近年知識密集產業的快速發展，政府與民間投入許多資源於創新活動當中，期待藉由資源的投入，創造出足夠的創新產出，以提供產業成長的動力。在這趨勢下，產生一系列關於創新的研究，想要找出創新的驅動力量究竟是什麼？ㄧ般認為Schumpeter最先發現創新活動對產業發展的影響，近年來學術界進ㄧ步注意到了創新活動的地理現象，Freeman提出國家創新系統與Asheim提出區域創新系統後，加上矽谷等高科技地區的發展經驗，學者開始以系統的觀點看待地區內創新活動的關連性，將地區視為創新的生產單位。
傳統關於創新效率的研究，常以巨觀的分析方式，比較國家之間創新能力的差異，或以微觀的角度，了解個體廠商研發活動的差異。相關研究較少以兩尺度間的區域尺度，去比較地區的創新能力與效率。因此，本研究以地區作為分析單元，希望了解創新資源是如何分配於空間當中，創新的產出是集中在哪些地區？最後評估這些資源是否有被有效的利用，以最適的配置方式產出創新？
本研究以專利申請數做為創新的替代變數，分析不同創新活動的分佈，並以資料包絡法(DEA)評估地方創新產出效率。本研究將達成以下三點目的：
1.了解不同創新行為的空間分佈與變遷概況。
2.評估地區創新效率。
3.提出效率改善方向與分析內外因素對創新效率之影響。
研究結果發現，創新產出多集中於北部區域，且不同類型的創新，於空間中的分佈略有差異，其中以發明專利的集中性最為明顯。發明專利以新竹市最多，並擴散至鄰近的桃園縣、新竹縣等縣市。
在創新效率方面，以嘉義市、台南市及嘉義縣的創新總效率表現最佳。在純技術效率的評估方面，具備純技術效率的縣市較少，顯示大多數創新無效率的縣市是因為資源分配的不適當。有半數縣市規模效率佳，顯示大部分縣市僅需些微調整生產規模，即可增加創新效率。創新效率的改善方向，在投入部分應減少各地區的儀器設備費用；產出部分則應增加新式樣專利的申請量。所採取的變項對創新效的影響上，以投入項中的研發部門數，及產出項的發明專利對效率計算結果的影響力最大。南部區域、較小創新生產規模與基礎產業為民生工業之地區，創新生產效率較高。

In recent years, there has been fast growth in Knowledge Intensive Industries. This is largely attributed to allocation of resources by government and private enterprises into the research and development (R & D) sector in attempt to encourage innovation in these industries. Such trends have resulted in a chain of studies being conducted to identify factors that stimulate innovation. Joseph Schumpeter is the first scholar who developed theories directly associating innovation and its impact on industry.
Later under the influence of Freeman and Asheim’s proposition of National and Regional Innovation Systems consecutively, scholars began adopting a systematic view towards geographic innovation studies and treated area as a production unit of innovation.
Traditional studies on innovation efficiency can be divided into two levels. The macro level compare differences between countries and the micro level compare differences between firms or R&D institutions. These research studies rarely take area as an analysis unit.
This study takes area as an analysis unit, to find out how resources for innovation are distributed and identify regions where innovation is mostly carried out. Lastly, the efficiency at which these resources are used will be reviewed.
For this study, patents will be used as innovation indicators, and innovation efficiency is calculated by Data Envelopment Analysis. There are three main purposes of this study:
1. To understand distribution of different innovation inputs and outputs.
2. To calculate production efficiency of innovation in different areas.
3. To find out what factors would affect production efficiency of innovation.
The results of the study show that innovations are concentrated in Northern Taiwan. Different types of innovation are concentrated in different areas. Invention patents are more clustered than most other patents. Hsinchu city has more invention patents than other cities.
Chiayi city produces innovation much more efficiently in terms of technical proficiency and scaling. Most cities in Taiwan are not efficient due to poor allocation of their resources. However half of the cities in Taiwan are good in scale efficiency. Improvement in production efficiency can be achieved by slightly adjusting their scale of production. To further improve innovation efficiency, cities in Taiwan should decrease equipment fees and increase the number of design patents. This study finds that the most important input variable is the number of R & D sectors, and the most important output variable is invention patents. The characteristic of area will affect innovation efficiency. In the southern parts of Taiwan, a small scale innovation production area and area where essential industry is commodity industry will produce innovation more efficiently.

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