Carbon emission factor of each category.

Within the context of advancing global sustainable development goals, universities are recognized as leaders in energy conservation and emissions reduction within the education sector. Universities should actively engage in the accounting and analysis of carbon emissions. This study uses Sichuan University Jinjiang College(Hereafter referred to as J University) in Sichuan, China, as a case study, where the campus’s carbon emissions for the year 2023 were calculated using the Emission Factor Method and the Delphi Method. The uncertainty associated with these emissions was further explored using Monte Carlo simulation. The results indicate that the net carbon emissions of J University amounted to 44,584.33 tons of CO2 equivalent (tCO2e), with per capita emissions of 1.89 tCO2e. The primary sources of campus carbon emissions, in descending order, include electricity (18879.94tCO2e), natural gas (8647.25tCO2e), business travel (5224.55tCO2e), campus commuting (3852.33tCO2e), food (3444.67tCO2e), and thermal energy (2566.63tCO2e). Among these sources, the carbon emissions from electricity, natural gas, and thermal energy were closely correlated with seasonal and regional factors. The uncertainties related to commuting and business travel had the most significant impact on the overall carbon emissions accounting for the campus. The study presents a framework for campus carbon emission accounting, providing a concrete case study for future researchers in this field. In particular, an in-depth exploration of statistical uncertainties is conducted, offering a scientific basis for the accurate calculation of carbon emissions in future studies.

在推进全球可持续发展目标的背景下，高校被视为教育领域节能减排的领军者。高校应积极开展碳排放核算与分析工作。本研究以中国四川省四川大学锦江学院（以下简称J大学）为案例对象，采用排放因子法（Emission Factor Method）与德尔菲法（Delphi Method）核算了该校2023年的校园碳排放量，并通过蒙特卡洛模拟（Monte Carlo simulation）进一步探究了本次碳排放核算的不确定性。研究结果显示，J大学2023年净碳排放总量达44584.33吨二氧化碳当量（tCO2e），人均碳排放量为1.89 tCO2e。校园碳排放的主要来源按占比从高到低依次为：电力（18879.94 tCO2e）、天然气（8647.25 tCO2e）、商务出行（5224.55 tCO2e）、校园通勤（3852.33 tCO2e）、餐饮（3444.67 tCO2e）以及热能（2566.63 tCO2e）。其中，电力、天然气与热能相关碳排放与季节及区域因素密切相关。校园通勤与商务出行相关的不确定性对整体校园碳排放核算结果的影响最为显著。本研究构建了校园碳排放核算框架，为该领域后续研究者提供了具体实践案例；尤为关键的是，本研究深入开展了统计不确定性分析，可为未来碳排放精准核算研究提供科学依据。