This paper examines how artificial intelligence (AI) adoption influences the total factor productivity (TFP) of Chinese A-share manufacturing firms from 2010 to 2023. Results show that AI significantly raises TFP, robust across multiple specifications and instrumental variable tests. AI also boosts operational efficiency by accelerating accounts receivable and inventory turnover, revealing a “technology–operation–productivity” pathway. The positive effect is stronger in regions with better digital infrastructure and in firms with stronger governance. The findings provide fresh evidence on AI’s productivity effects and offer policy implications for intelligent transformation and high-quality manufacturing development.

This study investigates the impact of Total Factor Productivity(TFP) growth in China's high-tech industry on household consumption, examining the distinct roles of labor and capital factor productivity from the perspective of biased technological progress. We innovatively construct a sequential proportional NDDF-Luenberger index. This index not only provides a theoretically consistent measure of TFP but also enables its precise decomposition into labor factor productivity and capital factor productivity, allowing for the quantitative identification of the degree and direction of technological bias. Our analysis yields three key findings. First, China's high-tech industry TFP evolved through a three-phase pattern of "surge–retreat–recovery," characterized by persistent capital-biased technological progress. Second, at the national level, improvements in overall TFP, labor factor productivity, and capital factor productivity all significantly promote household consumption, validating the theoretical pathway where supply-side efficiency gains stimulate demand. Third, significant regional heterogeneity exists: the Eastern region exhibits a "capital-led" growth pattern with weaker consumption effects from labor productivity; the Central and Western regions show "factor synergy," where both productivities contribute to consumption; whereas the Northeastern region suffers from a blocked transmission mechanism, where technological progress fails to significantly boost local consumption due to insufficient integration with the regional economy. By integrating supply-side TFP with demand-side consumption through the lens of biased technological progress, this research provides critical insights for fostering a virtuous cycle between innovation and domestic demand, offering valuable implications for industrial and regional policy design aimed at sustainable and inclusive growth.

The commodity and financial characteristics of carbon emission allowances play a pivotal role within the Carbon Emission Trading Scheme (CETS). Evaluating the effectiveness of the scheme from the perspective of carbon price is critical, as it directly reflects the underlying value of carbon allowances. This study employs a time-varying Difference-in-Differences (DID) model, utilizing data from publicly listed enterprises in China over the period from 2010 to 2023, to examine the effects of carbon price level and stability on Total Factor Productivity (TFP). The results suggest that both an increase in carbon price level and stability contribute to improvements in TFP, particularly for heavy-polluting and non-stateowned enterprises. Mechanism analysis reveals that higher carbon prices and stability can stimulate corporate engagement in green innovation, activate the Porter effect, and subsequently enhance TFP. Furthermore, optimizing the system environment proves to be an effective means of strengthening the scheme's impact. The study also finds that allocating initial quotas via payment-based mechanisms offers a more effective design. This research highlights the importance of strengthening the financial attributes of carbon emission allowances and offers practical recommendations for increasing the activity of trading entities and improving market liquidity.

Utilizing data from Chinese A-share listed companies, this study investigates the effects of digital transformation (DT) on total factor productivity (TFP) and the moderating function of ESG performance. The results indicate that DT boosts TFP, but ESG performance negatively moderates this effect, revealing the green paradox. A dynamic model of factor allocation efficiency shows that DT improves capital allocation by reducing financing constraints, information asymmetry, and enhancing operational capacity. However, ESG weakens the positive link between DT and operational capacity, thus diminishing its impact on TFP. Similarly, DT increases labor productivity, but ESG undermines this effect by weakening the link between DT and labor efficiency. The positive impact of DT is stronger when firms focus on ‘Practical Application Technologies’ rather than ‘Underlying Technologies’. This effect is especially evident in smaller, asset-intensive, non-state-owned firms, and those located in the Beijing-Tianjin-Hebei region. Additionally, ESG’s negative moderation is more pronounced where DT exerts a stronger positive influence. A notable distinction emerges: asset-intensive firms gain more from DT in terms of TFP, whereas ESG’s adverse effect is stronger in labour-intensive firms. This study offers a novel perspective on the interplay between DT, ESG performance, and productivity. It provides valuable insights for firms seeking to align digital strategies with ESG goals, thereby fostering technological innovation alongside sustainable development.

The impact of Artificial Intelligence (AI) on firm performance is an emerging issue in both practice and research. However, discussions surrounding the effect of AI on productivity are enshrouded in a paradoxical quandary. This study examines the relationship between AI implementation and total factor productivity (TFP), considering the moderation effects of digital infrastructure quality, business diversification, and demand uncertainty. Using data from 2155 Chinese firms over 2016-2021, our empirical analysis reveals a nuanced pattern: while moderate AI implementation achieves the best TFP, excessive and insufficient implementation yields diminishing returns. The curvature of this inverted U-shaped relationship flattens with higher levels of digital infrastructure quality but steepens when firms undertake diversified businesses and face heightened demand uncertainty. The findings suggest that the impact of AI on TFP is not universally beneficial, and the relationship between AI and TFP varies across different contexts. These findings also provide implications on how firms can strategically implement AI to maximize its value.

Fintech has significantly influenced the traditional financial industry by introducing advanced technologies and innovative business models with profound impacts. We aim to study the effect of Fintech development on labor allocation efficiency, and to explore its underlying mechanisms. Using a set of companies on Chinese A-share market over the years of 2011- 2020, we find that Fintech development plays a positive role in labor allocation efficiency, mainly through suppressing labor overinvestment. This positive effect is further reinforced by market competition. In addition, our investigation reveals that the primary pathways through which Fintech enhances labor allocation efficiency are lowering information asymmetry, mitigating agency issues and substituting low-skilled labor. Moreover, we show that the dimensions of depth and digitalization are particularly important in improving labor allocation efficiency among the three dimensions of Fintech development. Lastly, we find that Fintech development enhances total factor productivity by improving labor allocation efficiency.

ESG is emerging as a new benchmark for measuring a company's sustainable development capabilities and social impact. As a measure of ESG performance, ESG ratings are increasingly receiving attention from companies, the general public, and government institutions, and are becoming an important reference factor influencing their decision-making. This paper investigates the impact of corporate ESG ratings on Total Factor Productivity (TFP) and its mechanisms of action. Focusing on listed companies in China, we find that higher ESG ratings contribute to improving a company's TFP, and this conclusion remains valid after robustness tests and addressing endogeneity issues. Further exploration into the reasons behind this result reveals that ESG ratings can be seen as a signal that a company sends to the outside world, representing its overall performance. Higher ESG ratings enhance a company's TFP by reducing market financing constraints and obtaining government subsidies. Heterogeneity analysis shows that the positive impact of ESG ratings on TFP is more pronounced for companies with higher levels of attention, reputation, and audit quality. Additionally, we explore whether ESG ratings can serve as a predictive indicator for measuring a company's TFP. This hypothesis was tested using machine learning algorithms, and the results indicate that models incorporating ESG rating indicators significantly improve the accuracy of predicting a company's TFP capabilities.

The role of Market-incentive environmental regulation (MIER) within the framework of environmental governance is patently evident. While extant literature lauds the advantageous outcomes attributed to the environmental protection tax (EPT) which as a representative of MIER, our empirical inquiry presents a contrasting narrative. By employing the sophisticated Difference-in-Difference-in-Difference (DDD) methodology and utilizing data from A-share listed firms in Shanghai and Shenzhen from 2015-2022, our investigation reveals a significant decrease in firms’ total factor productivity (TFP) following the implementation of EPT. Our core assertion is fortified through the discernment of two plausible mechanisms, namely, the production downsizing effect and the production capital crowding-out effect. Building upon this revelation, we delve into the nuanced pathways through which firms can strategically mitigate the impacts of EPT, encompassing the enhancement of human capital, amplification of research and development (R&D) investments, and fortification of overall firm resilience. Heterogeneity analysis discloses a notably heightened impact of EPT on TFP of state-owned enterprises (SOEs), larger enterprises and enterprises located in eastern regions. Ultimately, an approximately cost-benefit analysis conclusively demonstrates that the benefits derived from EPT far surpass the costs incurred by the concomitant industrial output reduction, which further illustrates the rationale for the implementation of EPT.

This paper examines the impact of asset bubbles on innovation and long-run economic growth within a semi-endogenous growth framework, incorporating idiosyncratic productivity shocks and endogenous credit constraints in the R&D sector. It demonstrates that pure bubbles tied to intrinsically useless assets and equity bubbles linked to intermediate goods firms can coexist, relaxing credit constraints and boosting entrepreneurs’ total factor productivity (TFP), which stimulates R&D and enhances growth along the transitional path. However, these bubbles generally do not influence the long-run economic growth rate. The model’s mechanisms and predictions are supported by aggregate and firm-level evidence, showing a positive correlation between equity bubbles and R&D investment, with stronger effects during periods of tightened financial constraints.

As digital transformation strategies have emerged as a primary approach for enterprises to enhance their Total Factor Productivity (TFP), it is crucial to empirically examine the impact of these strategies on TFP. For this purpose, this study considers these transformation strategies as a quasi-natural experiment and employees a propensity score-weighted difference-indifferences methodology on data from Chinese firms listed on the A-share market between 2007 and 2020. The key findings include: (1) digital transformation has a significant positive influence on TFP; (2) Generalized boosted regression trees analysis reinforces this finding after controlling for other TFP determinants; (3) notably, non-state-owned and technology-intensive enterprises exhibit a more distinct enhancement in TFP following digital transformation. These results underscore the need for firms to increase investment in research and development capabilities and digital competencies.