Search Results (367)

Effective management of cascade reservoir systems is essential for balancing hydropower generation, flood control, and ecological sustainability, especially under increasingly uncertain runoff conditions driven by climate change. Traditional optimization methods, while widely used, often struggle with high dimensionality and fail to adequately address inflow variability. This study introduces a novel deep reinforcement learning (DRL) framework that tightly couples probabilistic runoff forecasting with adaptive reservoir scheduling. We integrate a Long Short-Term Memory (LSTM) neural network to model runoff uncertainty and generate probabilistic inflow forecasts, which are then embedded into a Proximal Policy Optimization (PPO) algorithm via Monte Carlo sampling. This unified forecast–optimize architecture allows for dynamic policy adjustment in response to stochastic hydrological conditions. A case study on China’s Xiluodu–Xiangjiaba cascade system demonstrates that the proposed LSTM-PPO framework achieves superior performance compared to traditional baselines, notably improving power output, storage utilization, and spillage reduction. The results highlight the method’s robustness and scalability, suggesting strong potential for supporting resilient water–energy nexus management under complex environmental uncertainty. Full article

Strengthening climate resilience constitutes a foundational approach through which cities adapt to climate change and mitigate associated environmental risks. However, research on the influence of economic policy environments on climate resilience remains limited. Guided by institutional theory and dynamic capability theory, this study employs a panel dataset comprising 272 Chinese cities at the prefecture level and above, covering the period from 2009 to 2023. It constructs a composite index framework for evaluating the green business environment (GBE) and urban climate resilience (UCR) using the entropy weight method. Employing a two-way fixed-effect regression model, it examined the impact of GBE optimization on UCR empirically and also explored the underlying mechanisms. The results show that improvements in the GBE significantly enhance UCR, with green innovation (GI) in technology functioning as an intermediary mechanism within this relationship. Moreover, climate policy uncertainty (CPU) exerts a moderating effect along this transmission pathway: on the one hand, it amplifies the beneficial effect of the GBE on GI; on the other hand, it hampers the transformation of GI into improved GBEs. The former effect dominates, indicating that optimizing the GBE becomes particularly critical for enhancing UCR under high CPU. To eliminate potential endogenous issues, this paper adopts a two-stage regression model based on the instrumental variable method (2SLS). The above conclusion still holds after undergoing a series of robustness tests. This study reveals the mechanism by which a GBE enhances its growth through GI. By incorporating CPU as a heterogeneous factor, the findings suggest that governments should balance policy incentives with environmental regulations in climate resilience governance. Furthermore, maintaining awareness of the risks stemming from climate policy volatility is of critical importance. By providing a stable and supportive institutional environment, governments can foster steady progress in green innovation and comprehensively improve urban adaptive capacity to climate change. Full article

Digestate valorization is essential for sustainable waste management and circular economy strategies, yet large-scale adoption faces technical, economic, and environmental challenges. Beyond waste-to-energy conversion, digestate is a valuable soil amendment, enhancing soil structure and reducing reliance on synthetic fertilizers. However, its agronomic benefits depend on feedstock characteristics, treatment processes, and application methods. This study reviews digestate composition, treatment technologies, regulatory frameworks, and environmental impact assessment through Life Cycle Assessment. It analyzes the influence of functional unit selection and system boundary definitions on Life Cycle Assessment outcomes and the effects of feedstock selection, pretreatment, and post-processing on its environmental footprint and fertilization efficiency. A review of 28 JCR-indexed articles (2018–present) analyzed LCA studies on digestate, focusing on methodologies, system boundaries, and impact categories. The findings indicate that Life Cycle Assessment methodologies vary widely, complicating direct comparisons. Transportation distances, nutrient stability, and post-processing strategies significantly impact greenhouse gas emissions and nutrient retention efficiency. Techniques like solid–liquid separation and composting enhance digestate stability and agronomic performance. Digestate remains a promising alternative to synthetic fertilizers despite market uncertainty and regulatory inconsistencies. Standardized Life Cycle Assessment methodologies and policy incentives are needed to promote its adoption as a sustainable soil amendment within circular economy frameworks. Full article

Urban population growth is intensifying pressure on mobility systems, with road transportation contributing to environmental and sustainability challenges. Policymakers must navigate complex uncertainties in addressing rising mobility demand while pursuing sustainability goals. Advanced technologies offer promise, but their real-world effectiveness in urban contexts remains underexplored. This meta-review comprised three complementary studies: a broad analysis of sustainable mobility with Norwegian case studies, and systematic literature reviews on digital twins and Big Data/AI applications in urban mobility, covering the period of 2019–2024. Using structured criteria, we synthesised findings from 72 relevant articles to identify major trends, limitations, and opportunities. The findings show that mobility policies often prioritise technocentric solutions that unintentionally hinder sustainability goals. Digital twins show potential for traffic simulation, urban planning, and public engagement, while machine learning techniques support traffic forecasting and multimodal integration. However, persistent challenges include data interoperability, model validation, and insufficient stakeholder engagement. We identify a hierarchy of mobility modes where public transit and active mobility outperform private vehicles in sustainability and user satisfaction. Integrating electrification and automation and sharing models with data-informed governance can enhance urban liveability. We propose actionable pathways leveraging Big Data and AI, outlining the roles of various stakeholders in advancing sustainable urban mobility futures. Full article

This paper examines the changing role of emissions trading systems (ETSs) within the macro-financial framework of energy markets, emphasizing price dynamics and systemic spillovers. Utilizing monthly data from seven ETS jurisdictions spanning January 2021 to December 2024 (N = 287 observations after log transformation and first differencing), which includes four auction-based markets (United States, Canada, United Kingdom, South Korea), two secondary markets (China, New Zealand), and a government-set fixed-price scheme (Germany), this research estimates a panel vector autoregression (PVAR) employing a Common Correlated Effects (CCE) model and augments it with machine learning analysis utilizing XGBoost and explainable AI methodologies. The PVAR-CEE reveals numerous unexpected findings related to carbon markets: ETS returns exhibit persistence with an autoregressive coefficient of −0.137 after a four-month lag, while increasing inflation results in rising ETS after the same period. Furthermore, ETSs generate spillover effects in the real economy, as elevated ETSs today forecast a 0.125-point reduction in unemployment one month later and a 0.0173 increase in inflation after two months. Impulse response analysis indicates that exogenous shocks, including Brent oil prices, policy uncertainty, and financial volatility, are swiftly assimilated by ETS pricing, with effects dissipating completely within three to eight months. XGBoost models ascertain that policy uncertainty and Brent oil prices are the most significant predictors of one-month-ahead ETSs, whereas ESG factors are relevant only beyond certain thresholds and in conditions of low policy uncertainty. These findings establish ETS markets as dynamic transmitters of macroeconomic signals, influencing energy management, labor changes, and sustainable finance under carbon pricing frameworks. Full article

The transition to sustainable agriculture is a critical challenge for the U.S. food system. A sustainable food system must support the production of healthy and nutritious food while ensuring economic sustainability for farmers and ranchers. It should also reduce negative environmental impacts on soil, water, biodiversity, and climate, and promote equitable and inclusive access to land, farming resources, and food. This narrative review synthesizes U.S. social science literature to identify the key factors that support or impede the adoption of sustainable agricultural practices in the U.S. Our analysis reveals seven overarching factors that influence producer decision-making: awareness and knowledge, social factors, psychological factors, technologies and tools, economic factors, implementation capacity, and policies and regulations. The review highlights the critical role of social science in navigating complexity and uncertainty. Key priorities emerging from the literature include developing measurable, outcome-based programs; ensuring credible communication through trusted intermediaries; and designing tailored interventions. The findings demonstrate that initiatives will succeed when they emphasize measurable benefits, address uncertainties, and develop programs that capitalize on identified opportunities while overcoming existing barriers. Full article

Accurate forecasting of household waste generation is essential for sustainable urban planning and the development of data-driven environmental policies. Conventional statistical models, while simple and interpretable, often fail to capture the nonlinear and multidimensional relationships inherent in waste production patterns. This study proposes a machine learning-based regression framework utilizing Random Forest and XGBoost algorithms to predict annual household waste generation across four metropolitan regions in South Korea Seoul, Gyeonggi, Incheon, and Jeju over the period from 2000 to 2023. Independent variables include demographic indicators (total population, working-age population, elderly population), economic indicators (Gross Regional Domestic Product), and regional identifiers encoded using One-Hot Encoding. A derived feature, elderly ratio, was introduced to reflect population aging. Model performance was evaluated using R², RMSE, and MAE, with artificial noise added to simulate uncertainty. Random Forest demonstrated superior generalization and robustness to data irregularities, especially in data-scarce regions like Jeju. SHAP-based interpretability analysis revealed total population and GRDP as the most influential features. The findings underscore the importance of incorporating economic indicators in waste forecasting models, as demographic variables alone were insufficient for explaining waste dynamics. This approach provides valuable insights for policymakers and supports the development of adaptive, region-specific strategies for waste reduction and infrastructure investment. Full article

Drawing on a panel dataset of 27,972 firm-year observations from Chinese A-share listed companies spanning 2009 to 2022, this study employs fixed-effects models to examine the nonlinear relationship between firm-level climate policy uncertainty (FCPU) and corporate green governance expenditure (GGE). The results reveal a robust inverted U-shaped pattern: moderate levels of FCPU encourage firms to increase GGE, while excessive uncertainty discourages it. Financing constraints mediate this relationship; specifically, FCPU exhibits a U-shaped impact on financing constraints, initially easing and then tightening them. Older top management teams accelerate the GGE downturn, while government environmental expenditure delays it, acting as a buffer. Heterogeneity analyses reveal the inverted U-shaped effect is more pronounced for non-polluting firms and state-owned enterprises (SOEs). This study highlights the complex dynamics of FCPU on corporate green behavior, underscoring the importance of climate policy stability and transparency for advancing corporate environmental engagement in China. Full article

Nature-Based Solutions (NBSs) offer promising pathways to enhance ecological resilience and address urban water challenges, particularly in heritage cities where conventional gray infrastructure often fails to balance environmental needs with cultural preservation. This study proposes a strategic framework for the integration of NBSs into historic urban landscapes by employing Internal–External (IE) matrix modeling and an impact–uncertainty assessment, grounded in a structured evaluation of key internal strengths and weaknesses, as well as external opportunities and threats. The Internal Factor Evaluation (IFE) score of 2.900 indicates a favorable internal environment, characterized by the multifunctionality of NBS and their ability to reconnect urban populations with nature. Meanwhile, the External Factor Evaluation (EFE) score of 2.797 highlights moderate support from policy and public awareness but identifies barriers such as funding shortages and interdisciplinary coordination. Based on these findings, two strategies are developed: an SO (Strength–Opportunity) strategy, promoting community-centered and policy-driven NBS design, and a WO (Weakness–Opportunity) strategy, targeting resource optimization through legal support and cross-sectoral collaboration. This study breaks new ground by transforming theoretical NBS concepts into actionable, culturally sensitive planning tools that enable decision-makers to navigate the unique challenges of implementing adaptive stormwater and environmental management in historically constrained urban environments. Full article

Container terminals are facing significant challenges in meeting the increasing demands for volume and throughput, with limited space often presenting as a critical constraint. Key areas of concern at the quayside include the berth allocation problem, the quay crane assignment, and the scheduling problem. Effectively managing these issues is essential for optimizing port operations; failure to do so can lead to substantial operational and economic ramifications, ultimately affecting competitiveness within the global shipping industry. Optimization models, encompassing both mathematical frameworks and metaheuristic approaches, offer promising solutions. Additionally, the application of machine learning and reinforcement learning enables real-time solutions, while robust optimization and stochastic models present effective strategies, particularly in scenarios involving uncertainties. This study expands upon earlier foundational analyses of berth allocation, quay crane assignment, and scheduling issues, which have laid the groundwork for port optimization. Recent developments in uncertainty management, automation, real-time decision-making approaches, and environmentally sustainable objectives have prompted this review of the literature from 2015 to 2024, exploring emerging challenges and opportunities in container terminal operations. Recent research has increasingly shifted toward integrated approaches and the utilization of continuous berthing for better wharf utilization. Additionally, emerging trends, such as sustainability and green infrastructure in port operations, and policy trade-offs are gaining traction. In this review, we critically analyze and discuss various aspects, including spatial and temporal attributes, crane handling, sustainability, model formulation, policy trade-offs, solution approaches, and model performance evaluation, drawing on a review of 94 papers published between 2015 and 2024. Full article

In the context of the intensifying global climate crisis, the power industry, as a significant carbon emitter, urgently needs to promote low-carbon transformation using market mechanisms. In this paper, a multi-objective stochastic optimization scheduling framework for regional power grids integrating carbon trading (CET) and green certificate trading (GCT) is proposed to coordinate the conflict between economic benefits and environmental objectives. By building a deterministic optimization model, the goal of maximizing power generation profit and minimizing carbon emissions is combined in a weighted form, and the power balance, carbon quota constraint, and the proportion of renewable energy are introduced. To deal with the uncertainty of power demand, carbon baseline, and the green certificate ratio, Monte Carlo simulation was further used to generate random parameter scenarios, and the CPLEX solver was used to optimize scheduling schemes iteratively. The simulation results show that when the proportion of green certificates increases from 0.35 to 0.45, the proportion of renewable energy generation increases by 4%, the output of coal power decreases by 12–15%, and the carbon emission decreases by 3–4.5%. At the same time, the tightening of carbon quotas (coefficient increased from 0.78 to 0.84) promoted the output of gas units to increase by 70 MWh, verifying the synergistic emission reduction effect of the “total control + market incentive” policy. Economic–environmental tradeoff analysis shows that high-cost inputs are positively correlated with the proportion of renewable energy, and carbon emissions are significantly negatively correlated with the proportion of green certificates (correlation coefficient −0.79). This study emphasizes that dynamic adjustments of carbon quota and green certificate targets can avoid diminishing marginal emission reduction efficiency, while the independent carbon price mechanism needs to enhance its linkage with economic targets through policy design. This framework provides theoretical support and a practical path for decision-makers to design a flexible market mechanism and build a multi-energy complementary system of “coal power base load protection, gas peak regulation, and renewable energy supplement”. Full article

Climate change risk has significant impacts on corporate financial activities. Using firm-level data from A-share listed companies in China from 2010 to 2022, we examine how climate risk affects corporate debt financing capacity. We find that climate change risk significantly weakens firms’ ability to raise debt, leading to lower leverage and higher financing costs. These results remain robust across various checks for endogeneity and alternative specifications. We also show that reducing corporate carbon emission intensity can mitigate the negative impact of climate risk on debt financing, suggesting that supply-side credit policies are more effective than demand-side capital structure choices. Furthermore, we identify three channels through which climate risk impairs debt capacity: reduced competitiveness, increased default risk, and diminished resilience. Our heterogeneity analysis reveals that these adverse effects are more pronounced for non-state-owned firms, firms with weaker internal controls, and companies in highly financialized regions, and during periods of heightened environmental uncertainty. We also apply textual analysis and machine learning to the measurement of climate change risks, partially mitigating the geographic biases and single-dimensional shortcomings inherent in macro-level indicators, thus enriching the quantitative research on climate change risks. These findings provide valuable insights for policymakers and financial institutions in promoting corporate green transition, guiding capital allocation, and supporting sustainable development. Full article

The electric vehicle (EV) business model integrates advanced battery technology, dynamic power train architectures, and intelligent energy management systems with ecosystem strategies and digital services. It incorporates environmental sustainability through lifecycle analysis and renewable energy integration. China, with 9.49 million EV sales in 2023 (33% market share), faces infrastructure gaps constraining further growth. China is strategically mitigating CO₂ emissions while fostering economic expansion, notwithstanding constraints such as suboptimal battery technology advancements, elevated production expenditure, and enduring ecological impacts. This Political, Economic, Social, Technological, Legal, Environmental (PESTLE) assessment, operationalized through a survey of 800 stakeholders and Statistical Package for the Social Sciences IBM SPSS SPSS (Version 28) quantitative analysis (factor loading = 0.73 for Technology; eigenvalue = 4.12), identifies infrastructure gaps as the dominant barrier (72% of stakeholders). Political factors (β = 0.82) emerged as the strongest adoption predictor, outweighing economic subsidies in significance. The adoption of EVs in China presents a significant prospect for reducing CO₂ emissions and advancing technology. However, economic barriers, market dynamics, inadequate infrastructure, regulatory uncertainty, and social acceptance issues are addressed in the assessment. The study recommends prioritizing infrastructure investment (e.g., 500 K fast-charging stations by 2027) and policy stability to overcome adoption barriers. This study provides three key advances: (1) quantification of PESTLE factor weights via factor analysis, revealing technological (infrastructure) and political factors as dominant; (2) identification of infrastructure gaps, not subsidies, as the primary adoption barrier; and (3) demonstration of infrastructure’s persistence post-subsidy cuts. These insights redefine EV adoption priorities in China. Full article

Alternative fuels obtained from renewable sources, providing low greenhouse gas emissions and high energy efficiency, offer significant advantages in terms of sustainability. In addition, the wide applicability of these fuel types in sectors such as housing, transportation, and industry creates significant opportunities in terms of reducing dependence on fossil fuels. Alternative fuels should be evaluated not only according to their environmental contributions but also based on multi-dimensional criteria such as economic cost, technical suitability, sustainability level, fuel properties, infrastructure requirements, and social acceptance. In this context, a comparative analysis of alternative fuel types in terms of various basic parameters is no longer optional, but a necessity. These parameters generally include symmetrical relationships such as balanced trade-offs between economic and environmental dimensions or mutual effects between technical and social criteria. However, they also show variability and uncertainty depending on the fuel type. Therefore, Spherical Fuzzy Multi-Criteria Decision Making (SF-MCDM) methods, which can effectively represent symmetry in membership and hesitation degrees, have been used to achieve more realistic and reliable results in uncertain decision environments. The proposed model provides a systematic and flexible evaluation structure that helps decision makers determine the most appropriate alternative fuel options and contributes to the formation of sustainable energy policies. Full article

This study introduces a novel approach for optimizing residential energy systems by combining linear policy graphs with stochastic dual dynamic programming (SDDP) algorithms. Our method optimizes residential solar power generation and battery storage systems, reducing costs through strategic charging and discharging patterns. Using stylized test data, we evaluate battery storage optimization strategies by comparing various SDDP model configurations against a linear programming (LP) benchmark model. The SDDP optimization framework demonstrates robust performance in battery operation management, efficiently handling diverse pricing scenarios while maintaining computational efficiency. Our analysis reveals that the SDDP model achieves positive financial returns with small-scale battery installations, even in scenarios with limited photovoltaic generation capacity. The results confirm both the economic viability and environmental benefits of residential solar–battery systems through two key strategies: aligning battery charging with renewable energy availability and shifting energy consumption away from peak periods. The SDDP framework proves effective in managing battery operations across dynamic pricing scenarios, achieving performance comparable to LP methods while handling uncertainties in PV generation, consumption, and pricing. Full article