銀行業、中間目標と排出目標のコスト：データと複製コード

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:炭素市場制度設計と目標達成コストのモデル比較手法、複製可能なコードとデータの参考になる。

🏢実務担当者:バンキング制約や中間目標が自社の炭素価格エクスポージャとコンプライアンスコストに与える影響を理解できる。

🏛政策担当者:中間目標とバンキング上限の設定が目標達成コストと炭素価格経路に与える影響を制度設計に反映できる。

This dataset contains the model outputs and the Python code that reproduce the quantitative results of the paper “Banking, Interim Targets and the Cost of Emission Targets: Theory and an Application to Korea’s 2035 Nationally Determined Contribution” by Ick Jin. The paper studies how restrictions on allowance banking and interim targets affect the cost of meeting an emissions target. Compliance is solved under five regimes (end-year target, annual compliance, cumulative budget without borrowing, cumulative budget with a 2030 interim target, and banking caps) in the CO-STIRPAT equilibrium system, a quarterly input–output macro model calibrated to Korea. The model is documented in SSRN Working Paper 7028438 (https://doi.org/10.2139/ssrn.7028438). Files analysis_data.xlsx: model outputs used in the paper. author: author and paper. readme: list of sheets. regime_meta: regime names and price segments. regime_paths: annual paths by regime, 2024–2050 (carbon price, net emissions, allowance path, consumption, real GDP, carbon revenue). baseline: baseline without a carbon price. case_paths: end-year path under alternative specifications. case_summary: 2035 power-sector and macroeconomic indicators. recycling: revenue recycling at the target-meeting price. mc_draws: fixed-path Monte Carlo, 200 Latin hypercube draws of the assumed coefficients. robust_paths: regime comparison under alternative solver settings, median coefficients and the 2019 K-ETS carry-over limit. mc_regime_draws: regime comparison across 30 Latin hypercube draws, with R2 and R3 re-solved for each draw. analysis_code.py: reproduces Tables 2–6 and C.1–C.5, Fig. 2, the partial-equilibrium surrogate (Section 5.3) and the in-text results of Sections 5.4 and 5.6 from analysis_data.xlsx. README.md: software, usage and the source of each result in the paper. Usage Run “python analysis_code.py analysis_data.xlsx”. The script writes analysis_results.xlsx (one sheet per result) and fig2.png, and prints all results. It requires Python 3.12 with numpy, pandas, scipy, openpyxl and matplotlib (see README.md for versions). Units Carbon prices in KRW per tonne of CO2eq (2024 prices, annual averages); emissions in Mt CO2eq; consumption, GDP and revenue in trillion KRW (2024 prices). Present values are discounted at 4.5% a year to 2027. Note The data are simulated outputs of the CO-STIRPAT equilibrium system, not observed statistics.

• openalex https://doi.org/10.17632/src5wfx4vkfirst seen 2026-11-12 04:44:35 · last seen 2026-11-14 04:49:15