earticle

논문검색

Rural Population Dynamics under Long-Term Drying : Analysis of Mongolia

초록

영어

Rural population dynamics in Mongolia are shaped by strong climatic variability, long-term drying trends, and the vulnerability of pastoral livelihoods to environmental stress. This study examines how desertification related indicators, quarterly climate anomalies, and socioeconomic conditions influence rural population patterns across 21 provinces except Ulaanbaatar from 2006 to 2025. Using a quarterly provincial-level panel dataset, the analysis integrates vegetation conditions (NDVI), soil moisture, long-term drought severity (SPEI-12), temperature anomalies, wind speed, livestock density, and real income. A fixed-effects panel regression framework is employed to control for unobserved provincial characteristics and isolate the temporal effects of environmental and economic variables on rural population. The national-level model shows that cooling anomalies significantly reduce rural population, while warming exerts a modest positive influence. To capture spatial heterogeneity in climate impacts, provinces are separated into drying and stable groups based on long-term SPEI-12 trends. Results indicate that drying regions react more strongly to climate stress: soil moisture becomes significant only in these areas, and livestock density has nearly four times stronger demographic influence than in stable regions. Quarterly temperature models reveal that the positive warming effect originates from winter and spring, while summer cooling intensifies drought stress and reduces rural population. Overall, the findings highlight the combined importance of quarterly climate variability, long-term drying, and rural economic resilience in shaping population dynamics in Mongolia.

목차

Abstract
1. Introduction
2. Research background
3. Analysis method
3.1. Panel regression
3.2. Fixed Effects Model (FE)
3.3. Within Transformation (Derivation of the FE Estimator)
4. Data introduction
5. Analysis and Result
5.1. Description analysis
5.2. National Model: Full Data Panel Regression
5.3. Sub-group Panel Regression
6. Conclusions
Acknowledgements
References

저자정보

  • Onon-Ujin Otgonbayar Department of Computer Science and Statistics, Chosun University, Gwangju 61452, Korea
  • Youn Su Kim Institute of Well-Aging Medicare CSU G-LAMP Project Group, Chosun University, Gwangju 61452, Korea
  • In Hong Chang Department of Computer Science and Statistics, Chosun University, Gwangju 61452, Korea

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