Empirical analysis of carbon tariff's effect on the export structure of China's manufacturing industry and social welfare based on the GTAP model

In recent years,carbon emissions have gradually evolved from an environment issue into a political and economic one.Carbon tariff has brought about new trade barriers of developed countries,and in order to enhance the industrial competitiveness of developed countries,it will produce unfavorable impact on developing countries.Concentrated on the manufacturing industry,which is the most intensive high-carbon industry in China's export structure,this article studies the relationship between carbon tariff policy and industry structure of export trade and builds up a relation between climate change and international trade.First,by means of establishing a partial equilibrium model,it applies geometric analysis and mathematical analysis to compute the impact on China's manufacturing export trade and the consequences of the introduction of the US carbon tariff to China's manufacturing industry that has already imposed a domestic shipping carbon tax.Furthermore,with the application of the GTAP model,it estimates the overall economic and welfare effects on China's manufacturing industry if the US and Europe introduce carbon tariff by means of four ways,and then analyzes the influence on China's manufacturing industry export structure and social welfare as well.The result shows that the introduction of the US carbon import tariff lowers China's export price and export volume,and the implementation of a domestic carbon tax justifies a higher export price and a lower export volume for China.However,the degree of export reduction is smaller than that under the effect of the US carbon tariff.In the case of developed countries imposing carbon tariff on China's energy-intensive industries,such as chemical rubber products,oil and coal-processing industry and paper industry,whose export would be reduced,the negative impact on the paper industry is the severest,which will decrease the paper industry's export ranging from 1.79%to 6.05%,whereas the other industries' export will increase.Anyhow,it will promote China's manufacturing industry to adjust the export structure to a certain extent.In addition,it will lead to a decrease in China's welfare,with a decrease between $2,134 billion and $8,347 billion.Finally,this paper provides information on international coordination,export structure adjustment and green manufacturing adjustment as a reference for the development of China's manufacturing industry.

The Wolf Is Coming?——Multinationals's Entry and Effect on China's Manufacturing Industry

Nowadays,China has become the country that introduces the largest amount of foreign capital.Up to June 2008,the accumulative total number of foreign- invested enterprises in China is over 632 thousand:the total amount of actual use of foreign capital is over US$582 billion.There are over 400 multination- als of top 500 investing in China,30 of which set up regional headquarters。

Should China's Manufacturing Industry Transition Towards Both Ends of the "Smiling Curve" ?——A Comparative Analysis between China and Traditional Manufacturing Powers

The manufacturing sector in China has surpassed that of the United States in terms of scale.Yet it still suffers from the shocks of an international economic downturn,the lack of core technologies,and the impact of "re-industrialization" of the developed economies.Based on an evaluation of the "smiling curve" theory and selected trade data from 1991 to 2011,this paper performs a comparative analysis of the differences between the manufacturing sectors of China and those of the traditional manufacturing powers in terms of product categories and architecture.This analysis is conducted in order to explore the question of whether China should transition from being the "world's manufacturing plant" to both ends of the "smiling curve".The paper then introduces Japan's "inverted smiling curve " to explore whether profit margins remain in the manufacturing sector.Research shows that China's manufacturing capacity,especially its "integrated" product assembly capacity,has not been fully realized,and that the development of the manufacturing sector can still yield high market returns.As a result,it is necessary for China to continue the path of being the "world's manufacturing plant".

Research on the Industrial Transfer and Restructuring Path of the Energy-Intensive Manufacturing Industry in Western China

The orderly transfer of the manufacturing industry is a major action in China’s industrial restructuring.From the perspective of industrial transfer,we used the concentration ratio to depict the trend of the industrial transfer of energy-intensive manufacturing in the eastern,central,and western regions since the policy of large-scale development of western China was implemented.We measured the total factor productivity(TFP)of western China using the DEAMalmquist index method.We conducted a regression analysis to measure the effect of western China’s undertaking of the transfer of the energy-intensive manufacturing industry.The findings of this study show that during 2000–2019,eleven provinces(as well as autonomous regions and municipalities)in western China undertook the transfer of the energy-intensive manufacturing industry from the eastern and central regions to varying degrees,exhibiting significant phase features regarding the rate and scale of transfers.Further investigation also demonstrated that the transfer of energy-intensive manufacturing industries has a U-shaped enabling effect on TFP in western China with the scale effect greater than the technology effect.Therefore,it is necessary to transition from“extensive industrial transfer”at the cost of the labor force,land,and resources to“modern industrial transfer”featured by technology and efficiency improvements to contribute to industrial restructuring in western China effectively.

On the Digital Transformation of the Automobile Manufacturing Industry in the Chengdu-Chongqing Economic Circle:Mechanism of Action and Feasible Paths

With a long industrial chain and a powerful ability to drive other industries,the automobile manufacturing industry has a prominent strategic position in the national economy.In recent years,many countries have put on their agenda the digitalization of the automobile manufacturing industry,leading to an connected,autonomous,shared,and electric(also known as CASE)①development trend in the industry.As one of the six major automobile industry clusters in China,the Chengdu-Chongqing economic circle has achieved initial results in the digital transformation of the automobile manufacturing industry.However,the region is still faced with some constraints,such as insufficient digital infrastructure,relatively slow development of new automobile products,insufficient innovation ability of the automobile industry,and complex digital transformation of small and medium-sized automobile enterprises(automobile SMEs).This paper intends to construct a framework for the mechanism of action of the digital transformation in the automobile manufacturing industry,analyze the effects of the digital transformation of the automobile manufacturing industry in the Chengdu-Chongqing economic circle,and propose feasible paths for the digital transformation of the automobile manufacturing industry in the region by drawing on domestic and international experience in this regard.The specific paths include:(a)Smoothing the“dual-core”data chain to facilitate the digital transformation of the automobile manufacturing industry;(b)Developing the new energy vehicle(NEV)industry to upgrade the quality of automobile products;(c)Achieving corner overtaking in the digital transformation of the automobile manufacturing industry with digital technology;(d)Jointly building the automobile industrial park to promote the digital transformation of the industry;(e)Addressing problems facing automobile SMEs in digital transformation via targeted policy tools.

How can technology and efficiency alleviate the dilemma of economic growth and carbon emissions in China's industrial economy? A metafrontier decoupling decomposition analysis

This paper attempts to explore the decoupling relationship and its drivers between industrial economic increase and energy-related CO_(2) emissions(ICE). Firstly, the decoupling relationship was evaluated by Tapio index. Then, based on the DEA meta-frontier theory framework which taking into account the regional and industrial heterogeneity and index decomposition method, the driving factors of decoupling process were explored mainly from the view of technology and efficiency. The results show that during2000-2019, weak decoupling was the primary state. Investment scale expansion was the largest reason hindering decoupling process of industrial increase from ICE. Both energy saving and production technology achieved significant progress, which facilitated the decoupling process. Simultaneously, the energy technology gap and production technology gap among regions have been narrowed, and played a role in promoting decoupling process. On the contrary, both scale economy efficiency and pure technical efficiency have inhibiting effects on decoupling process. The former indicates that the scale economy of China's industry was not conducive to improve energy efficiency and production efficiency, while the latter indicates that resource misallocation problem may exist in both energy market and product market.

Method for Detecting Industrial Defects in Intelligent Manufacturing Using Deep Learning

With the advent of Industry 4.0,marked by a surge in intelligent manufacturing,advanced sensors embedded in smart factories now enable extensive data collection on equipment operation.The analysis of such data is pivotal for ensuring production safety,a critical factor in monitoring the health status of manufacturing apparatus.Conventional defect detection techniques,typically limited to specific scenarios,often require manual feature extraction,leading to inefficiencies and limited versatility in the overall process.Our research presents an intelligent defect detection methodology that leverages deep learning techniques to automate feature extraction and defect localization processes.Our proposed approach encompasses a suite of components:the high-level feature learning block(HLFLB),the multi-scale feature learning block(MSFLB),and a dynamic adaptive fusion block(DAFB),working in tandem to extract meticulously and synergistically aggregate defect-related characteristics across various scales and hierarchical levels.We have conducted validation of the proposed method using datasets derived from gearbox and bearing assessments.The empirical outcomes underscore the superior defect detection capability of our approach.It demonstrates consistently high performance across diverse datasets and possesses the accuracy required to categorize defects,taking into account their specific locations and the extent of damage,proving the method’s effectiveness and reliability in identifying defects in industrial components.

Advances of embedded resistive random access memory in industrial manufacturing and its potential applications

Embedded memory,which heavily relies on the manufacturing process,has been widely adopted in various industrial applications.As the field of embedded memory continues to evolve,innovative strategies are emerging to enhance performance.Among them,resistive random access memory(RRAM)has gained significant attention due to its numerousadvantages over traditional memory devices,including high speed(<1 ns),high density(4 F^(2)·n^(-1)),high scalability(~nm),and low power consumption(~pJ).This review focuses on the recent progress of embedded RRAM in industrial manufacturing and its potentialapplications.It provides a brief introduction to the concepts and advantages of RRAM,discusses the key factors that impact its industrial manufacturing,and presents the commercial progress driven by cutting-edge nanotechnology,which has been pursued by manysemiconductor giants.Additionally,it highlights the adoption of embedded RRAM in emerging applications within the realm of the Internet of Things and future intelligent computing,with a particular emphasis on its role in neuromorphic computing.Finally,the review discusses thecurrent challenges and provides insights into the prospects of embedded RRAM in the era of big data and artificial intelligence.

Review of China's Hydropower Manufacturing Industry

China's hydropower equipment manufacturing technology has benef ited from the policy of reform and opening-up and the "Three Gorges Model." The latter means an approach of self-supply of hydropower equipment. Based on the foundations laid through independent research and development, by way of digesting, absorbing and innovating the technologies introduced from abroad for the Left Bank Station of the Three Gorges Hydropower Station, domestic suppliers independently designed and manufactured large hydropower units for the Right Bank Station of the Three Gorges and many other large hydropower stations in China.

Fundamental Theories and Key Technologies for Smart andOptimal Manufacturing in the Process Industry

Given the significant requirements for transforming and promoting the process industry, we present themajor limitations of current petrochemical enterprises, including limitations in decision-making, produc-tion operation, efficiency and security, information integration, and so forth. To promote a vision of theprocess industry with efficient, green, and smart production, modern information technology should beutilized throughout the entire optimization process for production, management, and marketing. To focuson smart equipment in manufacturing processes, as well as on the adaptive intelligent optimization of themanufacturing process, operating mode, and supply chain management, we put forward several key scien-tific problems in engineering in a demand-driven and application-oriented manner, namely：intelligentsensing and integration of all process information, including production and management information; collaborative decision-making in the supply chain, industry chain, and value chain, driven by knowledge; cooperative control and optimization of plant-wide production processes via human-cyber-physical in-teraction; and Q life-cycle assessments for safety and environmental footprint monitoring, in addition totracing analysis and risk control. In order to solve these limitations and core scientific problems, we furtherpresent fundamental theories and key technologies for smart and optimal manufacturing in the processindustry. Although this paper discusses the process industry in China, the conclusions in this paper can beextended to the larocess industry around the world.

Opportunities and Challenges of Artificial Intelligence for Green Manufacturing in the Process Industry

Smart manufacturing is critical in improving the quality of the process industry. In smart manufacturing, there is a trend to incorporate different kinds of new-generation information technologies into process- safety analysis. At present, green manufacturing is facing major obstacles related to safety management, due to the usage of large amounts of hazardous chemicals, resulting in spatial inhomogeneity of chemical industrial processes and increasingly stringent safety and environmental regulations. Emerging informa- tion technologies such as arti cial intelligence (AI) are quite promising as a means of overcoming these dif culties. Based on state-of-the-art AI methods and the complex safety relations in the process industry, we identify and discuss several technical challenges associated with process safety: ① knowledge acquisition with scarce labels for process safety;② knowledge-based reasoning for process safety;③ accurate fusion of heterogeneous data from various sources;and ④ effective learning for dynamic risk assessment and aided decision-making. Current and future works are also discussed in this context.

Intelligent Manufacturing for the Process Industry Driven by Industrial Artificial Intelligence

Based on the analysis of the characteristics and operation status of the process industry,as well as the development of the global intelligent manufacturing industry,a new mode of intelligent manufacturing for the process industry,namely,deep integration of industrial artificial intelligence and the Industrial Internet with the process industry,is proposed.This paper analyzes the development status of the existing three-tier structure of the process industry,which consists of the enterprise resource planning,the manufacturing execution system,and the process control system,and examines the decision-making,control,and operation management adopted by process enterprises.Based on this analysis,it then describes the meaning of an intelligent manufacturing framework and presents a vision of an intelligent optimal decision-making system based on human–machine cooperation and an intelligent autonomous control system.Finally,this paper analyzes the scientific challenges and key technologies that are crucial for the successful deployment of intelligent manufacturing in the process industry.

Future Digital Design and Manufacturing: Embracing Industry 4.0 and Beyond

Digital design and manufacturing have been around for several decades from the numerical control of machine tools and automating engineering design in 1960s, through early Computer Aided Design （CAD）/Computer Aided Engineering analysis （CAE）/Computer Aided Manufacturing （CAM）, to modem digital design and manufacturing [1], and cloud manufacturing [2] converging into product lifecycle management （PLM） [3, 4] and Internet-enabled personalized manufacturing [5].

Industry Linkage and Low-carbon Development--Based on Logistics Industry & Manufacturing Industry

Key project of " manufacturing industry and logistics industry linkage"was proposed in the Logistics Adjustment and Revitalization Plan by the state council in 2009. However the consumption and pollution generated by manufacturing industry and logistics industry linkage in China are also large at present. How to conduct manufacturing industry and logistics industry linkage by the low-carbon manner is one of most important issues under current low-carbon economy background. In this paper,the issue is studied and analyzed by constructing system dynamics model,which could propose suggestions for low-carbon linkage development of manufacturing industry and logistics industry.

Impact of the green credit policy on external financing,economic growth and energy consumption of the manufacturing industry

China attempts to achieve energy conservation,emission reduction and environmental protection through the implementation of the green credit policy,but its implementation impact is still controversial.An important content of the green credit policy is to require banking and financial institutions to tighten the credit exposure of industries of‘high pollution and high energy consumption’and industries with overcapacity,so as to use economic leverage to curb their blind expansion and reduce energy consumption by controlling external financing.This paper examined the impact and the lingering effects of the green credit policy on external financing,economic growth and energy consumption in the manufacturing industry,which was most influenced by the green credit policy,from 2003 to 2016 by using the DID method.Furthermore,this paper estimated the dynamic endogenous relationships among external financing,economic growth and energy consumption with two-step system GMM model to investigate the influential path of the green credit policy.The results showed that:the green credit policy had a significant negative impact on the external financing of manufacturing industry,but its negative impact on the economic growth and energy consumption of manufacturing industry was not statistically significant,and the effect of the green credit policy had a dynamic feature of weakening with time.Additionally,in the manufacturing industry,there was a bilateral causal relationship between the energy consumption and economic growth of the control group industry and the processing group industry.There was a bilateral causal relationship between the economic growth and external financing of the control group industries in the manufacturing industry.There was a unilateral causal relationship between the economic growth and external financing of the processing group industries in the manufacturing industry,while the external causality existed between the control group industries and the processing group industries in the manufacturing industry.The causal relationship between the financing and energy consumption was not statistically significant.At present,the transmission path of the green credit policy is that the green credit policy controls external financing,then affects economic growth and ultimately inhibits energy consumption,but the effectiveness of the path is not statistically significant.The conclusion of this paper provides policy reference and scientific basis for the adjustment and improvement of green credit.

Spatial Organization of Innovation in the Oil Equipment Manufacturing Industry: Case of Dongying, China

As innovation and technological change have become increasingly important for the competitiveness and sustainable growth of firms,cooperative innovation is now crucial for traditional industries in the context of globalization.This paper proposes a framework for analyzing the spatial pattern of cooperative innovation for traditional industries in developing countries.Based on in-depth interviews with 35 firms in the oil equipment manufacturing industry in Dongying City,China,this study argues that different firms in the innovation pyramid have various innovation activity preferences and spatial patterns.Firms with high innovation abilities tend to cooperate with various partners that are geographically dispersed and continuously expanding,while firms with inferior abilities usually cooperate with nearby fixed partners.Due to the differences in innovation environment and actor locations,firms tend to make different choices regarding innovation types and models,which highlight the importance of personnel training and basic scientific research at the global scale and practical product research and development at the national scale.Additionally,talent flow is the most important way to realize relationships for firm innovation activity.

Optimization Model in Manufacturing Scheduling for the Garment Industry

The garment industry in Vietnam is one of the country’s strongest industries in the world.However,the production process still encounters problems regarding scheduling that does not equate to an optimal process.The paper introduces a production scheduling solution that resolves the potential delays and lateness that hinders the production process using integer programming and order allocation with a make-to-order manufacturing viewpoint.A number of constraints were considered in the model and is applied to a real case study of a factory in order to viewhowthe tardiness and latenesswould be affected which resulted in optimizing the scheduling time better.Specifically,the constraints considered were order assignments,production time,and tardiness with an objective function which is to minimize the total cost of delay.The results of the study precisely the overall cost of delay of the orders given to the plant and successfully propose a suitable production schedule that utilizes the most of the plant given.The study has shown promising results that would assist plant and production managers in determining an algorithm that they can apply for their production process.

Effects of Global Value Chain Position on the Carbon Emissions of Manufacturing Industry in China: An Empirical Study Based on the STIRPAT Model

Based on the input-output data from the World Input-Output Database( WIOD),the global value chain( GVC) position of China's manufacturing industry from 2003 to 2014 was calculated,and the relationship between the carbon emissions and global value chain position of China's manufacturing industry was studied based on the improved STIRPAT model. The results show that the improvement of global value chain position could significantly reduce the carbon emissions of China's manufacturing industry. In addition,foreign investment and energy structure hindered the low-carbon development of China's manufacturing industry. The effects of population size and research intensity on the carbon emissions of manufacturing industry were not significant. In the process of participating in the global value chain,China's manufacturing industry should effectively reduce carbon emissions by strengthening environmental regulation,optimizing energy structure and improving production technology.

Development on manufacturing technology of railway locomotive and rolling stock industry in China

This paper described progress and development on the welding technology and vehicle manufacture during last decade, which promoted the technical progress for development and creation a considerable variety of high quality products in railway locomotive and rolling stock industry.

Green transformation of manufacturing industry for ecological protection in Jiangsu Province

Under the new normal economy, the manufacturing industry is developing rapidly as the focus of economic development in Jiangsu. Its rapid development has brought great opportunities to our society, but at the same time, great challenges has been brought to the natural environment. The resource consumption and environmental pollution problems have become increasingly prominent. In the face of this situation, we must try every means to reduce pollution and save energy. Therefore, the transition trend of manufacturing industry in the future is bound to take the green economy as the background. Thus, this paper analyzes the drawbacks of the manufacturing industry in Jiangsu province. And the status of traditional manufacturing enterprises and advantages of green manufacturing are analyzed. Then, the paper proposed to explore the practice of green manufacturing mode for the ecological protection, so as to achieve the industrial transformation and upgrading of manufacturing industry in Jiangsu province.