Search Results (530)

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

Energy communities (ECs) are becoming a key institutional instrument for decentralizing the European energy transition, yet their implementation remains constrained by fragmented legal interpretation, uneven national transposition, and unresolved socio-technical coordination problems. This review synthesizes the peer-reviewed literature, EU primary legal texts, and national legislation to clarify the distinction between Renewable Energy Communities (RECs) and Citizen Energy Communities (CECs), alongside the amendment relationship between the RED II and RED III directives. The analysis demonstrates that the scalability of these initiatives depends less on theoretical legal recognition and more on aligning operational frameworks, including metering, settlement, cybersecurity, and equitable allocation rules. The Romanian case illustrates this challenge clearly: rapid prosumer growth creates valuable distributed generation but also exposes physical grid constraints, asymmetric socio-economic participation capacity, and weak experience with cooperative energy governance. To address these vulnerabilities, this paper contributes a focused analytical framework linking energy justice, peer-to-peer game-theoretic modeling, and the strategic integration of “anchor-prosumers.” The study argues that larger renewable self-consumers can act as stabilizing community anchors when internal energy prices are designed between wholesale export values and retail import prices, thereby improving both producer incentives and consumer affordability. Future research developments, including targeted surveys and longitudinal empirical validations, will sustain this claim and optimize the socio-economic resilience of decentralized energy markets. Full article

As a cornerstone of global technological advancement, the semiconductor industry depends critically on supply chain stability, which directly influences the global economy and technological innovation. To address uncertainty in semiconductor supply chains, this study develops a Stackelberg game model incorporating Nash bargaining fairness concern to examine pricing strategies, ESG effort decisions, and their implications for supply chain stability under different fairness concern scenarios. A cost-sharing contract-based coordination mechanism is proposed, and numerical simulations verify the effects of fairness concern and ESG effort on stability, as well as the coordinating role of the cost-sharing contract under market uncertainty. The results show the following: (1) Manufacturer fairness concern boosts its profit and ESG effort, but excessive price hikes erode retailer profit and undermine stability. (2) Retailer fairness concern prompts the manufacturer to rebalance profit allocation via lower wholesale prices and reduced ESG effort, weakening supply chain competitiveness. (3) Cost-sharing contracts effectively mitigate the adverse effects of fairness concern and enhance semiconductor supply chain stability. This study provides a verifiable framework for semiconductor firms to improve cooperative stability and sustainable development. 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

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

Sustainable industrial transition requires productivity evidence that separates real efficiency improvement from scale expansion, capital deepening, and reporting change. This study develops a reproducible frontier-productivity diagnostic for provincial leading industry policy, using official 2013–2023 sector panels for 23 two-digit manufacturing sectors in Shandong Province and a matched 2019–2023 benchmark against Jiangsu. The framework combines input-oriented Banker–Charnes–Cooper (BCC) data envelopment analysis (DEA), Simar–Wilson bootstrap bias correction, Malmquist total factor productivity change (TFPCH) decomposition, producer price index (PPI) deflation diagnostics, scale-productivity classification, and targeted sensitivity tests. Bootstrap correction lowers mean BCC efficiency from 0.77 to 0.69 in Shandong and from 0.79 to 0.70 in Jiangsu. Uniform provincial PPI deflation leaves constant-returns-to-scale (CRS) Malmquist estimates almost unchanged, whereas asymmetric deflation creates measurable sensitivity. Direct sector-cluster resampling places Shandong’s aggregate TFPCH at 1.016 with a 95% interval of 0.995–1.045, supporting a near-stationary interpretation rather than a broad upgrading surge; Jiangsu’s corresponding estimate is 0.976 with a 95% interval of 0.955–0.997. The study does not measure environmental performance directly. It shows how frontier-productivity evidence should be stress-tested and paired with environmental indicators before it is used in sustainability-oriented industrial policy. Full article

There are significant difficulties with power quality and stability as a result of active cooperation between renewable energy sources and load demand. To maintain power stability between renewable energy supplies and the microgrid/utility grid, novel solutions must be implemented. By using an artificial and computational intelligence controller to schedule power from multiple sources (photovoltaic, wind, grid, and battery) under a set of constraints, such as weather, load-shedding hours, and peak pricing hours, this paper introduces a novel approach for power management in grid-connected hybrid renewable systems with PV–wind and energy storage systems. The approach involves using an artificial neural network (ANN) to process all of the inputs and creating an ANN rule set from a modelled hybrid renewable system. A rule-based power scheduler is developed, and simulations are run for a full day. The suggested fuzzy control approach can detect ongoing variations in grid load-shedding patterns, PV–wind power generation, load demands, and battery state-of-charge to enable prompt and accurate decision-making. The proposed ANN rule-based scheduler can handle nonlinearity by integrating metaheuristics into computer-assisted decision-making and can function effectively with imprecise inputs, negating the need for an exact numerical model. The MATLAB/Simulink R2023a software was used for simulation, and the system operated as efficiently as possible. The simulation results suggested that an ANN offers a foundation for extension to handle numerous particular scenarios. Full article

The Gulf Cooperation Council (GCC) countries, heavily reliant on oil revenues, have long aimed to diversify their economies to mitigate the volatility of global oil prices and foster sustainable growth. Two countries, Saudi Arabia, representing the biggest economy, and Kuwait, the third biggest economy in the GCC, were chosen based on their promising economic visions, while being considered as the more historically conservative countries. Both countries represent case studies to reflect on the effectiveness of their diversification measures on GDP/capita as one of the main macroeconomic indicators for prosperity. The paper aims to use time series data over the period 2000–2024 for both countries to reflect the diversification efforts on GDP per capita. A straightforward multivariate regression model is employed, utilizing the value-added contributions of the three primary sectors—industry, agriculture, and services—to examine whether recent economic transformations and policy reforms have influenced GDP per capita and to identify in which country reforms exerted the greatest impact. Findings are expected to reflect a bigger impact of diversification aims on GDP/capita in Saudi Arabia due to the pace of reforms that have been implemented. This research shall provide valuable insights for policymakers, highlighting the need to promote policy reforms to foster sustainable economic growth. The outcome of this study will provide hydrocarbon-dependent GCC economies with an updated, replicable methodological framework to support a better formulation of policy and strategy connecting digital transformation and sustainability agendas in line with efforts related to the Saudi Vision 2030 and Kuwait Vision 2035, which shall present a benchmark that can be applicable for the other GCC economies. Full article

Against the backdrop of the continuous advancement of the market-oriented allocation of data factors, problems such as high data circulation costs, unclear pricing mechanisms, insufficient value conversion, and unreasonable profit allocation have constrained the coordinated development of the data supply chain. To address these issues, this paper develops a model of transactions in data and its derivative products among the data provider, the data service provider, and the data demander. On the basis of considering data integrity, privacy sensitivity, privacy protection investment, and data analytics capability, it compares the optimal strategies and total supply chain profit under four modes: non-cooperation, cooperation between the data provider and the data service provider, cooperation between the data service provider and the data demander, and tripartite cooperation. It further employs an improved Shapley value to allocate and coordinate cooperative profits. The results show that improvements in data integrity and data analytics capability help expand data scale, increase the prices of data and data products, and improve the target performance of the application product, whereas increases in data processing cost, data analytics cost, privacy sensitivity, and privacy protection investment inhibit the optimal strategies and total profit of the data supply chain. Cooperation modes can effectively mitigate the efficiency loss caused by decentralized decision-making, and the more complete the scope of cooperation, the better the overall performance of the supply chain. A higher degree of cooperation can further improve total supply chain profit under cooperation modes. The improved Shapley value can effectively coordinate the data supply chain and enhance the fairness of profit allocation and the stability of cooperation. This study enriches the theory of pricing and revenue coordination in data supply chains and provides decision support for the efficient circulation of data factors and multi-party collaborative governance. Full article

The Pearl River Delta region suffers from congestion in the urban road network, noise, air pollution, and other “urban diseases”. Vigorously developing inland water transportation can greatly alleviate these “urban diseases”. However, it is difficult to take advantage of the inland waterway transportation cost advantages due to the Pearl River Delta’s short haul distance characteristics. In recent business practice, a novel, environment-friendly, and competitiveness-enhanced inland waterway transportation mode has emerged in the area, called the leftover-cargo mode in this paper. This mode is composed of first-tier (big companies) and second-tier (small companies) inland barge companies, which establish a cooperative relationship and jointly meet the needs of shippers and can lead to a modal shift from inland truck to inland waterway transportation. In real practice, the pricing methods of this novel mode still rely on experience. We propose four pricing game theory models based on channel leadership in order to investigate how decision-making impacts the pricing and income of the two-tier companies. We find that, if the market price ceiling is low, second-tier inland barge companies always benefit more than first-tier companies, which is very interesting and counter to the existing literature. These findings offer pricing insights into economically viable leftover-cargo cooperation and its role in supporting sustainable road-to-waterway freight modal shift in the Pearl River Delta. Full article

To address the output volatility of distributed photovoltaics, the low utilization efficiency of energy storage resources, and the challenge of optimal revenue for PV-storage virtual power plants (VPPs) in multi-market environments, this paper proposes a three-stage stochastic optimal operation strategy for PV-storage VPPs under multi-market synergy and develops a benefit allocation model based on the Nash–Harsanyi bargaining game. A Monte Carlo simulation was adopted to capture the uncertainties of market electricity prices and PV power output, and the stochastic dual-dynamic-programming (SDDP) algorithm was employed to solve the three-stage optimization framework consisting of day-ahead bidding, real-time optimization, and real-time frequency regulation. Bargaining power was quantified from four dimensions—the marginal contribution rate, PV prediction accuracy, energy storage capacity, and utilization rate—to establish a fair and reasonable internal benefit allocation mechanism. Case studies verified that the proposed method improved the single-day market revenue by up to 20.79% compared with traditional operation modes, achieved a near-zero curtailment rate for distributed PV, and maintained frequency regulation performance scores above 0.4 at all times. The benefits of all investment entities in the alliance increased by 3.36–99.43%, significantly enhancing the multi-market profitability of PV-storage VPPs and the stability of alliance cooperation. Full article

With the ongoing low-carbon transition of energy systems and the increasing penetration of distributed energy resources, the coordinated operation of heterogeneous prosumers has become essential for improving the economic and environmental performance of integrated energy systems. However, existing studies have not sufficiently addressed the joint coordination of electricity sharing, carbon emission trading, green certificate trading, and demand-side flexibility. To address this gap, this paper proposes a hybrid game-based optimal operation model for a multi-energy prosumer alliance coordinated by an Electricity Balance Service Provider (EBSP). The model is developed under coupled carbon emission trading (CET) and green certificate trading (GCT) markets. A piecewise linear dynamic pricing mechanism and a mutual recognition rule are introduced to describe the interaction between CET and GCT. Meanwhile, a price-based demand response model considering reducible and shiftable loads is incorporated to exploit load-side flexibility. On this basis, a Stackelberg-cooperative hybrid game is formulated to coordinate electricity pricing, integrated dispatch, electricity sharing, and benefit allocation between the EBSP and the prosumer alliance. The proposed model is solved using particle swarm optimization and the alternating direction method of multipliers. Case studies show that, compared with the corresponding benchmark scenarios, the proposed method reduces the alliance operating cost by 7.19%, the carbon trading cost by 41.35%, and total carbon emissions by 3.66%. It also decreases the peak-to-valley load difference ratio by 3.78 percentage points. These results demonstrate the effectiveness of the proposed method in improving economic performance, promoting low-carbon operation, and enhancing the peak-shaving and valley-filling capability of the prosumer alliance. Full article

Abrupt maritime-energy disruption can generate system-wide production losses before firms and policymakers can adjust. Existing assessments usually emphasize direct exposure or long-run equilibrium responses, which makes them less suitable for short-run risk assessment in energy-dependent production systems. We develop a threshold-cascade framework that combines dual-track dependence topology, edge-level inventories, smooth operability bands, and a separate price-validation step to identify the blockade intensity at which a localized chokepoint shock becomes systemic production loss. The framework is evaluated against the March 2021 Suez blockage and the 2022 Russia–Ukraine producer-price episode, and then applied to a 2026 Strait of Hormuz stress scenario using the Organisation for Economic Co-operation and Development (OECD) Inter-Country Input-Output (ICIO) tables, 2025 edition, with the 2022 benchmark year. Under the baseline 150-day horizon, terminal loss first reaches 50% at about 32% blockade intensity, with a broader calibrated threshold band of 32–46%. Losses spread beyond the point of origin and become concentrated in East and Southeast Asian manufacturing supply chains and in downstream consumer markets after inventories at connected hubs are depleted. Policy experiments show that single-channel interventions shift the threshold only modestly, whereas an integrated package that relaxes logistics, inventories, and upstream scarcity moves the threshold to about 46% in this calibration. The analysis targets the weeks-to-months interval before substitution, contract renegotiation, and broader market adjustments dominate. Within that interval, the model identifies when buffers fail, how production losses spread, and which intervention packages delay systemic disruption. Full article

With the large-scale integration of new energy, electric vehicles, and other new loads, disorderly electricity consumption has led to surging peak loads and heightened overload risks for distribution transformers. Particularly in aging, high-density urban areas constrained by the cost and space limitations of upgrading distribution facilities, there is an urgent need to tap into the flexible load control potential of existing power distribution systems to ensure system safety. This paper proposes a multi-timescale cooperative scheduling framework for flexible loads in distribution systems, deeply integrating the dynamic load capacity of transformers with the dispatchable characteristics of a flexible load. First, a day-ahead scheduling layer based on multi-agent reinforcement learning is constructed to optimize electricity plans and smooth peak–valley loads in the distribution system. Second, a dynamic transformer-rating model for distribution transformers is established to uncover their dynamic load capabilities under varying environmental conditions. Finally, an intraday scheduling layer for flexible loads is developed. It dynamically matches the regulation demands of distribution transformers and flexible loads via real-time optimization of consumption strategies to address electricity price fluctuations and user behavior randomness. Case study results demonstrate that the methods described in this paper effectively reduce power load fluctuations, ensuring the safe and stable operation of distribution and power supply systems. Full article

Against the backdrop of the digital economy, digital transformation has increasingly evolved from a firm-level upgrading process into a collaborative decision-making issue among supply chain members. From the perspective of intelligent supply chain management, this study develops a two-echelon game model of a vertical manufacturer–retailer supply chain to examine digital collaborative decision-making under heterogeneous power structures. By comparing a centralized cooperative benchmark with decentralized non-cooperative scenarios, the study investigates how power structures affect firms’ digital transformation efforts, pricing decisions, and system-level outcomes, while also considering the role of knowledge spillovers. The results show that, under the same power structure, cooperation leads to higher digital transformation effort levels and greater total supply chain profit than non-cooperation. Knowledge spillovers further strengthen firms’ incentives to invest in digital transformation and improve market demand, consumer surplus, and social welfare. Compared with asymmetric power structures, a balanced power structure generates lower retail prices, higher market demand, and better overall supply chain performance. Numerical simulations further show that higher digital transformation costs weaken collaborative gains, whereas greater market sensitivity to digitalization strengthens them. Overall, this study suggests that digital collaboration contributes to supply chain sustainability by improving coordination efficiency, enhancing adaptive operations, and promoting system-level value realization under heterogeneous governance structures. Full article