Future Forecasts Research Articles

THE SITUATION ON THE UKRAINIAN RESIDENTIAL REAL ESTATE MARKET IN THE PRE-WAR AND WAR PERIODS

The War and the Ukrainian Housing Market: Overview and Forecasts: The article examines the domestic housing market dynamics in the pre-war and war periods. Quantitative indicators of the housing stock of Ukraine are analyzed, and their dynamics and structure are highlighted. The State Program "eRestoration": The Way to Housing Restoration: The features of implementing the "eRestoration" program aimed at compensating damaged and destroyed housing are considered. Trends in price changes in the primary, secondary, and rental housing markets in different regions of Ukraine are substantiated and conceptualized. Resumption of construction: Dynamics and prospects: The article examines aspects of the resumption of construction in Ukraine: the number of residential complexes sold in 2023 and apartments put into operation are analyzed. The state's role in the recovery of the housing market emphasizes the critical role of the state in stimulating the development of the housing market and providing affordable housing for people who lost it due to the war. Housing market: challenges and prospects. 1. Functioning of the housing market: the housing market as a system of economic relations; its role in stimulating savings, redistribution of resources, and investment; the impact of the war on the domestic housing market. 2. Market trends in 2023: growing demand for ready or almost ready housing; higher real estate prices, especially in the western regions; internal and external population migration; slow construction and accumulation of pent-up demand. 3. Future forecasts: stabilization of the market in the absence of escalation of the war; conversion of pent-up demand into new transactions; price correction; opportunities for further market development; stimulation of demand for housing and commercial real estate; development of urban infrastructure. 4. The role of the state: the leading role of the state in the formation of the regulatory framework; implementation of programs to eliminate imbalances in housing provision; elimination of the adverse effects of the war; 5. Conclusion: The restoration and development of the housing market of Ukraine is a complex task that requires joint efforts of the state, businesses, and citizens. Improving the uniqueness of the text: new subheadings were added to improve the structure of the text; more dynamic and emotional words and phrases were used; author's opinions and analytics were added; specific examples and statistics were provided; complex sentences were rephrased to improve readability; a different writing style was used to emphasize the author's handwriting.

Assessing the viability of hybrid renewable energy systems in Nigeria

AbstractThe implementation of renewable energy strategies has been on the rise due to recent global initiatives on sustainable development. In this work, meteorological data obtained from geographically separated stations in Nigeria is utilized to assess the economic benefits of off‐grid renewable energy projects specifically WT/battery, PV/battery, and PV/WT/battery systems for upcoming projects in six different sites, with anticipated start dates in 2023, 2026, and 2029. By utilizing future forecasts of capital costs, this analysis aims to facilitate the decision‐making process regarding the optimal timing and placement of these installations. The WT/battery systems have a levelised cost of electricity (LCOE) that is 20–35 times more in comparison to both PV/WT/battery and PV/battery systems. In 2023, the LCOE for photovoltaic (PV)/battery systems varies from $0.16/kWh in Yola to $0.169/kWh in Port Harcourt. On the other hand, PV/WT/battery systems have the lowest LCOE, ranging from $0.261/kWh in Jos to $0.319/kWh in Port Harcourt. The analysis additionally demonstrated that in the event of project delays, PV/battery systems are projected to exhibit the lowest LCOE across all locations by 2029. This is attributed to the anticipated accelerated reduction in PV and battery expenses compared to the decline in costs associated with WTs. In the specified year, Yola exhibited the most favorable LCOE at $0.0748 per kilowatt‐hour, while Port Harcourt recorded the highest LCOE at $0.113 per kilowatt‐hour. It has been discovered that solar energy exhibits more cost‐effectiveness compared to wind energy, mostly attributable to its superior ability to match generation with load on an hourly and monthly basis, particularly in the presence of heavy loads. This study provides a comprehensive geographical overview that will assist policymakers in the strategic selection of cities in Nigeria for the deployment of off‐grid renewable energy (RE) systems.

Combining distribution modelling and phylogeography to understand present, past and future of an endangered spider

BackgroundUnderstanding how endangered species respond to climatic changes is fundamental for their conservation. Due to its restricted geographic range, its sensitivity to the ongoing global warming and its continuing decline, the Southwestern-Alpine endemic wolf spider Vesubia jugorum is currently classified as Endangered in the IUCN Red List. Here, we combined species distribution modelling (SDM) and phylogeographic inference to describe the present, the past and the future of this species in light of the mtDNA genetic structure of extant populations.ResultsPhylogenetic and network analyses show a high level of genetic differentiation and a strong genetic structure of the populations, likely explicable by a long history of isolation and survival in separate refugia. The SDM projection into past climatic conditions supports these results by showing a smaller distribution range compared to present, mostly restricted to the Maritime and Ligurian Alps, which possibly served as main refugium. Future forecast shows a significant shift in the bioclimatic range towards higher altitudes and latitudes, with a drastic decrease of habitat suitability in the central and south-eastern parts of the range, with consequent general loss of haplotype diversity.ConclusionSDM and phylogeographic inference support the hypothesis that the current distribution and the genetic structure of the extant populations mirror the survival in situ of Vesubia jugorum across repeated glacial and interglacial phases, in line with the ‘long-term stability hypothesis’. Future predictions show a significant shift in the bioclimatic range that V. jugorum will be likely unable to track, with profound impact on its long-term survival and its genetic diversity. Our considerations have implication for conservation genetics, highlighting the pivotal role of the transboundary protected areas of the SW-Alps in promoting conservation efforts for this species.

Feasibility of forecasting future critical care bed availability using bed management data

ObjectivesThis project aims to determine the feasibility of predicting future critical care bed availability using data-driven computational forecast modelling and routinely collected hospital bed management data.MethodsIn this proof-of-concept, single-centre data...

Listening to Stakeholders III: Potential Users Evaluate Product Content and Design for Subseasonal Extreme Precipitation Forecasts

Abstract Extreme precipitation over a 2-week period can cause significant impacts to life and property. Trustworthy and easy-to-understand forecasts of these extreme periods on the subseasonal-to-seasonal time frame may provide additional time for planning. The Prediction of Rainfall Extremes at Subseasonal to Seasonal Periods (PRES2iP) project team conducted three workshops over 6 years to engage with stakeholders to learn what is needed for decision-making for subseasonal precipitation. In this study, experimental subseasonal-to-seasonal (S2S) forecast products were designed, using knowledge gained from previous stakeholder workshops, and shown to decision-makers to evaluate the products for two 14-day extreme precipitation period scenarios. Our stakeholders preferred a combination of products that covered the spatial extent, regional daily values, with associated uncertainty, and text narratives with anticipated impacts for planning within the S2S time frame. When targeting longer extremes, having information regarding timing of expected impacts was seen as crucial for planning. We found that there is increased uncertainty tolerance with stakeholders when using products at longer lead times that typical skill metrics, such as critical success index or anomaly correlation coefficient, do not capture. Therefore, the use of object-oriented verification that allows for more flexibility in spatial uncertainty might be beneficial for evaluating S2S forecasts. These results help to create a foundation for the design, verification, and implementation of future operational forecast products with longer lead times, while also providing an example for future workshops that engage both researchers and decision-makers.

Main historical stages in the field of physical culture and sports management (analysis of the past, present, future forecast)

The article substantiates the need for a theoretical analysis of the main historical stages in the field of industrial management. Attention is focused on the military–applied and professionally applied orientation of physical culture, which has been preserved for more than a century, including the present. In connection with the identified shortcomings, it is proposed to increase the number of hours for teaching basic military training in the form of a separate subject in grades 5–11, as was the case in Soviet secondary schools. Also, approval at the legislative level of the introduction of communicative and digital forms of industrial gymnastics in all organizations, regardless of the types of activities and organizational and legal forms of ownership, as it was during the existence of the Soviet Union. A structural unit of educational organizations is a boarding school with a sports profile, 24 hours a day, accepting children from the age of 7 at the expense of budget funding, as was the case in the USSR. This will make it possible to prepare a reserve for the national teams of the constituent entities of the Russian Federation and the country in aesthetic sports, the specificity of which is early specialization. The resulting socio–economic and political circumstances in the early 1990s prompted the emergence of industry–specific sports management and many innovations. But time shows the need to study the past to avoid shortcomings in the present, to use the positive experience of continuity from the Soviet management of physical culture and sports for its application in modern Russia.

ATIN: Attention-embedded time-aware imputation networks for production data anomaly detection

Effective identification of anomalous data from production time series in the oilfield affects future analysis and forecasting. Such time series is often characterized by irregular time intervals due to uneven manual sampling, and missing values caused by incomplete measurements. Therefore, the identification task becomes more challenging. In this paper, an Attention-Embedded Time-Aware Imputation Network (ATIN) with two sub-networks is proposed for this task. First, Time-Aware Imputation LSTM (TI-LSTM) is designed for modeling irregular time intervals and incomplete measurements. It decays the long-term memory component as the producing well conditions may be varied during the water cut stage. Second, Attention-Embedding LSTM (ATEM) is designed to improve the effectiveness of anomaly detection. It focuses on the correlation between the last and historical measurements in a given sequence. Comparison experiments with several state-of-the-art methods, including mTAN, GRU-D, T-LSTM, ATTAIN, and BRITS are conducted. Results show that the proposed ATIN performs better in accuracy, F1-score, and area under curve (AUC).

The Efficiency of Financing Environmental Protection Measures in the Context of Ukraine’s Future Membership in the EU

In recent decades, humanity has had a significant negative impact on the environment. This problem can be solved only by establishing a rational environmental management policy and ensuring an effective financial policy in the context of balancing emissions and expenditures on environmental protection measures. The purpose of this article is to analyze the efficiency of financing environmental protection measures by determining the dependence of pollutant emissions on environmental protection expenditures in Ukraine and the European Union. The following methods were used for the study: analysis, synthesis, generalization, comparison, specification, and statistical and graphical methods. The statistical information was systematized on the basis of open data from the Open Budget web portal, the State Statistics Service, and Eurostat. This study identified the dynamics of revenues from environmental taxes in Ukraine; taxes on air emissions prevail. Most of the expenditures are made from the state budget. According to the functional classification, environmental expenditures are mainly aimed at preventing and eliminating environmental pollution. The dynamics of capital investments in environmental protection were also studied. To assess the effectiveness of the state policy in the field of environmental protection, we analyzed the dependence of pollutant emissions in Ukraine, Poland, and Romania on the amount of environmental expenditures and investments in this area and revenues from environmental tax. In Ukraine, the amount of pollutants released into the atmosphere depends mostly on investments in this area; in Poland—on revenues from environmental taxes; and in Romania—on expenditures on environmental protection. It has been established that the obtained models are adequate and can be used to build future forecasts of pollutant emissions. Directions for the development of financial and environmental policy are proposed. Post-war restoration of the environmental situation should be carried out on the basis of sustainable development, focusing on the European Green Deal A triple-task approach should be implemented, including environmental restoration, the minimization of negative climate change and balanced use of resources, and the expansion of powers of the relevant ministry with a focus on the strategic goals of the state policy. It is necessary to develop methodological recommendations according to international standards to assess the real state of the environment.

Spatio-temporal forecasting for the US Drought Monitor

Abstract The US Drought Monitor is the leading drought monitoring tool in the United States. Updated weekly and freely distributed, it records the drought conditions as geo-referenced polygons showing one of six ordered levels. These levels are determined by a mixture of quantitative environmental measurements and local expert opinion across the entire United States. At present, forecasts of the Drought Monitor only convey the expected direction of drought development (i.e. worsen, persist, subside) and do not communicate any uncertainty. This limits the utility of forecasts. In this paper, we describe a Bayesian spatio-temporal ordinal hierarchical model for use in modelling and projecting drought conditions. The model is flexible, scalable, and interpretable. By viewing drought data as areal rather than point-referenced, we reduce the cost of sampling from the posterior by avoiding dense matrix inversion. Draws from the posterior predictive distribution produce future forecasts of actual drought levels—rather than only the direction of drought development—and all sources of uncertainty are propagated into the posterior. Spatial random effects and an autoregressive model structure capture spatial and temporal dependence, and help ensure smoothness in forecasts over space and time. The result is a framework for modelling and forecasting drought levels and capturing forecast uncertainty.

Production optimization in a fractured carbonate reservoir with high producing GOR

The Gas-Oil Ratio (GOR) is a crucial production parameter in oil reservoirs. An increase in GOR results in higher gas production and lower oil production, potentially leading to well shut-ins due to economic infeasibility. This study focuses on a real fractured oil field that requires urgent production operations to reduce the producing GOR. In this study, the static model for the field was developed using commercial software, involving steps such as data collection, fault modeling, meshing, and statistical analysis to prepare for dynamic simulation. The dynamic model incorporates geometry, gridding, and rock properties from the static model, utilizing a dual-porosity approach for the naturally fractured reservoir and the Peng-Robinson equation for fluid phase behavior. Initial reservoir conditions, production history, and rock-fluid interactions were defined, with relative permeability curves indicating a water-wet reservoir and low critical gas saturation affecting the GOR. To better understand the relationship between reservoir and production parameters, a detailed sensitivity analysis was performed using the Response Surface Methodology (RSM). Following the sensitivity analysis, a history matching process was conducted using the Designed Exploration and Controlled Evolution (DECE) optimizer to validate the model for future forecasts. Six operational scenarios were defined to decrease the production GOR and enhance final recovery from the field. The results indicate that the water injection scenario is effective in preventing the GOR increase by maintaining reservoir pressure, thereby sustaining production over a longer period. This scenario also improves oil recovery by approximately 6% compared to the base case. Finally, optimization was carried out using the DECE optimizer for each scenario to fine-tune the operational parameters. The goal was to maximize oil revenue for each scenario during the optimization process. This study stands out as one of the few that provides a comprehensive analysis of production behavior and development planning for a real fractured reservoir with high producing GOR.

How will education look like in the future?

There are many factors affecting educational practices. Changes in these factors create serious transformations in education. Some of the transformations are visible that imply a predictable future, while others have blurred influences which make the future forecasts difficult. Educational technologies often come and go but they all leave their marks on education when they are prevalent. In order to get prepared for the future, educators need to know which technologies will be dominant in the future practices of education. This article attempts to highlight the technologies that will be used commonly in the future education. It appears that virtual classroom, artificial intelligence, learning analytics, mobile applications, smart devices, extended reality, open educational resources, gamification, cloud computing, network society, post-truth, digital learning objects, online collaboration will be popular elements of educational practices in the future. On the other hand, social media, metaverse, and wearable technologies will lose their popularity in education to some degree. It is also likely that some of the current technologies will be modified while new ones will emerge to accommodate educational needs of the future.

An EEMD‐LSTM, SVR, and BP decomposition ensemble model for steel future prices forecasting

AbstractThe forecasting of steel futures prices is important for the steel futures market, even for the steel industry. We propose a decomposition ensemble model that incorporates the Ensemble Empirical Mode Decomposition (EEMD), Long Short‐Term Memory (LSTM), Support Vector Regression (SVR), and Back Propagation (BP) neural network to forecast steel futures prices. The forecasting procedures are as follows: (1) The price data are initially decomposed into several relatively independent Intrinsic Mode Functions (IMFs) and a residue using EEMD. (2) The IMFs are then reconstructed as components representing short‐term, medium‐term, and long‐term frequencies via fine‐to‐coarse. (3) LSTM, SVR, and BP neural network are utilized to forecast the short‐term, medium‐term, and long‐term reconstructed components, respectively. (4) The prediction results for each component are simply added to the final prediction results. The accuracy of the proposed model is compared with several benchmark models by experiments and evaluated by some prediction evaluation indexes. The experimental results show that our model outperforms other models in terms of forecast accuracy, confirming its strong predictive capabilities. This study provides some suggestions for investment and decision making by participants in the steel futures market. It may promote the smooth operation of the steel futures market and shed some light on the operation of the steel industry.

Unlocking Online Insights: LSTM Exploration and Transfer Learning Prospects

Machine learning algorithms can improve the time series data analysis as compared to the traditional methods such as moving averages or auto-regressive approaches. This advancement has helped to unlock several challenging problems since machine learning not only helps to forecast the overall trend of the data, but it also helps to keep the historical track of changes in factors, influencing this trend. These predictions play a pivotal role in almost all areas of research where the observations are time dependent, such as problems ranging from challenges of finance to public health, environmental and climate change challenges. A key challenge of these domains is the higher number of attributes and predictors since managing and manipulating data from many attributes is itself a significant challenge for future forecasting. Addressing these challenges is possible with Recursive Long Short-Term Memory models. The application of such models is crucial, and their efficacy is further amplified when considering transfer learning. During this research, a detailed and comprehensive description of such models is addressed. Practical application is illustrated through an example, emphasizing that these models, when transferred to complex and large datasets using transfer learning, hold great promise.

Scientific evidence of the hydrological impacts of nature‐based solutions at the catchment scale

AbstractThe introduction of nature‐based solutions (NbS) in catchments has the potential to increase the cost‐effectiveness, flexibility, and reliability of water management practices aimed at improving water security. However, the scientific‐evidence base of the hydrological impacts of NbS is still weak, and there is therefore a risk that catchment interventions might not lead to the desired hydrological outcomes. This is especially important when assessing NbS‐based catchment interventions before their implementation, as this requires robust simulation tools capable of effectively managing the uncertainties associated with future forecasts. This study aims to review the hydrological impacts of different NbS intervention types for water management. First, we present an NbS typology and the corresponding dominant hydrological impacts. We then use this typology to review the strength of the current evidence of the effect of NbS interventions on the hydrological response at the catchment‐scale. Our results demonstrate that the effectiveness of each NbS type hinges on specific conditions such as location, design, and environmental factors. For instance, micro‐reservoirs notably enhance surface storage and evaporation, while infiltration trenches reduce runoff but can increase soil erosion. Our global analysis highlights the need for an improved understanding of NbS catchment impacts and careful planning of NbS interventions as a key for successful long‐term implementation of NbS. These include participatory approaches with stakeholder involvement in NbS co‐design, knowledge co‐production, and novel data collection to support locally relevant adaptation strategies, and to increase water security on the long term.This article is categorized under: Science of Water > Hydrological Processes Engineering Water > Planning Water Water and Life > Conservation, Management, and Awareness

Current Issues Regarding the Recycling of Consumer Products and Transport Performance from the Perspective of the Green Circular Economy

Abstract The circular economy and the principles by which it is guided, has become the subject of more and more scientific works, the authors defining this green part of consumerism in multiple and diverse ways, but all focusing on the decision-making power of consumers and producers regarding the consumption of goods and services, respectively of the practical measures to maximize the recycling and reuse of the resulting waste. This research follows the evolution of the impact of the human factor on the environment at the level of Romania in the period 1948-2024, of the European Union, Switzerland, Norway and Iceland (UEFTA-Liechtenstein) in the period 2010-2022, respectively 2004 - 2021. The main purpose of the article is to to determine the economic behavior models of the main actors on the global market, regarding the circular economy, with the aim of understanding their motivations and obviously to recommend the changes in mentality and implicitly the reactions, respectively the actions perceived to be strictly necessary for a sustainable use of raw materials and materials used in production processes. Of course, the concept of circular economy is one of vital importance for the entire human society, especially in the conditions of the demographic explosion predicted in the foreseeable future. For multiple reasons, this concept is absolutely decisive for avoiding the economic, social and ecological collapse of the entire planet we live on. The fundamental question we want to answer is “What are the main behaviors of consumers and producers regarding the circular economy?”, in order to be able to determine the current motivations, possibly future forecast and rewards that prove beneficial in the implementation of the principles of the green economy. The study is based on data related to GDP/capita, the recycling rate, the amount of greenhouse gases emitted and the number of vehicles with alternative propulsion.

Digital Revolution in Food Entrepreneurship: Perspectives from Select Small and Micro Food Enterprises

This study is entitled Digital Technologies and Its Integration In The Operations Of Micro And Small Food Enterprises: Basis For Strategic Plan which intended to describe the profile of the companies namely, Henan Little Jade Foods, The Garden of Clear Fragrance Foods, Tianjin Joint Venture Foods, and Tianjin the EatSmart Way Technology Development Company. These four food enterprises located in Henan and Tianjin, China integrate digital technologies in their business operations. This research aims to provide a description of digital technologies integrated in the operations of micro and small food enterprises as basis for strategic plan. The description of the profile of the four companies dealt with the nature of business, products and services offered, number of years in the business, existing market, platform of distribution, and business or asset size as of 2022. In addition, this study delves into the use of digital technologies in the business areas of management and decision making, marketing, production/operations and finance. Regarding the integration of digital technologies in small microfood businesses, all respondents strongly agreed on the use of digital technologies in the four main areas of management and decision making, marketing, production/operations and finance. In the area of management and decision making, 98% of the respondents strongly agreed with the following item statements:In the marketing area, 96% of respondents strongly agreed that digital technology "helps to broaden the customer base, pricing, customer trends and future demand forecasting, helps to develop a customer-centered marketing strategy, and helps to build a competitive brand." In the production/operations area, 92% of respondents strongly agreed with the statement that the following items: "facilitates the implementation of more effective risk assessment and risk mitigation, brings innovation to the company, facilitates an effective transition from traditional to modernized production models, improves the efficiency of customer relationship management, and better connects the company to the community." In the area of finance, 93% of respondents felt strongly that digital technology has caused "confusion and struggle due to lack of preparedness on issues such as the digital economy, cyberthreats and attacks, financial management software, and digital investments." In marketing, respondents agreed that digital technology has created "confusion due to loss of control over customer interactions, poorly trained staff, and mistrust among customers." They also agreed that on the production/operations side, digital technology has brought about internal conflicts and difficulties in complying with regulatory policies due to different perspectives on understanding digital". Based on these findings, we propose a strategic plan for food companies to study and adapt. The researcher recommends further research into the potential for micro and small food businesses to participate in a centralized data management system. However, this will require more training for employees to ensure that everyone is up-to-date with the industry's digitalization and in sync with customer platforms. The challenges that food companies face in integrating digital technologies must be examined by top management and employees alike to determine what issues the company must strategically address to ensure sustainability and productivity, which are the ingredients for survival in the world market. Keywords: digital technologies, integration, micro and small food enterprises, management and decision-making, marketing, production/operations, finance

Future forecast and economic perspective of the distribution of greenhouse gas emissions by sectors

Various policies aim to reduce greenhouse gas (GHG) emissions, placing specific limits on different sectors, and impacting the environment and the economy. However, fossil-based energy systems are continuously developing in rapidly industrializing regions. Balancing the immediate costs of transitioning with the long-term benefits of sustainability is key in addressing the distribution of GHG emissions across sectors in Turkey from an economic perspective. This study aims to provide a future forecast and economic perspective on the distribution of GHG emissions by sectors in Turkey. Depending on this purpose, Econometric Views (EViews) 12 software was used to analyze the collected data. The Autoregressive Integrated Moving Average (ARIMA) model has been used to predict future values. The study predicts that greenhouse gas emissions will increase by 5.5% in 2026 compared to 2022. The energy sector will continue to have the highest share of emissions, while the agricultural sector will experience the most significant increase. Additionally, emissions from industrial processes and product use are expected to rise, while emissions from waste are expected to decrease between 2022 and 2026.

Implementation of the Vector Autoregressive (VAR) Model in Electricity Supplier Companies

The government's policy regarding setting special prices for coal as fuel for power plants is a dilemma for investors because if the policy is implemented there is a possibility that it will affect the company's profitability and impact the company's share price. Scientific forecasting of a company's future based on share price volatility with various considerations including political, social and economic aspects of the country are things that must be taken into account when making decisions. The method that will be used in this research is quantitative descriptive through the application of the VAR model to be able to describe the causal relationship between variables. The result is a VAR model for each variable that is used as a forecasting model for the daily share price volatility of companies supplying coal to PLN, so that investors can make strategic steps in investing.

The Implementation of Holt-Winters Method to Forecast the Loan Interest Rate of Indonesia

This study aimed to anticipate the rupiah loan interest rates at commercial banks in Indonesia by employing the Holt-winters method. This study employs data on rupiah loan interest rates from commercial banks in Indonesia. The data comprises a time series element, with monthly intervals spanning from January 2013 to November 2015, which was obtained from the official website of BPS Indonesia. The study demonstrates that the Holt-winters technique yields the most accurate forecasts, as indicated by a Root Mean Square Error (RMSE) of 0.19720630. The parameters alpha, beta, and gamma, set at 0.6, 0.6, and 0.6 respectively, constitute the optimal configuration for this method. These results indicate that the Holt-winters method is an effective tool for capturing seasonality, trends, and patterns in credit interest rate data, making it a reliable choice for future loan interest rate forecasting. The findings of this study are expected to significantly contribute to strategic decision-making in the banking sector, particularly in risk management and loan interest rate strategy determination.

The dominant factor in extreme dust events over the Gobi Desert is shifting from extreme winds to extreme droughts

Gobi Desert (GD) is one of the major global dust sources, where dust events are frequent. Based on satellite observations, we compiled extreme dust events (EDEs) over the GD during spring since 2000. Among all 43 extreme events, EDEs on 4–10 April 2001 (EDE 2001) and 14–19 March 2021 (EDE 2021) are the most pronounced, due to the longest duration and the most intense strength, while the generation mechanisms underlying these two EDEs are different. EDE 2001 is associated with extreme westerlies, which are caused by an abnormal low-pressure due to the extreme Eurasian teleconnection (EU). However, EDE 2021 is attributable to extreme droughts, which are caused by an abnormal high-pressure due to the extreme Arctic Oscillation (AO) and West Pacific teleconnection (WP). Moreover, the trends in EU, AO, and WP combined with the analysis of all EDEs indicate a shift in the dominant factor of EDEs over the GD from extreme winds to extreme droughts. Therefore, regional droughts should be given more importance in future EDE forecasts.