本研究旨在建構一套適用於臺灣市區公車產業之營運制度與效率分析架構，透過整合「以租代購」機制與「績效運籌模式」，系統性評估電動大客車政策推動下之產業效能轉化效果。隨著全球淨零碳排目標與我國綠色交通政策加速落實，電動大客車在交通能源轉型中扮演關鍵角色，惟現行制度下，政府補助未能有效引導產業效率提升，亦缺乏績效導向之整合平台與可量化之評估機制。為解決上述問題，本研究提出一個結合效率模型與制度模擬的實證架構，針對政策導入前後之生產與營運表現進行縱向比較，並作為制度設計與商業模式優化之依據。
本研究採用兩階段資料包絡分析法（Two-Stage DEA）分別評估製造端與營運端效率，並輔以 Malmquist 檢驗總要素生產力變化。分析樣本包含五家取得國產化資格之電動大客車製造商及其對應營運單位，以2025年為現況基準，並模擬2026與2027年在「零方案」、「保守」、「中估」、「樂觀」四種制度情境下之發展軌跡。投入與產出變數根據文獻回顧與資料取得之考量選擇成本投入、技術研發、車輛性能與營運效益等四項變數，並依不同情境設定合理之成長假設，反映制度預期成果。
實證結果顯示，多數受評單位在模擬情境中皆展現效率改善，尤其在中估與樂觀方案下，技術與財務表現明顯優化，且於整體階段呈現效率穩定提升。差額變數分析指出，部分製造與營運指標仍存在冗餘資源，應透過制度性整合與資訊揭露強化改善彈性；而超效率分析進一步突顯個別廠商之潛在領先優勢，提供作為政策差異化激勵與示範補助依據。研究亦進行敏感度分析，以驗證模擬假設對效率評估之穩健性，提升整體模型之應用價值與政策解釋力。
綜合而論，本研究建議未來應建立以績效為核心之資源配置機制，將效率表現與補助制度掛鉤，並導入標準化資料回報、分級績效評估，提升產業系統運作之透明性與彈性。並透過跨部門協作與平台式治理設計，形成涵蓋製造端、營運端、政府部門與供應鏈單位之績效循環架構，以利未來政策之穩健落地與制度永續發展。

This study proposes a performance-oriented institutional evaluation framework for Taiwan’s urban electric bus sector, integrating a “leasing instead of procurement” mechanism with a two-stage Data Envelopment Analysis (DEA) and the Malmquist Productivity Index. It aims to systematically assess production and operational efficiency before and after policy implementation. Motivated by global net-zero carbon goals and the nation’s transition to green mobility, the study addresses the current gap between subsidy mechanisms and measurable performance outcomes.

The empirical sample comprises five certified domestic electric bus manufacturers and their affiliated operators. Using 2025 as the baseline year, the study simulates four policy scenarios—baseline, conservative, moderate, and optimistic—for 2026 and 2027. Principal Component Analysis (PCA) extracts four key efficiency variables: cost input, R&D investment, vehicle performance, and operational benefit. These variables form the input-output structure for DEA efficiency modeling across different scenario assumptions.

Results indicate notable efficiency improvements under the moderate and optimistic leasing scenarios. Super-efficiency analysis identifies potential policy-leading firms, while slack variable analysis reveals residual resource redundancies. These findings suggest that institutional coordination and enhanced data transparency can significantly strengthen system performance. Sensitivity testing further validates the robustness of the simulation framework and enhances its policy relevance.However, findings are derived under specific modeling assumptions, variable frameworks, and simulation designs. Thus, conclusions should be interpreted with caution. Future research is recommended to validate the framework across different regional and institutional contexts to enhance external generalizability and policy relevance.

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