https://scholar.gist.ac.kr/handle/local/7928 Fri, 02 Oct 2026 01:40:16 GMT 2026-10-02T01:40:16Z https://scholar.gist.ac.kr/handle/local/34508 Title: Explainable AI for Economic–Environmental Trade-offs and Regional Heterogeneity in Land-Use Composition Author(s): Juyoung Park Abstract: Tropical forests face accelerating pressure from land-use transformation, yet whether economic development necessarily entails environmental degradation remains contested. Resolving this tension requires moving beyond aggregate analyses to examine how land-use composition simultaneously shapes both dimensions across structurally diverse local contexts. This study therefore examines how land-use compositions shape economic and environmental outcomes at the municipal level in Pará, Brazil, a state where extensive land conversion has generated disproportionate greenhouse gas emissions alongside persistent economic underdevelopment. Employing a framework that integrates machine learning model comparison, SHAP-based interpretation, Gaussian Mixture Model （GMM） clustering, and spatial autocorrelation analysis, the results reveal substantial nonlinear and heterogeneous effects. Pasture and mining drive upward environmental pressure without commensurate economic returns, while perennial crops tend toward more environmentally favorable configurations. A key finding is that economic and environmental outcomes more frequently move in the same direction than in opposition, suggesting that land-use composition reflects broader structural conditions shaping both dimensions concurrently. The classic conservation-development trade-off is therefore not inevitable; pathways exist in which environmental protection and economic performance are mutually compatible, contingent on local structural context. The resulting municipal typologies further display significant positive spatial autocorrelation, indicating that similar trade-off structures cluster geographically across the state. Building on these findings, this study proposes an empirically grounded decision-support framework that translates municipal trade-off profiles into spatially differentiated planning typologies, highlighting the importance of context-sensitive strategies over uniform average-effect approaches. Wed, 31 Dec 2025 15:00:00 GMT https://scholar.gist.ac.kr/handle/local/34508 2025-12-31T15:00:00Z https://scholar.gist.ac.kr/handle/local/34488 Title: Changes in Operational Carbon and Selected Infrastructure Embodied Carbon under Hourly Grid-Import Cap Stringency for a Fixed-Load Data Center Author(s): Seunghwan Lee Abstract: Data centers require large and continuous electricity supply, but clean-electricity procurement is often evaluated through annual renewable matching. Annual matching is useful as a volume benchmark, yet it does not show whether clean electricity is available in the same hours as a fixed load. Existing 24/7 clean-electricity studies clarify this annual-to-hourly distinction, and data-center carbon studies emphasize the need to consider both operational and embodied carbon. However, less attention has been given to how higher hourly grid-import cap stringency shifts carbon burden between grid operation and selected clean-energy infrastructure in a fixed-load data-center pathway. This thesis evaluates that mechanism using a deterministic single-node linear optimization model based on CAISO 2022 hourly input data. The model represents a fixed 100 MW data-center load, PV and wind generation, renewable-charged 4-hour battery storage, grid imports, and model-internal curtailment. Hourly stringency is represented as a grid-import cap, and the objective minimizes modeled annual carbon within a defined accounting boundary. The carbon metric combines operational carbon from grid imports with selected PV, wind, and battery infrastructure embodied carbon. The annual benchmark shows that a 100% annual 50:50 PV/wind portfolio requires 410.641 MW of combined PV and wind capacity, or 4.106 times the fixed load. When this annual portfolio is checked hour by hour without storage or time shifting, the same-hour clean-served share is about 67.5%. This diagnostic shows that annual MWh adequacy alone does not ensure hourly feasibility for a continuous fixed load. The core alpha sweep, where alpha denotes hourly grid-import cap stringency, shows a carbon-burden shift. From alpha = 0.80 to alpha = 1.00, operational carbon decreases by 3,727.952 tCO2/year, while selected infrastructure embodied carbon increases by 13,980.540 tCO2/year. As a result, total modeled annual carbon increases by 10,252.587 tCO2/year in the base case. At alpha = 1.00, the model selects 348.415 MW of PV, 564.738 MW of wind, and 2,414.965 MWh of battery energy capacity. The resulting 913.153 MW PV/wind value is the renewable component selected jointly with battery energy capacity, not a no-battery hourly requirement. These findings show that annual MWh adequacy, same-hour feasibility, battery energy capacity, and selected infrastructure embodied carbon must be interpreted together. Within the modeled CAISO fixed-load PV/wind/4-hour-battery pathway, higher hourly grid-import cap stringency reduces grid operational carbon but can shift carbon burden to selected infrastructure. Wed, 31 Dec 2025 15:00:00 GMT https://scholar.gist.ac.kr/handle/local/34488 2025-12-31T15:00:00Z https://scholar.gist.ac.kr/handle/local/34478 Title: An Appropriate-Technology Approach to On-Premise AI Multilingual Translation of Village Broadcasting Author(s): Chaikyung LIM Wed, 31 Dec 2025 15:00:00 GMT https://scholar.gist.ac.kr/handle/local/34478 2025-12-31T15:00:00Z