Operational-level synergistic strategy for enhancing carbon benefits in coal-fired power plants

Collaborative optimization of integrated energy systems in energy-intensive industrial clusters considering carbon-green-certificate trading

It is critical to the sustainable development of agricultural production that balances between increased grain yield with economic returns and carbon emissions in intensive oasis irrigation agroecosystems. Crop rotation and nitrogen management have been shown to have great potential in enhancing soil carbon sequestration and reducing carbon emissions. However, the comprehensive assessment of different planting systems in terms of crop productivity, economic benefits, soil carbon sequestration and greenhouse gas emissions remains unclear. A continuous field experiment was conducted since 2018 in the Hexi Oasis irrigation area, and data were collected from 2022 to 2025. Four planting patterns (W: spring wheat continuous cropping; WV: spring wheat-common vetch; WWV: spring wheat-winter wheat-common vetch; WRV: spring wheat-winter rapeseed-common vetch) and three nitrogen rates (N2: local conventional nitrogen amount, 360 kg ha −1 ; N1: 270 kg ha −1 , nitrogen amount reduced by 25%; N0: no nitrogen application, 0 kg ha −1 ) were set up as experiment treatments. The results showed that rotation significantly increased equivalent yield compared to continuous cropping. The cropping systems followed a decreasing yield order: WRV> WWV> WV. Compared to the spring wheat continuous cropping with local conventional nitrogen amount (WN2), the wheat-winter rapeseed-common vetch rotation combined with a 25% nitrogen reduction (WRVN1) significantly increased the equivalent yield by 10.7%. Furthermore, WRVN1 increased the soil organic carbon content by 3.9%, organic carbon stock, and carbon management index compared to WN2. In addition, crop rotation reduced carbon emissions (excluding WV), while the carbon emission efficiency of WV pattern was higher than W pattern. The carbon emission efficiency of WRVN1 was 24.2% higher than WN2 and had no significant difference with WRVN2. Furthermore, crop rotation increased net income (excluding WV). And net income of WRVN1 increased by 29.7% compared to WN2. Incorporating winter crops with leguminous green manure and moderate nitrogen reduction can achieve a trade-off between agronomic performance, economic benefits and ecological development in oasis irrigation districts. Our case provides pathways for N management and sustainable production practices in oasis irrigation agroecosystems. ● Diversifying continuous wheat with winter rapeseed and vetch increased system yield and farm net income in the Hexi Oasis. ● Winter crop–legume rotations with 25% less N maintained or increased wheat-equivalent yield while reducing soil CO₂ emissions. ● A spring wheat–winter rapeseed–vetch rotation with less N optimized trade-offs among yield, profit, and carbon sequestration.

Apply machine learning to predict greenhouse gas emissions in aerobic composting and achieve emission reduction by nanomembrane covering mode.

Intelligent optimization of steam-curing for precast concrete tunnel lining: Insight from sample data parameters analysis and interpretable machine learning

Healthcare contributes considerably to global greenhouse gas emissions, with operating theatres amongst the most energy-intensive hospital environments. While carbon footprints have been quantified for several surgical procedures, the environmental impact of hand surgery, characterised by high case volumes and short procedures, remains poorly studied. This study aims to quantify carbon emissions of hand surgery procedures. This single-centre observational pilot study quantified the carbon emissions associated with hand surgery procedures performed during two half-day theatre lists at a UK NHS hospital. Data was collected under the Greenhouse Gas Protocol Scopes and emissions calculated using UK Government greenhouse gas conversion factors. Data collected included theatre electricity and heating, anaesthetic use, staff and patient transport, waste incineration, supply-chain emissions, and instrument sterilisation. Five trauma hand surgery cases were analysed. Case-level emissions ranged from 8.32 to 22.56kg CO2. When combined at a list level, total emissions were substantial, reaching 311.36kg CO2 and 285.30kg CO2 per half-day list. Purchased electricity (Scope 2) was the largest contributor, followed by heating and anaesthetic gases (Scope 1). Scope 3 emissions were largely attributed to staff travel and single-use consumable supply-chain emissions, while waste disposal and reusable instrument sterilisation contributed comparatively little. Individual hand surgery procedures have a relatively low carbon footprint, but the cumulative emissions at list-level are large. Theatre energy use, heating and staff transport represent key targets for emission reduction. Interventions focusing on energy-efficient infrastructure, renewable energy, greener staff travel, and reduced reliance on single-use consumables may result in meaningful environmental benefits. Larger multicentre studies with improved energy metering are needed to refine estimates and guide sustainable surgical practice. Quantifying the carbon emissions associated with common hand surgery procedures may help hand surgery teams and healthcare organisations identify opportunities to reduce emissions.

This scoping review explores the environmental impact of nursing interventions in acute care settings, focusing on waste reduction, energy consumption, and carbon emissions, while identifying nurse-led sustainability practices, assessment frameworks, and implementation barriers/enablers. Guided by Arksey and O'Malley's framework and reported per PRISMA-ScR guidelines. Data Sources: PubMed, CINAHL, Scopus, and Google Scholar were searched for peer-reviewed, English-language studies published between 2020 and 2025. A five-stage process was employed: (1) research question formulation, (2) comprehensive literature search, (3) study selection using the Population-Phenomenon-Context (PPC) framework, (4) data charting via a structured extraction form, and (5) thematic synthesis. Methodological quality was appraised using Joanna Briggs Institute (JBI) tools. Of 400 identified records, 25 met inclusion criteria. Findings show nurses reduce environmental impact through reusable linen use (e.g., 496 kg/year ICU waste reduction), energy-efficient equipment, and improved waste segregation. However, time constraints, limited resources, and inadequate sustainability training impede consistent implementation. Awareness among nurses and students is moderate but rarely translates into practice. Nursing interventions hold significant potential to reduce healthcare's environmental footprint. Embedding sustainability competencies into curricula and reinforcing them with institutional policies and leadership support are essential. This review informs nurses and clinical leaders that sustainable practices-such as switching to reusable linens and optimizing energy use-are both feasible and impactful in acute care. It provides actionable evidence for reducing waste and carbon emissions while maintaining patient safety, supporting the integration of environmental stewardship into daily nursing practice. Findings from this review highlight the measurable environmental benefits of nurse-led sustainability interventions, such as waste reduction and energy conservation in ICUs. The evidence supports updating nursing curricula, clinical guidelines, and hospital policies to equip nurses with the knowledge and tools needed to lead sustainability efforts, thereby reducing healthcare's carbon footprint without compromising care quality.

The rapid expansion of data centers (DCs) has led to substantial increases in global energy consumption and carbon emissions. Moreover, the strong coupling among workload scheduling, IT, cooling, and energy subsystems makes sustainable DC microgrids highly complex, especially given the difficulty of accurate modeling and the presence of significant uncertainties and rapid dynamics. To address these challenges, this paper proposes a multi-critic deep reinforcement learning (DRL) approach based on a mixture-of-experts (MoE) with multi-head architecture. The problem is formulated as a multi-reward Markov Decision Process (MDP) with independent reward signals for workload efficiency, energy consumption, and carbon footprint, providing richer feedback compared to traditional single-reward formulations. In the proposed approach, each reward is assigned a dedicated critic, thereby avoiding the conflicts and interference that arise when a single critic attempts to learn multiple competing rewards simultaneously. The shared MoE foundation with the multi-head architecture enables each critic head to focus on learning the value function for its specific optimization target, while the integrated adaptive gating mechanisms facilitate dynamic leveraging of both common and task-specific knowledge. This architecture improves learning stability, accelerates convergence, and enhances adaptability to complex, multi-objective learning tasks. Extensive experiments on a DC microgrid show that the proposed method outperforms state-of-the-art DRL and rule-based baselines in reducing the overall energy consumption and carbon emissions. All codes can be found at https://github.com/ikelq/Mixture-of-experts-based-Multi-Critic-DRL-for-Sustainable-Management-of-Data-Center-Microgrids . • Formulate the sustainable management of a DC microgrid as a multi-reward MDP. • Propose a multi-critic DRL framework with each reward assigned to a dedicated critic. • Design a multi-head architecture with a shared MoE structure within the multi-critic DRL framework.

Structural changes in the wheat supply chain and their environmental footprints through satellite yield forecasting

• A nested optimization framework for marine hybrid propulsion systems is proposed. • The framework integrates optimal power management and component sizing. • Pareto-optimal solutions minimize system investment cost and GHG emissions. • Case study shows 27% less emissions or 6% investment cost reduction compared to requirement-based design. • The framework is tested for robustness to ship power request variations. The growing demand for decarbonization of the shipping sector calls for integrated design strategies that simultaneously address energy management and propulsion system sizing. This paper presents a nested optimization framework for designing ship hybrid propulsion systems that identifies the Pareto-optimal front balancing economic and Well-to-Wake environmental performance. The framework utilizes a multi-objective genetic algorithm (NSGA-II) in the outer layer to efficiently explore the design space for battery capacity and generator sizing. For each candidate design, an inner optimization layer determines the minimum achievable greenhouse gas emissions through optimal power resource management. This nested approach ensures that each point on the resulting Pareto frontier represents a design in which both sizing and operation are simultaneously optimized. The methodological accuracy is validated by benchmarking NSGA-II against an exhaustive grid search, while its effectiveness is demonstrated in a small ferry case study by comparing results with a standard requirement-based design (RBD) approach. The results demonstrate that the optimized framework can achieve up to a 27% reduction in emissions at the same investment cost, or a 6% reduction in investment cost at the same level of emissions, compared to the RBD baseline. A sensitivity analysis is conducted to assess the method’s robustness to realistic variations in power demand and to evaluate the impact of component cost fluctuations on the resulting Pareto frontier. The proposed optimization framework serves as a decision-support tool for ship designers, enabling them to make informed, consistent choices when designing marine hybrid propulsion systems. The proposed structure is generalizable to a wide range of vessel types and operational scenarios.

Packaging significantly affects the loss and waste rate of fresh fruit and vegetables (FV), and thus the environmental impact. The usage of reusable plastic crate (RPC) can reduce food loss and waste (FLW), energy consumption, and thus the relevant environmental impact. However, studies combining FV and all these considerations are limited. This article applies life cycle assessment (LCA), from farm to store shelf, to evaluate the environmental impact of FV and their packaging, including Global Warming Potential (GWP), Freshwater Eutrophication Potential (EP), Freshwater Ecotoxicity Potential (FETP) and Water consumption Potential (WCP). It shows that the RPC usage can significantly reduce environmental impacts. Considering the food loss and waste reduction and energy optimisation brought by RPC usage, RPC usage can reduce GWP increase by 32.36 %–50.38 % as compared to cardboard (CB) packaging, FETP by 3.1 %–16.8 %, EP by 1.9 %–13.4 % and WCP by 3.4 %–16.9 %. The most notable reduction in GWP and FETP is observed for cabbage among the selected FV due to the highest loss rate. When combining food and packaging two life cycles together, the RPC usage can reduce GWP by 4.50 %–16.61 % compared to CB, . Electricity and FLW rate are identified as highly sensitive factors to total environmental impact, while electricity and fertilizer demonstrated a high sensitivity for GWP and FETP, respectively. The study shows that RPC has coupling advantages over CB in terms of indirect and direct impacts on GHG emissions and other environmental impacts. The results support a further deployment of RPC as a means to reduce the environmental influence and waste. • Reusable plastic crate (RPC) reduces Global Warming Potential (GWP) compared to cardboard packaging. • The reduction of CO 2 emissions using RPC can be enhanced by considering food loss and energy use. • Cucumber has the highest GWP compared to other investigated fruit and vegetables without packaging. • Using RPC has the most positive impact on cabbage-related CO 2 emissions among all investigated fruit and vegetables. • Electricity and fertilizer use are key sensitivity factors that influence GWP and Freshwater Ecotoxicity Potential (FETP).

Incorporation of postconsumer recycled (PCR) plastics in asphalt mixes is reported to improve the mechanical performance of asphalt mixes when used at a lower dosage. However, overstiffening of asphalt mixes due to the addition of higher amounts of plastic has been a serious concern. To this end, this study aims at increasing the percentage of plastic in asphalt mixes by incorporating a biorejuvenator. For this purpose, a control mix was designed using the balanced mix design (BMD) approach and then modified with two different types of PCR plastics, namely low-density polyethylene (LDPE) and linear low-density polyethylene (LLDPE). The asphalt mixes were then further modified by adding a biorejuvenator. The volumetric properties were determined, and the mechanical performances (rutting, cracking, and moisture-induced damage resistance) of the asphalt mixes were evaluated using the Hamburg Wheel Tracking (HWT) and Indirect Asphalt Tensile Cracking Test (IDEAL-CT). The optimum dosage of plastics was determined using the BMD criteria. The optimum dose of LDPE was found to be 1.5% with 2% waste cooking oil (WCO)-modified binder. In addition, environmental impact analyses were performed on the plastic-modified mixes. A significant reduction in greenhouse gas emission was observed from the use of plastic in asphalt mixes. A minimum of 7.5% reduction in greenhouse gas generation was found by using optimum LDPE and WCO-modified asphalt mixes.

• Chengdu led global CO 2 emissions followed by Luoyang, Chongqing, and Los Angeles. • Katowice had highest CH 4 emissions, trailed by São Paulo, Lahore, and Delhi. • CO 2 levels rose from 394.470–394.477 ppm in 2003 to 394.501–394.510 ppm in 2020. • CH 4 levels increased from 1831.3–1833.3 ppb to 1832–1834.5 ppb over 17 years. • Minimum land surface air temperature rose by 2 °C, from −54.49 °C to −52.20 °C. This study examined global atmospheric variations in carbon dioxide (CO 2 ) and methane (CH 4 ), which are two major greenhouse gases (GHGs). The main objectives were as follows: (1) identify the top 50 cities with the highest CO 2 and CH 4 emissions, (2) analyze 17-year trends in emissions in cities worldwide, (3) conduct a spatiotemporal analysis of CO 2 and CH 4 emissions from 2003 to 2020, (4) quantify changes in GHG emissions during this period, and (5) assess the impact of GHG emissions on land surface air temperature (LSAT). These objectives were achieved using the global ERA5 reanalysis data from the Copernicus Climate Change Service. The findings indicated that Chengdu (China) had the highest cumulative CO 2 emissions between 2003 and 2020, followed by Luoyang (China), Chongqing (China), Myitkyina (Myanmar), Louangphrabang (Laos), Lampang (Thailand), Louang Namtha (Laos), Aizawl (India), Nola (Central African Republic), and Los Angeles (USA). Katowice (Poland) exhibited the highest CH 4 emissions, followed by São Paulo (Brazil), Lahore (Pakistan), Delhi (India), New Delhi (India), Moscow (Russia), Chengdu (China), Anshan (China), Andijan (Uzbekistan), and Fergana (Uzbekistan). Between 2003 and 2020, the mean annual atmospheric CO 2 concentration increased from 394.470–394.477 ppm to 394.501–394.510 ppm, whereas the CH 4 concentration increased from 1831.3–1833.3 ppb to 1832–1834.5 ppb. The analysis revealed significant increasing trends in CO 2 and CH 4 emissions globally, with certain cities exhibiting sharper increases. The LSAT also increased during the study period, with the minimum LSAT increasing by 2 °C (from − 54.49 °C to − 52.20 °C). This comprehensive analysis highlights the urgent need to address GHG emissions to mitigate their environmental and climatic effects.

Greenhouse gas emissions and water-carbon cost-adjusted yield of drought-tolerant rice under varying irrigation amounts in the Jianghan Plain of China

Using Spirulina (Limnospira platensis) as an alternative feedstuff for poultry: Effects on ammonia and greenhouse gas emissions from excreta during storage.

Mitigating container port congestion and carbon emissions through AI and capacity sharing

• Introducing a novel feature value substitution methodology to simulate potential energy consumption savings for specific retrofit options. • Quantifying energy-savings on large-scale real-world data and solving data scarcity issues by introducing a feature value substitution methodology. • Providing a retrofit index to the research community that combines energy savings, carbon emission reduction, retrofit costs, and local subsidies into a comprehensible measurand. • Mitigating the uncertainty of homeowners and energy auditors in the decision-making process to identify the most suitable retrofit option for individual residential buildings. • Scenario analysis for different retrofit budgets and energy prices to identify the retrofit measure with the highest energy saving potential. The global building sector is one of the main contributors to annual global greenhouse gas emissions, yet homeowners remain hesitant regarding specific retrofit measures to reduce carbon emissions. This is unsurprising as the link between retrofits that reduce energy consumption and corresponding economic and ecological benefits remains elusive. Therefore, this study addresses the intersection of building energy performance, carbon emission reduction, and financial subsidies by quantifying expected energy savings based on specific energy-related retrofits with a real-world dataset containing 25,000 German residential buildings. The simulated energy savings for specific retrofit measures are based on a novel feature value substitution methodology and three sophisticated machine learning models, namely XGBoost, CatBoost, and LightGBM. This study then combines potential ecological gains, household investment budgets, and expected local governmental subsidies into a single informative yet comprehensible retrofit index to overcome the uncertainty regarding retrofits. The results show that glazing is the most impactful feature for potential energy savings of residential buildings, followed by heating system changes from oil to electric heating pumps. In contrast to the neglectable impact of better facade conditions on building energy performance, roof and wall insulation improvements lead to significantly lower energy consumption. This study underscores potential ecological savings of targeted retrofit measures and enables practitioners to cut expenses and reduce the associated financial risks.

From coal to code: The transformative role of fintech and renewable energy in China's low-carbon transition

Network-aware coordinated multi-microgrid energy management with carbon emission considerations under uncertainty: a multi-agent double deep Q networks approach

Progress and performance in synergizing the reduction of pollution and carbon emissions in China from 2015 to 2022