This study investigates the influence of smart supply chain management on the Environmental, Social, and Governance (ESG) performance of Chinese manufacturing firms spanning from 2009 to 2022. Our findings reveal a positive association between smart supply chain management and enhanced ESG performance, a relationship consistently upheld across various analytical methodologies. Additionally, we uncover that smart supply chain practices stimulate corporate social responsibility (CSR) disclosure, contributing to heightened transparency and subsequently bolstering ESG metrics within firms. Furthermore, our analysis demonstrates that the positive effect of smart supply chain management on ESG outcomes is particularly pronounced among firms that are operating in less competitive and more environmentally impactful industries, receiving heightened media scrutiny, and influenced by Confucian principles. This research provides actionable insights for firms seeking to advance their ESG initiatives.

The commodity and financial characteristics of carbon emission allowances play a pivotal role within the Carbon Emission Trading Scheme (CETS). Evaluating the effectiveness of the scheme from the perspective of carbon price is critical, as it directly reflects the underlying value of carbon allowances. This study employs a time-varying Difference-in-Differences (DID) model, utilizing data from publicly listed enterprises in China over the period from 2010 to 2023, to examine the effects of carbon price level and stability on Total Factor Productivity (TFP). The results suggest that both an increase in carbon price level and stability contribute to improvements in TFP, particularly for heavy-polluting and non-stateowned enterprises. Mechanism analysis reveals that higher carbon prices and stability can stimulate corporate engagement in green innovation, activate the Porter effect, and subsequently enhance TFP. Furthermore, optimizing the system environment proves to be an effective means of strengthening the scheme's impact. The study also finds that allocating initial quotas via payment-based mechanisms offers a more effective design. This research highlights the importance of strengthening the financial attributes of carbon emission allowances and offers practical recommendations for increasing the activity of trading entities and improving market liquidity.

The growing significance of sustainable development has underscored the importance of integrating corporate sustainability indicators into corporate strategies. As external stakeholders increasingly emphasize corporate environmential performance, social responsibility and governance (ESG), understanding its impact on corporate value becomes essential, especially in emerging markets like China. This research aims to bridge these knowledge gaps by empirically investigating the influence of ESG ratings on firms’ value among Chinese listed firms, with a special emphasis on the mediating roles played by financial distress and financing constraints. By analyzing data from listed companies of China over the period 2018 to 2022, this research explores the correlation between firms’ value and ESG ratings. The findings indicate a positive association between firms’ value and ESG ratings. Enhanced ESG ratings directly boost market valuation and indirectly elevate firm value by mitigating financing constraints and financial distress. Further analysis reveals the positive effects of ESG ratings are more noticeable in industries that are not heavily polluting and in state-owned enterprises. This research provides valuable insights for enterprise management by systematically examining how ESG ratings contribute to corporate value through the mitigation of financial distress and constraints, while also highlighting the variations in ESG strategy implementation across different types of enterprises.

With the increasing penetration of renewable energy (RE) in power systems, the electricity spot market has become increasingly uncertain, presenting significant challenges for generation companies (GenCos) in formulating effective bidding strategies. Most existing studies assume that GenCos act as perfectly rational decision makers, overlooking the impact of irrational bidding behaviors in uncertain market environments. To address this limitation, we incorporate prospect theory to model the decision-making process of bounded rational GenCos operating under risk. A bilevel stochastic model is developed to simulate strategic bidding in the spot market. In addition, a distributional re-inforcement learning algorithm is proposed to tackle the decision-making challenges faced by bounded rational GenCos with risk considerations. The proposed model and algorithm are validated through simulations using a 27-bus system from a region in eastern China. The results demonstrate that the algorithm effectively captures market uncertainties and learns the distribution of GenCo’s profits. Furthermore, simulated bidding strategies for various types of GenCos highlight the applicability of prospect theory to describe bounded rational decision-making behavior in electricity markets.

The frequent occurrence of extreme weather events not only poses serious challenges to global economic growth and financial stability but also affects firms negatively across multiple dimensions. Using a sample of Chinese A-share listed firms from 2006-2016, this study aims to explore the effect of climate risk on firm export sophistication. The findings show that climate risk inhibits firm export sophistication, with the results varying depending on firm and industry types. Specifically, climate risk (i) inhibits export sophistication for firms with low government subsidies more than for firms with high government subsidies; (ii) restraints export sophistication for firms in high-tech industries rather than for low-and medium-tech industries; and (iii) reduces export sophistication for firms in low-marketization regions more than for firms in high-marketization regions. In addition, channel analysis shows that climate risk inhibits firm export sophistication by increasing financial constraints and reducing human capital.

Environmental disclosure in emerging markets remains poorly understood, despite its critical role in sustainability governance. Here, we analyze 42,129 firm-year environmental disclosures from 4,571 Chinese listed firms (2008-2022) using machine learning techniques to characterize disclosure patterns and regulatory responses. We show that increased disclosure volume primarily comprises boilerplate content rather than material information. Cross-sectional analyses reveal systematic variations across industries, with manufacturing and high-pollution sectors exhibiting more comprehensive disclosures than consumer and technology sectors. Notably, regional rankings in environmental disclosure volume do not align with local economic development levels. Through examination of staggered regulatory implementation, we demonstrate that market-based mechanisms generate more substantive disclosures compared to command-and-control approaches. These results provide empirical evidence that firms strategically manage environmental disclosures in response to institutional pressures. Our findings have important implications for regulatory design in emerging markets and advance understanding of voluntary disclosure mechanisms in sustainability governance.

This paper examines the impact of low-cost carriers (LCCs) on the pricing strategies of full-service carriers (FSCs) in the Chinese airline market. We first analyze the effect of LCC presence and find that LCCs exert significant downward pressure on FSC fares, with the magnitude of this impact varying across carriers and routes. Next, we explore the dynamic responses of incumbent FSCs to the entry and the threat of entry by LCCs. Our findings reveal that FSCs begin lowering fares well in advance of LCC entry, with fare reductions of approximately 11%–18% occurring as early as the 8th quarter before entry. The fare reductions intensify as the entry date approaches and persist beyond it. On the other hand, FSCs do not seem to respond to LCC entry threats. Our analysis highlights the importance of considering the dynamic pricing responses of FSCs rather than relying solely on LCC presence which is commonly used in the literature studying Chinese LCCs.

This study examines how full-time external supervisors affect corporate irregularities using listed Chinese state-owned enterprises (SOEs) as a research sample. We find that full-time external supervisors restrain corporate irregularities. This outcome continues to hold after accounting for potential endogeneity concerns. Further mediating effect analysis shows that full-time external supervisors mitigate corporate irregularities by curbing managers' opportunistic behavior. Additionally, the heterogeneity analysis demonstrates that the impact of full-time external supervisors on corporate irregularities varies significantly across different types of SOEs and internal control environments. Overall, this paper enriches and expands the literature on the effectiveness of full-time external supervisors in emerging economies and provides new insights for dealing with corporate irregularities.

The impact of Artificial Intelligence (AI) on firm performance is an emerging issue in both practice and research. However, discussions surrounding the effect of AI on productivity are enshrouded in a paradoxical quandary. This study examines the relationship between AI implementation and total factor productivity (TFP), considering the moderation effects of digital infrastructure quality, business diversification, and demand uncertainty. Using data from 2155 Chinese firms over 2016-2021, our empirical analysis reveals a nuanced pattern: while moderate AI implementation achieves the best TFP, excessive and insufficient implementation yields diminishing returns. The curvature of this inverted U-shaped relationship flattens with higher levels of digital infrastructure quality but steepens when firms undertake diversified businesses and face heightened demand uncertainty. The findings suggest that the impact of AI on TFP is not universally beneficial, and the relationship between AI and TFP varies across different contexts. These findings also provide implications on how firms can strategically implement AI to maximize its value.

Based on 5-minute high-frequency trading data, we examine the time-varying causal relationship between herding behavior and multiscale spillovers (return, volatility, skewness, and kurtosis) in the Chinese stock market. We employ the novel time-varying Granger causality test proposed by Shi et al. (2018), which is based on the recursive evolving algorithm developed by Phillips et al. (2015a, 2015b), to identify real-time causal relationships and capture possible changes in the causal direction. Our findings reveal a strong relationship between herding and spillover effects, particularly with odd-moment (return and skewness) spillovers. For most of the study period, a bidirectional causal relationship was found between herding and odd-moment spillovers. These results imply that herding behavior is a key driver of spillover effects, especially return and skewness spillovers, which are primarily transmitted through the information channel. By contrast, volatility and kurtosis spillovers are more strongly driven by real and financial linkages. Furthermore, spillover effects also affect herding behavior, highlighting the intricate feedback loop between investor behavior and risk transmission.