Search Results (1,329)

To address the issues that carbon trading fails to cover the full life cycle and that traditional demand response achieves poor emission reduction due to a lack of accurate carbon-intensity feedback in park integrated energy systems (PIESs) during low-carbon transition, this study proposes a two-layer optimal scheduling method synergizing life-cycle stepwise carbon trading and low-carbon demand response (LCDR) to balance low-carbon performance and economic efficiency. Firstly, based on life cycle theory, embodied carbon from new energy equipment manufacturing and transportation is incorporated into accounting, with a stepwise carbon trading mechanism designed. Secondly, corrected dynamic carbon emission factors for power and heating networks are constructed to quantify real-time carbon intensity. A dual-driven LCDR model (electricity price and carbon factor) is established to coordinate shiftable and sheddable electric-thermal loads and is combined with a two-layer scheduling model (pre-scheduling and re-scheduling) targeting the minimal total operation cost. Simulation results of a South China park show that life-cycle stepwise carbon trading reduces emissions by 16.7%, and LCDR further cuts 4.05%. Their synergy achieves significant carbon reduction with a slight cost increase, while supplementary sensitivity analyses further confirm the scalability and robustness of the proposed framework under varying load levels and demand response capabilities. Full article

Fuel cell technologies are increasingly investigated as alternatives to conventional auxiliary diesel generators in order to enhance shipboard energy efficiency and reduce greenhouse gas emissions. This study presents a unified and uncertainty-driven system-level assessment of solid oxide fuel cell (SOFC) and proton exchange membrane fuel cell (PEMFC) systems operating as auxiliary power sources on a 200 m bulk carrier. Both technologies are evaluated under identical vessel characteristics, operating profiles, auxiliary load levels (360–600 kW), and cost assumptions, and are benchmarked directly against a conventional three–diesel-generator configuration. A modular numerical framework is developed to model propulsion–auxiliary interactions for ship speeds between 10 and 14 knots. SOFC systems are assessed using grey, bio-derived, and green natural gas pathways, while PEMFC systems are examined under grey, blue, and green hydrogen supply routes. Performance indicators include annual fuel consumption, carbon dioxide (CO₂) emission reduction, net present value (NPV), internal rate of return (IRR), payback period (PBP), and marginal abatement cost (MAC). Economic uncertainty is explicitly embedded in the framework through Monte Carlo simulation, where fuel prices (±20%) and capital costs are sampled across defined ranges, generating probabilistic distributions rather than single deterministic estimates. This uncertainty-centred approach enables assessment of robustness, downside risk, and probability of profitability. Results show that replacing a single operating 600 kW diesel generator with fuel cell systems reduces auxiliary fuel energy demand by 25–35% for SOFC and approximately 15–25% for PEMFC relative to the diesel benchmark. Annual CO₂ reductions range from 1.1 to 1.3 kt for SOFC systems and 1.8–2.8 kt for PEMFC configurations. Under grey fuel pathways, median NPVs reach approximately 2–4.5 M$ for SOFC and 9–17 M$ for PEMFC as load increases, with IRRs exceeding 15% and 30%, respectively. Transitional pathways exhibit narrower margins, while renewable pathways remain more sensitive to fuel price variability. The findings demonstrate that fuel pathway cost dominates lifecycle outcomes under uncertainty and that hydrogen-based PEMFC systems exhibit the strongest economic resilience within the examined market ranges. The framework provides structured, uncertainty-aware decision support and establishes a foundation for integration into model-based systems engineering (MBSE) environments for early stage ship energy system design. Full article

Recent deep exploration in the northern Sichuan Basin has advanced our understanding of Lower Cambrian Canglangpu Formation carbonate reservoirs. However, the characteristics, genesis, and distribution of the reservoir, as well as future exploration targets, remain unclear. Specifically, core and thin-section analyses indicate that these reservoirs are notably tight, with virtually no visible macroporosity and low permeability (0.01–1 mD). However, helium porosity measurements reveal values of 2–5%, suggesting significant storage potential. An integrated approach utilizing optical and scanning electron microscopy (SEM), high-pressure mercury injection capillary pressure (MICP), nuclear magnetic resonance (NMR), and micro-computed tomography (micro-CT) was employed to characterize the pore systems. Quantitative thin-section analysis reveals visible areal porosity markedly lower than helium porosity, indicating predominance of micropores; mercury intrusion and NMR demonstrate that intragranular and intergranular micropores constitute most pore volume, although effectively connected throat sizes remain below 1 µm. Comparative stratigraphic evaluations show that porosity is more developed in the dolomite-rich upper and middle intervals of the depositional cycles, whereas the lower intervals are less porous. Early subaerial exposure promoted dolomitization and dissolution, which facilitated pore development. However, the influence of sediment mixing led to a reduction in porosity. And deep burial subjected the rocks to intense compaction and cementation, destroying most of the primary pore space. Consequently, reservoir quality is ultimately governed by the interplay between the original depositional environment and the later diagenetic history, with paleotopographic highs identified as the most promising exploration targets. These findings establish a predictive framework for reservoir quality in tight carbonate rocks, which holds significant implications for analogous plays worldwide. Full article

In order to reach net-zero by 2050, we need to have strong decarbonization policies, especially in hard-to-abate clean-ups like steel (8% of the global emissions), cement (7%), and power generation (30%), and negative emissions through direct air capture (DAC) and bioenergy with carbon capture and storage (BECCS). This review paper summarizes the progress in CO₂ capture, compression, transportation, and storage technologies between 2020 and 2025, including energy penalty (20–40%) and cost (15–30%) reductions, with innovations such as metal–organic frameworks (MOFs), bio-inspired catalysts, ionic liquids, and artificial intelligence (AI)-based optimization. This paper, as a new input into the carbon capture and storage (CCS) field, uses the Weighted Sum Model (WSM) as a multi-criteria decision-making tool to rank the best technologies in the capture, storage, monitoring, and transportation sectors. The weights of the criteria are calculated based on Shannon entropy, and the assessment is performed in three conditions, namely, optimistic, pessimistic, and expected. The weights are computed with sensitivity analysis to make the assessment robust. The viability of key projects, such as Northern Lights (Norway, 1.5 MtCO₂/year), Porthos (The Netherlands, 2.5 MtCO₂/year), Quest (Canada, 1 MtCO₂/year), and Petra Nova (USA, 1.6 MtCO₂/year), is evident, and it is projected that, globally, CCS will reach 49 MtCO₂/year across 43 plants in 2025. The review incorporates socio-economic and environmental justice, including barriers such as high costs ($30–600/MtCO₂), energy penalties (1–10 GJ/tCO₂), and opposition between people (20–40% in EU/US). In comparison with previous reviews, this article has a more comprehensive focus, provides quantitative synthesis through WSM, and discusses the implications for researchers, policymakers, and stakeholders towards achieving faster CCS implementation on the path to net-zero. Full article

Global poultry production continues to expand rapidly to meet the growing demand for affordable and high-quality animal protein. However, this growth raises pressing concerns about environmental sustainability, natural resource use, and public health. Although current initiatives, such as improved housing systems, optimized feeding practices, and partial soybean meal substitution, have helped mitigate some impacts, comprehensive integrated solutions remain underexplored. This review synthesizes emerging nutritional and management innovations that enhance the sustainability of poultry production while maintaining profitability. It addresses three central research questions: (1) Which alternative feed ingredients most effectively preserve animal performance while minimizing environmental burdens? (2) How can environmental management practices enhance resource efficiency and waste valorization? (3) What roles do life cycle assessment methodologies and policy frameworks play in advancing sustainable poultry systems? Evidence from 100 peer-reviewed studies, industrial data, and field analyses reveals that incorporating insect meals, algae, and agro-industrial by-products can reduce dependence on soybean meal by 20–40% and improve feed efficiency by 5–12% across various poultry production systems. Furthermore, integrating environmental management strategies, such as manure valorization, efficient water and energy use, and the adoption of renewable energy, substantially reduces greenhouse gas emissions and promotes circular economic principles. Life cycle assessment studies confirm that combined dietary and management interventions yield greater reductions in carbon footprint than isolated measures. Future research should focus on optimizing interactions among feed strategies, environmental management, and policy frameworks through digital technologies, nanomaterial-based feed additives, and region-specific sustainability plans to accelerate the transition toward resilient, climate-smart poultry production systems. Full article

This study investigates trade-in channel selection in a closed-loop supply chain under a hybrid carbon policy framework that integrates cap-and-trade and carbon taxation. Game-theoretic models are developed for three manufacturer-led channels: manufacturer trade-in (M-CX), retailer trade-in (R-CX), and third-party trade-in (T-CX). The analysis examines pricing strategies, profitability, and carbon emission reductions across these channels. The key findings are as follows: (1) Carbon tax consistently compresses manufacturer profits, whereas cap-and-trade mechanisms exhibit a non-linear U-shaped effect. Manufacturer profits remain highest under the M-CX channel, irrespective of policy intensity. (2) Retail prices are most sensitive to carbon policies under the T-CX channel, where trade-in rebates increase with carbon intensity. The R-CX channel sustains higher retail prices and rebates than M-CX, while T-CX surpasses both under conditions of high carbon intensity. (3) Carbon emission reductions decline sharply under M-CX and R-CX as policy stringency increases. In contrast, the T-CX channel establishes a buffering mechanism through rising rebates, exhibiting the slowest rate of decline. At low carbon intensity, T-CX yields the lowest reduction levels; however, under high intensity, it overtakes the other channels to achieve the highest reduction. This study offers insights for manufacturers’ channel selection and government policy coordination under hybrid carbon regulation regimes. Full article

To address the challenges of low-carbon economic optimization and collaborative management for multiple Virtual Power Plants (VPPs), this paper proposes a low-carbon economic optimization and collaborative management method based on a Stackelberg game framework. Firstly, a Stackelberg game model is constructed with the Distribution System Operator (DSO) as the leader and multiple VPPs as followers. The leader (DSO) guides the followers’ behavior through dynamic pricing strategies to maximize its own utility. Meanwhile, the followers (VPPs) develop energy management strategies to minimize their individual costs, taking into account factors such as energy transaction costs, fuel costs, carbon trading costs, operation and maintenance (O&M) costs, compensation costs, and renewable energy generation revenues. Furthermore, the strategy spaces of all participants are defined, and an optimization model is established subjected to constraints including energy balance, energy storage operation, power conversion, and flexible load response. The CPLEX solver and Nonlinear-based Chaotic Harris Hawks Optimization (NCHHO) algorithm are employed to solve the proposed game model. Simulation results demonstrate that the proposed method effectively facilitates collaboration between the DSO and multiple VPPs. While ensuring the safe operation of the system, it balances the profit between the DSO and VPPs, and incentivizes renewable energy consumption and indirect carbon reduction, thereby validating the effectiveness and superiority of the method and providing reliable technical support for the low-carbon collaborative operation of multiple VPPs. Full article

Livestock manure management contributes substantially to agricultural greenhouse gas emissions, making the adoption of low-carbon approaches urgent in ecologically sensitive regions. This study focuses on the County-wide Livestock Manure Resource Utilization Project in Danjiangkou City, the core water source area of China’s South-to-North Water Diversion Project. Based on field survey data, IPCC Guidelines, and a life cycle assessment framework, this study established a carbon accounting boundary covering excretion, collection, storage, treatment, and utilization stages. A scenario analysis was conducted to compare 2023 baseline emissions with 2026 project emissions and to quantify the carbon reduction potential. The research findings indicate that the overall carbon reduction rate following the project’s implementation reached 40.8%. However, the effectiveness varied considerably across the four management models. The Sedimentation–Crop Model and the Housing–Bedding Integrated Model, which employed integrated systemic interventions, achieved reductions of 61.50% and 60.09%, respectively. In contrast, the “124” Healthy Breeding Model and the Raised-Bedding Composting System, which relied primarily on single-stage upgrades, achieved reductions of only 32.04% and 27.70%. This disparity suggests that in decentralized livestock operations, isolated technological improvements fall short; meaningful decarbonization requires systemic interventions across the entire manure management chain. The findings provide a reference for low-carbon livestock manure management and regional development in ecologically sensitive areas. Full article

Long-term energy sustainability in the built environment depends not only on deploying renewables but also on maintaining high energy efficiency that consistently lowers demand and enables more effective use of low-carbon electricity over time. This paper presents an illustrative case study that demonstrates a low-data, EPC/audit-based screening workflow for assessing operational energy, carbon, and water-related indicators in a non-residential building. An explanatory case study is conducted for a mixed-use logistics facility in Poland (≈610 m²), combining approaches to useful/final/primary energy indicators with operational carbon and water footprints. The operational water footprint is evaluated as a screening metric (L/kWh) applied to the annual electricity balance and tested across PV self-consumption levels (25/50/75%) to reflect the role of energy management and flexibility. The results indicate that an efficiency-oriented modernization pathway supported by PV integration (≈64 kWp; ~57,350 kWh/yr) reduces the primary energy performance indicator EP from 154 to 62.5 kWh/m²·yr, corresponding to a 59% reduction in annual primary energy demand. The operational water footprint indicator decreases nearly linearly with increasing PV self-consumption, demonstrating that long-term benefits depend on sustained efficiency and on maximizing on-site renewable utilization through controls, demand shifting, and/or storage. Overall, the framework supports transparent benchmarking and the development of staged pathways for integrating renewable and low-carbon energy systems into logistics-building portfolios, while maintaining an analytical focus on operational energy, carbon, and water performances. Full article

This paper develops and applies a techno-economic optimization framework for sizing photovoltaic (PV) and battery energy storage systems (BESSs) in grid-connected energy communities. An in-house developed modeling platform featuring custom MATLAB (R2025a) code implements a mixed-integer linear programming (MILP) model that minimizes differential net present value (NPV) over a 25-year lifetime, integrating capital expenditures, operating cash flows, and carbon taxation. The formulation captures temperature-dependent PV efficiency, battery round-trip efficiency, and time-varying electricity prices, and is validated on a real campus energy community with hourly demand, irradiance, and tariff data. Two design scenarios are examined: the optimal unconstrained case and a budget-constrained configuration (CAPEX ≤ 2.0 M€). Results show the unconstrained system installs 3.19 MW_p PV and 12.3 MWh storage, achieving 78.9% self-sufficiency and a 78.9% emissions reduction. The constrained case installs 0.99 MW_p and 1.68 MWh, achieves 32.0% self-sufficiency, and delivers a 4.46 M€ NPV with payback in 3.9 years. Under current costs and tariffs, PV-dominated configurations provide the highest value, with limited battery benefit except under generous budgets or higher carbon prices. A dedicated CAPEX sensitivity analysis explores PV and battery cost variability and its impact on optimal sizing and economic outcomes. The core methodological contribution is a master-planning formulation that solves design decision variables and optimal dispatch concurrently within a single MILP. The flexible platform enables future reassessment as technology, tariff, and policy landscapes evolve. Full article

In the context of global energy system transformation and the pursuit of regional sustainability, China’s Air Pollution Control and Prevention Action Plan (APPA) targets both pollution reduction and carbon mitigation, serving as a critical policy instrument for coordinating the energy-economy-environment nexus in the “2+26” cities. This study employs a quasi-natural experiment with a difference-in-difference (DID) method to assess the synergistic impact of this energy-related policy on these cities. Results show that APPA significantly reduces PM2.5 and carbon emissions by 5.56% and 9.89%, respectively, demonstrating a successful alignment of short-term environmental targets with long-term decarbonization goals. Heterogeneity analysis reveals that large cities with higher institutional capacity are more effective in reducing both pollutants, while resource-based cities achieve more PM2.5 reduction, and non-resource-based cities excel in low-carbon energy transition. Mechanism analysis indicates that APPA promotes these outcomes by optimizing the energy-intensive industrial structure and fostering green technological innovation. This study highlights the effectiveness of integrated governance frameworks in enhancing air quality and reducing carbon emissions, providing crucial insights for redesigning sustainable energy policies and managing the socio-economic disruptions of just transitions in rapidly developing regions. Full article

Globally, the transport sector accounts for almost a quarter of CO₂ emissions from fuel combustion and generates large amounts of pollutants, placing significant pressure on the environment and human health. By 2050, the European Green Deal requires a 90% reduction in transport-related emissions, making sustainability necessary across all modes of transport. Based on the relevant literature, this study examines the role and potential of railways in decarbonising the EU transport sector. Railway is highly efficient, consuming just 1.9% of transport sector energy while handling 16.9% of freight and 5.1% of passenger transport in the EU, yet is responsible for only 0.4% of total emissions. According to studies, greenhouse gas emissions can be reduced by improving energy efficiency, using low-carbon or renewable energy, and expanding train electrification. The greatest potential for decarbonisation lies in a modal shift to rail. However, this requires significant infrastructure investment: raising line speeds to at least 160 km/h, expanding networks, building terminals, digitalisation, and alignment with TEN-T standards. Although the EU supports the modal shift with funding programmes, the transition is not progressing as expected—the share of road freight transport increased from 74% in 2013 to 78% in 2023. Stronger investment is needed in Member States’ national policies for the development and modernisation of railways. The authors developed a Path Evaluation Matrix (PEM), a quantitative decision framework integrating the fields of energy, transport, politics, and economics. The PEM results indicate that BEMU (battery electric multiple units) is optimal for 68% of secondary lines in south-eastern Europe. Full article

Energy-efficient retrofit of existing residential buildings is essential for reducing heating energy demand and carbon emissions in severe cold regions. However, the absence of a structured quantitative evaluation approach often limits effective decision-making in practice. This study develops a multi-dimensional evaluation framework integrating the Fuzzy Delphi Method and Analytic Hierarchy Process (AHP) to assess and prioritize building envelope retrofit strategies. A representative non-energy-efficient residential building in Changchun, China, is selected as a case study. Based on expert consultation, a hierarchical indicator system is established, and indicator weights are determined with satisfactory consistency (CR < 0.1). The results indicate that envelope thermal performance and energy–carbon benefits are the dominant factors influencing retrofit decisions. At the parameter level, insulation thermal conductivity and external wall heat transfer coefficient are identified as the most critical variables. The findings suggest that prioritizing improvements in envelope thermal performance can effectively enhance energy-saving and carbon-reduction performance under practical constraints. The proposed framework provides a practical and transferable decision-support tool for energy-efficient retrofit planning for existing residential buildings in severe cold regions. Full article

Energy management of multi-carrier energy hubs (MCEHs) is a challenging task, particularly when fuel cell electric vehicle (FCEV) stations are included, due to the stochastic nature of FCEV demand, system loads, and integrated renewable energy resources (RERs) such as wind turbines (WTs) and photovoltaic (PV) systems. This paper aims to optimize the energy management of an MCEH-based microgrid to simultaneously minimize total operating costs and emissions. To this end, a novel enhanced quadratic interpolation optimization (EQIO) algorithm is proposed. The proposed EQIO algorithm incorporates two key improvements: a best-to-mean quasi-oppositional-based learning (BMQOBL) strategy and an evaluation mutation (EM) strategy. The performance of EQIO is evaluated using the CEC 2022 benchmark functions, and the obtained results are compared with those of other optimization techniques. Three case studies are investigated: (i) energy management of the MCEH microgrid without RERs, (ii) sustainable operation (with RERs), and (iii) sustainable operation with RERs combined with the application of demand-side response (DSR). Moreover, the proposed framework explicitly supports long-term sustainability goals by enhancing renewable energy utilization, reducing the carbon footprint, and promoting cleaner transportation through efficient integration of FCEV infrastructure. The results demonstrate that integrating RERs reduces operating costs and emissions by 51.47% and 59.69%, respectively, compared to the case without RERs. Furthermore, the combined application of RERs and DSR achieves cost and emission reductions of 55.26% and 53.93%, respectively, compared to the case without RERs. Full article

The paper proposed an adaptive preference-based multi-objective optimization framework of intelligent energy management in smart microgrids that are dynamically adapted to operational priorities with regard to real-time grid conditions, stakeholder preferences, and environmental constraints. The suggested hierarchical algorithm combines an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with an advanced dynamic preference weight distribution system that can trade off between minimization of operational cost. Reduction of carbon emission, enhancement of voltage stability, enhancement of power quality and maximization of system reliability and adaptability to different operational conditions, such as renewable energy intermittency, demand response schemes and emergencies. The framework presents a new multi-layered preference-learning module that represents the intricate stakeholder priorities in terms of more sophisticated fuzzy logic-based decision matrices, neural network preference prediction, and adaptive reinforcement learning methods and transforms them into dynamic optimization weights with feedback mechanisms. Large-scale simulations on a modified IEEE 33-bus test system coupled with various renewable energy sources, energy storage facilities, electric vehicle charging points, and smart appliances demonstrate superior improvements in performance: 23.7% operational costs reduction, 31.2% carbon emissions reduction, 18.5% system reliability improvement, 15.3% voltage stability increase and 12.8% reduction of deviations in power quality. The proposed system has an adaptive nature with better performance in a variety of operating conditions such as peak demand times, renewable energy intermittency events, grid-connected and islanded operations, emergency load shedding situations, and cyber–physical security risks. The framework is shown to be highly effective under different conditions of uncertainty and variation in parameters and communication delay through intense sensitivity analysis and robustness testing, thus demonstrating its practical applicability in real-world applications of smart grids. Full article