隨著全球淨零碳排政策與永續發展目標的推動，企業面臨日益嚴格的碳排放管理與揭露要求。然而，傳統碳盤查方法多屬靜態分析，難以即時反映商業模式調整對碳排放的影響，進而限制企業在商務模式創新階段的決策效率。
本研究結合生成式人工智慧（Generative AI, GAI）、商業模式畫布（Business Model Canvas, BMC）與國際碳排放計算標準之ISO 14064-1與 ISO 14067，提出一套可快速模 擬企業碳排放之模擬估測的方法。透過設計結構化的提示指令，引導 GAI 理解企業商業模式內容，並依據碳排類別對照表模擬各構面之碳排放活動，進而完成碳排估算與熱點分析。
本研究以服務型商業模式（Uber Eats）與產品型商業模式（發現茶）作為案例進行方法的實測，結果顯示所提出的方法能有效支援「As-is」與「To-be」商業模型之不同情境的碳排量比較，協助企業關注關鍵碳排之構面，並評估不同商業模式之可能調整方案的減碳潛力。研究結果證實，GAI 可作為永續管理輔助之工具，因其具備快速分析、彈性調整與跨產業應用之可行性。
本研究所提出之方法可作為企業在永續轉型與商業模式創新過程中的決策支援的工具，亦為未來結合人工智慧與碳排管理之研究提供參考。

With the increasing pressure from global net-zero policies and carbon-related trade regulations, enterprises are required to incorporate carbon considerations into strategic decision-making processes. In particular, carbon emissions should be evaluated not only during operational stages but also at the early phases of business model design and innovation. However, conventional carbon accounting approaches are primarily retrospective and static, making them unsuitable for assessing the carbon implications of frequent business model adjustments.
This study proposes a carbon emission forecasting method that integrates Generative Artificial Intelligence (GAI), the Business Model Canvas (BMC), and international carbon accounting standards, including ISO 14064-1 and ISO 14067. By applying structured prompt engineering, GAI is guided to interpret business model configurations, identify carbon-emitting activities across BMC components, and estimate emissions under both “As-is” and “To-be” scenarios. The proposed method emphasizes relative comparison and decision support rather than replacing formal carbon accounting or verification processes.
To validate the applicability of the proposed method, two case studies are conducted: a service-oriented platform business (Uber Eats) and a product-oriented business (Teascovery). The results demonstrate that the method can effectively identify carbon emission hotspots associated with different business model components and support rapid comparison of alternative business model designs. This research highlights the potential of GAI as a practical decision-support tool for carbon-aware business model innovation and sustainable corporate strategy.

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