The structural change and influencing factors of carbon transfer network in global value chains

Research on the evolution of biotechnology cooperation networks - a study based on patent data in China from 2004 to 2023

Introduction

Biotechnology has significant potential in public health, offering critical support for communicable disease control, chronic illness management, and drug development. To foster biotechnology innovation, governments increasingly incentivize cooperations among organizations, resulting in more interconnected biotechnology cooperation networks. However, research on the evolution of these networks rely primarily on static network analysis and neglect the micromechanisms under the evolution, which lead to deviations in policymaking.

Methods

Using temporal exponential random graph model (TERGM), which accounts for dynamic network correlations, and based on micromechanisms framework consisting of agency, opportunity and inertia, this study analyzes the impacts of both endogenous and exogenous factors on the evolution of biotechnology cooperation networks.

Results

The empirical analysis based on China's biotechnology patent data from 2004 to 2023 reveals the following findings and policy recommendations. First, the evolution of the biotechnology cooperation networks is temporally dependent, highlighting the need for awareness of policy lags. Second, two endogenous factors - transitivity and convergence - emerge in the evolution, implying the need for government to create information platforms, establish targeted project subsidies, and enforce technical confidentiality policies. Finally, with regard to exogenous factors, the networks exhibit geographical homogeneity, implying the needs for government to promote cross-regional cooperation by establishing innovation centers and unified standards to mitigate lock-in effects and barriers.

Network structures and mitigation potential of trade linked global agricultural greenhouse gas emissions

Since agriculture is a major source of greenhouse gas emissions, accurately calculating these emissions is essential for simultaneously addressing climate change and food security challenges. This paper explores the critical role of trade in transferring agricultural greenhouse gas (AGHG) emissions throughout global agricultural supply chains. We develop a detailed AGHG emission inventory with comprehensive coverage across a wide range of countries and emission sources at first. Utilizing this inventory, the multi-regional input-output analysis is integrated with the complex network analysis to specifically reveal the characteristics of global AGHG flow networks. Finally, scenario analyses reflecting current trends and policy changes in global agriculture are conducted to investigate the emission reduction potential of different networks. The results show that the community division and characteristics of different AGHG networks vary, with more communities in the rice-CH4 and livestock-CH4, N2O networks, and fewer in the cropland-N2O network. Production-side technology improvements (reducing direct carbon emission intensities) and consumption-side livestock products substitution can contribute to the reduction of global AGHG emissions. At the same time, these impacts may propagate to other countries through AGHG networks. In contrast, localization substitution has minimal impact on AGHG emissions and may even result in slight negative effects. It is suggested that enhancing cooperation between countries from a network perspective, such as strengthening the transfer of advanced production technologies within communities, could help reconceptualize global agricultural decarbonization.

Spatial correlation network structure of operational efficiency and its driving factors: A case study of star-rated hotels in China

Accurately grasping the spatial network correlation structure of operational efficiency in star-rated hotels and its influencing factors is of great significance for promoting high-quality coordinated development of star-rated hotels and the regional tourism reception industry. Using a comprehensive approach integrating the Super-DEA model, modified gravity model, and social network analysis to explore the evolving characteristics of spatial network structure in operational efficiency of provincial star-rated hotels in China from 2013 to 2022, and their underlying mechanisms. The results indicate that: (1) During the study period, the average operational efficiency of star-rated hotels in China was 0.618, with spatial efficiency values showing a distribution pattern of "Eastern > Central > Western > Northeastern". (2) The spatial correlation network of operational efficiency among provincial star-rated hotels in China overall exhibits characteristics of densification, hierarchical structure, and reinforcement. Provinces such as Beijing, Tianjin, Shanghai, and Jiangsu in the eastern region play central roles as "central actors" in the network, while provinces in the western and northeastern regions such as Heilongjiang, Tibet, and Xinjiang play roles as "passive actors" in the spatial network. (3) Members of the "Net benefit" and "Two-way spillover" are primarily from the eastern and some central provinces, while members of the "Net overflow" are mainly from the northeastern and western provinces. (4) Factors such as economic development level, residents' consumption level, and distance from provincial capital cities collectively drive the evolution and optimization of the spatial network structure of operational efficiency among provincial star-rated hotels in China. This study not only enriches the research findings on hotel operational efficiency but also provides a reference for constructing a cross-regional collaborative mechanism to enhance the operational efficiency of star-rated hotels.

The roots of carbon emission differences between China and USA: A quantitative study based on global value chains

China and the USA, as preeminent contributors to global carbon emissions, demonstrate discernible differentials in both magnitude and trajectories of their respective carbon outputs. This article employed two methods, Structural Decomposition Analysis (SDA) and Quantitative Structural Modeling, to scrutinize the underpinnings of these disparities through the lens of the global value chain. Drawing upon data from the World Input-Output Database (WIOD), our analysis revealed that the compounded influences of output composition, input intensity, input composition and input origin collectively elevated China's aggregate carbon footprint from 2000 to 2014, while the scale effect made China's carbon emissions lower than of the USA. Notably, China's carbon emissions surpassed those of USA, with the gap accentuating over time. The quantitative results of the structural model showed that the difference in carbon emissions between China and USA predominantly stem from disparities in productivity, production technology, factor intensity, factor endowment and direct carbon intensity. Differences in trade costs exhibited some discernible impact, their influence remains relatively marginal, whereas distinctions in consumption behaviors and trade imbalances minimally contribute to the observed differentials. These findings have important policy implications for global carbon reduction efforts and China's trajectory towards a low-carbon economic paradigm.

Transboundary cooperation in Arctic climate change governance under geopolitical tensions

Political conflicts or geopolitical tensions can create uncertainty in addressing climate change and environmental management in the Arctic. Dissecting how actors interact with each other and form networks is important for understanding ecological and environmental management challenges during geopolitical tensions, as well as promoting better governance. We construct transboundary networks for Arctic climate change governance (ACCG) from 2013 to 2021 based on the Global Database of Events, Language, and Tone (GDELT). Further, we used network descriptive statistical analysis and Temporal Exponential Random Graph Models (TERGM) to explore the structure of ACCG networks and the key factors influencing cooperation formation. The findings suggest that the overall cooperation density of the ACCG is low, and the dominant position of core actors is continuously strengthening. Non-state actors are less likely to be seen as partners and their participation depends largely on cooperation with states. The results also show that actors with similar stances and problem exposure are more likely to cooperate, but those exposed to high latitudes often choose not to cooperate; first-comers are more likely to perceive as cooperating yet they are inclined to establish internal cooperation. Additionally, two geographically proximate actors are more likely to cooperate. This indicates that under geopolitical tensions, the ACCG faces challenges not only due to the limited capacity of non-state actors to perform transboundary functions but also because the cooperation mechanisms are influenced by regional political logic. Accordingly, we further suggest policy recommendations from developing binding international frameworks to guide transboundary cooperation, enhancing cooperation among non-state actors, and ensuring the representativeness and fairness of non-Arctic actors' participation. This research provides insights into transboundary environmental management under political tensions, while also offering new pathways for analysing large-scale environmental governance structures.

A comprehensive study on carbon emissions flows and key drivers within China's domestic and global value chains

Numerous studies have examined the transfer of carbon emissions in China's trade. However, few papers have established a link between China's domestic value chains (DVCs) and the global value chains (GVCs) to comprehensively trace the flows of carbon emissions within a unified framework. To address this research gap, our paper aims to connect China's DVCs with the GVCs and map the carbon emissions flows of China's eight regions in both domestic and international trade. Using structural decomposition analysis (SDA), it studies the driving factors behind change in carbon emissions transfer (CET). Our findings are as follows: Firstly, in 2018, the total carbon emissions transfer of China's eight regions amounted to 5122.0mt, a significant increase of 31.1 % compared to 2012. Notably, the carbon emissions transfer through intermediate product trade is more than four times that of the final product trade. Secondly, significant variations exist in the sources and destinations under different trade patterns, which have been largely overlooked in the existing literature. Over 70.0 % of carbon emissions resulting from final product trade are directed towards regions outside of Chinese mainland, while approximately two-thirds of carbon emissions resulting from intermediate product trade are allocated to Chinese mainland's internal regions. Thirdly, the reduction of carbon emissions transfer increment is primarily driven by the carbon intensity, while the indirect demand scale effect of Chinese mainland's internal regions emerges as the most significant driving factor, playing a substantial role in the increase of carbon emissions transfer. Additionally, the unreasonable input-output structure between regions within Chinese mainland has contributed to the rise in carbon emissions transfer. The research findings offer valuable insights and policy recommendations for the formulation of regional carbon reduction policies in China.

Carbon neutrality along the global value chain: an international embedded carbon network analysis

Using the value-added method in the global input-output framework, embedded carbon transfers between economies are measured. Then, the international embedded carbon network (IECN) models are constructed and analyzed to reveal the real pattern of the IECN. Influential factors of the IECN are further explored with Quadratic Assignment Procedure (QAP). The main findings are as follows: first, the IECN has the characteristics of small world and scale-free. Developed economies led by the USA have high centrality indicators and always occupy the core position in the network; the network position of China and India has risen significantly with different network characteristics. Second, the subgroup relationships of the carbon network implied by international trade are becoming more and more complex and diverse, economies' participation in the global carbon chain is gradually increasing, and the number of edge nodes are decreasing. Third, the results of QAP analysis show that in addition to the negative impact of geographical distance on embedded carbon transfers between economies, inter-country trade agreement relations, differences in industrial structure, energy intensity, environmental regulation, economic size, and population size have a positive impact on embedded carbon transfers between economies. These findings can provide important practice implications for the global carbon neutrality.

Analysis of spatial correlation networks of carbon emissions in emerging economies

Studies have shown that energy consumption from economic development leads to an increase in carbon emissions. Emerging economies, as important sources of carbon emissions with high growth potential, play a crucial role in global decarbonisation efforts. However, the spatial pattern and evolution trend of carbon emissions in emerging economies have not been studied in depth. Therefore, this paper uses the improved gravitational model and carbon emission data from 2000 to 2018 to construct a spatial correlation network of carbon emissions in 30 emerging economies around the world, aiming to reveal the spatial characteristics and influencing factors of carbon emissions at the national level. The results show that the spatial network structure of carbon emissions in emerging economies is closely linked, forming a "big network" of interconnection. Amongst them, Argentina, Brazil, Russia, Estonia, etc. are at the centre of the network and play a leading role. Geographical distance, economic development level, population density, and scientific and technological level have a significant impact on the formation of spatial correlation between carbon emissions. Further use of GeoDetector shows that the explanatory power of two-factor interaction on centrality is greater than that of a single factor, indicating that a single economic development cannot well enhance the influence of countries in the carbon emission network, and needs to be combined with factors such as industrial structure and scientific and technological level. These results are helpful to understand the correlation between carbon emissions between countries from the perspective of the whole and part and provide a reference for optimizing the carbon emission network structure in the future.

Extreme weather raises the prices of regional emission allowances in China

Extreme weather is an unexpected shock to the socioeconomic, which is likely to create climate risks in the process of global warming mitigation. The aim of this study is to investigate the impact of extreme weather on prices of China's regional emission allowances, by using the panel data of four representative pilots in China (Beijing, Guangdong, Hubei, and Shanghai) from April 2014 to December 2020. The overall findings reveal that extreme weather, especially extreme heat, has a short-term lagged positive impact on carbon prices. In particular, the specific performance of extreme weather under different conditions is as follows: (i) carbon prices in tertiary-dominated markets are more sensitive to extreme weather, (ii) extreme heat has a positive effect on carbon prices while extreme cold does not, and (iii) the positive impact of extreme weather on carbon market is significantly stronger during compliance periods. This study provides the decision-making basis for emission traders to avoid losses caused by market fluctuations.

CO2 emissions and global value chains indicators:new evidence for 1995-2018

Globalization and the configuration of production processes around Global Value Chains (GVCs) have become key factors for explaining the recent evolution of environmental and economic indicators. Indeed, previous research found evidence on the significant impact of GVCs indicators (participation and position) on CO₂ emissions. Additionally, results obtained in previous literature vary depending on the time period and geographical areas considered. In this context, the main aims of this paper are to analyze the role the GVCs in explaining the evolution of CO₂ emissions, and to identify possible structural breaks. This study uses the Multiregional Input-Output framework to calculate a position indicator and two different measures of participation in GVCs (interpreted either as trade openness or international competitiveness). The analysis useS Inter-Country Input-Output tables (ICIO) as main database, which includes 66 countries and 45 industries and covers the period 1995-2018. It is first concluded that upstream positions in GVCs are associated to lower global emissions. Additionally, the effect of participation depends on the measure used: trade openness is linked to lower emissions, while a higher competitiveness in international trade leads to higher emissions. Finally, two structural breaks are identified in 2002 and 2008, revealing that position is significant in the two first subperiods, while participation becomes significant from 2002 onwards. Thus, policies to mitigate CO₂ emissions might to be different before and after 2008: currently, reductions in emissions can be achieved by increasing value-added embodied in trade while decreasing the volume of transactions.

Research on the structural characteristics and influencing factors of global environmental services trade networks

The development of the environmental services trade is crucial to achieving climate goals and a green economic transition. Based on environmental services trade data from 2001 to 2019, this work uses the social network analysis (SNA) method to depict the structural features of global environmental services trade networks, empirically testing the influencing mechanism of network evolution based on the quadratic assignment procedure (QAP) model. The results indicate that the global environmental services trade is now in a rebound stable period. The market of the environmental services trade is becoming increasingly diversified, the accessibility and convenience of trade are constantly being enhanced, and there is an obvious core-edge structure in the network. Belgium, Italy, and the Netherlands are at the center of the global environmental services trade network; Greece, as the most prominent trade catch-up country, has gradually become an important "bridge" and "hub." Climate change, geographical distance, and population size are the key factors affecting the global trade network of environmental services; environmental regulation, economic distance, and green technology distance have no significant impact on the development of the global environmental services trade, and language difference is no longer an obstacle to the growth of this trade. The results indicate that countries should develop in-depth transnational trade cooperation in the field of environmental services and do so with a more open attitude. The government should give sufficient policy support to the import and export of environmental services, guaranteeing the development of the environmental services trade.