Downstream Enterprises Research Articles

The hidden benefit: Emission trading scheme and business performance of downstream enterprises

There seems to be no empirical study on the impact of emission trading scheme (ETS) on downstream industries. However, carbon trading may affect the performance of downstream firms via cost effect or spillover effect through the industrial chain. Based on the data of listed companies, this paper innovatively constructs three-dimensional data of “upstream firm - downstream firm - time”, with a total of 40,437,001 valid observations, simulates the upstream and downstream relationship between enterprises, and estimates the impact of carbon trading on the downstream performance using the Pair Weighted Difference in Difference in Difference (PW-DDD) model. The results show that the effects of carbon trading on downstream performance is negative in the short run, but it has positive significance in the long run. Heterogeneity is also found: the downstream firms with significant industrial chain relationships are more positively affected, such as chemical, petrochemical, and cement. In addition, carbon trading may improve productivity in different sectors in the same region or the same industry in other areas, thus improving the performance of non-ETS-covered enterprises.

Industrial Policy and Technological Innovation of New Energy Vehicle Industry in China

Promoting the development of new energy vehicles is one of the important measures to ensure energy security and deal with global warming. Technological innovation is an inexhaustible driving force for the development of the new energy vehicle industry. This study considered listed enterprises in China’s new energy vehicle industry as research samples and used the fixed effect model to study the impact of government subsidies on the quantity and quality of technological innovation in the new energy vehicle industry. The empirical results show that government subsidies have a significant positive impact on the quantity of technological innovation in the new energy vehicle industry; however, government subsidies have no significant impact on the quality of technological innovation. Government subsidies increase the quantity of technological innovation in the new energy vehicle industry by increasing R&D investment, mitigating financing constraints, and improving the external attention of enterprises. Compared to downstream enterprises in the industrial chain, government subsidies have a better incentive effect on the technological innovation of upstream enterprises, which increases the number of patents and enhances the quality of utility model patents. Government subsidies have a better effect on promoting the quantity of technological innovation in large enterprises.

The configuring pathways of green technology advance, organizational strategy and policy environment for realizing low-carbon manufacturing from the perspective of simmelian tie: A qualitative comparative analysis of listed companies in China

Low-carbon manufacturing is an emerging industrial trend and an important way to achieve sustainable development. As the country with the largest manufacturing scale and carbon dioxide emissions, it is of great practical and theoretical significance to study China's low-carbon manufacturing issues. Based on the ternary analysis perspective of simmelian tie, this paper chooses to identify the influencing factors through the technology-organization-environment research framework, and derives the pathways of low-carbon manufacturing through the fuzzy-set qualitative comparative analysis method. The research results show that: (1) Under the low-carbon manufacturing goal, stable industrial chain structure and policy-sensitive are the formation reasons for the simmelian tie. (2) From the perspective of simmelian tie, low-carbon manufacturing will be influenced by the driving role of green technological advances, the supporting role of organizational strategies, and the regulatory role of the policy environment. (3) Manufacturing enterprises' low-carbon pathways are directly related to their industrial chain position. Compared with upstream enterprises, downstream enterprises have more diverse low-carbon manufacturing pathways. (4) Active governmental support can more effectively promote the realization of low-carbon manufacturing than environmental pressure.

Valuation Analysis of New Energy Vehicle Industry Stock Price Based on HP-ARIMA Method

In recent years, new energy vehicles have become one of the mainstream trends in the global auto market. By analyzing the valuation of the stock price of the new energy automobile industry, the actual value of its stock can be found, thus providing a specific reference for investors. Based on this, this paper investigates the fluctuation of daily average stock prices using the HP-ARIMA method, and performs predictive analysis, robustness test and heterogeneity analysis on the given data. After separating the short-term fluctuations and long-term trends of daily average stock prices based on the HP filtering method, the predictions were made by the ARIMA model. The predicted results were tested for robustness by OLS regression and Tobit model, respectively, and the following findings were obtained: (1) Through the analysis of HP filtering method, the cyclical fluctuation of the stock price of the new energy automobile industry is relatively large, and upstream enterprises affect the middle and downstream enterprises to a certain extent. (2) The prediction analysis by ARIMA shows that the prediction accuracy of upstream enterprises' share prices is relatively high, and the prediction results have good robustness. (3) The regression analysis shows that the fit is good for upstream and downstream enterprises, and the change trend shows a positive correlation, while the midstream enterprises show the opposite trend.

Application of Blockchain Based on Deep Learning Algorithm in Enterprise Internet of Things System

The purpose of an IoT enterprise business system is to form a global open network structure. The main task of this process is to build the Internet of Things system based on the internal enterprise, and link the upstream and downstream enterprises to interact with the Internet of Things system. However, existing blockchain systems often have limited performance and capacity, so they cannot be used in many scenarios, resulting in reduced scalability. The most common solution is to use the deep learning algorithm to find the optimal blockchain parameters. Based on deep learning and blockchain technology, this article proposes an enterprise Internet of Things system, which divides the whole Internet of Things into three layers—device layer, edge layer, and application layer, so as to build a reliable edge layer platform, so as to integrate the application network and blockchain network of the enterprise Internet of Things system and ensure the data security of the enterprise Internet of Things system. Finally, in order to verify the design proposed in this article, we build a distributed IoT business system, implement the deployment of experimental datasets on multiple hardware platforms, and design experiments to verify the security and effectiveness of the system designed in this work. By using blockchain technology based on the deep learning algorithm, the accuracy and time efficiency of the enterprise Internet of Things system are improved, and the burden of enterprise operators is greatly reduced. This article studies the deep learning algorithm and blockchain technology, and applies it to the design process of the enterprise Internet of Things system, thus promoting the development of the enterprise Internet of Things system.

Influencing Factors Analysis of Collaborative Development Level of New Retail and Cold Chain Distribution of Agricultural Products under Low-Carbon

With the annual growth of agricultural products， which has greatly met People’s daily needs. It is of great practical significance to introduce the low-carbon concept into the retail and cold chain logistics of agricultural products. However, few studies have designed the coupling relationship between the new retail of agricultural products and the development of cold chain distribution. In this context, this work uses principal component analysis (PCA) to extract the common core factors that affect the operational benefits of cold chain low-carbon distribution and the consumer experience of new agricultural retail. Then, the coupling linkage model of cold chain low-carbon distribution and new retail of agricultural products was constructed by using grey correlation analysis. Finally, a new retail and agricultural products cold chain coupling mechanism under low-carbon distribution is designed. The work is helpful to improve the new downstream supply chain retail enterprise management ability of agricultural products, promote cold-chain distribution business benefit and business value, and also helps to promote the implementation of the strategy of rejuvenating and promote the country. This study is conducive to improving the downstream supply chain management ability of new agricultural retail enterprises, enhancing the cold chain distribution business efficiency and business value, and ultimately promoting the implementation of a rural revitalization strategy.

A differential game for basin ecological compensation mechanism based on cross-regional government-enterprise cooperation

Cross-regional cooperation among upstream and downstream governments and enterprises is crucial for the efficiently implementation of basin ecological compensation mechanism. In this study, we propose and study a differential game model for basin ecological compensation mechanism based on cross-regional cooperation among governments and enterprises. We have considered three different cooperation settings: mechanisms with no cost sharing, mechanisms with cost sharing and collaborative cooperation mechanism. Under these three settings, all the players' optimal ecological protection efforts, cost sharing rates and optimal revenues are derived. Comparison analysis shows that (1) all the players’ revenues are increasing in the partially ordered set of vectors of cost sharing; (2) the total revenue of the whole system achieves optimal under the collaborative cooperation mechanism. Finally, we conduct several numerical experiments to verify the performance of the solutions to the model, and reveal many interesting managerial insights after hence.

Construction of Credit Risk Assessment Index System of SMEs based on Blockchain-driven Supply Chain Finance

blockchain-driven supply chain finance (BC-driven SCF) can effectively integrate the credit resources of upstream and downstream enterprises and enhance the financing capacity of all enterprises in the chain, taking the core enterprise as the center and the blockchain technology as the platform, which plays an important role in the upgrading of the industrial chain. In order to accurately and objectively evaluate the SMEs’ credit risk based on BC-driven SCF, we need to establish a scientific and complete credit risk assessment index system, formulate strict design ideas and select appropriate design method. By analyzing the formation mechanism of blockchain-driven supply chain finance model, and using relevant theoretical and empirical methods, this paper puts forward a credit risk assessment index system suitable for the supply chain financial environment. Finally, 27 three-level indicators, 13 two-level indicators and 5 primary indicators were established in index system. This conclusion provides a reference for the application of blockchain in supply chain to reduce the credit risk. At the same time, the selected indicators are more comprehensive, which provides a strong basis for the subsequent construction of credit risk assessment model

Does External Innovation Promote the Exports of Private Enterprises? A Market Stakeholder Perspective.

Using the theoretical perspective of market stakeholders, we analyze the impact of external innovations from upstream enterprises, downstream enterprises, and competitors on the exports of private enterprises. By using data from the China Industrial Enterprises Database, we find that external innovations from upstream suppliers, downstream customers and horizontal competitors show positive impacts on the export propensity, intensity and scale for private enterprises. The results of a heterogeneity analysis indicate that the positive relationships between the external innovations of stakeholders and the exports of private enterprises are stable in different factor-intensive industries. In addition, while the exports of private enterprises are positively correlated with their external innovations in the eastern and central regions, this relationship is not significant in the western region. Further, the mechanism analysis confirms that enterprise innovation played an important mediating role for the external innovations of stakeholders to promote the exports of private enterprises. This study provides useful policy implications for enhancing the export competitiveness of private enterprises.

Government regulation to promote coordinated emission reduction among enterprises in the green supply chain based on evolutionary game analysis

The green supply chain realizes the unification of economic and environmental benefits through green manufacturing, green circulation and reverse logistics, and represents an important way to reduce emissions. The characteristics of the green supply chain network, however, can encourage some companies to exhibit “free-riding” behavior, participating without being willing to reduce emissions directly themselves. This paper applies an evolutionary game model to a two-level green supply chain composed of green suppliers and green manufacturers in order to analyze a variety of internal and external factors that affect the behavior of both parties in the game, and thence numerically simulate the evolution and stability trend of coordinated reductions in emissions. The results identify many cases of system evolution but the only stable evolution strategy is when both (1) the sum of collaborative emission reduction benefits and government subsidies is greater than the sum of collaborative emission reduction input costs and "free rider" benefits, and (2) the increased rate of unilateral emission reduction benefits is greater than the ratio of costs to original benefits. Income arising from collaborative reductions in emissions, enterprise original income, income from unilateral reductions in emissions increase ratio, government subsidy coefficient and achievement reward base can directly affect the system evolution path. The larger these value, the greater the probability of green suppliers and green manufacturers choosing collaborative reductions in emissions, and the faster the system convergence speed. Only the imposition of regulatory punishments above the threshold will affect the "free riding" behavior of enterprises and drive the upstream and downstream enterprises of the green supply chain to reduce emissions faster.

A blockchain and IoT-based lightweight framework for enabling information transparency in supply chain finance

Supply Chain Finance (SCF) refers to the financial service in which banks rely on core enterprises to manage the capital flow and logistics of upstream and downstream enterprises. SCF adopts a self-testing and closed-loop credit model to control funds and risks. The key factor in a successful SCF service is the deployment of SCF business-oriented information systems that allow businesses to form partnerships efficiently and expedite cash flows throughout the supply chain. Blockchain Technology (BCT), featuring decentralization, tamper-proofing, traceability, which is usually paired with the Internet of Things (IoT) in real-world contexts, has been widely adopted in the field of finance and is perfectly positioned to facilitate innovative collaborations among participants in supply chain networks. In this paper, we propose a BCT and IoT-based information management framework (named BC4Regu), which works as the regulatory to improve the information transparency in the business process of SCF. With BC4Regu, the operation cost of the whole supply chain can be significantly reduced through the coordination and integration of capital flow, information flow, logistics and trade flow in the supply chain. The contributions in this paper include: (1) proposing a novel information management framework which leverages Blockchain and IoT to solve the problem of information asymmetry in the trade of SCF; (2) proposing the technical design of BC4Regu, including the Blockchain infrastructure, distributed ledger-based integrated data flow service, and reshaped SCF process; and (3) applying BC4Regu to a group of scenarios and conducting theoretical analysis by introducing the principal-agent model to validate the BC4Regu.

Research on government subsidy strategies for new drug R&D considering spillover effects

This paper studies the pharmaceutical production supply chain system composed of upstream and downstream pharmaceutical enterprises, and explores the impact of government subsidy strategies on the new drug research and development (R&D) decision variables and profits of pharmaceutical enterprises as well as social welfare, when considering both the horizontal spillover effects within the industry and the vertical spillover effects between industries. Comparing and analyzing the impact of these strategies including non-government subsidy strategy, pharmaceutical enterprise innovation input subsidy strategy, pharmaceutical enterprise innovative product subsidy strategy, patient price subsidy strategy, and patient medical insurance subsidy strategy. By establishing a four-stage Cournot duopoly model incorporating spillover effects, the equilibrium solutions are obtained by backward induction, and the impact of spillover effects on decision variables is investigated accordingly. Studies have shown that: (a) Government subsidy strategies can promote pharmaceutical enterprises’ R&D investment and have a positive incentive effect on the pharmaceutical enterprises’ profits and social welfare. (b) The patient medical insurance subsidy strategy is the optimal subsidy strategy, which can generate higher profits for pharmaceutical enterprises and higher social welfare. (c) The horizontal and vertical spillover effects have different effect on decision variables, enterprises’ profits and social welfare under various subsidy strategies.

The technological innovation efficiency of China's lithium-ion battery listed enterprises: Evidence from a three-stage DEA model and micro-data

Large-scale clean energy deployment and energy consumption electrification are important measures for China to respond to severe climate challenges and achieve carbon neutrality goals, and the development of lithium-ion battery storage technology is essential to enable clean energy transition. Using three-stage DEA and Tobit model, this paper evaluated the real technological innovation efficiency (TIE) of China's lithium-ion battery listed enterprises (CLBLEs) during 2009–2018, and explored how external environment and enterprise management factors influence the TIE. The results demonstrate that there exist inefficiency problems of the TIE of CLBLEs due to the diseconomies of scale. The average TIE of CLBLEs is low, at 0.39. The stated-owned enterprises, large-scale enterprises, and downstream enterprises have the highest TIE, and mainly concentrated in eastern region. For external environmental factors, regional economic level and government subsidy promote TIE. While for internal enterprise management factors, firm size, financial leverage, profitability, and employee quality significantly promote TIE. Thus, for policy makers, strengthening the implementation of industrial support policies, enhancing industrial agglomeration, and promoting the sharing of innovative resources are conducive to improving the scale efficiency of CLBLEs. In addition, improving management capabilities and personnel composition is also an important direction to promote the TIE of CLBLEs.

Automobile Industry under China’s Carbon Peaking and Carbon Neutrality Goals: Challenges, Opportunities, and Coping Strategies

China has already committed to peaking carbon dioxide emissions by 2030 and achieving carbon neutrality by 2060 (referred to as the 30·60 Target), which has brought both daunting challenges and great opportunities to the automobile industry in China. However, there is still a lack of comprehensive and in-depth studies on the challenges, paths, and strategies for reducing carbon emissions to fulfill the 30·60 Target in automobile industries. Therefore, this paper proposes low-carbon development strategies for China’s automobile industry. This study’s method is to integrate the results from different literature to summarize the status, challenges, opportunities, and refine the coping strategies for carbon emission of the automobile industry. The results indicated that the paths for achieving the 30·60 Target include joint carbon emission reduction by upstream and downstream enterprises inside the industry. It also needs cross-industry and cross-sector coordinated decarbonization outside the industry. Meanwhile, the low-carbon policy and regulation system should be established to provide a direct driving force and fundamental guarantee for the low-carbon development of China’s automobile industry.

Research on the Risks and Countermeasures of Rare Earth Industry from the Perspective of Supply Chain Finance——Taking Northern Rare Earth as an Example

Since 2021, the price of rare earth raw materials has risen sharply, and the upstream and downstream enterprises in the supply chain in the rare earth industry are facing a serious shortage of funds. In view of the current development status of the rare earth industry in the Inner Mongolia Autonomous Region, Baotou City, Inner Mongolia Autonomous Region, has begun to vigorously promote the effective combination of supply chain finance and local characteristic industries. This paper takes the upstream and downstream enterprises in the supply chain with the core of China Northern Rare Earth (Group) High-Tech Co., Ltd. as the research object, and analyzes the risks that may occur in each link of each subject in the supply chain finance and explores the reasons. And then put forward corresponding risk control suggestions for the government, core enterprises, financial institutions and related upstream and downstream small and medium-sized enterprises, and provide reference opinions for each enterprise whether to choose supply chain finance for financing.

Retracted] Path Optimization of Enterprise Network Innovation Performance Management Based on Deep Learning and Internet of Things

In today's global competition environment with rapid changes in market and technology, it is more and more difficult for enterprises to fully grasp the latest knowledge and learn all the technologies by relying on their own strength. It is particularly important for enterprises to establish a network relationship of a certain intensity with other external entities (upstream and downstream enterprises, peer enterprises, scientific research institutions, government departments, financial institutions, and other organizations) for their technological learning and improvement of technological innovation performance. From different perspectives, the academic circles have confirmed that the strength of network relationship does have an impact on the technological innovation performance of enterprises. This paper will explore the measurement scale of technology learning cost, and from the basic perspective of technology learning cost, deeply explore and analyze how enterprise technology innovation performance is affected by the strength of enterprise network relationship. This paper argues that the strength of network relationship can affect the cost of technology learning and therefore the performance of technology innovation. The main contents of this study include the following. (1) Through the collation and review of the related theories of network relationship strength, technological learning cost and technological innovation performance, and the existing research results, the theoretical model of this study is established, and the theoretical assumptions of this study are put forward. (2) A presurvey is carried out first, and the data collected from the presurvey are used to test the reliability and validity of the scale of this study, and the appropriate measurement scales for the strength of network relationship, technology learning cost, and technology innovation performance are determined. (3) Adopt the method of regression analysis combined with the SME method, to verify this paper builds the theoretical model, and further clarify the network relation intensity, technology acquisition cost, and cost of technological learning all dimensions and performance of technological innovation and network strength, technological learning mechanism between cost, and performance of technological innovation.

Organization Strategies of Innovation Forces for the Breakthrough of Key Core Technologies in Aero-Engine Industry

Achieving breakthroughs of key core technologies in the aero-engine industry is urgently required for guaranteeing national strategic security, and it demands effective organization of innovation forces. This study first analyses the major innovation forces and their problems regarding collaborative innovation in the current aero-engine scientific research and production system in China. Subsequently, it clarifies the roles of relevant forces in the research process of key core technologies, including the government, the army, the Aero Engine Corporation of China, professional maintenance enterprises, upstream and downstream enterprises, universities, and research institutes. Furthermore, we suggest that the breakthrough practice should be planned and organized in an overall manner, corresponding institutions and mechanisms should be created to encourage the innovation forces to closely align with national strategies, the driving role of the demand side should be emphasized, a number of innovation consortia aiming at user needs and product problems should be established by leading enterprises, and an innovation ecosystem should be established relying on major projects.

Jointly modeling transfer learning of industrial chain information and deep learning for stock prediction

The prediction of stock price has always been a main challenge. The time series of stock price tends to exhibit very strong nonlinear characteristics. In recent years, with the rapid development of deep learning, the ability to automatically extract nonlinear features has significantly attracted scholars’ attention. However, the majority of the relevant studies have concentrated on prediction of the changes of stock market based on the data of the specific stock (e.g., transaction data, financial data, etc.), while those studies ignored the interaction between stocks of different industries, especially the interaction between the stocks of upstream enterprises and downstream enterprises in the industrial chain. This paper aims to propose a combination of transfer learning of industrial chain information and deep learning models, including multilayer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), for stock market prediction. These models are used to predict the trend of the 379 stock market indices by industry in China, and the DM test was employed for validation of the prediction results. It can be concluded that RNNs are not necessarily such an optimal choice for the prediction when dealing with specific time series data, and it could be justified by using the local interpretable model-agnostic explanations (LIME) algorithm. Hence, the MLP was selected to effectively improve the accuracy of the prediction of the stock market indices based on the transfer learning of industrial chain information. The investment strategy is constructed according to the prediction results, and the yield of maturity exceeds that of the buy-and-hold strategy.

Supply Chain Contract Choice with Different Behavior Preferences of Retailer

Retailer may exhibit irrationality when facing the risk of demand uncertainty; therefore, we consider four retailer behavioral preferences: risk neutral (RN), waste aversion (WA), stockout aversion (SA), and stockout-waste aversion (SW). The decision-making and contract choice of upstream and downstream enterprises in cases where demand depends on supplier’s effort are studied. The results show that if the retailer has only SA or RN preferences, then the supplier prefers to choose a wholesale price contract, while the retailer does the opposite, if the retailer has only WA, then the supplier prefers to choose a revenue sharing contract, but the retailer’s contract choice depends on the degree of waste aversion, and if the retailer has SW, then the contract choice of upstream and downstream enterprises is related to the degree of waste aversion and stockout aversion.

Research on Ordering and Transportation Strategy of Raw Materials in Manufacturing Enterprises

This paper mainly studies the impact of upstream suppliers on downstream manufacturing enterprises. Firstly, this paper makes an abstract qualitative analysis on the supply characteristics of suppliers from the four aspects of supplier quality, supplier scale, supply stability and supply continuity, and then quantifies these four qualitative aspects layer by layer into five indicators: supply and demand matching degree, weekly average supply, supply weeks, supply law and the longest continuous supply weeks, The quantitative evaluation index is constructed, the weight is given by entropy weight method, and the supply characteristic measurement index system based on TOPSIS evaluation model is established by Python software to compare the advantages and disadvantages of different suppliers, and the 5 most important suppliers are sorted by scoring.