国际比较视域下建筑业发展与城镇化关联分析及预测

田沁菡

摘  要

改革开放以来，城镇化为中国建筑业发展提供机遇与窗口，建筑业则通过提供物质投入与城镇化发生关联。随着建筑业与城镇化均步入“提质”发展阶段，参考发达国家建筑业随城镇化发展先行经验，对建筑业健康持续发展意义重大。

研究首先分析中国背景下建筑业发展与城镇化关联。将国民经济指标作为城镇化水平辅助衡量指标，计算建筑业发展与城镇化相关性，并从劳动力、技术与装备以及建筑工程材料三个层面阐述二者关联。其次，对建筑业发展与城镇化关联展开国际比较。选取英、美、日、韩作为发达国家样本，以城镇化率与人均GDP衡量城镇化水平，以建筑业人均增加值衡量建筑业发展水平，提取发达国家建筑业发展与城镇化关联特征。最后，基于发达国家与中国样本，建立贝叶斯向量自回归模型，量化分析城镇化对建筑业发展影响在国家间的差异，并对未来五年中国建筑业发展短期趋势作出预测。

研究主要获得三点发现。第一，在长期发展趋势层面，当城镇化率高于80%时，建筑业人均增加值趋于稳定，中国建筑业需把握2050年前发展窗口。在发达国家人均GDP达3万美元前，人均GDP与建筑业人均增加值均快速增长；在跨越3万美元台阶后，人均GDP保持低速增长，而建筑业人均增加值增速则趋于平缓。第二，在短期发展趋势层面，在中国建筑业从业人数增速与“十三五”期间一致的情况下，预计“十四五”期间建筑业增加值年均增长率为3.34%，相比“十三五”期间放缓。第三，尽管建筑业发展与城镇化在经济指标上存在相关性，但建筑业不可过度依赖城镇化，而应主要关注行业自身增速，加快提升生产率。

关键词：建筑业；城镇化；贝叶斯向量自回归模型；国际比较；关联分析

Abstract

There has been a tight bond between construction industry and urbanization process since the Chinese economic reform. Urbanization process facilitates construction industry by creating needs while the industry offers material input in infrastructure and housing construction. As China puts more emphasis on quality improving in the development of urbanization and construction industry, it is of great significance for the construction industry to refer to relationship between urbanization and the development of construction industry in developed countries.

The research content is divided into three main parts. First, the relationship between urbanization and the development of construction industry in China is analyzed. The national economic index is taken as an extra indicator to measure the level of urbanization. The correlation between the development of construction industry and urbanization is calculated. The relationship between urbanization and construction industry is explained from three aspects: labor, technology and equipment, and construction materials. Second, the relationship between urbanization process and the development of construction industry is analyzed based on international comparative perspective. The United Kingdom, the United States, Japan and South Korea are selected as the samples of developed countries. The urbanization rate and per capita GDP are used to measure the urbanization level, and the per capita added value of construction industry is used to measure the development status of the construction industry. The key correlation characteristics between urbanization and construction industry in developed countries are extracted later. Third, based on the samples of developed countries and China, the bayesian vector autoregressive model is established to quantitatively analyze the impact of urbanization on the construction economy among countries. The growing trends of construction industry in China in the next five years is forecasted based on the model.

There are three main findings based on analyses in this research. First, according to the selected developed countries, the growth of per capita added value of construction industry slows down when the urbanization rate approaches 80%. Therefore, it is crucial for the growth of construction industry in China before 2050. Both per capita GDP and per capita added value of construction industry grow rapidly when per capita GDP is below $30000. When per capita GDP exceeds $30000, it keeps growing in low growth rate while per capita added value in construction industry barely grows. Second, it is estimated that the average annual growth rate of added value of construction industry during the 14th Five-Year Plan period would be 3.34% under the condition that the growth rate of the number of employed people in the industry be in consistent with last five years, meaning the construction industry in China will grow slower than the last five years. Third, although there is a correlation between the development of the construction industry and urbanization in terms of economic indicators, the construction industry should focus within the industry to increase productivity instead of relying on the demands created in the urbanization process.

Key words: Construction Industry; Urbanization; BVAR Model; International Comparative Perspective; Correlation Analysis