「取引モードと炭素価格の商品先物との関連性：中国全国排出量取引制度からの証拠」の研究コード

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

🔬研究者:炭素価格と商品市場の動的連動を扱う計量手法（階層的交互作用・Newey–West・ローリング窓）の再現可能な実装例。

🏢実務担当者:炭素価格変動が調達コストや商品エクスポージャーに波及しうる点を、リスク管理・ヘッジ検討の参考にできる。

🏛政策担当者:ETS部門拡大が価格波及経路をどう変えうるか、制度設計・市場監視の論点として示唆。

This repository contains the Python research code accompanying the manuscript entitled "Trading modes and carbon-price associations with commodity futures: Evidence from China's national emissions trading system" by Wenhui Shao. Study overview The study examines how listed-agreement and block-agreement trading activity in China's national emissions trading system (ETS) is associated with subsequent commodity-futures returns. It combines daily China Emission Allowance (CEA) composite closing prices and trading volumes from the Shanghai Environment and Energy Exchange with contract-level data for eleven commodity-futures products from the Shanghai Futures Exchange. The sample covers July 16, 2021, to August 14, 2026, and the primary empirical analysis contains 1,229 daily observations. The analysis uses a hierarchical interaction model to compare trading-mode-specific carbon-price associations across periods defined by the March 2025 national ETS sectoral expansion. The main specification employs calendar-year and month-of-year fixed effects with Newey–West heteroskedasticity and autocorrelation consistent standard errors. Additional analyses examine alternative specifications, product classifications, permutation sensitivity, rolling-window estimates, event-time intervals, and alternative cutoff dates. Repository contents 01_cneeex_carbon_data.py: Retrieves and processes official carbon-market and commodity-futures data, constructs analysis panels, and performs data integrity checks. 02_main_empirical_analysis.py: Constructs next-day commodity-futures returns, estimates the primary hierarchical interaction model, and performs robustness and sensitivity analyses. 03_dynamic_price_discovery.py: Examines the temporal evolution of trading-mode associations through rolling-window estimation, event-time analysis, cutoff sensitivity, and trading-volume composition. 04_publication_figures_and_tables.py: Generates figures and tables corresponding to the empirical analysis. Data and reproducibility The scripts are intended to be executed sequentially, from 01 to 04. This repository contains source code only; raw exchange data and constructed analysis datasets are not included. The data acquisition script accesses official exchange sources, and execution requires the relevant Python dependencies and access to those sources. Interpretation The study documents period-dependent conditional associations between carbon-market trading activity and subsequent commodity-futures returns. These associations should not be interpreted as separately identifying the causal effects of the 2025 sectoral expansion or subsequent changes in the construction of the official CEA composite price benchmark.

• openalex https://doi.org/10.5281/zenodo.23289387first seen 2026-10-13 04:37:20