The emphasis on environmental sustainability outcomes (ESO) in emerging economies has increased recently, requiring in-depth research to manage the complex interplay between socioeconomic and sociopolitical factors. With a particular focus on China, this study describes this significant relationship using a good technique that comprises standardized questionnaires administered to a large sample of different organizational levels. This research examined several factors and their effects on ESO. First, the results show that the kind of economic system (EST) directly affects ESO; no direct relationship was discovered between ESO and the industrialization level (LI). Furthermore, it was demonstrated that government funding for eco-sustainability projects (GFESP) and community engagement and eco-education initiatives (CEEEI) had a direct impact on ESO. Secondly, the results demonstrated that implementing eco-policies and regulations (IEPR) had a full mediating effect between EST and ESO. Third, political instability (PI) and corruption level (CL), which are essentially the outcome of China's current political and environmental issues, diminish the correlation between EST and ESO. These findings demonstrate that China's environmental sustainability has to be improved using a multimodal approach. The public is encouraged to push legislators to bolster eco-laws and policies in order to emphasize environmental preservation. Initiatives to reduce corruption and maintain political stability may also bolster the beneficial effects of economic frameworks on ESO.

Taking China’s environmental “fee-to-tax” reform as a quasi-natural experiment, this paper employs a difference-in-differences (DID) approach to examine the impact of the green tax reform on firms’ cost of equity on a sample of A-share listed firms from 2012 to 2023. The results show that the implementation of the Environmental Protection Tax Law significantly reduced the cost of equity for heavily polluting firms. This finding remains robust after a series of robustness checks. Analyses of the underlying mechanisms reveal that the green tax reform lowered the cost of equity primarily through the channel of “Porter effect.” Moreover, the effect is more pronounced among firms located in the eastern China, those with stronger internal governance, and those operating in a stricter external monitoring environment.

Traditional portfolio optimization typically relies on a single fitted return distribution, thus ignores non-normality and uncertainty across distributional states. Mean–variance–ambiguity (MVA) models, motivated by the smooth-ambiguity preferences of Klibanoff, Marinacci, and Mukerji (2005) and the KMM-to-MVA Arrow–Pratt reduction of Maccheroni, Marinacci, and Ruffino (2013), extend conventional risk penalties to account for distributional uncertainty, but they missed how state-conditional return distributions affect portfolio optimization stability. We develop Deep Mean–Variance–Ambiguity (Deep MVA) to resolve this gap by learning state-conditional return distributions and embedding the resulting conditional information loss into the ambiguity penalty. The resulting local certainty equivalent combines reduced-law mean–variance risk with a quadratic penalty for second-order disagreement. Conditional-mixture state resolution yields a Deep ambiguity covariance equal to the Traditional ambiguity covariance plus a positive-semidefinite state-feature increment. This increment is the conditional L2 loss from averaging state-specific distributions before optimization. On compact convex feasible sets, we show that the state-resolving program has stronger ambiguity-relevant curvature, lower mean-input sensitivity, and a unique global solution under restricted positive curvature or a common ridge; its projected iterations converge linearly. An LSTM-MDN learns state-conditional distribution densities from 60-minute bars, and Probability and VoV scores enter weekly MVA, with GARCH_Resid as a comparator. Tests on the SSE 50, CSI 300, and CSI 500 universes from 2017–2025 find statistically significant Sharpe out-of-sample improvements over Traditional MVA and MVO across all three universes and ambiguity specifications.

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.

This article examines the link between foreign institutional holdings and firms’ efficiency in labor investment in the setting of Chinese markets. We find that foreign institutional investors enhance firms’ labor investment outcomes primarily through mitigating asymmetric information and by strengthening internal governance. Specifically, the influence of foreign institutional investors on a firm’s labor investment efficiency is stronger when the firm faces greater labor adjustment frictions. This effect is more evident when foreign institutional investors are originated from countries or areas with stronger cultural connections to China, stronger governance quality, common law traditions, or stronger bargaining power in the firms’ governance. Our paper contributes to the literature in that it documents the monitoring role of foreign institutional investors from the perspective of firms’ labor investment decisions, and adds to the literature on the drivers of firms’ labor investment choices.

Recent technological advancements have enabled the emergence of business organizations fully managed by algorithms, such as decentralized autonomous organizations (DAOs) or through artificial intelligence (AI), as observed in China’s online food delivery sector. These organizations are collectively referred to as algorithm-managed firms (AMFs). Given machines’ capabilities in data collection and analysis, human directors are increasingly being replaced by algorithms or AI in specific sectors. This article contends that algorithms can effectively take over human directors’ managerial, monitoring, and mediating roles. The diminishing role of human directors raises certain concerns of stakeholder protection. Unlike human directors, algorithm directors or managers would not consider stakeholders’ interests unless clearly instructed to do so. However, the algorithm supplier and the AMFs may lack the incentives to fully consider stakeholders because they do not always internalize the social costs. To address the challenges of AMFs, policymakers need to consider different regulation strategies. First, they must choose between command-and-control regulations and target-based regulations. Command-and-control regulations often do not work well because regulators lack enough information or control over complex algorithms. Instead of setting detailed technical rules, policymakers should adopt target-based regulations that let the algorithm balance various interests and regulate its own operations. Second, policymakers should decide between entity-based and algorithm-based regulations. Algorithm-based regulation is more suitable because it prevents companies from passing costs onto society. The state could consider regulating the composition of the board of directors of the algorithm supplier to ensure that they incorporate the concerns of stakeholders’ interests in the development of the algorithm. Additionally, corporate law doctrines that protect creditors and other stakeholders, such as piercing the corporate veil and limiting liability for corporate torts, must be revisited and modified because their foundational assumptions no longer align with the realities of AMFs.

在实现“双碳”与共同富裕目标并进的背景下，环保规制的公平后果日益受到关注。本文以2013、2014与2016年分批启动的碳排放权交易试点为准自然实验，基于2000—2023年中国区县面板数据，系统评估市场型环保规制的分配效应。结果变量方面，本文使用区县夜间灯光构建的不平等指标（基尼、泰尔、阿特金森）刻画县域经济活动分布，并以县域城乡居民收入对数差距作为补充。识别策略方面，除区县与年份双向固定效应的TWFE-DID外，进一步引入适用于分批采用的更强识别方法：（1）Sun-Abraham分组事件研究用于动态效应检验并规避传统事件回归在异质处理效应下的加权偏误；（2）Callaway-Sant’Anna ATT(g,t)在“尚未受处理/从未受处理”对照组框架下估计分组—时期平均处理效应；（3）合成双重差分（SDID）同时估计单位权重与时间权重，以匹配处理前趋势并降低对严格平行趋势的依赖。研究发现：在TWFE-DID下，试点显著降低了县域夜间灯光基尼与阿特金森指数；更强识别（ATT(g,t)、SDID）在城市层面同样指向夜间灯光泰尔指数的下降，但幅度更为温和。机制检验表明，试点显著降低城市单位GDP能耗，支持“绿色转型—要素再配置—分配格局改善”的作用链条。

This paper investigates the gambling preference of Chinese investors in the convertible bond (CB) market through a natural experiment—the 2018 amendment of Article 142 of the Company Law. Utilizing CB issuance data from 2016 to 2023, we employ a cohort difference-in-difference approach and find a 4% to 7% increase in IPO premiums for high-repurchase-expectation CBs across various measures. This significant increase indicates that the legal revision reshapes investors’ expectation and adjusts their valuation of CBs. Furthermore, the event-study analysis reveals the escalating impact of legal revision, driven by the herding behavior of gambling investors.

This study was conducted to understand better how rules influence China's energy performance; this research on these policies' efficacy that facilitating the transition to sustainable energy sources is of tremendous significance, particularly in light of the severe problems climate change poses. To determine whether or not strict regulations are beneficial to China's energy transition efforts, this research makes use of a substantial amount of data about China's environmental laws and environmental transition policies. This paper thoroughly analyses the impact of strict environmental regulations on various energy transition measures. These metrics include the availability of green energy, carbon emissions, and energy efficiency. The results provide insights into how environmental restrictions have affected China's transition to a different energy source. Policymakers and stakeholders may use this information to build efficient plans to expedite the transition to a low-carbon, renewable energy system in China and abroad.

The 2008 Labor Contract Law of China stipulates that all employment relationships must be covered by a written labor contract. This regulation considerably strengthened employment protection for workers. Using a unique longitudinal dataset, the China Labor-force Dynamics Survey (2012, 2014, and 2016 waves), this article estimates the impact of the formal contractualization of labor relations on workers’ labor market outcomes, social insurance participation, and job satisfaction. We find that gaining a labor contract was strongly associated with an increase in salary, a decrease in working overtime hours, and greater participation in unemployment and pension insurances. In terms of job satisfaction, workers who gained a labor contract reported being less satisfied with their workplace environment and income than they had anticipated. This finding suggests that workers had higher expectations from the benefits gained through contractualization, than what they actually derived.