「移行への資金調達：金利・政策不確実性・制度と各国の再生可能電力移行、2010〜2025年」の複製パッケージ

Replication package for 'Financing the Transition: Interest Rates, Policy Uncertainty, and Institutions in the Cross-Country Renewable Electricity Transition, 2010–2025 (原題)

ATIQUZZAMAN

Zenodo (CERN European Organization for Nuclear Research)データセット2026-10-13#エネルギー転換Origin: Global経営インパクト: 資金調達対象セクター: power

DOI: 10.5281/zenodo.23343065

原典: https://doi.org/10.5281/zenodo.23343065

🤖 gxceed AI 要約

日本語

本稿は、18カ国を対象に2010〜2025年の再生可能電力移行を金利・政策不確実性・制度の観点から分析した論文の複製パッケージである。システムGMMやパネルARDL、合成コントロール法などの計量手法を再現するスクリプトとデータを公開し、再現性を担保する。金利や政策不確実性が再エネ投資を左右するという実証的枠組みを提供する。

English

This is the replication package for a study analyzing the cross-country renewable electricity transition (2010–2025) through interest rates, policy uncertainty, and institutions. It provides scripts and data reproducing system GMM, panel ARDL, and synthetic control analyses for 18 countries, ensuring reproducibility of the empirical framework linking financing conditions to renewable investment.

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

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

再エネ移行における金利・政策不確実性の役割は、日本のGX推進・トランジション・ファイナンスや電力部門の脱炭素投資判断に直結する。再現可能な計量枠組みは、国内の政策評価や投資環境分析に応用できる。

In the global GX context

The paper speaks to global debates on how monetary conditions and policy uncertainty shape renewable deployment, relevant to transition finance and climate investment under frameworks like TCFD/ISSB. Its reproducible econometric design offers a template for cross-country climate-finance research.

👥 読者別の含意

🔬研究者:再エネ移行と金利・政策不確実性の関係を扱う計量研究の再現可能な手法とデータ設計を学べる。

🏢実務担当者:再エネ投資判断における金利・政策リスクの影響を理解する参考になる。

🏛政策担当者:政策不確実性の低減が再エネ投資を促す可能性を示唆し、制度設計の重要性を示す。

📄 Abstract（原文）

# Replication package: "Financing the Transition"Replicates every table and figure in the manuscript"Financing the Transition" (submitted to Energy Policy).## EnvironmentPython 3.11 with: pandas 3.0.2, numpy 2.4.4, statsmodels 0.15.0, linearmodels 7.0,pydynpd 0.2.2, pysyncon, matplotlib 3.10.9 (see `requirements.txt`).**Known issue (pydynpd 0.2.2).** With recent NumPy versions, `pydynpd/specification_tests.py`fails with `TypeError: only 0-dimensional arrays can be converted to Python scalars`(Hansen and Arellano-Bond tests). Wrapping the three conversions in `np.asarray(...).item()`(the `float(hansen_test)`, `float(p_value)` and `math.sqrt(d1 + d2 + d3)` / `float(d0 / ...)`lines) fixes it and reproduces the reported results exactly.## Run order (from `scripts/`)| Script | Produces | Manuscript item ||---|---|---|| `01b_build_extended_panel.py` | `data/processed/panel_extended_2010_2025.csv`, `panel_analysis_2010_2024.csv` | Data (Section 4) || `08_descriptives_extended.py` | `summary_statistics_extended.csv`, `correlation_matrix_extended.csv` | Tables 1, 2 || `03b_extended_fe_h2_h3.py` | `extended_fe_h2_h3_results.txt` | Table 3, CD test, Hausman test || `04b_extended_system_gmm.py` | `extended_system_gmm_results.csv/.txt` | Table 4 (Hansen/AR tests print to console) || `05b_extended_panel_ardl.py` | `extended_panel_ardl_results.txt`, `..._long_run_multipliers.csv` | Tables 5, 6 || `06b_synthetic_control_h4_real.py` | `synthetic_control_us_2025_h4.txt`, US figures | Figure 1, Figure B2 || `06_synthetic_control_demo.py` | `synthetic_control_india_2015.txt`, India figures | Figure B1 (pipeline validation) || `07_cips_test.py` | `cips_test_results.txt`, `cips_cadf_by_country.csv` | Section 7.3, Table B1 |`data/raw/` holds the raw pulls used by `01b`. The full Our World in Data file (about 9 MB) is notincluded; `owid_extract_18country.csv` is the 18-country extract taken from the Ember / Our World in DataYearly Electricity Data (github.com/owid/energy-data). Running `01b` regenerates both processed panelsbyte-for-byte from the raw files. `06_synthetic_control_demo.py` reads `data/processed/panel_core18_2010_2024.csv`,which is supplied.Figures are saved without in-image titles at 300 dpi (journal requirement); captions are in the manuscript.## Data sources (see `docs/DATA_ACCESS_LOG.md` and `docs/CODEBOOK.md`)World Bank WDI and WGI (World Bank API / Data360), World Uncertainty Index (FRED country series,Ahir, Bloom and Furceri 2022), Ember / Our World in Data Yearly Electricity Data (2026 release).Data were retrieved on 2026-09-15 and 2026-09-16. Check each provider's terms before redistribution.## VerificationAll tables were re-generated from the scripts and data in this folder and compared with the savedoutputs; results were identical (apart from run timestamps).

🔗 Provenance — このレコードを発見したソース

openalex https://doi.org/10.5281/zenodo.23343065first seen 2026-10-16 04:51:39

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