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Sentiment analysis (SA) has been an active research subject in the domain of natural language processing due to its important functions in interpreting people’s perspectives and drawing successful opinion-based judgments. On social media, Roman Urdu is one of the most extensively utilized dialects. Sentiment analysis of Roman Urdu is difficult due to its morphological complexities and varied dialects. The purpose of this paper is to evaluate the performance of various word embeddings for Roman Urdu and English dialects using the CNN-LSTM architecture with traditional machine learning classifiers. We introduce a novel deep learning architecture for Roman Urdu and English dialect SA based on two layers: LSTM for long-term dependency preservation and a one-layer CNN model for local feature extraction. To obtain the final classification, the feature maps learned by CNN and LSTM are fed to several machine learning classifiers. Various word embedding models support this concept. Extensive tests on four corpora show that the proposed model performs exceptionally well in Roman Urdu and English text sentiment classification, with an accuracy of 0.904, 0.841, 0.740, and 0.748 against MDPI, RUSA, RUSA-19, and UCL datasets, respectively. The results show that the SVM classifier and the Word2Vec CBOW (Continuous Bag of Words) model are more beneficial options for Roman Urdu sentiment analysis, but that BERT word embedding, two-layer LSTM, and SVM as a classifier function are more suitable options for English language sentiment analysis. The suggested model outperforms existing well-known advanced models on relevant corpora, improving the accuracy by up to 5%.

Karachi is located in the southern part of Pakistan along the Arabian Sea coast. Relevant institutions are concerned about the possibility of ground subsidence in the city, contributing to the comparative sea-level rise. So yet, no direct measurement of the subsidence rate and its relation to city submergence danger has been made. SAR (Synthetic Aperture Radar) interferometry is a powerful method for obtaining millimeter-accurate surface displacement measurements. The Sentinel-1 satellite data provide extensive geographical coverage, regular acquisitions, and open access. This research used the persistent scatterer interferometry synthetic aperture radar (PS-InSAR) technology with Sentinel-1 SAR images to monitor ground subsidence in Karachi, Pakistan. The SARPROZ software was used to analyze a series of Sentinel-1A images taken from November 2019 to December 2020 along ascending and descending orbit paths to assess land subsidence in Karachi. The cumulative deformation in Line of Sight (LOS) ranged from − 68.91 to 76.06 mm/year, whereas the vertical deformation in LOS ranged from − 67.66 to 74.68 mm/year. The data reveal a considerable rise in subsidence from 2019 to 2020. The general pattern of subsidence indicated very high values in the city center, whereas locations outside the city center saw minimal subsidence. Overall, the proposed technique effectively maps, identifies, and monitors land areas susceptible to subsidence. This will allow for more efficient planning, construction of surface infrastructure, and control of subsidence-induced risks.

There is a consensus among the empirics regarding the positive role of renewable energy in mitigating the effects of climate change. Hence, it is vital to search for the factors that can promote renewable energy demand. As a result, this analysis investigates the impact of educational attainment, environmental law, and innovation on renewable energy consumption (REC) in China. From empirical estimates, we confer that the long-run estimates attached to the environment-related taxes and environmental policy stringency are positive and significant, implying that both these factors increase the REC in China in the long run. Similarly, the estimated coefficients of environment-related technologies and patent applications are significantly positive, confirming that environmental and other technologies give rise to REC in the long run. Likewise, the long-run estimates of education are significantly positive in both models, which confer that REC increases along with an increase in average years of schooling. Lastly, the estimates of CO2 emissions are significantly positive in the long run. These results imply that policymakers should invest in research and development activities that are crucial for promoting eco-innovation and renewable energy demand. In addition, strict environmental laws should be introduced to induce firms and businesses to invest in clean energy.

Landslide classification and identification along Karakorum Highway (KKH) is still challenging due to constraints of proposed approaches, harsh environment, detail analysis, complicated natural landslide process due to tectonic activities, and data availability problems. A comprehensive landslide inventory and a landslide susceptibility mapping (LSM) along the Karakorum Highway were created in recent research. The extreme gradient boosting (XGBoost) and random forest (RF) models were used to compare and forecast the association between causative parameters and landslides. These advanced machine learning (ML) models can measure environmental issues and risks for any area on a regional scale. Initially, 74 landslide locations were determined along the KKH to prepare the landslide inventory map using different data. The landslides were randomly divided into two sets for training and validation at a proportion of 7/3. Fifteen landslide conditioning variables were produced for susceptibility mapping. The interferometric synthetic aperture radar persistent scatterer interferometry (PS-InSAR) technique investigated the deformation movement of extracted models in the susceptible zones. It revealed a high line of sight (LOS) deformation velocity in both models’ sensitive zones. For accuracy comparison, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve approach was used, which showed 93.44% and 92.22% accuracy for XGBoost and RF, respectively. The XGBoost method produced superior results, combined with PS-InSAR results to create a new LSM for the area. This improved susceptibility model will aid in mitigating the landslide disaster, and the results may assist in the safe operation of the highway in the research area.

Landslides are the most catastrophic geological hazard in hilly areas. The present work intends to identify landslide susceptibility along Karakorum Highway (KKH) in Northern Pakistan, using landslide susceptibility mapping (LSM). To compare and predict the connection between causative factors and landslides, the random forest (RF), extreme gradient boosting (XGBoost), k nearest neighbor (KNN) and naive Bayes (NB) models were used in this research. Interferometric synthetic aperture radar persistent scatterer interferometry (PS-InSAR) technology was used to explore the displacement movement of retrieved models. Initially, 332 landslide areas alongside the Karakorum Highway were found to generate the landslide inventory map using various data. The landslides were categorized into two sections for validation and training, of 30% and 70%. For susceptibility mapping, thirteen landslide-condition factors were created. The area under curve (AUC) of the receiver operating characteristic (ROC) curve technique was utilized for accuracy comparison, yielding 83.08, 82.15, 80.31, and 72.92% accuracy for RF, XGBoost, KNN, and NB, respectively. The PS-InSAR technique demonstrated a high deformation velocity along the line of sight (LOS) in model-sensitive areas. The PS-InSAR technique was used to evaluate the slope deformation velocity, which can be used to improve the LSM for the research region. The RF technique yielded superior findings, integrating with the PS-InSAR outcomes to provide the region with a new landslide susceptibility map. The enhanced model will help mitigate landslide catastrophes, and the outcomes may help ensure the roadway’s safe functioning in the study region.

Various old nexuses are getting new empirical attention in advanced econometric. Therefore, we examine the asymmetric influence of remittances and FDI on CO2 emissions by using the NARDL approach for China from 1981 to 2019. Based on NARDL empirical findings, a negative change in remittances has also positive effects on CO2 emissions in the short and long run. We found that positive and negative change in FDI has also a positive effect on CO2 emissions, while a positive change in FDI is relatively more effective on CO2 emissions than a negative change in FDI in long run. Asymmetry is observed in the only magnitude but not in direction. Our study implies that the China government should redesign the environmentally friendly policies and enforces the foreign investors to role play in environmental quality.

Karakoram Highway (KKH) is an international route connecting South Asia with Central Asia and China that holds socio-economic and strategic significance. However, KKH has extreme geological conditions that make it prone and vulnerable to natural disasters, primarily landslides, posing a threat to its routine activities. In this context, the study provides an updated inventory of landslides in the area with precisely measured slope deformation (Vslope), utilizing the SBAS-InSAR (small baseline subset interferometric synthetic aperture radar) and PS-InSAR (persistent scatterer interferometric synthetic aperture radar) technology. By processing Sentinel-1 data from June 2021 to June 2023, utilizing the InSAR technique, a total of 571 landslides were identified and classified based on government reports and field investigations. A total of 24 new prospective landslides were identified, and some existing landslides were redefined. This updated landslide inventory was then utilized to create a landslide susceptibility model, which investigated the link between landslide occurrences and the causal variables. Deep learning (DL) and machine learning (ML) models, including convolutional neural networks (CNN 2D), recurrent neural networks (RNNs), random forest (RF), and extreme gradient boosting (XGBoost), are employed. The inventory was split into 70% for training and 30% for testing the models, and fifteen landslide causative factors were used for the susceptibility mapping. To compare the accuracy of the models, the area under the curve (AUC) of the receiver operating characteristic (ROC) was used. The CNN 2D technique demonstrated superior performance in creating the landslide susceptibility map (LSM) for KKH. The enhanced LSM provides a prospective modeling approach for hazard prevention and serves as a conceptual reference for routine management of the KKH for risk assessment and mitigation.

The concrete cover is an important factor that influences the corrosion of rebar in concrete. When the specified cover depth is not achieved due to improper placement of formwork or sagging of rebars at the construction stage of RC structures, it results in a partial cover thickness. This partial cover thickness would create an electrochemical imbalance enhancing the macrocell current. This study investigated the influence of partial cover thickness, water-cement (W/C) ratio, and chloride ions on the corrosion of rebar in mortar. A special divided bar was used to make cylindrical specimens with a full cover thickness of 20 mm and partial cover thickness (20 mm and 7.5 mm) with W/C ratios of 0.30 and 0.70. Electrochemical methods such as macrocell, microcell, electric resistivity, and polarization curves were used for measurement. The corrosion current density for a partial cover thickness of 7.5 mm with a W/C of 0.30 was 3.46 μA/cm2, and for 20 mm, it was 1.85 μA/cm2. This study concludes that for a low W/C of 0.30; decreasing the cover thickness partially by 62.5% (7.5 mm) will increase the magnitude of total corrosion current density by 47%. However, for a W/C of 0.70, a partial cover thickness had no influence on the corrosion rate.

Agricultural subsidies have been a key financial mechanism in ensuring agricultural productivity and food security. They have recently attracted growing interest in their potential to deliver long-term sustainable food systems and climate action. This article consolidates state-of-the-art research on the roles of agricultural subsidies and their linkages to climate adaptation and mitigation, identifying research gaps beneficial in leveraging financial restructuring of agrifood systems for synergized climate action. Applying a systematic review using PRISMA, we analyzed 331 peer-reviewed articles (2000–2024) that discuss agricultural subsidies and finance with climate linkages in country-based studies from an initial 3434 Scopus-indexed articles. Through bibliometric evaluation and full-text analysis, this review reveals that prevalent approaches focus on the immediate impact of subsidies, signaling the discrepancy between limited studies on subsidies’ benefits and their long-term sustainability outcomes. Findings suggest that existing literature can benefit from comprehensive research on optimizing subsidies that are co-opting adaptation and mitigation, broadening examination to include developing countries and subregions, building more empirical studies rooted in practice, and understanding country-specific subsidy reform for climate synergies. This review provides insights for facilitating the restructuring of agricultural subsidy systems to deliver climate action. It offers future research a conceptual framework integrating subsidies, finance, and sustainability.

Natural hazards such as landslides are among the most harmful and recurring hazards to infrastructure, communities, and the environment around the world. In Pakistan, the Balakot Valley is prone to severe landslides, especially along the Balakot–Naran route, which is a major economic and tourist route. This route requires accurate landslide susceptibility mapping (LSM) to mitigate landslide risk. However, existing approaches mainly rely on statistical methods, which do not sufficiently address the complexity of spatial patterns and characteristics between landslide conditioning factors (LCFs) and their prevalence. In this study, small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) measurements of slope deformation (Vslope) were employed to update the landslide inventory. Following this update, an LSM was generated to examine the causal variables that are associated with landslide occurrences. Several machine learning (ML) classifiers, which include Adaptive Boosting (AdaBoost), Light Gradient Boosting (LightGBM), Extreme Gradient Boosting (XGBoost), and a hybrid (ADA + LGBM + XGB), are utilized for mapping landslide susceptibility. A total of 14 LCFs were considered, with 70% of the dataset being trained and 30% tested. To evaluate the significance of these variables, Recursive Feature Elimination (RFE) and the Shapley Additive Explanations (SHAP) were used. Results indicate that the hybrid model exhibits superior efficiency in the area under the curve (AUC) (88.00%), precision (84.69%), accuracy (84.52%), F1-score (84.69%), and recall (84.70%). The hybrid classifier, when combined with InSAR predictions, generates an improved LSM for the route. In conclusion, the improved LSM can effectively identify areas that are prone to landslides along the Balakot–Naran route.