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ABSTRACTThe concept of “blue carbon” is, in this study, critically evaluated with respect to its definitions, measuring approaches, and time scales. Blue carbon deposited in ocean sediments can only counteract anthropogenic greenhouse gas (GHG) emissions if stored on a long‐term basis. The focus here is on the coastal blue carbon ecosystems (BCEs), mangrove forests, saltmarshes, and seagrass meadows due to their high primary production and large carbon stocks. Blue carbon sequestration in BCEs is typically estimated using either: 1. sediment carbon inventories combined with accretion rates or 2. carbon mass balance between input to and output from the sediment. The inventory approach is compromised by a lack of accurate accretion estimates over extended time periods. Hence, short‐term sedimentation assays cannot be reliably extrapolated to long timescales. The use of long‐term tracers like 210Pb, on the other hand, is invalid in most BCEs due to sediment mobility by bioturbation and other physical disturbances. While the mass balance approach provides reasonable short‐term (months) estimates, it often fails when extrapolated over longer time periods (> 100 years) due to climatic variations. Furthermore, many published budgets based on mass balance do not include all relevant carbon sources and sinks. Simulations of long‐term decomposition of mangrove, saltmarsh (Spartina sp.), and eelgrass (Zostera sp.) litter using a 3‐G exponential model indicate that current estimates of carbon sequestration based on the inventory and mass balance approaches are 3–18 times too high. Most published estimates of carbon sequestration in BCEs must therefore be considered overestimates. The climate mitigation potential of blue carbon in BCEs is also challenged by excess emissions of the GHG methane (CH4) and nitrous oxide (N2O) from biogenic structures in mangrove forests and saltmarsh sediments. Thus, in many cases, carbon sequestration into BCE sediments cannot keep pace with the simultaneous GHG emissions in CO2 equivalents.

PurposeThis study aims to deepen the understanding of what stakeholders talk about when it comes to sustainable fashion on social media and how. Sustainable fashion is a broad umbrella term, which can distract attention from the differences between the individual subtopics and the sentiments ascribed to them. However, little systematic research exists on how the stakeholder activity and dominant sentiments vary across different sustainable fashion topics.Design/methodology/approachThis study is based on a social media analysis of 19,179 tweets authored by 1,819 distinct stakeholders on Twitter (now “X”) from 2007 to 2022. A large language model, a type of artificial intelligence (AI) that focuses on understanding and generating human language, is used to conduct a sentiment analysis of six stakeholder groups and 81 keywords linked to sustainable fashion. Two case examples are used to highlight the differences in stakeholder perceptions of sustainable fashion.FindingsThe social media analysis demonstrates how subcategories of sustainable fashion significantly differ in terms of stakeholder interest, activity and sentiments. For instance, tweets on circular economy and relevant subcategories (closed loop, recycling, upcycling, etc.) are popular, whereas issues linked to environmental, social and governance (ESG) and due diligence receive little attention on social media. While sentiments toward sustainable fashion are in general positive, discussions on topics such as labor rights issues are consistently associated with negative sentiments across most stakeholder groups.Originality/valueThis study contributes to the literature by demonstrating how stakeholders and sentiments vary across different topics linked to sustainable fashion on social media, which has become one of the main channels for communicating sustainability content. The findings thereby shed new light on dominant stakeholder positions regarding a wide variety of sustainable fashion topics.

Wetlands are important carbon sinks for mitigating climate warming. In this paper, greenhouse gas (GHG) fluxes and carbon sequestration capacity of freshwater wetlands, coastal wetlands and constructed wetlands around the world are evaluated, and strategies to improve carbon sequestration by wetlands are proposed based on the main influencing factors. Air temperature and average annual rainfall are significantly positively correlated with CH4 flux and N2O flux in freshwater wetlands and coastal wetlands. While chemical oxygen demand (COD) and total nitrogen (TN) concentrations of influent are found to be the main factors affecting GHG fluxes in constructed wetlands. The main factors affecting wetland carbon storage include the presence and species of wetland vegetation, ecological water level, and ecological pattern. Strategies for protecting and restoring existing wetlands, creating new wetlands, and strengthening the carbon sequestration capacity of wetlands are proposed. Fully realizing the carbon sequestration potential of wetlands holds the prospect of a more effective and sustainable response to global climate change.

With the growing global demand for climate change mitigation, the development and utilization of renewable energy have become crucial for energy transition. This study introduces an innovative optimization framework for clean energy systems on energy islands, integrating offshore wind power, hydrogen production, and hydrogen storage. Advanced forecasting models based on Long Short-Term Memory (LSTM) and Attention-enhanced Convolutional Neural Networks combined with Bidirectional LSTM (Attention-CNN-BiLSTM) are proposed, achieving an impressive prediction accuracy of 98 % for both wind power and residential electricity load. A multi-objective optimization approach, combining the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), is employed to perform 24-h rolling scheduling optimization of the energy system. The optimization model finds a compromise between maximizing profits and minimizing power fluctuations. Compared with the results of non-optimization, the power stability of the optimized system is improved by 45 %. When the wind power capacity is sufficient, the system operating profit reaches 4.41 million CNY, and the power fluctuation is 4.26 GW. This study provides a new theoretical basis and practical guidelines for the design and operation of energy islands, highlighting the potential applications of clean energy technologies in modern energy systems.

Numeric simulation tools are commonly used to predict buildings' energy performance for different purposes, such as for certification or optimisation measures. However, the actual buildings' energy use often deviates from the predicted (simulated) building energy use. This deviation is typically indicated as energy performance gap (EPG). As such, there can be multiple reasons for this mismatch. Recently, especially the impact of building users has been suggested to be a key contributor to the EPG. To this end, this effort critically reflects building users' impact on the energy performance of buildings in three steps. First, the spectrum of potential uncertainties in building energy use predictions are outlined. Second, two illustrative case studies are discussed that explore the impact of building users' behaviour and interaction with building interfaces on the energy performance via different modelling approaches and simulation techniques. Finally, the paper discusses potential mitigation strategies that could support both control opportunities for building users and energy efficiency goals.

Perceptions of environmental sustainability among radiographers can potentially be influenced by individual experiences and educational backgrounds. This study aims to investigate the perceptions and practices of radiographers regarding sustainability initiatives, emphasizing the significance of understanding their diverse experiences and backgrounds.This was an online survey involving 104 radiographers from various regions worldwide to evaluate their training related to global warming, their perceptions of sustainability, current practices, and the barriers they face in implementing sustainability practices.Participants' knowledge regarding sustainability was significantly influenced by their social networks, including friends and family (χ2 = 12.505, p = 0.004). Notable correlations were observed between years' of experience and the belief in pollution's contribution to climate change (χ2 = 8.096,p = 0.038), as well as the perception of human activities as the primary cause of climate change (χ2 = 22.68,p = 0.011). Furthermore, involvement in environmental protection initiatives (χ2 = 19.268, p = 0.033) and the perception of local climate impacts (χ2 = 22.478, p = 0.012) were positively correlated with experience. In the field of radiography, the adoption of energy-efficient practices (χ2 = 10.482, p = 0.011) and the recycling of imaging waste (χ2 = 25.778, p = 0.004) were significantly associated with levels of experience. Lastly, the barrier identified as "Lack of Authority to make change" also exhibited a significant relationship with years of experience (χ2 = 9.449, p = 0.022).This study indicates that staff experiences play a significant factor influencing sustainability engagement among radiographers. Barriers identified include financial constraints, insufficient leadership, safety concerns, and inadequate training.The current study highlights the essential requirement for customized strategies based on radiographers' experiences to improve sustainable practices in radiography. It acknowledges the impact of organizational barriers and suggests that progress toward a more sustainable future can be achieved through individual empowerment and collaboration within the healthcare sector.

This work, conducted exergy and thermoeconomic analyses of sugarcane biorefineries for different biomass allocations and conversion technologies. Using the Kriging method to determine biomass allocation, this article evaluated the performance of a sugarcane biorefinery based on the 2nd Law of Thermodynamics. By this combination of techniques, it is possible to provide better guidance to decision-making. Therefore, through exergy analysis, it is possible to improve the allocation of biomass and energy resources to be more appropriate. Therefore, biorefineries can be more efficient and synthesize products at lower costs. Moreover, the highest exergy efficiency, 43.7 %, occurred when all sugarcane bagasse was destined for the thermochemical route, 60 % for Fischer-Tropsch synthesis, and 40 % for a BIG-GTCC. On the other hand, the lowest exergy efficiency, 39.63 %, was observed in the conventional case, indicating that prioritizing biomass conversion to any kind of fuel is more productive than allocating it to produce electricity in a Rankine Cycle, regardless of technology and biofuel. Moreover, from the exergoeconomic insight, a promising trade-off was determined: the thermochemical routes proved to be better in efficiency, both energetically and exergetically, while the biochemical routes, indicated potential profitability. Furthermore, the exergoeconomic analysis demonstrated that increasing the exploration of second-generation biomass in the biorefinery lowers the exergy costs of every product. Overall, this research highlights the potential associated with sugarcane biorefineries going into expansion and modernization since its resources can be valorized in terms of efficiency and monetary value.

In this study, we provide a detailed technical assessment of an integrated system using direct air capture based on temperature-vacuum-swing-adsorption (TVSA-DAC) as the carbon source for e-methanol production.