Search Results (1,267)

Gibberellic acids (GAs) are a class of tetracyclic diterpene carboxylic acid compounds produced by green plants, fungi, and bacteria, which have a wide range of applications in agricultural production and food ingredients processing. Owing to the continuously growing market demand, enhancing GA yield has become imperative. The biosynthesis of GAs is a multi-enzymatic synergistic process that can be enhanced through genetic and metabolic engineering strategies. In this review, we first summarize recent advances in GA production by Fusarium fujikuroi. We then highlight key metabolic engineering strategies, including biosynthetic pathway engineering, cluster-specific channeling of geranylgeranyl diphosphate biosynthesis, cofactor engineering, as well as regulatory mechanisms involving nitrogen modulation and histone modification. Finally, we discuss promising approaches for constructing high-efficiency microbial cell factories, such as implementation of the CRISPR/Cas9 system, the application of strong promoters, the development of target-specific technologies for small molecules, and the employment of genome-scale metabolic models. Recent metabolic engineering efforts have achieved GA3 titers of up to 3.16 g/L through multi-target nitrogen regulation strategies, highlighting the potential for further yield improvement. Full article

We re-conceptualize corporate debt default risk (EDF) as an emergent state variable of a coupled financial system and ask how capital-market opening reshapes its equilibrium. Extending the structural credit-risk framework with three interacting subsystem channels—external financing, investment efficiency, and information disclosure—we derive a closed-form result showing that an exogenous increase in liberalization strictly reduces the system-level corporate debt default probability through three complementary channels. We then exploit the staggered roll-out of China’s Shanghai–Hong Kong and Shenzhen–Hong Kong Stock Connect (HSGT) programs as a quasi-natural experiment on a panel of 21,351 firm-year observations over 2011–2023. A difference-in-differences (DID) estimator confirms a significant stabilizing effect on the firm’s market-implied default probability that is robust to an extensive battery of identification and specification checks; mechanism regressions confirm all three model-implied channels. The stabilizing effect is further amplified in firms facing greater environmental uncertainty and greater customer concentration—precisely the regimes in which our model predicts the underlying subsystem coupling to be most fragile. Our findings recast capital-market opening as a system-level intervention that simultaneously re-balances financing, investment, and information subsystems of the financial system, with implications for financial-stability policy in emerging economies. Full article

Against the background of China’s “dual carbon” goals and the continued advancement of the green and low-carbon transformation of resource-based industries, resource-based enterprises urgently need to rely on green innovation to overcome development constraints characterized by high resource dependence, strong environmental pressures, and mounting transformation challenges. Patient capital, with its long-term orientation, stable support, and risk-sharing characteristics, can provide sustained financial backing and governance support for green innovation in resource-based enterprises; however, its underlying mechanism remains to be further explored. Drawing on patient capital theory, this study constructs a “capital–ESG–innovation” analytical framework to examine the impact of patient capital on green innovation in resource-based enterprises and its mechanism of action. Using Chinese A-share listed resource-based enterprises from 2014 to 2023 as the research sample, this study measures patient capital from two dimensions, namely stable equity and relational debt, and conducts empirical analysis through panel regression and multiple robustness tests. The results show that patient capital significantly promotes green innovation in resource-based enterprises, with both relational debt and stable equity playing positive roles. Mechanism tests reveal that ESG performance serves as an important mediating channel through which patient capital promotes green innovation. Further analysis indicates that the level of regional marketization strengthens the green innovation effect of patient capital, and this effect is more pronounced in large enterprises, enterprises subject to stronger media supervision, and enterprises whose executives have higher green cognition. This study enriches the literature on the relationship between patient capital and green innovation and provides empirical evidence for cultivating long-term capital and promoting the green and low-carbon transformation of resource-based enterprises. Full article

Accurate carbon price forecasting is essential for emission trading risk management and low-carbon investment decisions. In existing decomposition-prediction frameworks, secondary decomposition targets are typically selected based on statistical complexity rather than domain-informed causality, and standard Long Short-Term Memory (LSTM)-Transformer architectures discard the cell state that encodes long-term temporal memory. These limitations are particularly pronounced where energy-driven causal structures and regime-switching volatility coexist. This study proposes Causal State-driven Network (CaStNet), an intelligent forecasting framework with two core innovations. A Policy-Causality-guided Residual Secondary Decomposition (PCRSD) module replaces entropy-based criteria with Granger causality to select intrinsic mode functions (IMFs) exhibiting significant energy-carbon causal linkages for targeted variational mode decomposition (VMD). A Cell-State-Driven Dual-function Attention (CSDA) mechanism repurposes the LSTM cell state for simultaneously injecting long-term memory into the Transformer and employing the cell-state differential velocity as a volatility proxy to adaptively regulate Top-k attention sparsity. The Artificial Lemming Algorithm (ALA) globally co-optimizes decomposition dimensions and attention boundaries. A Shapley Additive exPlanations (SHAP)–Local Interpretable Model-agnostic Explanations (LIME) interpretability analysis reveals horizon-dependent driver transitions from short-term autoregressive momentum to long-term energy fundamentals, uncovering threshold nonlinearities in energy-carbon transmission channels. Validation on the Shanghai market (2013–2025) achieves point-forecast RMSE = 0.8326 and R² = 0.9777, outperforming all twelve benchmark models. Cross-market testing on the Hubei market yields R² = 0.9487, and expanding-window five-fold cross-validation on the Shanghai dataset yields mean R² = 0.9704, jointly confirming generalization robustness. Full article

Marine ecosystem degradation may reduce state revenues, increase recovery spending, and weaken fiscal sustainability, yet Indonesia does not yet have a routine governance mechanism that links marine natural capital valuation to fiscal-risk assessment in the State Budget Financial Note. This article develops a governance design, Blue Natural Capital Value at Risk (BNC-VaR), to translate changes in marine ecosystem conditions into fiscal-exposure signals for Indonesian public finance. Ecological condition indicators, such as fish-stock status, coral-reef condition, and mangrove extent, are converted into traceable valuation parameters and then into structured outputs, including fiscal-exposure scenarios, budget-relevance notes, and medium-term fiscal-sustainability readings across revenue, expenditure, deficit, and financing channels. The design treats ecological change as affecting the fiscal position through mediated and disclosable pathways rather than automatic causal effects. It adapts Value at Risk as a risk logic for public fiscal governance rather than as a conventional market-based probabilistic measure. Using theory synthesis and a model-paper approach across six analytical stages, the study produces five design principles, four formal propositions, and a five-component institutional architecture, with the Directorate General of State Assets Management positioned as a valuation custodian. As a conceptual contribution, BNC-VaR offers an operational architecture and implementation roadmap for future empirical testing in Indonesia and other archipelagic or marine-resource-dependent fiscal systems. Full article

This study examines whether stablecoin and Bitcoin transaction volumes in Brazil are associated with domestic macroeconomic conditions. Using monthly data from August 2019 to December 2025, the analysis compares the association of domestic economic activity, proxied by the IBCBr, and the BRLUSD exchange rate with the transaction volumes of stablecoins and Bitcoin reported in the open data records of the Receita Federal do Brasil, the Brazilian Tax Administration. The empirical strategy distinguishes between long-run relationships in log levels and short-run dynamics in log differences, applies Johansen cointegration and ARDL bounds testing, and estimates an error correction model for stablecoins. Global crypto market controls are used as complementary measures to assess the contrast between the two assets. The results show that stablecoin transaction volume is positively and significantly associated with Brazilian economic activity in both long-run and short-run specifications and that this association is not explained by global stablecoin activity. The exchange rate is associated with stablecoin volume mainly through a structural long-run channel rather than immediate monthly effects. In contrast, Bitcoin transaction volume does not exhibit a robust association with domestic economic activity and is instead more strongly associated with global Bitcoin volume. The findings suggest that stablecoins may act as a domestically embedded macro-financial instrument in Brazil. This finding reflects transactional demand, liquidity management, or demand for dollar-linked assets, whereas Bitcoin behaves as a more globally oriented and comparatively detached digital asset. This distinction has important implications for policy, as stablecoins may have stronger implications for monetary transmission, digital dollarisation, and financial intermediation than Bitcoin-focused analyses indicate. Full article

This study investigates the long-run and short-run relationships between capital market development, foreign direct investment, trade openness, and real GDP per capita in Romania over 2003–2024, employing the Autoregressive Distributed Lag (ARDL) bound testing approach, complemented by lag-augmented VAR Granger-causality analysis and a comprehensive set of diagnostic and stability tests. The bounds tests strongly reject the null of no cointegration, confirming a long-run relationship that remains robust under finite-sample critical values. The causality analysis demonstrates a supply-leading mechanism from the equity market to real economic activity, while economic growth in turn Granger-causes both market liquidity and trade openness, pointing to demand-following dynamics for these channels. The analysis shows that foreign direct investment, market liquidity, and trade openness exert positive and significant short-run effects; yet their long-run coefficients are negative, significantly for FDI (foreign direct investments), capturing an asymmetry between immediate output gains and durable structural contribution that is characteristic of emerging European economies. The error-correction term is positive, demonstrating that real GDP (gross domestic product) per capita does not adjust back toward the long-run relationship in the conventional sense, but, instead, it behaves as a forcing variable that leads the financial and trade channels rather than being led by them. All in all, the findings describe an economy with functional short-run transmission channels, but limited long-run structural anchoring, with direct relevance for Sustainable Development Goals 8 and 17 and Romania’s ongoing OECD accession. Full article

Under China’s dual carbon goals, low-carbon transition risk has become an important source of corporate sustainability risk and climate-related financial risk. This study treats the carbon emissions trading pilot (CETP) as a quasi-natural experiment and uses panel data of Chinese A-share listed firms from 2006 to 2024 to examine whether carbon trading reduces corporate low-carbon transition risk (CTR). CTR is measured as the sensitivity of firm stock returns to return shocks from a stranded-asset portfolio, thereby capturing market-implied exposure to high-carbon asset revaluation risk. The results show that the CETP significantly reduces corporate CTR. Economically, the fully controlled DID coefficient is about one tenth of the standard deviation of CTR, indicating a meaningful decline in firms’ exposure to stranded-asset shocks. The conclusion remains robust after using alternative CTR measures, shortening the sample period, applying staggered DID based on actual pilot launch years, controlling for province-level time-varying factors and province-specific trends, controlling for concurrent green policies, conducting placebo tests, applying PSM-DID, and retaining the instrumental-variable test. Mechanism tests provide evidence consistent with a carbon performance channel. Evidence on capital expenditure is interpreted cautiously because Capex is a broad proxy for investment intensity and asset adjustment rather than a direct measure of green upgrading. Heterogeneity analysis shows that the risk-reducing effect is stronger among non-state-owned firms, high-tech firms, and firms located in eastern China. These findings suggest that carbon pricing can serve not only as an emissions-reduction instrument but also as a mechanism for mitigating climate-related financial risk. Full article

Over-the-counter (OTC) hearing aids offer a cost-effective solution for adults with mild-to-moderate hearing loss, whereas their market penetration remains low despite regulatory support and technological advancements. In this paper, we analyze OTC hearing aid patients’ behavior and experiences of integrating them into sustainable healthcare logistics across China. As an econometric toolkit, we use multiple linear regression models for the purpose of predictive modeling and hearing aid policy formulation. The focus is on patient awareness and attitudes towards the role of corporate social responsibility (CSR) in shaping sustainable purchase decisions. This study also examines the challenges related to insufficient Chinese hearing-impaired patients’ understanding of some trusted sales channels (TSCs) like digitization-driven platforms. The outcomes show that CSR initiatives increase purchase intention. In contrast, we observe that reliance on non-traditional TSCs may be associated with failure to build patient confidence in comparison with hospital-recommended brands of OTC hearing aids. From a strategic healthcare economic perspective, we highlight the potential of the healthcare market with a growing aging population and rising demand for equitable hearing solutions. The transferable framework provides insights for policymakers regarding healthcare accessibility and affordability in China to bridge the gap between market offerings and patient needs. Full article

This study examines how client ESG performance affects supplier total factor productivity (TFP) from a knowledge spillover perspective, using matched client–supplier–year data for Chinese A-share listed firms from 2010 to 2023. The results show that client ESG significantly improves supplier TFP; specifically, a one-unit increase in client ESG is associated with an average increase of approximately 8.3% in supplier TFP. These results remain robust across a series of robustness tests. Mechanism analysis indicates that client ESG enhances supplier productivity through three knowledge spillover channels: technical assistance, management sharing, and innovation induction. Heterogeneity analysis further shows that this positive effect is more pronounced in long-term cooperative relationships, among clients with stronger market power, for state-owned suppliers, and when clients and suppliers have aligned ownership structures. Further analysis shows that the positive effect of client ESG persists for at least three fiscal years and is more pronounced in industries characterized by lower volatility. These findings suggest that policymakers and firms should strengthen supply chain ESG governance to promote knowledge spillovers and improve productivity. Full article

Household livelihood resilience is an important foundation for sustainable rural development, particularly in rural areas exposed to climate risks, market fluctuations, and demographic pressures. This study examines whether participation in new rural collective economic organizations (NRCEOs) is associated with household livelihood resilience and explores the mechanisms and contextual heterogeneity underlying this association. Using survey data from 837 rural households in Jiangxi Province, China, we construct a multidimensional livelihood resilience index and apply ordinary least squares, propensity score matching, and lasso Regression, together with an exploratory IV-2SLS sensitivity analysis. The results show that participation in NRCEOs is positively associated with household livelihood resilience, and this relationship remains stable across alternative estimation strategies. Mechanism analysis provides evidence consistent with two pathways: land, labor, and capital allocation support the resource-allocation pathway, while production efficiency and agricultural income support the agricultural production pathway; the sales channel estimate remains inconclusive because online sales are rare in the sample and statistical power is limited. Overall, the findings indicate that the relationship between collective economic participation and household livelihood resilience varies across mechanism dimensions and local development contexts. Full article

Vertically linked commodity markets—global futures, regional spot, and farm-gate prices—transmit information through directed cross-market channels whose strength varies with latent volatility regimes. Standard deep learning forecasters absorb both the directed cross-market dependence and the regime dependence of intrinsic-mode-aligned latent components into shared model weights, with no explicit architectural mechanism that exposes either as an inspectable structure. This paper proposes HVB-RA, a modular framework that combines two such mechanisms with a per-tier Variational Mode Decomposition and bidirectional LSTM backbone: (i) a directed cross-market attention layer in which the upstream-to-downstream topology is supplied from domain knowledge and the time-varying upstream-source attention intensities at the farm-gate tier (the regional-spot tier, with a single upstream key, reduces algebraically to a fixed residual upstream fusion) are extracted from data, and (ii) a regime-informed modal-weighting layer that mixes two trainable softmax weight profiles over IMF-aligned latent components through a filtered Markov-switching state probability fitted in a separate stage. An auxiliary post hoc projection enforces an exact linear constraint defined by long-run sample-mean ratios across tiers; the paper does not claim that these descriptive ratios are cointegrating relations or equilibrium coefficients. The framework is evaluated on three tiers of daily natural-rubber prices spanning 2038 trading days, against three external benchmarks (random walk, ARIMA(2,0,2), and an exogenous-only LSTM) and a contemporary neural hierarchical-interpolation forecaster (NHITS). Root mean squared error is reported per tier-horizon cell; a decision-aware income-smoothing metric quantifies the operational value of $h=5$ farm-gate forecasts under a 5-day selling rule; and a within-method comparison evaluates the marginal contribution of the auxiliary constraint projection. On the present single-regime test window, HVB-RA attains a lower point error than the contemporary NHITS baseline at every tier-horizon cell, while no method—including HVB-RA—improves on the random-walk floor at most cells; the regime-conditional components of the architecture are not identifiable because every calibration and test origin is classified as a high-volatility regime by the trained Markov-switching model. The paper contributes to machine learning and knowledge extraction by demonstrating how time-varying upstream-source attention intensities at the farm-gate tier and regime-dependent latent-component-weight profiles—two forms of latent structure typically absorbed into model weights—can be exposed as explicit, inspectable, and individually testable components of a multi-tier forecasting architecture, and by providing a reproducibility package documenting the conditions under which each component is expected to be identifiable. Full article

Environmental, Social, and Governance (ESG) performance has become a central criterion for evaluating corporate sustainability. Yet the empirical relationship between ESG and financial performance remains contested, especially in emerging markets where institutions are evolving. This study examines how ESG performance relates to corporate financial performance among Chinese A-share listed companies and tests whether corporate innovation functions as a transmission mechanism. Using a balanced panel (2015–2023), we combine System Generalized Method of Moments (System GMM) with a non-parametric Bootstrap mediation procedure. We decompose ESG into environmental, social, and governance dimensions and distinguish between innovation input (R&D investment) and innovation output (patent generation). The results indicate a positive directional association between overall ESG performance and return on assets (ROA), but the direct financial effect is primarily driven by the governance dimension. Environmental and social performance do not show robust direct effects. However, ESG significantly promotes corporate innovation, especially patent output. Bootstrap mediation results confirm that patents represent a robust and universal channel through which ESG contributes to financial performance, while the R&D pathway is more conditional. The findings also indicate ownership heterogeneity between state-owned and private enterprises. By distinguishing between innovation input and output, this study explains how ESG practices may be translated into economic value in an emerging market context distinct from advanced economies. Full article

The rapid expansion of e-commerce into rural areas has emerged as a prominent policy initiative aimed at promoting digital economic development and facilitating structural transformation in developing countries. However, empirical evidence on whether and how such policies affect rural labor markets remains limited. This paper investigates the impact of China’s E-commerce into Rural Areas Policy on non-agricultural employment among rural residents. We develop a theoretical framework in which an individual’s utility derives from commodity consumption, leisure, and social recognition associated with e-commerce participation. The model predicts that reducing the investment price of e-commerce activities—the primary intervention of the policy—increases non-agricultural labor supply through both direct and indirect channels. Using panel data from the China Family Panel Studies (CFPS) spanning 2010 to 2022 and exploiting the staggered rollout of the Rural E-commerce Demonstration Pilot as a quasi-natural experiment, we employ a difference-in-differences (DID) approach to estimate the policy’s causal effects. The results show that the policy marginally significantly increases the probability of non-agricultural employment by approximately 1.1 percentage points, an effect that remains robust after parallel trend tests, placebo tests, and controlling for concurrent policies. Mechanism analysis reveals that the policy operates through two distinct channels: promoting regional industrial development, particularly the growth of the tertiary sector, and generating village-level peer economic incentives that encourage participation through social networks and information spillovers. Heterogeneity analysis indicates that the policy’s effects are larger for males, younger individuals, those with higher educational attainment, and residents of poor counties or regions with e-commerce potential. These findings contribute to the literature on digital development and rural labor markets by providing rigorous causal evidence and identifying the mechanisms underlying the policy’s effectiveness. The results also offer practical insights for policymakers seeking to leverage digital technologies for rural employment generation and structural transformation. Full article

Digital financial inclusion (DFI) can broaden financial access for households historically underserved by traditional finance, yet the magnitude of its effect on household income and the channels through which it operates remain underexplored. This study examines the causal effect of DFI on household income and its mechanisms. We match the Peking University Digital Financial Inclusion Index (2011–2023) with household microdata from the China Family Panel Studies (2012–2022) at the prefecture-year level, yielding an unbalanced panel of 32,789 household-year observations across 105 prefecture-level cities. Using two-way fixed-effects models with instrumental-variable estimation to address endogeneity, we find that DFI significantly raises household income, a result robust to alternative measures, sample restrictions, and a difference-in-differences design. The effect operates through two channels, improved credit accessibility and greater participation in risky assets, and these gains are not accompanied by higher household leverage or financial fragility. The effects are stronger where local financial and market institutions are more developed, indicating a capability-dependent form of inclusion. By clarifying the causal effect and mechanisms linking DFI to household income, the study offers practical guidance for policies promoting sustainable, broadly shared income growth through digital finance. Full article