制造业向智能制造转型升级是中国实现制造强国战略的必由之路。本文采用双重差分法，以《中国制造2025》政策出台为准自然实验，利用A股上市公司2010-2019年数据实证检验了智能制造政策对企业全要素生产率的作用效果及机制。研究发现：（1）智能制造政策能有效促进智能制造领域企业全要素生产率的提高；（2）智能制造政策一方面诱导企业增加无意义或低效率的研发投入，降低企业全要素生产率，另一方面引导企业增加有效发明专利数，进而提高企业全要素生产率；（3）智能制造政策作用效果在不同所有制性质、不同市场化程度地区的企业中存在显著差异。上述结果证实了智能制造政策的作用效果，并部分打开其作用机制黑箱，对下一阶段智能制造产业政策的制定与落实具有重要借鉴和启示意义。

The transformation and upgrading of manufacturing industry to intelligent manufacturing is the inevitable choice for China to realize the strategy of manufacturing power. According to the double difference method, this paper takes the introduction of "made in China 2025" policy as the quasi-natural experiment, and empirically tests the effect and mechanism of intelligent manufacturing policy on enterprise total factor productivity by using the data of A-share listed companies from 2010 to 2019. First, "Made in China 2025" has effectively promoted the improvement of total factor productivity of smart manufacturing enterprises. As an incentive policy, this policy will give target companies preferential benefits in terms of capital, taxation, land use, and talents, which can effectively guide and help companies reduce costs and enhance competitiveness, thereby promoting the improvement of corporate total factor productivity. Second, "Made in China 2025" has two opposite mechanisms for the total factor productivity of enterprises. On the one hand, incentive policies induce enterprises to increase meaningless or inefficient R&D investment and reduce the total factor productivity of enterprises. This partly stems from Adverse selection behaviors caused by information asymmetry between enterprises and governments; on the other hand, they guide enterprises to increase the number of effective invention patents, thereby increasing their total factor productivity. Third, compared with state-owned enterprises, the effect of policy implementation on the total factor productivity of non-state-owned enterprises is more obvious. Due to the inherent lack of political resources and derivative advantages of non-state-owned enterprises, they are more eager to get support from policies, and their response speed and degree to policies will be higher. Therefore, policies have a stronger marginal promotion effect on their corporate factor productivity. Fourth, compared with enterprises in low-market areas, the impact of policy implementation on the total factor productivity of enterprises in high-market areas is more obvious. In regions with a high degree of marketization, government policies will be more inclined to consider supporting companies with more competitive market advantages, so as to give full play to the role of policy resources in promoting total factor productivity. The results confirm the effect of intelligent manufacturing policy, and partially open the black box of its mechanism, which has important reference and enlightening significance for the formulation and implementation of intelligent manufacturing industry policy in the next stage.

本文基于2011-2019年中国30个省份的面板数据测度工业智能化指标，实证检验了工业智能化与碳生产率之间的影响机制。结果显示：工业智能化显著提高了碳生产率，这一结论在进行一系列稳健性检验后仍然成立。机制分析表明，工业智能化主要是通过多元化集聚间接提高碳生产率，由于产业集聚生命周期的存在，工业智能化对碳生产率的影响呈现出“先增后减”的非线性特征。空间计量分析表明，工业智能化不仅可以提高本地的碳生产率，还可以提高邻近地区的碳生产率，有助于形成地区间绿色协调发展的空间格局。异质性分析表明，在非资源依赖、工业化水平高、环境规制强度低的地区，工业智能化提高碳生产率的作用更显著。研究结果为提高碳生产率进而实现“双碳”目标提供了一条可行的路径，也为政府部门积极谋划区域工业智能化战略布局提供了有益的借鉴。

Industrial intelligence with "intelligent manufacturing” as the core has provided a crucial driving force for China to achieve the goal of "carbon peak and carbon neutrality." As the world's largest emerging economy, China is accelerating the construction of emerging infrastructure such as 5G, cloud computing, and artificial intelligence. At the same time, as global warming continues to accelerate, China stresses the need to protect the global environment and achieve a harmonious coexistence between man and nature. In 2021, for the first time, China included "carbon peak and carbon neutrality" in its government work report. The vigorous development of industrial intelligence characterized by "machine for man" can not only reduce production costs of enterprises to improve production efficiency through the application of intelligent machines but also reshape economic geographic patterns by changing factor endowment conditions, realize resource sharing among enterprises, and reduce carbon emissions. Therefore, studying the relationship between industrial intelligence and carbon productivity and its mechanism is of great theoretical and practical significance. Based on the panel data of 30 provinces in China from 2011 to 2019, this paper measures the industrial intelligence indicators from three aspects: the basic conditions of intelligence, the degree of intelligent application, and the achievements of intelligent technology, and empirically tests the spatial-temporal evolution characteristics and influence relationship between industrial intelligence and carbon productivity. This paper includes industrial agglomeration into the research framework and expounds on the nonlinear relationship between industrial intelligence and carbon productivity based on the life cycle theory of industrial agglomeration to analyze the influencing mechanism between industrial intelligence, industrial agglomeration, and carbon productivity. In addition, this paper also takes resource dependence, industrialization level, and environmental regulation intensity into consideration to explore whether it has a heterogeneous impact on the relationship between industrial intelligence and carbon productivity, and improve the theoretical framework of the relationship between industrial intelligence and carbon productivity. The results show that industrial intelligence significantly improves carbon productivity. And this conclusion remains valid after a series of robustness tests including the introduction of instrumental variables, the replacement of the regression method, and the replacement of the calculation method of explanatory variables. Mechanism analysis shows that industrial intelligence promotes diversified agglomeration and specialized agglomeration. But it mainly indirectly improves carbon productivity through diversified agglomeration. Due to the existence of the life cycle of industrial agglomeration, the influence of industrial intelligence on carbon productivity presents a nonlinear feature of "first increase and then decrease." Spatial econometric analysis shows that industrial intelligence has a spatial spillover effect. Industrial intelligence can improve carbon productivity not only in local areas but also in neighboring areas, which will be conductive to shape a spatial pattern of coordinated green development among regions. Heterogeneity analysis shows that the role of industrial intelligence in promoting carbon productivity is more significant in regions with non-resource dependence, high industrialization levels, and low environmental regulation intensity. The results of this study provide a feasible path to increase carbon productivity and achieve the "double-carbon" goal and also provides a beneficial reference for government departments to plan the strategic layout of regional industrial intelligence energetically.

基于直接传导机制、间接传导机制和异质性传导机制三个维度，利用文本挖掘的方法，以中国A股制造业上市公司为样本，阐述了智能化发展是否促进企业进行颠覆性创新，探讨两者之间的内在机理，并从实证方面检验了颠覆性创新在智能化发展和企业绩效之间的中介效应。研究发现，智能化发展显著提高了企业颠覆性创新水平，是新时代企业实现颠覆性创新的主要途径；智能化发展通过加大固定资产投资和优化人力资本结构的机制间接促进企业颠覆性创新；在考虑企业异质性特征时，发现国有企业、资本技术密集型企业智能化发展对颠覆性创新的影响更明显；最后，企业智能化发展可以通过颠覆性创新提高企业的绩效，助推企业高质量发展。研究结论为推动企业智能化发展、实现颠覆性创新提供了一条可行的路径。

A new round of industrial revolution with intelligent manufacturing as the core is sweeping across the world. As the starting point and primary goal of industry 4.0, intelligent manufacturing is the main direction of "made in China 2025", and disruptive innovation is an important way for Chinese manufacturing enterprises to gain core competence under the background of intelligent manufacturing. Therefore it is very crucial to study the relationship between intelligent development and disruptive innovation from the enterprise level. In order to comprehensively explore the relationship between intelligent development and disruptive innovation, this paper analyzes the impact mechanism of intelligent development on disruptive innovation of manufacturing enterprises, which is based on three dimensions of direct transmission mechanism, indirect transmission mechanism and heterogeneous transmission mechanism, so as to provide reference for intelligent development of enterprises and make up for the shortcomings of existing research. Taking China's A-share listed manufacturing companies from 2000 to 2017 as samples, this paper uses text mining method to construct indicators that are difficult to quantify, studies whether intelligent development promotes China’s A-share manufacturing listed companies’ disruptive innovation and discusses the internal mechanism between intelligent development and disruptive innovation. At the same time, disruptive innovation is divided into disruptive business model innovation and disruptive technological innovation, the purpose is to deeply understand the internal meaning of disruptive innovation. In further analysis, the econometric model is used to empirically test the intermediary effect of disruptive business model innovation and disruptive technological innovation between intelligent development and enterprise operating performance, getting through the transformation path of "intelligent development-disruptive innovation-enterprise high-quality development", and deeply understand the transformation mode of manufacturing enterprises in line with the background of intelligent manufacturing. It is found that intelligent development significantly improves disruptive business model innovation and disruptive technological innovation of manufacturing enterprises, which is the main way for enterprises to realize disruptive innovation in the new era. Intelligent development can indirectly promote enterprise disruptive business model innovation and disruptive technological innovation, and this mechanism is realized by increasing enterprise fixed asset investment and improving the level of human capital. Manufacturing enterprises can create good development conditions for enterprise disruptive innovation by increasing investment in intelligent infrastructure and introducing high-quality talents; When considering the characteristics of enterprise heterogeneity, the samples of listed companies are divided into two groups according to the characteristics of ownership and factor intensity, it is found that the intelligent development of state-owned enterprises, capital-intensive enterprises and technology-intensive enterprises has a more significant impact on disruptive innovation, while intelligent development of non-state-owned enterprises and labor-intensive enterprises have less influence on disruptive business model innovation and disruptive technological innovation. It can be seen that state-owned enterprises, capital-intensive enterprises and technology-intensive enterprises have obvious advantages in promoting disruptive innovation in the era of intelligent manufacturing; Finally, the development of enterprise intelligence can improve enterprise performance and boost the high-quality development of enterprises through disruptive business model innovation and disruptive technological innovation. The research conclusion enriches the theoretical framework of the relationship between intelligent development and enterprise disruptive innovation, and provides a feasible path for promoting enterprise intelligent development and realizing disruptive innovation.

We examine the concerns that new technologies will render labor redundant in a framework in which tasks previously performed by labor can be automated and new versions of existing tasks, in which labor has a comparative advantage, can be created. In a static version where capital is fixed and technology is exogenous, automation reduces employment and the labor share, and may even reduce wages, while the creation of new tasks has the opposite effects. Our full model endogenizes capital accumulation and the direction of research toward automation and the creation of new tasks. If the long-run rental rate of capital relative to the wage is sufficiently low, the long-run equilibrium involves automation of all tasks. Otherwise, there exists a stable balanced growth path in which the two types of innovations go hand-in-hand. Stability is a consequence of the fact that automation reduces the cost of producing using labor, and thus discourages further automation and encourages the creation of new tasks. In an extension with heterogeneous skills, we show that inequality increases during transitions driven both by faster automation and the introduction of new tasks, and characterize the conditions under which inequality stabilizes in the long run. (JEL D63, E22, E23, E24, J24, O33, O41)