Unlocking time-quantile impact of energy vulnerability, financial development, and political globalization on environmental sustainability in Turkey: Evidence from different pollution indicators

Journal of Environmental Management, Год журнала: 2024, Номер 365, С. 121499 - 121499

Опубликована: Июль 2, 2024

Язык: Английский

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A multi-stage forecasting system for daily ocean tidal energy based on secondary decomposition, optimized gate recurrent unit and error correction

Journal of Cleaner Production, Год журнала: 2024, Номер 449, С. 141303 - 141303

Опубликована: Март 12, 2024

Язык: Английский

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An Optimized Extreme Learning Machine Composite Framework for Point, Probabilistic, and Quantile Regression Forecasting of Carbon Price

Process Safety and Environmental Protection, Год журнала: 2025, Номер unknown, С. 106772 - 106772

Опубликована: Янв. 1, 2025

Язык: Английский

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A hybrid model for carbon price forecasting based on SSA-NSTransformer: Considering the role of multi-stage carbon reduction targets

Journal of Environmental Management, Год журнала: 2025, Номер 375, С. 124237 - 124237

Опубликована: Янв. 29, 2025

Язык: Английский

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Evaluation of the planting performance for planting substrate using the SQI-CRITIC combination model based on the least squares method

Frontiers in Environmental Science, Год журнала: 2025, Номер 13

Опубликована: Фев. 6, 2025

Background To improve the scientificity of evaluation results planting performance Yellow River sediment based on substrate. Methods This study replaced natural soil with sediment, used cement as cementing material, added different proportions organic matter and amendment habitat material to prepare substrate carried out experiments by using oats proposed a combined SQI-CRITIC weighting calculation method least square for its evaluation. Results The showed that (1) led significant variations in sediment: among 5# mix ratio, plant height (12.5 cm) biomass (3.06 g) reached extreme values. Photosynthetic rate (1.97 μmol∙m−2∙s−1 ) transpiration (0.25 id="m2">mmol∙m−2∙s−1 were significantly higher than other fitness ratio components ( P < 0.05 ), while stomatal width (87.03 μm) was largest density (19.6%) lowest ). (2) calculated combination model are scientific effective, more accurate line actual situation. (3) comprehensive analysis recommended use quality (mixture matter, = 100:3:12:4) ecological restoration achieve best growth effect. Conclusion experimental can be extended areas need sandy restoration, view providing theoretical basis evaluation, water conservation control, construction Basin.

Язык: Английский

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Supply and demand behaviors in the Chinese emission allowances pledge credit market under different value type: An evolutionary game analysis

Journal of Cleaner Production, Год журнала: 2024, Номер 455, С. 142374 - 142374

Опубликована: Апрель 27, 2024

Язык: Английский

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An optimal weight heterogeneous integrated carbon price prediction model based on temporal information extraction and specific comprehensive feature selection

Energy, Год журнала: 2024, Номер 312, С. 133654 - 133654

Опубликована: Ноя. 1, 2024

Язык: Английский

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Carbon emission price forecasting in China using a novel secondary decomposition hybrid model of CEEMD-SE-VMD-LSTM

Systems Science & Control Engineering, Год журнала: 2023, Номер 12(1)

Опубликована: Дек. 18, 2023

Effective forecasting of carbon prices helps investors to judge market conditions and promotes the environment economic sustainability. The contribution this paper is constructing a novel secondary decomposition hybrid price model, namely CEEMD-SE-VMD-LSTM. It noteworthy that sample entropy introduced identify highly complex signals rather than empirically determined in previous studies. Specifically, complementary ensemble empirical mode (CEEMD) model used decompose original signals. (SE) variational (VMD) are conducted recognize components, while long short-term memory (LSTM) employed forecast by summing up predicted intrinsic function (IMF) components. conclusion shows proposed has smallest errors with values RMSE, MAE MAPE 0.2640, 0.1984 0.0044, respectively, models better other primary performances LSTM-type those GRU-type models. Further evidence convinces us accuracy superior long-term forecasting. Those conclusions innovation can provide valuable reference for make trading decisions.

Язык: Английский

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A carbon price ensemble prediction model based on secondary decomposition strategies and bidirectional long short‐term memory neural network by an improved particle swarm optimization

Energy Science & Engineering, Год журнала: 2024, Номер 12(6), С. 2568 - 2590

Опубликована: Май 13, 2024

Abstract To further enhance the precision of carbon trading price forecasting, this research proposes a combined forecasting model, CEEMDAN–VMD–IPSO–BiLSTM, considering unsatisfactory high‐frequency sequence decomposition and reliance on unidirectional neural networks in current price‐prediction models. First all, original prices is decomposed into multiple independent subsequences through completely ensemble empirical mode with adaptive noise (CEEMDAN) technique. The sample entropy values each subsequence are calculated to reconstruct them as high‐frequency, low‐frequency, trend sequences. Second, we employ variational (VMD) approach decompose series. obtained subsequences, along low‐frequency sequences, separately input an improved particle swarm optimization (IPSO) optimized bidirectional long short‐term memory network (BiLSTM) model for forecasting. Finally, IPSO–BiLSTM used integrate outcomes from previous step, yielding ultimate results predicting prices. case studies reveal that compared benchmark exhibits superior predictive universality. It offers theoretical support optimizing market operations fostering low‐carbon economic growth, holding practical importance.

Язык: Английский

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Carbon Price Point–Interval Forecasting Based on Two-Layer Decomposition and Deep Learning Combined Model Using Weight Assignment

Journal of Cleaner Production, Год журнала: 2024, Номер unknown, С. 144124 - 144124

Опубликована: Окт. 1, 2024

Язык: Английский

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