Department of Sociology, Zhejiang University, Hangzhou, China.
Culture and Knowledge Lab, Zhejiang University, Hangzhou, China.
PLoS One. 2023 Mar 16;18(3):e0278469. doi: 10.1371/journal.pone.0278469. eCollection 2023.
The increasing prominence of urban scaling laws highlights the importance of a systematic understanding of the variational scaling rates for different economic activities. In this article, we utilize several datasets to provide the first systematic investigation of the urban scaling of manufacturing industries in China. Most existing literature assumes that the divergence in urban scaling can be explained by returns to agglomeration, with a few exceptions instead highlighting the role of knowledge complexity or a mixture of both. Our main purpose in this paper is to explain the inter-sector variation of urban scaling rates. In doing this, we provide a clearer approach to demonstrating the relations between urban scaling, returns to agglomeration, and knowledge complexity. Our findings are twofold. First, after uncovering the scaling rates (denoted as urban concentration) and returns to agglomeration (denoted as urban productivity) for each sub-manufacturing sector, we prove that, rather than being a positive predictor, returns to agglomeration is slightly negatively associated with urban scaling rates. This finding reveals that urban concentration of manufacturing may not simply be a natural consequence driven by the maximization of performance. We also show that this result of the manufacturing system contrasts with what would be found in other pure knowledge systems such as patents. Secondly, we measure the complexity for each sector and demonstrate that the variation of urban concentration can be largely explained by their complexity, consistent with the knowledge complexity perspective. Specifically, complex manufacturing sectors are found to concentrate more in large cities than less complex sectors in China. This result provides support for the view that the growth of complex activities hinges more on diversity than on efficiency. The findings above can greatly reduce the current level of ambiguity associated with urban scaling, returns to agglomeration and complexity, and have important policy implications for urban planners, highlighting the significance of a more balanced and diversified configuration of urban productive activities for the growth of innovation economy.
城市规模法则的日益凸显，突出了系统理解不同经济活动的变尺度率的重要性。在本文中，我们利用多个数据集，首次对中国制造业的城市规模进行了系统研究。大多数现有文献认为，城市规模的差异可以用集聚回报来解释，但也有少数例外，强调了知识复杂性的作用或两者的混合作用。我们本文的主要目的是解释城市规模变化率的部门间变化。为此，我们提供了一种更清晰的方法来展示城市规模、集聚回报和知识复杂性之间的关系。我们的研究结果有两点。首先，在揭示了每个次制造业部门的规模率（表示为城市集聚）和集聚回报（表示为城市生产力）之后，我们证明，集聚回报并不是城市规模率的正预测因子，而是略微负相关。这一发现表明，制造业的城市集聚可能不仅仅是一种追求性能最大化的自然结果。我们还表明，制造业系统的这一结果与其他纯知识系统（如专利）中的结果形成对比。其次，我们测量了每个部门的复杂性，并证明城市集聚的变化可以很大程度上由其复杂性来解释，这与知识复杂性的观点一致。具体来说，在中国，复杂制造业部门的集聚程度比不那么复杂的部门更高。这一结果为这样一种观点提供了支持，即复杂活动的增长更多地取决于多样性，而不是效率。上述发现可以大大降低当前与城市规模、集聚回报和复杂性相关的模糊性，对城市规划者具有重要的政策意义，突出了城市生产活动更均衡、更多样化的配置对创新经济增长的重要性。
