Strategic frameworks for carbon emission mitigation
炭素排出削減のための戦略的フレームワーク (AI 翻訳)
J. Merlin Rosia
🤖 gxceed AI 要約
日本語
本論文は、二酸化炭素排出削減の重要性と、炭素取引制度がもたらす不平等を指摘。LSTMモデルを用いたGDPセクター別のCO2予測を提案し、政府政策による排出削減フレームワークを提示。AI活用の政策立案ツールの必要性を強調している。
English
This paper highlights the necessity of CO2 mitigation and the inequality exacerbated by carbon trading. It proposes an LSTM-based forecasting model for GDP sectors and a framework for government policies to reduce emissions, emphasizing the need for AI-enabled planning tools for monitoring and risk management.
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
日本ではカーボンプライシングやGX推進政策が議論される中、本論文はAIを用いた排出予測と政策立案支援の枠組みを提供。データ駆動型の政策立案に参考となる。
In the global GX context
This paper contributes to global climate policy by addressing inequalities in carbon trading and demonstrating how machine learning can support sectoral emission forecasting and policy optimization, relevant for just transition discussions.
👥 読者別の含意
🔬研究者:Provides a methodological framework integrating LSTM forecasting with policy analysis for carbon mitigation.
🏢実務担当者:Offers a model for companies to forecast emissions and align with policy-driven reduction targets.
🏛政策担当者:Highlights the need for AI-based tools to design and monitor equitable carbon reduction policies.
📄 Abstract（原文）
Carbon dioxide emission mitigation is more essential to prevent global warming and to promote a low-carbon economy. The primary source of climate change is carbon dioxide emissions. Due to the increase in carbon dioxide emission the problems arises are extreme changes in weather, rise of sea level, melting of polar ice caps, and glaciers. It is well known that climate change is one of the greatest challenges humanity faces as it strives to achieve sustainable development goals, especially in terms of limiting CO2 emissions. This significant rise of emissions continually occurs from 2000 to 2026 for various reasons, such as the pressure of population growth, energy consumption, industrialization and urbanization, technological development and economic growth. High- and low-income countries diverge both in capacity to mitigate and emissions produced. A carbon trading system, therefore, bestowes favour on rich nations and exacerbates inequalities with poor countries benefiting little or not at all. To have control over the sectors to reduce the CO2 emission, we use a machine learning algorithm, such as an LSTM-based CO2 forecasting model, in all the GDP sectors. The research suggests a framework to reduce carbon dioxide emissions in GDP sectors through Government policies. This research also highlights that policy-makers should consider carbon emissions before framing a policy, and an AI-enabled planning tool is necessary for monitoring, system optimisation, and risk management.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1007/s43621-026-03247-0first seen 2026-05-15 17:29:25
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