This study evaluates whether electric vehicles (EVs) in China have delivered on their “green” promise by improving air quality and reducing private ownership costs. Combining city–month data on EV adoption and air pollution with detailed vehicle-level transaction records, we find little evidence that rising EV adoption reduces concentrations of major pollutants or greenhouse gases. Both the traditional instrumental variable (IV) and the Bartik IV estimations show that the null effects persist after addressing potential endogeneity. The limited environmental gains are largely attributable to the continued dominance of coal in electricity generation and to a sizable rebound effect whereby lower operating costs increase vehicle usage. On the private side, although EVs reduce energy expenditures by roughly CNY 16,747, their faster depreciation leads to an average net loss of about CNY 18,175 over the ownership period relative to fuel vehicles (FVs). Counterfactual simulations show that EVs match the emissions of fuel vehicles when the coal share falls to about 55%, deliver measurable benefits below 49%, and require a reduction to around 31% once higher utilization is considered. The results show that the environmental benefits from EV adoption depend on a transition toward cleaner electricity generation.

This paper studies the unintended spillover effects of China's 2020 "Three Red Lines" policy – a macroprudential intervention aimed at deleveraging real estate developers – on employment in the upstream manufacturing firms. Exploiting variation in industries' exposure to the real estate sector via input-output linkages and employing a generalized difference-in-differences (DID) design on panel data of listed manufacturing firms (2017-2022), we find a significant decline in employment among upstream suppliers, while there is no substantial effect on downstream firms. We identify two transmission channels: a demand propagation channel, evidenced by falling sales and inventory overhang, and a balance sheet contagion channel, where liquidity strain is passed upstream via extended payables and a shift to riskier short-term debt. Furthermore, we show that downward nominal wage rigidity shaped the adjustment, as firms resorted to layoffs rather than wage cuts for ordinary workers. From a regional labor market perspective, the employment losses represent a net reduction at the aggregate level, as displaced workers are not absorbed by other local firms in the same city or province. Our study reveals a significant real economic cost of sectoral macroprudential regulation and clarifies how such shocks propagate through production networks and interact with labor market frictions.

We investigate how investors assess transition risks arising from the discretionary regulatory mandates for nature conservation. We focus on the UN Biodiversity Conference in Kunming, China, a policy shock signaling heightened political commitment to protection. During the conference, firms exhibited negative abnormal stock returns on average. Using a machine learning approach, we identify historical nature-related subsidies to capture firm-level variations in expectations of mandated conservation engagement, beyond measuring corporate environmental (ecological) impacts. This metric negatively correlates with returns during the conference, particularly for firms with high operational volatility, and those in regions with limited fiscal flexibility and stringent conservation pressures. Investor concerns primarily stem from policy uncertainty, without positive earning expectations from conservation. Our findings underscore the role of authoritative discretion in shaping nature transition risks.

This paper examines how traditional informal institutions shape preferences for old-age support and the emergence of initiative-based long-term financial planning. Using data from the China Health and Retirement Longitudinal Study combined with clan genealogy records, we estimate the causal impact of exposure to clan culture on old-age support preferences. We find that stronger exposure to clan culture in early life is associated with a higher likelihood of adopting modern preferences for old-age support, characterized by greater reliance on self-initiated savings and pension participation rather than dependence on children. The results are robust to instrumental variable estimation and placebo test. Importantly, the effect operates exclusively through exposure to clan culture during early life, while clan culture encountered in adulthood has no significant influence, particularly among individuals who relocate later in life. Mechanism analysis suggests that peer effects, generalized trust, income growth, and financial literacy jointly mediate the relationship between clan culture and old-age support preferences. We further show that the expansion of the formal pension system complements, rather than crowds out, the influence of clan culture, consistent with a process of co-evolution between informal and formal institutions. The effects are stronger among individuals with favorable attitudes toward clans, rural residents, and men. Finally, modern preferences induced by clan culture translate into higher pension participation and larger financial asset holdings. Overall, the findings highlight how informal institutions can shape economic preferences and facilitate the adoption of formal financial arrangements.

We study machine-learning forecasts and portfolio allocation for Chinese government bonds. Penalised linear regressions and gradient-boosted trees predict next-month total returns for eight maturity buckets. Their forecasts feed mean–variance, Black–Litterman, and risk-budgeting implementations with weight, duration, and trading-cost controls; convexity, tail-risk, and attribution constraints are analysed separately as theoretical extensions. From January 2019 through December 2025, the three optimised cash portfolios earn net excess-return Sharpe ratios of 1.94–2.26 and maximum drawdowns of 0.18–0.25%. Three long-only bond-selection variants attain Sharpe ratios of 1.39–1.70. A futures-hedged arbitrage variant attains 1.08 under an 80% hedge-P&L scaling and basis-cost assumption. A stylised 10-year futures overlay reduces rather than improves cash-portfolio Sharpe ratios.

This study examines how emerging market multinational enterprises (EMNEs) respond to cross-national diversity in environmental regulation. Prior research suggests that internationalization exposes EMNEs to diverse environmental standards and may facilitate regulatory learning, yet offers a limited understanding of the mechanisms through which such exposure translates into green innovation. Drawing on institutional theory, we argue that green innovation is particularly fostered when EMNEs operate across host countries whose environmental regulatory regimes are more stringent than those in the home country, and when such exposure varies across multiple host locations. We theorize that this form of environmental regulatory diversity creates learning pressures and legitimacy incentives that motivate EMNEs to upgrade their environmental capabilities through green innovation. Using panel data comprising 9,362 firm-year observations from 1,088 Chinese MNEs over the period 2008–2021, we find support for our arguments. We further show that these effects are contingent on organizational and institutional conditions, specifically top management teams’ technological expertise and the strength of intellectual property enforcement in home-country locations. These findings advance research on EMNE learning and sustainable innovation under complex global environmental governance.

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.

货币政策传导效果具有显著状态依赖特征，其强度和路径随经济周期、金融条件与市场预期动态调整。本文结合叙事符号约束结构向量自回归(SVAR)与平滑迁移局部投影(ST-LP)，基于2006年3月至2025年12月的中国宏观经济月度数据识别外生货币政策冲击，考察不同周期阶段的非对称传导效应，并从利率、信用和预期渠道分析其机制。研究发现：第一，货币政策具有显著经济周期非对称效应，相比扩张期，扩张性货币政策在收缩期对产出和通胀的促进作用更强，但冲击初期存在时滞；第二，利率渠道在两种状态下均有效传导，收缩期宽松政策能推动信用扩张，但信用利差未同步收窄；第三，预期渠道主体分化，企业信心在收缩期明显改善并与产出修复呼应，消费者信心响应较弱。这为完善逆周期宏观调控提供新的经验证据。

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.

Households form beliefs by recalling past experiences, but recall is context dependent. Variation in contexts alone can therefore generate non-fundamental disagreement. A stylized model à la Wachter and Kahana (2024) illustrates this cognitive source of disagreement. When combined with salience-based frictions in memory encoding, context-dependent recall also generates testable predictions for belief skewness. Using weather as a contextual cue that triggers the recall of past shopping experiences, I construct a measure of memory-based household inflation disagreement in the U.S. and show that it explains survey-based disagreement, with stronger effects in unusual contexts, and predicts greater belief skewness. The memory effect is distinct from experience and mood effects and generalizes across countries and outcome domains. Finally, memory-based disagreement weakens monetary policy transmission by attenuating intended consumption responses.