Effective Climate Change Adaptation Research Articles

Deciphering climate change impacts on resource extraction supply chain: a systematic review

AbstractMining is becoming increasingly vulnerable to the effects of climate change (CC). The vulnerability stems from changing weather patterns, leading to extreme weather events that can cause damage to equipment, infrastructure, and mining facilities and disrupt operations. The new demand from governments and international agreements has placed additional pressure on mining industries to update their policies in order to reduce greenhouse gas emissions and adapt to CC. This includes implementing carbon pricing systems, utilizing renewable energy, and focusing on sustainable development. Most mining and exploration industries prioritize reducing mining’s impact on climate change rather than adapting to extreme weather events. Therefore, it is important to study and investigate the impacts of climate change on the mining sector. This paper aims to investigate the challenges and strategies for adapting to and mitigating the impacts of climate change on mining through a systematic literature review. The results indicate that the majority of proposed models and strategies in the mining field are still in the conceptual phase, with fewer practical implementations. It has been identified that there is a requirement for long-term planning, improved risk management plans, and increased awareness and education within the industry. Practical strategies such as integrating renewable energy, enhancing operational safety, and improving water and tailings management have been recognized as crucial for effective climate change adaptation and mitigation.

Constraining future surface air temperature change on the Tibetan Plateau

Abstract The rapid warming of the Tibetan Plateau (TP) in recent decades has led to severe consequences, including the melting of glaciers and snow cover, which further accelerates warming. Accurately projecting the magnitude of future warming is crucial for effective climate change adaptation. However, the projection of future temperature change is model dependent. In this study, we demonstrate a significant correlation between the historical intermodel warming trend and future temperature change, suggesting this relationship could be used to calibrate the best estimate of projections and reduce the uncertainty by observations. For a high emission scenario, the constraint helps to narrow down the uncertainty range of annual and summer temperature change on the western TP by up to 2 °C and 4 °C, respectively, in the end of this century. The most substantial calibrated increase of future change is in winter up to 2 °C, followed by autumn with an increase of up to about 1 °C. Discrepancies of historical warming trend among different observation datasets expose the largest impact on the constrained best estimate compared with emergent relationship derived from different climate models and historical periods.

Regional Climate Change and Drought Dynamics in Tunceli, Turkey: Insights from Drought Indices

Global warming and climate change are causing temperatures to rise, which is having a negative impact on water resources. Climate change is a worldwide problem that affects and will continue to affect the frequency and intensity of natural disasters in many regions of the world. Tunceli region in Turkey, which until 10 years ago was known as an environmentally friendly city with abundant water resources and frequent rainfall, is experiencing a decrease in precipitation during the snowy winter season. This situation has made the investigation of climate change impacts an important issue in the region. Therefore, effective climate change adaptation strategies need to be developed. To determine these strategies, in this study, we assessed long-term drought conditions using multiple drought indices such as Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), Normal Precipitation Index (PNI), and Aridity Index (AI). The SPI and RDI analyses were performed in annual reference periods on a time scale of 3, 6, 9, and 12 months using temperature, precipitation, and evaporation data. Consequently, the SPI and RDI results were compared, and both indices show similar behavior in dry, wet, and normal seasons. Nevertheless, RDI shows less variation between different time scales, which is an advantage over SPI and is probably due to the inclusion of potential evapotranspiration in RDI. The variations in PNI between humid and dry sub-humid categorizations throughout the years, combined with the AI results, indicate that the Tunceli region predominantly experiences a climate ranging from dry sub-humid to semi-arid. This study could help decision-makers take effective measures to become more resilient to climate change in temperate climate regions and take important steps toward sustainable water resources management.

Nexus between Indigenous Knowledge Systems and Adaptation to Climate Change Strategies by Farmers in Kajiado County, Kenya

Indigenous knowledge (IK) has played an important role in adaptation to climate change in traditional agricultural communities. These approaches have been shown to be more effective when integrated with scientifically developed strategies. In this study, a perception survey questionnaire was used to find out whether there is a nexus between IK systems and the uptake of modern Climate Change Adaptation Strategies (CCAS) among farmers in Kajiado County, Kenya. A mixed research approach that involved the use of household questionnaire surveys, key informant interviews, focus group discussions (FGD), and observations was used to obtain data. The two theories, namely the Situated Learning Theory (SLT) and the Theory of Planned Behavior (TPB), formed the basis for illustrating the relationship between variables, while the Model of Private Proactive Adaptation to Climate Change (MPPACC) was the key theoretical model employed. A total of 382 small-scale rural household representatives randomly selected from 3 Kajiado sub-counties were the principal respondents. The key informants included officers in the departments of agriculture, environment, meteorology, trade, and industry, as well as local administration and village elders. Statistical tools such as chi-square, correlation, and binary logistic regression analysis were used to determine associations. Thematic analysis of data collected from key informants and focus group discussions (FGD) was conducted to triangulate the survey results. Farmers' awareness of climate change, risk perceptions of impacts, existing locally developed approaches to climate change adaptation, and perceptions of CCAS were the main areas of investigation. The majority of farmers (85%) indicated that prolonged drought was the main extreme event that affected their farming activities. Of the indicators observed, drought (X2 = 13.861, p =.000) was perceived as the greatest risk associated with climate change to their agricultural productivity. Their IK adaptation strategies resulted from their interactions with the effects of drought on their livelihoods over the years. Farmers who were nomadic (IK practitioners) were not willing to apply for CCAS. For example, they were 72% less likely to increase their irrigated cropped area and 79% less likely to increase the number of better livestock breeds. Among the CCAS approaches, water resource management was the only preferred strategy. This particular finding shows that climate change policies can be easily adopted by indigenous farming communities if they are developed around an issue that matches their socio-ecological priorities. Without taking into account the prevailing social, cultural, and geo-environmental context of specific communities, the adoption of CCAS by farmers would be significantly affected. The study recommends that integrating indigenous agricultural knowledge into climate change adaptation policies enhances community resilience. Aligning adaptation strategies with local responses enriches farmer knowledge and boosts resilience against climate change impacts. Effective climate change adaptation plans should incorporate local weather prediction, environmental conservation, and proven community-based strategies, especially in water resource management for arid and semi-arid pastoralist communities.

Opinion: Optimizing climate models with process knowledge, resolution, and artificial intelligence

Abstract. Accelerated progress in climate modeling is urgently needed for proactive and effective climate change adaptation. The central challenge lies in accurately representing processes that are small in scale yet climatically important, such as turbulence and cloud formation. These processes will not be explicitly resolvable for the foreseeable future, necessitating the use of parameterizations. We propose a balanced approach that leverages the strengths of traditional process-based parameterizations and contemporary artificial intelligence (AI)-based methods to model subgrid-scale processes. This strategy employs AI to derive data-driven closure functions from both observational and simulated data, integrated within parameterizations that encode system knowledge and conservation laws. In addition, increasing the resolution to resolve a larger fraction of small-scale processes can aid progress toward improved and interpretable climate predictions outside the observed climate distribution. However, currently feasible horizontal resolutions are limited to O(10 km) because higher resolutions would impede the creation of the ensembles that are needed for model calibration and uncertainty quantification, for sampling atmospheric and oceanic internal variability, and for broadly exploring and quantifying climate risks. By synergizing decades of scientific development with advanced AI techniques, our approach aims to significantly boost the accuracy, interpretability, and trustworthiness of climate predictions.

The Effect of Credit Access on Climate Change Adaptation Strategies Among Dairy Farmers in East Java, Indonesia

This study investigated the critical issue of credit access and its implications for climate change adaptation among dairy farmers in East Java, Indonesia. Using survey cross-sectional data from a sample of 70 dairy farmers, this research employs a combination of probit modelling and propensity score matching (PSM) to examine the determinants of credit access and evaluate its impact on climate change adaptation. The findings indicate that credit access is positively and significantly associated with factors such as farming experience, family size, and the number of employed household members. These factors emerge as pivotal determinants shaping farmers' ability to secure credit. The analysis employing PSM further reveals a notable effect of credit access on climate change adaptation. Farmers with access to credit demonstrate a greater propensity to adopt and implement a greater number of climate change adaptation strategies. The positive association between credit access and climate change adaptation underscores the potential role of financial support in enhancing farmers' resilience to environmental challenges. This suggests that facilitating credit access for farmers could significantly contribute to promoting sustainable agricultural practices in the context of climate change. These findings have implications for policymakers, agricultural practitioners, and financial institutions. By leveraging these insights, targeted interventions can be devised to improve credit accessibility for farmers, thereby fostering effective climate change adaptation strategies within the agricultural sector.

Climate change impacts on agricultural trade and food security in emerging economies: case of Southern Africa

Climate change has been a significant threat to sustainable agriculture impacting trade and food security. This research investigates the impacts of climate change on agricultural trade and food security in emerging economies focusing on Southern Africa. The research employed the dynamic panel Generalised Method of Moments (GMM) using panel data for the period 2012 to 2021 obtained from a sample of 12 Southern African countries selected based on data availability. The main independent variables to the research were climate change variables (temperature and precipitation). Other control variables included were population growth, food inflation and agricultural growth. The study found significant negative effects of climate change on agriculture trade. Whilst temperature changes were found to have insignificant effects, precipitation changes were found to have significant positive effects. The research recommends concerted efforts towards climate change adaptation and mitigation for sustainable agriculture. Based on the Findings, sustainable food security and agricultural trade Southern African emerging economies may be attained through effective climate change mitigation and adaptation approaches particularly promoting climate-smart agriculture (CSA).

Local Knowledge on Climate Change: Insights from Orange Farmers in Namsaling Village, Ilam District, Nepal

This journal article investigates the knowledge and experiences of orange farmers in Namsaling village, situated in the Ilam District of Nepal, concerning climate change. The study aims to assess farmers' awareness of climate change, its impact on orange cultivation, and its effects on their family economy. Data were collected through semi-structured interviews and focus group discussions with orange farmers. The findings demonstrate that farmers possess a substantial understanding of climate change and its implications for their agricultural practices. They have observed changes in temperature, rainfall patterns, and occurrences of pests and diseases, directly affecting orange production. Farmers have adopted various adaptive measures, including altering cultivation practices, cultivating genetically modified crops, implementing irrigation systems, and using organic fertilizers to sustain their family economy. However, challenges such as limited access to climate information, financial constraints, and the need for additional training hinder effective climate change adaptation in the region. This study offers valuable insights into the experiences and perspectives of orange farmers, emphasizing the importance of targeted interventions to enhance their adaptive capacity and promote sustainable orange cultivation amidst the challenges posed by climate change.

Exploring Climate Change Adaptation Practices and Agricultural Livelihoods among Rice Farmers of the Brahmaputra Valley in Northeast India.

Global climate change has seriously threatened agriculture and connected sectors, especially in developing countries like India. The Brahmaputra Valley in Assam, Northeast India, is vulnerable to climate change due to its agrarian economy, fragile geo-ecological setting, recurrent floods and droughts, and poor socioeconomic conditions of the farmers. The climate-induced hindrances faced by the rice farming community of this region and the local adaptation practices they employ have not been adequately studied. Therefore, we carried out a survey among 635 rice farmers across four agro-climatic zones of Assam, namely the Upper Brahmaputra Valley Zone, North Bank Plain Zone, Central Brahmaputra Valley Zone, and Lower Brahmaputra Valley Zone, to understand how they perceive and respond to climatic changes. The survey revealed that all the respondents have perceived an increase in ambient temperature, and 65% of the respondents have perceived a slight change in rainfall characteristics over the years. Most farmers reported adjusting the existing farming practices and livelihood choices to adapt to the changing climate. Farming adjustments were made mainly in terms of field preparation and management of water, rice variety, nutrients, and pests. Environmental variables like rainfall, flood, drought, and pest level, and socioeconomic variables like family size, education, farming experience, training, digital media exposure, and land area were found to influence farmers' adaptation choices. The findings imply that policies to strengthen flood, drought, pest management, education, land-use planning, agricultural training, and digital media applications in agriculture are needed for effective climate change adaptation in this region.

Stepwise clustering ensemble downscaling for future drought prediction under climate change: A case study of the Yangtze River Basin

Drought is a highly destructive natural disaster that can even lead to other complex and volatile extreme weather events in the Yangtze River Basin (YRB). Researching potential changes in future drought occurrences is crucial for developing effective climate change adaptation strategies under a warming climate. In this study, a stepwise clustered ensemble drought downscaling (SCEDD) model has been proposed, and two internationally recognized drought indices (the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI)) are used to evaluate drought events in YRB. The validation results show that the proposed model can be used to generate robust climate projections (daily precipitation) based on simulation. On this basis, SPI and SPEI are calculated from historical and future forecasts (Tmean and precipitation). The study suggests that predicted precipitation will increase dramatically in the future (2075–––2099) at all stations under the SSP245 and SSP585 scenarios. Drought events under SPI increased by 1.5% and decreased by 1.2% during 2075–––2099 under SSP245 and SSP585, respectively. In contrast, drought events in SPEI increased by 16.5% and 33.7%, respectively. In addition, drought events under SPI present an increase in spring, summer, and fall, with a decrease in winter under both SSPs. For comparison, drought events under SPEI will increase substantially in all seasons. In particular, drought predictions made using SPEI are more accurate and trustworthy because SPEI takes temperature into account. The results obtained can provide decision-makers in the water resources management sector with a reliable forecast of drought and minimize drought-related losses.

Perceptions of environmental changes among a climate-vulnerable population from Bangladesh

Effective climate change adaptation requires a thorough understanding of whether and how affected populations perceive climatic and environmental changes. Existing research has been inconclusive regarding the consistency of these perceptions compared to objective meteorological indicators. Moreover, no systematic comparison has been done for the perception of discrete environmental events such as floods or erosion. This study relies on novel panel survey data of approximately 1700 households residing along the Jamuna River in Bangladesh as well as on unique individual-level, satellite-based erosion data. It compares respondents’ perceptions of environmental events, namely riverbank erosion, and three climate change indicators, specifically long-term temperature change and changes in precipitation during wet and dry seasons, to objective measurements using satellite imagery and climatic time-series data (CRU TS). I find that long-term temperature change is perceived more accurately than long-term changes in precipitation. Given that educational attainment and climate change literacy among the study population are low, this indicates that global temperature increases are felt even by remote populations who have never heard the term climate change. Erosion is strongly overestimated, especially by those respondents who had been personally affected by it. Since human behavior is guided by perceptions rather than objective data, this has important policy implications, underlining the importance of considering people’s perceptions if the goal is to assist them in adapting to environmental changes.

Interhemispheric Temperature Gradient and Equatorial Pacific SSTs Drive Sahel Monsoon Uncertainties under Global Warming

Abstract The Sahel is one of the most vulnerable regions to climate change. Robust estimation of future changes in the Sahel monsoon is therefore essential for effective climate change adaptation. Unfortunately, state-of-the-art climate models show large uncertainties in their projections of Sahel rainfall. In this study, we use 32 models from CMIP6 to identify the sources of this large intermodel spread of Sahel rainfall. By using maximum covariance analysis, we first highlight two new key drivers of this spread during boreal summer: the interhemispheric temperature gradient and equatorial Pacific sea surface temperature (SST) changes. This contrasts with previous studies, which have focused mainly on the Northern Hemisphere rather than the global scale, and in which the Pacific Ocean has been neglected in favor of the Atlantic. Next, we unravel the physical mechanisms behind these statistical relationships. First, the modulation of the interhemispheric temperature gradient across the models leads to varying latitudinal positions of the intertropical convergence zone and, consequently, varying Sahel rainfall intensity. Second, models that exhibit less warming than the multimodel mean in the equatorial Pacific, thereby projecting a less “El Niño–like” mean state, simulate enhanced precipitation over the central Sahel in the future through modulations of the Walker circulation, the tropical easterly jet, the meridional tropospheric temperature gradient, and hence regional zonal wind shear. Finally, we show that these two indices collectively explain 62% of Sahel rainfall change uncertainty: 40% due to the interhemispheric temperature gradient and 22% through equatorial Pacific SST.

Let’s Do It for Real: Making the Ecosystem Service Concept Operational in Regional Planning for Climate Change Adaptation

The application of ecosystem service (ES) knowledge to planning processes and decision-making can lead to more effective climate change adaptation. Despite the increased attention given to the ES concept, its degree of integration and use in spatial planning processes are still below the expectations of those who are promoting this concept. Barriers hindering its operationalisation cover a span of aspects ranging from theoretical to procedural and methodological issues. Overall, there is a general lack of guidance on how and at what point ES knowledge should be integrated into planning processes. This study aims to promote the inclusion of ES knowledge into spatial planning practices and decision-making processes to enhance climate change adaptation. A replicable GIS-based methodology is proposed. First, the potential supply of ESs that can support climate change adaptation (ESCCAs) is defined, mapped, and quantified. Then, a need for an ESCCA supply is identified, and territorial capacities to respond to the expected climate change impacts on natural and socio-economic sectors are assessed. The methodology is applied to the Friuli Venezia Giulia Autonomous Region (Italy) as an illustrative case study. The results reveal that areas with similar geomorphological characteristics tend to respond similarly. Forest ecosystems, inland wetlands and specifically salt marshes can potentially supply a greater variety of ESCCAs. In the case study area, about 62% of the supplied ESCCAs can contribute to reducing the impacts in more than 50% of the impacted sectors. The territory of the study site generally shows good preparedness for expected impacts in most of the analysed sectors; less prepared areas are characterised by agricultural ecosystems. This reading approach based on land cover analyses can thus assist in developing policies to enhance different territorial capacities, ultimately leading to better and more sustainable decision-making.

GIS-based revision of a WUDAPT Local Climate Zones map of Bern, Switzerland

Urban areas are particularly affected by heatwaves through the intensification of heat stress by the urban heat island effect. For effective climate change adaptation, information about microscale surface cover, structures, and human activity in cities is needed to depict the underlying causes of urban heat stress. The framework of “Local Climate Zones” (LCZs) classifies and standardizes urban areas based on such characteristics. To date, most LCZ mapping workflows use satellite imagery as input. The resulting maps may lack some important details, and thus benefit from the use of additional geodata. We introduce a novel approach that combines the geodata of urban canopy parameters with the remote sensing-based LCZ map of Bern, Switzerland. City-specific urban canopy parameters are calculated and used to adjust established value ranges, if necessary. The most common misclassification patterns are identified and misclassified pixels are corrected using a decision tree and k-nearest-neighbor algorithm. Results show that the conformity with the urban canopy parameter values markedly increased, especially in the distinction of water surfaces, non-built areas, and building height. However, for high-resolution LCZ maps, this also leads to unnecessary heterogeneity, which may require further postprocessing. Given sufficiently available urban canopy parameter data, the proposed workflow is simple and easily adaptable for other cities. It could prove useful in urban climate studies and city planning to enhance an existing LCZ map in a contextualized manner quickly.

Application of relative importance metrics for CMIP6 models selection in projecting basin-scale rainfall over Johor River basin, Malaysia

The most recent set of General Circulation Models (GCMs) derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) was used in this work to analyse the spatiotemporal patterns of future rainfall distribution across the Johor River Basin (JRB) in Malaysia. A group of 23 GCMs were chosen for comparative assessment in simulating basin-scale rainfall based on daily rainfall from the historical period of the Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS). The methodological novelty of this study lies in the application of relative importance metrics (RIM) to rank and select historical GCM simulations for reproducing rainfall at 109 CHIRPS grid points within the JRB. In order to choose the top GCMs, the rankings given by RIM were aggregated using the compromise programming index (CPI) and Jenks optimised classification (JOC). It was found that ACCESS-ESM1–5 and CMCC-ESM2 were ranked the highest in most of the grid. The final GCM was then bias-corrected using the linear scaling method before being ensemble based on the Bayesian model averaging (BMA) technique. The spatiotemporal assessment of the ensemble model for the different months over the near-future period 2021–2060 and far-future period 2061–2100 was compared with those under Shared Socioeconomic Pathways (SSPs), namely, SSP1-2.6, SSP2-4.5, SSP3–7.0, and SSP5-8.5. Heterogeneous changes in rainfall were projected across the JRB, with both increasing and decreasing trends. In the near-future and far-future scenarios, higher rainfall was projected for December, indicating an elevated risk of flooding during the end of the North East monsoon (NEM). Conversely, August showed a decreasing trend in rainfall, implying an increasing risk of severe drought. The findings of this study provide valuable insights for effective water resource management and climate change adaptation in the region.

Building Quality-Oriented Societies in Asia Through Effective Water-Related Disaster Risk Reduction and Climate Change Adaptation

Asia-Pacific countries are facing growing risks from water-related disasters that are being exacerbated by climate change, urbanization, population growth, and development activities. Effective disaster risk reduction (DRR) and climate change adaptation (CCA) are crucial for building quality-oriented societies. This study proposes approaches to DRR and CCA by examining cases and approaches from a special session at the 9th International Conference on Flood Management. This session was held to follow up on the Kumamoto Declaration adopted at the 4th Asia-Pacific Water Summit in Kumamoto in April 2022, and demonstrated the determination of heads of states and governments to resolve water issues in the region. The recent disaster cases in Pakistan, the Republic of Korea, the Philippines, and Indonesia highlight the unprecedented scale of water-related disasters. These countries have developed integrated structural and non-structural measures as fundamental solutions, including planning supported by scientific evidence, institutional reforms, and capacity building. However, there is a need to prioritize and strengthen urban planning and land use regulations for effective DRR and CCA. The Kumamoto Declaration emphasized three critical approaches: science and technology, finance, and governance, while the session clarified the effectiveness of these approaches. Leveraging science and technology can help societies develop and implement effective strategies to mitigate climate risks and safeguard vulnerable populations and ecosystems. However, there is a significant investment gap for flood protection, estimated at USD 64 billion per year. Thus, financial arrangements must be established. Meanwhile, good governance is essential for collaboration between local bodies, national governments, and international assistance. Such governance can leverage green infrastructure as a key solution and promote disaster resilience that is both locally driven and nationally relevant.

Carbon stock potential in rehabilitated mangrove in Lubuk Kertang Village, North Sumatra, Indonesia

Mangrove forests are one of the most productive and efficient long-term natural carbon sinks, tropical natural resources that have social, economic and ecological benefits. Even though the area of mangrove forests is relatively large in of the world, the consequences of various anthropogenic cause various damages. Given their global significance as a large carbon sink, preventing mangrove loss would be an effective climate change adaptation and mitigation strategy. This research was conducted to analyze carbon stocks in mangrove forests resulting from the rehabilitation of former pond land in Lubuk Kertang Village, West Brandan District, Langkat, North Sumatra, Indonesia. The census method was applied in this study, to calculate the value of carbon stocks using an allometric equation with DBH > 5cm. The aboveground biomass was 15.54 Mg/ha. Soil sampling for organic carbon content by collecting a sub-sample 5 cm from the center of the core. The total value of the total carbon stock above and below the soil surface is 9.42 Mg/ha1 and soil carbon is 685 MgC/ha1.

Sustainable livelihood capital and climate change adaptation in Pakistan's agriculture: Structural equation modeling analysis in the VIABLE framework

This study aims to assess the role of sustainable livelihood capital, the mediation of investments and farming purposes, and the moderation of climatic and non-climatic factors in the adaptation process, particularly in the aspects of Crop, Farm, Irrigation, and Economic Management. Moreover, guided by the VIABLE (Values and Investments for Agent-Based Interaction and Learning in Environmental Systems) framework, we analyze stakeholders' actions, priorities, and goals in the climate change adaptation process. A structured questionnaire was designed based on a five-point Likert scale covering the concepts of livelihood capital, climate change adaptation, investment priorities, farming constraints, and farmers' decision-making factors. Field data were collected from 800 farmers during December 2021 to February 2022 in the irrigated agricultural regions in the Indus Plain of the Punjab and Sindh provinces, Pakistan. We employed the Partial Least Square Structural Equation Modeling approach to the VIABLE framework (VIABLE-SEM) to analyze the collected data. The results confirm livelihood capital as the most significant determinant (beta = 0.57, effect size = 0.503) for farmers’ adaptation strategies in the Indus plain. Other variables, such as the principal purpose of farming, available investment options, natural and human constraints, appear less important. We identified 13 significant viability pathways that show investment priorities, farming purposes, and constraints faced by the farmers in climate change adaptation. The study also found that non-climatic factors negatively influence (beta = −0.156) the relationship between capital and adaptation, while climatic factors positively influence (beta = 0.050) this relationship. Interestingly, the presence of these influencing factors increases the adaptive capacity of farmers. These findings have important implications for policymakers and researchers in designing and implementing effective climate change adaptation strategies in the agricultural sector of Pakistan.

Cover crops, crop insurance losses, and resilience to extreme weather events

AbstractThis study investigates whether cover crop adoption reduces extreme‐weather‐related crop insurance losses. To achieve this objective, we utilize a county‐level panel data set with information on cover crop adoption acres, crop insurance losses (i.e., specifically due to drought, excess heat, or excess moisture), and a number of weather variables. The data cover the main row crop production region in the Midwestern United States (US) for the period 2005 to 2018. We utilize linear fixed effects econometric models and a number of robustness checks in the empirical analysis (i.e., a fractional regression approach, two “external‐instrument‐free” estimation procedures, and a variety of alternative empirical specifications). The estimation methods used take advantage of the panel nature of the data to address various specification and endogeneity issues. We find evidence that counties with higher cover crop adoption tend to have lower crop insurance losses due to drought, excess heat, or excess moisture. Our analysis also indicates that cover crops likely have stronger loss mitigation effects against excess moisture events (like floods) and somewhat weaker loss mitigation impacts against droughts and excess heat. Nonetheless, our results overall suggest that cover crops can enhance resilience to extreme weather events and have the potential to be an effective climate change adaptation strategy in US agriculture.

Machine Learning for Weather Forecasting: XGBoost vs SVM vs Random Forest in Predicting Temperature for Visakhapatnam

Climate change, a significant and lasting alteration in global weather patterns, is profoundly impacting the stability and predictability of global temperature regimes. As the world continues to grapple with the far-reaching effects of climate change, accurate and timely temperature predictions have become pivotal to various sectors, including agriculture, energy, public health and many more. Crucially, precise temperature forecasting assists in developing effective climate change mitigation and adaptation strategies. With the advent of machine learning techniques, we now have powerful tools that can learn from vast climatic datasets and provide improved predictive performance. This study delves into the comparison of three such advanced machine learning models—XGBoost, Support Vector Machine (SVM), and Random Forest—in predicting daily maximum and minimum temperatures using a 45-year dataset of Visakhapatnam airport. Each model was rigorously trained and evaluated based on key performance metrics including training loss, Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R2 score, Mean Absolute Percentage Error (MAPE), and Explained Variance Score. Although there was no clear dominance of a single model across all metrics, SVM and Random Forest showed slightly superior performance on several measures. These findings not only highlight the potential of machine learning techniques in enhancing the accuracy of temperature forecasting but also stress the importance of selecting an appropriate model and performance metrics aligned with the requirements of the task at hand. This research accomplishes a thorough comparative analysis, conducts a rigorous evaluation of the models, highlights the significance of model selection.