Search Results (321)

This work develops a hybrid kernel sparse Gaussian process regression integrated with kernel density estimation (HCSGPR-UQ) to resolve three critical drawbacks of conventional lithium-ion battery State of Energy (SOE) estimators: degraded accuracy under dynamic loads, high computational overhead, and inadequate uncertainty quantification. A composite covariance kernel is built by weighting the radial basis function (RBF) and Matérn 5/2 kernels to simultaneously model global smooth SOE decay trends and local nonlinear fluctuations induced by abrupt current/temperature variations. Inducing-point sparse approximation is adopted to accelerate model inference, while kernel density estimation (KDE) generates nonparametric prediction bounds for quantitative uncertainty evaluation. Validations are carried out on a 75 Ah traction lithium-ion cell across −5 °C to 35 °C under Dynamic Stress Test (DST) and Beijing Bus Dynamic Stress Test (BBDST) cycles. Experimental results reveal that the proposed method yields mean absolute errors (MAEs) of only 0.32% (DST) and 0.38% (BBDST), runs roughly 15× faster than full Gaussian process regression (GPR), and attains a 94.7% coverage probability for nominal 95% prediction intervals. Balancing estimation precision, real-time inference speed and statistical reliability, the proposed framework delivers a viable online SOE estimation solution for vehicle battery management systems (BMSs). Full article

Carbon-intensive firms face growing pressure to develop strong corporate climate risk management (CCRM), yet its firm-level and country-level antecedents remain unevenly understood. Drawing on stakeholder and institutional theory, this study examines three potential antecedents of CCRM: sustainability governance, voluntary climate-membership commitments, and regulatory quality. The data cover 1295 firm-year observations across 43 countries over 2018–2022. Ordered logistic regressions are estimated with lagged independent variables, supplemented by ordered probit, two-step system GMM, sample-selection corrections (Heckman two-step and inverse probability weighting), and sub-sample robustness checks. In the main specification, sustainability governance and regulatory quality are both positively and significantly associated with CCRM; climate membership exhibits a sector-conditional association concentrated in energy and basic materials. Sub-sample analyses reveal that internal governance is the stronger correlate among non-state-owned firms, while among state-owned firms (a sub-sample heavily concentrated in Chinese SOEs), regulatory quality is the stronger correlate. The latter is framed as suggestive of context-conditional institutional substitution rather than as a universal feature of state ownership, consistent with comparative-institutional arguments that strong country-level institutions and firm-level governance can act as functional substitutes. CCRM is highly persistent, suggesting that climate risk management is best understood as a path-dependent organizational capability built incrementally over time. Firms seeking to strengthen CCRM should prioritize establishing a board-level sustainability committee with a clear climate mandate; regulators should treat regulatory-quality reform as complementary to direct climate mandates. Full article

Second- and third-order elastic constants (SOEs and TOEs) are essential parameters for characterizing the nonlinear elastic behavior of metallic materials. However, their determination in small-diameter slender rods remains challenging due to the stringent requirements of existing bulk-wave acoustoelastic and resonant ultrasound methods on the specimen dimensions and measurement conditions. This study proposes a two-stage guided-wave acoustoelastic inversion method for identifying the second- and third-order elastic constants of isotropic metallic rods. A high-accuracy forward model based on the wave finite element (WFE) method is developed to calculate the L(0,1) guided-wave dispersion and acoustoelastic responses under different combinations of elastic constants and uniaxial prestress. The sensitivity characteristics of group velocity dispersion and acoustoelastic coefficients are systematically investigated to provide a basis for objective function construction and test frequency selection. A surrogate-assisted Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is employed to solve the resulting ill-conditioned and non-separable inversion problem. Numerical validations demonstrate that the proposed method can accurately recover both SOEs and TOEs while substantially reducing the computational cost of iterative inversion. Full article

National governments increasingly promote corporate sustainability through approaches ranging from rigid mandates to voluntary market-based compliance. State auditing may offer a middle-ground governance mechanism, but its role in improving Corporate Sustainability Performance (CSP) remains underexplored. China provides a distinctive setting: the China National Audit Office (CNAO), the country’s supreme audit institution under the State Council, audits state-owned enterprises (SOEs). Exploiting staggered CNAO interventions, we apply a staggered difference-in-differences design to 7883 firm-year observations of Chinese A-share-listed SOEs from 2009 to 2022. Government audit exposure is associated with a statistically significant but modest 0.118-point increase in CSP, measured using Huazheng (Sino-Securities) ESG ratings, equivalent to about 0.12 standard deviations. The result is robust to propensity score matching, parallel-trend and placebo tests, ESG-pillar decomposition, and external validation using green patent filings. Information disclosure quality and media attention strengthen the effects, indicating that transparency and public scrutiny amplify government oversight. Effects are more pronounced among non-heavily polluting industries, larger firms, firms with fewer financing constraints, and enterprises located in eastern China. These patterns suggest that audited SOEs respond as cost-benefit-sensitive market participants rather than passive state agents. These findings imply that structured governmental oversight can promote sustainability when complemented by transparency mechanisms and adequate firm-level capacity. Full article

This study examines whether state capital refocusing promotes innovation-driven development by reshaping the scale and technological direction of innovation in state-owned enterprises (SOEs). Using 499 firm-year observations for 42 Beijing municipal SOEs from 2013 to 2024, we distinguish innovation output from innovation allocation across invention-oriented, green, and internationally oriented patents. Two-way fixed-effects estimates show that a one-standard-deviation increase in core business focus is associated with increases of 0.610, 0.468, 0.383, and 0.090 log points in overall, invention-oriented, green, and internationally oriented innovation output, respectively. Refocusing also raises the shares of invention-oriented and green innovation, while leaving the international share unchanged. The main patterns remain broadly robust to alternative measures, additional controls, alternative specifications, and a continuous-treatment difference-in-differences design based on the 2020–2022 SOE Reform Three-Year Action Plan. Mechanism analyses provide evidence consistent with industrial-chain control as an important transmission channel, while heterogeneity analyses show that functional missions, industrial-chain positions, and strategic-sector status condition the effects. The findings indicate that state capital refocusing influences not only how much SOEs innovate, but also where innovation resources are directed, providing firm-level evidence on how public-capital reallocation can support mission-oriented innovation. Full article

Drawing on a panel of Chinese A-share listed firms covering 2007 to 2024, we construct a firm-level measure of climate risk exposure based on textual analysis of annual reports. Employing a three-way fixed effects model combined with endogeneity corrections and a battery of robustness checks, we empirically identify the causal effect of climate risk on the cost of debt, as well as its underlying transmission mechanisms and heterogeneous boundary conditions. Our analysis yields three core findings. First, climate risk exerts a statistically significant and economically meaningful positive effect on the cost of debt, indicating that greater climate risk exposure amplifies firms’ debt financing burdens. Second, the impact operates through two parallel transmission channels. On the one hand, climate risk erodes corporate financial fundamentals by disrupting production and operations and elevating default risk. On the other hand, it damages non-financial reputation by triggering downgrades in Environmental, Social, and Governance (ESG) ratings and weakening long-term financing credibility. Third, the relationship between climate risk and the cost of debt is significantly moderated by firm- and industry-level characteristics: high-quality information disclosure attenuates the adverse financing impact of climate risk, while affiliation with heavily polluting industries strengthens this positive association. These findings remain robust to alternative measures of climate risk and the cost of debt, alternative clustering specifications, high-dimensional interactive fixed effects, and subsample tests with restricted sample windows. To address endogeneity concerns stemming from reverse causality and omitted variable bias, we adopt two complementary identification strategies: using one-period lagged values of the core explanatory variable and conducting instrumental variable estimation via two-stage least squares (2SLS). Estimates from both approaches remain statistically and economically consistent with our baseline results. Further heterogeneity analyses show that the cost-increasing effect of climate risk is more pronounced for firms without ESG fund ownership, non-state-owned enterprises (non-SOEs), and firms located in non-eastern regions of China. Overall, this study provides novel firm-level evidence on the microeconomic consequences of climate risk in emerging economies, develops a dual transmission framework integrating financial fundamentals and non-financial reputation, and offers actionable implications for policymakers, financial institutions, and firms to improve climate risk governance and optimize the financing environment amid the low-carbon transition. Full article

Against the backdrop of escalating financial regulation in China, this study examines how regulatory enforcement intensity affects corporate liquidity risk among A-share listed non-financial firms over 2015–2024. We construct a composite regional regulatory intensity index (Enforce) integrating the frequency and monetary magnitude of administrative penalties issued by local securities regulators and employ firm- and year-fixed-effects panel regressions with the current ratio (CR) as the primary liquidity measure. We find that tighter regulatory enforcement significantly depresses the current ratio, consistent with a compliance-cost channel that constrains short-term debt-servicing capacity. Mediation analysis—conducted separately for each ESG sub-dimension and verified via bootstrap tests—reveals that the corporate governance dimension (G) generates a significant positive indirect effect (consistent partial mediation), the social responsibility dimension (S) generates a significant negative indirect effect (competing partial mediation), and the environmental dimension (E) yields no statistically significant indirect effect. Ownership-type heterogeneity tests confirm that non-state-owned enterprises (non-SOEs) are substantially more sensitive to regulatory tightening than state-owned enterprises (SOEs). Moderation analysis further shows that financial leverage plays a non-monotonic role: the regulation–liquidity effect is negative at low leverage levels and reverses to positive above an estimated threshold (Lev ≈ 0.56). Robustness is established through subsample regressions and a lagged-variable endogeneity test. These findings enrich the institutional finance literature and provide evidence-based guidance for differentiated regulatory policymaking. Full article

This study investigates the asymmetric impact of Data Elements (DE) on Corporate Environmental Performance (CEP) in China, using a sample of 310 A-share listed firms from 2012 to 2021. The results show that DE significantly enhances CEP, with stronger effects observed in State-Owned Enterprises (SOEs) compared to non-SOEs. Additionally, heavily polluting firms are more responsive to DE than lightly polluting firms, indicating that DE has a stronger effect in industries with greater environmental challenges. The study also highlights regional differences, with firms located in areas with stricter environmental regulations experiencing a more substantial improvement in CEP. Mechanism analysis reveals that DE improves environmental performance through optimizing labor force structure, enhancing management efficiency, and alleviating financing constraints. These findings suggest that the impact of DE on CEP is not uniform, and firms should leverage DE more effectively, particularly in high-pollution industries, regulated regions, and state-owned enterprises, to support green development. The study provides valuable insights for policymakers and business leaders aiming to foster a green transformation in China’s economy. Full article

Regional digital–real integration (RDRI) refers to the deep integration of digital technologies into the real economy, through which digital technologies reshape industrial production, organizational processes, and value creation. Amid global industrial restructuring and increasing economic uncertainty, RDRI has become a key driver of high-quality economic development in China. Using A-share listed manufacturing firms, this study constructs a multidimensional index of enterprise resilience (ER) covering resistance, recovery, development, and innovation. It then applies the entropy weight method and the coupling coordination model to examine the impact of RDRI at the city level on ER. The results show that RDRI significantly improves ER, and the conclusion remains robust after endogeneity checks and robustness tests. Moreover, the results show that the positive effects of RDRI are stronger for firms in eastern China, capital-intensive firms, State-Owned Enterprises (SOEs), and high-tech firms. RDRI also enhances ER by optimizing resource allocation, improving firm productivity, and easing financing constraints. The paper provides theoretical insight and empirical evidence for deepening RDRI and strengthening ER. Full article

The startup process of gas turbines driven by the static frequency converter (SFC) exhibits complicated electromechanical coupling characteristics. Conventional simulation methods fail to integrate physical modeling with sequence of event (SOE) data and cannot support co-simulation of multiple startup schemes at the power station level. In this paper, a hierarchical digital twin architecture oriented to gas turbine SFC startup is established to realize intelligent deduction of sequential control and break through the technical limitations of traditional simulations. Relevant waveforms and data of the F-class heavy-duty gas turbine during startup are obtained via the digital twin. The maximum effective value of voltage is 12.07 kV, the maximum effective value of current is 1.6 kA, and the peak output power of the SFC reaches 15.67 MW. The system achieves the rated speed (3000 rpm) within an acceptable start-up duration, demonstrating satisfactory dynamic response. All the above data conform to the preset startup parameters and operation control logic of heavy-duty gas turbines. Full article

Understanding the pricing of climate risks in bond markets is relevant to financial stability. The real estate sector, characterized by geographically fixed and long-duration assets, exhibits high exposure to environmental shocks; yet, empirical matching between specific climate channels and real estate bond pricing remains sparse. This analysis examines the impact of climate risks on corporate bond credit spreads within the real estate sector by constructing three thematic indicators: transition risk (CTRI), chronic physical risk (ChroCPRI), and acute physical risk (AcuCPRI). Initial feature selection via machine learning suggests all three risk categories as predictive covariates for bond pricing. Subsequent regression estimations indicate that climate transition risk and acute physical risk expand credit spreads, whereas chronic physical risk compresses them—with these statistical patterns being more pronounced among state-owned enterprises (SOEs). Mechanism analyses yield threefold insights: first, transition risk elevates spreads by tightening financing constraints and restricting corporate asset growth, a channel concentrated in short-term tranches and low-liquidity firms; second, the counterintuitive spread-compressing effect of chronic risk is localized among firms with lower credit ratings and lower profitability, consistent with institutional climate support frameworks and strategic green adaptations; third, acute physical risk widens spreads by compressing operational cash flows and exacerbating financing friction, particularly for smaller enterprises. These channels align with the structural attributes of SOEs, which are characterized by larger asset scales, superior capital liquidity, and a higher propensity to secure state guarantees. 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

Environmental uncertainty (EU) has become one of the key determinants influencing corporate decision-making, yet the existing literature has not sufficiently explored its effects. Based on the data from Chinese non-financial public companies during the period from 2010 to 2023, we examine the impact of EU on carbon emission. The empirical results show that EU has a significant negative impact on corporate carbon emission. Specifically, a one-unit increase in EU leads to approximately a 9.13 percent decline in carbon emission. Furthermore, we find that EU increases firms’ financing constraints, thereby reducing capacity-utilization and carbon emission. Meanwhile, EU can spur innovation, resulting in active decarbonization. Finally, the finding is more pronounced in non-state-owned enterprises (N-SOEs). The above findings shed light on promoting carbon reduction for policymakers and corporate operators. Full article

The smart cities policy (SCP) is one of the key initiatives driving digital transformation in China, delivering a significant digital impact on enterprises within the region. This paper employs a multiple-period difference-in-differences (DID) model to systematically analyze the impact of the SCP on total factor productivity (TFP) of A-share listed companies in China. The results show that the SCP significantly enhances enterprise TFP. Heterogeneity analysis reveals that the policy has a more pronounced positive effect in regions with stronger intellectual property protection, as well as in non-manufacturing sectors and state-owned enterprises (SOEs). Furthermore, the SCP improves enterprise TFP through various dimensions, such as boosting innovation capacity, optimizing human capital structure, reducing operational costs, and improving investment efficiency. This study provides a solid theoretical foundation and policy recommendations for the further optimization and promotion of the SCP. Full article

The Chinese state-owned enterprise (SOE) reform at the end of the twentieth century displaced millions of workers into a rapidly shifting market. How do displaced workers navigate the coexistence of historical structures, of relations of production and social reproduction, both old and new? This article takes up these questions through two films by director Zhang Meng—Piano in a Factory and Lucky Dog—that center on former SOE workers amidst postsocialist China’s new economic order. In both films, characters navigate life outside the SOE structure by improvising with limited resources and repurposing what remains available, by way of both creativity and constraint. Reading these acts, this article proposes the term labor of making-do to name a practical, creative activity through which subjects navigate limited material conditions, competing subjectivities, and disrupted social relations in the shifting reality. Drawing on embodied socialist skills and memory, making-do as a particular form of labor reproduces subjects in a new historical structure. In the face of the market, it sometimes functions outside market logics. While informed by the historical legacy of the socialist ideology of labor, the labor of making-do is not primarily a nostalgic attempt to restore socialist ideals; nor is it a deliberate resistance to capitalism. Rather, it demonstrates a lived, spontaneous and often unglamorous process by which precarious subjects negotiate sustenance and survival, and through which revised subjectivities are produced. It is through such contradictory and quotidian processes that social reproduction unfolds across coexisting historical structures. Full article