Circular economy and waste production models for sustainable development goals 12 and 14: Evidence from cruise sustainability reporting

Sustainable Development, Год журнала: 2024, Номер 32(6), С. 6686 - 6702

Опубликована: Май 22, 2024

Abstract The relationship between the practices and initiatives governing “waste production models” (sustainable development goal [SDG]12) marine biodiversity goals (SDG14) is relatively unexplored. Aiming to bridge this gap by drawing on stakeholder legitimacy theories, study examines onboard cruise ships' circular economy (CE)‐based waste management initiatives, correlating SGDs 12 14. Consequently, Carnival Corporation Plc's 2020–2022 sustainability reports are analyzed using content analysis both Leximancer software (ver. 5.0) manual methods. results highlight corporation's increasing commitment green technologies for achieve SDG14. However, its provide unclear evidence of impact biodiversity. Findings implies that practitioners should partner invest in Besides being first explore link two SDGs within CE framework, advances insights into models,” enhancing understanding sustainable practices.

Язык: Английский

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Metal-organic frameworks as potential catalysts for biodiesel production and biomass conversion: Mechanism and characteristics

Industrial Crops and Products, Год журнала: 2024, Номер 211, С. 118232 - 118232

Опубликована: Фев. 21, 2024

Язык: Английский

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Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches

International Journal of Green Energy, Год журнала: 2024, Номер 21(12), С. 2771 - 2798

Опубликована: Март 14, 2024

Examining the game-changing possibilities of explainable machine learning techniques, this study explores fast-growing area biochar production prediction. The paper demonstrates how recent advances in sensitivity analysis methodology, optimization training hyperparameters, and state-of-the-art ensemble techniques have greatly simplified enhanced forecasting output composition from various biomass sources. argues that white-box models, which are more open comprehensible, crucial for prediction light increasing suspicion black-box models. Accurate forecasts guaranteed by these AI systems, also give detailed explanations mechanisms generating outcomes. For models to gain confidence processes enable informed decision-making, there must be an emphasis on interpretability openness. comprehensively synthesizes most critical features a rigorous assessment current literature relies authors' own experience. Explainable encourage ecologically responsible decision-making improving forecast accuracy transparency. Biochar is positioned as participant solving global concerns connected soil health climate change, ultimately contributes wider aims environmental sustainability renewable energy consumption.

Язык: Английский

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Catalytic co-pyrolysis and kinetic study of microalgae biomass with solid waste feedstock for sustainable biofuel production

Journal of Analytical and Applied Pyrolysis, Год журнала: 2024, Номер unknown, С. 106755 - 106755

Опубликована: Сен. 1, 2024

Язык: Английский

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A review of the co‐liquefaction of biomass feedstocks and plastic wastes for biofuel production

Biofuels Bioproducts and Biorefining, Год журнала: 2024, Номер 18(5), С. 1799 - 1820

Опубликована: Май 25, 2024

Abstract Interest has emerged recently in addressing the long‐standing issue of waste plastic disposal and environmental challenges through co‐liquefaction plastics with eco‐friendly renewable biomass resources, including microalgae lignocellulosic biomass, to produce biofuels. Co‐liquefaction provides a viable alternative for managing while contributing biofuel production. The purpose this article is provide comprehensive review advances various mixtures different types feedstocks (lignocellulosic algal) production influence reaction parameters, such as feedstock composition (blending ratio), temperature, catalyst type loading, solvents, time on product yield are explored. synergistic interaction during distribution properties products also discussed. findings demonstrate that maximum yields vary depending final blending ratio plays crucial role determining liquefaction products. Of particular interest biocrude oil, components which influenced by material. organic elements biochar contingent upon used. Although analysis gas‐phase often overlooked, medium's shown impact resulting gas composition. Finally, based insights gleaned from literature, presents future perspectives subject matter. In general, process offers option sustainable promising approach address effectively, valorization achieve circular bioeconomy future.

Язык: Английский

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Transforming Biowaste to Bioenergy: The Role of Catalysts in Advancing Thermochemical Torrefaction

Process Safety and Environmental Protection, Год журнала: 2025, Номер 196, С. 106779 - 106779

Опубликована: Янв. 15, 2025

Язык: Английский

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Monitoring and Managing Endocrine Disrupter Pesticides (EPDS) for Environmental Sustainability

Опубликована: Янв. 1, 2025

Язык: Английский

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Enhancing co-pyrolysis process of biomass and coal using machine learning insights and Shapley additive explanations based on cooperative game theory

Energy & Environment, Год журнала: 2025, Номер unknown

Опубликована: Фев. 17, 2025

The co-pyrolysis process is an essential method for energy extraction from waste biomass and coal although the technology of presents a complex engineering challenge. To address these challenges, modern data-driven ensemble tree-based machine learning approaches offer promising solution. This study provides comprehensive analysis various techniques, including linear regression (LR), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), adaptive (AdaBoost) to predict outcome models pyrolysis oil yield, syngas char lower heating value coal. are evaluated using different statistical metrics. DT-based yield model outperformed other four (LR, RF, XGBoost, AdaBoost) in predicting with robust accuracy, achieving R 2 0.999 mean squared error (MSE) close zero during training phase. Similarly, showed high near-zero MSE while based excelled others negligible In subsequent phase, explainable artificial intelligence-based Shapley additive explanation (SHAP) values were estimated feature importance analysis. SHAP identified key features blending ratio reaction time being most crucial, temperature important LHV model.

Язык: Английский

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Effect of CaO addition on fast pyrolysis behavior of solid waste components using Py GC/MS

Journal of Analytical and Applied Pyrolysis, Год журнала: 2025, Номер unknown, С. 107055 - 107055

Опубликована: Фев. 1, 2025

Язык: Английский

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A new method for the preparation of biomass-based solid fuels: Pyrolysis-impregnation-cobaking

Energy, Год журнала: 2025, Номер unknown, С. 135522 - 135522

Опубликована: Март 1, 2025

Язык: Английский

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