Search Results (163)

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

Against the backdrop of increasingly stringent environmental regulation and increasing uncertainty in supply chain operations, this study examines how environmental penalties affect corporate supply chain resilience. Using Chinese A-share listed firms from 2009 to 2024, this paper constructs a firm-level panel dataset and employs a two-way fixed-effects model to estimate the relationship between environmental penalty intensity and supply chain resilience. Environmental penalty intensity is measured by the annual penalty amount imposed on each firm, while supply chain resilience is captured through an entropy-weighted index reflecting both resistance and recovery capacities. To alleviate endogeneity concerns, this study further uses an instrumental-variable approach based on the interaction between a firm’s one-year lagged penalty amount and city-level thermal inversion days. The results show that environmental penalties reduce corporate supply chain resilience. This negative effect is heterogeneous across firm characteristics and is partially mediated by reduced operational efficiency and crowded-out R&D investment. This conclusion remains robust after replacing the dependent variable, changing the clustering level of standard errors, and excluding observations from the COVID-19 pandemic period. Mechanism tests suggest that environmental penalties weaken supply chain resilience partly by reducing operational efficiency and crowding out R&D investment. Heterogeneity analysis indicates that the negative effect is more pronounced among young firms, non-high-tech firms, and firms located in regions with lower environmental regulation intensity. This study contributes to the literature by distinguishing environmental penalties from broader environmental regulation and by examining their implications for supply chain resilience. The findings also suggest that environmental enforcement should maintain deterrence while improving transparency, predictability, and targeted compliance guidance. Full article

Achieving carbon neutrality requires simultaneous advances in both the quantity and quality of low-carbon technology innovation (LCTI). This paper uses a country–industry–year three-dimensional panel dataset covering 25 EU member states and 39 two-digit NACE Rev. 2 industries over the period 2003–2020 to examine the effects of energy market integration (EMI) on LCTI quantity and quality. An EMI index is constructed based on cross-national energy price dispersion, and the analysis employs Poisson pseudo-maximum likelihood estimation with three-way fixed effects, complemented by a Bartik instrumental variable and double/debiased machine learning as supporting robustness evidence. Results show that: (1) EMI exerts significant positive effects on both LCTI quantity and quality; (2) Mechanism tests reveal that EMI operates through two channels: expansion of energy R&D investment and intensification of cross-border knowledge spillovers; (3) Heterogeneity analysis shows that the promoting effects are concentrated in countries with adequate R&D investment and active energy market competition, and in industries with low emission intensity and low energy intensity. These findings suggest that deepening regional energy market integration constitutes a meaningful institutional complement to conventional low-carbon innovation policy. Full article

This paper examines whether total factor productivity (TFP) in Israeli agriculture has genuinely slowed or declined in recent years, or whether the reported trend is primarily driven by methodological choices, data limitations, and measurement error. We compare two widely used approaches to TFP measurement—those of the Bank of Israel and the U.S. Department of Agriculture (USDA)—which differ in their definitions of output, treatment of inputs, and assumptions regarding factor shares. We reconstruct and refine the underlying datasets, addressing important limitations in the existing measures, including the omission of foreign labor, inconsistencies in agricultural land measurement, and the application of non-representative input shares. Despite data improvements and methodological adjustments, both approaches yield similar qualitative conclusions. Following rapid increase in earlier decades, TFP growth in Israeli agriculture appears to have stagnated or declined since the early 2010s. A decomposition of output growth further indicates that recent production patterns have been driven primarily by greater input intensity per unit of land rather than by technological progress or efficiency gains. As a result, agricultural output has shown little or no net growth over the past decade. We discuss potential explanations for this slowdown, including climate change, the growing reliance on reclaimed and other marginal water sources, and the long-term decline in agricultural research and development (R&D) investment relative to sectoral output. Overall, the findings suggest that the productivity slowdown is real rather than an artifact of measurement and underscore the need for renewed investment in agricultural innovation and climate adaptation to sustain domestic production and strengthen food security. Full article

In the context of global market volatility and the pursuit of sustainable development, improving the financial resilience of manufacturing firms lays a critical foundation for high-quality development of the real economy. To explore the key channels through which ESG practices sustain financial stability amid external shocks, this study selects listed manufacturing enterprises in the Shanghai and Shenzhen A-share markets from 2015 to 2024 as the research sample based on the CSMAR database. It employs the entropy weight method to measure corporate financial resilience, uses a two-way fixed-effects model for benchmark regression, and conducts mechanism tests through mediation and moderation analyses to explore the underlying channels between ESG performance and financial resilience in manufacturing enterprises. The results indicate that improved ESG performance significantly enhances corporate financial resilience, and these findings remain robust after robustness tests and endogeneity treatments. ESG performance primarily enhances the financial resilience of manufacturing enterprises by alleviating financing constraints, increasing R&D investment intensity, and strengthening corporate environmental governance. Heterogeneity tests show that the positive impact of ESG performance on financial resilience is more pronounced in state-owned enterprises, manufacturing enterprises located in Central China, and those in the recession phase. Based on the above conclusions, this paper puts forward targeted suggestions for the government, manufacturing firms, and investors to promote ESG practices and boost financial resilience. Full article

Increasing external uncertainty, supply disruptions, and market volatility have made resilience enhancement increasingly important for sustainable agricultural supply chains. While existing studies mainly examine agricultural supply chain resilience from macro or operational perspectives, limited attention has been paid to how firms’ strategic AI investment reshapes organizational resilience under external shocks. Using panel data on Chinese agricultural-related listed firms from 2010 to 2024, this study examines whether and how strategic AI investment enhances supply chain resilience. Empirical results show that strategic AI investment significantly improves both dimensions of supply chain resilience, namely resistance capacity and recovery capacity. Mechanism analyses indicate that this effect mainly operates through supply diversification, technological innovation, and information transparency. Further analyses reveal heterogeneous effects across supply chain positions, ownership structures, and regional digital development environments. In addition, compatibility analyses show that strategic AI investment not only strengthens supply chain resilience but also improves operational efficiency, R&D investment intensity, and financial stability. Overall, this study highlights strategic AI investment as an important organizational capability for strengthening agricultural supply chain resilience under increasing external uncertainty. Full article