This study investigates the influence of smart supply chain management on the Environmental, Social, and Governance (ESG) performance of Chinese manufacturing firms spanning from 2009 to 2022. Our findings reveal a positive association between smart supply chain management and enhanced ESG performance, a relationship consistently upheld across various analytical methodologies. Additionally, we uncover that smart supply chain practices stimulate corporate social responsibility (CSR) disclosure, contributing to heightened transparency and subsequently bolstering ESG metrics within firms. Furthermore, our analysis demonstrates that the positive effect of smart supply chain management on ESG outcomes is particularly pronounced among firms that are operating in less competitive and more environmentally impactful industries, receiving heightened media scrutiny, and influenced by Confucian principles. This research provides actionable insights for firms seeking to advance their ESG initiatives.

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Environmental disclosure in emerging markets remains poorly understood, despite its critical role in sustainability governance. Here, we analyze 42,129 firm-year environmental disclosures from 4,571 Chinese listed firms (2008-2022) using machine learning techniques to characterize disclosure patterns and regulatory responses. We show that increased disclosure volume primarily comprises boilerplate content rather than material information. Cross-sectional analyses reveal systematic variations across industries, with manufacturing and high-pollution sectors exhibiting more comprehensive disclosures than consumer and technology sectors. Notably, regional rankings in environmental disclosure volume do not align with local economic development levels. Through examination of staggered regulatory implementation, we demonstrate that market-based mechanisms generate more substantive disclosures compared to command-and-control approaches. These results provide empirical evidence that firms strategically manage environmental disclosures in response to institutional pressures. Our findings have important implications for regulatory design in emerging markets and advance understanding of voluntary disclosure mechanisms in sustainability governance.

This paper examines the impact of green mergers and acquisitions (M&As) on firms’ transition to green exports. We develop a “Technology-Qualification” theoretical framework and conduct the empirical analysis using a matched dataset of Chinese listed manufacturing firms and customs records. The findings show that green M&As significantly promote firms’ green exports, and this effect remains consistent across a series of robustness test. Mechanism analysis reveals that green M&As promote green exports through two key channels: green innovation spillovers and green qualification spillovers. Further heterogeneity analysis indicates that the positive impact of green M&As on green exports is more pronounced among firms with stronger operational performance, weaker green foundations, and those involved in processing trade. In addition, green M&As not only stimulate green exports but also prevent the entry of polluting products and reduce the exit of green product, thereby driving a green-oriented dynamic restructuring of firms’ export structure. This paper offers micro-level insights into how firms can navigate the dual challenges of enhancing green production capabilities and overcoming barriers to green trade during their transition to green exports.

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.

We evaluate the impact of digital finance on the ESG performance of manufacturing enterprises and whether digital and traditional finance play a complementary or substitute role in promoting the ESG performance. First, we find that developing digital finance can alleviate financing constraints and promote technological innovation, thereby increasing enterprises' investment in environmental, social, and governance, providing sufficient technical support, and improving their ESG performance. Furthermore, digital finance and traditional finance have a direct impact on the ESG performance and further enhance their influence through complementary effects. Therefore, this paper may provide a valuable reference for finance to support manufacturing enterprises' development effectively.

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Using the staggered intercity but within-province deregulation of local banks in China as exogenous variations, we evaluate the effect of banking integration across geographical segmentation on capital misallocation. Based on an administrative data set comprehensively covering Chinese manufacturing firms, we find that for firms with initially high marginal revenue products of capital (MRPK), the integration increases physical capital by 19.3%, and reduces MRPK by 33.1% relative to low MRPK ffrms. Our findings are more pronounced for non-statedowned firms and firms with higher exposure to integrated banks. Integration also significantly increases the responsiveness of firms’ investments to deposit shock on other cities within the same province.

Based on the panel data of listed manufacturing companies in China from 2010 to 2019, the artificial intelligence (AI) index is constructed using the industrial robot data provided by the International Federation of Robotics, and the two-way fixed effect model is used to test the impact of AI on the total factor productivity (TFP) of enterprises. The results show that AI significantly improves the TFP of manufacturing enterprises, and this conclusion remains valid after robustness tests and endogeneity processing. AI promotes TFP by improving the level of human capital and technological innovation, and management and operational levels positively regulate the promotional effect of AI on the TFP of enterprises. Compared with manufacturing enterprises in the central and western regions, AI boosts the TFP of those in the eastern region; compared with non-state-owned enterprises, AI boosts the TFP of state-owned enterprises; and AI significantly boosts the TFP of high-tech and non-high-tech enterprises.

This study examines the effects of the robotic subsidy policy in China’s manufacturing sector. The demand-side subsidy policy aims at encouraging manufacturing firms to invest in robotics by lowering the cost of purchase. Our difference-in-difference analysis reveals distributional impacts of municipality-level robot subsidies on manufacturing firms of different scales. Although the subsidy brings a 14.2% increase in the application of robot patents, the facilitated access to robotics has not transformed into new firm entries. Strikingly, new firm entry decreases by 23.5% after the policy implementation. On the other hand, robot subsidies have increased the revenue, total asset, and employment of larger manufacturing firms by 9.8%, 6.9%, and 6.7%, respectively. To interpret the mechanism, we develop a simplified framework incorporating financial frictions into a task-based model. The model reveals that idiosyncratic borrowing costs lead to an inefficient equilibrium by generally depressing automation adoption and creating automation dispersion across firms. Such ex-ante distortion results in a uniform subsidy disproportionately benefiting firms with better capital access, thus creating a trade-off in terms of efficiency: while the subsidy can enhance overall automation, it simultaneously exacerbates automation dispersion. To quantify the efficiency implications, we embed this simplified model into a dynamic heterogeneous-agent framework, calibrated to the 2010 productivity distribution, financial frictions, and robot density in the industrial sector in China. Our dynamic model reveals that a 20% robot subsidy narrows the gap between mean and optimal automation level by 22% percentage points, while raises automation dispersion by 49%. This results in a 1.23% increase in aggregate output at the cost of a 2.40% decline in TFP. This dynamic model proposes a novel mechanism that automation exacerbates capital misallocation by enlarging asset accumulation dispersion between workers and entrepreneurs. Controlling for this dynamic feedback could enhance the subsidy-induced output gain by an additional 26%