How we model

We build capacity-expansion and economic dispatch models using PyPSA — a Python-for-Power-System-Analysis framework that solves linear and mixed-integer linear programmes over networked electricity systems.

Core formulation. Each model minimises total system cost (annualised capital + variable operating costs) subject to energy balance, generation capacity limits, transmission capacity, storage state-of-charge, and unit commitment constraints. For large national grids we use linear relaxations; for islanded systems (Jeju, energy island concepts) we may retain integer variables to capture startup/shutdown cycles.

Korean grid specifics. PyPSA-KR-PLANiT encodes Korea's seven regional bidding zones, hourly demand profiles, time-series capacity factors for 25 GW of existing nuclear and coal, and pending grid-connection corridors. simplePyPSA_KR strips the network to a single-node "copper-plate" approximation for rapid scenario scanning. pypsa_jeju_simple models Jeju Island as an isolated network with a single HVDC cable to the mainland.

Cross-border interconnection. KRJP-EnergyIsland extends the model to a Korea–Japan offshore energy island, evaluating optimal HVDC cable sizing and shared renewable build-out under different carbon price trajectories.

Accessibility. pypsa_oneclick wraps the solver call in a Streamlit web app so non-technical stakeholders can adjust scenario parameters and re-run the model in a browser. pypsa_gui provides a desktop Qt interface for visualising network topology.

We model sector-level decarbonisation trajectories using Marginal Abatement Cost Curves (MACC) combined with exogenous or endogenous carbon price paths.

MACC construction. Each abatement option is characterised by its technical potential (tCO₂/yr) and marginal cost (USD/tCO₂). Costs include incremental CAPEX, OPEX, fuel switching costs, and avoided energy costs. The curve is sorted by marginal cost to yield the classic "hockey stick" shape — cheap measures first, expensive ones last.

Pathway optimisation. emissionpathway solves an inter-temporal cost-minimisation problem: given a carbon budget constraint (e.g., 1.5 °C-consistent sectoral budget), find the deployment schedule for each abatement option that minimises total net present cost. energypathway applies the same logic specifically to the energy supply mix, coupling with PyPSA outputs for power-sector abatement costs.

Carbon pricing scenarios. We model three price regimes — a regulatory floor (Korea ETS), an IMO levy, and a social cost of carbon — and test how the optimal pathway changes under each. systempathway integrates economy-wide interactions between sectors, accounting for fuel substitution and induced demand.

Petrochemical application. petrochemical_macc_2025 constructs a plant-level MACC for Korea's petrochemical complex using disclosed energy intensity data and technology costs from IEA, IRENA, and operator reports. Each plant's abatement cost is estimated from process-specific hydrogen substitution rates, electrification potential, and carbon capture retrofit costs.

Our shipping models evaluate compliance costs and fuel transition economics for vessel operators under evolving IMO greenhouse gas regulations (IMO GHG Strategy 2023, FuelEU Maritime, CII ratings).

Carbon price pass-through model (shipping_carbonprice). This tool calculates the Revenue Unit (RU) and Surplus Unit (SU) dynamics under IMO's proposed GHG levy mechanism. For a given vessel type, route, and fuel mix, it solves:

• The implied carbon intensity (CII score) using the attained EEXI/CII formula
• The levy payable per voyage under a dual-rate carbon price structure
• The break-even fuel price at which alternative fuels become cost-competitive with conventional VLSFO

The model is parameterised for bulk carriers, container ships, and tankers across the Korea–Japan–Singapore trade lane.

Operator decision model (shipping_operator). This module models the ship operator's fuel choice as a discrete optimisation problem over a planning horizon of 5–20 years. The operator chooses among: continue on VLSFO, switch to LNG, switch to methanol, switch to ammonia, or drydock for retrofitting. The objective is to minimise the net present cost of compliance over the vessel's remaining life, incorporating:

• Fuel CAPEX and OPEX uncertainty (Monte Carlo over fuel price distributions)
• Regulatory risk scenarios (IMO levy adoption timeline, CII rating stringency)
• Stranded asset risk from lock-in to a transitional fuel

We use Leontief input-output (I-O) analysis to trace how sectoral decarbonisation propagates through supply chains — quantifying both direct and indirect economic and emissions effects.

Core I-O framework (iotable). The library ingests the Bank of Korea's official supply-use and symmetric input-output tables and builds the Leontief inverse matrix L = (I − A)⁻¹, where A is the matrix of technical coefficients (intermediate demand as a share of total output). A demand shock vector Δf (e.g., reduced demand for coal in steelmaking) is multiplied by L to yield total output changes across all linked sectors.

We extend the standard framework in three ways:

1. Emissions multipliers. Each sector's CO₂ intensity (tCO₂/KRW output) is appended so that output changes automatically translate to scope-1, scope-2, and scope-3 emission changes.
2. Value-added and employment multipliers. Gross value added per unit output and FTE-equivalents per unit output are tracked, allowing us to estimate job impacts of industrial transition.
3. Import leakage. We separate domestic from import coefficients to model carbon leakage — the risk that Korean decarbonisation simply shifts production (and emissions) offshore.

Steel sector application (steel_iotable). This repository applies the framework to Korea's integrated steel industry. It models the transition from blast-furnace/basic-oxygen-furnace (BF/BOF) routes to electric arc furnace (EAF) and direct-reduced-iron/hydrogen (DRI-H₂) routes, tracing cascading effects on coke, iron ore, scrap, and electricity sectors.

We combine top-down carbon budget allocation with bottom-up asset-level risk modelling to estimate the financial implications of the climate transition for Korean industrial assets.

Carbon budget allocation (carbonbudget). Starting from the IPCC AR6 global remaining carbon budget for 1.5 °C and 2 °C pathways, we apply a convergence-based effort-sharing approach to derive Korea's national sectoral budgets. The library implements three allocation principles — equal per-capita, ability to pay, and historical responsibility — and outputs sector-year budget matrices compatible with MACC and I-O models.

Transition risk screening (climate_risk). For a given asset (power plant, steel mill, chemical plant), the model estimates:

• Physical risk exposure: based on asset location, flood risk maps (FATHOM Global, K-water), and temperature anomaly projections (SSP2-4.5, SSP5-8.5)
• Transition risk exposure: the net present value loss from early retirement if a carbon price path renders the asset uneconomic before end of its technical life
• Stranded value: undepreciated book value × probability of early retirement under each carbon price scenario

Risk premium quantification (climate_risk_premium). This module estimates the additional financial return (risk premium) that rational investors should demand to hold climate-exposed Korean industrial bonds. It calibrates a reduced-form credit risk model (Merton-style) with transition risk overlaid as a shock to asset value, and maps estimated default probabilities to spread levels consistent with Korean corporate bond market data.

We build financial models for clean energy project economics: levelised cost benchmarking, corporate power purchase agreement (PPA) structuring, and grid infrastructure cost allocation.

Levelised cost (levelisedcost). The library computes the Levelised Cost of Energy (LCOE) and Levelised Cost of Hydrogen (LCOH) for a wide range of technology configurations. The standard LCOE formula discounts all lifetime costs (CAPEX, OPEX, fuel, decommissioning) by a weighted average cost of capital (WACC) and divides by lifetime energy production. We extend this to:

• Technology-specific degradation curves (solar PV performance ratio decay, wind blade fatigue)
• Capacity factor distributions using regional time-series (hourly for Korea, Japan, offshore Northeast Asia)
• Sensitivity surfaces over WACC × fuel cost × carbon cost parameter space

PPA pricing (pyPPA, simplePPA). These libraries model the economics of bilateral renewable electricity supply contracts between a generator and a corporate offtaker. pyPPA is the full-featured version: it structures a fixed-price or indexed PPA, simulates the generator's revenue under the contract against the wholesale market price (Korea Power Exchange), and solves for the PPA price that achieves a target project IRR. simplePPA is a lightweight version for quick screening.

Grid and transmission cost (gridcost). Korea's transmission tariff embeds substantial cross-subsidies between user classes. This library decomposes grid access costs using a postage-stamp and MW-mile methodology, estimates the locational marginal premium for congested corridors, and models how large-scale offshore wind connection charges affect industrial electricity consumers.

We use geospatial analysis to quantify renewable energy potential, map land-use conflicts, and site clean energy infrastructure — combining Korean government spatial datasets with global remote sensing products.

vWorld API downloader (vworldAPIdownloader). Korea's vWorld platform provides authoritative spatial datasets: cadastral land parcels, topographic contours, agricultural land classification, protected area boundaries, transmission line corridors, and administrative boundaries. Our downloader automates paginated API calls, handles coordinate reference system conversions (EPSG:5174 → EPSG:4326), and stores results as GeoPackage files for downstream GIS analysis.

Renewable siting pipeline. Built on top of the downloader, our siting pipeline layers multiple exclusion and weighting criteria:

• Agricultural land grades (to identify agrivoltaic candidates while respecting food security constraints)
• Slope and aspect from a 5 m DEM (Korea NGII)
• Distance to existing 154 kV and 345 kV transmission corridors
• Exclusion of protected habitats, flood plains, and military zones
• Wind speed climatology at 100 m hub height (ERA5 reanalysis, bias-corrected with KMA surface stations)

Offshore wind resource assessment. For offshore areas, we combine GEBCO bathymetry (to classify fixed-foundation vs. floating feasibility by depth), ERA5 wind speed distributions, and Korea's marine spatial planning exclusion zones to produce area-weighted capacity factor maps at 0.25° × 0.25° resolution.

Integration with energy models. Siting outputs feed directly into PyPSA as time-varying capacity factor time series, and into levelisedcost as location-specific LCOE inputs, closing the loop between spatial analysis and system economics.