Search Results (1,055)

The transition toward sustainable energy systems remains challenging as conventional energy still dominates despite environmental and security concerns. Integrated Energy Services (IES) provide a promising mechanism by optimising energy planning, operation, and delivery through integrated solutions. While previous studies have emphasized technological or policy aspects of IES, little is known about how consumers’ cognition and perceptions shape their acceptance of IES. This study investigates how awareness of conventional energy drawbacks and recognition of IES advantages influence acceptance by surveying 450 households in Beijing, Tianjin, and Shanghai. Descriptive statistics, Spearman’s correlation, and mediation analysis were employed to identify key behavioral pathways. Results reveal that planning and design influence service performance through operation and maintenance, and service efficiency affects price acceptance through perceived service quality. City-level analysis shows that Beijing residents emphasize reliable planning and operations, Tianjin respondents focus on efficiency and responsiveness, while Shanghai consumers place the greatest importance on service quality and fairness. These findings provide new insights into the consumer-level mechanisms of IES acceptance and offer practical guidance for tailoring city-specific strategies to enhance IES implementation and support China’s low-carbon transition. Full article

In dynamic, high-interference industrial and logistics environments, multi-AGV cooperative tasks are often affected by communication delays and data loss, leading to information staleness and reduced control accuracy. Traditional handshake frequency hopping communication strategies introduce additional overhead in high-load environments, and channel selection strategies struggle to adapt to dynamic changes. To address challenges related to communication delay, task coordination, and real-time information exchange, we propose a control and communication co-optimization method based on a nonlinear Age of Information (AoI) penalty and an adaptive handshake frequency hopping mechanism. The method constructs a coupled control-communication model, designs an adaptive handshake period and multi-channel frequency hopping strategy to reduce channel conflicts, and introduces a nonlinear AoI penalty function that prioritizes the update of critical timely information, improving communication success rates and path control accuracy. Furthermore, by integrating the differential dynamics model, state estimation under communication delay and control error modeling, we propose a cooperative optimization algorithm for perception control and communication based on nonlinear AoI optimization (PPO-CCBNA). The algorithm achieves efficient solution based on approximate policy optimization (PPO). Simulation results demonstrate that PPO-CCBNA significantly outperforms benchmark algorithms in communication success rates, control stability, and energy efficiency, validating its effectiveness and feasibility in complex multi-AGV cooperative tasks. Full article

This study focuses on highway service areas. Building upon prior research that identified key influencing factors through surveys and ISM–MICMAC analysis, it constructs a tripartite evolutionary game model involving the government, service area operators, and carbon reduction technology providers based on stakeholder theory. Combined with MATLAB simulations, the model reveals the dynamic patterns of the carbon reduction system. The results indicate that government strategies exert the strongest influence on the system and catalyze the other two parties, followed by service area operators. Carbon reduction technology providers adopt a more cautious stance in decision-making. Government actions shape system evolution through a “cost-benefit-incentive” triple mechanism, with its strategies exhibiting significant spillover effects on other actors. Enterprise behavior is markedly influenced by Xinjiang’s regional characteristics, where the core barriers to corporate carbon reduction lie in the costs of proactive equipment and technological investments. The willingness of technology providers to cooperate primarily depends on two drivers: incremental baseline benefits and enhanced economies of scale. The core trade-off in government decision-making lies between the cost of strong regulation (Cg1) and the cost of environmental governance under weak regulation (Cg2). An increase in Cg1 prolongs the government’s convergence time by 233.3% and indirectly suppresses the willingness of enterprises and technology providers due to weakened subsidy capacity. Enterprises are relatively sensitive to the investment costs of carbon reduction equipment and technology, with convergence time extending by 120%. Technology providers are highly sensitive to incremental baseline returns (Rt), with stabilization time extending by 500%. Compared to existing research, this model quantitatively reveals the “cost-benefit-incentive” triple transmission mechanism for carbon reduction coordination in “grid-end” regions, identifying key parameters for strategic shifts among stakeholders. Based on this, corresponding policy recommendations are provided for all three parties, offering precise and actionable directions for the sustainable advancement of carbon reduction efforts in service areas. The research conclusions can provide a replicable collaborative framework for decarbonizing transportation infra-structure in grid-end regions with high clean energy endowments. Full article

Against the global rollout of Carbon Border Adjustment Mechanisms (CBAMs), carbon tariffs have emerged as a core tool for developed economies to internalize environmental externalities—especially for energy-intensive imports that dominate cross-border carbon flows. However, emission information asymmetry, a critical barrier to implementing cross-border energy and environmental policies, undermines the design of optimal carbon tariffs, as it distorts the link between tariff levels and actual fossil energy-related emissions. This study develops a two-country analytical model to examine how biased assessments of exporters’ carbon intensity influence optimal tariff settings, exporters’ strategic behavior, and aggregate carbon emissions—with a focus on energy-intensive production contexts. The results show that underestimating carbon intensity reduces exporters’ compliance costs, incentivizing emission concealment; this weakens tariffs’ environmental stringency and may raise global emissions. Overestimation, by contrast, inflates exporters’ marginal costs, discouraging green investment and causing emission displacement rather than reduction. The analysis highlights a policy feedback loop wherein misjudged emission information distorts both trade competitiveness and environmental performance. This study concludes that a transparent, accurate, and internationally verifiable carbon accounting system is essential: it not only facilitates the effective implementation of CBAM but also aligns optimal carbon tariffs with CBAM’s dual goals of climate action and trade equity, while supporting global energy transition efforts. Full article

Intelligent connected vehicles (ICVs) face challenges in handling intensive onboard computational tasks due to limited computing capacity. Vehicular edge computing networks (VECNs) offer a promising solution by enabling ICVs to offload tasks to mobile edge computing (MEC), alleviating computational load. As transportation systems are dynamic, vehicular tasks and MEC capacities vary over time, making efficient task offloading and resource allocation crucial. We explored a vehicle–road collaborative edge computing network and formulated the task offloading scheduling and resource allocation problem to minimize the sum of time and energy costs. To address the mixed nature of discrete and continuous decision variables and reduce computational complexity, we propose a hybrid hierarchical deep reinforcement learning (HHDRL) algorithm, structured in two layers. The upper layer of HHDRL enhances the double deep Q-network (DDQN) with a self-attention mechanism to improve feature correlation learning and generates discrete actions (communication decisions), while the lower layer employs deep deterministic policy gradient (DDPG) to produce continuous actions (power control, task offloading, and resource allocation decision). This hybrid design enables efficient decomposition of complex action spaces and improves adaptability in dynamic environments. Results from numerical simulations reveal that HHDRL achieves a significant reduction in total computational cost relative to current benchmark algorithms. Furthermore, the robustness of HHDRL to varying environmental conditions was confirmed by uniformly designing random numbers within a specified range for certain simulation parameters. Full article

Against the backdrop of China’s “carbon peaking” and “carbon neutrality” goals, as well as the advancement of new industrialization, the oil and gas industry is undergoing a critical transformation from resource-dependent growth toward innovation-driven, low-carbon, and high-quality development. The integrated advancement of high-end, intelligent, and green transformation—collectively referred to as the “Three Modernizations”—has become a vital pathway for promoting industrial upgrading and sustainable growth. Based on panel data from 30 Chinese provinces from 2009 to 2023, this study constructs a comprehensive evaluation index system covering 19 secondary indicators across three dimensions: high-end, intelligent, and green development. Using the entropy-weighted TOPSIS method, kernel density estimation, Dagum Gini coefficient decomposition, and σ–β convergence models, the study examines the spatiotemporal evolution, regional disparities, and convergence characteristics of HIG integration, and further explores its driving mechanisms through a two-way fixed effects model and mediation effect analysis. The results show that (1) the overall HIG integration index rose from 0.34 in 2009 to 0.46 in 2023, forming a spatial pattern of “high in the east, low in the west, stable in the center, and fluctuating in the northeast”; (2) regional disparities narrowed significantly, with the Gini coefficient declining from 0.093 to 0.058 and σ decreasing from 7.114 to 6.350; and (3) oil and gas resource endowment, policy support, technological innovation, and carbon emission constraints all positively promote integration, with regression coefficients of 0.152, 0.349, 0.263, and 0.118, respectively. Heterogeneity analysis reveals an increasing integration level from upstream to downstream, with eastern regions leading in innovation-driven development. Based on these findings, the study recommends strengthening policy and institutional support, accelerating technological innovation, improving intelligent infrastructure, deepening green and low-carbon transformation, promoting regional coordination, and establishing a long-term monitoring mechanism to advance the integrated high-quality development of China’s oil and gas industry. Overall, this study deepens the understanding of the internal logic and spatial dynamics of the “Three Modernizations” integration in China’s oil and gas industry, providing empirical evidence and policy insights for accelerating the construction of a low-carbon, secure, and efficient modern energy system. Full article

Scientific, accurate, and interpretable carbon price forecasts provide critical support for addressing climate change, achieving low-carbon goals, and informing policy-making and corporate decision-making in energy and environmental markets. However, the existing studies mainly focus on deterministic forecasting, with obvious limitations in data feature diversity, model interpretability, and uncertainty quantification. To fill these gaps, this study constructs an interpretable hybrid system for carbon market price prediction by combining feature screening algorithms, deep learning models, and interpretable explanatory analysis methods. Specifically, this study first screens important variables from twenty-one multi-source structured and unstructured influencing factor datasets on five dimensions affecting carbon price using the Boruta algorithm. Immediately after that, this study proposes a hybrid architecture of bidirectional temporal convolutional network and Informer models, where a bidirectional temporal convolutional network is used to extract local spatio-temporal dependent features, while Informer captures long sequences of global features through the connectivity mechanism, thus realizing staged feature extraction. Then, to improve the interpretability of the model and quantify the uncertainty, this study introduces Shapley additive explanations to analyze the feature contribution in the prediction process, and the Monte Carlo dropout method is used to achieve interval prediction. Finally, the empirical results in China’s Guangdong and Shanghai carbon markets show that the proposed model significantly outperforms benchmark models, and the coverage probability of the obtained prediction intervals significantly outperforms the confidence level. The Shapley additive explanation analysis reveals regional heterogeneity drivers. In addition, the proposed model is also intensively validated in the European carbon market and the U.S. natural gas market, which also demonstrate an excellent prediction performance, indicating that the model has good robustness and applicability. Full article

The electro-hydraulic hybrid system has emerged as a critical technology in new energy vehicles, owing to the remarkable power density and efficient energy regeneration capabilities of hydraulic technology, coupled with the high energy density of electric power. This system effectively enhances vehicle range and battery life. We developed an energy management strategy (EMS) for the electro-hydraulic hybrid system (EHHS) to ensure smooth energy conversion, while ensuring the full utilization of electrical and hydraulic energy within a reasonable and efficient range. To enhance the system’s overall performance, it is imperative to address pivotal technologies, including power coupling and energy management. In this research, the structure of an electro-hydraulic hybrid vehicle (EHHV) is classified, compared and discussed. The application of existing EHHVs is studied. Subsequently, an analysis and summary are conducted on the current status and development trends of EMSs and collaborative operation control strategies (COCSs), and a novel mechanical-electro-hydraulic power-coupled system (MEHPCS) is put forward that successfully converts mechanical, electrical, and hydraulic energy in performance. Simultaneously, other applications of the system are forecasted. Finally, some suggestions for the electro-hydraulic hybrid systems’ future development are made. This study can promote the development of sustainable transportation technologies. The system integrates mechanical engineering, control theory, and environmental science, enabling interdisciplinary methodological innovation. In addition, relevant studies provide data support for policy makers by quantifying energy consumption indicators. Full article

In recent years, the European Union has played a key role in global efforts to combat climate change and the energy transition, focusing on creating fiscal, legal and regulatory policies and instruments capable of supporting the decarbonization process and ensuring a sustainable energy future. Environmental taxation has been considered not only as an essential tool to discourage pollution but also to stimulate cleaner energy production, the integration of renewable sources and energy efficiency. Our research analyses the impact of environmental tax revenues on CO₂ across 27 EU member states from 2012 to 2023. A mixed-method research approach is used, combining policy and strategy analysis, bibliometric mapping and econometric data analysis using OLS, as well as fixed and random effects models that are selected based on the Hausman test. The methodological mix approach provides empirical evidence on how fiscal instruments can simultaneously support environmental sustainability and energy resilience. The results show that environmental taxes are associated with greenhouse gas emission reductions and an increase in the share of renewable energy, especially when integrated into a coherent national policy framework. The policy analysis highlights the role of the Climate Action Budgetary Mechanism (CABM) and the Effort Sharing Regulation (ESR), underlining their importance for the European Union’s energy strategy. The bibliometric results indicate the existence of thematic clusters focused on carbon pricing, renewable energies and international comparisons, particularly with China. Finally, this study suggests that the maximum efficiency of environmental taxes is achieved when the revenues generated are reinvested in green infrastructure, innovation and sustainable jobs. Furthermore, policies should be adapted to the specificities of each Member State to ensure a fair and sustainable energy transition at the EU level. Full article

Against the accelerating of global climate change and carbon neutrality transitions, energy price volatility exerts complex effects on the employment dimension of economic sustainability through both industrial and agricultural channels as intermediaries. This study employed a mixed-frequency vector autoregression model to statistically analyze the weekly prices of four major industries and 24 sub-markets in China. The main outcome was the urban unemployment rate in China, and it was verified against the urban unemployment rates in 31 cities and the unemployment rates by age group (YUR/LUR). The study investigated the employment dimension of economic sustainability. Energy and energy metal prices represent the energy transition, while food and industrial goods prices characterize the intermediary linkages. Unemployment rates serve as the employment dimension of economic sustainability. The findings reveal bidirectional interactions and heterogeneous transmission mechanisms between prices and unemployment: energy prices exhibit weaker direct links to unemployment, partly influenced by demand inelasticity and policy adjustments; agricultural products face more persistent impacts, reflecting policy interventions and demand constraints; chemical products demonstrate the highest sensitivity and fastest response to unemployment shocks; metals show significant internal variations, with sub-market-level impacts being more pronounced yet shorter-lived. These insights advance climate and energy economics by guiding low-carbon transition policies, optimizing resource allocation, and managing energy market risks for resilient economic sustainability. Full article

Energy efficiency presents significant potential, especially for Western Balkan (WB) countries, if effectively addressed through energy efficiency measures. The building sector, which includes residential, commercial, and public buildings, is the most energy-intensive sector globally. Public buildings in the Western Balkan countries are characterized by poor energy efficiency performance. The average energy consumption in public buildings is anticipated to exceed double the European Union (EU) requirement, given that more than 60-70% of these structures were built over 60 years ago with no regard for energy efficiency. This study assesses the Public Building–Energy Efficiency Readiness Index (PB-EERI) to evaluate how legislative specificity, institutional capacity, financing mechanisms, renovation guidelines, energy market conditions, and societal awareness collectively influence the readiness of Western Balkan economies to enhance energy efficiency in public buildings. The index serves as an operational diagnostic to identify the presence of enabling conditions, determine the most significant gaps, and prioritize policy efforts accordingly. This study presents a novel approach by integrating, within a single transparent index, (i) the existence of energy laws, (ii) market feasibility, (iii) renovation needs of public buildings, and (iv) societal awareness. The awareness pillar is both central and novel. By utilizing harmonized Regional Cooperation Council (RCC) data, this article quantifies societal awareness, thereby ensuring that the index accurately reflects the importance of stakeholder comprehension in the success of renovating initiatives for public buildings. The theoretical framework derives from the application of composite indicators in numerous studies and reports to illustrate the status of energy or energy efficiency. The methodology for developing this indicator is derived from the Organization for Economic Co-operation and Development (OECD) Handbook on Constructing Composite Indicators. For the aggregation method, the summation of weighted and normalized sub-indicators was used. The PB-EERI reveals considerable regional variations, with total scores ranging from around 39 to 72% and concentrating around the mid-0.5s. The findings reveal systematic differences in most indicators’ performance. The legal framework indicator significantly influences variation between countries, together with market conditions and societal awareness. Energy efficiency in public buildings, praised as the “first fuel”, should be prioritized beyond mere compliance with EU regulations. The PB-EERI emphasizes that success relies more on the capacity to transform formal strategies into concrete renovation programs, quantifiable objectives, and higher awareness of society to ensure uptake of the renovation measures. Full article

Artificial Intelligence (AI) is increasingly central to sustainable development, yet its advancement varies across G7 economies. This study employs Method of Moments Quantile Regression (MMQR) to examine how Financial Technology (FinTech), Economic Growth (EG), Human Capital (HC), and Renewable Energy Consumption (RENC) influence AI development in G7 countries from 2000 to 2022. By analyzing heterogeneous effects across quantiles, the study captures stage-specific drivers often overlooked in average-based models. Results indicate that FinTech and human capital significantly promote AI adoption in lower and middle quantiles, enhancing digital inclusion and innovation capacity, while RENC becomes relevant primarily at advanced stages of AI adoption. Economic growth exhibits negative or inconsistent effects, suggesting that GDP expansion alone is insufficient for technological transformation without alignment to supportive policies and institutional contexts. The lack of long-run cointegration further highlights the dominance of short- and medium-term dynamics in shaping the AI–sustainability nexus. These findings provide actionable insights for policymakers, emphasizing targeted FinTech development, skill-building initiatives, and renewable-powered AI solutions to foster sustainable and inclusive AI adoption. Overall, the study demonstrates how financial, human, and environmental factors jointly drive AI development, offering a mechanism-based perspective on technology-driven sustainable development in advanced economies. Full article

This review provides a comprehensive analysis of the technological, environmental, economic, regulatory, and social dimensions shaping the development and operation of agricultural biogas plants. The paper adopts a primarily European perspective, reflecting the comparatively high share of agricultural inputs in anaerobic digestion (AD) across EU Member States, while drawing selective comparisons with global contexts to indicate where socio-geographical conditions may lead to different outcomes. It outlines core principles of the AD process and recent innovations—such as enzyme supplementation, microbial carriers, and multistage digestion systems—that enhance process efficiency and cost-effectiveness. The study emphasises substrate optimisation involving both crop- and livestock-derived materials, together with the critical management of water resources and digestate within a circular-economy framework to promote sustainability and minimise environmental risks. Economic viability, regulatory frameworks, and social dynamics are examined as key factors underpinning successful biogas implementation. The paper synthesises evidence on cost–benefit performance, investment drivers, regulatory challenges, and support mechanisms, alongside the importance of community engagement and participatory governance to mitigate land-use conflicts and ensure equitable rural development. Finally, it addresses persistent technical, institutional, environmental, and social barriers that constrain biogas deployment, underscoring the need for integrated solutions that combine technological advances with policy support and stakeholder cooperation. This analysis offers practical insights for advancing sustainable biogas use in agriculture, balancing energy production with environmental stewardship, food security, and rural equity. The review is based on literature identified in Scopus and Web of Science for 2007 to 2025 using predefined keyword sets and supplemented by EU policy and guidance documents and backward- and forward-citation searches. Full article

The urgent pursuit of net-zero emissions presents a critical challenge for modern societies, necessitating a speedup of transformative shifts across sectors to mitigate climate change. Predicting trends and drivers in the integration of energy technologies is essential to addressing this challenge, as it informs policy decisions, strategic investments, and the deployment of innovative solutions crucial for transitioning to a sustainable energy future. Despite the importance of accurate forecasting, current methods remain limited, especially in leveraging the vast, unlabelled energy literature available. However, with the advent of large language models (LLMs), the ability to interpret and extract insights from extensive textual data has significantly advanced. Sentiment analysis, in particular, has just emerged as a vital tool for detecting scientific opinions from the energy literature, which can be harnessed to forecast energy trends. This study introduces a novel multi-agent framework, EnergyEval, to evaluate the sentiment and factuality of the energy literature. The core novelty of the multi-agent framework is found to be the use of heterogeneous energy-specialised roles with different LLMs. This investigation, using both multiple persona agents and different LLMs, provides a bespoke collaboration mechanism for multi-agent debate (MAD). In addition, we believe our approach can extend across the energy industry, where deep application of MAD is yet to be exploited. We apply EnergyEval to the case of UK offshore wind literature, assessing its predictive performance. Our findings indicate that the sentiment predicted by the EnergyEval effectively aligns with observed trends in increasing the installed capacity and reductions in Levelised Cost of Energy (LCOE). It also helps us to identify key drivers in offshore wind development. The advantage of employing a multi-agent LLM debate team allows us to achieve competitive accuracy compared to single-LLM-based methods, while significantly reducing computational costs. Overall, the results highlight the potential of EnergyEval as a robust tool for forecasting technology developments in the pursuit of net-zero emissions. Full article

Plastic waste accumulation poses a critical environmental challenge, while the road construction industry continues to rely heavily on energy intensive, non-renewable binders. Integrating waste plastics into asphalt offers a dual solution to these issues by enhancing pavement performance and promoting circular economy principles. This review provides a comprehensive and data-driven synthesis of global research on plastic-waste-modified asphalt (PWMA), covering six major plastic types and both wet- and dry-processing technologies. Unlike prior reviews, this study employs a systematic PRISMA-based selection framework to evaluate 42 peer-reviewed experimental studies from 2000 to 2024, quantitatively comparing rheological, mechanical, and environmental outcomes. The review identifies polymer bitumen compatibility mechanisms, microstructural interactions revealed through microscopy, and the role of pre-treatment processes (glycolysis and pyrolysis) in improving dispersion and stability. Life Cycle Assessment (LCA) data reveal 20–35% reductions in carbon emissions and 10–12% life cycle cost savings compared to conventional and SBS-modified asphalt. The review proposes a strategic roadmap addressing performance variability, microplastic emissions, and compatibility challenges. By integrating material science, sustainability assessment, and field implementation data, this review advances a novel multidisciplinary perspective on waste plastic valorization in road infrastructure, bridging the gap between laboratory research and policy-ready, scalable applications. Full article