Fine particulate matter (PM_2.5) is one of the main air pollution problems that occur in major cities around the world. A country’s PM_2.5 can be affected not only by country factors but also by the neighboring country’s air quality factors. Therefore, forecasting PM_2.5 requires collecting data from outside the country as well as from within which is necessary for policies and plans. The data set of many variables with a relatively small number of observations can cause a dimensionality problem and limit the performance of the deep learning model. This study used daily data for five years in predicting PM_2.5 concentrations in eight Korean cities through deep learning models. PM_2.5 data of China were collected and used as input variables to solve the dimensionality problem using principal components analysis (PCA). The deep learning models used were a recurrent neural network (RNN), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM). The performance of the models with and without PCA was compared using root-mean-square error (RMSE) and mean absolute error (MAE). As a result, the application of PCA in LSTM and BiLSTM, excluding the RNN, showed better performance: decreases of up to 16.6% and 33.3% in RMSE and MAE values. The results indicated that applying PCA in deep learning time series prediction can contribute to practical performance improvements, even with a small number of observations. It also provides a more accurate basis for the establishment of PM_2.5 reduction policy in the country.

Walking is the primary means of mobility and a daily activity for the elderly. Despite the need to ensure pedestrian safety given their physical limitations, elderly pedestrian traffic accidents in South Korea occur at a rate 7.7 times higher than in OECD member countries. In preparation for an aging society, there is a growing need to create a safe walking environment for the elderly. This study focuses on Seoul, analyzing the factors that compromise pedestrian safety for the elderly and identifying the characteristics of vulnerable areas. By using elderly pedestrian traffic accident data provided by the Road Traffic Authority and applying factors influencing accident occurrence to the MaxEnt model, the study identified priority elements for ensuring pedestrian safety. Additionally, the study predicted the regional vulnerability of elderly pedestrian accidents with the increasing elderly population in the future and reviewed possible measures to mitigate the risks. The study indicates that areas where elderly pedestrian safety is vulnerable tend to have lower budget allocations for road management, suggesting a need for future policy support. The prediction of elderly pedestrian accident occurrences through this study is expected to be useful in identifying areas with vulnerable pedestrian safety in Seoul, which can be utilized in prioritizing road improvement projects.

Population decline due to low fertility and aging causes socioeconomic challenges such as a shrinking labour force and regional decline. In response to these challenges, there is a growing discussion about accepting immigrants to mitigate the side effects of population decline. This implies the importance of analyzing local peoples' perceptions as a basis of policy and planning in anticipation of demographic transitions toward a multicultural society. While there are many studies on local problems caused by depopulation and perceptions of immigrants based on contact theory and group threat theory, there has been relatively little research on the relationship between depopulation and individual perceptions of immigrants. This study fills this gap by using survey data of Seoul, Korea, the city experiencing both population decline and an increase in immigrants. It employs the multilevel ordered logit model to explore how the decrease in local population associates with individuals' perception on the need for multicultural immigration policies. The results suggest that individuals are more likely to have a positive view of multicultural immigration policies if their region is experiencing a higher population decline. This finding is significant as it demonstrates that a shift to an unfavourable demographic structure can have a positive impact on perceptions of immigrant acceptance, offering a new perspective on the relationship between immigrants and native residents.

Addressing the interconnected challenges of aging populations and climate change requires a thorough understanding of their complex interactions. Climate change is largely driven by anthropogenic carbon emissions, making it crucial to explore how population dynamics, such as aging, influence these emissions as we strive for carbon neutrality. While previous studies have largely focused on older adults as consumers, increasing life expectancy has allowed many older individuals to remain active in the workforce, thereby also affecting carbon emissions through productive engagement. This study examines the relationship between the economic participation of the older population and carbon emissions, employing fixed-effects panel analysis and instrumental variable techniques, with a focus on South Korea—a country experiencing rapid aging, high life expectancy, and a growing proportion of older workers. This study employs the STIRPAT framework and analyzes data from 16 provinces in South Korea covering the years 2000 to 2019. The findings indicate a strong correlation between the economic participation rate of the older population and their contribution to carbon emissions, driven by rising life expectancy and structural inefficiencies in the pension system. Furthermore, this impact is projected to intensify in the future, particularly as the dependency ratio increases. These findings offer new insights into the relationship between aging and carbon emissions and highlight the need for targeted policy measures that promote sustainability in an aging society.

The phenomena of extreme weather due to climate change and changes in population structure will become important issues as the elderly population, who are particularly vulnerable to climate change, continues to increase. Recognizing the damage caused by climate change, it is necessary to enhance the climate resilience of local governments to mitigate and prevent such damage. Furthermore, efficient allocation of budgets is essential for the establishment of policies and infrastructure by local governments to improve climate resilience. Accordingly, this research analyzed the relationship between the reduction of heatwave mortality rates in the elderly population and budgets for social welfare and environmental protection, as a preemptive response to potential damage related to climate change and demographic changes. Analyzing ten years of financial, demographic, heat index, and mortality rate data from 227 cities through panel analysis revealed a positive correlation between higher social welfare and environmental protection budget proportions and improved climate resilience. This increase in budget proportion had a negative relationship with heatwave mortality rate among the elderly population, indicating the necessity of increasing the share of these budgets to improve climate resilience. These results have major implications for policy formulation to address climate change and demographic shifts.