Search Results (148)

As countries accelerate their transition toward low-carbon economies, understanding the drivers of green finance is essential for shaping effective sustainability policies. This study investigates how FinTech development, digitalization, financial access, and structural factors influence public renewable energy investment—a measurable dimension of green finance—across 29 European countries over 2000–2022, using the Method of Moments Quantile Regression (MMQR). Results reveal strong distributional heterogeneity: FinTech consistently promotes green investment across all quantiles, digital infrastructure amplifies this effect in advanced regimes, and financial access is most binding at lower quantiles. Natural resource dependence exerts a persistent resource curse constraint that intensifies at higher quantiles. Three robustness strategies—2SLS-IV and quantile fixed effects QFE confirm a causal positive FinTech effect. Quantile-specific policy implications are derived: early-stage green investors should prioritize financial access and digital infrastructure, while advanced economies should deepen FinTech adoption and address resource-dependence constraints. Full article

This study investigates the relationship between renewable energy (RE) expansion and economic diversification in Egypt over 1990–2023 using a nonlinear autoregressive distributed lag (NARDL) framework. Egypt’s fossil fuel share stands at approximately 93% of primary energy supply, yet the country has committed to a 42% renewable electricity target by 2035. Despite quadrupling utility-scale RE capacity from 2.8 GW to 11.2 GW between 2015 and 2023, the Economic Diversification Index (EDI) has remained broadly stagnant. The bounds test confirms long-run cointegration (F = 6.760), exceeding small-sample critical values at the 1% level. Long-run estimates reveal that positive RE shocks are associated with lower diversification (θ⁺ = −0.571, p = 0.035) and negative shocks exhibit a statistically similar adverse effect (θ⁻ = −0.271, p = 0.024). Oil rents exhibit a positive long-run association (β = 0.145, p = 0.003). The error-correction term (−0.569) indicates approximately 57% annual adjustment. The Wald test provides marginal evidence against long-run symmetry (F = 2.999, p = 0.097). To complement the Granger causality analysis and address small-sample concerns, we additionally implement the Toda and Yamamoto augmented VAR procedure, which confirms robust unidirectional temporal precedence from LRE to LEDI (χ² = 23.48, p < 0.001) without reverse feedback (χ² = 2.25, p = 0.133). These patterns are interpreted through the lens of the Green Dependency Paradox—a conceptually distinct framework characterized by three mechanisms absent from classical resource curse theory: technology-mediated capital flight, procurement-induced deindustrialization, and policy-reversible lock-in operating under conditions of high import content, absent local content mandates, and fragmented industrial policy coordination. A tri-phase, evidence-grounded policy framework is proposed. All findings are explicitly conditional on Egypt’s current institutional context. Full article

To address the challenges of combinatorial explosion and expensive evaluations in truck–drone (truck–UAV) collaborative delivery under complex geographical constraints, this paper proposes a Surrogate-assisted Rezone-Enhanced Multi-objective Adaptive Evolutionary Algorithm (SRE-MAEA). As a knowledge-driven decomposition-based surrogate-assisted framework, the proposed algorithm aims to synergistically optimize a four-dimensional conflicting objective space consisting of economic cost, social satisfaction, environmental emissions, and battery resilience. To overcome the curse of dimensionality in high-dimensional and strongly constrained environments, SRE-MAEA constructs an adaptive Rezone Search architecture. By dynamically deconstructing the decision space, it transforms global search pressure into refined knowledge mining within high-potential local regions. The core mechanism incorporates an intelligent sampling strategy based on the Multi-Armed Bandit (MAB). By utilizing real-time evolutionary feedback to dynamically prioritize the Pareto contribution of each rezone, the MAB achieves pruning-level scheduling of expensive evaluation resources. Simulation results on 15 benchmark instances with clustered, random, and mixed spatial distributions demonstrate that SRE-MAEA exhibits superior convergence boundaries and distribution uniformity in terms of IGD and HV metrics, significantly outperforming state-of-the-art regression-based strategies. Furthermore, computational efficiency analysis confirms that by precisely identifying invalid search paths via the MAB mechanism, SRE-MAEA maintains a high-precision Pareto front while reducing the average CPU time by approximately 35.2–48.5%. This effectively resolves the computational bottleneck caused by complex battery resilience integral models. This research provides an efficient algorithmic paradigm for resilient logistics scheduling in extreme environments and holds significant academic value and engineering application prospects. Full article

Resource dependence pressure helps explain why some manufacturing firms remain tied to resource-based business paths even when green transformation is needed. Existing resource curse studies have mainly examined countries, regions, and resource-based cities, while less is known about how resource dependence works inside firms. We examine whether resource dependence pressure is associated with green innovation performance among Chinese manufacturing firms. Resource dependence pressure is defined as firm-level pressure arising from resource-linked customers and markets, resource-oriented suppliers and production chains, local policy/project opportunities, and inherited operating routines. Drawing on resource dependence theory, organizational inertia theory, and the digital dynamic capability perspective, we propose that organizational inertia mediates the relationship between resource dependence pressure and green innovation performance, while digital capability weakens the association between resource dependence pressure and organizational inertia. Survey data from 504 Chinese manufacturing firms are analyzed using PLS-SEM, with multi-group analysis comparing firms in resource-based cities and non-resource-based cities. The results show that resource dependence pressure is negatively associated with green innovation performance and positively associated with organizational inertia. Organizational inertia mediates this relationship. Digital capability weakens the resource dependence pressure-organizational inertia relationship, and this moderating pattern is stronger among firms in resource-based cities. The study extends resource curse research to the firm level and shows how digital capability is linked to weaker resource-induced organizational lock-in. Full article

Background: Birth defects remain a major global public health concern, particularly in low-resource settings where awareness and preventive practices are limited. Maternal knowledge, attitudes, and practices (KAP) are critical in the prevention and management of birth defects. This study explored contextual factors influencing maternal KAP using a mixed-methods approach in three rural districts of the Eastern Cape, South Africa. Methods: A convergent mixed-methods cross-sectional study was conducted among 72 mothers selected through purposive sampling. Quantitative data were collected using a structured questionnaire administered in English only, covering socio-demographic characteristics, obstetric history, knowledge, and preventive practices. Qualitative data were obtained through interviews exploring beliefs, perceptions, and cultural explanations of birth defects. Quantitative data were analysed using descriptive statistics and linear regression analysis to identify factors associated with birth defects, while qualitative data were thematically analysed to provide contextual understanding. Results: Most participants resided in the Amathole district (63.89%), followed by Alfred Nzo (18.06%) and Joe Gqabi (18.06%). Most women were aged between 20 and 35 years (52.78%), while 15.28% were younger than 20 years and 6.94% were older than 45 years. Over half of the respondents were single (55.56%), 34.72% were married, and the remainder were either separated (4.17%) or divorced (5.56%). Numerous participants had primary education (56; 77.78%), followed by secondary (11; 15.28%) and tertiary education (5; 6.94%). The majority were unemployed (56; 77.78%), while smaller proportions were employed (10; 13.89%) or engaged in other income-generating activities (6; 8.33%), indicating limited participation in formal employment among respondents. Nearly all participants (95.83%) had experienced pregnancy, with 70.83% reporting pregnancy-related complications. Only 2.78% reported having a child with a birth defect, while 90.28% reported a family history of birth defects. Knowledge of genetic causes was relatively high (69.23%), but awareness of modifiable risk factors was limited. Although 93.06% recognized alcohol use during pregnancy as harmful, fewer participants identified smoking or medication use (18.06%) and advanced maternal age (26.39%) as risk factors. Only 13.89% acknowledged the preventive role of antenatal care. Qualitative findings revealed strong cultural influence on perceptions of birth defects, with causes attributed to medical factors (38.89%), supernatural beliefs such as witchcraft or curses (18.06%), immoral behaviour (12.50%), and dietary taboos (11.11%). Traditional health-seeking behaviour was common, with 91.67% consulting traditional healers during pregnancy. Linear regression analysis identified significant predictors of birth defects, including family history (β = 1.36, p = 0.008), alcohol use during pregnancy (β = 1.13, p = 0.050), and inadequate antenatal care attendance (β = 0.99, p = 0.040). Advanced maternal age showed a weaker and non-significant association (β = 0.79, p = 0.080). Conclusions: The study highlights substantial gaps in maternal knowledge and the strong influence of cultural beliefs on birth defect prevention. Strengthening culturally sensitive health education, improving antenatal care services, and engaging traditional healers in community-based interventions are essential to improve maternal health outcomes in rural South Africa. Full article

This research adopts a model that has been developed around the Ecological Modernization and Resource Bless/Curse theories to offer practical insights on environmental sustainability. Data from 28 selected Caribbean and Latin American economies (grouped into 11 Small Island and Developing States (SIDSs) and 17 Large Continental Economies (LCEs)) for the period 2004 to 2024 is utilized in the analysis. ‘Method of Moments’ quantile regression and the two-stage least squares technique are employed for robust direct findings, while path analysis—a subset of structural equation modelling—is employed to examine the mediation and moderation relationships. Key findings shows that renewable energy is the key driver of sustainability in all Caribbean and Latin American economies regardless of the economy type. Consequently, digitalization and trade openness worsens environmental problems in the SIDSs. However, in the LCEs, digitalization advances sustainability up to a certain threshold, after which it becomes ineffective. Trade openness also tends to present a weak drive to sustainability in the LCEs. Green finance improves sustainability symmetrically in the SIDSs and asymmetrically in the LCEs as it only advances sustainability in highly environmentally stressed LCEs. The meditation role of green finance is significant in the SIDSs and insignificant in the LCEs, while the moderating role of institutional quality is insignificant in all Caribbean and Latin American economies. This study recommends channeling resources for supporting the development of green financial mechanisms for sustainable environments. Policy reforms that align economic efforts in advancing environmental sustainability can be adopted. Full article

Accelerating the digital transformation of resource-based cities is a key measure for breaking the “resource curse” and fostering new drivers of development. Based on TOE theory and employing the fsQCA method, this paper explores the driving mechanisms of digital transformation in resource-based cities through the synergistic interaction of multiple factors. The findings reveal that: (1) The digital transformation of resource-based cities is not driven by a single condition, but rather results from the synergistic interaction of multiple factors. (2) There are six configuration pathways leading to high levels of digital transformation, which can be further categorised as technology-driven, innovation–organisational synergy, collaborative composite linkage, and technology–environmental linkage; there are four configurations leading to non-high levels of digital transformation, which exhibit asymmetric characteristics relative to those of high digital transformation. (3) Technological conditions form the core foundation of the transformation and play a central role in most pathways leading to high levels of digital transformation; environmental pressures act as important catalysts for the transformation; organisational conditions exhibit strong characteristics of flexible substitution. The study reveals the synergistic mechanisms of multiple conditions in the digital transformation of resource-based cities, providing pathway options and theoretical references for these cities to advance their transformation in a manner suited to local conditions. Full article

Multi-Agent Reinforcement Learning (MARL) faces key challenges in credit assignment and the curse of dimensionality as agent numbers grow. In cooperative settings, uniform treatment of agents often exacerbates these issues. We argue that an agent’s importance depends on its personalized attributes and environment states and propose concentrating computational resources on key agents while others act simply, alleviating dimensionality explosion and improving generalization. We propose the Decision of Nonsynchronous Framework (DNF), which identifies and prioritizes key agents at each time step for optimized decision-making, while assigning predefined or simplified behaviors to the remaining agents based on computational outcomes. To realize this, we introduce a Core Extractor (CE) architecture that categorizes agents into Priorities Key Agents (PKAs) and followers. Although agents are differentiated by priority, we still adhere to the Centralized Training with Decentralized Execution (CTDE) paradigm. This approach reduces the dimensionality of the joint state-action space, mitigates the dimensionality explosion problem in MARL, and fosters improved collaboration among agents. Experimental results demonstrate that DNF achieves a 100% win rate on multiple SMAC maps, including 3m, 2s3z, and 1c3s5z, and achieves 98.9–100% win rates on challenging hard and super-hard scenarios such as 2c_vs_64zg and Corridor, significantly outperforming baseline methods like QMIX and QPLEX in both final performance and training stability, while incurring only a modest increase in computational overhead. In the continuous MPE, DNF matches or exceeds HAPPO in performance and demonstrates substantially higher time efficiency, with both advantages growing more pronounced as the number of agents increases. Full article

Active Distribution Networks (ADNs) rely on the precise coordination of flexible resources to mitigate the stochasticity of high-penetration renewables. However, the hierarchical and partitioned nature of modern ADNs transforms the day-ahead scheduling problem into a high-dimensional many-objective optimization task, typically involving conflicting objectives across multiple regions. Standard evolutionary algorithms often struggle with the “curse of dimensionality” in such scenarios. To address this limitation, this study formulates a hierarchical partition-based scheduling model for many-objective optimization and introduces a novel adaptive MOEA/D algorithm. Specifically, a double-layer weight generation method and an adaptive neighborhood adjustment strategy are introduced to balance global search capability with local convergence speed. The methodology is validated using a practical 47-node ADN case study in Panzhihua, China. Comprehensive analysis of evaluation metrics (e.g., Hypervolume and IGD) indicates that the proposed algorithm achieves enhanced performance at the expense of a marginal increase in cost. Furthermore, it demonstrates strong competitiveness against advanced heuristic algorithms in solving high-dimensional scheduling problems, effectively balancing economic efficiency and voltage stability under renewable uncertainty. Full article

Oil-dependent economies function as complex socio-economic systems in which resource revenues, institutional structures, and investment outcomes are dynamically interlinked. When governance is weak, oil windfalls can trigger reinforcing feedback loops that erode institutional capacity, reduce investment quality, and deepen resource dependence. This paper examines whether governance may function as a system-level regulating mechanism that weakens these self-reinforcing dynamics. We study 13 oil-dependent economies over 2000–2023 using the Incremental Capital–Output Ratio (ICOR)—the investment required to produce one unit of output—as a proxy for system-level capital efficiency. Panel ARDL–PMG estimation, which separates short-run perturbations from long-run equilibrium, shows that oil rents are associated with a higher ICOR in the long run, indicating declining system efficiency. A composite governance index, and anti-corruption capacity in particular, are associated with a substantially lower ICOR and weaken the oil–inefficiency transmission. Notably, anti-corruption is the only governance dimension operating across both temporal scales, functioning as both an immediate corrective mechanism (preventing procurement fraud) and a structural stabilizer (shaping the institutional environment over time). Results are robust across alternative ICOR specifications, supported by Granger causality tests, and stable when any single country is dropped. The findings identify governance—especially anti-corruption capacity—as a critical leverage point for improving system-wide capital efficiency in resource-dependent developing economies. Full article

This study examines how institutional quality (INST) affects the contribution of natural resource endowments (NREs) to citizens’ happiness and life satisfaction. It also identifies the INST threshold above which NREs enhance citizens’ life satisfaction and happiness. Consistent with challenges of low happiness levels, weak institutions, and the pervasive resource curse in Africa’s resource-rich economies, we analyse a dataset of these economies from 2012 to 2022. This study employs a robust standard-error Driscoll–Kraay nonparametric covariance matrix, dynamic common correlated effects, fully modified least squares, the method-of-moments quantile regression, and a dynamic panel threshold estimator. The findings suggest that natural resource endowment is a curse that lowers life satisfaction. Meanwhile, threshold analysis indicates that most resource-rich African countries fall short of the institutional development required to transform this curse into a blessing by encouraging the efficient allocation of resource earnings to initiatives that increase people’s happiness. Most of Africa’s resource-rich economies operate below this threshold. This study concludes that in Africa’s resource-rich countries, institutions are vital to incentivise the effective distribution of the proceeds from these resources to initiatives that enhance citizens’ happiness. Full article

As the global response to climate change and energy crises accelerates, the large-scale integration of heterogeneous distributed energy resources (DERs) is rapidly transforming traditional passive distribution networks into active distribution networks. However, the massive quantity and high stochasticity of these underlying devices trigger a severe “curse of dimensionality,” creating significant computational and communication bottlenecks for coordinated system dispatch. To overcome these challenges, the “clustering followed by equivalence” aggregation modeling paradigm has emerged as a critical technical pathway. This paper reviews the state-of-the-art clustering and aggregation methodologies for distribution networks with high DER penetration. The review begins by synthesizing multi-dimensional feature extraction techniques and cutting-edge clustering algorithms that establish the foundation for dimensionality reduction. It then delves into refined aggregation models tailored to heterogeneous resources, including dynamic data-driven equivalence for renewable generation, Minkowski sum-based boundary approximations for energy storage, and thermodynamic alongside Markov chain mapping methods for flexible loads. Building upon these models, the paper comprehensively discusses the practical applications of generalized aggregators, such as microgrids and virtual power plants, in feasible region error evaluation, coordinated network control, multi-agent market games, and privacy-preserving architectures. Finally, the review outlines future research trajectories, emphasizing hybrid data-model-driven architectures for real-time dispatch, distributionally robust optimization (DRO) for enhancing grid resilience and self-healing, and decentralized trading ecosystems to ensure equitable system-level surplus allocation. This review aims to provide a systematic theoretical reference for the coordinated management and aggregated trading of flexibility resources in novel power systems. Full article

As the most critical binding constraint in the Yellow River Basin, the endowment and allocation efficiency of water resources significantly influence the stability and sustainability of urban economic systems. However, the direction, intensity, and heterogeneity of the impacts of water resource abundance and water use efficiency on urban economic resilience remain unclear. Therefore, to explore the intrinsic relationship between water resources and urban economic resilience and to identify effective pathways for enhancing urban risk resistance, this paper employs a fixed-effects model to empirically examine the differential impacts based on panel data from 78 prefecture-level cities in the Yellow River Basin from 2011 to 2023. The results show that: (1) Water resource abundance exerts a significant inhibitory effect on urban economic resilience, while water use efficiency exhibits a significant promoting effect. (2) Market demand, government intervention and opening up exacerbate the negative impact of water resource abundance and also strengthen the positive impact of water use efficiency. (3) The negative impact of water resource abundance is significant only in resource-based cities, water-abundant cities, cities in the lower reaches, and cities with high economic development, high urbanization, and high technology input. In contrast, the positive impact of water use efficiency is significant in most cities, and it is more pronounced in resource-based cities, water-abundant cities, cities in the middle reaches, and cities with high economic development and high urbanization. These findings provide important insights for enhancing urban resilience and promoting sustainable development. Full article

The rapid development of space exploration demands real-time backhaul of massive sensing payload data in space-ground integrated telemetry, tracking, and command (TT&C) networks. However, traditional narrow-band TT&C links suffer from severe congestion during massive data backhaul. Since most TT&C applications are inherently task-oriented and do not require pixel-perfect data reconstruction, we propose a task-oriented joint resource allocation framework based on semantic communications. Specifically, we introduce an adaptive semantic split computing mechanism that extracts and transmits only compact, decision-critical features instead of raw bitstreams, fundamentally mitigating the bandwidth bottleneck. The joint optimization of computation offloading, semantic splitting, and continuous on-board computing allocation is formulated as a stochastic mixed-integer nonlinear programming (MINLP) problem. We propose a decoupled algorithm based on Hierarchical Multi-Agent Proximal Policy Optimization (HMAPPO) to solve it. An outer layer employs multi-agent reinforcement learning (MARL) for distributed discrete decision-making, while an inner layer utilizes a Karush–Kuhn–Tucker (KKT)-based solver for continuous space-based computing allocation. This bi-level architecture overcomes the curse of dimensionality and mathematically guarantees zero-violation of physical capacity constraints. Simulations demonstrate that HMAPPO rapidly converges and sustains a high weighted success rate under heavy traffic congestion, significantly improving system utility compared to state-of-the-art baselines. Full article

Nigeria entered the 2020 COVID-19-related oil price downturn without the fiscal buffers that numerous resource-rich economies had built over time. Despite heavy dependence on petroleum revenues, the country has made limited use of stabilization tools such as structured hedging programs, sovereign savings mechanisms, or strategic reserves, leaving public finances exposed to external shocks. Drawing on political choice theory and the resource governance literature, this study examines how institutional conditions shaped crisis management during the 2020 oil price collapse and the COVID-19 pandemic. The study combines qualitative institutional analysis with a stochastic counterfactual simulation. It compares Nigeria’s policy approach with those of oil-producing countries including Mexico, Saudi Arabia, the United Arab Emirates, Angola, and Ghana, using data from the IMF, World Bank, Afreximbank, and peer-reviewed sources. The counterfactual simulation is calibrated to Nigeria’s 2019 federal budget oil benchmark of US $60 per barrel, with the IMF’s 2019 petroleum price assumption used as a robustness check. The model treats hedging as a form of partial fiscal insurance rather than full stabilization. Results suggest that hedging sufficient to offset 10%, 20%, and 30% of the shock would have improved 2020 GDP decline from −1.80% to approximately −1.62%, −1.44%, and −1.26%, respectively. The analysis identifies institutional gaps in Nigeria’s use of hedging, sovereign savings, and reserve infrastructure. The counterfactual results indicate that even modest oil hedging could have meaningfully softened the 2020 downturn, with the 20% scenario reducing GDP contraction by an estimated 0.36 percentage points. These findings suggest that governance constraints contributed materially to fiscal vulnerability. The study proposes a four-pillar framework centered on risk hedging, revenue savings, strategic investment, and institutional reform to strengthen fiscal stability and resilience to external shocks. Full article