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.

Against the backdrop of escalating global carbon emission reduction urgency and China’s "dual carbon" goals, individual carbon trading (ICT) has emerged as a key measure to tap into consumer-side emission reduction potential. However, the current ICT mechanism remains in the exploratory stage, plagued by limited access to formal trading channels, strict entry barriers, and low public penetration, only 5 carbon exchanges in China allow individual participation, with fewer than 10,000 total participants. Focusing on mechanism integrity and integrating the carbon inclusive system, this study explores the influencing factors of ICT operation and constructs targeted optimization strategies. Based on Mechanism Design Theory, the Theory of Planned Behavior, and Value-Belief-Norm Theory, a theoretical model covering five core ICT components was established. Using data from 1,200 questionnaires across 15 Chinese cities, multiple linear regression and hierarchical regression were employed to analyze the impacts of institutional fairness, technical usability, social atmosphere, and individual cognition. Results show: (1) Institutional fairness increases quota compliance by 38% through transparent rules; (2) User-friendly technical tools boost engagement by 1.8x, enhancing accounting accuracy and platform efficiency; (3) Social atmosphere moderates the effect of individual cognition on incentive conversion, particularly strongly in rural areas; (4) Individual cognition mediates the relationship between social atmosphere and trading platform efficiency. Additionally, optimizing mechanism components is more critical for long-term operation than single incentives. This study proposes a multi-dimensional optimization framework, providing theoretical and policy references for improving ICT integrity and advancing "dual carbon" goals.

The development of industrialization runs through the history of the People’s Republic of China. In the early years of the founding of the People’s Republic of China, the Soviet Union provided important help for China’s industrialization construction, capital lending, talent training and technology transfer. By analyzing the proportion of China’s industrial output value in the total output value and the output of major industrial products during the period of the Soviet Union, it shows that the assistance of the Soviet Union had a very positive role in promoting the realization of ChinaIs industrialization, and helped China to lay the foundation of industrialization.In addition, some problems in accepting the Soviet Union’s aid to China also deserve our attention when promoting the new industrialization. The research explores both the internal and external factors that led to China’s acceptance of Soviet assistance.The study examines the political, economic, and historical contexts that created a favorable environment for such aid, analyzes the various modes and scales of the assistance implemented, and presents statistical data that underscore the transformative social and economic achievements derived from this partnership.Furthermore, the paper highlights the contributions of quantitative analyses in understanding the aid’s impact, discusses the experiences and lessons learned from the Soviet aid period, and assesses the evolving dynamics of Sino—Soviet relations,particularly the gradual divergence in economic development models. The findings shed light on how Soviet aid laid the groundwork for China’s industrialization while also foreshadowing future ideological and developmental separations.

The development of industrialization runs through the history of the People’s Republic of China. In the early years of the founding of the People’s Republic of China, the Soviet Union provided important help for China’s industrialization construction, capital lending, talent training and technology transfer. By analyzing the proportion of China’s industrial output value in the total output value and the output of major industrial products during the period of the Soviet Union, it shows that the assistance of the Soviet Union had a very positive role in promoting the realization of ChinaIs industrialization, and helped China to lay the foundation of industrialization. In addition, some problems in accepting the Soviet Union’s aid to China also deserve our attention when promoting the new industrialization. The research explores both the internal and external factors that led to China’s acceptance of Soviet assistance. The study examines the political, economic, and historical contexts that created a favorable environment for such aid, analyzes the various modes and scales of the assistance implemented, and presents statistical data that underscore the transformative social and economic achievements derived from this partnership. Furthermore, the paper highlights the contributions of quantitative analyses in understanding the aid’s impact, discusses the experiences and lessons learned from the Soviet aid period, and assesses the evolving dynamics of Sino—Soviet relations, particularly the gradual divergence in economic development models. The findings shed light on how Soviet aid laid the groundwork for China’s industrialization while also foreshadowing future ideological and developmental separations.

This study constructs a new mispricing factor for the Chinese equity market. We propose two-, three-, and four-factor models that incorporate this factor alongside the market, size, and value factors. Our models, especially the two-factor version, consistently outperform the Fama and French models and perform as well as other leading models in explaining Chinese anomalies. This study advances asset pricing literature specific to China and offers a promising new framework for analyzing mispricing in emerging markets.

The robustness of the securities market is a necessary condition for ensuring the stable operation of the financial market, and stock price fluctuations have always been a major concern for academia and investors. One of the main influencing factors of stock price fluctuations is macroeconomic monetary policy. As one of the important control tools of macroeconomic monetary policy, the impact of benchmark interest rates on stock price fluctuations cannot be ignored. This paper takes Chinese A-shares as the research object, analyzes the impact of benchmark interest rate changes on stock price fluctuations, and further introduces investor sentiment as a mediating variable to analyze its role in the transmission process of monetary policy. This paper selects monthly data of Chinese A-share listed companies from 2018 to 2024 and conducts empirical tests by constructing direct effect models and mediating effect models. The research results show that: First, benchmark interest rates have a significant negative impact on stock price fluctuations; second, benchmark interest rates can indirectly affect stock price fluctuations by influencing investor sentiment, with investor sentiment playing a partial mediating role. The research conclusions of this paper help to deepen the understanding of the transmission mechanism of monetary policy's impact on securities prices, and provide micro-evidence for monetary policymakers to assess the impact of policy adjustments on capital market stability, while also providing a reference for investors to understand the risk characteristics of the securities market under changes in the interest rate environment.

With the development of China's economy, more and more Chinese enterprises are active on the world stage, and cross-border M&A is the most effective and fastest way for enterprises to go abroad and make overseas investments, and it is also an important path for globalization after the enterprises have reached a certain stage of growth. Compared to domestic M&A, cross-border M&A is a more complex economic activity, requiring more factors to be considered and greater risks to be taken, with the slightest misstep often leading to operational difficulties for the acquiring company. It is important to consider whether cross-border M&A can improve business performance, the factors that influence the performance of cross-border M&A, and how to improve the performance of enterprises in cross-border M&A. This study takes 100 cross-border M&A events of Chinese listed companies in Shanghai and Shenzhen during the period of 2017-2020 as a sample, and on the basis of reviewing the research results of cross-border M&A at home and abroad, combined with the characteristics of cross-border M&A of Chinese enterprises, from different perspectives, a number of financial indicators are selected to construct comprehensive performance evaluation indicators using factor analysis, and the preliminary analysis shows that after cross-border M&A, the companies with increased performance The preliminary analysis showed that the number of companies whose performance increased after cross-border M&A increased year by year. The impact of industry relevance and transaction equity on M&A performance is not significant; the ratio ofM&A amount to current assets negatively affects firm performance in the year of M&A. Finally, based on the empirical results, relevant policy recommendations are made to encourage better development of private enterprises and improving cross-border M&A performance.

The global AI inference market—reaching approximately $90–100 billion annually and growing at 18% CAGR—operates without organized exchange infrastructure. We document three market failures: resource misallocation (80% of China’s newly built compute capacity sits idle), price opacity (100-fold price dispersion across providers of equivalent quality), and unhedged risk exposure (85% of enterprises miss AI cost forecasts by more than 10%). Following the market design tradition of Roth [2002] and Budish et al. [2015], we propose the AI Service Right (ASR) as a transferable property right on AI compute and the AI Service Unit (ASU) as a quality-adjusted, cross-platform unit of account grounded in hedonic price theory [Rosen, 1974]. The ASU is modality-neutral: billing prices across text, image, video, and speech modalities are unified via eq-token conversion factors (κimg ≈ 2,667 eq-tokens per image; κvid ≈ 2,667 per second of video; κspc ≈ 7 per second of audio), and modality-appropriate benchmark sets (MMLU/HumanEval for language; FID/CLIP Score for image; FVD/CLIPSIM for video; MMBench for multimodal) supply the quality in dex via PCA. We design a hybrid secondary market architecture synthesizing mechanisms from four orthogonal market traditions: foreign exchange markets (cross-platform exchange rates and PPP-analog arbitrage via the ASU); equity markets (Central Limit Order Book, market making, clearing); electricity markets (Compute Locational Marginal Pricing for spatial scarcity signals); and decentralized finance (Automated Market Maker for long-tail liquidity). We establish nine formal propositions: bilateral trading is generically inefficient; Compute Locational Marginal Pricing decomposes nodal prices into system marginal cost, capacity congestion, and bandwidth premia; no-arbitrage equi librium holds with capital constraints (extending Shleifer and Vishny 1997); the ASR market Pareto-improves over bilateral trading; market prices are more in formative under ASR; the hybrid CLOB-AMM architecture weakly dominates either mechanism alone; platform adoption admits multiple equilibria with a coordination trap; financialization may improve or reduce price informativeness depending on speculator-hedger composition; and a hedonic micro-foundation justifies the ASU definition. Calibrated agent-based simulation (500 steps, 30 Monte Carlo runs) provides computational validation: the hybrid architecture reduces price dispersion by 90% relative to bilateral trading, and order-of-magnitude welfare estimates suggest enterprise procurement cost savings of 0.2–20% (net of ASR transaction costs; see Table 7) and potential TFP gains from compute reallocation of up to $29.9 billion annually. We propose a phased implementation roadmap from shadow ledger to full financialization, and we engage critically with the concern that financialization may not reduce intermediation costs [Philippon, 2015].

Whether risk aversion is time varying and countercyclical is central to modern asset pricing, yet evidence remains limited and is based mainly on experimental, survey, or aggregate stock market data. We provide individual-level evidence from 10 million Chinese auto insurance contracts from 2011 to 2017, estimating policyholders’ risk aversion from deductible choices. We find that risk aversion is time varying and countercyclical. The estimates are negatively related to lottery and stock trading, positively related to insurance sales and bond trading, and vary with psychological factors, including seasonal mood, “zodiac year,” and calendar events.

This paper revisits two widely studied predictors of commodity futures returns, basis and basis momentum, whose conventional measures using first-nearby futures as proxies for spot prices may limit their ability to capture fundamental spot-market risks. Motivated by this limitation, we construct two spot-based signals from observed spot and futures prices, which are theoretically shown to contain incremental information beyond conventional measures. Using 41 Chinese commodity futures, we find that these signals robustly predict first-nearby contract returns and remain significantly priced in time-series and cross-sectional tests, even after controlling for their conventional counterparts. We then develop a spot-enhanced three-factor model, including the market factor and the two spot-based factors, which consistently outperforms three widely used benchmark models in pricing competing factors and explaining return anomalies.