摘要
针对开放式创新驱动与绿色发展融合下工业绿色增长路径及动力来源多元化特征,基于中国工业2009-2015省际面板数据,通过考虑非期望产出的Super-SBM模型测算环境和能源约束下的工业绿色增长结构,并采用改进的动态门槛回归模型,从地区知识积累视角系统探究外部知识源化路径驱动工业绿色增长机制的知识积累动态门槛效应及其时空异质性。研究表明:中国工业绿色增长整体水平较低,地区异质性显著;总体上,外部知识源化对工业绿色增长起到了正向作用,但其存在显著的知识积累异质门槛特征:知识积累一旦突破了临界规模,外部知识源化路径驱动工业绿色增长的正向效应则呈现减弱,且知识积累门槛时空分布存在较大差异。
With the increasingly rigid constraints of energy and environmental factors on industrial growth, industrial pollution emissions and energy consumption have approached the environmental bearing limit. As the main body of China′s economy, industry plays a significant role in economic growth, energy consumption and environmental pollution, and the contradiction between industrial energy environment and economic society is extremely sharp. In this context, the strategy of innovation-driven green development has become an inevitable choice for industrial growth, and how to deeply integrate and improve the industrial green development and innovation-driven path is the key to its transformation. "Green growth", as a sustainable growth mode that pursues economic development and pays attention to environmental protection, biodiversity and resource recycling, is an important strategy to achieve "climate friendly" and "low-carbon" sustainable growth. On the other hand, based on innovation-driven development, technological innovation and green growth are carried out for the purpose of reducing pollution, saving resources and improving the environment to further drive sustainable development. Meanwhile, driven by the global open innovation, Chinese industrial enterprises try to create and acquire knowledge through external cooperation to continuously generate new knowledge flow to accumulate and update knowledge stock, and realize the transformation of innovation paradigm in the interaction of cooperative R&D, R&D outsourcing, entrusted technology development and other modes with enterprises, universities and research institutions with technological advantages. External knowledge sourcing has become one of the important paths for Chinese industry to carry out technological innovation and green growth.Therefore, based on the provincial panel data of 2009-2015 in China′s industry, this paper measures the industrial green growth structure with the restriction of environment and energy by using the Super-SBM model which considering undesirable outputs. Moreover, the improved dynamic threshold regression model is used to systematically explore the dynamic threshold effect of knowledge accumulation and its temporal and spatial heterogeneity of the industrial green growth mechanism driven by external knowledge sourcing. We provide an effective reference for exploring the driving source, path choice and policy design of promoting the transformation of industrial green innovation.The study shows:(1) the overall level of China′s industrial green growth is low, there is a large space for development. In most regions, the level of industrial green growth did not increase significantly in the sample period, and even showed a certain decline trend, especially in the central and western regions, the industrial green growth index has been floating at a low level, and the regional heterogeneity is significant.(2) the impact of industrial environmental regulation, per capita energy consumption and urbanization on its green growth is significantly negative. At present, the development of China′s industrial industry needs to bear the cost of energy and environmental technology constraints to a large extent, and the "Porter effect" has not been realized in a short time.(3) on the whole, external knowledge sourcing has positive effect on industrial green growth, but there is a heterogeneity threshold characteristic of regional knowledge accumulation: knowledge accumulation once broke through the critical mass, the positive effect of external knowledge sourcing is decreasing, so the driving effect of external knowledge sourcing is not significant in areas with high knowledge accumulation level.(4) the heterogeneity of regional knowledge accumulation driven by external knowledge sourcing is significant, and the spatial-temporal distribution is quite different. The number of low knowledge accumulation areas is decreasing, while the number of high knowledge accumulation areas is increasing continuously. The eastern coastal and central regions have significantly crossed the threshold of high knowledge accumulation level, while a few provinces in the western and northeast regions are still in low knowledge accumulation areas.Accordingly, this paper puts forward some policy suggestions. First of all, the overall green growth level of the industry is relatively low, and there are obviously some problems of environmental pollution and resource consumption control. On the one hand, the green development strategy driven by open innovation has become an inevitable choice for industrial growth. On the other hand, it is necessary to fully consider the regional gap of industrial green growth, balance regional development planning, and strictly unify environmental standards while flexibly selecting regional environmental regulation tools. Secondly, in the practice of industrial green development, external knowledge sourcing is an effective driving factor and strategy for industrial enterprises to achieve green development in the initial and transformation stage. The path and driving source of industrial green growth are diversified. In the trend of open innovation, we should make full use of external knowledge to make up for the shortage of industrial green technology resources and R&D accumulation, actively seek external resources to establish an open connection, so as to cope with the increasing environmental energy cost and the complexity of clean technology and green production process, and promote the integration of green technology and production. To optimize green production and technological conditions, in turn, to increase the stock of environmental technology, expand the path of industrial green growth, and reduce technology costs and green R&D risks. Finally, we should pay attention to the spatial-temporal heterogeneity effect of knowledge accumulation of regional industrial green growth path, and choose the best driving path correctly. At present, the industrial knowledge accumulation foundation of the eastern coastal areas is well developed, and the industrial technology strength is strong, while there are diversified driving factors in the industrial green development that can be effectively used. After having the ability to increase investment in R&D, we need to be alert to the long-term cost, intellectual property rights, digestion and absorption of external R&D under the advantage of higher knowledge accumulation, and more. It is based on proprietary intellectual property rights and more efficient internal R&D for industrial clean technology and production. However, the western regions with low knowledge accumulation level consider more effective external sources, reduce the risk of low-carbon environmental protection technology and the cost of clean process transformation, and alleviate the urgent problems of energy consumption, pollution emissions and industrial growth. In brief, we should establish a "bridge" between the industrial green technology foundation and external R&D, combine with the government guidance mechanism, and effectively transform and utilize the existing diversified driving factors;Meanwhile, we should flexibly adjust and control the multiple driving path system according to the heterogeneous constraints of industrial knowledge accumulation in different regions, so as to realize the green and industrial innovation-driven transformation and growth.
作者
侯建
王刚
陈建成
Hou Jian;Wang Gang;Chen Jiancheng(School of Economics and Management,Beijing Forestry University,Beijing 100083,China)
出处
《科研管理》
CSSCI
CSCD
北大核心
2020年第3期91-100,共10页
Science Research Management
基金
中央高校基本科研业务费专项资金资助项目(2019RW20)
中国博士后科学基金资助项目(2019M650519)。
关键词
外部知识源化
知识积累
工业绿色增长
动态门槛模型
异质性
external knowledge sourcing
knowledge accumulation
industrial green growth
dynamic threshold model
heterogeneity