Search Results (1,789)

The integration of renewable energy into power grids is imperative for reducing carbon emissions and mitigating reliance on depleting fossil fuels. In this paper, we develop symmetric and asymmetric evolutionary game-theoretic models to analyze how user–grid cooperation in peak shaving can be enhanced by incorporating whole-process democracy (deliberative governance) into decision-making. Our framework captures excess returns, cooperation-driven profits, energy pricing, participation costs, and benefit-sharing coefficients to identify equilibrium conditions under varied subsidy, cost, and market scenarios. Furthermore, this study integrates the theory, path, and mechanism of deliberative procedures under the perspective of whole-process democracy, exploring how inclusive and participatory decision-making processes can enhance cooperation in renewable energy systems. We simulate seven scenarios that systematically adjust subsidy rates, cost–benefit structures, dynamic pricing, and renewable-versus-conventional competitiveness, revealing that robust cooperation emerges only under well-aligned incentives, equitable profit sharing, and targeted financial policies. These scenarios systematically vary these key parameters to assess the robustness of cooperative equilibria under diverse economic and policy conditions. Our findings indicate that policy efficacy hinges on deliberative stakeholder engagement, fair profit allocation, and adaptive subsidy mechanisms. These results furnish actionable guidelines for regulators and grid operators to foster sustainable, low-carbon energy systems and inform future research on demand response and multi-source integration. Full article

The European Union Emissions Trading System (EU ETS) and its underlying market structure play a central role in the EU’s climate policy. This study analyses how the network of trading relationships within the EU ETS has evolved from a hub-dominated architecture to one marked by structural change and the emergence of new trading dynamics. Using transaction data from Phases I–IV, we apply complex network analysis to assess changes in connectivity, centrality, and community structure. We then construct a Digital Twin of the EU ETS, integrating graph neural networks and logistic regression models to simulate the entry of new participants and predict future trading links. The results indicate shifts in network composition and connectivity, especially in Phase IV, where regulatory innovations and institutional mechanisms appear to play a key role. While our analysis focuses on structural dynamics, these patterns may have broader implications for market performance and policy effectiveness. These findings underscore the importance of monitoring the evolving trading network alongside price signals to support a resilient, efficient, and environmentally credible carbon market. Full article

Enhancing market integration levels is crucial for advancing sustainable regional collaborative development and achieving ecological protection and high-quality development goals within the Yellow River Basin, fostering a balance between economic efficiency, social equity, and environmental resilience. This study analyzed the retail price data of goods from prefecture-level cities in the Yellow River Basin from 2010 to 2022, employing the relative price method to measure the market integration index. Additionally, it examined the temporal and spatial evolution patterns and driving factors using the Dagum Gini coefficient and panel regression models. The results indicate the following. (1) The market integration index of the Yellow River Basin shows a fluctuating upward trend, with an average annual growth rate of 9.8%. The spatial pattern generally reflects a situation where the east is relatively high and the west is relatively low, as well as the south being higher than the north. (2) Regional disparities are gradually diminishing, with the overall Gini coefficient decreasing from 0.153 to 0.104. However, internal differences within the downstream and midstream areas have become prominent, and contribution rate analysis reveals that super-variable density has replaced between-group disparities as the primary source. (3) Upgrading the industrial structure and enhancing the level of economic development are the core driving forces, while financial support and digital infrastructure significantly accelerate the integration process. Conversely, the level of openness exhibits a phase-specific negative impact. We propose policy emphasizing the need to strengthen development in the upper reach of the Yellow River Basin, further improve interregional collaborative innovation mechanisms, and enhance cross-regional coordination among multicenter network nodes. Full article

Given the compromising effect of energy rebound on energy conservation efforts and environmental sustainability, plentiful research has focused on evaluating its size and scope in the past; however, there is a scarcity in the exploration of its potential drivers, especially the impacts of the digital economy. With the accelerating pace of worldwide digitalization, how the digital economy affects the energy rebound effect deserves special attention. We explored the underlying impacts of the digital economy on energy rebound and its influencing mechanisms for the first time in this study based on a panel dataset from China. Results show that most of the regions in China exhibited a partial rebound effect over the period 2007–2022, with an average value of 77.14%. Digital economy development exhibits a threshold effect on energy rebound with regard to energy efficiency improvement. That is, when the energy efficiency is low, digital economy development positively impacts the energy rebound, however, as the energy efficiency increases and surpasses a certain critical threshold, the digital economy can help mitigate the energy rebound effect. Energy prices and environmental regulation present a significant negative impact on energy rebound. Finally, several policy implications are highlighted based on the main findings of this study. Full article

This study focusses on the transmission of carbon pricing mechanisms in shaping trade dynamics between the Eurozone and key partners: the USA and China. Using Bayesian variable selection methods and a Time-Varying Structural Vector Autoregressions (TV-SVAR) model, the research identifies the key variables impacting EU carbon emissions over time. The results reveal that manufactured products from the US have a diminishing positive impact on EU carbon emissions, suggesting potential exemption from future regulations. In contrast, manufactured goods from the US and petroleum products from China are expected to increase emissions, indicating a need for stricter trade policies. These findings provide strategic insights for policymakers aiming to balance trade and environmental objectives. Full article

This study employs k-means clustering to analyze local organizations responsible for land improvement in Greece, identifying four distinct groups with consistent geographic patterns but divergent financial and operational characteristics. By integrating unsupervised machine learning with spatial analysis, the research offers a novel perspective on irrigation water pricing and cost recovery. The findings reveal that organizations located on islands, despite high water costs due to limited rainfall and geographic isolation, tend to achieve relatively strong financial performance, indicating the presence of adaptive mechanisms that could inform broader policy strategies. In contrast, organizations managing extensive irrigable land or large volumes of water frequently show poor cost recovery, challenging assumptions about economies of scale and revealing inefficiencies in pricing or governance structures. The spatial coherence of the clusters underscores the importance of geography in shaping institutional outcomes, reaffirming that environmental and locational factors can offer greater explanatory power than algorithmic models alone. This highlights the need for water management policies that move beyond uniform national strategies and instead reflect regional climatic, infrastructural, and economic variability. The study suggests several policy directions, including targeted infrastructure investment, locally calibrated water pricing models, and performance benchmarking based on successful organizational practices. Although grounded in the Greek context, the methodology and insights are transferable to other European and Mediterranean regions facing similar water governance challenges. Recognizing the limitations of the current analysis—including gaps in data consistency and the exclusion of socio-environmental indicators—the study advocates for future research incorporating broader variables and international comparative approaches. Ultimately, it supports a hybrid policy framework that combines data-driven analysis with spatial intelligence to promote sustainability, equity, and financial viability in agricultural water management. Full article

Stock price forecasting remains a persistent challenge in time series analysis due to complex inter-stock relationships and dynamic textual signals such as financial news. While Graph Neural Networks (GNNs) can model relational structures, they often struggle with capturing higher-order dependencies and are sensitive to noise. Moreover, sentiment signals are typically aggregated using fixed time windows, which may introduce temporal bias. To address these issues, we propose DASF-Net (Diffusion-Aware Sentiment Fusion Network), a multimodal framework that integrates structural and textual information for robust prediction. DASF-Net leverages diffusion processes over two complementary financial graphs—one based on industry relationships, the other on fundamental indicators—to learn richer stock representations. Simultaneously, sentiment embeddings extracted from financial news using FinBERT are aggregated over an empirically optimized window to preserve temporal relevance. These modalities are fused via a multi-head attention mechanism and passed to a temporal forecasting module. DASF-Net integrates daily stock prices and news sentiment, using a 3-day sentiment aggregation window, to forecast stock prices over daily horizons (1–3 days). Experiments on 12 large-cap S&P 500 stocks over four years demonstrate that DASF-Net outperforms competitive baselines, achieving up to 91.6% relative reduction in Mean Squared Error (MSE). Results highlight the effectiveness of combining graph diffusion and sentiment-aware features for improved financial forecasting. Full article

Credit-linked notes (CLNs) are vital for transferring and diversifying credit risks in asset securitization, yet their application in China remains limited despite policy support. This paper optimizes China’s CLN pricing mechanism by developing the structured model incorporating the dynamic default boundary and the probability of government implicit guarantees. The model transforms the pricing problem into a semi-unbounded problem via partial differential methods, yielding an explicit pricing solution through Poisson’s formula. Empirical analysis reveals that government implicit guarantees are observed in systemically important institutions in the domestic CLN market and significantly reduce credit risk premiums, with Monte Carlo simulations indicating an approximately positive linear correlation between guarantee probability and CLN prices. Our results demonstrate the dual impact of implicit guarantees—lowering risk premiums while potentially hindering market discipline. This research advances China’s credit derivative pricing theory, offering institutions a pricing tool and further providing policy and practical suggestions for regulatory authorities. Full article

This study focuses on the impact of China’s digital economy on sustainable modernization from 2011 to 2021, using provincial panel data for empirical analysis. By applying threshold and mediation models, we find that the digital economy promotes modernization through industrial upgrading (with a mediating effect of 38%) and trade openness (coefficient = 0.234). The research reveals “U-shaped” nonlinear threshold effects at specific levels of digital development (2.218), market efficiency (9.212), and technological progress (12.224). Eastern provinces benefit significantly (coefficient ranging from 0.12 to 0.15 ***), while western regions initially experience some inhibition (coefficient = −0.08 *). Industrial digitalization (coefficient = 0.13 ***) and innovation ecosystems (coefficient = 0.09 ***) play crucial roles in driving eco-efficiency and equity, in line with Sustainable Development Goals 9 and 13. Meanwhile, the impacts of infrastructure (coefficient = 0.07) and industrialization (coefficient = 0.085) are delayed. Economic modernization improves (coefficient = 0.37 ***), yet social modernization declines (coefficient = −0.12 *). This study not only enriches economic theory but also extends the environmental Kuznets curve to the digital economy domain. We propose tiered policy recommendations, including the construction of green digital infrastructure, carbon pricing, and rural digital transformation, which are applicable to China and offer valuable references for emerging economies aiming to achieve inclusive low-carbon growth in the digital era. Future research could further explore the differentiated mechanisms of various digital technologies in the modernization process across different regions and how to optimize policy combinations to better balance digital innovation with sustainable development goals. Full article

Given the significant shifts in rural labor mobility patterns and their continuous influence on the transformation of the land factor market, it is crucial to understand the relationship between labor factor prices and land factor prices. This understanding is essential to keep land factor prices within a reasonable range. This study establishes a theoretical framework to investigate how migrant workers’ return shapes land price formation mechanisms. Using 2023 micro-level survey data from eight counties in Jiangsu Province, China, this study empirically examines how migrant workers’ return affects land transfer prices and its underlying mechanisms through OLS regression and instrumental variable approaches. The findings show that under the current pattern of labor mobility, the outflow factor alone is no longer sufficient to exert substantial downward pressure on land transfer prices. Instead, the localized return of labor has emerged as a key driver behind the rise in land transfer prices. This upward mechanism is primarily realized through the following pathways. First, factor substitution effect: this effect lowers labor prices and increases the relative marginal output value of land factors. Second, supply–demand effect: migrant workers’ return simultaneously increases land demand and reduces supply, intensifying market shortages and driving up transfer prices. Lastly, the results demonstrate that enhancing the stability of land tenure security or increasing local non-agricultural employment opportunities can mitigate the effect of rising land transfer prices caused by the migrant workers’ return. According to the study’s findings, stabilizing land factor prices depends on full non-agricultural employment for migrant workers. This underscores the significance of policies that encourage employment for returning rural labor. Full article

The Carbon Emissions Trading System (ETS) serves as a market-based mechanism to drive renewable energy (RE) investments, yet its heterogeneous impacts on different stakeholders remain underexplored. This paper treats the carbon market as an exogenous shock and develops a multi-agent equilibrium model incorporating carbon pricing, encompassing power generation enterprises, power transmission enterprises, power consumers, and the government, to analyze how carbon prices reshape RE investment layouts under dual-carbon goals. Using panel data from Zhejiang Province (2017–2022), a high-energy-consumption region with 25% net electricity imports, we simulate heterogeneous responses of agents to carbon price fluctuations (CNY 50–250/ton). The results show that RE on-grid electricity increases (+0.55% to +2.89%), while thermal power declines (–4.98% to −15.39%) on the generation side. Transmission-side RE sales rise (+3.25% to +9.74%), though total electricity sales decrease (−0.49% to −2.22%). On the consumption side, RE self-generation grows (+2.12% to +5.93%), yet higher carbon prices reduce overall utility (−0.44% to −2.05%). Furthermore, external electricity integration (peaking at 28.5% of sales in 2020) alleviates provincial entities’ carbon cost pressure under high carbon prices. This study offers systematic insights for renewable energy investment decisions and policy optimization. Full article

This study introduces a novel tripartite evolutionary game model to analyze the strategic interactions among electric vehicle (EV) aggregators, local governments, and EV users in vehicle–grid interaction (VGI) markets. The core novelty lies in capturing bounded rationality and dynamic decision-making across the three stakeholders, revealing how policy incentives and market mechanisms drive the transition from disordered charging to bidirectional VGI. Key findings include the following: (1) The system exhibits five stable equilibrium points, corresponding to three distinct developmental phases of the VGI market: disordered charging (V0G), unidirectional VGI (V1G), and bidirectional VGI (V2G). (2) Peak–valley price differences are the primary driver for transitioning from V0G to V1G. (3) EV aggregators’ willingness to adopt V2G is influenced by upgrade costs, while local governments’ subsidy strategies depend on peak-shaving benefits and regulatory costs. (4) Increasing the subsidy differential between V1G and V2G accelerates market evolution toward V2G. The framework offers actionable policy insights for sustainable VGI development, while advancing evolutionary game theory applications in energy systems. Full article

Effective energy management is vital for enhancing reliability, reducing operational costs, and supporting the increasing penetration of electric vehicles (EVs) and renewable energy sources (RESs) in distribution networks. This study presents a dynamic reconfiguration strategy for distribution feeders that integrates EV charging stations (EVCSs), RESs, and capacitors. The goal is to minimize both Energy Not Supplied (ENS) and operational costs, particularly under varying demand conditions caused by EV charging in grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes. To improve optimization accuracy and avoid local optima, an improved Whale Optimization Algorithm (IWOA) is employed, featuring a mutation mechanism based on Lévy flight. The model also incorporates uncertainties in electricity prices and consumer demand, as well as a demand response (DR) program, to enhance practical applicability. Simulation studies on a 95-bus test system show that the proposed approach reduces ENS by 16% and 20% in the absence and presence of distributed generation (DG) and EVCSs, respectively. Additionally, the operational cost is significantly reduced compared to existing methods. Overall, the proposed framework offers a scalable and intelligent solution for smart grid integration and distribution network modernization. Full article

Societies are aiming to have a higher ecological consciousness in wastewater treatment operations and achieve a more sustainable future. With this said, global demands for larger quantities of resources and the consequent waste generated will inevitably lead to the exhaustion of current municipal wastewater treatment works. The utilization of biosolids (particularly microbial proteins) from wastewater treatment operations could generate a sustainable bio-adhesive for the wood industry, reduce carbon footprint, mitigate health concerns related to the use of carcinogenic components, and support a more circular economic option for wastewater treatment. A techno-economic analysis for three 10 MGD wastewater treatment operations producing roughly 11,300 dry pounds of biosolids per day, in conjunction with co-feedstock defatted soy flour protein at varying ratios (i.e., 0%, 15%, and 50% wet weight), was conducted. Aspen Capital Cost Estimator V12 was used to design and estimate installed equipment additions for wastewater treatment plant integration into an urban biorefinery process. Due to the mechanical attributes and market competition, the chosen selling prices of each adhesive per pound were set for analysis as USD 0.75 for Plant Option P1, USD 0.85 for Plant Option P2, and USD 1.00 for Plant Option P3. Over a 20-year life, each plant option demonstrated economic viability with high NPVs of USD 107.9M, USD 178.7M, and USD 502.2M and internal rates of return (IRRs) of 24.0%, 29.0%, and 44.2% respectively. The options examined have low production costs of USD 0.14 and USD 0.19 per pound, minimum selling prices of USD 0.42–USD 0.51 per pound, resulting in between 2- and 4-year payback periods. Sensitivity analysis shows the effects biosolid production fluctuations, raw material market price, and adhesive selling price have on economics. The results proved profitable even with large variations in the feedstock and raw material prices, requiring low market selling prices to reach the hurdle rate of examination. This technology is economically enticing, and the positive environmental impact of waste utilization encourages further development and analysis of the bio-adhesive process. Full article

Aiming at the economic problems of industrial users with wind power, photovoltaic, and small hydropower resources in clean energy consumption and trading with superior power grids, this paper proposes a distributed pumped storage capacity optimization configuration method considering clean energy systems. First, considering the maximization of the investment benefit of distributed pumped storage as the upper goal, a configuration scheme of the installed capacity is formulated. Second, under the two-part electricity price mechanism, combined with the basin hydraulic coupling relationship model, the operation strategy optimization of distributed pumped storage power stations and small hydropower stations is carried out with the minimum operation cost of the clean energy system as the lower optimization objective. Finally, the bi-level optimization model is solved by combining the alternating direction multiplier method and CPLEX solver. This study demonstrates that distributed pumped storage implementation enhances seasonal operational performance, improving clean energy utilization while reducing industrial electricity costs. A post-implementation analysis revealed monthly operating cost reductions of 2.36, 1.72, and 2.13 million RMB for wet, dry, and normal periods, respectively. Coordinated dispatch strategies significantly decreased hydropower station water wastage by 82,000, 28,000, and 52,000 cubic meters during corresponding periods, confirming simultaneous economic and resource efficiency improvements. Full article