This paper examines how economic policy uncertainty (EPU) affects mergers and acquisitions (M&As) between companies with different levels of financing constraints. Existing literature overlooks the interactive effect of EPU and financing constraints on M&As, and empirical evidence regarding EPU's influence on financially constrained firms remains limited. China's unique ownership structure provides a valuable context for this analysis, as state-owned enterprises (SOEs) face fewer financing constraints than private firms. Using a 2007-2021 sample of Chinese listed state-owned enterprises (SOEs) and private companies, we find that high EPU decreases the likelihood of private firms acquiring SOEs, while increases the likelihood of private firms being acquired by SOEs. These results suggest that under high EPU, financially constrained firms experience greater survival pressure, limiting their capacity to alleviate constraints by acquiring less-constrained targets. Conversely, less-constrained firms enhance their bargaining power and are more likely to acquire financially stressed counterparts. EPU facilitates control transfers from high-constraint to low-constraint firms, contributing to long-term market returns and improving financial market allocation efficiency. Our study contributes to the literature by shedding light on how EPU shapes divergent M&A behaviors based on firms’ financing constraints.

Utilizing microevaluation data on the extent to which firms advance the achievement of the UN’s Sustainable Development Goals (SDGs) provided by Robeco, this paper examines the influence of corporate sustainability on stock price performance and its underlying economic mechanisms. The empirical results suggest that firms’ sustainability has a significant negative effect on excess returns, particularly the contribution of firms to the social dimension of sustainability. Firms’ SDG performance can alleviate financing constraints and reduce financial risk, but it does not significantly enhance financial performance, leading to market capital outflows from high SDG-performing firms, especially from individual investors. Furthermore, our results suggest that high SDG-performing firms are undervalued and do not increase the information content in their stock prices, which may be the main reason for the negative effect of SDG performance. We also conduct a series of heterogeneity tests, which show that firms from regions with high environmental regulatory intensity and less economic development, as well as heavily polluting firms and firms with poorer information environments, experience greater negative effects. These findings have implications for investors to properly understand corporate sustainability and for regulators to promote the development of a low-carbon economy.

Innovation is crucial for long-term corporate value and competitive advantage; however, it can misalign the interests of managers and investors. Balancing managers’ short- and long-term goals is a pivotal challenge in promoting innovation incentives. Therefore, this study examines innovative incentives for managers of publicly traded firms to address the issue of agency problems. The study focuses on economic value-added (EVA) reform implemented by China’s State-Owned Assets Supervision and Administration Commission (SASAC), which encourages EVA-driven R&D investments as the primary management metric. The policy effectively motivates key corporate managers by reducing capital costs and stimulating increased innovation. Following this policy’s implementation, notable innovation disparities exist between state-owned enterprises and firms not subject to the reform. Furthermore, innovation incentives significantly affect overconfident company managers, yielding positive effects on innovation.

To address the challenges posed by renewable energy integration in power systems, China is advancing the development of Pumped Hydro Storage (PHS). However, the rapid growth of PHS installations, coupled with strict regulations and a high reliance on capacity compensation, has led to increasing financial burdens on other utilities. One solution is to reallocate the capacity compensation through market-based approaches to implement the “beneficiary-pays” principle. To achieve this goal, an operational policy named ’partial-regulated dispatch’ is proposed in this study. The analysis of this policy encompasses two crucial dimensions: the dispatch mechanism and business models. The dispatch mechanism evaluates PHS’s capacity contribution to grid stability, while the business models focus on enhancing PHS profitability to reduce dependency on capacity compensation while ensuring long-term economic sustainability. Furthermore, the flexibility of PHS is introduced as a criterion for assessing system security contributions, considering both individual unit vibration characteristics and multi-unit commitment strategies. The case study shows that through partial-regulated dispatch, PHS can reduce its reliance on capacity compensation by nearly 50% while ensuring its regulation service via flexibility compensation. This policy effectively balances economic viability with system support capabilities. Moreover, flexibility compensation provides PHS operators with a risk mitigation strategy in the complex power market environment. Under an appropriate operational strategy and policy incentives, the flexibility can be enhanced by nearly 30% in a fully marketized scenario, contributing to both system stability and operational efficiency.

Climate change presents a global threat, prompting nations to adopt low-carbon development pathways to mitigate its potential impacts. However, current research lacks a comprehensive framework capable of integrating multiple variables and providing dynamic optimization capabilities. This article focuses on designing pathways for developing a low-carbon economy to tackle climate challenges. Specifically, we construct a low-carbon economy model that incorporates economic, environmental, social, energy, and policy factors to analyze the drivers of economic growth and carbon emissions. We utilize economic model predictive control and tracking model predictive control to optimize development pathways aligned with various low-carbon targets, creating and validating a comprehensive framework for low-carbon policy design using historical data from China. This study highlights significant advantages in analyzing low-carbon pathways through advanced techniques like hierarchical regression and model predictive control, providing a robust framework that enhances our understanding of causal relationships within the LCE system, captures system feedback, dynamically optimizes pathways, and accommodates diverse policies within a comprehensive low-carbon economy system.

We empirically examine how institutional investors react to adjustments in environmental policies in China. We observe a seemingly counterintuitive phenomenon: when environmental policies intensify, fund managers do not increase their holdings in high ESG-rated firms as might typically be expected; instead, they significantly divest from these firms. This behavior stems from the fact that, under stringent environmental policies, maintaining a high level of ESG investing leads to financial losses and fund outflows, especially in the short term, which impair fund managers’ compensation and raise career concerns. Further, within the context of environmental policy adjustments, our heterogeneity analysis tries to disentangle the true motivations behind institutional investors' ESG adoptions. We demonstrate that both pro-social preferences and financial incentives play pivotal roles, and that fund managers do not tolerate unlimited financial losses when ESG investing underperform. Our findings reveal the economic impact of environmental policies on institutional investors and shed light on the contentious and complex nature of the ESG concepts.

This paper proposed an innovative WTC-LSTM-ASSA-NSTformer framework for gold price forecasting. The model integrates Wavelet Transform Convolution, Long Short-Term Memory networks (LSTM), and an improved Nyström Spatial-Temporal Transformer (NSTformer) based on Adaptive Sparse Self-Attention (ASSA), effectively capturing the multi-scale features and long- and short-term dependencies of gold prices. Additionally, for the first time, various financial and economic uncertainty indices (including VIX, GPR, EPU, and T10Y3M) are innovatively incorporated into the forecasting model, enhancing its adaptability to complex market environments. An empirical analysis based on a large-scale daily dataset from 1990 to 2024 shows that the model significantly outperforms traditional methods and standalone deep learning models in terms of MSE and MAE metrics. The model’s superiority and stability are further validated through multiple robustness tests, including varying sliding window sizes, adjusting dataset proportions, and experiments with different forecasting horizons. This study not only provides a highly accurate tool for gold price forecasting but also offers a novel methodological pattern to financial time series analysis, with important practical implications for investment decision-making, risk management, and policy formulation.

We study tail-risk contagion in Belt and Road (BRI) stock markets by conditioning on shocks from China and global commodities. We construct time-varying contagion indices from conditional higher co-moments (CoHCM) estimated within a DCC-GARCH model with generalized hyperbolic innovations, and apply them to daily data for 32 BRI markets. The higher-moment index isolates two channels: a China-driven financial-institutional channel and a WTI-driven commodity-real-economy channel, whereas a covariance benchmark fails to recover this separation. Furthermore, the system-GMM estimates link the China-conditional channel to institutional quality and financial depth, and the WTI-conditional channel to real activity. In out-of-sample portfolio tests, the WTI-conditional signal improves risk-adjusted performance relative to equally weighted and mean-variance benchmarks, while the China-conditional signal does not. Tail-based measurement thus sharpens identification of contagion paths and yields information that is economically relevant for risk management in interconnected emerging markets.

Background: How to cope with the rapid growth of LTC (long-term care) needs for the old people without activities of daily living (ADL), which is also a serious hazard caused by public health emergencies such as COVID-2019 and SARS (2003), has become an urgent task in China, Germany, Japan, and other aging countries. As a response, the LTCI (longterm care insurance) system has been executed among European countries and piloted in 15 cities of China in 2016. Subsequently, the influence and dilemma of LTCI system have become a hot academic topic in the past 20 years.Methods: The review was carried out to reveal the effects of the LTCI system on different economic entities by reviewing relevantliterature published from January 2008 to September 2019. The quality of 25 quantitative and 24 qualitative articles was evaluated using the JBI and CASP critical evaluation checklist, respectively. Results: The review systematically examines the effects of the LTCI system on different microeconomic entities such as caretakers or their families and macroeconomic entities such as government spending. The results show that the LTCI system has a great impact on social welfare. For example, LTCI has a positive effect on the health and life quality of the disabled elderly. However, the role of LTCI in alleviating the financial burden on families with the disabled elderly may be limited. Conclusion: Implementation of LTCI system not only in reducing the physical and mental health problems of health care recipients and providers, and the economic burden of their families, but also promote the development of health care service industry and further improvement of the health care system. However, the dilemma and sustainable development of the LTCI system is the government needs to focus on in the future due to the sustainability of its funding sources.

The 16th annual China International Conference on Insurance and Risk Management (CICIRM 2026) will be held on July 8-11, 2026 at the Yunnan Lianyun Hotel in Kunming, Yunnan, China. The conference is organized by the China Center for Insurance and Risk Management, School of Economics and Management, Tsinghua University, and co-organized by the School of Finance, Yunnan University of Finance and Economics.