Abnormal Climate Research Articles

Multi-factor coupled forest fire model based on cellular automata

The risk of forest fires is substantial due to uneven precipitation distributions and abnormal climate change. This study employs cellular automata principles to analyze forest fire behavior, taking into account meteorological elements, combustible material types, and terrain slopes. The Wang Zhengfei model is utilized to compute fire spread speed, and a multifactor coupled forest fire model is developed. Comparisons with experimental data show a mean calculated fire spread speed of 0.69 m/min, which is consistent with the experimental results. Using the forest fire in Anning city, Yunnan Province, as a case study with a mean burned area of 2281 ha, the burned area, rate of change in burned area, and burning area demonstrated an increasing trend, with fluctuating states in the rate of change of the burning area. Employing the controlled variable method to examine forest fire spreading patterns under varying factors such as wind speed, vegetation type, and maximum slope reveals that under wind influence, the fire site adopts an elliptical shape with the downwind direction as the major axis. Quantitatively, when the wind speed increases from 2 m/s to 10 m/s, the burned area expands by a factor of 1.37. The ratio of the combustible material configuration coefficient to the burned area remains consistent across the different vegetation types, and the burned area increases by a factor of 1.92 when the maximum slope increases from 5° to 25°.

Abnormal Climate in 2022 Summer in Korea and Asia

Abnormal Climate in 2022 Summer in Korea and Asia

Development of an evaluation indicator for highway climate change adaptation projects based on analytical hierarchy process in South Korea

AbstractOwing to climate change, abnormal climate phenomena occur frequently, damaging aging highway facilities. Therefore, it is necessary to develop adaptation projects for highway systems. Adaptation projects focus on mitigating the impact of climate change on highway facilities and enabling safe use. The present study aims to develop quantified evaluation indicators that are necessary for planning and selecting appropriate climate change adaptation projects. By analysing previous studies, the most important factors to be considered in the evaluation process of climate change adaptation projects were defined, and 20 preliminary evaluation indicators were divided into three layers. A feasibility study for each class was performed on the preliminary evaluation indicators by an expert panel; 16 evaluation indicators were selected through a feasibility study, and the weight of each indicator was calculated using the analytical hierarchy process (AHP) method. The priority of the evaluation indicators was service improvement through adaptation projects (weight 8.76), cost reduction and job creation (weight 8.46), and climate change impact reduction and vulnerability reduction (weight 8.31). These quantified evaluation indicators can help organizations that manage highways respond to climate change and establish adaptation projects.

Actions after Experiences: CEO Abnormal Climate Exposure and Corporate Carbon Reduction

Actions after Experiences: CEO Abnormal Climate Exposure and Corporate Carbon Reduction

ОРГАНІЗАЦІЯ ЗАПИЛЕННЯ ПЛОДОВО-ЯГІДНИХ КУЛЬТУР САДУ ІЗ ЗАЛУЧЕННЯМ РІЗНИХ ВИДІВ КОМАХ

The purpose of the research – to study the mutual existence of different types of insects competing for food sources and involved in garden crops. Methods and materials. The research was carried out on garden crops (cherry plum, apricot, cherry, pear, domestic apple) using honey bees, bumblebees, and osmium under the influence of temperature on the efficiency of pollination in the forest-steppe of the Kyiv region. Results. The organization of pollination of the main garden crops by insects begins with cherry plum, apricot, and cherry, the honey productivity of which is 40 kg/ha. Temperature fluctuations and unfavorable weather conditions do not allow honey bees to pollinate effectively, so other insect species more adapted to abnormal climate changes were used. The use of osmia, bumblebees, and honey bees for pollination was more effective since they complement each other due to different flight modes and different food requirements. Bumblebees and osmia fly out to collect food before honeybees, and they can also collect nectar with a sugar content within 4%, and bees prefer nectar with a content of 50-60%, but not less than 16-18%. When weather conditions improve, more flowers bloom, the nectar thickens, and as a result, the mobilization of the honey bee increases, which with its numbers covers the plant’s need for pollination. Conclusions. The simultaneous use of honey bees, osmia and bumblebees for the pollination of fruit and berry crops makes it possible to obtain an ovary on cherry plum of 86.8%, which is 11.2% more than when using only bees as pollinators; on apricot - 39.2% ( + 10.8%), cherries – 35.7% (+23.3%), pears – 64.8% (+29.2%), plums – 78.4% (+36.1%), domestic apple trees – 78.9 (+16.7%). Keywords. Honey bee, bumblebee, osmia, pollination, cherry plum, apricot, cherry, pear, plum, domestic apple tree.

IT 플랫폼 기업의 ESG 경영이 소비자의 구매의도에 미치는 영향

As the global pandemic situation continues and abnormal climate conditions continue, ESG has received more attention, and the introduction of ESG management by IT platform companies has become active. Therefore, the ESG characteristics of IT platform companies will affect corporate image and consumer trust, and influence consumers' purchase intentions. This paper focused on the impact of ESG management of IT platform companies on consumer purchase intention. Therefore, based on previous research, this study classifies the characteristics of each ESG area into environmental characteristics, social characteristics, and governance characteristics, and presents two parameters, corporate image and consumer trust, to determine their impact on purchase intention. A corresponding research model was constructed. Samples were collected by means of questionnaires and empirical analysis was conducted using SmartPLS 4.0 and IBM SPSS 23.0 to verify the hypotheses. The following conclusion are drawn. First, ESG management activities, environmental, social, and dominant structural characteristics all have a positive impact on corporate image. H1 was adopted. Second, environmental characteristics do not affect consumer trust. thus, H2-1 was rejected. H2 was partially adopted. Third, corporate image has a positive impact on consumer trust. H4 was accepted.. Corporate image has a positive impact on purchase intention. H4 was accepted. Consumer trust has a positive impact on purchase intention, so H5 was adopted.

Sustainability Research of Building Systems Based on Neural Network Predictive Models and Life Cycle Assessment (LCA)–Emergy–Carbon Footprint Method

Facing the abnormal climate changes and the goal of carbon neutrality, the ecological sustainability research of building systems has become a focus of attention for experts in this field. However, the definition of sustainable buildings is broad. This article discusses the quantitative analysis of sustainable buildings from the perspectives of an ecological emergy and carbon footprint. It also establishes the long-term sustainability of buildings through predictive neural networks. The research findings indicate that the emergy and carbon emissions during the operational and materials phases dominate the entire system. The calculation and analysis of the emergy sustainability indicator (ESI) demonstrate a decreasing trend in the sustainability of the building system over three time periods (10 years, 20 years, and 30 years), with results of 0.58, 0.238, and 0.089, respectively. As the operational time increases, carbon emissions from the building system also increase, further exacerbating the pressure on the building and reducing its overall sustainability. To address this dilemma, sustainable retrofit measures have been proposed, such as rainwater harvesting and embedded applications of distributed energy sources, which reduce the burden of emergy and carbon emissions. The effectiveness of these measures has been validated in this article, demonstrating their potential to enhance building sustainability and providing references for architects and building managers.

기후변화 관련 어휘 사용 및 기후위기 담론의 전개 양상

If there is a problem that has emerged as a global disaster along with the COVID-19 pandemic, it is climate change. This is why the discourse of “climate crisis” has been actively conducted from various angles in recent years. Based on these facts, this study focused on analyzing the use of words related to climate change and how the ideological tendencies pursued by media companies correlate with climate issues, especially the “climate crisis” reports that have been increasingly used recently. The results of the study are summarized as follows.
 First, based on data from BIGKinds, a news big data analysis service provided by the Korea Press Promotion Foundation, it was confirmed that relatively various language variations have been appearing in climate change-related terms over the past 30 years, such as “climate change,” “weather variation,” “abnormal climate,” “climate crisis,” “climate disaster,” and “extreme climate.” Second, in our society, the term “climate change” has been used more frequently since the 2000s instead of “weather change” over the climate issue, which is markedly different in nature from natural fluctuations in the past, and “climate change” has been the most frequently used to date.
 Third, “climate change” showed a sudden upward curve in contrast to other years by showing 15,455 times in 2009 and 26,945 times in 2021. It was confirmed that there is a close correlation between the climate change cause discourse of “global warming” in 2009 and the climate change response discourse of “greenhouse gas reduction” and “carbon neutrality” in 2021.
 Fourth, the second most frequent vocabulary after “climate change” is “climate crisis,” which has shown a total frequency of 33,827 over the past 30 years, and this phenomenon has appeared as a distinct trend, especially during the COVID-19 pandemic.
 Fifth, media reports that focus on highlighting the “climate crisis” issue have been confirmed to have a clear correlation with the ideological tendencies of media companies, and in newspaper editorials as well as general articles, liberal media companies are more actively approaching the “climate crisis” issue than conservative media companies.

Pathway to sustainable economic growth: Linkage among energy consumption, carbon emissions, climate change and technological innovation

This paper investigates the impact of climate change and technological innovation on economic growth, energy consumption, and carbon emissions by using annual observations of a wide range of variables of Asian and European countries from 1996 to 2021. The study uses a dynamic GMM model and reveals significant regional heterogeneity and time-varying effects of technological innovation on economic growth. Technological innovation plays a more substantial role in promoting sustainable economic growth in European countries compared to Asian countries. The study also uncovers a spatial linkage and dependence between climate change and technological innovation. The technological transfer from European countries has increased energy consumption and carbon emissions in Asian countries, leading to abnormal climate change. The paper underscores the importance of targeted technological innovation strategies in Asian countries to improve energy efficiency and mitigate climate change. The study also confirms the EKC hypothesis with an observed inverted U-shaped relationship between economic growth and carbon emissions.

The Evaluation of the Temperature Reduction Effects of Cool Roofs and Cool Pavements as Urban Heatwave Mitigation Strategies

Various types of damage, including health risks, caused by abnormal climate conditions have gradually increased in recent years, particularly in cities with a high degree of complexity. In particular, it is important to address heatwave damage risk from both technical and policy perspectives because it poses risks to older adults and/or disadvantaged individuals. This study was performed to assess the quantitative effects of two heatwave mitigation measures, cool roofs and cool pavements, in the Jangyumugye district of Gimhae, Republic of Korea. Thermal imaging cameras were used to measure surface temperatures after the installation of these measures; after the installation of cool roofs, indoor temperatures were also measured using temperature measurement data loggers. Then, a survey was conducted to obtain feedback from users of the associated facilities. From August to October 2019, surface temperatures were significantly reduced following the installation of cool roofs and cool pavements. Cool roofs constructed using slabs and panels provided average temperature reductions of 15.5 °C and 11.6 °C, respectively. Cool pavements showed a temperature reduction of ≥4 °C in both parking lot and alley sites. More than 30% of users noticed a reduction in temperature as a result of heatwave measure installation. These results may be used by the city to make informed policy decisions regarding the implementation of heatwave mitigation measures.

Effects of Climate Change and Drought Tolerance on Maize Growth.

Climate change is affecting all regions of the world with different climates, and the scale of damage is increasing due to the occurrence of various natural disasters. In particular, maize production is highly affected by abnormal climate events such as heat waves and droughts. Increasing temperatures can accelerate growth and shorten the growing season, potentially reducing productivity. Additionally, enhanced temperatures during the ripening period can accelerate the process, reducing crop yields. In addition, drought stress due to water deficit can greatly affect seedling formation, early plant growth, photosynthesis, reproductive growth, and yield, so proper water management is critical to maize growth. Maize, in particular, is tall and broad-leaved, so extreme drought stress at planting can cause leaves to curl and stunt growth. It is important to understand that severe drought can have a detrimental effect on the growth and reproduction of maize. In addition, high temperatures caused by drought stress can inhibit the induction of flowering in male flowers and cause factors that interfere with pollen development. It is therefore important to increase the productivity of all food crops, including maize, while maintaining them in the face of persistent drought caused by climate change. This requires a strategy to develop genetically modified crops and drought-tolerant maize that can effectively respond to climate change. The aim of this paper is to investigate the effects of climate change and drought tolerance on maize growth. We also reviewed molecular breeding techniques to develop drought-tolerant maize varieties in response to climate change.

스타트업의 ESG경영지표 개발 및 활용에 대한 연구

The expansion of start-up standards for corporate value from technology and idea business models to eco-friendly management has emerged as an important issue in recent research. Interest in abnormal climate phenomena and corporate social responsibility due to global warming is being studied as important issues in business administration, and ESG management strategies for sustainable management are being studied by developing policies and indicators around advanced countries as a solution. In particular, companies themselves are also releasing sustainable research reports by strengthening ESG indicators from existing management evaluation indicators as a countermeasure for sustainable management and risk management. However, there is still a lack of systematic and standardized research on ESG management indicators, and more than 600 domestic and foreign evaluations are being conducted by industry and company. Against this background, positive expectations and negative concerns for ESG evaluation indicators coexist. Indicators tailored to the need for ESG management, including strengthening the demand for non-financial performance of companies, establishing investment standards through ESG evaluation, and due diligence on supply chains, will have important implications. While no systematic standards have been established for domestic and international ESG management indicators, research on start-up perceptions of ESG management and ESG management evaluation indicators is still in its infancy. The purpose of this study is to develop ESG indicators suitable for start-up companies and SMEs based on domestic and international prior studies and ESG indicators. To this end, ESG indicators of start-up companies were derived for ESG experts, and detailed indicators were developed by dividing them into three stages: upper, middle, and lower indicators through AHP analysis.
 The results of the study are as follows. First, the environment was evaluated as the most important factor in the importance and priority of the startup ESG management indicators of the ESG expert group in order of environment, social, and governance. The overall importance of the median indicators was in the order of industry and product safety, second place environmentally friendly production, and third place waste pollutant emission, and the overall importance of the sub-indicators was first place for the CEO's willingness to practice, second place for product safety, third place for greenhouse gas emissions and verification, fourth place for the industrial safety and health promotion system/industrial accident rate, and top five for ESG management and information disclosure. These findings can present baseline indicators that should be recognized or performed first to start-up executives considering ESG management, and can be meaningful in contributing to start-up ESG management and ESG system construction.

Calculated Damage of Italian Ryegrass in Abnormal Climate Based World Meteorological Organization Approach Using Machine Learning

Calculated Damage of Italian Ryegrass in Abnormal Climate Based World Meteorological Organization Approach Using Machine Learning

A multi-source data-driven model of lake water level based on variational modal decomposition and external factors with optimized bi-directional long short-term memory neural network

An accurate prediction of lake water levels is of great significance to water resource regulation, flood prevention and mitigation. However, water level fluctuations have been increasingly serious due to abnormal climate and extreme events. In view of this, a VMD-EF-OBILSTM model was constructed for lake water levels based on multiple sources of hydrological and meteorological variables. In this model, water level data are transformed into low-frequency internal and high-frequency external terms by variable modal decomposition (VMD), and they are combined with external factors (EF) for multivariate prediction. The optimized bi-directional long short-term memory (OBILSTM) invokes the attention mechanism and optimizes the model's hyperparameters by whale optimization algorithm (WOA). Ultimately, the predictions of each component are linearly combined to obtain the forecast values. The empirical results with water level data from Poyang Lake in China show that the multi-source deep learning model can achieve higher prediction accuracy and lower prediction uncertainty.

Habitat Suitability of Pine Wilt Disease in Northeast China under Climate Change Scenario

Pine wilt disease (PWD), caused by Bursaphelenchus xylophilus, is one of the most severe forest diseases worldwide. PWD causes devastating disasters to Chinese pine trees, seriously threatening forestry production and the forest ecological environment, and causes economic losses of over ten billion yuan per year to China. Previous studies have shown that the spread of PWD is closely related to climate factors. Today, PWD is spreading rapidly owing to abnormal climate changes. In order to provide a reference for controlling the spread of PWD in China, in this study, we accurately assessed the risk of the continued spread of PWD in Northeast China; a correlative species distribution model (MaxEnt) (RM = 1, AUC = 0.9904) was used to evaluate China’s climate suitability for PWD. The effects of climate factors on the spread of PWD in Northeast China were studied using Liaoning Province as an example by analyzing the relationship between the changes in average precipitation, average temperature, average relative humidity, average vapor pressure deficit, average wind speed, average sunshine duration and the area of the PWD epidemic over the past five years. These results suggest that with the change in climate, the areas suitable for PWD have expanded, and certain previously unsuitable areas for its distribution have become suitable. Temperature and precipitation were found to play key roles in the occurrence and damage of PWD, and hot and arid conditions favored the spread of PWD. It is recommended that for areas within the suitable range of PWD but not yet epidemic areas, quarantine should be strengthened to prevent the further spread of PWD. In addition, special attention should be paid to epidemic areas with high temperatures and arid while the monitoring of PWD should be strengthened to achieve the early detection and timely treatment of infected epidemic trees. Our results indicate that PWD undoubtedly poses a major threat to Northeast Chinese pine species if climate change proceeds as projected. In the future, more attention should be paid to monitoring the northward spread of PWD, and further studies should consider meteorological data forecasts, which could facilitate timely control measures.

Watershed environmental impact assessment for extreme climates based on shared socioeconomic pathway climate change scenarios

Abnormal climate phenomena that exceed conventional weather observations occur worldwide, such as severe floods, droughts, and environmental issues, and have attracted increasing attention. To avoid indiscriminate industrialization and achieve sustainable development, environmental experts have presented various opinions based on climate change scenario data. In this study, shared socioeconomic pathway (SSP) climate change scenarios that consider socioeconomic conditions were used to assess the environmental impact of extreme climate conditions on watersheds. Using the UK Earth System Modelling (UKESM1) SSP scenarios, we analyzed the following: 1) Heat index for each SSP climate change scenario using the H-Index; 2) extreme climate for each SSP climate change scenario using the standardized precipitation index (SPI); 3) the impact of extreme climate on non-point pollution using event mean concentration (EMC); and 4) the environmental flow caused by extreme climate by combining the K-water Distributed RUnoff Model (K-DRUM) and global environmental flow calculator (GEFC) models. According to the heat index analysis regarding SSPs climate change scenarios, the heatwave index will continue to rise if high carbon emissions persist. Temperature serves as an important indicator that has the most meaningful impact on ecosystems. The SPI analysis showed an increase in “extremely dry” conditions, and overall, more severe droughts are likely to occur due to high carbon emission-induced climate change. The nonpoint source pollution is also higher in climate change scenarios with high carbon emissions. The extreme drought event assessment using SPI and environmental flow revealed a shift from the “moist conditions” category to grade C. Our assessments in this study conclusively indicate that drought frequency will increase along with nonpoint source pollution. Furthermore, environmental flows will shift to grade C, resulting in the disappearance of some sensitive ecological species and an increase in invasive species. These analyses determined that high carbon emission-induced climate change caused leads to severe droughts and significant alterations in the overall water circulation, thereby complicating water resource management. Furthermore, we identified watersheds that are highly vulnerable to climate change and designated these as “mid-watersheds” that first require non-point pollution management. Here, the aquatic ecosystem environment can be affected by climate change without any artificial environmental influence. Various research-based analysis methods based on modeling have been presented using SSP climate change scenarios, and are highly beneficial for establishing policies and coping strategies for environmental preservation and sustainable development.

인공지능 및 토양 모니터링 기반 스마트 수목 보호틀 통합 관리 시스템

For years, average temperature have risen and precipitation have constantly increased. And tree in cities are being damaged by diseases, pests, death beacause of abnormal climate. The existing management system for tree protection have used the system based on not tree data but weather data. So, in order to reduce damage to tree, we are necessary to improve tree management technology. In this study, we proposed a smart tree protection frame and management system using AI technology. We proposed a system that collects tree condition data using a smart tree protection frame, diagnoses and manages trees through an analysis system. Unlike previous studies, this study has the advantage of being able to manage tree without the help of tree expert by utilizing artificial intelligence models in the use of system.

Inflow Prediction of Centralized Reservoir for the Operation of Pump Station in Urban Drainage Systems Using Improved Multilayer Perceptron Using Existing Optimizers Combined with Metaheuristic Optimization Algorithms

Owing to the recent increase in abnormal climate, various structural measures including structural and non-structural approaches have been proposed for the prevention of potential water disasters. As a non-structural measure, fast and safe drainage is an essential preemptive operation of a drainage facility, including a centralized reservoir (CRs). To achieve such a preemptive operation, it is necessary to predict the inflow of the drainage facilities. Among the drainage facilities, CRs are located downstream of the drainage area, and their pump stations are operated according to the CR water level. The water level of a CR depends on the inflow, as does the preemptive operation of its pump station. In this study, as a nonstructural measure, the inflow prediction for the CR operation in an urban drainage system was proposed. For predicting the inflow of a CR, a new multilayer perceptron (MLP) using existing optimizers combined with a self-adaptive metaheuristic optimization algorithm, such as an improved harmony search, was proposed. Compared with the adaptive moment, which yields the best results among other existing optimizers, an MLP using an existing optimizer combined with an improved harmony search improves the mean square error and mean absolute error by 0.1767 and 0.0349, respectively.

RCP 시나리오를 활용한 이상기후에 따른 마늘 수급 영향 분석

Our study investigated the impact of the implementation of greenhouse gas reduction (GGR) policies on the garlic market. We set an abnormal climate scenario; (1) emitting greenhouse gases as the current trend, (2) the greenhouse gas reductioin policy. Then, we analyzed whether the GGR policy was implemented or not. When abnormal weather occurred in 2030, Scenario 1 showed a larger decrease in yields per garlic unit than Scenario 2. On the other hand, in the case of price increase, Scenario 2 was found to be smaller than scenario 1. The results imply that if the GGR policy is implemented in abnormal climate conditions, the decrease in garlic yield per unit is relatively small due to favorable climate conditions, and it will positively contribute to stabilizing garlic prices. The results of the study suggest the need to implement GGR policies in the agricultural sector at a time when abnormal climate phenomena are inevitable. Since voluntary participation in farmers is essential to achieve carbon neutrality in the agricultural sector, policies should be implemented to increase farmers' participation rate, such as providing economic incentives when reducing greenhouse gases in the agricultural process.

Calculation of Damage to Whole Crop Corn Yield by Abnormal Climate Using Machine Learning

Calculation of Damage to Whole Crop Corn Yield by Abnormal Climate Using Machine Learning