To address the challenge of insufficient effective demand, stimulating consumption has become a core priority of China's macroeconomic policy. Stimulating consumption requires simultaneous efforts from both the demand and the supply sides. On the one hand, the demand for consumption can be boosted by improving the well-being of people. On the other hand, the supply of consumption can be improved by facilitating the innovation of goods and services. How to achieve consumption stimulation and social welfare improvement through coordinated efforts on both demand and supply sides constitutes a critical topic that requires in-depth investigation.
Fiscal policy, which can provide direct targeted support for key areas, has unique advantages in addressing economic downturns and structural problems. However, as China's fiscal imbalance becomes increasingly prominent, it is essential to optimize the fiscal expenditure structure and improve fiscal spending efficiency. Meanwhile, structural monetary policy can incentivize financial institutions to provide financing for targeted sectors. Nonetheless, due to its endogeneity, structural monetary policy must be implemented in coordination with other policy instruments to stimulate consumption and expand aggregate demand. This paper aims to construct a multi-sector DSGE model based on China's macroeconomic characteristics to systematically analyze the effects of fiscal expenditure restructuring on consumption from both the demand and supply sides, and further explores the coordination mechanism of structural monetary policy with fiscal expenditure structural adjustment. In particular, China's economy exhibits a distinct vertical industrial structure, with state-owned enterprises largely concentrated in the upstream sectors of the industrial chain and private enterprises mainly located in the downstream sectors. Private enterprises, with their strong innovation capability and rapid adaptability to market changes, serve as a key driver of improvements in consumption supply. Accordingly, this paper incorporates a vertical industrial structure into the model to clarify the impact of various policies on the supply side of consumption.
Based on the evolution of China's fiscal expenditure structure over the past decade, this paper classifies fiscal expenditure into three categories: livelihood expenditure, infrastructure expenditure, and government consumption. We examine the impacts of two structural adjustment approaches on consumption demand and consumption supply: (1) expanding the livelihood expenditure and cutting traditional infrastructure expenditure at the same time, and (2) expanding the livelihood expenditure and cutting government consumption at the same time. Increasing the livelihood expenditure can stimulate people's willingness to consume on the demand side. Infrastructure expenditure generates demand for intermediate goods produced by state-owned enterprises, thus reducing such expenditure helps redirect investment and credit funds from the state-owned sector to the private sector, thereby improving consumption supply. Under the vertical industrial structure, both government and households consume final goods produced by the private sector, hence cutting government consumption also contributes to improving consumption supply for households.
This paper employs impulse response analysis and comparative static analysis to examine the short-run and long-run effects of fiscal expenditure structural adjustment, respectively. The results indicate both approaches can effectively boost consumption demand. In the short run, Approach (1) delivers a more significant expansion of consumption supply. In the long run, however, public capital accumulated through infrastructure expenditure generates positive spillover effects on the production sector. Consequently, the long-run boosting effect of Approach (1), which cuts infrastructure expenditure, is weaker than that of Approach (2). Therefore, increasing livelihood expenditure and reducing government consumption at the same time can better achieve the coordinated expansion of consumption supply and demand in the long run.
Given that both approaches of fiscal expenditure structural adjustment can increase the credit spread for the private sector, this paper argues that implementing structural monetary policy alongside such fiscal expenditure structural adjustment can provide targeted liquidity to the private sector, thereby better leveraging the role of fiscal expenditure structural adjustment in promoting consumption supply.
These findings carry important policy implications. First, stimulating consumption requires coordinated fiscal and monetary policies. Second, efforts should be made to promote sound interaction between supply and demand, with coordinated measures from both sides. Third, fiscal expenditure structure should be optimized by increasing livelihood expenditure and cutting government consumption.

This paper studies how the expansion of Chinese local government debt affects corporate default risk and how this risk transmits to financial stability. The rapid accumulation of local government liabilities has raised concerns not only about fiscal sustainability but also about the interaction between local public balance sheets and corporate financing conditions. Local debt expansion can alter firms' borrowing costs and access to credit through higher market interest rates, credit crowding-out, weakened expectations about local fiscal capacity, and heterogeneous debt structures. Against this background, the paper addresses three questions: whether local government debt and default risk are linked in a non-linear manner; through which channels this relationship is transmitted and how it affects financial stability; and whether policy adjustments alter this relationship.
The theoretical framework integrates local government debt, corporate default risk, financial stability, and central regulatory intensity into a unified dynamic system. In a deterministic setting, the model allows for threshold switching, so that key parameters change across debt regimes. The stochastic extension adds diffusion terms and Poisson jumps to capture both continuous macro-financial disturbances and discrete institutional or policy events. Under optimal-control conditions, the paper derives state-contingent policy responses and expresses them as estimable reaction functions, yielding testable sign restrictions and interpretable thresholds.
The empirical analysis is based on a 2014–2022 firm–year panel. The data are compiled from WIND, CSMAR, the Bank of China Database (CBD), and official statistics from the National Bureau of Statistics and the Ministry of Finance. Local government debt is measured using publicly available indicators of explicit debt. Corporate default risk is measured using a Merton-type structural approach and further combined with liquidity, profitability (ROE), leverage, and market risk in a multi-factor specification to obtain a composite default risk score.Financial stability is measured from the banking side, the market side, and a PCA-based composite index. From the banking perspective, we use the loan-to-deposit ratio (LDR) to proxy liquidity resilience and reliance on funding sources. On the market side, a market instability index (MSI) is built from high-frequency returns in CSMAR by aggregating tail-risk indicators. The analysis also constructs a firm-level proxy for shadow-banking activity.
The baseline identification uses two-way fixed effects and explicitly tests for quadratic non-linearity between local government debt and corporate default risk. The paper reports a statistically significant quadratic relationship: at relatively low debt levels, higher local debt is associated with lower measured default risk, whereas as debt rises the marginal effect increases and eventually turns risk-increasing. To address potential endogeneity, the paper further implements a two-step fitted-value regression and a stepwise TWFE set-up. Regarding policy effects, it employs a multi-period DID design with multiple policy nodes to assess the stage-wise impact of different governance milestones.To study transmission to financial stability, the empirical strategy decomposes total effects into direct and indirect channels and conducts mediation analyses that incorporate both corporate default risk and shadow banking as parallel mechanisms.
The results indicate that both mediation paths are statistically significant. Extending the mechanism tests, the paper examines banking-side instability, market-side instability, and the PCA composite. Within the sample, local government debt is in general statistically negatively associated with the instability measures, but corporate default risk is positively and significantly related to banking-side instability, highlighting that the credit channel is most clearly reflected in bank liquidity vulnerability. Evidence on non-linearity is stronger for the banking-side measure.The results suggest that the effect of local government debt on corporate default risk is non-linear and exhibits threshold behavior. At relatively low debt levels, fiscal resources may help buffer risk, whereas beyond a critical level, rising financing costs, crowding-out effects, and worsening expectations are associated with higher default risk. Corporate default risk further acts as an important channel linking local government debt to financial instability. The multi-node DID evaluation further shows that average treatment effects differ across governance nodes. Taken together, these results imply that debt-risk assessment and policy design should take account of regime heterogeneity and coordinate actions across the debt, corporate and financial-system dimensions.

Total factor productivity (TFP) is a core indicator of firm performance and economic growth, and a key metric for evaluating technological progress and the effects of economic policies. The existing literature on the production function and productivity estimation largely rests on the assumption that the functional form of the production function is exogenously determined and stable. However, with the rapid advancement of intelligent manufacturing, this assumption is facing significant challenges. The defining feature of intelligent manufacturing is the substitution of machines for labor, which not only alters the ratio of capital to labor inputs but also makes the factor input share of firms endogenously determined by relative factor prices, factor-specific productivity, and the scope of substitution. In this context, continuing to rely on conventional methods to estimate production functions and TFP may lead researchers to mistake firms' endogenous adjustment of input choices for productivity changes. This, in turn, generates systematic bias and distorts the evaluation of technological progress, resource allocation efficiency, and industrial policy. Consequently, accurately estimating production functions and TFP in the context of intelligent manufacturing remains a critical theoretical and empirical challenge.
To address this question, this paper develops a theoretical model with endogenous technology choice and proposes a new method for production function estimation. Relative to existing approaches, our method does not treat factor shares as exogenous constants. Instead, it explicitly incorporates firms’ endogenous technology choices, thereby allowing for more consistent identification of production function parameters and, in turn, more accurate measurement of TFP. We first assess the performance of the proposed estimator through Monte Carlo simulations. The results show that our method recovers the true parameters with relatively high accuracy, whereas conventional methods, namely the LP and ACF estimators, as well as the nonparametric GNR methods, exhibit significant bias. We then apply our method to Chinese manufacturing firms for the period of 1998 to 2013 to estimate production functions and TFP, and further examine the distribution, dynamics, and heterogeneity of productivity. We leverage data from this period because capital-labor substitution is not unique to the era of intelligent manufacturing; intelligent manufacturing mainly expands the range of substitution and reduces its cost. Therefore, if the gap between existing methods and our method is already substantial during the early stage of intelligent manufacturing, it will inevitably be magnified in periods when intelligent manufacturing is more pervasive.
This paper yields several main findings. First, the Monte Carlo simulations show that both conventional and nonparametric methods suffer from sizable bias in estimating production functions. Second, our estimates indicate that manufacturing firms in China do exhibit substantial capital-labor substitution and that the production function is not of the standard Cobb-Douglas form, thereby validating the model specification. At the same time, capital productivity is generally higher than labor productivity, with the former being roughly two to three times the latter. This suggests that firms substitute capital for labor not only because relative prices change, but also because capital is more productive. Third, compared with conventional and nonparametric methods, our approach yields a substantially greater degree of within-industry productivity dispersion. This implies that existing methods seriously underestimate the true dispersion of productivity because they fail to account for firms’ endogenous technology choice. Fourth, the productivity decomposition shows that productivity growth in Chinese manufacturing over 1998 to 2013 was driven mainly by productivity improvements within incumbent firms, rather than by the extensive-margin contribution of firm entry and exit. Fifth, firm heterogeneity is pronounced: productivity differs substantially across ownership types, with non-state-owned firms outperforming state-owned firms on average. By contrast, productivity differences between exporters and non-exporters are not statistically significant.
The contributions of this paper are fourfold. First,motivated by the fact that intelligent manufacturing fundamentally changes firms’ mode of production, we show that the existing production function estimation literature has overlooked the crucial role of endogenous technology choice. By building a theoretical model around this mechanism and proposing a new estimation strategy, we extend the literature on production function and productivity estimation. Second, we link intelligent manufacturing and automation to the problem of productivity measurement, showing that existing empirical studies on the effects of intelligent manufacturing may be contaminated by productivity mismeasurement. In this sense, this paper provides a new methodological foundation for reassessing the economic consequences of digital technologies and automation. Third, we show that ignoring endogenous technology choice leads to a systematic underestimate of within-industry productivity dispersion. This implies that existing estimates of resource misallocation may be overly conservative, and thus offers a new perspective for re-evaluating the extent of misallocation in Chinese manufacturing and the potential gains from improving allocation efficiency. Finally, our analytical framework is broadly adaptable. It is applicable not only to firm-level productivity analysis, but also to broader work on resource allocation, industrial policy, and general equilibrium analysis.
This paper emphasizes several key policy implications. First, as intelligent manufacturing continues to deepen, neither academic research nor policy evaluation should continue to rely on conventional methods that assume an exogenous and invariant production function by default. Instead, both should explicitly take into account that firms adjust technology choices endogenously in response to input prices and technological conditions. Reliable inference on technological progress and policy effects requires accurate measurement of productivity as a prerequisite. Second, since productivity growth in Chinese manufacturing is driven mainly by efficiency gains within incumbent firms, while the reallocation effect across firms is negative, the next stage of reform should not only continue to support technological upgrading and intelligent transformation at the firm level, but also deepen market-oriented factor reforms so as to channel resources toward more efficient firms and improve allocative efficiency. Third, given the substantial productivity gap that remains across ownership types, continued reform of state-owned enterprises, together with better incentive design and resource allocation mechanisms, remains essential for raising aggregate productivity.
This paper also offers several directions for future research. On the one hand, our estimation framework can be applied to more recent firm-level microdata to examine how endogenous technology choice affects production function estimation and productivity measurement in the context of the wider diffusion of artificial intelligence, industrial robots, and platform-based production. On the other hand, our framework can be combined with the measurement of resource misallocation to reassess thereal magnitude of allocative efficiency losses in Chinese manufacturing and even in services. More broadly, institutional features such as subsidies, tax incentives, credit constraints, and R&D policies can be incorporated into the model to study how they shape firms’ technology choices, the distribution of productivity, and industry dynamics.

Taxation is deeply linked to China's economic growth. A robust fiscal and tax framework is essential for efficient resource allocation, market integration, social equity, and long-term national stability, and constitutes the institutional foundation of a well-functioning market economy.
Since the reform and opening-up, Chinese firms have made remarkable progress in “going global”, bolstered by strong policy support. As a key policy instrument facilitating the dual circulation of domestic and international economies, the export tax rebate effectively reduces the tax burden on exporters and enhances their international competitiveness. Before 2015, export tax rebates were jointly financed by central and local governments. However, uneven regional development and increasing local fiscal pressures led to inefficiencies in rebate payments and delays in capital recovery for exporting firms. When exporters rely on suppliers’ funds for financing, liquidity risk is transmitted upstream, undermining supply chain security and stability. Addressing exporters’ financing constraints and revitalizing supplier liquidity through fiscal reform has thus become an urgent policy priority. The reform of the export tax rebate-sharing in 2015 represents an important institutional innovation. The export tax rebate-sharing mechanism defines how the central and local governments share fiscal responsibility for export tax rebate payments. By transferring rebate expenditure entirely to the central government, the reform alleviates local fiscal burdens, shortens rebate processing time, and accelerates capital recovery for exporters, thereby reducing the tie-up of suppliers’ funds. This paper raises three research questions:(1) Does the reform generate spillover effects across supply chains? (2) Through which mechanisms do these effects occur? (3) Do regional and firm-level heterogeneities exist? Answering these questions contributes to a deeper understanding of the reform’s significance for safeguarding supply chain security and stability.
Using data form A-share listed companies over the period 2007-2024, this study documents several key findings. First, following the reform of the export tax rebate-sharing mechanism, exporting firms significantly reduced their encroachment on suppliers’ working capital relative to non-exporting firms. The reform operates through two key channels, alleviating financing constraints and facilitating cross-regional procurement, which ease firms’ financing pressure and lower procurement costs, thereby generating both direct and indirect spillover effects along the supply chain. Second, these spillover effects exhibit notable heterogeneity. At the regional and industry level, the effects are stronger in regions with higher government efficiency, better business environments, and more competitive industries. At the firm level, the effects are more pronounced among non-state-owned enterprises and exporters located closer to ports. Third, the results suggest that adjustments to the rebate-sharing ratio help promote balanced regional development and optimize the tax structure. Moreover, the reform mitigates asymmetric supply-demand fluctuations at the industry level and improves the efficiency of capital circulation across the supply chain. These results remain robust across a battery of alternative specifications and robustness tests.
This study makes several contributions. First, it extends the literature on the export tax rebate-sharing reform by incorporating a supply chain spillover perspective. While prior research has focused primarily on firm-level outcomes, such as export performance and procurement location choices, it has largely overlooked how the reform transmits effects through supplier–customer networks linked by trade credit. This paper addresses that gap by providing systematic empirical evidence of such spillover effects and offering policy implications for further refining the reform. Second, it identifies the mechanisms through which the reform shapes inter-firm trade credit arrangements, thereby contributing to the broader literature on supply chain spillovers. Third, the findings provide empirical evidence that can guide further refinement of the export tax rebate-sharing mechanism and inform the design of proactive fiscal policies aimed at strengthening China's macroeconomic governance.
The findings offer several policy and managerial implications. First, it is essential to clearly delineate fiscal responsibilities between central and local governments to ensure timely and efficient disbursement of export tax rebates. In particular, enhanced financial support should be directed toward non-state-owned enterprises to improve capital flows, mitigate the risk of supply chain disruptions, and safeguard supply chain stability. Second, policymakers should promote innovation in supply chain finance to diversify firms’ funding sources. A collaborative credit management framework should be established to facilitate supplier credit evaluation and risk assessment, thereby enhancing the resilience of both industrial and supply chains. Third, at the firm level, companies should deepen collaboration with supply chain partners to foster coordinated upstream and downstream development. Through stronger financial cooperation and deeper supply chain integration, firms can bolster their long-term competitiveness and sustainable growth capacity.
In sum, this study provides robust evidence that the export tax rebate-sharing reform generates significant positive spillover effects on supply chain performance, offering both theoretical insights and practical guidance for further fiscal reform and supply chain governance.

Promoting green and low-carbon development of the economy and society is essential for achieving high-quality development. Green finance plays a significant role in promoting the green transition of the economy and society. In recent years, China's banking system has made remarkable progress in green credit, with the balance reaching 44.7 trillion yuan by the end of 2025, ranking first globally in terms of scale. However, due to the incomplete green standards and information disclosure systems, green credit has encountered the issue of greenwashing, where borrowers misappropriate green loan funds. Such post-loan greenwashing distorts the allocation of green financial resources and undermines the effectiveness of green finance in supporting low-carbon development. In response, regulatory authorities have prioritized the quality and effectiveness of green loans, thereby incentivizing banks to fulfill their environmental responsibilities to ensure that these loans yield the expected environmental benefits. Meanwhile, some banks, such as the Industrial and Commercial Bank of China and Bank of Jiangsu, have enhanced post-loan management through regular inspections and environmental risk reporting. This indicates that banks are actively fulfilling environmental responsibilities and strengthening supervision to mitigate greenwashing risks. Furthermore, the green reputation banks have built through fulfilling environmental responsibilities may also deter borrowers from greenwashing. An important question remains: can banks’ fulfillment of environmental responsibilities effectively constrain borrowers’ post-loan greenwashing behavior? Does this constraint stem from strengthened post-loan management or the accumulation of green reputation? Investigating these questions helps clarify the quality and effectivenes of green finance in serving the green transformation of the real economy.
To address this, based on a sample of 2012 green loans issued by 27 Chinese listed banks from 2013 to 2022, this paper investigates the impact and underlying mechanisms of banks’ environmental responsibility fulfillment on enterprises’ post-loan greenwashing behavior. Furthermore, it explores how green standards and post-loan management incentives and constraints moderate this relationship. The results show that banks’ fulfillment of environmental responsibilities significantly curbs borrowers’ post-loan greenwashing behavior, primarily through strengthened post-loan management measures (e.g., enhanced on-site inspections, exit threats, and reductions in credit lines and terms), rather than through green reputation accumulation. Moreover, clearer green standards and stronger post-loan management incentives and constraints reinforce this effect.
This paper offers the following policy insights: First, while prioritizing green finance and fulfilling environmental responsibilities, banks should not only expand the scale and share of green credit but also pay attention to the quality and effectiveness of green credit assets. Second, strengthening post-loan management is a fundamental measure to curb greenwashing behavior and enhance the quality and effectiveness of green finance. Third, it is essential to further improve green standards and strengthen the incentive and constraint effects of green finance policies.
The contributions of this paper are as follows: First, it contributes to the research on the economic effects of banks’ environmental responsibility fulfillment. While previous studies have primarily focused on the impact of green loan issuance on banks’ operational performance and risk-taking, as well as on the environmental performance and technological innovation of borrowers, this study investigates the impact of banks’ environmental responsibility fulfillment on borrowers’ greenwashing behavior and its mechanisms, which helps reveal the quality and effectiveness of banks’ green finance practices. Second, it advances the research on corporate greenwashing by shifting the focus from strategic green information disclosure and pre-loan project packaging to post-loan misappropriation of credit funds. Finally, it enriches the research on the evaluation of green finance policy effects, from the perspective of moderating effects. This paper examines how green finance-related regulatory policies moderate the inhibitory effect of banks’ environmental responsibility fulfillment on greenwashing in green loans, and provides micro-level empirical evidence from bank credit management for regulatory authorities to further improve the external environment for banks’ green credit governance.

In recent years, the tension between human economic activities and the degradation of the natural environment has become increasingly pronounced. Balancing economic development with environmental protection has emerged as a critical concern for most economies. To address this, targeted climate policies are implemented to establish a green, low-carbon, and circular economic and social system. Green and low-carbon development are pivotal to achieving high-quality growth. Within this transition, commercial banks, as key participants and intermediaries in the financial market, play an essential role in ensuring the efficient allocation of social resources. Consequently, enhancing banks' green responsiveness is not only crucial in the short term for overcoming green project financing bottlenecks and improving the quality and efficiency of real economy transformation, but also represents a necessary long-term pathway for deepening financial supply-side structural reform and strengthening the financial system's resilience to climate change and its capacity for sustainable development.
In proactive response to climate change and to foster a green and low-carbon transition, China has progressively established a climate governance and green finance policy system guided by its “dual carbon” goals. However, the inherent unpredictability of climate change, combined with numerous uncertainties across various stages of climate policy formulation, has significantly heightened the instability of the policy environment. International academic research has demonstrated that climate risks and policy adjustments are increasingly becoming key factors influencing the decisions of financial institutions, particularly commercial banks. Climate risks can propagate through the financial system via bank-firm credit channels, significantly impacting banks' asset quality, performance, and risk-taking, while also creating spillover effects on monetary policy transmission and the effectiveness of macroprudential policies. Regarding climate policy adjustments, scholars have examined their impact on banks' micro-level performance, including their risk-taking levels, liquidity creation, and behavioral decisions. While existing literature provides a crucial foundation for understanding the link between climate policy and bank behavior, certain limitations remain. On one hand, most studies adopt a passive perspective focused on risk aversion, relatively neglecting the proactive, strategic, and complex nature of bank decision-making. On the other hand, the discussion tends to concentrate on core business activities, with insufficient research dedicated to banks' green information disclosure and communication.
Following this research trajectory, this paper empirically examines the impact of climate policy uncertainty (CPU) on banks' green responsiveness using a sample of A-share listed banks from 2012 to 2023. It fills a gap in the literature by shifting focus from macro-level climate risks towards the transmission of climate policy risks. The study reveals two key findings regarding the “actions” and “words” of banks' green responsiveness. Regarding “actions”, CPU is found to inhibit banks' green credit allocation. As for “words”, this paper constructs a theoretical framework based on the additive mechanism of the cost-benefit effect, following Haans et al. (2016). The empirical tests reveal an inverted U-shaped relationship between CPU and banks' green information disclosure. These conclusions remain consistent across a series of robustness checks. Mechanism analysis indicates that CPU suppresses green credit allocation by intensifying information friction between banks and enterprises and by tightening banks' resource constraints. Conversely, the inverted U-shaped effect of CPU on green disclosure operates through changes in banks' allocation of attention resources to green governance. Drawing on the attention-based view, the study finds that management's trade-off between the marginal benefits and marginal costs of attention allocation forms the micro-level cognitive basis driving their disclosure strategies. Furthermore, the paper constructs an indicator of “words-actions deviation” in green responsiveness, discovering that high levels of CPU tend to make banks' green responsiveness prone to “actions lagging behind words”. Heterogeneity analysis reveals that in banks located in areas affected by natural disasters, those with executives possessing environmental protection backgrounds, and those with higher ESG ratings, the inhibitory effect of CPU on green credit allocation is significantly mitigated, and the inverted U-shaped relationship with green disclosure also becomes insignificant. This research holds significant policy implications for stabilizing expectations and enhancing the effectiveness of bank actions during the climate policy transition period. Based on the findings, corresponding policy recommendations are proposed. The government, regulatory agencies, banks, and firms should collaboratively build a climate finance support system characterized by “stable policy expectations, regulatory incentive compatibility, and positive bank-firm interaction”, guiding financial resources to be precisely channeled into green sectors and injecting lasting momentum into high-quality economic development.

As sustainable development gains global consensus, ESG rating agencies have emerged as critical information intermediaries connecting firms with capital markets, with their assessments profoundly shaping corporate reputational capital and financing costs. Meanwhile, deepening supply chain integration increasingly exposes focal firms to ESG-driven reputational contagion from upstream and downstream partners. While existing research has extensively examined direct supply chain ESG risk management strategies, including supplier screening, on-site audits, and compliance standards, scholarly understanding remains limited regarding whether firms strategically adjust supply chain information disclosure as a lower-cost and more covert alternative. This gap motivates our central inquiry: under intensifying ESG scrutiny pressure, do firms strategically reduce supply chain disclosure transparency to sever risk transmission channels?
Two competing theoretical hypotheses frame our analysis. The risk avoidance hypothesis predicts that ESG rating coverage will prompt firms to reduce supply chain disclosure transparency: by restricting the supply chain information available to ESG information stakeholders, firms take advantage of investors' limited attention to weaken the perceived linkage between supply chain ESG incidents and focal firm performance, thereby reducing external reputational pressure. The signaling hypothesis predicts the opposite: firms proactively enhance disclosure to satisfy ESG information demands and signal superior governance capabilities to the market. Which hypothesis prevails remains an empirical question.
We exploit a quasi-natural experiment arising from the staggered coverage of Chinese A-share listed companies by seven major ESG rating agencies, including both domestic and international institutions, between 2007 and 2023. Leveraging the voluntary nature of supply chain disclosure in China, we employ a difference-in-differences framework to identify the effect of ESG scrutiny pressure on supply chain disclosure transparency and examine its real economic consequences.
Our findings consistently support the risk avoidance hypothesis. ESG rating coverage significantly reduces supply chain disclosure transparency, with the effect intensifying as the number of covering agencies increases. Cross-sectional analyses reveal that strategic disclosure is more pronounced when supply chain members exhibit weaker ESG performance, that is, when reputational contagion risk is most acute, and when firms face elevated media attention, which amplifies the potential reputational damage from supply chain ESG incidents. These patterns collectively confirm that firms deliberately reduce transparency to sever the risk transmission chain between supply chain ESG events and their own market standing.
Analysis of real effects reveals an asymmetry in economic consequences. On one hand, reduced supply chain transparency lowers firms' capital engagement in supply chain relationship maintenance, generating near-term cost savings. On the other hand, the information asymmetry created by strategic disclosure may persistently accumulate unresolved uncertainty in capital markets, potentially elevating firms' future stock price crash risk.
This study contributes to the literature in three respects. First, we identify strategic information disclosure as a supply chain ESG risk management approach, extending the existing research framework beyond direct intervention measures to the domain of information management. Second, we document an unintended consequence of soft ESG regulation: while ESG rating coverage is intended to promote sustainability, the scrutiny pressure it generates may paradoxically suppress voluntary information transparency. Third, by introducing an ESG risk transmission avoidance motive, we extend supply chain disclosure motivation theory as a complement to traditional explanations grounded in proprietary costs and related considerations.
These findings carry concrete policy implications. Regulators should further improve the regulatory framework governing supply chain information disclosure. ESG rating agencies should incorporate supply chain transparency as an independent evaluative dimension, monitor disclosure trends dynamically, and cross-validate assessments through diverse channels including regulatory enforcement records. Investors should look beyond ESG rating scores to the continuity and completeness of supply chain disclosure, incorporating anomalous transparency changes into investment decision frameworks as potential early warning signals of latent risk.

Enterprises are the primary drivers of innovation activities in China. Tech-oriented SMEs with high-level technology and strong innovation capabilities are critical driving forces for strengthening the main position of enterprise innovation. However, SMEs have been facing financing difficulties, specifically high financing costs and limited financing channels, which becomes a real dilemma that has long restricted their business operations and growth. In particular, tech-oriented SMEs, characterized by small size, large credit needs, light assets, long time horizons and uncertain profits, typically face more severe financial constraints. In China's bank-dominated financial system, bank credit is the main financing channel for tech-oriented SMEs. Accordingly, commercial banks play a vital role in facilitating the development of technology finance.
The unique difficulty of bank lending to tech-oriented SMEs lies in the adverse selection and moral hazard problems caused by the high level of information asymmetry between these firms and potential lenders. The institutional arrangement of intellectual property rights (IPR) protection is particularly important for the financing of tech-oriented SMEs. However, in theory, it is not clear how strengthening IPR protection affects the pricing of bank loans for tech-oriented SMEs. On the one hand, IPR protection can optimize the intellectual property authorization and confirmation process and increase the disclosure of corporate innovation information. Additionally, it can effectively reduce external infringement risks and the uncertainty of IPR value and future returns. All these factors help alleviate information asymmetry and reduce the risks and loan spreads for tech-oriented SMEs. On the other hand, strengthening IPR protection may cause tech-oriented SMEs to significantly increase their investment in cutting-edge technology research, which entails high risks and uncertainties. Simultaneously, the monopoly rent effect may also weaken the incentives for enterprises to improve their products and technologies. Consequently, these factors may exacerbate operational risks of tech-oriented SMEs and cause banks to require higher risk premiums, ultimately raising their loan spreads.
Using transaction-level loan data from a commercial bank in Shandong province, this paper exploits the exogenous change in IPR protection caused by the implementation of Regulations of Shandong Province on the Protection and Promotion of Intellectual Property. Employing a difference-in-differences strategy, it identifies the incremental effects of IPR protection on bank financing costs for tech-oriented SMEs and its working mechanisms, thereby verifying the vital role of IPR protection in facilitating the development of technology finance. We find strong evidence that tech-oriented SMEs experience significant reductions in the spread of bank loans after the strengthening of IPR protection. Importantly, the positive effects of enhanced IPR protection on loan spreads are more pronounced for unsecured loans and enterprises in industries with fierce competition and complex technology as well as those with strong innovation capabilities. Our results suggest that alleviating the degree of information asymmetry by reducing IPR-related risks is an effective channel through which IPR protection affects the costs of bank financing for tech-oriented SMEs. In addition, the reduction of the financing costs is concentrated in firms with limited access to bank credit and digital credit.
This paper not only builds on and contributes to the related studies on the economic effects of IPR protection and the determinants of financing for innovative firms based on the sample of non-listed tech-oriented SMEs, but also provides empirical evidence based on China's tech-oriented SMEs for the classic information asymmetry and signaling theory and joins a growing literature that documents the importance of institutions for financial development. More importantly, the conclusions of this paper offer policy implications for advancing technology finance by improving IPR protection system, including persistently refine the IPR protection system to transform the IPR institutional strengths into drivers of growth for technology finance, steadily advance IPR finance by strengthening the involvement of financial institutions in the protection, creation and commercialization of intellectual property, deepen the innovation and application of digital technologies to promote the coordinated development of technology finance and digital finance.

Measuring firm-level artificial intelligence (AI) investment is central to understanding technology adoption, productivity upgrading, and market valuation, yet direct accounting measures are limited because most AI inputs are intangible and rarely separated in financial statements. Existing proxies based on annual-report wording, IT hardware spending, or patent output capture only part of the process and can blur real investment behavior. Building on the view that AI adoption is human-capital intensive, this study develops a recruitment-based measure that tracks whether firms are actually allocating resources to AI capability development. The core idea is that hiring demand for AI-related skills reflects concrete investment decisions at the implementation stage, thereby providing a scalable, behavior-based indicator that complements disclosure-based metrics.
To operationalize this idea, we construct a Chinese labor-skill dictionary with 99,463 skills by integrating Lightcast, O*NET, and ESCO and then localizing the lexicon with a BERT-based named-entity-recognition pipeline trained on Chinese job texts. Using skill co-occurrence with four anchor concepts (AI, machine learning, image recognition, and natural language processing), we compute an AI relevance score for each skill, aggregate scores to the posting level, and classify a posting as AI-relatedwhen its average relevance exceeds 0.05. We then define AIRatio at the firm-year level as the share of AI-related postings in total postings. This framework follows a transparent skill-to-position-to-firm aggregation logic and can be extended to other technologies by replacing anchor skills.
The empirical sample combines 7.13 million postings from theZhaopin platform (organized by CnOpenData and matched to listed firms) with annual-report disclosures from CNINFO, AI patent information identified under China National Intellectual Property Administration (CNIPA) rules, and accounting and market variables from CSMAR, covering the period from 2015-2022. We validate AIRatio through three complementary tests. First, benchmark validation shows strong discrimination: firms on the CEIDI Top 100 AI list have a much higher mean AIRatio than other firms (0.0617 versus 0.0097). Second, patent validation shows a significantly positive association between AIRatio and AI patent applications. Third, a capital-market validation based on the release of ChatGPT on November 30, 2022, shows that high-AIRatio portfolios outperform low-AIRatio portfolios in China's event window (form December 30, 2022 to April 1, 2023), and firm-level cumulative abnormal returns are positively related to AIRatio.
UsingAIRatio, we document a substantial increase in AI investment among Chinese listed firms from 0.0023 in 2015 to 0.0157 in 2020, followed by a mild normalization to 0.0130 in 2022. Industry heterogeneity is substantial, with information services and finance leading, while agriculture, mining, and hospitality lagging behind. We then compare investment-based and disclosure-based AI measures and find only a modest correlation with frequent firm-level mismatch, including many cases of high disclosure but low contemporaneous hiring. Mechanism analysis indicates that mismatch is not purely cosmetic: disclosure contains forward-looking signals that predict next-period hiring, and it also captures technology positioning and business-AI relatedness beyond current input. In asset pricing, a disclosure premium is broad, while an investment premium is strongest when disclosure is already high, indicating a disclosure-first information hierarchy.
The study contributes in three ways.First, it introduces a scalable and behavior-based AI investment metric that is less dependent on voluntary narrative disclosure. Second, it clarifies the different information content of disclosure and investment indicators. Third, it provides evidence on how AI adoption in the real economy is transmitted into equity valuation under a major technology shock. Policy implications are to support AI diffusion in traditional sectors through application-oriented incentives, strengthen AI talent training and mobility, monitor widening inter-firm AI gaps, and improve verifiable AI disclosure standards by linking narrative claims to recruitment, R&D, and patent evidence. Future research can extend the framework to non-listed firms, add new input channels such as cloud and compute spending, and develop stronger causal designs for productivity, labor, and competition effects of AI diffusion.

Competition gradually deepens from products to technologies and then to standards. Moreover, the relaxation of market access regulations can lead to systematic changes in the investment and output levels of innovation resources in various technological directions of enterprises. However, the existing literature largely confirms that the relaxation of market access regulations can enhance the overall innovation investment and output levels of enterprises through the mechanism of product market competition.
Relaxation of market access regulations may either reduce or increase the technological diversification of enterprises. Relaxation of market access regulations canfoster diversification when it increases the net benefits of technological diversification, and deter it when it reduces the net benefits of technological diversification. Industry leaders can choose compromise, dominance and control strategies from weak to strong based on their industry dominance. For example, the leading control group may focus on the direction of their advantageous technologies, while the industry followers can choose compliance, avoidance and resistance strategies from weak to strong based on their competitive strength. For example, the avoidance-following group may develop new technology directions. Stronger enterprises may also develop new technology directions, while weaker enterprises may abandon secondary technology directions. These choices will change the technological diversification of enterprises, and they need to adjust the innovation resource allocation in each technological direction to cope with the relaxation of market access regulations.
This paper is based on the data of A-share listed companies in China's Shanghai and Shenzhen stock markets from 2010 to 2020. The negative list system for market access is taken as an exogenous shock, and the multi-period difference-in-differences method is used to test the above logic. The study finds that the negative list system for market access significantly improves the technological diversification level of enterprises in the pilot areas. Enterprises exhibit an increase in research and development projects, research and development investment, the number of technical personnel and inventors, and patents of new IPC categories. At the same time, it reduces the patent output on existing IPC numbers, improves the technological efficiency of enterprises, and significantly increases the degree of enterprises’ participation in standard formulation, especially technical standard formulation, the possibility of using external forces to promote innovation, and the degree of product market competition they face. Moreover, the effect of technological diversification improvement in enterprises with relaxed market access regulations is more pronounced among firms with higher technological uncertainty, wider coverage of inventors' knowledge, higher digitalization of enterprises, and lower financing constraints. Overall, it is conducive to improving the disruptive innovation level and total factor productivity of enterprises.
This study can, from the perspective of technological diversification, refine the research on the innovative effects of relaxing market access regulations, extending research from the overall innovation performance of enterprises to the innovation in each technological direction. Furthermore, from the perspective of competitive heterogeneity, it expands the competitive mechanism of the innovative effects resulting from relaxing market access regulations from product market competition to innovation competition and standard competition.
These findings indicate that the government should not only focus on which technological fields or directions should have their regulations relaxed, but also pay attention to and guide the changes after the relaxation of regulations in these fields or directions towards intended policy outcomes, and take corresponding measures to reduce the negative effects of relaxing market access regulations. Nevertheless, future research must continue to examine how various factors influence different elements of corporate innovation, and further clarify the internal mechanism through which firms adjust their innovation strategies in response to various institutional, competitive, and profit-driven pressures.

Investment is a strategic pillar of high-quality economic development. At present, corporate under-investment in China has continued to worsen. The growth rate of fixed-asset investment across China decreased from 17.3% in 2013 to -3.8% in 2025. From January to December 2025, the purchasing managers' index (PMI) for China's manufacturing sector remained below the 50-point threshold for nine months, with weak effective demand and insufficient corporate willingness to invest exerting dual pressures. The lack of investment may not only cause enterprises to miss growth opportunities, hindering the effective flow of capital to the real economy, but also suppress household employment and income growth, constrain the release of demand potential from the supply-side and further weaken the endogenous power of economic development. Demand-side reform, such as consumption stimulus policies, can stabilize market expectations and boost investment. Nevertheless, to fundamentally address enterprises' core dilemma of inadequate investment capacity due to financing constraints and contractual frictions, it is still necessary to rely on supply-side reforms. These measures can in turn drive consumption upgrading, stimulate corporate investment, and foster a virtuous cycle between market demand and supply, by expanding employment and raising labor compensation. Therefore, unblocking investment bottlenecks from the supply-side perspective has become a critical practical challenge in the current context of stabilizing growth and promoting development.
Bond covenants constitute a core mechanism in bond contracts for balancing the interests of creditors and debtors. In China, bond covenants consist of two types: creditor-protection covenants, which are designed to safeguard creditors’ interests, and debtor-protection covenants, which serve to protect the interests of issuing firms. Owing to the inherently conflicting interests of their respective beneficiaries, the two types of covenants differ fundamentally in their allocation of rights. The former controls agency risk by restricting corporate behavior, while the latter enhances financial flexibility by granting enterprises autonomy. This likely results in differential impacts on corporate investment decisions. Current research predominantly analyzes bond covenants as a single homogeneous group, overlooking the systematic differences between the two types of bond covenants. Meanwhile, existing literature focuses only on single-dimensional aspects such as “investment decision constraints” or “agency risk mitigation”, failing to explain the nonlinear relationship between creditor-protection covenants and investment efficiency.
In conclusion, this paper takes micro-level data from A-share listed firms issuing corporate bonds in China between 2007 and 2021 as the research sample. It classifies bond covenants into creditor-protection covenants and debtor-protection covenants according to the protected parties to investigate the effects and underlying mechanisms of bond covenants on corporate under-investment. First, we establish a dual analysis framework of benefits and costs for creditor-protection covenants along two dimensions: financing cost reduction and decision-making constraints. These covenants can send a positive signal to the market about the sound operation of enterprises, thus reduce bond financing costs and encourage enterprises to invest, by constraining opportunistic behavior of shareholders and managers. However, they can also reduce corporate decision-making flexibility, thereby inhibiting firms’ capacity for optimal decisions and leading to the loss of valuable investment opportunities. Second, we analyze the effectiveness of debtor-protection covenants from two aspects:enhanced financial flexibility and risk premium transmission. These covenants can provide financial flexibility to mitigate under-investment caused by maturity mismatch. However, with the transfer of rights, they may also increase the uncertainty of creditors’ cash flows, further pushing up the bond risk premium and reducing space for corporate investment.
The results are as follows. First, there is a positive U-shape relationship between creditor-protection covenants and corporate under-investment. This means that moderate protection for creditors can alleviate under-investment, like a stepping stone. However, excessive protection becomes a stumbling block, which can weaken the investment willingness of enterprises.These negative effects of creditor-protection covenants on under-investment can be attenuated by a good business environment, effective corporate governance, state-owned ownership and heavy-asset operation. Second, debtor-protection covenants have an aggravating effect on under-investment. The effect can be mitigated when macro interest rates decline or enterprises exhibit weak short-term solvency. Third, the classification research of creditor-protection covenants shows that both preventive restriction covenants and preventive option covenants have a positive U-shaped influence on under-investment, and remedial restriction covenants can significantly stimulate corporate investment.
The policy implications of this study are threefold. First, use bond covenants moderately to stimulate enterprise investment. When formulating bond indentures, enterprises shall adhere to long-term development strategies, comprehensively consider the multi-dimensional impacts of covenants and guard against adverse effects arising from excessive decision-making constraints or risk premiums. Second, enhance the application of quantitative indicators to improve the binding force and enforceability of covenants. Regulatory authorities and self-regulatory organizations should strengthen formatting guidelines for bond covenants to encourage enterprises to establish explicit quantitative thresholds for financial indicators within bond covenants. They should also closely monitor the legal validity and enforcement of such covenants in the market. For vague and dispute-prone general covenants, risk warnings may be introduced as appropriate. Third, continuously promote effective market development and strengthen the protection of creditors' rights. This study finds that a favorable business environment, along with sound corporate governance mechanisms, can significantly mitigate the negative impacts of creditor-protection covenants on corporate investment. Therefore, supporting institutions should be further developed and improved.