우리 센터는 구성원 간의 긴밀한 학술적 교류를 증진하고, 창의적이면서도 비판적인 연구 문화를 확산하기 위한 《DHCSS BBS: Brown Bag Seminar》를 개최합니다.
본 세미나는 센터 내에서 수행 중인 연구 성과를 공유하고, 연구 주제에 대한 심층적 논의를 통해 디지털 인문사회과학 분야의 학제적 가능성과 연구 역량을 함께 모색하는 학술적 장으로 기획되었습니다.
구성원 여러분의 많은 관심과 참여를 부탁드립니다. 이번 세미나에서는 다음과 같은 발표가 진행될 예정입니다.

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????주최: KAIST 디지털인문사회과학센터(DHCSS)
☎️연락: 042-350-8182
????메일: kjh258@kaist.ac.kr

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Desy Caesary (DHCSS Post-doc) 

Carbon pricing implemented through emissions trading systems (ETS) has emerged as a key policy instrument for encouraging cleaner production and accelerating the transition toward a low-carbon economy. As ETS markets continue to expand, accurate carbon price forecasting is essential to ensure market stability, particularly in newly established or data-scarce systems. This study proposes a transfer learning-based machine learning framework to predict carbon prices across heterogeneous ETS environments, incorporating integrated global energy prices with local variables, such as economic indicators and ETS mechanism features. Using EU data as the source-domain and Korean data as the target-domain, the model demonstrates that knowledge learned from a mature market can be effectively transferred and refined through fine-tuning to a target domain, leading to high predictive accuracy in the target market. Beyond the significant influence of economic indicators and fossil fuel prices, ETS mechanism variables play a more dominant role in carbon price formation, demonstrating the importance of institutional market design in shaping price dynamics. These findings indicate insights for ETS design reforms, such as allowance cap adjustment and allocation restructuring, that can strengthen carbon market efficiency and enhance emissions reduction outcomes. The proposed framework provides policymakers and market participants with a robust, data-driven decision-support tool to enhance forecast reliability and offer practical guidance for newly established or data-scarce ETS markets, contributing to more effective carbon market governance.