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Edge computing moves data and storage to one end of edge nodes. The advantages of direct data collection and intelligent analysis are gradually being considered as disruptive technologies to promote social progress. Many fields and industries are exploring the use of edge technologies. To achieve the goal of improving efficiency and optimizing business models, the supply chain is one of the areas where edge computing technology can be prioritized. Therefore, the organization and coordination of the supply chain must take into account both energy saving and emission reduction and intelligent decision-making effects. This paper establishes a basic decision-making model for the supply chain under the carbon tax constraint and compares and analyzes the optimal decision-making problem of the supply chain between the centralized and decentralized decisions of producers and retailers under the carbon tax constraint. Then, the supply chain optimization under the three conditions of considering the repurchase contract, the subsidy policy and the joint strategy of both the repurchase and the subsidy under the constraint of carbon tax are discussed. Research shows that carbon tax can play a role in reducing carbon emissions, but for some industries with smaller profit margins, relying solely on carbon tax policy may lead to reduced benefits and make business development difficult. Therefore, considering the combined strategy of repurchase and subsidy at the same time, the dual goals of emission reduction and economic benefits can be achieved. INDEX TERMS Edge computing, carbon tax constraint, low carbon supply chain, intelligent decision.

Affected by the Internet, computer, information technology, etc., building a smart city has become a key task of socialist construction work. The smart city has always regarded green and low-carbon development as one of the goals, and the carbon emissions of the auto parts industry cannot be ignored, so we should carry out energy conservation and emission reduction. With the rapid development of the domestic auto parts industry, the number of car ownership has increased dramatically, producing more and more CO2 and waste. Facing the pressure of resources, energy, and environment, the effective and circular operation of the auto parts supply chain under the low-carbon transformation is not only a great challenge, but also a development opportunity. Under the background of carbon emission, this paper establishes a decision-making optimization model of the low-carbon supply chain of auto parts based on carbon emission responsibility sharing and resource sharing. This paper analyzes the optimal decision-making behavior and interaction of suppliers, producers, physical retailers, online retailers, demand markets, and recyclers in the auto parts industry, constructs the economic and environmental objective functions of low-carbon supply chain management, applies variational inequality to analyze the optimal conditions of the whole low-carbon supply chain system, and finally carries out simulation calculation. The research shows that the upstream and downstream auto parts enterprises based on low-carbon competition and cooperation can effectively manage the carbon footprint of the whole supply chain through the sharing of responsibilities and resources among enterprises, so as to reduce the overall carbon emissions of the supply chain system.

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This paper is intended to investigate the role of Venture-Capital Syndication (VCS) background in the relationship between intellectual capital (IC) and portfolio firm performance (PFP); specifically, this article examines the moderating effect of VCS’s leading firm background and member heterogeneity on the effect of IC on PFP. This study used a modified VAIC model to measure IC to compose a 4-component variable including human capital, structural capital, relational capital, and innovation capital. The data were collected from VCS-backed and listed firms in China during 2014 to 2018 applying the pooled OLS model for hypotheses test, Generalized Method of Moments (GMMs) to reduce endogeneity and unobserved factor control, and also return on equity (ROE) instead of ROA for the robustness test. Empirical results showed that IC and its components can improve PFP for VCS-backed firms in China; in detail, IC showed greater impact on performance of firms invested by foreign lead investors than in private or government VCS, specially reflected in the impact of innovation capital on PFP. Furthermore, IC showed weaker impact on PFP of mixed VCS-backed firms compared to pure VCS-backed firms and showed diminished effect on higher VCS member heterogeneity mainly reflected in the impact of relational capital on firm performance. These findings propose a new way of combining IC and VC to improve firm performance and are beneficial to theoretical development of IC and VC as well as a perspective for VC firm managers to choose suitable partners prior to join a VCS.