中国における原油価格・気候リスク・炭素価格間のスピルオーバー効果：動的効果と主要ドライバー

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🔬研究者:原油価格ショックの分解と周波数領域スピルオーバーを組み合わせた炭素市場連関分析の実証手法が参考になる。

🏢実務担当者:炭素価格・気候リスク・為替・流動性の連動を踏まえ、炭素エクスポージャーの短期リスク管理に活用できる。

🏛政策担当者:地域炭素市場と全国ETSの流動性・対象部門の差異を踏まえ、市場安定化策や情報開示設計を検討する材料になる。

Against the backdrop of escalating global climate change and volatile international oil markets, the interdependence among energy prices, climate-risk information, and carbon markets has become increasingly prominent. Clarifying their dynamic spillover patterns is important for climate governance, energy security, and carbon-market risk management. This study examines the dynamic interactions among decomposed oil price shocks, physical and transition climate-risk indices, and China’s regional carbon price represented by the Guangdong pilot market. The research design is structured in three stages. First, oil price fluctuations are decomposed into supply, demand, and risk shocks following Ready (2018), and a time-varying parameter vector autoregressive (TVP-VAR) model is employed to capture evolving spillovers. Second, the Baruník–Křehlík (2018) frequency-domain spillover framework is used to distinguish short- and long-horizon connectedness. Third, the XGBoost–SHAP framework is used as a post-estimation predictive complement to identify nonlinear feature contributions to changes in spillover intensity; the resulting SHAP values are interpreted as feature-association evidence rather than structural causal estimates. The results show that: (1) oil price shocks, carbon prices, and climate risks exhibit substantial volatility spillovers, with short-term spillovers considerably stronger than long-term spillovers (baseline short-term TCI = 24.91 versus long-term TCI = 5.30, using a 1–10 trading-day short-term band); (2) physical and transition climate-risk information has strong bilateral spillover links with carbon prices, while oil-related risk shocks are more relevant in the short term and supply- and demand-related oil shocks display stronger long-horizon connections; and (3) SHAP-based variable-importance analysis indicates that exchange rate dynamics, China’s economic policy uncertainty, and market liquidity are important predictors of time-varying spillover intensity. Robustness checks using the Hubei pilot carbon market, together with additional frequency-domain evidence under the baseline 1–10 trading-day partition, confirm that the main conclusions remain qualitatively stable. The findings should be interpreted in light of the fact that regional pilots differ from the national ETS in liquidity, sector coverage, and allowance fungibility, and that the climate-risk indices are constructed from global media sources.

• openalex https://doi.org/10.3389/fenvs.2026.1888062first seen 2026-11-13 05:24:54