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컨테이너 항만 하역장비 배출량의 영향요인 분석: 인천항을 중심으로

An Analysis of Determinants of Emissions from Container Terminal Handling Equipment: Focusing on the Port of Incheon

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Abstract

As international efforts to combat the climate crisis intensify, managing greenhouse gas (GHG) emissions has emerged as a top-priority policy issue in the port sector. This study systematically examines the GHG emissions from container-handling equipment at Incheon Port's container terminals and quantitatively investigates the interactive effects of zero-emission transition rates and container throughput on emissions. To this end, the emissions data from 2019 to 2024 are employed as the dependent variable, and container throughput and electrification transition rates are adopted as key explanatory variables in a multiple regression model. The analysis reveals that even when replacing conventional emission-estimation metrics (i.e., fuel consumption and operating hours) with port operational indicators—namely throughput and electrification transition rates—the model achieves an R2 of approximately 0.976, indicating that these two variables collectively explain about 97.6% of the variability in handling equipment emissions. Specifically, when the centered value of throughput increases by 1,000 TEU, the logarithm of emissions rises by about 0.0027, corresponding to a 1.0027 increase. In contrast, the electrification transition rate of handling equipment shows a significant negative correlation with emissions. This finding empirically demonstrates that replacing fossil fuel-based equipment with electric or eco-friendly power sources can effectively mitigate carbon emissions during actual port-handling operations. This study contributes to the literature by statistically validating that variations in container throughput and the zero-emission transition rate—both principal port indicators—can be used to predict handling equipment emissions, and that converting equipment to zero-emission technology constitutes an effective strategy for realizing carbon neutrality. Moreover, because the core indicators used in this model are already routinely collected and managed in port operations, the findings suggest substantial applicability across diverse ports.

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