Small scale mining has always been part of life in Ghana, but its illegal form known as galamsey has grown into one of the country’s biggest problems. Galamsey gives work to many people, yet still it destroys rivers, forests and farms, leaving communities struggling to survive. This paper looks at the laws that guide small scale mining in Ghana, from the days of traditional mining, through the colonial period, the Small Scale Gold Mining Law of 1989, and the Minerals and Mining Act, 2006 (Act 703) as amended by Act 995. It explains how weak enforcement, corruption, political interference and the role of foreigners, especially some Chinese nationals, have made the laws less effective. The paper also shows, through recent court cases such as Republic v Fynn and Republic v Domotey and Others, that Ghana’s courts are ready to punish offenders when cases are well presented. The courts have given long prison terms and heavy fines, showing that they are not the weak link in the fight. The real problem is poor enforcement and selective justice, since many foreigners are deported instead of being put on trial. The paper argues that solving the galamsey problem needs strong enforcement, freedom from political interference, the setting up of special environmental courts, and better job opportunities for rural communities. With these steps, Ghana can protect the environment while also supporting its people to make a living in lawful ways.

High-quality development of water conservancy (HDWC) is of critical value for safeguarding the stability of production systems, livelihoods, and ecosystems. As the digital revolution intersects with China’s “dual carbon” targets, development of water conservancy must transition from traditional engineering approaches to data-driven ecological models. Drawing on institutional logic theory and employing dynamic qualitative comparative analysis across 30 Chinese provinces, this study examines how digital ecology facilitates the HDWC. The findings reveal that none of digital government, digital infrastructure, digital economy, digital capability, or digital society constitutes a necessary condition for the HDWC. Instead, it is the result of the combined effects of multiple institutional logics. Five configurations leading to HDWC are identified and categorized into four types: government-market-driven model, government-market-society-driven model, market-society-driven model, and government-society-driven model. The consistency of the configurations significantly increased during the study period. Furthermore, their distribution showed substantial regional differences. There are two configurations that inhibit the HDWC, namely the government-market-absence type and the market-society-absence type. Digital society emerges as a critical factor. This research uncovers multiple pathways through which digital ecology can empower HDWC, providing valuable insights for optimizing regional digital environments.

Understanding the impact of local government debt on economic development has emerged as a focal issue for both academic research and policymakers. This study adopts a financing structure perspective and utilizes panel data from 214 cities and 3,228 A-share listed companies in China (2017–2023) to empirically investigate the impact of local government debt on corporate “shift from virtual to real economy” and its underlying mechanisms. The expansion of local government debt significantly promotes enterprises “shift from virtual to real economy”. The positive impact of local government debt on enterprises” transition from financialization to the real economy is more pronounced among firms in first tier and new first-tier cities, non-state-owned enterprises, and labor-intensive industries. Further analysis indicates that local government debt drives capital reallocation from financial investments to real investments by alleviating corporate financing constraints. This study proposes policy recommendations including optimizing debt fund allocation, further optimizing the financing environment, implementing differentiated regulatory measures. These suggestions provide both a theoretical foundation and practical references for synergistically advancing debt governance and real economy revitalization.

We examine whether firms with high organization capital (OC) are more likely to purchase Directors’ and Officers’ (D&O) liability insurance, using a panel of Chinese A-share listed companies from 2009 to 2021. We document a robust positive association between OC and the propensity to carry D&O insurance. The effect remains statistically and economically significant after controlling for firm characteristics and employing multiple identification strategies to address endogeneity. We propose two economic channels through which OC affects D&O insurance demand, namely, agency and information asymmetry. Consistent with these mechanisms, we find that the positive OC–D&O relationship is significantly stronger in firms with weaker internal governance and those facing opaquer information environments. Additional cross-sectional analyses show that this effect is concentrated in privately-owned firms and in regions with more developed market institutions, suggesting that external pressures accentuate the value of insuring key decision-makers. Our results are robust to alternative model specifications and remain stable after using propensity score matching, instrumental variable approaches, and the Heckman two-stage model. Overall, the findings highlight OC as a critical internal driver of corporate insurance decisions. Firms with substantial intangible assets strategically obtain D&O coverage to strengthen governance and reduce information frictions, especially in emerging markets like China where formal investor protections are still evolving.

This study integrates microstructure theory with asset pricing to investigates how the idiosyncratic volatility (IVOL) puzzle operates through specialized liquidity risk channels in China’s A-shares market. We employ intraday transactions data to perform a novel decomposition of liquidity into its variable (informational) and fixed (transitory) components. We show that the anomalous negative relationship between IVOL and future returns emerges from the intricate interaction of liquidity risk exposure, information and arbitrage constraints, and measurement biases. Specifically, the variable component tied to informed trading and adverse selection exposes high-IVOL stocks to greater arbitrage risk during liquidity shocks, while the fixed component exacerbates their vulnerability to short-term market-making cost fluctuations. Our results reveal that the IVOL puzzle is not a statistical artifact but a rational pricing phenomenon driven by omitted liquidity risk, mediated by the country’s unique institutional environment and monetary conditions.

The essay delves into the impact of environmental uncertainty on asymmetric mispricing utilizing the data from listed firms in China spanning from 2007 to 2023. Our analysis reveals that environmental uncertainty amplifies stock mispricing within capital markets, whether upward or downward. Diverging from prior research, we distinguish between upward and downward mispricing and reveal the black box of environmental uncertainty affecting stock mispricing from dual channels. Specifically, environmental uncertainty intensifies upward mispricing through heightened earnings management and exacerbates downward mispricing by boosting investor irrationality. Furthermore, we explore the heterogeneous impact of different media coverage. In the downward mispricing sample, negative media exacerbated the relationship between the two, while positive coverage played a mitigating role. In the upward mispricing sample, only negative reports have a significant impact, and mitigate the impact of uncertainty on mispricing. Our research on media heterogeneity once again proves that it is a double-edged sword. Our research indicates that improving the capacity to recognize different mispricing mechanisms in various market directions can greatly boost decision-making efficiency. Meanwhile, it is vital to strengthen professional ethics in media organizations and encourage more objective reporting. These efforts can jointly contribute to improve he efficiency of emerging capital markets.

This paper studies missing firm characteristics in the Chinese stock market and their implications for empirical asset pricing. Relative to the U.S. market, missing firm characteristics in China remain underexplored despite substantial differences in data availability and disclosure environments. Using a dataset of 106 firm characteristics from 1992 to 2021, we document a pronounced cliff-shaped pattern in missingness, with missing rates falling sharply after 2000. We then compare expectation-maximization (EM) and mean imputation (MN) in both univariate characteristic-sorted portfolios and machine-learning applications that combine many predictors. Results indicate that, in univariate analysis, the two methods produce very similar return spreads because they assign largely the same stocks to the extreme deciles. In machine-learning applications, however, EM-imputed data generally produce better-performing prediction-sorted portfolios than mean-imputed data. These findings provide new evidence on missing firm characteristics in a major emerging market and highlight the importance of imputation choices in machine-learning asset-pricing applications.

Cross-firm momentum effects via shared analyst coverage are well-documented in de-veloped markets, but their robustness remains unclear in emerging markets, where information diffusion is asymmetric and analyst coverage is highly concentrated. Our work revisits this effect in an environment of extreme informational frictions — the Chinese market. We reconstruct the information transmission channel within the an-alyst coverage network by introducing a novel weighting scheme based on strength centrality (SC). This measure identiffes inffuential leader firms that command dis-proportionate attention from both analysts and the market. Our results demonstrate that SC-weighted connected-firm returns robustly predict cross-sectional stock returns, yielding significant and persistent profits even under a rigorous stock filter. This per-formance cannot be subsumed by strategies based on alternative weighting schemes or by explanations such as intra-industry cross-firm momentum and information discreteness. Further analysis reveals that the superiority of the SC-based approach stems from its ability to effectively identify firms with stronger cross-period fundamental linkages. In addition, high-SC stocks are characterized by higher investor attention, more efficient information processing, lower arbitrage costs, and greater internationa exposures. With this evidence, we further confirm a directional spillover: cross-firm momentum effects flow exclusively from these high-SC leaders to low-SC laggards, and there is no reverse spillover. Our findings suggest that cross-firm momentum may be systematically underestimated in many international markets due to methodological limitations rather than economic irrelevance. The SC-based framework therefore of-fers a portable tool for global investors and researchers operating in environments with asymmetric information.

We document “foreign bias” in AI financial predictions, reversing the classic home bias. U.S.-based ChatGPT is systematically more optimistic than China-based DeepSeek about Chinese firms—in price predictions and directional forecasts—yet significantly less accurate. Evidence supports an information-availability mechanism: bias is strongest when U.S. media coverage of Chinese firms is limited and attenuates for cross-listed firms. Crucially, injecting Chinese news eliminates the prediction gap. Both models produce similar forecasts for U.S. firms, consistent with broader worldwide coverage. LLMs trained in different information environments can create divergent signals, with implications for investors and policymakers as AI increasingly intermediates global markets.

Strengthening disclosure regulation and improving disclosure quality are central to China's transition to a full registration system and crucial for preventing capital market risks. Using prospectuses disclosed by IPOs on the STAR Market, ChiNext, and the Beijing Stock Exchange from 2019 to 2023, this study constructs four textual indicators from prospectuses—length, sentence complexity, technical term density, and uncertainty—and examines how they affect resource allocation efficiency under the registration system. We find that text length and sentence complexity improve resource allocation efficiency, consistent with an information effectiveness effect. In contrast, technical term density and uncertainty reduce efficiency, reflecting information redundancy. Further analysis shows that the registration system reform enhances the comprehensiveness and complexity of disclosures, but its net effect on efficiency depends on the balance between information effectiveness and redundancy. This study contributes to the international literature on “institutional environment—disclosure—resource allocation” with evidence from an emerging market, while also extending theories of information asymmetry and impression management. Our findings support Chinese regulators in optimizing prospectus standards and strengthening review oversight, and provide policy insights for other emerging markets seeking to improve capital allocation through more effective disclosure design.