Search Results (468)

Spatially explicit socioeconomic data are critical for regional analysis, yet they remain scarce at subnational scales in many African contexts. This study presents a transparent and reproducible open-data framework to generate high-resolution gridded Gross Domestic Product (GDP) and derived socioeconomic and energy indicators. The approach combines gridded population and Night-Time Light (NTL) through the LitPop method to downscale provincial GDP to 1 km resolution for the Inkomati-Usuthu Water Management Area (IUWMA) in South Africa. The resulting GDP dataset is subsequently used as a spatial proxy to disaggregate compensation of employees, gross capital formation, fixed capital stock, net exports, gross operational surplus and sectoral Total Final Energy Consumption (TFEC). Results show strong consistency with official provincial GDP totals, with deviations ±0.4% after 2017. In 2024, LitPop allocated 4.26 billion constant 2015 USD to the IUWMA, equivalent to 16% of Mpumalanga’s GDP, compared with 47.3% under area-based allocation and 51.3% under population-based allocation. These differences reveal the strong influence of spatially concentrated industrial and energy-intensive activity. The workflow provides a scalable and replicable solution to generate coherent gridded socioeconomic datasets for WEF Nexus modelling, although estimates remain proxy-based and sensitive to NTL-related biases, particularly the overrepresentation of highly illuminated industrial assets and the underrepresentation of less luminous activities. Full article

Climate change and low-carbon transition policies affect sustainable development by changing firms’ financing costs and investors’ capital allocation. This paper investigates whether and how climate-related information is priced in China’s equity market, focusing on firm-level carbon intensity and exposure to climate policy uncertainty (CPU). First, univariate-sorted portfolio tests confirm the existence of a carbon premium, as firms with high carbon intensity earn significantly higher average returns. However, the unconditional relation between CPU exposure and stock returns is insignificant. Bivariate-sorted portfolios reveal a strong interaction between the carbon premium and the CPU premium. The carbon premium is higher for firms with high exposure to CPU, whereas a significant and negative CPU premium appears among low-carbon firms and, in sector-level tests, is concentrated in non-energy firms. Further analysis demonstrates clear differences between energy and non-energy sectors, which may be attributable to cash flow risks and uncertainty in growth options. The findings contribute to climate-related asset pricing and sustainable finance research by showing that transition-risk pricing depends on the interaction between carbon exposure and policy uncertainty. Full article

Corporate balance sheets report aggregate equity and liability totals but conceal the internal allocation of financing sources across asset categories—an identification problem that conventional econometric methods cannot resolve without additional parametric assumptions. This paper develops a maximum entropy (ME) panel estimator to recover two latent scalar parameters: x ∈ (0,1), the share of equity capital directed toward long-term asset financing, and y ∈ (0,1), the corresponding debt allocation share. Grounded in maximum entropy principle, the estimator selects the unique parameter vector that satisfies the mean-level balance-sheet constraint while maximising joint Shannon entropy—the least-biassed solution consistent with observable data. The closed-form logistic representation yields a scalar Lagrange multiplier λ*, interpreted as a financing pressure index, recoverable via bisection in at most 21 iterations at tolerance ε = 10⁻⁵. Building on the ME estimates, we introduce a continuous matching alignment index M* = x* − y* that measures the degree of compliance with the financial matching principle along a continuous spectrum rather than as a binary categorisation. Applied to a ten-firm, cross-sectoral panel spanning Technology, Finance, Energy, and Automotive sectors over an observation window spanning 2001 to 2025 (with firm-specific subperiods reflecting differences in IPO dates and data availability), the framework reveals substantial heterogeneity in latent financing flows: equity allocation shares range from 30.1% (NVIDIA) to 75.1% (ExxonMobil), while debt allocation shares span 37.1% to 77.5%. Across the panel, only Meta exhibits substantial positive matching alignment, while Microsoft, ExxonMobil, Apple, and Tesla show only very slight differences that fall within the neutral band, and the remaining firms show varying degrees of structural departure from the matching benchmark; the thresholds used to summarise these descriptive labels are interpretive aids rather than re-imposed binary criteria, and the substantive ranking of firms along M* does not depend on the specific threshold values adopted. The ME solution’s entropy H(x*, y*) and the normalised diversification index D(x*, y*) describe allocation balance under the estimator’s information–theoretic criterion rather than independently observed firm complexity; in the present sample, the cross-firm ordering of these values is not recovered by firm size, leverage, or sector classification alone. These findings, based on a ten-firm case-study panel with time-invariant allocation parameters, should be interpreted as descriptive patterns of the present sample rather than statistically validated regularities. They provide a theoretically rigorous and computationally tractable identification of unobservable corporate financing flows, with potential implications for capital structure theory, financial risk assessment, and balance sheet analysis that would benefit from validation on larger and more representative samples in future work. Full article

This study examines the effect of low-carbon policy intensity on corporate financialisation using a panel of Chinese A-share-listed firms from 2012 to 2022. We find that low-carbon policy intensity increases corporate financialisation. The mechanism analysis indicates that this relationship is primarily driven by the short-term speculative motive rather than the “reservoir” motive. Greater stringency of low-carbon policies crowds out real investment and increases firms’ propensity to allocate idle funds to financial assets. Heterogeneity analysis further indicates that command-and-control policy instruments exert a significantly stronger positive effect on corporate financialisation than other policy components. We find that the positive association between low-carbon policies and corporate financialisation is more pronounced among firms in non-polluting industries, non-state-owned enterprises, and firms with higher financing constraints, compared with firms in polluting industries, state-owned enterprises, and firms with lower financing constraints. The effect is also stronger in regions with higher levels of financial development and a larger share of the secondary sector in GDP than in other regions. The findings are of significance in understanding the relationship between low-carbon policy intensity and corporate financialisation, providing inspiration for policy-makers to guide the capital back to the real economy. Full article

The rapid growth of the sharing economy has improved resource utilization in car-sharing, yet it has also sharpened market competition and diversified user demand. A persistent obstacle is the low coordination efficiency between asset-heavy operating companies and traffic-driven platforms, whose misaligned objectives waste social resources. This paper uses differential game theory to analyze their dynamic coordination strategies and benefit allocation mechanisms. The Nerlove–Arrow model captures the evolution of brand goodwill, while the company’s decisions on station layout, vehicle dispatch, and pricing, together with the platform’s advertising investment, form the core decision variables in a two-party game framework linking the asset side and the traffic side. Compared with the non-cooperative Nash equilibrium, the cooperative mode removes the double marginalization effect, strengthens the investment incentives of both parties, and raises the system’s steady-state goodwill and total profit, achieving a Pareto improvement. To ground the cooperative framework in rigorous theory, we supply a verification theorem confirming that the linear candidate value functions satisfy the Hamilton–Jacobi–Bellman equations over the entire admissible state space. A formal proof of instantaneous rationality ensures that neither party falls into a cooperation trap on the horizon $[0,T]$, and the asymptotic stability of the steady-state goodwill trajectory is established. We further endogenize the revenue-sharing coefficient through a generalized Nash bargaining model that admits asymmetric bargaining structures, and introduce a Stackelberg leadership benchmark as a third comparative regime. Sensitivity analyses with respect to the discount rate and user heterogeneity confirm the robustness of the findings. A dedicated discussion section bridges the gap between idealized parameterization and data-driven calibration, describing practical pathways via A/B testing, user churn metrics, and econometric estimation of demand parameters. The results offer a scientific decision-making reference for strategic cooperation in the car-sharing industry. Full article

Urban Unmanned Aerial Vehicle (UAV) logistics operations are frequently constrained by the intersection of limited battery endurance and dynamic ground traffic. When mobile depots are delayed by congestion, onboard drone fleets experience extended idling periods, leading to constrained sortie generation and reduced asset utilization. To address this bottleneck, this paper introduces a traffic-predictive multi-UAV dispatch framework for deterministic day-ahead planning under modeled urban operating conditions. By coupling a count-derived macroscopic speed surrogate learned using XGBoost with a Particle Swarm Optimization (PSO)–Mixed-Integer Linear Programming (MILP) optimization architecture, the framework synchronizes mobile depot trajectories with forecasted low-congestion windows and pre-allocates endurance-feasible parallel aerial sorties. Controlled computational experiments across 30 synthetic routing instances demonstrate the potential value of this approach within the stated modeling assumptions. Compared to baseline clustered deployments, the traffic-aware framework raises mean fleet utilization from 0.43 to 0.63—a 46.2% relative improvement driven by temporal compression of the mission window rather than an absolute increase in flight hours. Furthermore, the proposed framework reduces total mission completion time by 69.87% relative to the conventional truck-only baseline, while achieving a 29.58% incremental gain over static speed drone deployments. These findings suggest that incorporating predictive ground traffic information into day-ahead UAV scheduling can improve modeled fleet efficiency; however, field validation with measured route-level speeds, real delivery demand, and operational constraints remains necessary before deployment-level claims can be made. Full article

The inclusion of the cement industry into China’s national carbon emissions trading system in 2025 has fundamentally altered the compliance environment for high-emission enterprises, transforming carbon allowances from passive regulatory instruments into dynamic assets whose management directly affects financial performance. We develop a multi-scenario carbon asset management decision model tailored to the intensity-based benchmarking mechanism adopted by the national market. The model centres on the quota surplus-deficit variable EA4, which is computed from enterprise-level emission intensity relative to the industry benchmark, and decomposes the management problem into sequential selling and buying subproblems linked by coupled decision boundaries. A systematic parameter framework is constructed, and the model is applied to two cement enterprises—Enterprise A, a leading producer with a clear allowance surplus, and Enterprise B, a mid-tier producer operating near the benchmark boundary—through historical backtesting over the 2024–2025 period. Three principal findings emerge. First, the intensity benchmarking mechanism creates a dual-leverage effect whereby a 1.4% improvement in emission intensity (from 0.8112 to 0.8000 t/t) increases the quota surplus by 27%, a nonlinearity not captured by conventional compliance-cost models. Second, the model-driven strategy outperforms traditional experience-based approaches by 36.8% (baseline scenario, +95.20 vs. +69.58 MRMB) and 37.3% (risk scenario, −44.55 vs. −71.08 MRMB), with the improvement rate remaining consistent across both enterprises, suggesting that trading timing outweighs instrument selection in determining compliance cost outcomes. Third, dynamic CEA–CCER allocation captures an incremental 2.33 MRMB through the exploitation of a transient price inversion, a gain invisible to single-instrument strategies. Sensitivity analysis confirms that the relative advantage is robust to carbon price variations (±30%) and CCER offset caps (2–10%), while emission intensity and carry-over allowances represent the most consequential parameters for strategy direction, with EA4 crossing zero near the industry benchmark (I ≈ 0.85). The framework provides actionable decision support for cement and other high-emission enterprises navigating the unified carbon market, and contributes a quantitative methodology to the emerging field of environmental management accounting. This study contributes to Sustainable Development Goal 13 (Climate Action), Goal 7 (Affordable and Clean Energy), and Goal 9 (Industry, Innovation, and Infrastructure) by providing operational tools for decarbonisation in carbon-intensive industries. Full article

The transition of thermal power systems toward lower-carbon electricity raises a critical strategic question: whether to rely on cross-border electricity imports or reactivate domestic nuclear capacity under supply constraints. This study examines the trade-offs between these alternatives within a sustainable finance framework. A contingent-claim model is developed in which a life insurer provides long-term financing to a biomass-energy supplier, a thermal power plant, and a nuclear power plant operating under carbon-pricing regulation. The framework links electricity-market decisions with financial risk valuation, allowing the joint effects of biomass utilization, carbon regulation, electricity imports, and nuclear-security risks to be evaluated. The results show that biomass integration and tighter carbon regulation reduce short-term profitability in thermal generation but support long-run decarbonization. Cross-border electricity imports improve system flexibility and reduce operational volatility, strengthening the financial position of thermal producers. In contrast, nuclear-security disruptions significantly increase default risk for nuclear assets, reflecting their exposure to operational and regulatory uncertainty. By integrating energy-transition strategies with contingent-claim valuation, the analysis highlights the role of financial intermediation in shaping investment incentives and risk allocation in the electricity sector. The findings suggest that coordinated policies combining market integration, low-carbon transition strategies, and stable financing mechanisms can enhance system resilience. Full article

As a non-rivalrous, replicable, and non-consumable production factor, data offers conditions for resource-efficient value creation, and the conversion from scattered data resources into measurable data assets sits at the center of firm competitiveness and sustainable allocation of digital factors. How artificial intelligence supports this conversion, and how executive cognition shapes its strength, are taken up within a framework drawing on the resource-based view, dynamic capability, and upper-echelons theory. Using 24,251 firm-year observations from Chinese A-share listed firms over 2012–2022, panel fixed-effects estimation yields a positive association between AI and data asset formation, stable across instrumental-variable estimation, propensity score matching, Heckman correction, and alternative measures of both variables. AI deepens data mining capability through stronger research and development investment and widens data-carrying capacity through expanded digital infrastructure, with the two channels opening up the relationship. Cognitive flexibility improves the fit between AI and shifting business scenarios, while cognitive complexity supports balanced allocation of technological resources across competing constraints; both characteristics strengthen the main association. The pattern is more pronounced among state-owned enterprises and firms in eastern and central regions, with industry differences less clear-cut. The findings inform differentiated policy design for sustainable digital development in emerging-market settings. Full article

This study proposes a hybrid portfolio optimization framework integrating financial network analysis, Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and GA for asset allocation in BIST. The empirical analysis focuses on constituent firms within the BIST 30, BIST 50, and BIST 100 indices using daily stock market data covering the period 2000–2025. Financial network centrality indicators and technical analysis variables were employed to identify structurally influential assets and model nonlinear investment decision dynamics under market uncertainty. The ANFIS framework was utilized to capture complex relationships between technical indicators and portfolio allocation decisions, while Genetic Algorithms optimized portfolio weights under return maximization and downside-risk minimization constraints. To reduce overfitting risk, Principal Component Analysis (PCA) and K-fold cross-validation procedures were implemented during model training. The proposed framework was additionally evaluated using out-of-sample backtesting over the 2021–2024 period and compared against benchmark portfolio strategies, including Equal Weight and Minimum Variance portfolios. Empirical findings indicate that the ANFISGA framework achieved superior risk-adjusted performance, higher Sharpe and Sortino ratios, and lower maximum drawdown during volatile market conditions. The study contributes to the portfolio optimization literature by integrating financial network indicators with adaptive fuzzy decision systems and evolutionary optimization techniques within an emerging market context. The proposed framework is intended primarily as an adaptive portfolio decision-support system rather than a purely predictive forecasting model. Full article

This research paper addresses the persistent problem of environmental opacity in sustainable debt markets, exposing a structural flaw that incremental regulation alone cannot remedy. This study advances a radical, physics-grounded solution that fundamentally transforms environmental reporting from voluntary self-disclosure to instrumentally verified, quantum-limited measurement. The method integrates three mutually reinforcing analytical frameworks: the design of Quantum-Verified Green Bonds (QVGBs), the application of cryptographic quantum key distribution (QKD), and the formal apparatus of financial contract theory. The principal conceptual innovation resides in a three-tiered architectural structure—physical, cyber–physical, and financial—that collectively shifts the epistemological foundation of sustainable finance from institutional norms and managerial discretion to the immutable constraints of physical laws. By deploying nitrogen-vacancy (NV) centers in diamond as primary sensing arrays at industrial emission points, this system achieves environmental parameter estimation bounded by the Cramér–Rao quantum limits, a precision ceiling governed by Quantum Fisher Information, not corporate policy. This architecture acquires high-fidelity, real-time data on CO₂ and CH₄ flux densities, transforming atmospheric pollutant concentrations into physically attested, contractually actionable financial variables. A QKD layer further leverages the no-cloning theorem to render any upstream data manipulation physically self-revealing through statistically detectable elevations in the Quantum Bit Error Rate (QBER). The central contribution of this work lies in the algorithmic coupling of bond coupon structures to these quantum-verified state variables, enforced via smart contracts, thereby converting “environmental misinformation” from a viable managerial strategy into a strictly dominated equilibrium outcome. These findings carry substantial implications for bridging the “trust gap” in green financial markets, a gap sustained by chronically undervalued transition risks and deficient accountability mechanisms in air quality and carbon reporting. The QVGB framework stabilizes green asset prices by subordinating capital allocation decisions to physical constraints rather than political or institutional ones, thereby establishing a new ontological baseline for the global sustainable debt market. Full article

Power grid technical renovation projects are implemented through project-based supply chains involving equipment procurement, logistics coordination and on-site construction under market, delivery and carbon constraints. Their final cost is jointly affected by engineering quantities, supplier behavior, lead-time uncertainty, material price volatility and sustainability requirements. Existing studies usually emphasize technical parameters and direct expenditure, whereas supplier reliability, green procurement, carbon intensity and procurement contingency effects are only indirectly incorporated. This study develops a dynamic Bayesian model for carbon-adjusted cost forecasting and investment priority support in power grid technical renovation projects. Based on 800 anonymized project-level records, a random forest is first used to identify informative engineering, supply chain and sustainability variables. These variables are then organized in a Bayesian network that links observed evidence, intermediate cost nodes and the carbon-adjusted cost target. A dynamic evidence-weighting mechanism updates posterior cost beliefs as supplier, logistics, market and carbon information become available during implementation. Compared with static Bayesian inference, XGBoost, an improved BPNN and GRA-based benchmarks, the proposed model yields lower MAE and RMSE. Ablation and scenario analyses further show that supply chain and sustainability variables improve both predictive performance and decision interpretability. The results provide a quantitative basis for budget control, green procurement adjustment, contingency allocation and sustainable asset renewal prioritization in energy enterprises. Full article

The assumption that highly correlated financial assets share identical risk profiles often overlooks crucial distributional asymmetries. This study introduces a Goodness-of-Fit (GoF) framework to evaluate stochastic symmetry and structural alignment of equity returns. Moving beyond linear correlation, we apply non-parametric GoF tests—Kolmogorov–Smirnov, permutation-based Anderson–Darling, and Epps–Singleton—complemented by Energy Distance metrics, Extreme Value Theory (EVT) for 1% and 5% tail asymptotics, and robust L-moments to quantify tail asymmetry. We analyze major stocks against market indices and sectoral ETFs using ARMA-GARCH filtered innovations to isolate IID components. Our findings reveal a significant decoupling between correlation and stochastic symmetry; highly correlated assets frequently exhibit tail asymmetry and structural drift. Energy Distance decomposition isolates shape-driven deviations from scale-driven volatility. Furthermore, hierarchical clustering categorizes assets into distinct risk profiles, bridging structural divergence and left-tail risk. A 1000-iteration bootstrapped backtest shows that integrating our GoF framework with tail-risk penalties improves risk-adjusted performance, evidenced by superior Sharpe ratios (outperforming 80.3% of random allocations). In conclusion, high linear correlation does not guarantee distributional symmetry. The proposed framework offers deeper insights into asymmetric asset behavior than conventional second moment metrics, providing a robust tool for portfolio risk management under non-Gaussian market conditions. Full article

Regulatory sandboxes have evolved from specialised FinTech tools into broader mechanisms of regulatory experimentation. As artificial intelligence (AI) applications become embedded in credit decisioning, payment-fraud detection, identity verification, crypto-asset compliance, customer-facing advice and supervisory analytics, sandbox design increasingly affects how legal and institutional responsibility is allocated among regulators, participating firms, technology vendors and users. This article provides a comparative doctrinal and institutional analysis of accountability and liability in AI-related financial regulatory sandboxes. It clarifies the relevant AI modalities, distinguishes accountability (answerability and enforceability during sandbox participation) from liability (contractual, tort/product and regulatory/public law responsibility after harm), and maps framework-level safeguards across the European Union, the United Kingdom, Singapore, Norway and Hungary. The analysis does not seek to measure sandbox effectiveness empirically. Instead, it examines how publicly available legal and regulatory materials structure the allocation of duties before, during and after sandbox testing. The article shows that sandboxes generally do not operate as liability shields. Their legal significance lies in reallocating ex ante accountability duties—documentation, disclosure, monitoring, human oversight and exit planning—while preserving baseline liability rules. An Accountability and Liability Protocol is proposed to clarify roles, protect baseline consumer rights, support evidentiary traceability and connect sandbox learning to enforceable post-sandbox obligations. Full article

Against the backdrop of the deep integration of the “Dual Carbon” goals and the digital economy, whether digital asset allocation can improve corporate Environmental, Social, and Governance (ESG) performance has become an important topic of academic concern. Taking all Chinese A-share listed firms from 2013 to 2024 as the research population, this study obtains a final panel sample of 29,329 firm-year observations after excluding financial and insurance firms, ST/*ST firms, newly listed firms, and observations with missing key variables. Digital asset allocation is measured by the proportion of digital technology-related intangible assets to total intangible assets. The study employs a two-way fixed-effects panel model, firm-clustered robust standard errors, IV-2SLS estimation, robustness tests based on alternative measurements and sample restrictions, and Bootstrap sequential mediation analysis. The findings reveal that digital asset allocation significantly enhances corporate ESG performance. Mechanism tests indicate that digital asset allocation improves corporate ESG performance through internal control quality, green technological innovation, and the sequential pathway from internal control quality to green technological innovation. Further moderation analysis shows that the promotion effect is more pronounced in heavily polluting industries, while heterogeneity analysis indicates stronger effects among firms in the growth and decline stages, non-state-owned enterprises, and firms with lower financing constraints. This study provides empirical evidence and policy implications for optimizing corporate digital resource allocation, improving internal governance mechanisms, and advancing classified ESG. Full article