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.

This paper considers the relation between the exploration of new possibilities and the exploitation of old certainties in organizational learning. It examines some complications in allocating resources between the two, particularly those introduced by the distribution of costs and benefits across time and space, and the effects of ecological interaction. Two general situations involving the development and use of knowledge in organizations are modeled. The first is the case of mutual learning between members of an organization and an organizational code. The second is the case of learning and competitive advantage in competition for primacy. The paper develops an argument that adaptive processes, by refining exploitation more rapidly than exploration, are likely to become effective in the short run but self-destructive in the long run. The possibility that certain common organizational practices ameliorate that tendency is assessed.

针对创新组织的适应性行为，通过分析技术创新网络组织惯性产生的根源性，从结构惯性和认知惯性两个维度，探究组织惯性对渐进式创新和突破式创新的差异性影响，并引入网络嵌入性作为调节变量。以研发强度较高的行业为对象进行问卷调研，运用多元回归模型进行实证检验。研究结果表明：组织惯性对渐进式创新存在显著的正向影响，相比认知惯性，结构惯性更有助于促进渐进式创新；组织惯性与突破式创新之间存在显著的倒U型关系，相比结构惯性，适度的认知惯性更有助于促进突破式创新；关系嵌入在组织惯性对双元创新的影响过程中起到了显著的调控作用，结构嵌入的调控作用部分支持。研究结论有助于揭示技术创新网络组织的适应性行为特征，对于提升组织创新能力、维持网络稳定具有重要意义。

In order to deal with the adaptive behavior of innovation organization, this paper analysis the emergence characteristics of organizational inertia in technological innovation network. By constructing a framework of structure inertia and cognitive inertia to explore the effect on ambidexterity innovation in different network embeddedness. Then an empirical test is made by multiple regressions with high-tech industry data. The results show that organizational inertia can not only help to promote incremental innovation, but also the structure inertia is better than cognitive inertia for incremental innovation. The relationship between organizational inertia with radical innovation shows a reversed U-shape, the cognitive inertia is better than structure inertia for radical innovation. In the relationships between organizational inertia and ambidexterity innovation, the relational embeddedness’s moderated effect is confirmed. But structure embeddedness’s moderated effect is partly confirmed. This research can reveal the adaptive behavior of innovation organization in technological innovation network, which has significant meaning to improve the innovation ability and stability of technology innovation network.