Historically, power systems have been developed predominantly based on thermal or hydro resources, depending on the local resources availability and system needs. The distinct characteristics of these technologies shaped how adequacy requirements were defined, allowing systems to be classified as either capacity-constrained or energy-constrained. However, as variable renewable sources (VRS) such as wind and solar become increasingly dominant in system expansions, these one-dimensional approaches are no longer sufficient to ensure reliability or investment efficiency, due to the inherent uncertainty in resource availability and lack of dispatchability. Although some electricity markets already consider products related to energy and capacity dimensions independently, most adequacy mechanisms and market designs continue to focus on a single requirement — typically capacity — or are tailored to promote specific technologies. This paper aims to the ongoing debate on electricity market design by characterizing evolving adequacy requirements and proposing a redefinition of adequacy products that can induce efficient expansion. It emphasizes that adequacy products must be jointly designed and monitored across both the energy and capacity dimensions. The Brazilian power system, with its high share of hydropower and rapidly growing VRS participation, offers a relevant case study for these challenges and provides lessons that are broadly applicable to other contexts. The analysis shows that by aligning market products with multidimensional adequacy requirements, policymakers and regulators can avoid inefficient technological mandates, promote competitive neutrality, and support a more robust and cost-effective expansion of power systems in the decarbonization era. • In the decarbonization era, power systems requirements need to be recharacterized. • Adequacy mechanisms must consider jointly energy and power capacity dimensions. • Redefinition of adequacy products can induce efficient power system expansion. • Multidimensional adequacy products can avoid inefficient technological choices.

Behavioral economics provides tools to understand and influence decision-making; however, its application to immunization equity remains limited. This analytic essay examines how behavioral frameworks such as EAST (Easy, Attractive, Social, Timely) and MINDSPACE (Messenger, Incentives, Norms, Defaults, Salience, Priming, Affect, Commitments, Ego) can inform pediatric immunization strategies. Behavioral insights can simplify decisions and motivate vaccine uptake through design elements such as defaults, reminders, and trusted messengers. However, these approaches operate within broader social and structural contexts that shape whether families can act on their intentions. Integrating behavioral strategies with structural investments in maternal and early childhood health increases the effectiveness of health spending by improving the marginal efficiency of investment. Linking motivation with opportunity provides a practical framework for designing immunization policies that move beyond isolated nudges toward sustainable and equitable public health impact. (Am J Public Health. 2026;116(S3): S229-S234. https://doi.org/10.2105/AJPH.2026.308518).

We challenge a recently popular view that a negative interest-growth rate gap ([Formula: see text]) offers a "free lunch" for debt-financed government spending by formulating a model in which [Formula: see text] and [Formula: see text] are endogenous variables shaped by fiscal policy through its effects on equilibrium multiplicity and capital allocation. Observing [Formula: see text] can signal that sustained government deficits have generated multiple steady states, and the economy has converged to a stable low-efficiency equilibrium. With its heterogeneous entrepreneurs, the model's real interest rate serves as a screening device for investment efficiency. Causation runs from the fiscal regime to equilibrium selection and outcomes: Fiscal surpluses eliminate equilibrium multiplicity and anchor expectations that sustain a unique, high-productivity equilibrium, thereby rationalizing Alexander Hamilton's characterization of "debt as a blessing." Persistent deficits can push the economy into a "misallocation trap" characterized by scarce safe assets, low interest rates, survival of inefficient firms, depressed aggregate productivity, and self-validating low growth. Thus, costs of debt-financed fiscal deficits consist not only of deferred taxes, but also of permanently lower national productive capacity.

Complementary Growth: Midwives and Obstetrician-Gynecologists in the US Maternity Care System.

ABSTRACT Complex product development networks (CPDNs) are typically characterised by co-opetition relationships and restricted communication structures between manufacturers and suppliers. Traditional investment decision models often fail to capture these two inherent features simultaneously. To bridge this gap, this study proposes a novel biform game model that integrates the Position value from the graph cooperative game framework. In the non-cooperative stage, firms determine their investment levels. In the cooperative stage, collaborative benefits are allocated based on the Position value, which effectively reflects firms’ marginal contributions and their brokerage roles within the network topology. Furthermore, we explore the impact of key parameters on investment decisions and extend the model to networks with n suppliers to verify the robustness of our conclusions. The finding reveals that the optimal investments derived from the biform game model are higher than those from a pure non-cooperative model, underscoring the role of cooperation in incentivizing investment. This research presents a novel theoretical framework for analyzing strategic investment under co-opetition and communication constraints, offering practical insights to enhance investment efficiency and collaboration in CPDNs.

ABSTRACT This study examines the impact of common institutional ownership (CIO) on excessive leverage in Chinese listed firms. The results show that CIO significantly reduces excessive corporate leverage. Mechanism analysis indicates that CIO mitigates excessive leverage by improving equity financing and enhancing corporate governance. This effect is more pronounced in firms with more stable institutional investors and lower analyst coverage, and holds for both state-owned and non-state-owned enterprises. Furthermore, the deleveraging effect of CIO also helps reduce inefficient investment and lower debt default risk.

ABSTRACT This study investigates the impact of artificial intelligence (AI) applications on the overseas investment efficiency of Chinese multinational enterprises using firm-level data from 2008 to 2023. The results show that AI can significantly improve investment efficiency. Mechanism analysis indicates that AI plays roles by mitigating managerial myopia and enhancing dynamic capabilities. Heterogeneity analyses indicate that the positive effects of AI are more pronounced in host countries characterized by high economic policy uncertainty and greater institutional distance. Concurrently, the enabling effects of AI vary depending on firms’ organizational learning capabilities and political connectedness. These findings underscore the transformative potential of AI in refining multinational enterprises’ investment strategies, strengthening global competitiveness, and facilitating adaptation to diverse institutional and economic contexts, thereby providing actionable insights for improving international investment performance.

Introduction. One of the determining factors in shaping the directions for the development of construction infrastructure is the increase in the level of energy efficiency of enterprises. This factor is considered a necessary condition for sustainable development, for ensuring environmental and economic security, for facilitating integration into the European energy space, and for supporting the formation of an energy-independent national economy.Problem Statement. The improvement of organizational and technological solutions for the reconstruction of construction enterprises is becoming increasingly relevant in the context of rising requirements for energy efficiency and environmental security. These conditions necessitate the search for scientifically substantiated approaches to the modernization of production facilities and technological processes.Purpose. The purpose of this study is the development and scientific substantiation of effective organizational and technological solutions for the reconstruction of construction enterprises, taking into account contemporary requirements for energy efficiency, environmental safety, and economic feasibility, with the aim of increasing their competitiveness and ensuring sustainable development.Materials and Methods. The research methodology includes a systematic and comparative analysis of scholarly research publications of 2020—2025. In addition, the study has applied BIM-based modeling, INOVA analysis of innovative solutions, the weighted coefficient method, and economic methods for eva luating investment efficiency. The empirical base of the study consists of technical and design documentation of buildings, as well as statistical data on actual energy consumption collected over several years.Results. The study has demonstrated that the comprehensive implementation of energy-saving measures significantly improves the energy performance of construction enterprises. In particular, the obtained results have shown that electricity consumption may decrease by 30—35%, while thermal energy consumption may be reduced by 40—50%. Furthermore, the conducted INOVA analysis has revealed a high level of innovation potential in the proposed technological and organizational solutions.Conclusions. The application of the proposed approaches makes it possible not only to reduce energy consumptionand operational costs, but also to increase the overall sustainability of construction enterprises. The results of the study can serve as a basis for the development of regulatory and methodological frameworks aimed at the energy modernization of industrial and construction facilities.

ABSTRACT Smart cities, which integrate information and communication technologies (ICTs) into urban management systems, can differ from traditional cities in their approach to spatial hierarchy because they are realized through domains that combine physical spaces and virtual networks. Similar to conventional urban studies, the spatial hierarchy of smart cities has been examined primarily along two axes: city size and scale. However, prior research has tended to treat these concepts as separate analytical dimensions, and studies addressing either size or scale in smart cities have been conducted in a fragmented manner. To develop a more multidimensional understanding of smart city spatial hierarchies, it is therefore necessary to synthesize convergent and contrasting perspectives on size and scale and to clarify their relationships. Accordingly, this study aims to identify the key themes within smart city size and scale discourse and examine their relationships through a systematic literature review of 51 peer-reviewed articles. The findings indicate that size discourse mainly focuses on how a city’s physical magnitude influences investment efficiency and managerial complexity. In contrast, scale discourse reconceptualizes smart cities not as fixed territorial units but as multi-layered relational networks and technological spheres of operation spanning from the hyperlocal to the global. Furthermore, discussions on the size–scale relationship in smart cities revolve around three main issues: (1) the influence of urban size on technological scale-up, (2) the reconfiguration of functional scales through right-sizing strategies, and (3) tensions between universal expansion models and size-specific urban particularities.

ABSTRACT Digital transformation has emerged as a pivotal strategy for enterprises navigating the challenges of the modern economy. This study examines how corporate digital transformation affects investment efficiency in China’s food and beverage industry, using panel data from Shanghai and Shenzhen A-share listed companies over the period 2010–2023. Employing a text-mining approach to measure the degree of digital transformation and the Richardson’s model to estimate investment efficiency, we find that digital transformation significantly enhances corporate investment efficiency, we find that digital transformation significantly enhances corporate investment efficiency. Mechanism analysis reveals that this effect operates primarily through two channels: the reduction of agency costs and the alleviation of financing constraints. Heterogeneity tests further show that the positive impact is more pronounced among firms located in eastern China, state-owned enterprises, companies audited by non-Big Four accounting firms, and those in the growth and maturity stages of the corporate life cycle. These findings contribute to the literature on the economic consequences of digital transformation and offer practical insights for firms and policymakers seeking to leverage digitalization as a pathway to more efficient capital allocation in emerging market contexts.

The article provides an in-depth study of the functioning, investment policy, and efficiency of Georgia's funded pension system. The paper analyzes performance dynamics from 2019 to the present, the structure of the investment portfolio, and risk management mechanisms. Using empirical data, the Sharpe ratio, and international comparative analysis, the research identifies key challenges, such as low diversification and the underdeveloped local capital market. Finally, the study develops recommendations to ensure long-term financial sustainability. Keywords: pension reform, investment portfolio, diversification, financial margin, asset management.

ABSTRACT Nitrogen (N) management in Ethiopian lowland rice systems relies on outdated, suboptimal practices, leading to low N use efficiency (NUE) and significant environmental losses. A two-year field experiment was conducted across four sites to evaluate the effects of N application rate and timing on grain yield, NUE, chlorophyll content (SPAD), and economic returns. The study employed a factorial randomized complete block design with three N rates (92, 138, and 184 kg N ha−1) and five application schedules (two-, three-, and four-split regimes). Results showed that both N rate and timing significantly influenced productivity and efficiency, though their interaction was not significant, suggesting that optimal timing remains consistent regardless of the total N dose. The three-split strategy (1/3 basal +1/3 mid-tillering +1/3 panicle initiation) proved superior, achieving a maximum grain yield of 7.70 t ha−1 and biomass of 16.96 t ha−1 at the 184 kg N ha−1 rate. This strategy optimized sink-source dynamics and maintained higher SPAD values (36.3), while simultaneously enhancing agronomic N use efficiency by 20–30% compared to other regimes. Economic analysis revealed that while 184 kg N ha−1 yielded the highest net return (238,336 ETB ha−1), the 138 kg N ha−1 rate provided the greatest investment efficiency, with a Value-Cost Ratio of 8.7 and a Marginal Rate of Return of 765.8%. These findings demonstrate that synchronizing N supply with key growth stages maximizes both biological potential and economic stability. We recommend 184 kg N ha−1 in three splits for high-input systems, while 138 kg N ha−1 in three splits is the optimal recommendation for resource-constrained farmers to maximize profitability and minimize environmental risks. Future research should focus on sensor-guided and model-based precision N management to further refine these recommendations for Ethiopia’s lowland rice systems.

ABSTRACT As the core engine of the new round of technological revolution, the digital economy has made data factors a key driver of high-quality corporate development. This paper takes the establishment of data trading platforms as a quasi-natural experiment, uses data from A-share listed companies in Shanghai and Shenzhen from 2012 to 2022, and adopts a multi-period difference-in-differences (DID) model to examine the impact of data trading platform establishment on corporate investment efficiency and its internal mechanism. The research results show that the establishment of data trading platforms significantly reduces corporate investment inefficiency. Mechanism analysis indicates that data factor marketization mitigates corporate investment inefficiency by reducing information asymmetry, optimizing resource allocation efficiency, and promoting corporate digital technology innovation. Heterogeneity tests reveal that the above positive effects are more significant in mature and declining enterprises as well as non-state-owned enterprises, and mainly reduce investment inefficiency by curbing over-investment. From the perspective of investment efficiency, this paper reveals the positive impact of data factor marketization at the micro-enterprise level, providing empirical evidence for improving the construction of data factor marketization.

ABSTRACT As a crucial link connecting executive behavior and corporate resource utilization in corporate governance, the impact of perk consumption on firm-level total factor productivity (TFP) remains to be systematically elucidated. Our study combines the efficiency and agency perspectives on perk consumption, and employs a panel data set of Chinese A-share listed firms from 2010 to 2023 to explore how perk consumption affects firm-level TFP. The results document a nonlinear, inverted U-shaped association, whereby firm-level TFP increases at lower levels of perk consumption but declines once perk consumption exceeds a certain threshold. We further show that investment efficiency and innovation efficiency serve as important mediating mechanisms in this relationship. Our results also suggest that perk consumption more significantly affects firm-level TFP in firms with low ownership concentration, firms without managerial overconfidence, labor-intensive and technology-intensive firms, and firms with low financing constraints. Our study provides empirical support and theoretical insights for optimizing corporate governance structures, regulating senior executive consumption behavior, and improving the efficiency of corporate resource utilization.

Investments in restoring river connectivity are growing worldwide to support freshwater biodiversity. Although optimization methods exist for selecting cost-effective restoration portfolios, decisions are often guided by simple heuristic rules. For example, managers may prioritize restoring barriers blocking the largest amounts of high-quality upstream habitat, ignoring the position of other barriers in the system. These heuristics often rely on proxies for watershed connectivity and habitat quality. Using anadromous fish passage restoration in western Washington, USA, as a case study, we show that redesigning these heuristics can yield substantial performance gains. Benchmarking common heuristics against optimization outcomes reveals that connectivity proxies based on total upstream habitat can achieve 93% of optimal gains when increasing habitat quantity is the sole objective, but adding widely used proxies for habitat quality (e.g., percent of upstream natural land cover) can cut performance nearly in half. These findings underscore the importance of designing heuristics that more directly target high-quality habitat gains to improve investment efficiency and help close the science–practice gap between optimization research and on-the-ground restoration decisions.

Turning threats into opportunities: The impact of cybersecurity risk on labor investment efficiency

Share pledging by controlling shareholders and firm investment efficiency: Evidence from China

Improving energy efficiency in residential buildings is critical to combating climate change and reducing greenhouse gas emissions. Retrofitting existing buildings –that are major contributors to energy use– is therefore a key priority, particularly in regions with outdated building stock. Artificial Intelligence (AI) and Machine Learning (ML) can automate retrofit decision-making and find retrofit strategies. However, their implementation faces challenges of data availability, trust and alignment with trustworthiness guidelines, as well as compliance to AI regulations. This paper presents a trustworthy-by-design ML-based decision support framework that recommends energy efficiency strategies for residential buildings using minimal user-accessible inputs. The framework employs Conditional Tabular Generative Adversarial Networks (CTGAN) to augment limited and imbalanced data, while neural network-based multi-label classifier identifies potential combinations of retrofit measures. An Explainable AI (XAI) layer using SHAP is also incorporated to clarify the rationale behind recommendations, validate the model, and guide feature engineering. Two case studies on distinct datasets validate performance and replicability: i) a well-established, large Energy Performance Certificate (EPC) dataset for England and Wales; ii) an imbalanced post-retrofit dataset from Latvia (RETROFIT-LAT). Results demonstrate that the framework can handle diverse data conditions and improve performance up to 53% compared to the baseline model without XAI and synthetic data generation. Overall, the proposed framework provides a novel, user-friendly classification-based solution for building retrofit decision support that incorporates the trustworthiness aspects of transparency, human oversight, data governance, and fairness and aids stakeholders in achieving effective energy efficiency investments while aligning with AI regulation and ethical standards.

Organizations adopting generative AI (GenAI) face complex strategic tensions among management, departments, and employees that fundamentally determine adoption outcomes. This study develops a multi-level Bayesian game-theoretic framework modeling these multi-stakeholder interactions, identifying four distinct adoption patterns through formal equilibrium analysis. Our theoretical derivations establish that successful GenAI implementation requires three analytically-derived conditions: (1) strong strategic complementarity across departments, (2) efficient investment allocation, and (3) effective employee displacement mitigation. The formal model specifies explicit utility functions for three stakeholder groups — senior management, departmental units, and individual employees — and characterizes Bayesian Nash equilibria under incomplete information. Companies must simultaneously invest in cross-functional coordination mechanisms, establish shared governance structures, and implement workforce development programs that position GenAI as a capability enhancement rather than a job replacement. Our computational analysis, based on 10,000 Monte Carlo simulations with explicit parameter specifications and convergence criteria, demonstrates that coordination-focused strategies significantly outperform technology-focused approaches in organizational welfare, providing actionable guidance for AI transformation leadership. • Multi-level Bayesian game models GenAI adoption inside organizations. • Strategic complementarity drives coordinated GenAI value creation. • Employee displacement risks critically shape adoption equilibria. • Coordination strategies outperform technology-first GenAI adoption. • Formal thresholds distinguish value co-creation from co-destruction.

• Logistical rebound reduces the effectiveness of energy efficiency in South America. • Dominant road networks intensify consumption in countries like Brazil and Chile. • Integrated and modal policies are essential to mitigate rebound effects. • Logistics emerges as a central point in the sustainable energy transition. The pursuit of energy sustainability in South America has led to major efficiency investments in the transport sector, a key energy consumer. However, these gains are often offset by the transport energy rebound effect, whereby efficiency improvements paradoxically stimulate greater demand for transport services. This study examines direct and indirect rebounds across ten South American countries using log-linear econometric models and elasticity analysis. Results reveal marked regional heterogeneity, ranging from super conservation to backfire, shaped by infrastructure deficits, institutional fragility, and strong reliance on road-based systems. The findings highlight the risks of depending solely on technological advancements without structural or regulatory measures. The study recommends integrated policy frameworks involving demand-side regulation, targeted subsidies, modal shifts, and comprehensive externality pricing. By providing a systemic interpretation of rebound dynamics, the research offers insights for designing resilient, low-carbon transport strategies aligned with climate commitments.