The issue of global climate change is of paramount importance to the community with a shared future for mankind,emerging as the most pressing global agenda.Countries worldwide have introduced emission reduction policies to propel low-carbon transition.Against the background of the"dual carbon"goal in China,effectively identifying and quantifying low-carbon transition risks within financial markets holds significant value for maintaining both economic and financial security during the low-carbon transition and for preventing systemic financial risks.Based on the tensor autoregressive(TenAR)model,this paper incorporates the fundamental principles of the Fama-French three-factor model and proposes a modeling method for listed company attribute classification.Based on the attribute classification results,this paper decomposes the return series of A-share listed companies into tensor time series.Furthermore,by estimating the frontier TenAR model,it introduces a framework for measuring risk connectedness indices and constructing risk connectedness networks tailored to different attribute structures.Additionally,this paper combines the ripple effect of the stock market with information cascades from behavioral economics to provide a theoretical foundation for the horizontal and vertical transmission of low-carbon transition risks.Based on horizontal and vertical transmission characteristics,this paper,in empirical analyses,further identifies the low-carbon transition risk transmission paths of the ripple effect,and explores their driving factors and dynamic characteristics under low-carbon policies.Research results of this paper imply a significant ripple effect in the Chinese stock market,manifested by the strong risk connectedness effect among similar attribute categories.Along the horizontal path of the ripple effect of low-carbon transition risks,the risk aggregation center lies within the category of"high carbon emission,low price-to-book,high market value,and low beta"(CE4,PB1,MV3,Beta1).Along the vertical path,low-carbon transition risks are more likely to be transmitted from"high carbon emission,low price-to-book"(CE4,PB1)category to other carbon emission categories(CE3,CE2,CE1).In terms of dynamics,following the introduction of the"dual carbon"goal,the ripple effect of low-carbon transition risks significantly intensifies,with the greatest increase observed in the innermost ripple,exceeding the network's average connectedness effect by 381.85％.Additionally,the high carbon emission category(CE4)primarily drives the low-carbon transition risk ripple effect.However,under the constraints of the"dual carbon"goal,the primary driving category shifts towards low price-to-book(PB1)and high market value(MV3)categories,revealing the cross-attribute transmission characteristics of low-carbon transition risks.
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