Probabilistic Scenarios Reveal the Impacts of China’s Energy System Net-Zero Transition on the Water–Energy–Food Nexus
確率的シナリオが示す中国のエネルギーシステムのネットゼロ移行が水・エネルギー・食料ネクサスに与える影響 (AI 翻訳)
Shu Zhang, Wenying Chen
🤖 gxceed AI 要約
日本語
中国のカーボンニュートラルに向けたエネルギー移行が水・エネルギー・食料（WEF）ネクサスに与える影響を、ボトムアップモデルとモンテカルロシミュレーションを統合した確率的枠組みで評価。4000以上の不確実性ケースを探索し、中央値経路では2060年に再生可能エネルギーが一次エネルギーの60%以上を供給する一方、カーボンニュートラル達成確率はCCS展開や再生可能エネルギー拡大速度に強く依存することを示した。早期の排出削減行動や持続可能性対策が不確実性を圧縮し、政策設計への実践的示唆を提供する。
English
This study develops a probabilistic framework integrating bottom-up models of China's water-energy-food (WEF) systems with Monte Carlo simulations to assess the impacts of the net-zero energy transition. Exploring over 4,000 uncertainty cases, it finds that while the median pathway achieves deep decarbonization with renewables supplying over 60% of primary energy by 2060, the likelihood of carbon neutrality is highly sensitive to CCS deployment and renewable expansion pace. Early mitigation and sustainability measures reduce uncertainty and relax WEF constraints, offering actionable policy diagnostics.
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
中国のエネルギー移行がWEFネクサスに与える影響を確率的に評価した本手法は、日本でもエネルギー政策と水・食料資源の連関を考慮する際に参考になる。特に、SSBJ開示や統合報告書で気候関連リスクを評価する際、ネクサス全体の不確実性を考慮したシナリオ分析の重要性を示唆する。
In the global GX context
This study provides a transferable probabilistic framework for assessing nexus-wide impacts of energy transitions, relevant to global climate disclosure and transition planning. It highlights the importance of capturing tail risks and uncertainty in decarbonization pathways, aligning with TCFD/ISSB scenario analysis requirements. The findings offer insights for policymakers balancing net-zero goals with resource sustainability.
👥 読者別の含意
🔬研究者:Provides a novel probabilistic integrated modeling approach for WEF nexus under deep uncertainty, useful for advancing scenario analysis methodologies.
🏢実務担当者:Offers insights into how early mitigation and sustainability measures can reduce transition risks, informing corporate strategy and disclosure.
🏛政策担当者:Delivers actionable diagnostics for designing decarbonization strategies that reconcile net-zero ambitions with WEF sustainability, applicable to national planning.
📄 Abstract（原文）
Abstract China’s energy transition toward carbon neutrality tightens interactions across the water–energy–food (WEF) nexus, amplifying systemic uncertainties that are poorly captured in previous assessments. This study develops a probabilistic integrated framework that couples bottom-up models for China’s WEF systems with Monte Carlo simulations across technological, policy, and behavioral uncertainty dimensions. By exploring over 4000 uncertainty cases, the framework quantifies not only median trajectories but also the full spectrum of outcomes and the tail risks associated with multidimensional uncertainties. Results indicate large heterogeneity in the probability space: while the median pathway attains deep decarbonization with renewables supplying over 60% of primary energy by 2060, the likelihood of achieving carbon neutrality by 2060 is highly sensitive to carbon capture and storage deployment, the pace of renewable expansion, and cross-WEF-system resource constraints. Early mitigation actions, such as accelerated coal phase-out and rapid scale-up of low-carbon fuels, substantially reduce reliance on negative emission technologies and compresses outcome uncertainty. Sustainability-oriented measures, including water use efficiency improvements and dietary changes, materially relax WEF constraints. These findings deliver actionable diagnostics for policymakers to design decarbonization strategies that reconcile China’s net-zero ambitions with long-term WEF sustainability. The methodological framework is transferable to other national contexts where nexus interactions and deep uncertainty govern transition feasibility.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.1021/acs.est.6c03030first seen 2026-08-06 06:06:18
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。