To investigate the diffusion degree of low-carbon technology in competitive firms and the influence of different pollution regulations, we constructed an evolutionary game model representing three environmental mechanisms: total amount control and trading, carbon tax, and subsidy, within a complex firm network. By analyzing the effects of carbon quotas, tax rates, and subsidy rates, we comprehensively assessed the performance of these mechanisms in terms of diffusion degree, firm profits, consumer surplus, environmental damage, and social welfare. The study results demonstrated distinct outcomes for each environmental mechanism. Under the subsidy mechanism, low-carbon technology exhibited the highest diffusion degree, suggesting its efficacy in promoting widespread adoption. However, it is worth noting that the higher the subsidy, the more environmental pollution it led to, which presents a trade-off between environmental benefits and financial incentives. On the other hand, the carbon tax mechanism resulted in minimal pollution, reflecting its potential as an effective tool for environmental regulation. Finally, the cap-and-trade mechanism emerged as the most favorable option, as it maximized social welfare while effectively addressing environmental concerns.
We provide the first systematic analysis of the stock return lead-lag effect among firms connected through shared analyst coverage in China’s A-share markets. We measure the shared analysts-weighted average returns of connected firms （CF） and show that CF return is a significant positive predictor of future returns of the focal firms in the following one to 12 months. The CF-based long-short portfolio earns an abnormal return of 10% to 12% per year. The effect is robust to controls for the industry and geographic momentum effects. Further evidence shows that the CF momentum spillover effect is stronger when the focal firm shares more analysts with connected firms， is covered by more non-star analysts or analysts with lower levels of education， or is held by more stress-resistant institutional investors. Our findings contribute to the cross-asset momentum literature by documenting a new， strong， and long-lasting momentum spillover effect in the Chinese stock markets.
Additive network DEA model is one kind of highly nonlinear programming problem,which is difficult to be solved directly.The existing heuristic algorithm cannot be applied to obtain the exact solution of the model,and will consumes a lot of computing resources and times in the big data environment.In order to solve these two issues,the presented study mainly does two aspects of work.Firstly,aiming at the issue of solving precision of the model,a solving method of quadratic fractional programming is proposed in this study,which decomposes the model into a finite quadratic programming,and it is proved theoretically that the presented method can be applied to obtain the exact solution of the model.Then,in view of the issue of the slow solving speed in the big data environment,this work optimized the model's constraints by reducing the number of constraints and the consumption of computing resources while the feasible region remained unchanged,so as to improve the solving speed of the model.The numerical case results show that the proposed method can effectively improve both of the calculation accuracy and speed of the additive two-stages DEA model.
To reflect the actual relationships between sequences to the greatest extent and expand the field of negative grey relational analysis,this paper establishes a bidirectional grey A-S(Area-Slope)comprehensive relational model based on proximity and similarity.Firstly,according to the positions between correlation sequences,the same or inverse area relationship is determined,and the grey relational model based on area differences is established.Secondly,considering the directionality of the sequence slope,the sign function is introduced to build a bidirectional grey relational based on the slope differences.Then,the absolute value of the correlation degree of the two models is synthesized as the correlation strength of the sequences to characterize the proximity and similarity,and the direction of the grey correlation degree based on the slope differences is determined as the relational direction of the sequences,so as to construct a comprehensive relational model,and then the properties of the model are further discussed.Finally,through the analyses of the influencing factors of agricultural development in Jiangxi Province and China's milk powder import trade,the results based on the novel model are compared with the other eight classical grey relational models'.The results show that this model has the advantages in comprehensively representing the sequence relationship,reflecting the relational direction,and improving the correlation resolution.
In the wake of epochal international developments,such as Brexit,the Sino-American trade tensions,and global health crises exemplified by the outbreak of what is referred to as New-castle pneumonia,the impetus towards regional economic integration has escalated as a strategic imperative for economies seeking resilience against multifaceted economic shocks.Concomi-tantly,there is a discernible shift characterized by the re-shoring of manufacturing,offshoring of industrial chains,and domestication of production processes.This shifting landscape necessitates rigorous investigation into the stability of regional economic systems.Leveraging an innovative framework premised on ecological nested structure theory,this study undertakes a tripartite empirical examination of production network stability within the Asia-Pacific realm through measurement,simulation,and evolutionary analyses.Our findings illuminate that the morphol-ogy of global and regional production networks conforms to a"core-periphery"topology.The evolutionary dynamics and concomitant economic ramifications of this structure find congruence with the Local World Evolutionary Network model within network science.Furthermore,we posit that nested metrics offer robust quantification of the stability of these economic systems.Notably,the generalist industrial sectors that are situated within the nested core contribute to the network's stability,whereas specialist sectors induce potential destabilization.Simulation analyses using nested metrics reveal a strong correlation between a nation(region)'s role and its position within the production network,and the nation(region)'s level of economic develop-ment and capacity for risk mitigation.Moreover,an evolutionary examination of the Regional Comprehensive Economic Partnership(RCEP)affirms that augmenting trade volumes among member states fortifies the stability of(cross-border)production networks in the Asia-Pacific re-gion,which,in turn,fosters economic growth and industrial optimization within the constituent countries(regions).
