Recent studies have shown that air quality is an important amenity for population relocation in China. However, much of Chinese internal migration occurs due to non-discretionary reasons, such as government policies, family considerations, and military personnel reassignments. As such, estimates of the impact of environmental amenities on migration that do not control for migration reasons may be biased. Using the 2015 China Migrants Dynamic Survey, this paper estimates the impact of ambient air pollution on voluntary migration to other provinces for work. We find that more polluted days (air quality index (AQI) >150) at the original residence leads to a significant increase in labor out-migration to a province with better air quality, providing evidence of the current migration trend leading to declining populations in China’s megacities. Our findings indicate that environmental migration is more favored among households that are less educated, are older, work overtime, and have lower income, suggesting that environmental migration may result from environmental health inequalities in socially disadvantaged families.
This study examines the impacts of population aging on a wide range of economic indicators from a regional perspective. Many countries, including the United States, are experiencing demographic aging. This may have a dramatic impact on both the national and sub-national economies. However, there is little consensus about its impact on local sub-national economies. This study uses regional variation in age structure to explain economic outcomes at the metropolitan statistical areas (MSAs) level. In order to identify causal effects, Mahalanobis distances were calculated to identify the matched cities as instrumental variables. The study finds that regions with older age structures tend to have higher growth rates of GDP per capita and lower growth rates of unemployment, but such positive effects are likely to fade away in the long run. Additionally, there is no significant impact of age composition on income. The choice of variables is critical as it can lead to mixed results. The results are robust before, during and after the economic recession. Quantile regression is also used to explore potential heterogeneous effects among MSAs. The results show that MSAs, regardless of their size, are uniformly affected by the age structure.
The C5.0 decision model is used to study the distribution of vegetation
under global climate conditions, providing a theoretical basis for
vegetation conservation and coping with the effects of global climate
change on vegetation. Based on websites such as WorldClim, CHELSA, and
global vegetation distribution datasets, and using ArcGis for
meteorological and vegetation data extraction. Subsequently, global
meteorological and vegetation data were integrated into SPSS Modeler to
build C5.0 prediction model, and model analysis was performed using
accuracy, confidence, scatter plot, K-Means, and substituted into future
climate data for vegetation prediction, and finally a map of current and
future predicted vegetation types was drawn and overlaid with the global
administrative map to analyze the global vegetation distribution. The
results showed that the accuracy of the C5.0 model prediction was
69.67%, 68.23%, and 72.59% for the training set, test set, and
validation set, respectively, which had high accuracy and thus had some
reference significance for the study of global vegetation distribution.
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