This study aimed to identify the correlation between global climate phenomena, such as the ENSO, and South Korea’s Consumer Price Index (CPI) for a climate-sustainable economy. South Korea’s CPI has shown a linear upward trend, prompting a trend analysis and the subsequent removal of the linear trend for further examination. The correlation analysis identified statistically significant cases under the study’s criteria, with the Southern Oscillation Index (SOI) displaying the highest contribution and sensitivity. When comparing general correlations, the strongest relationship was observed with a 27-month lag. The Granger Causality Test, however, revealed causality with a 9-month lag between the CPI and El Niño–Southern Oscillation (ENSO) indices. This indicates the feasibility of separate analyses for long-term (27 months) and short-term (9 months) impacts. The correlation analysis confirmed that the ENSO contributes to explainable variations in the CPI, suggesting that CPI fluctuations could be predicted based on ENSO indices. Utilizing ARIMA models, the study compared predictions using only the CPI’s time series against an ARIMAX model that incorporated SOI and MEI as exogenous variables with a 9-month lag. Using the ARIMA model, this study compared predictions based solely on the time series of CPI with the ARIMAX model, which incorporated SOI and MEI as exogenous variables with a 9-month lag. Furthermore, to investigate nonlinear teleconnections, the neural network model LSTM was applied for comparison. The analysis results confirmed that the model reflecting nonlinear teleconnections provided more accurate predictions. These findings demonstrate that global climate phenomena can significantly influence South Korea’s CPI and provide experimental evidence supporting the existence of nonlinear teleconnections. This study highlights the meaningful correlations between climate indices and CPI, suggesting that climate variability affects not only weather conditions but also economic factors in a country.

Understanding the factors behind urban water leakage is crucial for developing a sustainable climate and protecting civil infrastructure. Water leaks not only waste essential resources but also increase urban vulnerabilities to climate-induced disasters. This study investigates the teleconnection between leakage incidents and climate change indices to establish predictive insight for water management. It focuses on climate phenomena such as El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD), which significantly influence global climate dynamics, affecting temperature and precipitation in South Korea. Using Pearson correlation analysis and Granger causality tests, this research examines climate indices and leakage data across South Korea’s inland regions from 2009 to 2022. The results indicate that ENSO indices exhibit a lead time of 6 to 30 months, with significant correlations in coastal areas, particularly Chungnam (west coast) and Gyeongnam (east coast). Inland regions such as Gimcheon and Chuncheon also showed notable correlations influenced by topographical factors. The findings highlight the importance of integrating climate teleconnection indices into risk management strategies. This approach allows for targeted monitoring and predictive modeling, enabling proactive responses to water leakage risks and contributing to sustainable urban development.

The correlation between ocean climate and Korean drought indices is key in explaining and predicting droughts in Korea. In this study, ocean climate index data, including El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) index values, were subjected to a Pearson correlation analysis with standardized precipitation index (SPI) data. The results of the analysis show that the SPI and ocean climate change are linked, which provides a possible explanation for the effect of ENSO and IOD on drought in Korea. However, because the magnitude of the observed link was not very large, the correlations ENSO and IOD indices, respectively, and SPI are statistically weak. On analysis of monthly data, significant correlations were mainly observed from January to May, with very weak correlations from June to December. Based on a regional analysis, twelve cities were divided into four groups with similar correlations between the analyzed indices.