中国经济增速自 2012 年起持续下行，学界对其潜在增速的判断长期存在分歧。本文基于2005Q1—2025Q4中国31个省级行政区季度面板数据，构建“双场景 + 双红利 + 双约束”统一分析框架，系统检验不同经济循环模式下的增长约束机制。对比多种计量方法的稳健性后，采用工具变量法、多期 DID、中介效应、滚动窗口门槛回归、反事实模拟及异质性企业 DSGE 模型开展实证分析，得到三项核心结论。第一，中国经济增长呈现清晰的四阶段演化特征，外需主导期与内需主导期的约束机制存在本质差异；仅在外需主导阶段，美元M2增速与GDP增速存在显著倒 U 型关系，7%—10%为最优黄金区间。第二，3.30% 实际融资利率与零边际利润率双重约束严格锁定内需主导期增长，一旦进入“越投越亏”区间，GDP增速将出现断崖式回落。第三，现有研究关于潜在增速的分歧，本质是无条件收敛假说与有条件收敛假说的视角差异。本文拓展了开放条件下的经济收敛理论，构建的双场景双约束分析框架，为理解中国经济增速换挡提供了新的理论视角与实证支撑。

菲利普斯经典分析体系诞生于低储蓄、直接融资的经济环境，将其套用于高储蓄经济体时，会出现明显的理论适配偏差。研究依托资金运行逻辑推导得到M1,t⋅Kt>QMt收益等价约束条件，据此搭建 LDR—K 双临界均衡分析框架，明确货币传导弹塑性断裂的定义与量化判定规则。面板实证结果表明，住户定活比（LDR）与实体收益倍数（K）的合理运行区间为 1.77∼1.83。当两项指标同步处于该区间，货币传导效率可提升4.3～4.8倍，M1/QM 将回归 1:2 的长期均衡水平。LDR 与 K 存在双向动态联动关系，居民存款结构主导短期传导，实体盈利水平决定中长期运行趋势。本文纠正了学界关于 M2/GDP、储蓄率与货币结构的三类认知误区，延伸了经典货币政策理论的适用范围。建议把 LDR、K 纳入央行常规监测指标，结合四类经济区制推行差异化调控，为高储蓄经济体搭建专属货币政策框架提供理论与实证依据。

西方租售比、房价收入比模型在中国房地产市场长期存在适用性缺陷。本文立足宏观审慎监管视角，构建以 "套" 为计价单位、名义 GDP 增速与商品房整体交易率为双锚的 CMH 估值模型。依托 2000—2019 年官方官方数据的检验显示，该框架确立的双向阈值 ——0.56-0.60 的过热预警区间、1% 交易率的过冷底线 —— 与历次房地产调控的政策时点高度吻合，能够为逆周期调控提供可量化、可复现的参考依据。

中国宏观经济长期存在一组结构性矛盾：货币供给总量持续扩张，制造业尤其是小微企业的流动性约束并未同步缓解，应收账款回收期居高不下，融资成本下行节奏明显滞后。现有研究多在“实体—金融”二元框架下讨论脱实向虚问题，忽略了债务履约具备法定刚性、优先偿付的特殊属性，难以解释总量宽松与结构性紧缺并存的现象。本文回归费雪交易方程的全口径内涵，将广义交易拆解为实体生产、金融资产交易与债务履约三个层级，构建三层流速分析框架；借鉴结构力学弹塑性变形逻辑，结合企业两期现金流约束模型内生推导实体流动性的非线性断裂阈值。基于2000—2024年中国省级面板数据，综合运用门槛回归、强度双重差分、马尔可夫区制转换、局部投影与工具变量法展开检验。研究发现：样本期内债务交易层流速持续显著高于金融层与实体层，传统单一GDP口径对真实货币流通压力存在系统性低估；债务流速存在约15.4%的临界阈值，突破阈值后对规上工业应收账款回收期的挤压强度呈非线性放大，且塑性断裂区间具备强粘性与低可逆性，总量型货币政策传导效率明显衰减；小微企业对应的断裂阈值更低、受冲击强度更大，弹塑性机制具备全域普适性。本文从流量分层视角拓展了资金脱实向虚的理论分析体系，可为地方债务长效治理与结构性货币政策设计提供量化依据。

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 examines the impact of Chinese local government hidden debt on corporate ESG greenwashing. Extending fraud theory, we reveal that hidden debt shifts the boundary between government and market that drives the factors behind ESG greenwashing. Using the ESG greenwashing indicator of listed firms in the A-share market and the hidden debt-to-GDP ratio of 31 provinces from 2012 to 2023, we find that local government hidden debt is positively correlated with corporate ESG greenwashing. The impact is more significant for firms that are state-owned, without active primary-level Party organizations, or not on China’s key pollution supervisory list. Mechanism analysis indicates that expansion of local government hidden debt brings firms with higher LGFVs’ share-holding for the SOEs, heavier environmental tax burden, and less social responsibility preference, all of which are related with ESG greenwashing. Reducing local government special debt and improving tax compliance can help alleviate this impact. These findings highlight the necessity of fiscal risk management in achieving genuinely sustainable corporate development.

Following the 18th National Congress of the Communist Party of China, the central government elevated the construction of ecological civilization to a central position within national strategy and introduced environmental governance indicators as mandatory criteria for evaluating officials, alongside GDP. These indicators served as an additional "threshold" for performance assessments. In the context of changes in the central government's development ideology and policies, this study utilizes matched data on the turnover of municipal party secretaries and local enterprise carbon emissions from 293 prefecture-level cities in China between 1990 and 2021. The research finds that turnovers of municipal party secretaries after the 18th National Congress have led to a significant reduction in carbon emissions from local enterprises, a trend that was not evident prior to the congress. This effect is more pronounced in situations where official turnover is primarily driven by promotion incentives, and less influenced by collusive behavior between the government and enterprises. Further analysis reveals that the decline in carbon emissions is more significant for private enterprises, non-heavy polluting enterprises, those located in the eastern region, and those in general prefecture-level cities, before and after municipal party secretary turnovers. This study enhances understanding of the relationship between the promotion incentives of Chinese officials and the carbon emissions of local enterprises, offering valuable insights for improving the official promotion assessment system and advancing local carbon reduction efforts.

In the late 1980s, Mexico opened itself to international trade and foreign investment, followed in the early 1990s by China. China and Mexico are still the two countries characterized as middle-income by the World Bank with the highest levels of merchandise exports. Although their measures of openness have been comparable, these two countries have had sharply different economic performances: China has achieved spectacular growth, whereas Mexico’s growth has been disappointingly modest. In this article, we extend the analysis of Kehoe and Ruhl (2010) to account for the differences in these experiences. We show that China opened its economy while it was still achieving rapid growth from shifting employment out of agriculture and into manufacturing while Mexico opened long after its comparable phase of structural transformation. China is only now catching up with Mexico in terms of GDP per working-age person, and it still lags behind in terms of the fraction of its population engaged in agriculture. Furthermore, we argue that China has been able to move up a ladder of quality and technological sophistication in the composition of its exports and production, while Mexico seems to be stuck exporting a fixed set of products to its North American neighbors.

Suzhou and Shenzhen are among the top cities in China by GDP, and both have performed exceedingly well in terms of cultivating technological industries and attracting foreign investment. This is in spite of the fact that neither city is a provincial capital nor a centrally administered city like Shanghai and Beijing. Yet, the two cities embody very different administrative models with respect to their relationship with the provincial and central governments. Shenzhen, in particular, has a closer relationship with the central government than almost any non-centrally administered city in China, whereas Suzhou is a city that remains closely in coordination with the provincial government even as its economy has grown by leaps and bounds. This begs the question of which city's model will prevail moving forward: the Shenzhen model, typified by "re-centralization" of power, or the Suzhou model, which represents more of the conventional regional decentralization model that has been prevalent in China since the 1980s. The article attempts to argue that even though Shenzhen is of pivotal importance to the central government's policies, it will remain an outlier for the time being so as to avoid disturbing the delicate balance between the central and provincial governments, barring an unforeseen economic or political crisis.

This paper investigates the contribution of futures market information to enhancing the predictive accuracy of macroeconomic forecasts, using data from China. We employ three cat-egories of predictors: monthly macroeconomic factors, daily commodity futures factors, and daily financial futures variables. Principal component analysis is applied to extract key fac-tors from large data sets of monthly macroeconomic indicators and daily commodity futures contracts. To address the challenge of mixed sampling frequencies, these predictors are incor-porated into factor-MIDAS models for both nowcasting and long-term forecasting of critical macroeconomic variables. The empirical results indicate that financial futures data provide modest improvements in forecasting secondary and tertiary GDP, whereas commodity futures factors significantly improve the accuracy of PPI forecasts. Interestingly, for PMI forecast-ing, models relying exclusively on futures market data, without incorporating macroeconomic factors, achieve superior predictive performance. Our findings underscore the significance of futures market information as a valuable input to macroeconomic forecasting.