制造业在世界经济发展和全球经济体系中占据重要的地位，由于及全球资源的稀缺性造成全球经济配置的空间依赖性，制造业优化资源配置的需求推动制造业对外投资，特别是在发达国家因高昂的土地人力成本需要将制造业企业外迁，而发展中国国家从发达国家引进外资也可以解决资金、技术、设备、外汇等要素短缺的困境，由此制造业的对外直接投资由此兴起，各地不同的要素禀赋吸引了不同类型的制造企业，他们因供应链、组织架构、市场等彼此联系，网状分布，形成制造业全球化趋势，也在经济和地理版图上构成特有的制造业绿地投资网络。

本文以社会网络分析理论为基础，旨在构建2005年、2013年、2018年和2022年四年的全球制造业跨国投资网络（以绿地投资为例），按照从行业到国家的分析思路，采用复杂网络分析的方法，从行业宏观上分析整体网络结构指标和网络节点权力指标，并绘制网络拓扑图。结果表明整体网络呈现稳步扩张的特点，网络密度小幅增大，互惠性增强。而节点权力方面，美国、德国在网络结构中占有绝对优势地位，且节点自主性和对网络影响力较强。发达国家指标优于于发展中国家。新兴发展中国家如中国、印度、俄罗斯在网络指标排名较为靠前，但其在网络中核心性不稳定。

同时，对于中国而言，本文梳理了中国制造业跨国投资的占比、规模和流向信息，结合其在网络结构中的核心-边缘地位以及节点中的中心度、结构洞指数的表现。总结出中国在全球制造业跨国投资网络的表现：具备一定核心性但不稳定 ，中心性靠前但不领先，结构洞指数显著但引领性不强

本文运用QAP的实证检验方式，以有权网络和无权网络分别作为被解释变量，选择地理距离差异、殖民关系、共同语言、贸易协定和经济实力差异作为解释变量，构建相应矩阵进行相关分析和回归分析。结果显示，地理距离差异与投资量与投资关系均呈现正相关关系，贸易协定与投资量与投资关系均呈现较为显著的正相关关系，共同语言与投资量存在显著的负相关关系。经济实力差异与投资量存在较为显著的负向关系。

最后，本文总结全文结论，并为回归本文落脚点，为中国制造业提升网络地位和影响力提供建议，具体为加强对对外直接投资的区位指导，积极参与区域经济一体化组织和推动自身产业结构升级。

The manufacturing industry plays a crucial role in global economic development and the global economic system. Due to the scarcity of global resources, the spatial dependence of global economic allocation is inevitable. The demand for optimizing resource allocation in the manufacturing industry has driven foreign investment, especially in developed countries where high land and labor costs require the relocation of manufacturing enterprises. At the same time, developing countries can address the shortage of funds, technology, equipment, and foreign exchange by attracting foreign investment from developed countries. As a result, foreign direct investment in the manufacturing industry has emerged, with different types of manufacturing enterprises being attracted to different regions based on their factor endowments. These enterprises are interconnected through supply chains, organizational structures, markets, etc., forming a global trend of manufacturing industry globalization and a unique manufacturing greenfield investment network on the economic and geographical map.

Based on social network analysis theory, this study aims to construct a cross-border investment network of the global manufacturing industry (using greenfield investment as an example) for the years 2005, 2013, 2018, and 2022. Following an analytical approach from industry to country, complex network analysis methods are used to analyze overall network structure indicators and network node power indicators at the industry macro level, and network topology diagrams are drawn. The results show that the overall network exhibits steady expansion, with a slight increase in network density and enhanced reciprocity. In terms of node power, the United States and Germany hold an absolute advantage in the network structure, with strong node autonomy and influence on the network. Developed countries perform better than developing countries in terms of network indicators. Emerging developing countries such as China, India, and Russia rank relatively high in network indicators, but their centrality in the network is unstable.

Furthermore, for China, this study summarizes the proportion, scale, and flow of China's cross-border manufacturing investment, combining its core-periphery status in the network structure and the performance of centrality and structural hole index in network nodes. It concludes that China possesses certain centrality but lacks stability, ranks high in centrality but not in a leading position, and has a significant structural hole index but lacks strong leadership.

Using the QAP empirical test method, this study takes weighted networks and unweighted networks as dependent variables and selects geographical distance differences, colonial relationships, common languages, trade agreements, and economic strength differences as explanatory variables. Corresponding matrices are constructed for correlation analysis and regression analysis. The results show that geographical distance differences and trade agreements are positively correlated with investment volume and investment relationships, and common languages is negatively correlated with investment volume, and economic strength differences are negatively correlated with investment volume .

Finally, this study summarizes the conclusions and provides suggestions for improving the network status and influence of China's manufacturing industry, including strengthening guidance for foreign direct investment locations, actively participating in regional economic integration organizations, and promoting industrial structure upgrading.