Search Results (1,824)

The goal of this paper was to develop an approach to managing the investment mechanism in an innovative financial economy, which would fit the modern era of global uncertainty. To achieve this, we conducted trend, correlation, and regression analyses of risk management in venture investing in BRICS+ based on statistics for the period of global uncertainty (2014–2025). The compiled econometric model of the effectiveness of risk management in venture investing in the innovative financial economy of BRICS+ amid global uncertainty highlighted differences in approaches to managing the investment mechanism in this economy, depending on the level of risk it entails. In the age of free trade, the approach involved the use of the two tools of risk management of venture investing within the state management of an innovative economy: acceleration of economic growth and energy transition. In the current age of global uncertainty, there is a need for a new approach. It is developed in this paper and involves the use of market management tools: high-tech exports and the export of intellectual property objects. The perspectives of accelerating the development of an innovative financial economy of BRICS+ in the age of global uncertainty include the revision of the approach to the management of the investment mechanism in an innovative financial economy. For this, it is recommended to increase revenues from selling rights for intellectual property objects at a higher rate compared to recent years and to make a transition to an increase in the share of high-tech exports in the structure of industrial exports. The advantages of the proprietary model include the disclosure of the poorly studied experience of developing countries, accounting for global uncertainty (in the world economy), and a larger period of empirical research of the economies of the countries of BRICS+, which encompasses 2014–2025 and ensures a fuller and more precise and reliable interpretation of the dynamics of risks of venture investing and return on the measures of risk management in these countries. Full article

This study investigates the macroeconomic impact of financial corruption and institutional weakness on Pakistan’s economy from 1996 to 2023, addressing a critical research gap in quantifying the simultaneous effects of shadow economy operations and poor governance on economic growth. Grounded in institutional economics theory, the research tested hypotheses that weak control of corruption and a large shadow economy negatively affect GDP growth, while also examining the roles of tax revenue, inflation, trade openness, and foreign direct investment. Utilizing a dual-methodological approach, this study employed multiple regression analysis with stationary testing to ensure robust inference, complemented by Random Forest machine learning with Leave-One-Out Cross-Validation for predictive accuracy and variable importance ranking. The econometric results identified shadow economy size and inflation rate as the most statistically significant barriers to growth, with a one percentage point increase in each associated with 0.32 and 0.08 percentage point reductions in GDP growth, respectively (p < 0.05). Control of corruption and institutional quality showed positive but statistically weaker effects. The machine learning analysis corroborated these findings, ranking shadow economy (31.8%) and inflation (24.5%) as the dominant predictors of GDP growth, with the Random Forest model achieving superior predictive performance (R² = 0.68) compared to traditional linear regression (R² = 0.45). Both techniques converged on the conclusion that formalizing informal activity and stabilizing prices represent the most impactful policy levers for growth enhancement, while institutional quality improvements operate through indirect channels. The findings underscore the urgent need for policymakers to prioritize inflation control through credible monetary policy and to formalize informal economic activity via simplified regulations and anti-corruption measures. This research provides a replicate dual-methodology framework for analyzing institutional economic issues in developing nations with limited data. Full article

Indonesia’s tropical peatlands hold vast carbon stores but face degradation from anthropogenic pressures such as agriculture, logging, and mining. The main objective of this study is to identify the determinants of community willingness to pay (WTP) for agrosilvofishery and to estimate its economic value to support sustainable peatland management. This study surveyed 617 residents of Perigi Village, Indonesia. A structured questionnaire was used to assess smallholder farmers’ WTP for agrosilvofishery models. Using a double-bounded dichotomous choice contingent valuation method and econometric estimation, the findings indicate that higher bid prices reduce WTP; respondents preferred low and medium bids. Overall, most respondents expressed a willingness to participate financially in agrosilvofishery practices. Significant factors influencing WTP include birthplace, income, regular income, expenditure, previous agrosilvofishery experience, experience with droughts or fires, expected profit, and environmental risk perception. Flood variables had no effect, while drought and fire significantly increased WTP. The findings highlight the importance of effective communication strategies and policy design to address perceived barriers and promote the benefits of agrosilvofishery. Full article

Oil remains a strategic input for global energy systems and petrochemical production, while in resource-dependent countries, it also plays a critical fiscal and macroeconomic role. In Mexico, this dual relevance is embodied by Petróleos Mexicanos, whose profitability has been persistently challenged by declining production, high fiscal pressure, and operational constraints. This study examines the dynamic relationship between fiscal burden, operational performance, and net profitability in Petróleos Mexicanos over the period of 2008–2024. Using quarterly audited financial data from institutional disclosures and investor reports, indicators of fiscal burden, operational performance, and profitability were constructed. The empirical strategy adopts a time-series econometric framework based on the Box–Jenkins methodology, combining ARIMA modeling with a transfer function approach to assess both contemporaneous and lagged effects. The results indicate that operational performance—captured by the operating margin before taxes and duties—exerts a statistically significant and persistent influence on net profitability, including delayed transmission effects. Once operational dynamics are explicitly accounted for, the direct impact of fiscal burden on profitability is not statistically confirmed. These findings suggest that fiscal pressure affects profitability primarily through indirect and mediated channels linked to operational performance rather than through an immediate direct mechanism. The study contributes dynamic empirical evidence to the fiscal–operational debate on state-owned oil companies and offers insights relevant for fiscal design, operational management, and the long-term financial sustainability of Petróleos Mexicanos. Full article

Background: The majority of the funding for the New Rural Cooperative Medical System (NCMS) is derived from fiscal subsidies, comprising central transfer payments and local fiscal matching subsidies. Local governments’ strategic behavior in response to central transfer payments may further impact NCMS compensation spending and medical economic risks. Methodology: Accordingly, this paper investigates, from both theoretical and empirical perspectives, the impact pathways through which local fiscal matching subsidies influence the medical economic risks faced by insured rural households, with central transfer payments serving as a moderating factor. This paper constructs a dynamic game framework involving the central government, local governments, and household sectors. It further applies a mediation effect model and related econometric methods to conduct empirical analysis using 87,630 observations from the China Family Panel Studies (CFPS). Results: The results show that, first, local fiscal matching subsidies significantly reduce catastrophic health expenditures for rural households under the income effect of central transfer payments. However, under the substitution effect, the opposite occurs, as local governments adopt non-cooperative strategies in response to central transfer payments. Second, these impacts exhibit regional heterogeneity, with stronger effects in eastern regions, regions with more developed secondary industries, and regions with higher fiscal self-sufficiency rates. Third, local fiscal matching subsidies influence medical economic risks through compensation spending, under the moderating role of central transfer payments. Conclusions: This paper provides a novel perspective on why the NCMS struggles to provide effective protection, thereby enriching the existing literature. Furthermore, it provides policy guidance for fiscal and healthcare reforms in countries with similar contexts to China. Based on these insights, we argue that, during the future integration process of the Basic Medical Insurance for Urban and Rural Residents, clear boundaries should be defined for local fiscal matching subsidies, and the moderating role of central transfer payments should be strategically leveraged. Full article

This study examines the False Reality Bias in treasury management, a cognitive distortion through which small and medium-sized enterprises (SMEs) infer financial stability from salient bank balances while overlooking pending obligations and cash-flow timing. Using a firm-level dataset of 50 Spanish meat-processing SMEs, the analysis develops two behavioral-finance indicators: the Liquidity Misperception Index (PEL), capturing the divergence between salient liquidity cues and effective short-term obligations, and the Liquidity Misconfidence Index (ICEL), measuring managerial overconfidence in liquidity assessments. Results show that 41% of firms overestimate liquidity (average PEL = 1.21), while 40% exhibit excessive confidence (ICEL > 1.3), both significantly associated with liquidity distress. Econometric estimates indicate that firms with PEL values above 1.2 are 4.48 times more likely to experience liquidity crises, even after controlling for bank balance levels. Predictive models are used in an exploratory capacity, achieving classification accuracies above 80% and supporting the robustness of the behavioral signals identified. In addition, AI-assisted cash-flow simulations reduce liquidity misperception by 34.7% (p < 0.01). Overall, the findings provide micro-level evidence that cognitive biases systematically distort SME treasury decisions but can be partially corrected through targeted decision-support tools, offering practical insights for managers, advisors, and policymakers. Full article

The state budget system in Vietnam functions within a cohesive structure that allocates financial resources between central and local governments; nevertheless, substantial disparities in socioeconomic conditions among provinces have resulted in increasing discrepancies in local budget revenue. This study, therefore, examines the impacts of fiscal decentralization policy, land utilization, urbanization, provincial competitiveness index, and human capital on local government revenue. The analysis utilizes quantitative panel-data techniques on a dataset encompassing all 63 Vietnamese provinces and municipalities from 2017 to 2022, totaling 378 observations. Econometric estimation employs pooled ordinary least squares, fixed-effects, random-effects, and viable generalized least squares models, along with diagnostic and robustness checks to mitigate unobserved heterogeneity and error dependence. The findings demonstrate statistically significant correlations between local budget revenue and five studied determinants. However, fiscal decentralization policy exerts the most significant influence on the revenue of the local government budget. The results suggest that enhancing municipal fiscal performance needs more than merely modifying revenue-sharing ratios, with significant ramifications. Full article

This paper addresses the growing sustainability fatigue in advanced economies. By analyzing Amazon’s artificial intelligence (AI) agent strategy as a model for “Rational Sustainability”, the study identifies a self-propagating growth trajectory that reconciles economic rationality with value creation. It provides a theoretical and empirical framework to overcome technological saturation and strategic homogenization in the generative AI era. To ensure methodological transparency, the analysis was conducted through two distinct stages: (i) Techno-econometric analysis (macro-level): Using an empirical dataset of 160 countries (40 advanced, 70 emerging, and 50 developing) from 2014 to 2024, the study utilized regression models to quantify the correlations and elasticities between three key proxies: GDP per capita (Y); the Human Capital Index (HCI), representing Institutional Capacity Building (ICB); and the E-Government Development Index (EGI), representing Endogenous Institutional Evolution (EIE). (ii) Hybrid AI analysis (case study): Utilizing process-tracing research, the paper examines Amazon’s R&D structure and AI agent strategy. This qualitative and structural analysis identifies how Amazon co-evolves EIE and ICB to conceptualize tacit knowledge and operationalize it into a competitive advantage. The findings reveal a marked disruption of the co-evolutionary mechanism in advanced economies, where the elasticity of EGI to GDP has declined since 2019, leading to a withdrawal state. In contrast, Amazon’s model demonstrates that the co-evolution of EIE and ICB creates a self-propagating growth engine. This research concludes that “Rational Sustainability”—grounded in evidence, economic rationality, and clear trade-offs—offers a viable pathway for revitalizing sustainability strategies in mature digital economies. Full article

This paper offers a hybrid forecasting approach that merges a local-level state space Kalman filter with a Long-Short-Term Memory (LSTM) neural network to assess the downside risk of the Botswana Pula versus the US Dollar (BWP/USD). Inspired by the inability of conventional econometric models to capture complex latent structural shifts and nonlinear patterns, our architecure uses a bottom-up hierarchical methodology in which the smoothed level component of the exchange rate is isolated by the Kalman filter and subsequently fed into the LSTM architecture. Three key indicators for assessing downside risk—Maximum Drawdown (MDD), Conditional Drawdown-at-Risk (CDaR), and Downside Deviation—are used to assess model performance across various time-frames (7, 30, 90, 180, and 240 days). As confirmed by Kupiec and Christoffersen’s backtesting processes, the findings show a high degree of alignment between projected and actual values, with negligible downside deviation bias and robust calibration. Moreover, global economic and geopolitical shocks, such as the COVID-19 pandemic, the Russia–Ukraine conflict, and the 2015–2016 Shanghai Stock Exchange crash, are important factors that influence exchange rate volatility, according to explainable artificial intelligence techniques, particularly SHAP (SHapley Additive exPlanations) analysis. Downside risk is also greatly increased by regional currency links, especially the impact of the ZAR/BWP exchange rate. On the other hand, domestic temporal variables, such as week, quarter, and month, have very little impact. These results emphasise how Botswana’s currency rate is structurally vulnerable to external shocks and how crucial it is to include both global and regional considerations in risk analysis. The research concludes that the accuracy and transparency of projections for exchange rate risk significantly improve when practical filtering is combined with deep learning and explainable AI. To improve macroeconomic resilience and guide successful financial risk management plans in emerging market environments, policymakers are advised to employ AI-driven forecasting techniques, enhance regional monetary coordination, and set up real-set learning systems. Full article

Under dual-carbon targets, electric vehicles (EVs) have become central to transport decarbonization, making EV sales a key indicator of market diffusion and policy effectiveness. Despite the growing body of research, studies on EV sales remain fragmented and lack systematic integration. This study provides a structured review of EV sales research published between 2016 and 2025. Based on searches in Scopus and Web of Science, 1518 records were identified, and 194 peer-reviewed journal articles were retained after a multi-stage screening process. Temporal analysis reveals a clear stage-based evolution of EV sales research, with limited publications prior to 2020 and a marked expansion after 2021. The literature is categorized into two main streams: (i) determinants of EV sales and (ii) forecasting approaches. For determinants, a macro–meso–micro analytical framework is developed to organize policy, market, and behavioral factors. For forecasting, quantitative analysis shows that econometric and statistical models remain dominant (54%), while machine learning (18%), behavior simulation (14%), hybrid models (8%), and deep learning (4%) are increasingly adopted. This indicates a gradual shift toward data-driven and model integration approaches. This review offers a structured synthesis of determinant mechanisms and forecasting paradigms, identifies methodological imbalances, and outlines future research directions toward improved multi-level integration and mechanism-based modeling of EV sales dynamics. Full article

In the context of China’s pursuit of high-quality economic development, enhancing agricultural productivity is crucial for ensuring food security and promoting common prosperity. This paper constructs a systematic IV-LP-ACF-SAR econometric framework to analyze agricultural Total Factor Productivity (TFP) growth using panel data from 31 Chinese provinces spanning 2014 to 2023 (n = 341 observations). The framework employs the instrumental variable (IV)-based Levinsohn–Petrin (LP) proxy variable method under the Ackerberg–Caves–Frazer (ACF) system to estimate a Translog production function while addressing endogeneity using multiple spatial weight matrices. TFP growth is decomposed into technical change (TC), technical efficiency (EC), and scale efficiency (SC). A Spatial Autoregressive (SAR) model with Dynamic Common Correlated Effects (DCCE) explores spatial spillover effects and regional heterogeneity. Results show that China’s agricultural TFP remained largely stagnant from 2014 to 2023 with an average annual growth rate of −0.18%, where technical efficiency decline (−0.33% annually) was the main constraint. Technical change remained neutral, while scale efficiency contributed positively (+0.15% annually). Mechanization showed the highest output elasticity (0.99), while fertilizers, pesticides, and labor exhibited negative marginal returns. Spatial analysis revealed significant negative scale efficiency spillovers with regional patterns of “scale synergy in the Northeast/Northwest” and “efficiency synergy in East/North China.” These findings suggest that productivity policy should shift toward a dual-driver model combining efficiency enhancement and optimal scaling, with differentiated regional policies and inter-provincial coordination mechanisms necessary to mitigate negative spillovers and enhance sustainable agricultural growth quality. Full article

This study examines the application of ensemble machine learning methods for identifying and flagging potentially risky transactions in military public procurement in Ukraine, a sector characterized by elevated financial and security sensitivity and limited capacity for comprehensive ex post control. Using an integrated dataset of procurement procedures conducted between 2021 and 2025, enriched with 56 financial, economic, and behavioral indicators of suppliers, the study develops and compares standard logistic and LASSO-penalized regression as econometric benchmarks, Random Forest, XGBoost, XGBoost with SMOTE balancing, and CatBoost classification models. The target variable is defined on the basis of officially detected violations identified through state monitoring. Model performance is evaluated using standard binary classification metrics, with particular emphasis on recall. Model uncertainty and predictive robustness are addressed through partial dependence analysis, temporal stability assessment, and out-of-sample residual diagnostics. The results indicate that the CatBoost model demonstrates the most balanced performance across evaluation measures. Feature importance analysis identifies expected contract value, procurement method, CPV code, and suppliers’ financial capacity as significant determinants of procurement-related risk. The findings provide empirical evidence on the usefulness of risk-oriented machine learning tools in supporting earlier detection and monitoring of irregularities in military procurement. Full article

The real estate appraisal sector is undergoing a profound transformation, shifting from traditional financial metrics toward multidisciplinary approaches that integrate civil engineering, territorial and environmental analysis, and advanced econometric modeling. This evolution addresses the hidden risks in so-called “zombie assets” and supports sustainable investment decisions. The INSPIRE Open Topic explores innovative methodologies to translate qualitative aspects—such as governance, environmental exposure, and social equity—into robust quantitative frameworks for both private and public decision-making. Fuzzy logic has emerged as a key tool for quantifying qualitative judgments, enhancing the transparency and reliability of ESG assessments. Empirical evidence shows that these approaches provide a realistic representation of risk, guide resilient urban strategies, and lay the foundation for scalable, sustainable, and transparent real estate evaluation. Full article

Assessing urban resilience under compound shocks requires observable and comparable process evidence that can inform resilient land governance and cross-jurisdiction planning. Using China’s Pilot Free Trade Zones (PFTZs) as a staged institutional setting, this research examines whether institutional exposure is associated with deviation–recovery trajectories of urban activity during the 2020 COVID-19 shock and whether these associations propagate through spatial spillovers with an identifiable scale profile. Institutional exposure is operationalized by the prefecture-level cities actually covered by PFTZ functional areas. With harmonized administrative boundaries, we construct an annual city-level VIIRS nighttime light (NTL) series for 2013–2024 and treat NTL as an activity-change signal rather than a direct proxy for output. We trace shock deviation in 2020 and subsequent recovery via staged differencing. Spatial interaction frictions are represented by least-cost path distance (LCPD) derived from a multi-source cost surface, which is used to build a gravity-based spatial weight matrix. Estimation relies on the Spatial Durbin Model (SDM), with LeSage–Pace impact decomposition to distinguish direct and spillover effects, complemented by distance-threshold diagnostics to map attenuation patterns. Results indicate persistent clustering within the PFTZ-related urban system. The shock year is characterized by compressed connectivity and fragmented brightening, whereas recovery proceeds in a layered manner with earlier core repair, partial corridor reconnection, and weaker adjustment at the periphery. Spatial dependence in activity change is statistically significant. Associations linked to institutional exposure are realized primarily locally, while structural and scale conditions more readily operate through spatial externalities. Spillovers are most detectable at meso-scales and attenuate gradually across distance thresholds. Overall, the integrated earth-observation and spatial-econometric framework provides replicable geospatial evidence to support resilient land governance and regional coordination under common shocks. Full article

This research examined the long-run effect of climate change on food security in Malawi, Madagascar, Mozambique and Zimbabwe using the Autoregressive Distributed Lag (ARDL) model. The study used nine variables for quantitative analysis using data for the four countries from the World Bank spanning from 2000 to 2023, using two models. The results were validated using the pooled mean group (PMG) estimator. The results from model 1 show that environmental temperature, fertiliser consumption, credit access, age dependency ratio, urbanisation and land size significantly affect the percentage of crop yields. The model 2 results show that all the aforementioned factors, including cereal temperature and yields, have an effect on the prevalence of malnutrition, which was a proxy for food security in this study. Furthermore, the study used the Granger causality test to indicate a unidirectional causality direction from both models’ independent variables to dependent variables. From the econometric analysis conducted, the findings highlight the urgent need for targeted interventions, such as promoting climate-resilient agriculture, expanding access to credit and social protection policies, to enhance nutritional well-being and improve resilience to climate shocks. Full article