摘要
制造业产业结构的优化调整既是"中国制造2025"的核心内容之一,亦是推动"供给侧改革"的重要抓手,但学术界往往未能充分利用开放经济的相关信息和技术水平的贡献作用,并缺乏对要素结构进行相关配套分析。为此,本文以中国制造业两位数行业为样本,对其进行产业结构的系统性优化,即先分析了2015年产出结构的优化调整目标及节能减排潜力,然后分析了各种要素投入的联动配套问题,并重点针对资本存量要素,测算并分析它的产能利用率状况。研究结果表明:(1)制造业产出结构具备较大的优化调整空间,可以为"经济增长和环境保护"双赢的实现提供支撑,能够让2015年能源强度和碳强度比原始值分别降低18.08%和17.42%。(2)为降低要素错配,制造业产出结构优化调整后需要各种投入要素进行联动配套,特别是资本存量水平需要有较大幅度的变动。(3)资本要素产能利用率水平的测算结果则进一步显示,受经济增速放缓和投资惯性的影响,2015年制造业产能利用率(56.14%)远低于国民经济十二五规划中后期(2008—2010)的均值水平(73.27%),而投入要素联动配套后的产能利用率则可以回升至后一水平。
Summary： The optimized adjustment of China＇s manufacturing industry structure, composed by output structure optimization and element structure optimization, is not only one of the core elements of ＂Made in China 2025＂, but also an important way to promote supply-side reform. How to further understand the upgrading of the industrial structure and the lack of rationalization in the manufacturing industry and the role they play in promoting economic development, quality, efficiency, and upgrading of the main battlefield have become major issues in academia. In the optimization of output structure, the current literature introduces energy conservation, efficient employment, industry coordination, and other factors in optimization analysis, but it often fails to take full advantage of relevant information on open economy and the contribution of technology. At the same time, the current literature discusses the issues of output structure optimization and optimal allocation of production factors, yet the ＂ two skins＂ phenomenon prevents these issues from being organically combined together. Based on the current literature, this paper first uses non-linear programming technology from the perspective of energy- saving emission reduction, accounting for efficient employment, industry balance, import and export potential, technical level contribution, and other factors, to optimize the output structure of China＇s double-digit industries in 2015. Then, we use the transcendental logarithmic production function model to extract the non-linear relationship between factor inputs and economic outputs and identify the relatively appropriate element structure of the optimized output structure. Finally, based on Data Envelopment Analysis technology, capital stock is used to estimate and analyze the capacity utilization level before and after optimization. The findings are as follows. First, the manufacturing industry structure has huge optimized adjustment potential and can reduce energy intensity and carbon dioxide intensity by 18.08% and 17.42% , respectively, compared with the 2015 values. Second, to decrease resource misallocation, the input factors of the manufacturing industry require linkage matching, especially the capital stock, which should be adjusted by a large amplitude after the output structure optimization. Third, the results of the calculation of the utilization level of capital stock further show that the utilization rate of manufacturing capacity （56.14%） in 2015 is much lower than the average level （73.27%） in the second half of the 12th Five-year Plan for the national economy （2008 -2010） , which is affected by the investment inertia and slowdown of economic growth, while the capacity utilization after the linkage matching of input factors can be improved to match the latter. The main contributions of this paper are the following. First, in the optimization of the manufacturing output structure, we focus on the import and export potential indicators from the ＂demand side＂ and the technical level contribution index from the ＂supply side＂. Second, we overcome the ＂two skins＂ phenomenon of the analyses in the current literature by organically combining output structure optimization and factor input matching. Third, in the study of element structure matching, this article follows the idea of inheritance and criticism; thus, it relies not only on the extraction of historical information to carry out the initial matching of the element structure but also analyzes the allocation of capital stock, focusing on the capital stock overcapacity problem.
作者
史丹
张成
SHI Dan ZHANG Cheng(Chinese Academy of Social Sciences Nanjing University of Finance and Economics)
出处
《经济研究》
CSSCI
北大核心
2017年第10期158-172,共15页
Economic Research Journal
基金
中国社会科学院创新工程项目“大数据技术在经济预测预警中的应用”
国家自科基金政策研究重点支持项目(71742001)
国家自科基金青年项目(71703065)
教育部人文社会科学基金青年项目(17YJC790195)的阶段性成果
关键词
产出结构
要素结构
产能过剩
节能减排
Industry Structure
Element Structure
Overcapacity
Energy-saving and Emission-reduction
