Search Results (1,273)

This study examines the role of circular services in generating economic value within the construction sector, focusing on firms belonging to the Sustainable Habitat Cluster in the Aburrá Valley, Colombia. The research analyzes how circular business model strengthening translates into economic outcomes through the implementation of circular service portfolios. Using a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach, the study evaluates the relationships between circular business model capabilities, circular service implementation, and circular revenue generation. The results confirm a sequential mechanism linking strategic capabilities to economic outcomes, where strengthening circular business models significantly enhances the implementation of circular services, which in turn strongly predicts the generation of circular revenues. The findings indicate that circular strategic orientation is a necessary but insufficient condition for economic value creation, as monetization occurs only when circular principles are translated into concrete service offerings. The study highlights the central role of circular services as the operational bridge between strategic readiness and economic performance, contributing to the literature on circular business models and Product–Service Systems (PSS) by providing empirical evidence of how circular strategies translate into revenue generation within the built-environment sector. Full article

Agrivoltaic systems, which enable simultaneous crop production and solar photovoltaic (PV) electricity generation on the same land, can support climate mitigation, food security, and rural development. Leguminous crops like beans are globally important, yet there is limited performance studies on diverse agrivoltaic trials. This limits appropriate policy guidance. To overcome these limitations, this study assessed organic green bush bean performance under thirteen PV configurations with varying transparency and spectral properties, comparing both agricultural outcomes against national yields and policy standards. The results in vegetative metrics indicated that blue-spectrum thin-film and intermediate-transparency c-Si modules supported growth near German productivity thresholds. Although no agrivoltaic system matched national average yields, combining crop and energy revenues revealed substantial benefits: the 44%—transparent c-Si configuration generated 340% more total revenue than traditional farming, and the blue 70%—transparent thin-film system achieved 94% of national yield but 164% of conventional farm revenue per acre. Electricity generation gains outweighed modest crop reductions, highlighting strong synergies between food and energy. The results of this study highlights the potential of agrivoltaic systems to enhance land-use efficiency, support renewable energy expansion, and improve rural economic resilience, while underscoring the need for multi-year trials and site-specific controls to validate long-term sustainability outcomes. Full article

The large-scale integration of wind and solar power introduces significant volatility into electricity markets, posing challenges for accurate day-ahead price forecasting for generation companies. This paper proposes a hybrid forecasting model, CEEMD-SE-IBA-LSTM, based on hyperparameter adaptation to improve prediction accuracy. First, a similar-day selection method integrating Random Forest and an Improved Grey Ideal Value approximation identifies the most relevant historical days. Second, Complete Ensemble Empirical Mode Decomposition with Sample Entropy (CEEMD-SE) decomposes and reconstructs the price series into stable components. Third, an Improved Bat Algorithm (IBA), incorporating differential evolution and adaptive weighting, is developed to optimize two key LSTM hyperparameters: the number of hidden layer neurons, which is treated as a model architecture hyperparameter, and the learning rate, which is treated as a training hyperparameter. The number of LSTM layers and the number of training epochs are kept fixed as model settings to ensure reproducibility. Using data from the US PJM market, the proposed model is validated against six benchmarks. The results show that CEEMD-SE-IBA-LSTM achieves superior performance, with a Mean Absolute Percentage Error (MAPE) of 3.73%, a Root Mean Square Error (RMSE) of 3.57 $/MWh, and a Mean Absolute Error (MAE) of 1.95 $/MWh. The method provides accurate price trends, offering effective decision support for new energy enterprises in price bidding to enhance revenue. Full article

Rural electrification in Sub-Saharan Africa faces a trilemma: cutting carbon emissions, making it economically viable, and achieving fair access to energy for all. This paper develops a multi-objective framework that optimises carbon revenue, net present value (NPV), total energy supply, cooking fuel (firewood and LPG), health costs, and benefit to society. The model uses continuous decision variables: daily energy allocation among four sources (solar, generator, firewood, LPG) to three population groups (men, women, children). The case study is a rural community of 7000 people in Nigeria (Tier 1 energy consumers). Six policy scenarios are considered: baseline, high carbon price, low carbon price, microfinance, government subsidy and community cooperative. This study compared algorithms and identified a hybrid Non-dominated Sorting Genetic Algorithm and Particle Swarm Optimisation II as the most suitable algorithm for solving the formulated optimisation problem. It was found that NPV and unit cost of energy would increase to $175,500 and 26.4 ¢/kWh, respectively, by increasing the price of carbon from $8/ton to $12/ton. Firewood generates health savings and carbon revenue in the range of $4100–$12,270/year. Prices below $8/ton do not induce optimal reconfigurations in the system. The best energy supply (2825 kWh/day) and the lowest unsatisfied demand occur in the government subsidy scenario with the greatest disparity index, displaying an equity-efficiency trade-off. The framework shows that sustainable access to energy can be unlocked using strategic integration of carbon finance, valuation of health benefits and equity constraints. Full article

Digital infrastructure may improve firm productivity, yet its economic value depends on whether firms can absorb external connectivity and embed it in production, management, and investment decisions. Using the staggered implementation of the “Broadband China” pilot policy as a quasi-natural experiment, this study examines the effect of city-level broadband infrastructure on the revenue-based labor productivity of Chinese A-share listed firms from 2009 to 2023. A multi-period difference-in-differences model shows that the pilot policy is associated with an increase in revenue per employee. The baseline estimate implies an economically meaningful increase of approximately 4.1%, and the result remains robust to alternative productivity measures, sample restrictions, stricter fixed effects, placebo tests, PSM-DID, and IPW-DID. CSDID estimates are positive but not statistically significant at conventional levels and are therefore interpreted as directionally consistent rather than independently confirmatory. Evidence based on total factor productivity, management expense intensity, and investment adjustment is consistent with production efficiency, management coordination, and organizational adjustment channels. Heterogeneity tests show stronger effects among non-state-owned, eastern region, and non-manufacturing firms. The findings suggest that broadband infrastructure generates productivity benefits when firms have the organizational absorptive capacity to convert external digital connectivity into internal operational efficiency. Full article

Energy storage systems (ESSs) installed alongside traditional photovoltaic (PV) power plants are primarily used to track planned output, which often results in low utilization rates and extended payback periods. Moreover, existing research inadequately addresses actual grid frequency fluctuation characteristics and lacks multi-timescale optimization frameworks. To address these issues, this paper proposes a day-ahead and intraday multi-market coordinated rolling optimization strategy that integrates energy market trading with Automatic Generation Control (AGC) frequency regulation services through a zone-based frequency regulation control strategy. The strategy first defines distinct regulation zones based on regional control deviations, enabling a dynamic power allocation approach for the energy storage system. Recognizing that conventional constant power control can lead to battery overcharging, over-discharging, and reduced cycle life, the strategy introduces state of charge (SOC)-based variable power charging and discharging constraint coefficients. These constraints ensure the battery operates safely within its optimal range. Furthermore, an electrochemical energy storage life decay model is developed to quantify battery degradation. To accommodate the uncertainty in PV output, Latin hypercube sampling is employed. A day-ahead dispatch model is established to maximize the system’s total daily operating revenue, and rolling optimization is applied during the intraday phase to correct deviations from the day-ahead forecast. Finally, simulation studies using actual data from a PV power plant demonstrate that the proposed strategy achieves a total daily revenue of 107,477 ¥, representing a 24.6% improvement over energy market-only participation; battery aging costs are reduced by 11.1% compared to the scenario without zone-based frequency regulation control. Results indicate that the proposed strategy effectively balances battery life degradation against market revenue, significantly improving the overall operational efficiency and economic viability of PV-storage hybrid systems. Full article

Weak grid infrastructure and the absence of flexible storage are among the principal barriers to reliable, low-carbon energy access in sub-Saharan transmission systems. This paper proposes a hierarchical multi-objective framework for the optimal siting and sizing of battery energy storage systems (BESSs), applied to the 130-bus Mali transmission network within the EMERGE project. The upper level employs NSGA-II to simultaneously maximize daily price arbitrage revenue and minimize active power losses; the lower level solves a network-constrained DC optimal power flow with thermal branch limits enforced as hard linear inequalities via the Power Transfer Distribution Factor (PTDF) matrix. Over 500 generations, the framework identifies Bus 91 (SIRAKORO II, 150 kV) as the dominant storage location, achieving a maximum daily revenue of approximately €10,033 at a marginal loss increment of $6.7\times {10}^{-3}$ MWh. The resulting Pareto front gives Mali system planners a quantitative tool for trading off private investment returns against grid-level environmental impact, demonstrating that rigorous network-constrained BESS planning is technically tractable and economically viable in the resource-constrained context of sub-Saharan energy transitions. Full article

Active labor market policies (ALMPs) targeting self-employment have become a well-established and relevant instrument within employment promotion strategies across many European countries. However, despite their strategic and economic importance, there is limited evidence on their potential performance prior to implementation. This paper aims to address this gap by conducting an ex-ante cost–benefit simulation of different types of ALMPs designed to promote self-employment in Spain. The methodology is based on estimating public costs per beneficiary and quantifiable potential benefits, including avoided welfare payments, additional tax revenues, and the generation of economic activity. These benefits are adjusted using two key parameters: additionality (the proportion of the effect genuinely attributable to the policy) and persistence (the duration of the impact over time). In addition, three sensitivity scenarios (conservative, baseline, and favorable) are developed. The results suggest that financing and access to credit policies exhibit the most robust returns, while direct subsidies, general tax incentives, and emergency policies are more sensitive to intervention design features. Consequently, the effectiveness of ALMPs targeting self-employment depends fundamentally on their ability to align with the specific frictions faced by potential entrepreneurs and on the persistence of their effects. Full article

To address the deepening electro-carbon coupling and flexibility shortages in active distribution networks with high renewable energy penetration, this paper proposes a bi-level collaborative planning strategy considering dynamic electro-carbon factors. First, considering the spatial–temporal correlation of wind and solar outputs, typical renewable energy scenarios are generated using the Frank-Copula function and clustering algorithms. Second, a bi-level planning model for the Soft Open Point integrated with an Energy Storage System (E-SOP) is established: the upper level optimizes the siting and sizing of E-SOPs to minimize the annualized comprehensive cost; the lower level incorporates a dynamic stepped carbon trading mechanism and a continuous price-based demand response (PBDR) mechanism to achieve optimal operational economy. For model solving, a hybrid bi-level decomposition strategy combining the Dhole Optimization Algorithm (DOA) and second-order cone programming (SOCP) is adopted, utilizing a coordinated dual-level solution interaction to favorably support numerical stability. Case studies on a modified IEEE 33-node system demonstrate that the proposed scheme reduces the annualized comprehensive cost by 12.3% and transforms the carbon trading expenditure into a net revenue, thereby significantly enhancing the low-carbon economic efficiency and operational flexibility of the distribution network. Full article

Smart grid private communication networks need to support heterogeneous services with varying requirements for reliability, security, bandwidth, and controllability. In such networks, service function chains (SFCs) can provide customized network services by deploying virtual network functions (VNFs) over a shared substrate infrastructure. However, sharing physical servers among different service categories may conflict with the physical isolation requirement between critical grid services and common grid services. To address this problem, this paper investigates physical-isolation-aware SFC deployment for smart grid private communication networks. We first formulate an integer nonlinear programming (INLP) model that maximizes the network resource usage revenue while considering server resource constraints, link bandwidth constraints, flow conservation constraints, virtual link mapping constraints, server energy consumption, and physical isolation constraints. The nonlinear constraints are then linearized into an integer linear programming (ILP) model, which can be solved by an optimizer and used as a benchmark. To reduce the computational cost, we propose a private-network-oriented service function chain isolation deployment (PNO-SSID) algorithm. The proposed algorithm selects a revenue-aware subset of SFC requests, determines the service category to be preferentially processed, selects server nodes based on VNF-layer traffic cost, deploys VNFs using a matching-game-based method, and maps virtual links based on shortest paths. Simulation results show that PNO-SSID requires much less execution time than CPLEX while achieving close revenue in small-scale cases. Compared with online profit maximization (OLPM) variants using different request preprocessing strategies, PNO-SSID achieves higher network resource usage revenue and request acceptance ratio under physical isolation constraints. A prototype platform based on a fifth-generation non-standalone private network and the OAI platform further validates the feasibility of server-level isolated core network service chain deployment under the considered service-category separation requirement. 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

Developing countries in which infrastructure development is heavily dependent on overseas development aid face significant sustainability challenges, including financing gaps and inadequate maintenance. Increasing private-sector investment is crucial for addressing these challenges. This paper proposes an innovative framework linking environmental, social, and governance (ESG) principles with a revised joint credit mechanism (JCM) to attract private investment in infrastructure development, particularly in Pacific Island countries facing the climate crisis. Under the revised JCM, by allocating generated carbon credits to participating Japanese companies, rather than the Japanese government, corporations can monetise credits through market transactions, creating compelling economic incentives for private-sector engagement. In ESG-advanced markets, credits serve as strategic instruments for corporate value enhancement beyond revenue generation, while corporations require continuous credit acquisition to sustain investor confidence. Our revised framework provides a sustainable solution to both financing gaps and infrastructure maintenance challenges. Our analysis demonstrates that integrating market dynamics and corporate incentives into bilateral climate mechanisms holds substantial potential for mobilising private capital for sustainable climate infrastructure finance. This approach represents a promising departure from traditional donor-dependent models, effectively aligning corporate interests with sustainable development objectives while advancing national emission reduction commitments. Full article

Wind turbine gearbox failures lead to substantial downtime and high maintenance costs. Although condition-monitoring systems are widely used, traditional life-extension methods that simply reduce power output often decrease revenue. Current research frequently treats life optimization and power generation independently, and as such lacks a quantitative link between control strategies and remaining useful life. To address this gap, this paper proposes a novel life-extension strategy that optimizes power generation by dynamically adjusting rotor speed and pitch angle. A transfer learning–long short-term memory model enhanced by multi-source information fusion is developed to predict remaining useful life accurately under conditions with limited fault data. Utilizing real operational data from 2 MW wind turbines in Northeast China, the study quantitatively analyzes the impact of variable-speed and pitch control. The results demonstrate that while both strategies extend life, variable-speed control offers superior effectiveness in improving remaining useful life. Furthermore, maximum power generation is achieved not at full capacity, but when the output is reduced to approximately 70% of the nominal power. At this optimal point, the proposed strategy increases power generation by up to 7.3%. This establishes a dynamic balance between operational safety and economic efficiency, overcoming the limitations of conventional methods. Full article

Achieving sustainable development requires decoupling economic growth from fossil fuel dependence—a challenge that places carbon pricing at the intersection of environmental policy, economic efficiency, and social equity. Carbon taxation is widely regarded among economists as the most cost-effective instrument for reducing greenhouse gas emissions, yet the United States has not adopted a federal carbon price. This study examines the macroeconomic and sectoral consequences of a hypothetical federal carbon tax using the Standard GTAPv7 computable general equilibrium model calibrated to GTAP Database version 12 (2023). A tax rate of 27.7% is derived from the Regional Greenhouse Gas Initiative (RGGI) average auction price of USD 12.81/t CO₂ for 2023—the lowest among active U.S. state carbon programs—and applied as a production tax shock to the fossil fuel sector. Simulations at the California (USD 32.93/t CO₂) and Washington state (USD 53.10/t CO₂) prices provide sensitivity bounds. Under the baseline scenario, U.S. real GDP falls by 0.09%, unskilled employment declines by 0.17%, and fossil fuel production and exports contract sharply. Outside the fossil fuel complex, most sectors record output and export gains, and total U.S. net exports improve by 0.33 percentage points. Bilateral GDP spillovers across eighteen trading partners range from −0.17% (South Korea) to −0.01% (Australia), principally through fossil fuel trade exposure. The results demonstrate that a federal carbon tax at the RGGI price can achieve meaningful emissions reduction at a contained macroeconomic cost, supporting the environmental pillar of sustainability. The concentration of adjustment burdens on unskilled workers highlights the social sustainability challenge of ensuring a just transition. The production reallocation from fossil-intensive to non-fossil sectors is consistent with the circular economy framework and contributes to long-run economic sustainability by reducing dependence on finite, non-renewable resources. Revenue recycling, just-transition provisions, and carbon border adjustment are identified as complementary policy instruments essential for aligning carbon taxation with the integrated environmental, economic, and social dimensions of sustainable development. Full article

Oil-price volatility represents a major challenge for hydrocarbon-dependent economies pursuing renewable-energy transition. In GCC countries, fluctuations in global oil markets may influence renewable-energy deployment through their effects on fiscal revenues, investment conditions, and long-term energy planning. While previous studies have largely examined the direct effects of oil prices, renewable energy, and financial development separately, limited evidence exists on whether financial development can mitigate the adverse implications of oil-market uncertainty for renewable-energy transition in GCC economies. Using annual data for six GCC countries over the period 1990–2024, this study investigates the links among oil-price volatility, financial development, and renewable-energy transition within a second-generation panel econometric framework that accounts for cross-sectional dependence and heterogeneity. The analysis employs Pesaran cross-sectional dependence tests, CIPS unit-root tests, Westerlund cointegration, common correlated effects mean group (CCE-MG), augmented mean group (AMG), and error-correction modeling. The results support the existence of a stable long-run relationship among the variables. Oil-price volatility is negatively associated with renewable-energy consumption, with a long-run coefficient of approximately −0.21. Financial development exhibits a positive association with renewable-energy transition, while the interaction between oil-price volatility and financial development remains positive and statistically significant. This finding suggests that stronger financial systems may partially reduce the adverse effects of oil-market instability. The short-run estimates also support the presence of a stable adjustment process toward long-run equilibrium. Robustness checks based on alternative financial-development proxies, lagged regressors, Driscoll–Kraay estimations, leave-one-out country analysis, and alternative volatility measures confirm the stability of the main findings. The findings suggest that financial development may strengthen the resilience of renewable-energy transition strategies in GCC economies exposed to volatile energy-market conditions. Full article