Search Results (168)

Driven by the strategies of high-quality development and manufacturing transformation and upgrading, China’s economy is gradually shifting to an innovation-driven growth model. Empowering firms to lift innovation quality through new-generation artificial intelligence has become a core priority for industrial development and policy support. This paper adopts unbalanced panel data of A-share listed manufacturing firms from 2012 to 2023 and employs the Double Machine Learning model to systematically investigate the impact and transmission mechanisms of new-generation artificial intelligence on manufacturing firms’ innovation quality. The results reveal that new-generation artificial intelligence can significantly boost manufacturing firms’ innovation quality, and this core conclusion remains valid after a series of robustness tests. Mechanism verification confirms that new-generation artificial intelligence promotes the upgrading of firms’ human capital structure, deepens firms’ digital transformation, and raises firms’ R&D investment intensity. These three channels work synergistically to indirectly empower the upgrading of firms’ innovation quality. Heterogeneity analysis verifies that the innovation quality improvement effect of new-generation artificial intelligence exhibits prominent stratified differences across regions, industrial sectors, and environmental regulatory scenarios. Specifically, the empowerment effect of new-generation artificial intelligence (GAI) is significantly stronger for samples from environmental pilot cities, Southern Coastal, Eastern Coastal, and Northern Coastal regions, as well as high-tech manufacturing firms. In contrast, its positive driving effect is weak or insignificant for samples in the middle reaches of the Yellow River and Northwest China, along with non-high-tech industries. This paper improves the theoretical analytical framework for artificial intelligence empowering micro-firm innovation and enriches relevant research on digital technologies and corporate innovation. It provides a theoretical basis and practical implications for China’s manufacturing sector to break through innovation bottlenecks via intelligent technologies and achieve comprehensive high-quality innovative development across the manufacturing industry. Full article

Innovation systems are commonly analysed through indicators of investment, performance, technological capacity, and research output, while less attention is paid to the composition of the research workforce. This article asks whether workforce composition can be understood as a structural dimension of national innovation systems and whether it is systematically associated with their capacity configuration. Using comparative 2023 data for 28 European and selected non-European national R&D systems, the study combines correlation analysis, a multidimensional capacity coordinate, and quadrant-based configurational mapping. Innovation-system capacity is operationalised through R&D intensity, innovation performance, and R&D expenditure per researcher. Workforce composition is measured by the share of women among researchers and is treated as a separate aggregate dimension rather than as a comprehensive measure of substantive inclusion. The results indicate a recurrent negative association between innovation-system capacity and the relative representation of women among researchers. Higher-capacity systems are more frequently positioned in configurations characterised by lower female representation, although cases combining high capacity with higher representation demonstrate that the pattern is not universal. The study does not establish causality, temporal development, or a deterministic trade-off. Sectoral, disciplinary, institutional, educational, and cultural factors may contribute to the observed cross-national differences. The article contributes to innovation-systems research by integrating aggregate workforce composition into systems-level analysis and by demonstrating how coordinate-based configurational mapping can reveal relationships that remain obscured in composite rankings. Full article

Using panel data on Chinese A-share listed firms from 2002 to 2024, this study examines the impact of the Air Pollution Prevention and Control Action Plan (the Air Ten policy) on firms’ cross-regional investment. We identify the policy effect using a multi-period difference-in-differences (DID) model with firm and year fixed effects. The findings remain robust across a series of tests, including the parallel trend test, propensity score matching combined with DID (PSM-DID), placebo tests, alternative variable specifications, additional control variables, and high-dimensional fixed effects. The results show that the Air Ten policy significantly promotes firms’ cross-regional investment. Mechanism analyses suggest three potential channels through which the policy operates: enhancing firms’ green innovation capability, improving human capital quality, and facilitating regional industrial upgrading. The resulting industrial upgrading creates a more favorable external environment for firms to expand their investment activities. Heterogeneity analyses show that the positive effect is more pronounced among firms with higher R&D intensity, weaker internal control, and those located in eastern China. We find no evidence that the policy-induced cross-regional investment is driven by pollution relocation. Instead, the results suggest that firms’ cross-regional expansion is more likely to reflect a capability-driven mechanism than regulatory arbitrage. This finding is broadly consistent with the pollution halo perspective and provides further support for the Porter hypothesis. Overall, this study offers new insights into how environmental regulation reshapes corporate capital allocation and geographic expansion strategies. Full article

The global energy sector is undergoing rapid and, in many respects, irreversible transformation driven by the convergence of digital disruption, sustainability mandates, and shifting investor expectations. Technologies such as artificial intelligence (AI), blockchain, and digital twin systems are fundamentally reshaping energy operations and strategic decision-making, while ESG governance quality and renewable energy adoption have emerged as two of the most consequential determinants of corporate financial competitiveness and equity valuation. Despite growing practitioner and regulatory interest in these dynamics, limited empirical evidence exists on how ESG governance, renewable adoption, and digital disruption jointly influence financial performance and environmental outcomes across multiple sectors simultaneously. This study addresses that gap using panel data from 26 large-cap US-listed firms across five sectors over 2015–2022 (N = 208 firm-year observations for Revenue/Market Cap/ROA models; N = 91 for the CO₂ model). A multi-method econometric framework is employed, comprising Fixed Effects and Random Effects panel regression with Hausman specification testing, Difference in Differences quasi-experimental analysis, and sequential OLS path analysis with HC3 robust standard errors. Three of four hypotheses are supported. ESG governance quality generates a significant market capitalisation premium of approximately 10–14% per unit Bloomberg ESG Score improvement, after controlling for firm size and R&D intensity; no significant revenue channel effect is found once firm size is properly accounted for. Renewable energy adoption shows a marginal association with market capitalisation at the 10% significance level (FE β = 0.019, p = 0.086; RE β = 0.016, p = 0.077), suggesting capital markets may price clean energy adoption as a forward-looking signal. ESG governance quality drives within-firm CO₂ emission reduction substantially more powerfully than renewable energy quantity alone, with the Fixed Effects estimator identifying a governance-led eco-efficiency mechanism. Firm profitability functions as a cross-model financial capacity moderator, enabling simultaneous ESG investment and environmental improvement. The findings carry direct implications for corporate managers, institutional investors, and policymakers aligned with SDG 7, SDG 9, and SDG 13. Full article

Focusing on China’s information technology policy system, this study investigates the spatio-temporal evolution of policy–reference patterns and their implications for policy diffusion. Using 33,702 policy documents and 3150 citation links from the PKULaw database, we construct a policy citation network and combine social network analysis with ordinary least squares (OLS) and geographically and temporally weighted regression (GTWR). The results show that observed policy–reference relationships remain strongly characterized by top-down administrative coordination, while the policy–reference network has expanded to involve a broader range of regions and increasingly diverse interregional connections. These changes are accompanied by increasing citation intensity and textual differentiation, while pronounced regional disparities persist. The regression results reveal that citation-based diffusion indicators are associated with regional development capacity. Economic and industrial foundations are positively associated with policy–reference outcomes, whereas the associations of urbanization and R&D investment vary across different diffusion dimensions. GTWR further reveals that these associations vary across space and time. By integrating policy citation networks with spatio-temporal analysis, this study advances understanding of policy-system evolution under centralized governance and informs differentiated digital policy coordination. Full article

Against the backdrop of the accelerating integration of the digital and real economies, exploring how digital-–real technology convergence enables corporate decarbonization and green upgrading represents a critical pathway. This development pathway is conducive to promoting the high-quality growth of industry while aligning with China’s strategic goals of carbon peaking and carbon neutrality. Based on panel data from Chinese A-share listed manufacturing enterprises during 2012–2023, and employing a fixed-effects model, this study empirically investigates how DRTC influences firms’ carbon performance, as well as the mechanisms through which this effect is transmitted. In addition, this research explores the intermediary function of green innovation and further investigates the contingent effects of R&D investment and financing constraints. The results show that DRTC contributes significantly to improving corporate carbon performance, with the validity of this conclusion supported by multiple robustness examinations. Green innovation acts as a partial mediator, channeling a portion of DRTC’s carbon-reduction benefits. R&D investment amplifies these positive effects, whereas financing constraints create a notable drag on DRTC’s effectiveness. Heterogeneity analysis adds nuance, showing that DRTC’s positive impact is substantially more pronounced in large-scale firms and asset-intensive enterprises. By clarifying the internal mechanisms and limiting conditions that underlie DRTC’s improvement of corporate carbon performance, this study enriches the interdisciplinary literature by linking research on the digital economy with discussions on green and low-carbon development. Full article

The impact of chain-leading (hub) firms’ forward-looking information disclosure on supply chain collaborative innovation remains underexplored. Based on panel data of Chinese A-share manufacturing listed firms from 2014 to 2024, this study adopts large-scale textual analysis to construct a time-varying indicator measuring hub firms’ forward-looking disclosure, and systematically examines its innovation spillover effects and internal mechanisms. The results show that hub firms’ forward-looking disclosure is positively associated with a significant increase in the R&D investment intensity of supply-chain node firms. This spillover effect is negatively moderated by node firms’ information absorptive capacity, reflecting a typical information substitution effect. Heterogeneity tests further reveal that the spillover effect is more pronounced among node firms with larger scale, higher supply-chain network centrality, and stronger supply-chain relationship specificity (proxied by higher customer concentration). In addition, such innovation spillovers are positively associated with improved corporate financial performance, and this profit-conversion effect is more pronounced among high-leverage firms, which is consistent with an implicit endorsement mechanism that helps alleviate financing constraints. Combining empirical evidence with industrial governance practice, this paper expands the theoretical boundary of supply chain collaborative innovation and provides actionable recommendations for optimizing information disclosure rules and formulating differentiated industrial innovation policies. Full article

Green innovation performance (GIP) in new energy enterprises emerges from complex interactions among technological, organizational, and institutional resource subsystems, yet existing research predominantly applies linear, single-factor approaches that fail to capture this systemic complexity. Drawing on the Resource-Based View (RBV) and systems thinking, this study employs fuzzy-set qualitative comparative analysis (fsQCA) on a sample of 54 Chinese A-share listed new energy enterprises—spanning wind power, solar power, hydrogen energy, energy storage, and new energy equipment manufacturing—observed over the 2019–2023 period, to examine the configurational pathways through which these firms achieve high GIP. Green patent grants serve as the outcome measure, and six conditions spanning three resource subsystems are considered: digital transformation and R&D intensity (technological subsystem), firm size and ownership structure (organizational subsystem), and government subsidies and carbon emission performance (institutional subsystem). Three key findings emerge. First, none of the six conditions is individually necessary for high GIP (all consistency scores below 0.90), indicating that high GIP reflects combinations of resources rather than a single driver. Second, the six sufficient configurations identified collapse into two distinct pathway clusters: a “SOE digital-empowerment-driven” cluster, in which digital transformation combines with R&D investment, government subsidies, or organizational scale within state-owned enterprises, and a “resource–capability synergy and substitution” cluster, in which scale resources, R&D investment, and policy support combine with or substitute for digital transformation regardless of ownership. Third, digital transformation appears in five of the six pathways, indicating that it functions as a key—but not universal—enabling element whose effectiveness depends on its alignment with other system components. Beyond confirming that multiple, equally valid resource combinations lead to high GIP, this study’s principal contribution is to embed RBV within an explicit systems framework, showing how technological, organizational, and institutional resources interact as subsystems of a single socio-technical system, and to translate the resulting configurations into differentiated, pathway-specific guidance for enterprises and policymakers navigating the low-carbon energy transition. Full article

As China’s new energy vehicle (NEV) industry shifts from scale expansion to sustainable competition, enterprise risk is increasingly shaped by price pressure, innovation investment, operational efficiency, and cash-flow quality. Conventional financial early warning models based on static accounting ratios are limited in capturing how such risks emerge and transmit within NEV business ecosystems. This study develops an AI-driven risk governance framework that combines a digital twin-inspired state representation, interpretable machine learning, Shapley additive explanations, and competitive scenario simulation. Using annual data from 2021 to 2025 for twelve listed Chinese NEV automakers, we construct forty-eight enterprise-year observations and predict next-period high-risk status from current-period financial, operational, and competitive state vectors. Logistic regression is used as a transparent benchmark, while XGBoost serves as the main nonlinear learner. The results show that NEV risk identification requires the joint consideration of profitability, R&D intensity, cash-flow quality, asset utilisation, and liquidity, rather than reliance on a single accounting indicator. Logistic regression provides stronger temporal stability, whereas XGBoost achieves higher recall and area under the receiver operating characteristic curve in cross-validation. SHAP results identify return on assets, R&D intensity, operating cash-flow ratio, fixed asset turnover, and current ratio as the leading contributors to model predictions. Scenario simulations reveal asymmetric resilience: low-risk firms can absorb moderate competitive shocks, while high-risk firms remain locked in elevated risk states. This study provides a practical decision-support framework for identifying risk drivers, evaluating competitive shocks, and improving risk governance in sustainable NEV business ecosystems. Full article

This study examines the comparative trade performance of India’s pharmaceutical industry vis-à-vis leading global pharmaceutical exporters, particularly the United States, Germany, and Switzerland, over the period 2005–2024, using sectoral (HS 30) and product-level (HS 3001–3006) trade data. The study employs a multi-index analytical framework integrating symmetric and weighted measures of revealed comparative advantage to evaluate export competitiveness and import dependence while accounting for product-weight distortions. The findings reveal that India maintains a positive and strengthening trade balance in the pharmaceutical sector, primarily driven by low-cost generics and bulk formulations. However, India continues to lag behind advanced economies in innovation-intensive segments such as biologics and patented formulations. The Weighted Revealed Export Advantage (WRXA) estimates indicate that India’s export competitiveness remains relatively limited and is still largely dependent on cost efficiency rather than innovation-led exports. In contrast, the Weighted Revealed Trade Advantage (WRTA) values consistently demonstrate strong competitiveness at the sectoral level. This study argues that India’s long-term export competitiveness cannot rely solely on generic pharmaceuticals and, therefore, requires strategic investments in biologics, advanced APIs, research and development, and innovation ecosystems. The findings further emphasise the need for policy support aimed at R&D financing, active pharmaceutical ingredient (API) self-sufficiency, and export diversification to transform India into a globally competitive hub for complex generics and high-value pharmaceutical products. Full article

A sustainable low-carbon transition requires policy that curbs emissions while accelerating green technological innovation. The EU Carbon Border Adjustment Mechanism (CBAM) imposes carbon costs on high-emission exports; yet, how it shapes exporters’ green innovation remains poorly understood. We develop an open-economy Environmental Dynamic Stochastic General Equilibrium (E-DSGE) model embedding three CBAM transmission channels: cost-driven (higher carbon-intensive production costs), awareness-driven (firms’ forward-looking expectations), and information-enhancement (lower green R&D financing costs). The model decomposes CBAM’s green-innovation effects by jointly endogenizing forward-looking green R&D investment and carbon disclosure quality in general equilibrium. Calibrated to Chinese data and solved in Dynare 7.0, the model is simulated over forty quarters. Under the baseline calibration, simulations suggest a CBAM shock raises green R&D investment by approximately 6.5% at its peak and the green technology level by approximately 12.5% by quarter 40, while brown emission intensity falls by approximately 10%. Within this window the policy carries a net welfare cost of approximately 0.34% of steady-state consumption, concentrated in transition-period labor disutility, with most gains accruing later. Combining CBAM with R&D subsidies modestly reduces the within-window welfare cost and raises long-run green technology. Realizing this sustainability potential requires policy credibility, carbon-information infrastructure, and coordinated innovation support. Full article

This study assesses the impact of research and development (R&D) tax incentives on the intensity of business R&D expenditure in Central and Eastern European countries and derives policy implications for Ukraine. The analysis uses a balanced panel of 11 new EU member states covering the period 2010–2023, based exclusively on officially published data. The main method is a two-way fixed-effects panel regression with country and year effects, clustered standard errors, alternative lag structures, heterogeneity analysis and robustness checks. The baseline specification shows that a 0.10 increase in the implicit subsidy rate is associated with an approximately 0.10 percentage point increase in Business Expenditure on R&D (BERD) to GDP two years later. However, the effect is strongly conditional on absorptive capacity: it is statistically significant in countries with a developed R&D base and practically absent in countries with a weak base. The estimated private R&D additionality ratio falls below one under the stated assumptions, indicating that the estimated effect does not imply more than one unit of additional private investment per unit of fiscal support. For Ukraine, the results support a gradual introduction of R&D tax incentives combined with structural measures that strengthen human capital, institutional capacity and links between science and business. Full article

Under the “dual carbon” targets, using green finance to support renewable energy use is an important way to reduce extreme climate risks. This study builds a balanced panel dataset of 271 Chinese cities from 2010 to 2021. We measured the level of Green Finance (GF) and renewable energy utilization (RE). Employing two-way fixed effects, the Spatial Durbin Model (SDM), and the Heterogeneous Spatial Autoregressive (HSAR) model, we systematically examine the promoting effects, transmission mechanisms, spatial heterogeneity, and economic–environmental consequences of GF on RE. The empirical results reveal that GF significantly enhances RE and generates pronounced positive spatial spillovers. Mechanism analysis indicates that R&D investment and environmental regulation serve as the primary transmission channels. The promotion effect is more pronounced in the eastern and central regions, as well as in areas with higher R&D investment and stricter environmental regulation, whereas the spatial spillover effect is particularly evident in coastal regions. Further consequence analysis demonstrates that GF contributes to reducing conventional energy intensity, improving green total factor productivity, and alleviating extreme climate events. Building on these findings, this study proposes spatially differentiated and sustainability-oriented policy strategies to advance China’s energy transition and foster coordinated economic and environmental sustainability. Full article

With the digital economy advancing at a fast pace, digital transformation plays a pivotal role in reinforcing firms’ innovation capability and promoting high-quality development. This study analyzes Chinese non-financial publicly listed firms on the A-share market over the period 2009–2023. Based on text mining of annual reports, this study constructs an index capturing digital transformation and empirically evaluate its impact on innovation output with firm and year fixed effects. The estimates suggest that digital transformation meaningfully increases firms’ innovation output; the inference is unchanged when applying instrumental-variable approaches and conducting extensive robustness checks. Mechanism analysis reveals two parallel channels: (1) the R&D investment mechanism, characterized by improvements in R&D intensity, capitalization rate, per capita efficiency, and investment growth; (2) the governance environment mechanism, reflected in enhanced internal control, improved information disclosure quality, and strengthened audit supervision. Once firms are stratified by characteristics, the estimated positive effect of digital transformation is most pronounced for firms with low financial constraints, large size, eastern locations, and state ownership. This study identifies both direct and indirect mechanisms linking digital transformation to innovation and highlights how firm- and region-specific features condition the magnitude of this effect, thereby offering empirical implications for corporate digitalization strategies and policy design. Full article

Accurate assessment of carbon storage dynamics and their driving factors is important for ecological sustainability and land management on the Loess Plateau under China’s dual carbon goals. In this study, the InVEST and PLUS models were integrated to evaluate carbon storage changes from 2000 to 2020 and simulate future carbon storage patterns for 2030 under four development scenarios, including natural development (ND), rapid development (RD), cropland protection (CP), and ecological protection (EP). In addition, the XGBoost-SHAP framework was employed to identify the dominant drivers and nonlinear response relationships controlling spatial variation in carbon storage. During 2000–2020, ecosystem carbon storage across the Loess Plateau generally increased, rising from 5.780 Pg to 5.893 Pg. Spatially, carbon storage displayed a pronounced pattern characterized by higher levels in the southeast and lower levels in the northwest, aligning with forest–grassland restoration belts. Scenario simulations showed that EP produced the largest carbon storage gain, with total carbon storage projected to reach 5.962 Pg in 2030. In contrast, RD reduced carbon storage to 5.858 Pg because of intensive construction land expansion. XGBoost-SHAP results identified net primary productivity (NPP) as the most influential factor controlling spatial variation in carbon storage, accounting for 57.3% of the total explanatory importance, whereas soil erosion (SE) exhibited a strong negative effect on carbon storage. Population density (POPD) also exerted a negative effect, whereas gross domestic product (GDP) showed positive contributions in economically developed counties. These findings enhance understanding of the spatial response characteristics of carbon storage under environmental gradients and human disturbance across the Loess Plateau. They further provide scientific support for differentiated ecological management and regionally adapted carbon mitigation planning. Full article