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Voluntary National Reviews (VNRs) provide a platform for participating countries to share their experiences, failures, and successes in achieving the United Nations (UN) Sustainable Development Goals (SDGs). The objective of this study is to gain a deeper understanding of the narrative elements, particularly the sentiment, in VNRs in order to more effectively assess and support global SDG progress. A total of 232 VNRs from 166 countries are analyzed using Aspect-Based Sentiment Analysis (ABSA) to extract each country's sentiment toward the 17 SDGs. The sentiment scores are then compared to the corresponding official UN SDG scores, and countries are grouped by their sentiment toward all 17 SDGs to identify potential common development pathways. The analysis uncovers a notable positive correlation between the reported sentiment and official SDG scores for SDG 2 (zero hunger) and SDG 11 (sustainable cities and communities), and a negative correlation for SDG 5 (gender equality). Conversely, this relationship is not significant for the majority of SDGs, suggesting that VNR narratives may not directly reflect actual progress. A t-distributed stochastic neighbor embedding (t-SNE) approach indicates a consistent sentiment score among developed countries. In contrast, there are greater differences in reporting sentiment among Emerging Markets, Frontier Markets, and Least Developed Countries (LDCs), where there is greater dispersion (especially among LDCs) and sentiment in reporting on SDG progress that appears to have changed from one reporting year to another. These findings highlight the need to interpret VNRs in the context of each country's unique situation and challenges specific to each country. [ABSTRACT FROM AUTHOR]

This study presents a comparative analysis of different neural network models, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) in predicting discharge within ungauged basins in Hesse, Germany. All models were trained on 54 catchments with 28 years of daily meteorological data, either including or excluding 11 static catchment attributes. The training process of each model scenario combination was repeated 100 times, using a Latin Hyper Cube Sampler for the purpose of hyperparameter optimisation with batch sizes of 256 and 2048. The evaluation was carried out using data from 35 additional catchments (6 years) to ensure predictions in basins that were not part of the training data. This evaluation assesses predictive accuracy, computational efficiency concerning varying batch sizes and input configurations and conducted a sensitivity analysis of various hydrological and meteorological. The findings indicate that all examined artificial neural networks demonstrate significant predictive capabilities, with a CNN model exhibiting slightly superior performance, closely followed by LSTM and GRU models. The integration of static features was found to improve performance across all models, highlighting the importance of feature selection. Furthermore, models utilising larger batch sizes displayed reduced performance. The analysis of computational efficiency revealed that a GRU model is 41 % faster than the CNN and 59 % faster than the LSTM model. Despite a modest disparity in performance among the models (<3.9 %), the GRU model's advantageous computational speed renders it an optimal compromise between predictive accuracy and computational demand. [ABSTRACT FROM AUTHOR]

Modern civilization relies on a complex, globally interconnected industrial agriculture system to produce food. Its unprecedented yields hinge on external inputs like machinery, fertilizers, and pesticides, rendering it vulnerable to disruptions in production and international trade. Such a disruption could be caused by large‐scale damage to the electrical grid. Solar storms, nuclear detonations in the upper atmosphere, pandemics, or cyber‐attacks, could cause this severe damage to electrical infrastructure. To assess the impact of such a global catastrophic infrastructure loss on major food crops (corn, rice, soybean, wheat), we employ a generalized linear model. The predictions show a crop‐specific yield reduction between 15% and 37% in phase 1, the year after the catastrophe, assuming rationed use of fertilizers, pesticides, and fuel stocks. In phase 2, when all stocks are depleted, yields decrease by 35%–48%. Soybean is less affected in phase 1, while all crops experience strong declines in phase 2. Europe, North and South America, and parts of India, China, and Indonesia face major yield reductions, potentially up to 75%, while most African countries are less affected. These findings underscore the necessity for preparation by highlighting the vulnerability of the food system. [ABSTRACT FROM AUTHOR]

Upscaling chamber measurements of soil greenhouse gas (GHG) fluxes from point scale to landscape scale remain challenging due to the high variability in the fluxes in space and time. This study measured GHG fluxes and soil parameters at selected point locations (n=268), thereby implementing a stratified sampling approach on a mixed-land-use landscape (∼5.8 km2). Based on these field-based measurements and remotely sensed data on landscape and vegetation properties, we used random forest (RF) models to predict GHG fluxes at a landscape scale (1 m resolution) in summer and autumn. The RF models, combining field-measured soil parameters and remotely sensed data, outperformed those with field-measured predictors or remotely sensed data alone. Available satellite data products from Sentinel-2 on vegetation cover and water content played a more significant role than those attributes derived from a digital elevation model, possibly due to their ability to capture both spatial and seasonal changes in the ecosystem parameters within the landscape. Similar seasonal patterns of higher soil/ecosystem respiration (SR/ER– CO2) and nitrous oxide (N2O) fluxes in summer and higher methane (CH4) uptake in autumn were observed in both the measured and predicted landscape fluxes. Based on the upscaled fluxes, we also assessed the contribution of hot spots to the total landscape fluxes. The identified emission hot spots occupied a small landscape area (7 % to 16 %) but accounted for up to 42 % of the landscape GHG fluxes. Our study showed that combining remotely sensed data with chamber measurements and soil properties is a promising approach for identifying spatial patterns and hot spots of GHG fluxes across heterogeneous landscapes. Such information may be used to inform targeted mitigation strategies at the landscape scale. [ABSTRACT FROM AUTHOR]