本研究主要目標是提出產業空間群聚之辨識模式與分析架構。產業空間群聚可定義為是由「空間關係維度」與「生產關係維度」之元素所組成的空間組織形式。「空間關係維度」是由產業空間集中與產業空間聚集組合而成，「生產關係維度」則是強調空間群聚內，產業的水平與垂直生產關係。根據以上操作定義，本研究發展出三階段產業空間群聚辨識模式，以台灣製造業為實證對象，整合工商暨服務業普查與產業關聯資料，分別辨識與分析產業水平、垂直與整合空間群聚與其空間區位及產業經濟特性。此辨識模式具有以下三大優點：(1)本模式結合水平與垂直的生產關聯與空間關係，可分別探討產業水平、垂直、整合空間群聚之發展趨勢；(2)本模式以空間集中與空間聚集共同辨識空間群聚，可避免因為空間單元劃定過小而無法辨識彼此空間鄰近之區塊，或空間單元劃定過大，導致辨識結果產生偏誤；(3)本模式結合總體與個體層次之分析，不僅可辨識由不同產業經濟特性所組成之整合空間群聚，亦可分析特定產業空間群聚在整體產業結構之角色與探討特定產業空間群聚中不同空間區位與空間結構之重要性。
根據實證結果本研究有以下六點重要發現：(1)國內製造業呈現高水平空間集中與高水平空間聚集之發展型態，製造業間同樣具有顯著空間垂直集中與空間聚集之現象。對照10年間之發展變化，顯示國內製造業有朝向水平空間分散，垂直空間群聚之發展趨勢，其中都會區扮演非常重要之角色，不同製造業會依據生產與市場需求座落都會區內之都會核心都市、都會都市、都會鄉鎮，進而形成不同程度之空間分工；(2)水平空間群聚產業之垂直空間集中與空間聚集效果顯著高於水平空間集中與非水平空間群聚產業。另外，兩年度間呈現顯著強生產關係之製造業組合，並不絕對具有顯著之垂直空間關係，本研究發現製造業間雖有顯著之垂直供需關係，其會以生產鏈內之產業組合在空間中群聚，相對生產鏈之間，則呈現空間分散之發展趨勢；(3)資本密集度、交易與運輸成本、國內自然資源投入依賴度、國內最終市場依賴度與產業水平空間群聚效果呈現負向關係；相對，技術密集度、勞力密集度、國外自然資源投入依賴度、自身製造業投入依賴度、其他製造業投入依賴度、製造業中間生產市場依賴度、國外市場依賴度與產業水平空間群聚效果呈現正向關係；(4)高垂直投入、高垂直關聯、高運費投入之製造業垂直組合、產業間使用製造業資源相似度越高、產業間使用自然資源相似度越低、上游產業屬於勞動密集型與技術密集型之產業、上游產業屬於出口為主之產業、下游產業屬於產業水平空間群聚，其產業垂直空間集中、聚集、集聚效果較高；相對，上游產業屬於資本密集型與國內最終市場為主之產業，則呈現反向效果；(5)整合五大類核心產業與整合空間群聚之辨識結果，本研究歸納出六大組整合空間群聚包括第一類塑化原料與其製品空間群聚、第二類紡織、成衣、服飾品空間群聚、第三類金屬、機械、運輸工具空間群聚、第四類電子產品空間群聚、第五類食品與飲料空間群聚、第六類其他原料及其製品空間群聚等六大類整合空間群聚；(6)第一組~第四組整合空間群聚多呈現北中南三個空間群聚區塊，北部區塊之完整度又顯著高於中南部區域，此組整合空間群聚多在都會都市形成高空間群聚之核心區塊，並在都會核心都市與都會鄉鎮形成外圍區塊。相對，第五組與第六組整合空間群聚，多屬於特定區域或特定地區型之地方化產業空間群聚，核心產業與上下游關聯產業並無顯著空間鄰近之趨勢，且多座落在都會鄉鎮、邊緣都市、邊緣鄉鎮。
整合以上分析，本研究提出以下兩點政策建議：(1)產業群聚並不單只是產業會出現空間群聚現象，其更會在不同的空間區位與空間結構下，出現最適的水平與垂直產業組合及最適的水平與垂直空間群聚型態，以發揮產業群聚效益；(2)產業群聚政策之擬定不能僅以單一產業進行考量，而是必須考量其上中下游產業之產業經濟特性與組合效益，並分析彼此之間的空間區位，才能擬定出適當的策略，引導產業與空間發展。

The purpose of this research is to propose a model to identify and analyze the spatial clustering of industries (SCI). The SCI is defined as a form of spatial organization composed by the elements from the both geographical and economic dimensions. The geographical dimension includes “geographical concentration” and “spatial agglomeration,” while the economic ones focus on the intra- and inter- production linkages in spatial clustering. Based on these definitions and related research, I develop a three-step procedure to identify and analyze inter-, intra- and integrated SCI and their locations and industrial economic characteristics, and take Taiwanese manufacturing industries as an empirical case study. This model has the following advantages: (1) It can examine the development pattern of inter-, intra- and integrated SCI by the identification of horizontal and vertical production linkage. (2) It can avoid the problems that can arise when space units are too small to tell the geographic proximity or too large to overestimate the clustering boundary. (3) It can not only identify the SCI formed by different economic characteristics, but can also analyze the role of specific spatial clusters of industries in the industrial structure of Taiwan’s manufacturing industries, and discuss the importance of different spatial locations and structures in a specific SCI by undertaking both macro- and micro- level analyses.
According to the empirical analysis, there are six important findings from this work: (1) The manufacturing industries in Taiwan are horizontally highly concentrated and agglomerated. Moreover, the spatial associations of inter-manufacturing industries in Taiwan are also highly vertically concentrated and agglomerated. The results also show the trend that manufacturing industries in Taiwan were moving toward the spatial dispersion of intra-industries and spatial clustering of inter-industries between 1991-2001. Most manufacturing industries cluster at the metropolitan scale, which shows that metropolitan cities play a key role in this process. (2) The effects of vertical spatial concentration and vertical spatial agglomeration of the horizontal SCI are significantly higher than those found for the horizontal spatial concentration of industries and non-horizontal spatial clustering of industries. The inter-manufacturing industries with strong input-output production linkages do not necessary have a significant spatial association. This shows that the manufacturing industries with significant vertical demand-supply linkage will develop inter-industrial spatial clustering on the intra-production chain, but inter-industrial spatial dispersion on the inter-production chain. (3) Capital intensity, transaction and transportation input, domestic natural resource input intensity, and domestic final market intensity have negative relationships with the effects of spatial clustering. Conversely, technology intensity, labor intensity, foreign natural resource input intensity, own manufacturing industry input intensity, other manufacturing industry input intensity, and foreign market intensity have positive relationships with the effects of spatial clustering. (4) Vertical input intensity, vertical linkage intensity, transportation input intensity, manufacturing input similarity of inter-industries, natural resource input similarity of inter-industries, upstream industries with higher labor and technology intensity, upstream industries with higher export-oriented intensity, and downstream industries with horizontal SCI have positive relationships with the effects of vertical spatial concentration, spatial agglomeration and spatial clustering. Conversely, upstream industries with higher capital intensity and domestic final market intensity have negative relationships with the effects of vertical spatial concentration, spatial agglomeration and spatial clustering. (5) By integrating the six types of core industries and their integrated SCI examined in this work, we develop six categories of SCI. The first category is chemical materials and chemical products. The second category is textile products, wearing apparel and clothing. The third category includes metal, machinery and transportation equipment. The fourth category includes electronic products. The fifth category includes food and beverages, while the sixth includes other materials and their products. (6) For the first to fourth categories, the results shows that the SCI tend to cluster into three blocks in northern, central and southern region of Taiwan, within which the completeness of inter-industry pairs in the northern part is higher than that for the central and southern parts. All four of these categories tend to cluster in the form of core blocks in the metropolitan cities and in the form of peripheral blocks around such cities. Conversely, the fifth and the sixth categories belong to localized SCI in specific regions or particular areas. The results show that these two categories of core industry and its up and down-stream industries do not have any significant trend with regard to spatial proximity. Furthermore, most of them are located in metropolitan counties, edge cities and edge counties.
Based on the findings, we have two policy suggestions: (1) Good spatial clusters of industries do not just have one industry or their up and down-stream industries in the same location, but instead have appropriate spatial patterns and industry combinations of intra- or inter-industries with the right distribution of spatial locations and spatial structures. (2) It is not possible to draw up a good industrial cluster policy by considering only one industry. Instead, it is necessary to consider the economic characteristics, composition benefits, and the spatial interaction of the core industry and its up and down-stream industries.

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