Cooperative management of an emission trading system:a private governance and learned auction for a blockchain approach

Although blockchain technology has received a significant amount of cutting-edge research on constructing a novel carbon trade market in theory,there is little research on using blockchain in carbon emission trading schemes(ETS).This study intends to address existing gaps in the literature by creating and simulating an ETS system based on blockchain technology.Using the ciphertext-policy attributed-based encryption algorithm and the Fabric network to build a platform may optimize the amount of data available while maintaining privacy security.Considering the augmentation of information interaction during the auction process brought about by blockchain,the learning behavior of bidding firms is introduced to investigate the impact of blockchain on ETS auction.In particular,implementing smart contracts can provide a swift and automatic settlement.The simulation results of the proposed system demonstrate the following:(1)fine-grained access is possible with a second delay;(2)the average annual compliance levels increase by 2%when bidders’learning behavior is considered;and(3)the blockchain network can process more than 350 reading operations or 7 writing operations in a second.Novel cooperative management of an ETS platform based on blockchain is proposed.The data access control policy based on CP-ABE is used to solve the contradiction between data privacy on the firm chain and government supervision.A learned auction strategy is proposed to suit the enhancement of information interaction caused by blockchain technology.This study provides a new method for climate change policymakers to consider the blockchain application of the carbon market.

A new hybrid method with data‑characteristic‑driven analysis for artificial intelligence and robotics index return forecasting

Forecasting returns for the Artificial Intelligence and Robotics Index is of great significance for financial market stability,and the development of the artificial intelligence industry.To provide investors with a more reliable reference in terms of artificial intelligence index investment,this paper selects the NASDAQ CTA Artificial Intelligence and Robotics(AIRO)Index as the research target,and proposes innovative hybrid methods to forecast returns by considering its multiple structural characteristics.Specifically,this paper uses the ensemble empirical mode decomposition(EEMD)method and the modified iterative cumulative sum of squares(ICSS)algorithm to decompose the index returns and identify the structural breakpoints.Furthermore,it combines the least-square support vector machine approach with the particle swarm optimization method(PSO-LSSVM)and the generalized autoregressive conditional heteroskedasticity(GARCH)type models to construct innovative hybrid forecasting methods.On the one hand,the empirical results indicate that the AIRO index returns have complex structural characteristics,and present time-varying and nonlinear characteristics with high complexity and mutability;on the other hand,the newly proposed hybrid forecasting method(i.e.,the EEMD-PSO-LSSVM-ICSS-GARCH models)which considers these complex structural characteristics,can yield the optimal forecasting performance for the AIRO index returns.

Medium-and long-term sustainable supply approaches and strategies for essential and typical strategic resources in China

This paper examines sustainable supply strategies for essential and strategic resources in China,addressing both domestic requirements and global supply uncertainties.In the context of intense global competition for resources and substantial internal demand,China’s significant role as a major consumer and global supplier is pivotal in the dynamics of the global supply chain.This study highlights China’s dependence on imports for essential resources and the critical need for resilient supply chains to enhance national security and promote environmental sustainability.By referencing international experiences and accounting for China’s specific circumstances,this study proposes strategic initiatives,including updating the strategic resource catalog,imposing export controls on key minerals,promoting resource conservation,and enhancing global cooperation.These strategies aim to reduce external dependencies and support global resource sustainability.The proposed framework can help policymakers ensure long-term resource security and manage resources more effectively in complex global landscapes.

Overview of research on carbon information disclosure

Against the background of addressing global climate change and carbon emission reduction,corporate carbon information disclosure(CID)has become an important measure to achieve carbon emission reduction worldwide and a research hotspot closely investigated by the academia.This study provides a systematic overview of literature on CID,including its research trend,theoretical basis,disclosing features,influencing factors,and consequences.Results indicate that,first,CID has been increasing in recent years,but the content and quality of the disclosure still need to be improved.Second,the main influencing factors of CID include company features,corporate governance,environmental performance,institutional characteristics,and stakeholders.Third,the consequences of CID are based mainly on company performance,ecological environment,and investors’decision-making.Lastly,most studies have confirmed the positive effect of CID on company performance and investors’decision-making,but the nexus of environmental performance and corporate CID remains to be investigated.Several important future research directions are also proposed based on these results.