高等教育機関におけるカーボンフットプリント評価とカーボンニュートラルへの道筋:インド・デヘラドゥン、アルパイン経営技術大学のケーススタディ
Carbon Footprint Assessment and Pathway to Carbon Neutrality in a Higher Educational Institute: A Case Study of the Alpine Institute of Management and Technology, Dehradun, India (原題)
Madhubala, Amit Goyal, Sanjay K. Sharma
🤖 gxceed AI 要約
日本語
インドの高等教育機関を対象に、ISO 14064-1とGHGプロトコルに準拠したボトムアップLCAで2022〜2025年の炭素フットプリントを算定。電力由来のScope 2が最大排出源(41〜44%)で、樹木調査による吸収量は呼吸を除けばScope 1-3を上回り既にカーボンニュートラル状態と評価。屋上太陽光・雨水利用・植林を追加吸収源として定量評価した。
English
A bottom-up LCA aligned with ISO 14064-1 and the GHG Protocol quantified the carbon footprint of an Indian higher-education institute over 2022-2025. Purchased electricity (Scope 2) dominated at 41-44%, and campus tree sequestration exceeded Scope 1-3 emissions when respiration was excluded, indicating existing carbon neutrality. Rooftop solar, rainwater harvesting, and afforestation were assessed as additional sinks.
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
日本では大学等の組織境界でのScope 1-3算定と吸収源評価の実務例は限られ、SSBJ・有報でのScope 3開示やカーボンニュートラル宣言を検討する教育機関・自治体にとって、算定境界の設計と吸収量評価の実践的ひな形となる。
In the global GX context
Adds empirical evidence to the underexplored higher-education subsector of corporate carbon accounting, offering a replicable template for Scope 1-3 boundary setting and sequestration assessment relevant to ISSB/CSRD-aligned organizational disclosure and net-zero planning.
👥 読者別の含意
🔬研究者:教育機関という未開拓セクターのScope 1-3算定と吸収源評価の方法論的枠組みを提供する。
🏢実務担当者:自組織の炭素会計で算定境界の設定と吸収量・オフセット計画の立案に活用できる。
🏛政策担当者:教育・公共機関の気候行動計画やSDG13報告の枠組み設計の参考になる。
📄 Abstract(原文)
The building sector is a major contributor to global greenhouse gas (GHG) emissions, and higher education institutions, with their diverse operational activities, represent a significant but underexplored subsector for carbon accounting. This study quantifies the carbon footprint of the Alpine Institute of Management and Technology, Dehradun (Uttarakhand, India), over four calendar years (2022-2025) using a bottom-up Life Cycle Assessment (LCA) approach aligned with ISO 14064- 1 and the GHG Protocol Corporate Standard. Emission sources were classified into Scope 1 (static and mobile combustion), Scope 2 (purchased electricity), and Scope 3 (employee commuting, waste disposal, water consumption, and paper/stationery use), with human respiration quantified separately as a supplementary source. Total institutional emissions under Scopes 1-3 were found to be 56,351.77 kg CO2-eq (2022), 60,131.82 kg CO2-eq (2023), 62,168.00 kg CO2-eq (2024) and 59,604.84 kg CO2-eq (2025), with purchased electricity (Scope 2) the dominant contributor at 41-44% of the total in every year studied, followed by Scope 1 combustion sources (38-40%) and Scope 3 activities (18-20%). Inclusion of human respiration more than doubled the reported footprint to a range of 126,536.77-134,468.84 kg CO2-eq, accounting for 55-58% of total emissions across the study period. A tree census conducted within the institutional boundary and evaluated using allometric biomass equations yielded an existing carbon sequestration potential of 73,332.00 kg CO2-eq, which exceeds the Scope 1-3 emissions for every year studied, indicating that the campus already functions as a carbon-neutral facility when respiration is excluded from the accounting boundary. When respiration is included, the sequestration deficit indicates a need for additional carbon sinks and offsets, for which rooftop solar photovoltaic potential (2,625 m2 of available roof area), rainwater harvesting, and afforestation initiatives are identified and quantitatively assessed. These results provide a replicable, data-driven template for carbon accounting and neutrality planning in resource-constrained educational institutions and contribute empirical evidence toward Sustainable Development Goal 13 (Climate Action).
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.22214/ijraset.2026.84887first seen 2026-09-19 05:04:23
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録(無料・3キーワードまで)。
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。