城市作為世界上人口最密集的地方，會自然地吸引人才與產業的群聚以促進區域繁榮，且是創新及企業家精神的驅動力。隨著世界愈趨重視未來城市的發展前景，過往缺乏以小範圍區域或城市級別為對象、大範圍客觀資料為基礎之城市創新文獻。本研究透過城市不同的環境因素、條件與經濟體發展程度，找出全球都市的發展模式與創新差異，並檢視環境因素與經濟發展條件是否對城市創新存在著不同影響性？藉此本研究希望能找出不同類型城巿在政策推動上應關注的要點。

本研究針對全球產官學研資料庫所公開之城市級別次級資料，蒐集以2021年為主之近四千筆數據，並將資料區分為交通運輸、硬體建設、軟體發展、創新表現與經濟體發展程度共五大類別18項指標。本研究透過交通運輸、硬體建設與軟體發展三種構面，進行非階層式集群分析以判斷各城市集群之創新程度，再透過相關性分析與多元線性迴歸分析，探討個別環境因素之間與經濟體發展程度對於城市創新表現的影響。

分析結果顯示，創新發展的表現與個別城市集群互有關聯之處有其區別，並分別以交通運輸及軟硬體發展的角度，確認了特定環境因素對於創新的影響程度差異性。本研究亦藉由納入經濟體發展程度之資料作為調節變數，了解區域發展間對於城市創新表現上的相異之處。在考量資源有限的情況下，城市管理者應針對個別城市之自身特性，制定相異的優先目標與發展策略來建構有利於創新的環境與條件。

As the most densely populated places, cities naturally attract clusters of talents and industries that contribute to regional prosperity and drivers of innovation and entrepreneurship. There was a lack of city innovation literature based on large-scale objective data at regional or city levels as the world pays more attention to the future development of cities. Therefore, through a variety of environmental factors, conditions, and economic development of cities, this study aims to find out what types of development patterns and innovation differences of urban characteristics exist and examines whether how these environmental factors and economic development conditions will impact innovations.

Accordingly, this study intends to analyze through city-level secondary data published by databases from governments, institutions universities, or businesses globally, and collects nearly four thousand data mainly in 2021. The data are divided into five categories and 18 kinds of factors: transportation, infrastructure, soft development, innovation performance, and economic development. Through the perspective of three urban conditions, this study conducts non-hierarchical cluster analysis, correlation analysis, and multiple linear regression analysis to understand the influences of city types, environmental factors, and regional development for policy implications of innovation and urban development.

The results show that the performance of innovation is different from city clusters in terms of mutual correlation, and the analysis from the perspective of transportation and soft and infrastructure development illustrates the difference in the degree of influence of specific environmental factors. By analyzing data on regional economic development, this study also confirms the degree of differences between regional development in the performance of city innovation. Considering resources being limited, governments should formulate different goals and strategies according to the conditions of each city to construct an environment conducive to innovations.

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