Chain Embedding Research Articles

Global Value Chain Embedding and Total Factor Productivity in Carbon Emission Reduction: A Multi-Country Analysis of the Paper Industry

Against the backdrop of carbon reduction and low-carbon economic development, this study takes the global paper industry chain as its research subject and employs the SBM-GML model and input–output model to measure the low-carbon TFP and value chain embedding levels of 42 major global economies from 2001 to 2021. Using fixed-effects and threshold effect models, the study examines both the overall and stage-specific impacts of value chain embedding on low-carbon TFP. The results reveal that between 2001 and 2021, while the low-carbon TFP of major countries in the global paper industry chain steadily increased, significant disparities persisted among them. Most countries experienced low-carbon TFP growth driven by technological progress, whereas only a few high low-carbon TFP nations also demonstrated upward trends in technical efficiency. The division of labour position in the value chain significantly promoted low-carbon TFP at a ratio of 1:0.26. In contrast, the low-carbon TFP effects of participation displayed a nonlinear impact with a threshold of 0.21: it exerted an insignificant inhibitory effect in the initial stage but, upon crossing the threshold, resulted in a significant positive effect with a ratio of 1:0.13. These findings highlight the importance of optimizing GVC strategies to achieve sustainable industrial transformation and growth.

The Markov chain embedding problem in a one-jump setting

Abstract The embedding problem of Markov chains examines whether a stochastic matrix $\mathbf{P} $ can arise as the transition matrix from time 0 to time 1 of a continuous-time Markov chain. When the chain is homogeneous, it checks if $ \mathbf{P}=\exp{\mathbf{Q}}$ for a rate matrix $ \mathbf{Q}$ with zero row sums and non-negative off-diagonal elements, called a Markov generator. It is known that a Markov generator may not always exist or be unique. This paper addresses finding $ \mathbf{Q}$ , assuming that the process has at most one jump per unit time interval, and focuses on the problem of aligning the conditional one-jump transition matrix from time 0 to time 1 with $ \mathbf{P}$ . We derive a formula for this matrix in terms of $ \mathbf{Q}$ and establish that for any $ \mathbf{P}$ with non-zero diagonal entries, a unique $ \mathbf{Q}$ , called the ${\unicode{x1D7D9}}$ -generator, exists. We compare the ${\unicode{x1D7D9}}$ -generator with the one-jump rate matrix from Jarrow, Lando, and Turnbull (1997), showing which is a better approximate Markov generator of $ \mathbf{P}$ in some practical cases.

Study on the Impact of National Value Chain Embeddings on the Embodied Carbon Emissions of Chinese Provinces

Accelerating the construction and optimization of national value chains is of great significance to reducing both pollution and carbon emissions and promoting green economic growth. In accordance with the input–output table and carbon emission statistics of China in 2012, 2015, and 2017, in this paper, we use the total trade decomposition method and the value chain decomposition method to decompose the embodied carbon emissions and the embeddedness of national value chains. Subsequently, we empirically study, for the first time, the impact of the degree of domestic value chain embedding on implicit carbon emissions using the calculated results. The results show the following: (1) The top three provinces with embodied carbon emissions are Shandong, Hebei, and Jiangsu, while the top four industries are the production and supply of electricity and heat; metal smelting and rolling processing; non-metallic mineral products; and transportation, warehousing, and postal services. (2) The degree of forward and backward national value chain embeddedness in Chinese provinces has increased, and the degree of forward embeddedness in most provinces and industries is lower than that of backward embeddedness. (3) The embeddedness of domestic value chains and embodied carbon emissions is always negatively correlated, and this conclusion is still valid after robustness and endogeneity tests. (4) There is industrial heterogeneity in the impact of the degree of embeddedness of domestic value chains on embodied carbon emissions.

Profit-Maximizing Service Function Chain Embedding in NFV-Based 5G Core Networks

Profit-Maximizing Service Function Chain Embedding in NFV-Based 5G Core Networks

Global value chain embedding and enterprise energy efficiency: A worldwide firm-level analysis

Global value chain embedding brings growth opportunities for small and medium-sized enterprises, despite their continuously controversial environmental effects. This study examines the impact of global value chain embedding on the energy efficiency of small and medium-sized enterprises using a worldwide firm-level micro data. Results support that global value chain embedding significantly improves enterprise energy efficiency, and this conclusion holds even after self-selection and endogeneity are considered. Global value chain embedding promotes enterprise innovation and labor productivity through market scale and induced learning effects and improves the energy efficiency. Moreover, the evidence only supports the lock-in effect of low value-added, while the lock-in hypothesis of pollution link and foreign technology is invalid. The positive impact of global value chain embedding on energy efficiency is greater for non-high-tech industries, mainly coming from export learning effect and innovation effect. Furthermore, there is no industry heterogeneity in the lock-in effect. Country heterogeneity in the effect of global value chain embedding on energy efficiency characteristics of regional embedding and industrialization, but not to economic development and human capital.

Research on the technology path selection of China's manufacturing industry under the high-quality development strategy

As the foundation and leading force of national economic development, manufacturing determines a country's comprehensive strength and international competitiveness, and is the key to achieving transformation, upgrading and high-quality development. Based on the perspective of global value chain embedding, this paper explores the issue of technology path selection and high-end development in my country's manufacturing industry. Against the background of Internet development, this paper analyzes the basis for technology path selection in my country's manufacturing industry to achieve intelligent transformation and high-quality development. By constructing an analytical framework for technology path selection from two aspects of internal and external factors, the theoretical basis for technology path selection in my country is proposed. Guided by high-quality development, this paper studies the internal mechanism of technology path selection that can promote the high-end, intelligent, service-oriented and green development of manufacturing industry, and discusses the technology path selection for manufacturing industry to achieve green and sustainable development. Based on an in-depth analysis of the impact and mechanism of global value chain embedding, Internet development, productive services and environmental constraints on technology path selection, this paper proposes the optimal decision equation for R&D resource allocation in my country based on factor endowment conditions, thereby promoting the transformation of manufacturing industry from factor-driven to innovation-driven, accelerating the process of achieving high-end, intelligent, service-oriented and green development, which is of great significance to promoting the high-quality development of manufacturing industry.

Global value chain participation and income inequality within enterprises: An empirical study based on Chinese-listed companies

In the debate about the causes of inequality, a growing strand of research focuses on the effects of globalization on income inequality. This study focuses on Chinese-listed companies from 2000–2016, examining the influence of GVC participation on intra-firm wage disparities. It finds a significant, non-linear inverted U-shaped relationship between GVC participation and wage inequality, which remains robust across various empirical specifications. The results also show that global value chain embedding has a more significant impact on income inequality within non-state-owned enterprises, larger enterprises, and enterprises with higher bargaining power among employees. This finding is qualitatively robust across various different empirical specifications.

Reinforcement-Learning-Assisted Service Function Chain Embedding Algorithm in Edge Computing Networks

Edge computing networks are critical infrastructures for processing massive data and providing instantaneous services. However, how to efficiently allocate resources in edge computing networks to meet the embedding requirements of service function chains has become an urgent problem. In this paper, we model the resource allocation problem in edge computing networks as a service function chain embedding problem model, aiming to optimize the resource allocation through reinforcement learning algorithms to achieve the goals of low cost, high revenue, and high embedding rate. In this paper, the basic concepts of edge computing network and service function chain are elaborated, and the resource allocation problem is transformed into a service function chain embedding problem by establishing a mathematical model, which provides a foundation for the subsequent algorithm design. In this paper, a service function chain embedding algorithm based on reinforcement learning is designed to gradually optimize the resource allocation decision by simulating the learning process. In order to verify the effectiveness of the algorithm, a series of simulation experiments are conducted in this paper and compared with other algorithms. The experimental results show that the service function chain embedding algorithm based on reinforcement learning proposed in this paper exhibits superior performance in resource allocation. Compared with traditional resource allocation methods, the algorithm achieves significant improvement in terms of low cost, high revenue, and high embedding rate.

GVCs Embeddedness and Its Effects on Industrial Productivity: Evidence from Turkish Manufacturing Industries

This study examines the impact of global value chain (GVC) embedding degree and position on productivity in Turkish manufacturing industries from 2003 to 2018 using a two-step system generalized method of moments (GMM). It explores variations in these effects between low-tech and high-tech industries. Results indicate a significant positive productivity effect for low-tech industries and an insignificant effect for high-tech and overall manufacturing industries. Furthermore, our findings suggest that embedding position has limited effects in all three contexts. This study reveals a statistically significant productivity impact of FDI, financial development, subsidies, and openness for overall manufacturing industries, with significant positive effects observed only for FDI and trade openness in low-tech and high-tech industries.

Foreign Direct Investment and Provincial National and Global Dual Value Chain Embedding——The Theoretical and Empirical Analysis under the New Development Paradigm

Foreign Direct Investment and Provincial National and Global Dual Value Chain Embedding——The Theoretical and Empirical Analysis under the New Development Paradigm

Effects of non-subsidized industrial policies on embedding position of power lithium-ion battery manufacturers in global value chain: Firm level evidence from China

Given the phase-out of subsidies, the Chinese government has issued a series of non-subsidized industrial policies to support the development of the power Lithium-ion Battery (PLiB) industry, recognized as pivotal for achieving carbon neutrality in the transportation department. However, existing literature provides limited evidence on the contribution of non-subsidized industrial policies, particularly to the PLiB industry. To bridge this gap, using data of 31 PLiB listed firms and 279 relevant non-subsidized industrial policies, this paper analyzes the effects of non-subsidized industrial policies on the global value chain (GVC) embedding position. The results reveal that non-subsidized industrial policies significantly raise the GVC embedding position of Chinese PLiB firms, and the positive effects are prone to increase over the research period. Further investigation shows that there is heterogeneity among PLiB firms, with the raising effects on the GVC embedding position being more significant in eastern and middle PLiB firms, private PLiB firms and midstream PLiB firms. The mechanism verification shows that non-subsidized industrial policies raise GVC embedding position of PLiB firms through technological innovation effect and scale economy effect, while competitiveness effect does not give full play. These findings provide important recommendations for better design of the industrial policies to improve the international competitiveness of PLiB industry and other strategic emerging industries.

Research on Service Function Chain Embedding and Migration Algorithm for UAV IoT

This paper addresses the challenge of managing service function chaining (SFC) in an unmanned aerial vehicle (UAV) IoT, a dynamic network that integrates UAVs and IoT devices for various scenarios. To enhance the service quality and user experience of the UAV IoT, network functions must be flexibly configured and adjusted based on varying service demands and network situations. This paper presents a model for calculating benefits and an agile algorithm for embedding and migrating SFC based on particle swarm optimization (PSO). The model takes into account multiple factors such as SFC quality, resource utilization, and migration cost. It aims to maximize the SFC benefit and minimize the migration times. The algorithm leverages PSO’s global search and fast convergence to identify the optimal or near-optimal SFC placement and update it when the network state changes. Simulation experiments demonstrate that the proposed method improves network resource efficiency and outperforms existing methods. This paper presents a new idea and method for managing SFC in UAV IoT.

Impact of global value chain embedding on decoupling between China's CO2 emissions and economic growth: Based on Tapio decoupling and structural decomposition

With the increasing fragmentation of global production, China's participation in cross-border production sharing activities has had a considerable impact on the nation's economy and carbon dioxide (CO2) emissions. This study applied the Tapio model to quantitatively evaluate the decoupling between CO2 emissions and economic growth in China, dividing the decoupling index based on global value chains (GVCs) and domestic production within the IO framework, and introducing structural decomposition analysis (SDA) to analyze the GVC-related factors to the decoupling. The relevant research results are fourfold. (1) From 2000 to 2018, China achieved weak decoupling between emissions and economic growth. Domestic and GVC effects each had a negative impact on the decoupling; however, after 2008, the GVC effect had a promotional effect and the negative domestic effect declined. (2) Emission intensity was the primary factor promoting decoupling through domestic and GVC effects, while the scale of final demand was the main hindrance. And the negative effects of GVC-related factors declined following the economic crisis. (3) The regional and sectoral structures of GVC production (58.44 % and 56.08 %) had promotional roles in the changes in GVC effects, while GVC production linkages (−20.19 %) had hindering effects. Various factors contributed to the hindering effect from the 2008 to 2011 index, whereas from the 2011 to 2018 index, all factors contributed to the promotional effect. (4) From 2000 to 2018, the average annual global value chain effect promoted the low-carbon development of China's labor-intensive and knowledge-based manufacturing. In order for GVCs to play a positive role in decoupling, China should promote trade facilitation through international platforms, support the advancement of production technology, reasonably guide China's industries to participate in the regional and industrial links of GVCs, and develop strategic emerging industries.

Analyzing the Impact of Digital Technology on Global Value Chain Embedding from the Perspective of Chinese Manufacturing Industry

Analyzing the Impact of Digital Technology on Global Value Chain Embedding from the Perspective of Chinese Manufacturing Industry

Performance-Aware Orchestration of P4-Based Heterogeneous Cloud Environments

The recent trend to deploy programmable packet processors in cloud environments enhances the packet processing capability without losing the flexibility to adapt the functions at runtime. In particular, distributed edge clouds can have a heterogeneous programmable processing substrate made up of different classes of devices: CPUs, NPUs, FPGAs, etc. However, managing the allocation of workloads in such a heterogeneous programmable processing substrate, in particular deciding where to instantiate a certain function, is a non-trivial task with many decisive functional and QoS-related factors. In this paper, we propose a mathematical model for optimizing the embedding of Service Function Chains implemented in P4, while considering the functional and QoS requirements associated with embedding requests, and the various types of processing devices that have different properties in terms of processing delay and supported features. To satisfy delay requirements, the problem formulation utilizes performance models to predict the forwarding latency associated with different candidate embedding options. Furthermore, a greedy solution is proposed to solve the problem in an efficient manner. Finally, a detailed numerical evaluation is conducted to evaluate the formulated model when different workload and infrastructure characteristics are varied and to evaluate the effectiveness of the proposed greedy solution.

The impact of global value chain embedding on the upgrading of China’s manufacturing industry

In the current international trade characterized by global value chains (GVCs), to improve the trade interests of China’s manufacturing industry and change the unfavorable situation of “big but not strong,” industrial upgrading is an important issue that needs to be solved urgently. Based on the analysis of the impact of embedding in GVCs on the acquisition of trade benefits, the paper empirically tests the impact of embedding GVCs on manufacture upgrading by using industry segmentation data of China’s manufacturing industry from the year 2000 to 2014 in the world input–output database. The research shows that 1) from 2000 to 2014, forward and backward participation increased from 11.2% and 15.2% to 14.6% and 15.9%, respectively; China’s manufacturing industry mainly participates in the GVC in a backward way, which significantly improves trade interests; 2) both forward participation and backward participation in the GVC are conducive to the increase of trade benefits and are positive at the 5% level, which has a significant driving effect on the upgrading of the manufacturing industry; and 3) heterogeneity testing shows that capital-intensive, technology-intensive, and high-tech industries gain significant trade benefits, and embedding GVCs is more conducive to upgrading. Therefore, integrating into the GVC at a higher level is the direction to promote the upgrading of China’s manufacturing industry.

Intelligent and Resource-Conserving Service Function Chain (SFC) Embedding

Network Function Virtualization (NFV) opens us great opportunities for network processing with higher resource efficiency and flexibility. In this respect, there is an increasing need for intelligent orchestration mechanisms, such that NFV can exploit its potential and live up to its promise. Genetic algorithms have emerged as a promising alternative to the proliferation of heuristic and exact methods for the Service Function Chain (SFC) embedding problem. To this end, we design and evaluate a genetic algorithm (GA), which computes efficient embeddings with runtimes on par with approximate methods. We present a GA model as state-space search in order to clarify the design choices of a GA. Our proposed GA utilizes a heuristic for the generation of the initial population, with the aim of directing the search towards the solution. Given the sensitivity of GAs on their various parameters, we introduce a parameter adjustment framework for GA fine-tuning. A comparative evaluation among a range of GA variants with diverse features sheds light on the impact of these features on SFC embedding efficiency. The GA variant that stands out is further benchmarked against a baseline greedy algorithm and a state-of-the-art heuristic. Our evaluation results indicate that the GA yields notable gains in terms of request acceptance and resource efficiency.

Slice-Based Service Function Chain Embedding for End-to-End Network Slice Deployment

This paper investigates the slice-based service function chain embedding (SBSFCE) problem, which is to embed the service function chains (SFCs) of flows from different slices on a physical network for end-to-end network slice deployment. Compared with regarding slice deployment as complete virtual network embedding (VNE), deploying slices from the perspective of SBSFCE is beneficial for achieving more delicate resource allocation and jointly optimizing virtual network function (VNF) mapping and link mapping without the need for particular virtual topology designs. However, performing effective SBSFCE also faces several key challenges like diversified and differentiated requirements of flows, inter-slice and intra-slice VNF sharing, priority-aware admission control, and VNF placement restrictions, and few existing SBSFCE works have comprehensively considered or solved these challenges. In view of this, we address the SBSFCE problem by jointly considering the above key challenges in this paper. Specifically, we formulate the SBSFCE problem as an integer linear programming (ILP) that aims to maximize flow acceptance ratios and minimize network resource costs. Then, we propose two novel heuristic algorithms, weight-oriented embedding (WOE) and weight-oriented ratio embedding (WORE), to solve the problem. Simulation results demonstrate that our algorithms outperform benchmark algorithms and achieve near-optimal performance.

Efficient embedding of service function chains in space-division multiplexing elastic optical networks

Virtual Network Functions (VNFs) are implemented as software instances on generic servers in Data Centers (DCs). A network service is implemented by an ordered list of VNFs, called Service Function Chain (SFC). SFC embedding decides which DC to place/instantiate a VNF, and how to route traffic through the instantiated VNFs/DCs. Benefits from its high capacity and flexibility, the Space-Division Multiplexing Elastic Optical Networks (SDM-EONs) have been proposed as a promising solution to provide connection service between DCs. In this paper, the problem of SFC embedding in SDM-EONs is studied. We first formulate an Integer Linear Programming (ILP) to minimize the Maximum Index of the Utilized Frequency Slot (MIUFS) for a given set of SFC requests. To achieve scalability, a greedy algorithm is proposed to embed SFCs separately. For each SFC, the lowest cost embedding solution can be found by enumeration would be selected for service embedding. A novel stretch algorithm is then designed to reduce the computing complexity further. The key idea is to place VNFs close to the least-cost path between the source and destination of a request. Three strategies to measure the path cost are evaluated. Simulation results show that our stretch algorithm gives the best overall performance in terms of running time, bandwidth consumption, MIUFS and request blocking probability.

Global value chain embedding mode and carbon emission efficiency: Evidence from China's manufacturing industry

With the in-depth development of the global value chain (GVC), it is important to find new ways to improve carbon emission efficiency (CEE) from the perspective of the GVC embedding mode. This study describes the current situation of the GVC embedding mode in China's manufacturing industry from the dual dimension system of GVC participation and length. It further investigates the impact of different GVC embedding modes on CEE to provide new ideas for the realization of low-carbon economic development in China. The study found that (1) the GVC backward embedding mode can improve the CEE, while the forward embedding mode has a restraining impact on CEE; (2) There is industry heterogeneity in the effect of the GVC embedding mode on CEE. The backward embedding mode inhibits CEE in low-energy-consumption industries, and the forward embedding mode characterized by the increase in forward production length promotes CEE in high-energy-consumption and non-technology-intensive industries; (3) The impact will show a significant threshold effect depending on different GVC statuses. With the rise in GVC status, the forward embedding mode, characterized by an increase in forward participation, and the backward embedding mode, characterized by an increase in backward participation, have an inverted U-shaped relationship with CEE.