Influence Of Climate Change Research Articles

Russia’s Arctic icebreaking capability: an assessment

ABSTRACT This paper explores the Russian icebreaker fleet capabilities, problems, and perspectives. It also analyses Russian civilian and military icebreakers, and their dual-use capabilities. The paper responds to a question grounded in Russia's Arctic policy: How critical is icebreaking capability to Russia’s Arctic strategy? Based on the findings, the paper concludes that the influences of climate change and Russia’s foreign and external security policy affect the development of its icebreaker fleet in the Arctic. In this respect, temperatures of the ice melting in the Arctic and delays in building new icebreakers, because of access to the technology caused by the sanctions, are stressed. On the other hand, Russia’s confrontation with the West following the invasion of Ukraine and the sanctions over it, increases the allocation of government finance to support local shipbuilding companies. Moreover, the paper argues that Russia’s economic difficulties and shipbuilding delays should enhance the dual-use capabilities of the Russian icebreakers in the coming years.

The influence of climate change and environmental concerns on participation in new voluntourism by younger generations

AbstractThis study investigates a new type of voluntourism that focuses on younger generations' environmental contribution in the context of a global climate crisis. The study describes the adoption process of new voluntourism by applying the Diffusion of Innovation (DOI) framework and examining the influence of its three innovation factors (compatibility, simplicity, and relative advantage along with the perceived risk of climate change on new voluntourism adoption attitude and behavioral intention). An online survey of 432 members of two demographic cohorts, Millennials and Generation Z, was conducted in South Korea. The results of structural equation modeling indicate that the perceived risk of climate change and DOI's innovation factors significantly affected new voluntourism adoption attitude and behavioral intention. The results confirmed that the perceived risk of climate change led to the diffusion and adoption of new voluntourism by younger generations. The theoretical and practical implications of the study's results are discussed.

137Cs radiotracer in investigating influence of hillslope positions and land use on soil erosion and soil organic carbon stock—A case study in the Himalayan region

AbstractThe topography and land use/land cover (LULC) of the hillslope play a significant influence on soil erosion because of water, which is considered as a principal factor for the reduction of soil organic carbon content. Reliable information on the impact of erosion mechanism on soil organic carbon stock (SOCS) is essential for effectively accounting for the carbon flux that influences climate change. The main objectives of this study were to determine soil erosion based on the variation of 137Cs (Radiocesium) radionuclide activity at various hillslope positions and LULC in a hilly and mountainous region of the north‐western Himalayas. Additionally, the relationship between 137Cs concentration, soil erosion rate and SOCS were examined. Fallout radionuclide‐137Cs have emerged as a suitable method for assessing soil erosion in hilly and mountainous regions where rugged topography and extreme weather events restrain the conventional soil erosion assessment. The study revealed very high soil erosion rates of 32.89 and 30.70 t ha−1 year−1 in the lower hillslope positions with cultivated fields. The lowest soil erosion was obtained with a mean of 0.47 t ha−1 year−1 from the ridge with grassland, followed by the upper hillslope (5.50 t ha−1 year−1 under deodar forest and 14.07 t ha−1 year−1 under pine forest), and the middle hillslope (1.58 t ha−1 year−1 for deodar and 7.77 t ha−1 year−1 for pine forest). The soil erosion rates differ significantly between cultivated and forested regions, and there is also a significant difference between deodar and pine forests. Moreover, a significant difference was found between topographic positions concerning 137Cs, SOCS and soil redistribution rate. This difference was more pronounced at hillslope positions with different LULC. In both disturbed (cultivated) (r2 = .111) and undisturbed (forested and grassland) (r2 = .356) soils, positive and statistically significant (p < .005) poor relationships were found between SOCS and 137Cs inventory. This indicates the presence of various factors influencing the soil organic carbon stock (SOCS) mechanism or the indirect contribution of soil erosion‐induced carbon loss. This suggests that forest cover can enhance SOCS in the soil, mitigating the adverse effects of soil erosion and climate change. Consequently, 137Cs could be effectively used to quantify the SOC stock in soil redistribution over the hillslope affected by soil erosion. Statistical analyses indicated that the 137Cs inventory, SOCS and erosion were significantly affected by various hillslope positions and LULC types.

Probing the Interplay Between Climate Change and Global Energy Security: Insights From Nonlinear Analysis

Motivated by the adverse effects of climate change on human well-being, this study investigated the influence of climate change on energy security. Employing a nonlinear autoregressive distributed lag (NARDL) approach, the study encompasses the timeframe from 1970 to 2022. The empirical findings unveil a harmful effect of climate change on energy security in the long term and present a varied impact in the short term.

Environmental and Climate Impacts on Physiological Aging: Comprehensive Pathways to Enhanced Age-related Resilience.

This perspective article highlights the pressing necessity to focus on heterogeneity in organ-specific aging, emphasizing the influence of environmental and climate changes, as well as social interactions, on the aging process of individual organs. It explores innovative and integrative strategies tailored to the unique aging mechanisms of different organs, aiming to preserve their biological clocks. By emphasizing the importance of addressing organ-specific vulnerabilities, the article calls for transcending traditional pharmacological treatments and organ transplants. It proposes a comprehensive approach that combines environmental management, social support systems, and advanced technological interventions to improve the well-being and longevity of aging populations.

Predicting Climate Change Impact on the Habitat Suitability of the Schistosoma Intermediate Host Oncomelania hupensis in the Yangtze River Economic Belt of China.

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

Can the best management practices resist the combined effects of climate and land-use changes on non-point source pollution control?

Climate and land-use changes have an overlying impact on non-point source (NPS) pollution in river basins. However, the control effect of Best Management Practices (BMPs) for NPS pollution is not yet clear under future scenarios. The Soil and Water Assessment Tool (SWAT) model was coupled with the entropy-weighted method, global climate patterns and land-use data to explore the dynamic variations in total nitrogen (TN) and total phosphorus (TP) loads in the Jing River Basin during the baseline (2000−2020) and future periods (2021–2065), evaluate the pollution reduction effectiveness of individual and combined BMPs, and propose practical BMP configurations. Results indicate that a future trend of urban land expansion, particularly in the economic scenario (LU_SSP585), leads to weakened environmental ecosystems, while the sustainable scenario (LU_SSP126) exhibits more balanced land development. The MIROC-ES2L model demonstrates higher Taylor skill scores, forecasted significant increases in precipitation, maximum, and minimum temperatures under the SSP585 scenario. Spatial heterogeneity in TN and TP loads is notable, showing an upward trajectory in the future. The interaction between land-use and climate change has complex effects on TN and TP loads, with land-use-induced TN changes being relatively small (4.6 %) and TP changes substantial (24.3 %). The spatial distribution, under overlying effects, leans towards the influence of climate change, emphasizing its dominant role in TN and TP load variations. Distinct differences exist in the reduction of NPS pollution loads among different BMPs, with combined BMPs demonstrating superior effectiveness. The environmental-cost effectiveness trends of BMPs remain consistent across various future scenarios. RG (Return agricultural land to grass), RG + TT (Terracing), and RG + FR10 (Fertilizer reduction: 10 %) + GW (Grassed waterway) + FS (Filter strip) + TT emerge as the most effective single, double, and multiple BMP combinations, respectively. The results offer valuable insights for preventing and mitigating future NPS pollution risks, optimizing land-use layouts, and enhancing watershed management decisions.

Exploring the influence of weather variability and climate change on health outcomes in people living with dementia: A scoping review protocol.

Environmental factors resulting from climate change and air pollution are risk factors for many chronic conditions including dementia. Although research has shown the impacts of air pollution in terms of cognitive status, less is known about the association between climate change and specific health-related outcomes of older people living with dementia. In response, we outline a scoping review protocol to systematically review the published literature regarding the evidence of climate change, including temperature and weather variability, on health-related quality of life, morbidity, mobility, falls, the utilization of health resources, and mortality among older adults living with dementia. This scoping review will be guided by the framework proposed by Arksey and O'Malley. Electronic search (Medline, Embase, PsycINFO, CINAHL, Scopus, Web of Science) using relevant subject headings and synonyms for two concepts (older people with dementia, weather/ climate change). No publication date or other restrictions will be applied to the search strategy. No language restriction will be applied in order to understand the impact of non-English studies in the literature. Eligible studies must include older adults (65+years) with dementia living in the community and investigate the impacts of climate change and/or weather on their health-related quality of life, morbidity, mobility, falls, use of health resources and mortality. Two independent reviewers will screen abstracts and select those for a full-text review, perform these reviews, select articles for retention, and extract data from them in a standardized manner. This data will then be synthesized and interpreted. OSF registration: DOI: 10.17605/OSF.IO/YRFM8.

Worldwide Research Trends and Networks on Flood Early Warning Systems

This review paper examined the global landscape of research on continental flood early warning systems (EWS), shedding light on key trends, geographic disparities, and research priorities. Continental floods stand as one of the most pervasive and devastating disasters worldwide, necessitating proactive measures to mitigate their impact. Drawing upon a comprehensive analysis of the scholarly literature indexed in the Web of Science repository, this study unveiled significant patterns in EWS research. While the emphasis on flooding is evident, a considerable portion of research focuses on precipitation as a variable and modeling approaches. Furthermore, the influence of climate change emerges as a prominent theme, though distinguishing between climate change and variability remains a crucial area for exploration. Geographically, Europe, particularly England and Italy, dominates research efforts in flood related EWS. Conversely, the limited representation of Central America and other regions such as Asia and Oceania, underscores the need for greater attention to regions facing significant flood risks. Importantly, the concept of total link strength emerges as a valuable metric, highlighting collaborative networks established by European countries and the United States. Based on these findings, recommendations are proposed to enhance the inclusivity and effectiveness of flood related EWS research, including a broader consideration of socio-economic factors, fostering collaboration among researchers from diverse regions, and prioritizing initiatives to strengthen research capacities in vulnerable areas. Ultimately, this study provides valuable insights for policymakers, researchers, and practitioners seeking to advance flood risk management strategies on a global scale.

Comprehensive Ecological Risk Changes and Their Relationship with Ecosystem Services of Alpine Grassland in Gannan Prefecture from 2000–2020

Alpine grassland is one of the most fragile and sensitive ecosystems, and it serves as a crucial ecological security barrier on the Tibetan Plateau. Due to the combined influence of climate change and human activities, the degradation of the alpine grassland in Gannan Prefecture has been increasing recent years, causing increases in ecological risk (ER) and leading to the grassland ecosystem facing unprecedented challenges. In this context, it is particularly crucial to construct a potential grassland damage index (PGDI) and assessment framework that can be used to effectively characterize the damage and risk to the alpine grassland ecosystem. This study comprehensively uses multi-source data to construct a PGDI based on the grassland resilience index, landscape ER index, and grass–livestock balance index. Thereafter, we proposed a feasible framework for assessing the comprehensive ER of alpine grassland and analyzed the responsive relationship between the comprehensive ER and comprehensive ecosystem services (ESs) of the grassland. There are four findings. The first is that the comprehensive ER of the alpine grassland in Gannan Prefecture from 2000–2020 had a low distribution in the southeast and a high distribution trend in the northwest, with medium risk (29.27%) and lower risk (27.62%) dominating. The high-risk area accounted for 4.58% and was mainly in Lintan County, the border between Diebu and Zhuoni Counties, the eastern part of Xiahe County, and the southwest part of Hezuo. Second, the comprehensive ESs showed a pattern of low distribution in the northwest and high distribution in the southeast. The low and lower services accounted for only 9.30% of the studied area and were mainly distributed in the west of Maqu County and central Lintan County. Third, the Moran’s index values for comprehensive ESs and ER for 2000, 2005, 2010, 2015, and 2020 were −0.246, −0.429, −0.348, −0.320, and −0.285, respectively, thereby indicating significant negative spatial autocorrelation for all aspects. Fourth, ER was caused by the combined action of multiple factors. There are significant differences in the driving factors that affect ER. Landscape index is the first dominant factor affecting ER, with q values greater than 0.25, followed by DEM and NDVI. In addition, the interaction between diversity index and NDVI had the greatest impact on ER. Overall, this study offers a new methodological framework for the quantification of comprehensive ER in alpine grasslands.

A study on the monitoring of heatwaves and bivariate frequency analysis based on mortality risk assessment in Wuhan, China.

The increasingly frequent occurrence of urban heatwaves has become a significant threat to human health. To quantitatively analyze changes in heatwave characteristics and to investigate the return periods of future heatwaves in Wuhan City, China, this study extracted 9 heatwave definitions and divided them into 3 mortality risk levels to identify and analyze historical observations and future projections of heatwaves. The copula functions were employed to derive the joint distribution of heatwave severity and duration and to analyze the co-occurrence return periods. The results demonstrate the following. (1) As the concentration of greenhouse gas emissions increases, the severity of heatwaves intensifies, and the occurrence of heatwaves increases significantly; moreover, a longer duration of heatwaves correlated with higher risk levels in each emission scenario. (2) Increasing concentrations of greenhouse gas emissions result in significantly shorter heatwave co-occurrence return periods at each level of risk. (3) In the 3 risk levels under each emission scenario, the co-occurrence return periods for heatwaves become longer as heatwave severity intensifies and duration increases. Under the influence of climate change, regional-specific early warning systems for heatwaves are necessary and crucial for policymakers to reduce heat-related mortality risks in the population, especially among vulnerable groups.

Spatio-temporal variations and influencing factors analysis of coastal wetlands in Tianjin, China

Coastal wetlands are one of the world’s most threatened ecosystems and the influences of climate change and anthropogenic factors on changes in coastal wetland distribution have been the subject of active research in recent years. However, the multi-year intergenerational trends in coastal wetland areas, and the factors influencing them in China have not been fully studied using geographic spatial data during the past 30 years. The relative contributions of climate change and anthropogenic factors to spatio-temporal variations in coastal wetland area also remain to be described. In this study, we discuss changes in the coastal wetland areas and the reasons behind them in Tianjin, which is one of China’s fastest developing regions, using remote sensing and geographic information system technology, Pearson correlation analysis, the relative weight method and the land expansion extraction module, the land expansion analysis strategy (LEAS) model of the Patch-generated Land Use Simulation (PLUS) model. The results showed that the total wetland area declined from 2233.59 km2 in 1984 to 1742.73 km2 in 2020. We conclude that the expansion of non-wetland land types is the main driver of natural wetland area decrease, with 830.86 km2 of natural wetlands having been converted to non-wetlands during this period. The large areas of shallow water and tidal marsh that have been converted to construction and bare land particularly emphasize the primary importance of urbanization and coastal reclamation in driving wetland reduction. The results showed that the temporal variation of natural wetlands area was correlated with population density (PD), total gross domestic product (GDP), annual mean temperature (AMT), average relative humidity (ARH), and primary (PI), secondary (SI), and tertiary industrial development (TI). PD, PI, TI, GDP, SI, AMT, and ARH contributed to 24.47 %, 18.21 %, 15.74 %, 14.16 %, 12.77 %, 7.09 %, and 4.86 % of the wetland area decrease, respectively. PD, distance to settlements (DS), distance to railways (DRW), AMT, distance to rivers (DR), and GDP contributed 17.61 %, 14.27 %, 13.83 %, 13.62 %, 12.07 %, and 10.92 % of the spatial variations of natural wetland area, respectively, which showing the predominant effects of anthropogenic variables on the spatio-temporal natural wetland areas. Our results are valuable in identifying the dynamic spatio-temporal variations in coastal wetlands to improve our understanding of the impacts of climate change and human activities on coastal wetlands to help in the future protection and restoration of wetlands.

A Bayesian hierarchical spatio-temporal model for summer extreme temperatures in Spain

A statistical study was made of the summer extreme temperatures over peninsular Spain in the last forty years. Records from 158 observatories regularly distributed over Iberia with no missing data were available for the common period from 1981 to 2020. For this purpose, a hierarchical spatio-temporal model with a Gaussian copula and a generalized extreme value parametrization of the extreme events was used. The temporal trend in maximum extreme temperatures was studied making use of both a stationary model and a nonstationary one that takes into account the influence of anthropogenic climate change on extreme temperatures using the global mean temperature as a function of time for the study period. The results led to a better fit of the nonstationary model, with there being a 3.5-fold greater increase in the 20-year return level of the extreme temperatures than in that corresponding to the global mean temperature.

Evaluation of best management practices for mitigating harmful algal blooms risk in an agricultural lake basin using a watershed model integrated with Bayesian Network approach

Lake eutrophication caused by nitrogen and phosphorus has led to frequent harmful algal blooms (HABs), especially under the unknown challenges of climate change, which have seriously damaged human life and property. In this study, a coupled SWAT-Bayesian Network (SWAT-BN) model framework was constructed to elucidate the mechanisms between non-point source nitrogen pollution in agricultural lake watersheds and algal activities. A typical agricultural shallow lake basin, the Taihu Basin (TB), China, was chosen in this study, aiming to investigate the effectiveness of best management practices (BMPs) in controlling HABs risks in TB. By modeling total nitrogen concentration of Taihu Lake from 2007 to 2022 with four BMPs (filter strips, grassed waterway, fertilizer application reduction and no-till agriculture), the results indicated that fertilizer application reduction proved to be the most effective BMP with 0.130 of Harmful Algal Blooms Probability Reduction (HABs-PR) when reducing 40% of fertilizer, followed by filter strips with 0.01 of HABs-PR when 4815ha of filter strips were conducted, while grassed waterway and no-till agriculture showed no significant effect on preventing HABs. Furthermore, the combined practice between 40% fertilizer application reduction and 4815ha filter strips construction showed synergistic effects with HABs-PR increasing to 0.171. Precipitation and temperature data were distorted to model scenarios of extreme events. As a result, the combined approach outperformed any single BMP in terms of robustness under extreme climates. This research provides a watershed-level perspective on HABs risks mitigation and highlights the strategies to address HABs under the influence of climate change.

Impact of climate change on the Vrana Lake surface water temperature in Croatia using support vector regression

Study regionThe case study presents Vrana Lake on Cres island in Croatia, which is the largest freshwater resource on the Mediterranean islands. It is used for the public water supply of the residents and tourists who inhabit this island during the summer months. Study focusUsing the Support Vector Regression (SVR), the influence of future climate changes on the lake surface water temperature (LSWT) was analysed. Input data were monthly air temperatures (AT). Model training and validation were based on measured LSWT and AT in the period 1981–2022. Expected LSWTs for the period 2023–2070 under the RCP8.5 emission scenario were forecast based on climate modelling data of monthly AT for the period 1971–2070. New hydrological insights for the regionThe results showed that the applied SVR model can effectively forecast monthly LSWTs, which was confirmed by a correlation coefficient of approximately 0.99 between the measured and simulated LSWTs. Root mean square errors were lower than 1 °C. The LSWT warming trend in 2023–2070 is expected to be lower than that observed in 1981–2022, and will vary from 0.2 °C dec-1–0.3 °C dec-1. The largest increase in LSWT can be expected in spring while the increase in LSWT will be the smallest in the summer months. In approximately 50 years, the expected LSWT could be higher by an average of 1.2 °C.

The overlooked carbon cache: Unveiling organic carbon storage in small floodplain lake sediments under humid continental climate changes

Small floodplain lakes (SFLs), characterized by their diminutive size (<10,000 m2), have been consistently overlooked in assessments of organic carbon (C) stock and the impacts of climate changes. This study aims to ascertain both the quantity and quality of carbon stored within the sediments of SFLs. Furthermore, we endeavour to evaluate the influence of ongoing climate change on the floodplain lake structure within the continental humid climate zone on the example of the Vistula River valley and estimate the CO2 emission from dry floodplain lakes. The sedimentary organic carbon content (TOC) ranged from 2.3–64 g kg−1, averaging at 3.4 g kg−1. Consequently, the sediments in the analysed SFLs of the river section potentially harbour up to 5,785 Mg TOC. Notably, the abundance of easily soluble carbon fractions (humic and fulvic acids) was relatively low (∼15 %), implying a stable immobilization of organic C compounds and minimal impact of sediments on water quality. Satellite imagery analysis for the years 2017–2020 revealed a 31 % reduction in the total lake surface area within a 100-km-long segment of the Vistula River. The decreasing surface area of floodplain lakes and rising mean air temperatures increase CO2 emissions and have an additional impact on climate warming. Our research underscores that investigating lake C storage concerning climate change demands a nuanced approach. While higher temperatures stimulate primary production, the reduction in lake surface area and the escalating role of small lakes, such as SFLs, in C storage should be paramount considerations.

The influence of climate change on the sesame yield in North Gondar, North Ethiopia: Application Autoregressive Distributed Lag (ARDL) time series model

Sesame is a major annual oil crop that is grown practically everywhere in tropical and subtropical Asia, as well as Africa, for its very nutritious and tasty seeds. Rising temperatures, droughts, floods, desertification, and weather all have a significant impact on agricultural production, particularly in developing countries like Ethiopia. Therefore, the main objective of this study is to examine the influence of climate change on the sesame yield in North Gondar, North Ethiopia, by using the autoregressive distributed Lag (ARDL) time series model. This study employed climate data from the Bahirdar Agrometeorological Center and secondary data on sesame production from the Ethiopian Statistical Service, spanning 36 years, from 1987 to 2023. Autoregressive Distributed LAG (ARDL) includes diagnostic tests for both short- and long-term autoregressive models. The results for the long-run and short-run elastic coefficients show a significant positive association between temperatures and sesame yield. Sesame yield and rainfall have a significant negative long-run and short-run relationship in North Gondar, North Ethiopia. ARDL results confirm that temperature and rainfall have significant effects on sesame productivity. Temperature had a considerable favorable effect on sesamen production, but rainfall had a negative effect in North Gondar, Ethiopia. Based on the evidence acquired from our study, we made several policy recommendations and suggestions to government officials, policymakers, new technologies, researchers, policy development planners, and other stakeholders in order to develop or implement new technology to halt its production and direct adaptation measures in light of the certainty of global warming and the characteristics of climate-dependent agricultural production.

Is human activity driving climate change? Perspectives from Australian landholders

Australian agriculture and the rural communities that depend upon it are expected to experience significant impacts from climate change. The recognition of the human role in climate change is central in the design and implementation of effective strategies to mitigate and adapt to its impacts. Understanding the extent to which members of the public, such as private landholders, acknowledge human-caused climate change is critical, given their role as custodians of large tracts of natural resources. Rural social benchmarking studies are a useful tool for understanding landholder values and beliefs. Here, we use a rural social benchmarking survey to examine landholder agreement regarding the extent to which humans contribute to climate change across four Australian agricultural regions. We perform hierarchical clustering analysis to determine subgroups of landholders with similar patterns of survey responses. We then evaluate this effect of cluster membership and demographic characteristics using Bayesian ordinal regression on levels of agreement with the statement “that human activities are influencing climate change.” Our findings reveal three distinct clusters based on patterns of responses to survey questions eliciting participants values beliefs and norms. Cluster membership exhibits the strongest positive influence on agreement (0.52, 95% CI: 0.37 to 0.67). This was followed by higher education levels (0.32, 95% CI: 0.22 to 0.41). Gender showed a moderately uncertain but positive influence. Years residing on the property, participant age, and property size showed very little influence, while rainfall zones showed a negative influence of-0.29 (95% CI: −0.47 to-0.12). Our results underscore the need for extension programs to consider landholder typologies based on a combination of lived experience and demographics.

Distribution patterns of Phytoseiulus persimilis in response to climate change.

The biological control agent Phytoseiulus persimilis is a commercialized specialist predator of two agricultural pest mite species Tetranychus urticae and Tetranychus evansi. Biocontrol of these pest species by P. persimilis has achieved success in biological control in some areas. However, the lack of precise information about the influence of global climate change on the worldwide distribution of this biocontrol agent hampers international efforts to manage pest mites with P. persimilis. With 276 occurrence records and 19 bioclimatic variables, this study investigated the potential global distribution of P. persimilis. The results demonstrated that the Maximum Entropy (MaxEnt) model performed well, with the area under the curve being 0.956, indicating the high accuracy of this model. Two variables, the minimum temperature of the coldest month (Bio_6) and precipitation of the coldest quarter (Bio_19) were the most important environmental variables that influenced the distribution of P. persimilis, contributing more than 30% to the model, respectively. The suitable area currently occupies 21.67% of the world's land area, spanning latitudes between 60°S and 60°N. Under shared socio-economic pathway (SSP) 5-8.5 (high-carbon emissions), the low suitable area would increase by 1.31% until the 2050s. This study successfully identified that south-eastern China, parts of countries in the Mediterranean coastal regions, including Libya, Algeria, Portugal, Spain, and France, are climatically favorable regions for P. persimilis, providing valuable information about the potential areas where it can be effectively exploited as biocontrol agents in classical biological control programs to manage pest spider mites environmentally friendly. © 2024 Society of Chemical Industry.

Global warming&amp;rsquo;s grip on agriculture: Strategies for sustainable production amidst climate change using regression based prediction

The intersection of climate change and food production is emerging as a critical area of research, focusing on both the potential benefits and the significant challenges posed by changing climate conditions. Elevated levels of carbon dioxide alongside rising global temperatures could theoretically boost crop yields, benefiting both human and animal consumption. This study examines the impact of various climate variables&amp;mdash;temperature, humidity, precipitation, and soil moisture&amp;mdash;on the primary production of essential foods such as rice, wheat, livestock, milk, eggs, vegetables, and fruits. Utilizing data from different countries spanning from 2000 to 2020, drawn from world development indicators, this research employs econometric analysis coupled with deep learning-based cluster analysis. Additionally, it projects future production trends up to 2100 using the moving average time series forecasting method. The findings reveal a direct correlation between climate variables and the production levels of vegetables and other food items, highlighting the immediate effects of climatic changes on agriculture. The study also points out the uneven distribution of these climate impacts, with developing countries facing more severe challenges due to their limited resources and adaptive capacities. This uneven impact contributes to increased uncertainty in food supply and affects market stability. Furthermore, concerns about food safety are intensifying under the influence of climate change, although some regions have implemented effective food conservation and control measures to mitigate these risks. This research underscores a complex landscape where the risks and benefits of climate change on food production are not uniformly distributed, but rather are influenced by a myriad of factors including geographic location, economic conditions, and the level of technological advancement in food safety practices. The nuanced understanding of these dynamics is crucial for developing targeted strategies to enhance food security in the face of a changing climate.