and steeper in those over 65 than in those under 65. Excess mortality has recently declined compared with that in the past. The impact of high summer temperatures on mortality changed between the two periods, suggesting improved population resilience.

Toward Climate Resilient Asphalt Binder Selection in Canada

Climate change and extreme weather events present a fundamental challenge to pavement engineering and planning practice, given that transportation infrastructure has traditionally been planned and designed using historical climate data under the implicit assumption that climate is stationary and future conditions will resemble past ones. Transportation professionals should consider future risks scenarios in road design, material selection and system operations. This will be challenging given the inherent uncertainties in any climate projections. However, changes might be needed since transportation professionals are expected to deliver cost-effective and climate-resilient transportation infrastructure. The material selection plays an important role in the durability of flexible roads; therefore, it is important to consider the whole road design life cycle in terms of climate change and extreme weather events impact on asphalt selection as well as to consider different traffic speeds. This led to the development of the NRC Climate Adaptation and Asphalt Selection Tool (CAAST).

Heterogeneous effects of climate change on displacement-inducing disasters

With an estimated 357.7 million internal displacements caused since 2008, weather-related disasters are a major driver of human mobility worldwide. As climate change is projected to increase the frequency and intensity of extreme weather events in many parts of the world, it is important to better understand how trends in weather patterns related to global warming have affected the intensity of disasters that have caused displacements. Here we combined observational and counterfactual climate data with global internal displacement records to estimate how climate change has affected precipitation and wind speeds at the time and location of floods and storms that led to internal displacements. We estimate that, on average, climate change increased precipitation and decreased wind speeds during such events by +3.7% and − 1.4%, respectively. However, the variability across events is considerable (±28.6 and ± 6.6%, respectively), highlighting the large signal of natural variability of the weather system as compared to the global warming signal. Our results caution against overstating the role of climate change in displacement-inducing disasters in the past, especially compared to socio-economic and development factors of vulnerability and adaptive capacity that determine whether weather-related hazards turn into disasters.’

Disaster Preparedness and capacity building for Resilience in Agriculture

Abstract. Agriculture faces unprecedented challenges due to the increasing frequency and severity of natural disasters and extreme weather events. Agriculture sector needs resilience to support food production and livelihoods because it is susceptible to cyclones, floods, droughts, soil erosion, pests, and disease outbreaks. Geospatial technology plays a significant role in disaster management for agriculture, offering tools for preparedness, response, and recovery. This study explores the role of geospatial technology in early warning systems and risk assessment for different types of natural disasters that impact agriculture activity. Sustainable farming practices that are essential for resilience include crop diversity, climate-resilience varieties, and adaptation strategies. Capacity building and training are vital for effective geospatial technology utilization, especially in developing countries like India, where infrastructure and technology access may be limited. Tailored capacity-building programs are essential, emphasizing climate-smart practices, sustainable land management, and post-disaster recovery strategies. Access to financial services and insurance schemes enhances resilience by helping farmers cope with losses and recover from disasters. In order to improve resilience and sustainability in agriculture, geospatial decision support systems make it possible to evaluate alternative planning, optimise resource allocation, and implement adaptive management.

Digital Mental Health Innovations in the Face of Climate Change: Navigating a Sustainable Future.

Climate change poses substantial challenges to mental health, with increased frequency of extreme weather events and environmental degradation exacerbating stress, anxiety, trauma, and existential concerns. Digital innovations, particularly artificial intelligence and digital phenotyping, offer promising avenues to mitigate climate-related mental health burdens. The integration of digital tools into climate-related mental health care necessitates careful consideration of issues of access to and adoption of solutions. Future research should evaluate the effectiveness and scalability of digital interventions to address the mental health impacts of climate change through collaborative efforts involving clinicians, environmental health specialists, bioethicists, policy makers, and technology developers.

Dynamic and thermodynamic contribution to the October 2019 exceptional rainfall in western central Africa

Abstract. Exceptional rainfall hit western central Africa in October 2019. To understand its underlying mechanisms, we examined the regional moisture and moist static energy (MSE) budgets, intending to highlight the importance of the dynamic and thermodynamic effects associated with this historic event. Analysis of the moisture budget reveals that the precipitation anomalies in October were mainly controlled by dynamic effects. Horizontal moisture advection induced by horizontal wind anomalies controls extreme precipitation north of western central Africa, while vertical moisture advection induced by vertical velocity anomalies controls extreme precipitation south of western central Africa. Changes in the thermodynamic effect, although not the key factor responsible for the events of October 2019, contribute up to 35 % of the total effect on the northern part and 15 % on the southern part of the domain. The residual term on the northern part is important and provides a caveat when estimating dynamic and thermodynamic processes. Diagnosis of the MSE balance averaged over the northern part of western central Africa shows that the anomalous vertical motion is dominated by the dynamic effect, i.e., the wet enthalpy advection induced by the horizontal wind anomalies. This is confirmed by the high spatial correlation (r=0.6) between the two terms compared to the other terms, whereas to the west of the Congo Basin, the increase in the net energy balance dominated the changes in vertical motion (r=0.51). The horizontal advection of the MSE induced by the anomalies of the wet enthalpy and the vertical advection of the MSE induced by the anomalies of the MSE seem less important (r=0.29 and −0.19 to the north and −0.17 and 0.03 to the south). The strong anomalies in the MSE balance in the north are linked to its meridional component, in particular the meridional wind anomalies in the dynamic effect and the meridional anomalies in latent heat in the thermodynamic effect. Our results suggest that dynamic and thermodynamic effects should be jointly considered for adequately anticipating this kind of extreme event. Understanding the associated mechanisms could help us improve our forecasts and projections and increase the region's population resilience to these extreme weather events.

Analyzing dynamics of extreme weather events (EWE) in India: unfolding trends through statistical assessment of 50 years data (1970–2019)

ObjectiveOver the last five decades, extreme weather events (EWEs) have made a substantial contribution to the overall climate change impacts in India. Intergovernmental Panel on Climate Change (IPCC) has predicted that India will experience an increase in extreme weather events in the future. The primary aim of the study was to gain insights into India’s overall occurrence of EWEs between 1970 and 2019 across different states.MethodsIn the present study, data from the Indian Meteorological Department (IMD) spanning the last five decades (1970–2019) was examined to understand how specific EWEs have changed in India over varying regions and time spans. The analysis involved descriptive statistics using heatmaps, and trend analysis using the Mann- Kendall test.FindingsIn the past 50 years (1970–2019), around 11,158 EWEs occurred in India. EM-DAT data shows a rise from 4 + events in the 1950s to 20 + in 2018, while IMD data indicates an increase from 50 + events in the 1970s to 400 + in 2019. The event-wise trend analysis of 50 years of data on EWEs in India revealed a consistent increase in the incidence of each EWE. The Mann–Kendall test, conducted to detect trends in EWEs over 50 years revealed a significant upward trend at 1% level for total EWEs, including heat waves, floods, heavy rains, and thunderstorms. Heat map and spatial analysis revealed that significant regions for heat waves include Maharashtra, Odisha, West Bengal, Uttar Pradesh, Rajasthan, and Uttarakhand. Cold waves have increased in Uttar Pradesh, Uttarakhand, Rajasthan, Bihar, and Jharkhand. Floods, heavy rains, thunderstorms, and lightning are common in Maharashtra, West Bengal, Kerala, Assam, and Karnataka.ConclusionThe findings of the study have critical implications for climate resilience strategies and disaster management in India. The increasing frequency and geographical concentration of EWEs call for region-specific preparedness and mitigation plans. By knowing the trends of EWEs and hotspot states through retrospective data records, the Government can proactively plan for future adverse events to prioritize high-risk areas, implementing targeted preparedness and mitigation strategies to prevent significant mortality and morbidity.

Small green housing makes a big difference: the impact of natural environmental uncertainty on consumers' willingness to purchase green housing

PurposeThe residential sector is a principal contributor to global energy consumption, underscoring the critical importance of promoting green housing initiatives to mitigate energy use and environmental degradation. The prevalence of uncertainty in the natural environment, exemplified by phenomena like extreme weather events, highlights the urgent need for adaptive strategies and sustainable practices to mitigate the impact on human communities and ecosystems. Against this backdrop, this paper presents a theoretical framework examining the influence of natural environmental uncertainty on consumers' willingness to purchase green housing.Design/methodology/approachThrough three experiments, this study modeled the mechanism by which the natural environment uncertainty affects consumers' willingness to purchase green housing, and then verified the mediating effect of the threat of ontological security and the moderating effect of the degree of consumers' natural connectedness.FindingsThis paper concludes (1) natural environmental uncertainty exerts a significant positive impact on the willingness to purchase green housing, with the threat to ontological security serving as a pivotal mediating variable; (2) the degree of natural connectedness significantly moderates the effect of ontological security threats on the purchasing intent for green housing.Originality/valueThis research contributes to the marketing literature by offering a novel perspective on the impact of natural environmental uncertainty on consumer behavior, augmenting the body of knowledge concerning the determinants of green housing purchase intentions, and provides new ideas for marketers.

Geospatial Analysis of Climate Change Impacts on Vegetation Dynamics in Owerri West, Imo State

Climate change is a global phenomenon with profound effects on the environment, economies, and societies. It refers to significant changes in global temperatures and weather patterns over time. This study explores the impact of climate change on vegetation in Owerri West, Imo State, Nigeria, using geospatial techniques over a 20-year period (2003–2023). By analyzing satellite imagery and Normalized Difference Vegetation Index (NDVI) values alongside key climatic variables such as precipitation and land surface temperature (LST), the research seeks to quantify the extent of vegetation changes due to climate variability. The study uses Landsat 8 satellite data to evaluate spatiotemporal trends in vegetation health. NDVI values in 2003 ranged from a high of 0.45, indicating healthy and dense vegetation, to a low of -0.04, representing barren areas. By 2013, NDVI values had drastically declined, with a maximum of 0.12 and a minimum of -0.33, despite an increase in precipitation from 3300 mm (2003) to 3900 mm (2013). This decline suggests that other factors, such as extreme weather events or urbanization, contributed to vegetation stress. In 2023, NDVI values showed partial recovery, with a maximum of 0.28 and a minimum of 0.05, although precipitation levels dropped to a range of 600 mm to 540 mm. The broader temperature range in 2023, with a maximum of 32°C and a minimum of 17°C, likely reduced heat stress, allowing for vegetation recovery, though still below the 2003 levels, The findings highlight the complex interplay between climatic variables and vegetation health, where precipitation and temperature changes significantly influence vegetation dynamics. However, the NDVI decline in 2013, despite high precipitation, suggests that anthropogenic factors like urbanization and land use changes also play a critical role. This research emphasizes the importance of integrating remote sensing and GIS for monitoring vegetation responses to climate change. The study calls for sustainable land management practices and climate adaptation strategies to enhance vegetation resilience in the region.

Extreme climate, local fiscal decentralization, and corporate sustainability

ABSTRACT Extreme weather events pose a significant threat to the sustainability of Chinese enterprises. This study investigates their impact on corporate sustainability and the moderating effect of local fiscal decentralization. Using a fixed-effects model with A-share listed companies from 2007–2021, it finds that extreme weather negatively impacts corporate sustainability, but fiscal decentralization mitigates this effect, notably in central-eastern regions and non-state enterprises. Recommendations include increasing local fiscal autonomy, accelerating SOE reforms, and enhancing environmental governance to bolster local governments’ climate resilience and foster overall economic and corporate sustainability.

Financial Inclusion and Climate Resilience: The Role for an AI-Enhanced Digital Wallet in Caribbean SIDS

Caribbean Small Island Developing States (SIDS) are highly vulnerable to extreme weather events and climate change. Caribbean SIDS climate vulnerability is worsened by their high level of financial exclusion. Many people do not have bank accounts and access to electronic fund transfer (EFT). As such, they cannot electronically receive funds before or after a natural disaster to cope with the effects. The financial exclusion problem can be addressed through a digital wallet. A digital wallet is a financial transaction application that securely stores a user’s banking and payment information on a cloud interface and allows the user to perform a transaction while hiding their banking information from a vendor. The biggest concern of users with regards to the use of digital wallets are its convenience and security. While digital wallets offer outstanding convenience of purchasing goods and services, data privacy and fraud risks deter people from adopting mobile payment. Potential fraud risk to digital wallets can be identified with anomaly detection techniques. The research problem investigated in this study is how the implementation of an artificial intelligence (AI) enhanced digital wallet can facilitate financial inclusion in the Caribbean, particularly in the context of disaster preparedness and recovery. Since security is an important aspect of a digital wallet, a sub-objective of this study is to derive an appropriate model for anomaly detection in financial transactions in a digital wallet. This study modifies Liu et al. [1] Gated Transformer Networks (GTN) architecture to allow for univariate time series classification. The corresponding new model is referred to as the Univariate Gated Transformer Network (UGTN). The UGTN is used for anomaly detection in financial data from a digital wallet. This study also provides policy recommendations for the implementation of a digital wallet to facilitate financial inclusion and climate resilience in Caribbean SIDS.

A Practical Approach on Reducing the Flood Impact: A Case Study from Romania

Recently, the frequency and intensity of extreme weather events have increased in many regions worldwide. Among them, floods, whose effects are devastating in many cases, have been recorded in Romania in the last few years. Built to reduce the flooding effects on the communities, structures such as embankments can sometimes accentuate the hazard. This article investigates such a situation and proposes solutions to reduce or even eliminate the flood impact on the community living in the Vărbilău Catchment in Romania. Recorded data series, field observations, GIS techniques, and hydraulic modeling were used to design the hazard maps and perform the 3D representations that illustrate the actual situation (when the small opening of the bridge favors the flooding) and the proposed solutions (extending the bridge opening and shortening the embankment). It is shown that adding seven pillars to the bridge would reduce the flooded surface by more than 1.5 times and the affected buildings’ surface by more than 3.5 times compared to the current situation.

The Impact of Climate Variability on the Livelihoods of Smallholder Farmers in an Agricultural Village in the Wider Belfast Area, Mpumalanga Province, South Africa

The purpose of this study was to investigate the impact of climate change on smallholder farmers in South Africa, particularly focusing on the relationship between agriculture and weather patterns. Understanding this connection is crucial for helping farmers adapt to changing climate conditions and improve their resilience and sustainability. This research analyses 33 years of climate data (1990–2023) from the Belfast weather station to identify long-term climate trends, seasonal shifts, and the frequency of extreme weather events. Statistical analysis, including the Mann–Kendall test, revealed significant changes in temperature, rainfall, and the intensity of extreme weather events, indicating that climate change is already affecting the region. Specifically, the research highlighted significant damage to agricultural infrastructure, such as greenhouses, due to climate-related wind events. This study emphasises the importance of using digital technologies to monitor weather patterns in real-time, aiding in decision-making, and enhancing agricultural efficiency. Additionally, it calls for further research into the social impacts of climate variability, including its effects on community cohesion, migration, and access to social services among smallholder farmers. These findings provide a foundation for developing effective interventions to support the resilience of smallholder farming communities in the face of climate change. Future studies need to consider how climate variability affects farmers’ abilities to access markets, both in terms of transport and product quality.

Investigating the role of community organizations in communicating extreme weather events in New York City: A content analysis.

The communication of extreme weather forecasts (e.g., heatwaves and extreme precipitation) is a challenge for weather forecasters and emergency managers who are tasked with keeping residents safe during often unprecedented situations. Weather models have inherent uncertainty, and the ability for potentially life-saving information to get to the people who need it most (e.g., those who need to evacuate) remains a challenge despite the proliferation of digital access to information and social media sites like Twitter. It is also unclear the role that community-based organizations and super-local governmental entities play or may play during weather events in transmitting weather information and providing assistance. In New York City, there remains robust inequality, with communities that are historically disadvantaged often suffering the highest number of deaths and level of destruction following weather events. Results from interviewing 26 New York City community leaders suggest that local organizations often act as intermediaries, passing on official weather information to members of their audience, regardless of the mission statement of their organization. Common challenges for communities in responding to extreme weather include lack of access to information, language barriers, and insufficient resources. Considerations for future weather communication strategies are discussed.

Econometrics of Climate Change Research

The econometrics of climate change explores the intricate interplay between economic activity and climate dynamics, offering empirical insights into the causes, impacts, and mitigation of global climate change. This research applies advanced econometric techniques to quantify the economic consequences of environmental changes and evaluate the effectiveness of climate policies. By leveraging panel data models, time-series analysis, and causal inference methods, this study investigates critical topics such as the economic costs of extreme weather events, the efficacy of carbon pricing mechanisms, and the role of renewable energy adoption in mitigating climate risks. Additionally, this research incorporates machine learning techniques to enhance predictive accuracy in modeling future climate-economic interactions. Through rigorous empirical analysis, the study provides actionable recommendations for policymakers to design sustainable strategies that balance economic growth with climate resilience. The findings contribute to a deeper understanding of the socioeconomic dimensions of climate change, emphasizing the importance of evidence-based decision-making in addressing one of humanity’s most pressing challenges.