Carbon emission prediction control coefficient analysis for public institutions towards carbon neutrality by 2060: A case study of Tianjin
2060年カーボンニュートラルに向けた公共機関の炭素排出予測制御係数分析：天津市の事例 (AI 翻訳)
Minchao Fan, Nengbin Cao, Chunmei Guo, Bin Yang, Chong Meng
🤖 gxceed AI 要約
日本語
本研究は、天津市の7000以上の公共機関建物のエネルギー消費と排出データ（2016-2024年）を分析し、グレイモデルとグレイマルコフモデルを用いて将来の炭素排出を予測した。現在の年間排出制御係数（k=0.98）では2060年のカーボンニュートラル達成に不十分であり、k≦0.92（残余排出5%）またはk≦0.87（1%）が必要であることを示した。
English
This study analyzes energy consumption and emissions data from over 7,000 public institution buildings in Tianjin (2016-2024), using Grey and Grey-Markov models to forecast carbon emissions. It finds that the current annual emission control coefficient (k=0.98) is insufficient for carbon neutrality by 2060, requiring strengthening to k≤0.92 for 5% residual emissions or k≤0.87 for 1%.
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
中国の公共機関における炭素排出削減の具体的な数値目標と経路を示しており、日本の自治体や公共施設の脱炭素計画策定にも参考となる可能性がある。ただし、中国特有の政策枠組みに基づくため、直接の適用には注意が必要。
In the global GX context
This paper provides a case study of emission prediction and mitigation pathway design for public buildings in China, offering a methodology that could be adapted for similar building stock elsewhere. It highlights the gap between current policy coefficients and the ambition required for 2060 neutrality, relevant for global building sector decarbonization.
👥 読者別の含意
🔬研究者:Provides a validated Grey-Markov model for building emission prediction; useful for methodology comparison.
🏢実務担当者:Offers specific control coefficients for building retrofits and energy system planning.
🏛政策担当者:Demonstrates that current emission reduction targets may need strengthening to meet long-term neutrality goals.
📄 Abstract（原文）
Accurate carbon emissions prediction and development of mitigation pathways are critical but challenging for achieving carbon neutrality in the public building sector. This study systematically analysed the energy consumption and emissions of over 7000 public institution buildings in Tianjin, China, using data from 2016 to 2024. The Grey model (1,1) and Grey–Markov models were employed to analyse historical trends and forecast future carbon emissions. Comparative results showed the Grey–Markov model performed better and was validated by external data, with actual values falling within the corresponding uncertainty intervals. Projections indicated that total carbon emissions and emission intensity for Tianjin's public buildings are to decline, reaching approximately 3.4290 million tCO 2 and 66.87 kgCO 2 /m 2 by 2035. However, scenario analysis revealed that the current annual emission control coefficient proposed by the government ( k = 0.98) is insufficient to achieve carbon neutrality by 2060. To limit residual emissions to 5% of the 2023 baseline by 2060 requires strengthening annual control to k ≤ 0.92; achieving 1% residual requires k ≤ 0.87. Achieving these pathways demands accelerated and coordinated efforts in building retrofits, deep energy system decarbonization, robust policy, and financial frameworks. This study provides a scientifically grounded methodology for emission prediction and targeted mitigation strategy formulation.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1177/1420326x261437397first seen 2026-05-05 19:13:55
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