The relationship between climate and energy consumption: The case of South Korea

ABSTRACT

Though the relationship between temperature and energy consumption has been rigorous, not all energy sources used for space heating and cooling are investigated. Moreover, most studies consider the usage of energy by limited sectors or even do not separate consumption of energy by sectors, but treat the economy as a whole. By subdividing energy sources into six (coal, petroleum, city gas, electricity, heat, and new & renewable energy) and sectors into five (residential, commercial, industrial, public, and transport), we investigate the relationship between temperature and energy consumption by energy sources and sectors. We used two different regression models, which are time series model and decomposed multiplicative model, using temperature and energy consumption data of South Korea. The result from time series model shows that consumption of city gas (by public and industrial sectors), electricity (by residential, commercial, industrial, and transport sectors), new & renewable energy (by transport sector) is U-shaped in relation with temperature, while consumption of electricity and heat by public sector shows diminishing marginal consumption. On the contrary, consumption of coal (by residential sector), heat (by residential sector), and new & renewable energy (by res/com sector) is linearly related to the temperature. On the other hand, the result from multiplicative model shows that consumption of city gas (by public sector), electricity (by commercial sector), and heat (by commercial and public sector) is U-shaped, while consumption of city gas (by res/com sector), heat (by residential sector), and new & renewable energy (by res/com sector) shows diminishing marginal consumption. However, city gas consumption by industrial sector and new & renewable energy consumption by public sector turns out to be linearly related to the temperature. The relationship between temperature and energy consumption by sectors is also visualized. The implications, limitations, and suggestions for future research are also presented.

KEYWORDS:

• Temperature
• energy consumption
• regression analysis
• time series model
• multiplicative model

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

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Additional information

Funding

Hongsuk Yang’s research was supported by the Institute of Management Research at Seoul National University.

Notes on contributors

Jaeho Shin

Jaeho Shin is a Ph.D. candidate of Graduate School of Business at Seoul National University in Seoul, Korea. His research interests focus on management of technology, supply chain management, and service operations management.

Hongsuk Yang

Hongsuk Yang is a professor of Graduate School of Business at Seoul National University in Seoul, Korea. His research focuses on inventory management, supply chain management, service operations, and simulation.

Changhee Kim

Changhee Kim is an assistant professor of Business School at Incheon National University in Incheon, Korea. His research interests include, but not limited to the following areas; measuring productivity and efficiency, service operations management, sports management, and data science.