[Carbon Emission Probability Density Forecasting and Carbon Peaking Potential Analysis： A Case of Anhui Province].
炭素排出確率密度予測と炭素ピークの可能性分析：安徽省を事例として (AI 翻訳)
Yaoyao He, Can Li
🤖 gxceed AI 要約
日本語
本研究は、安徽省を対象に分位点回帰ニューラルネットワーク（QRNN）を用いた炭素排出確率密度予測モデルを開発した。2022年の排出実績評価と2023～2035年の4シナリオ分析により、エネルギー革命シナリオが最適で、2028年に456.78 Mtでピークを迎えると予測した。不確実性を捉えた高精度予測を実現し、政策決定への示唆を提供する。
English
This study develops a carbon emission probability density forecasting model using quantile regression neural network (QRNN) for Anhui Province, China. It evaluates 2022 reduction progress and forecasts under four scenarios (2023-2035). The energy revolution scenario is optimal, with a carbon peak expected in 2028 at 456.78 Mt. The model captures uncertainty better than traditional point forecasts, providing scientific support for policy design.
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
本論文は中国安徽省の事例であるが、確率論的排出予測手法は日本の自治体や地域単位での炭素ピーク分析に応用可能。特にSSBJにおける将来排出見通しの開示要求が強まる中、不確実性を明示した予測は投資家対話でも有用。
In the global GX context
This provincial-level analysis demonstrates a probabilistic forecasting method for carbon emissions, relevant for global decarbonization planning. The QRNN approach offers a way to handle uncertainty in emission trajectories, which is increasingly important for TCFD/ISSB-aligned scenario analysis and transition plan disclosure.
👥 読者別の含意
🔬研究者:The QRNN-based density forecasting method provides a robust framework for capturing emission uncertainty, applicable to other regions or sectors.
🏢実務担当者:Provincial policymakers can use the scenario analysis to identify optimal peaking pathways and set evidence-based reduction targets.
🏛政策担当者:The finding that the energy revolution scenario leads to an earlier peak (2028) offers a concrete policy pathway for China and other developing regions.
📄 Abstract（原文）
China has committed to achieving carbon peaking before 2030， which calls for overcoming the limitations of traditional point prediction methods in addressing the uncertainties of carbon emissions. A more comprehensive forecasting model is essential to support scientific decision-making and policy design. As a key part of the Yangtze River Delta integration and the Central China Rise strategy， Anhui Province plays a critical role in promoting high-quality development and low-carbon transition. Therefore， Anhui Province is selected as the study area to develop a carbon emission probability density forecasting model based on the quantile regression neural network （QRNN）. It evaluates the actual emission reduction progress in 2022 and conducts carbon emission forecasts and peaking potential analysis under four scenarios for the period 2023-2035. The main findings are as follows： ① The proposed model not only achieved high-accuracy point prediction but also effectively captured the uncertainty of carbon emissions. ② In 2022， Anhui's carbon reduction performance was unsatisfactory， with a 79.01% probability that total emissions will continue to increase， and relative reduction targets were not achieved. ③ The energy revolution scenario was identified as the optimal development pathway， under which Anhui is expected to reach its carbon peak in 2028， with a peak value of 456.78 Mt. This study provides theoretical support and decision-making references for Anhui Province in formulating precise carbon peaking strategies and promoting green， sustainable regional development.
🔗 Provenance — このレコードを発見したソース
- openalex https://pubmed.ncbi.nlm.nih.gov/42473357first seen 2026-07-22 05:12:00
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