This study examines the nexus between energy, trade, urbanization and environmental degradation in Sri Lanka. The time series data has been checked for unit root problem along with unknown structural break. The bounds testing approach confirms the long-term relationship among carbon emissions, energy consumption, income, trade openness, and urbanization in the presence of structural break. The results of the study do not confirm the presence of the EKC (Environmental Kuznets Curve) hypothesis in Sri Lanka. This study finds that energy consumption leads to carbon emissions in both the long term and the short term. Trade openness is degrading environmental quality, as trade is responsible for the accumulation of carbon emissions in the atmosphere. The results of the study confirm that urbanization has been found to have significant and negative effect on carbon emissions. The study finds that the model is in equilibrium and the model will return to equilibrium from any external shock in less than two years. Policy measures are recommended for sustainable environment of the island.

The economic size of the Indian economy and its status as one of the major global emitters of carbon emissions makes the country a good place to study the determinants of environmental degradation in India. The study aims at analyzing the impact of energy, export variety, and foreign direct investment (FDI) on environmental degradation in India in the context of environmental Kuznets curve (EKC) hypothesis. The long run relationship was found between variables of the study through a cointegration test, whereas long run estimates were obtained through cointegration and dynamic ordinary least squares (DOLS). Results of the study reveal that energy consumption, export variety, FDI, and income positively contributed to environmental degradation in India. Results also unveil that the EKC hypothesis does not exist in India. Causality analyses document unidirectional causality from income and FDI to environmental degradation, and bidirectional causality was witnessed between energy consumption and environmental degradation and between export variety and environmental degradation in the long run. The long run and the short run causality highlight that India has to forego the short run economic growth in order to improve its environmental quality and reduce global carbon emissions; however, it will not affect its long term economic development process.

The clustering analysis approach treats multivariate data tuples as objects and groups them into clusters based on their similarities or dissimilarities within the dataset. However, in modern world, a significant volume of data is continuously generated from diverse sources over time. In these dynamic scenarios, the data is not static but continually evolves. Consequently, the interesting patterns and inherent subgroups within the datasets also change and develop over time. The researchers have paid special attention to monitoring changes in cluster solutions of evolving streams. For this matter, several algorithms have been proposed in the literature. However, to date, no study has examined the effect of variability in cluster sizes on the evolution of cluster solutions. Moreover, no guidance is available on determining the impact of cluster sizes on the type of changes they experience in the streams. In the present simulation study using artificial datasets, the evolution of clusters is examined concerning the variability in cluster sizes. The findings are substantial because tracing and monitoring the changes in clustering solutions have a wide range of applications in every field of research. This study determines the minimum sample size required in the clustering of time-stamped datasets.

This study explores the asymmetric impact of oil supply and demand shocks on the sectoral stock market returns of Pakistan. For this purpose, the study uses the non-linear autoregressive distributed lag (ARDL) approach based on monthly time series data for four sectors in the Pakistan Stock Exchange over the period 2005–2018. First, the findings of the unit root tests identified that all data series are stationary at first difference. Second, the F-bound test explored that oil supply and demand shocks have a cointegration relationship with sectoral stock market returns. Third, the study explored the asymmetric impact of oil supply shocks (OSS) and oil-specific demand shocks (OSDS) on sectoral returns of commercial banking and the symmetric impact of oil demand shocks (ODS) on sectoral returns of commercial banking. In addition, the asymmetric impact of oil supply shocks (OSS) and oil demand shocks (ODS) on sectoral returns of the power generation, chemical and fertilizer sectors was found. Moreover, the symmetric impact of oil-specific demand shocks (OSDS) on sectoral returns of the power generation, chemical and fertilizer sectors was explored. The study suggests important policy implications for policymakers and investors.

Reliability analysis is one of the significant applications of statistics. Many parametric and non-parametric techniques are available for reliability estimation based on precise life time observations, without considering the uncertainty of individu

This article comprehensively reviews the applications and algorithms used for monitoring the evolution of clustering solutions in data streams. The clustering technique is an unsupervised learning problem that involves the identification of natural subgroups in a large dataset. In contrast to supervised learning models, clustering is a data mining technique that retrieves the hidden pattern in the input dataset. The clustering solution reflects the mechanism that leads to a high level of similarity between the items. A few applications include pattern recognition, knowledge discovery, and market segmentation. However, many modern-day applications generate streaming or temporal datasets over time, where the pattern is not stationary and may change over time. In the context of this article, change detection is the process of identifying differences in the cluster solutions obtained from streaming datasets at consecutive time points. In this paper, we briefly review the models/algorithms introduced in the literature to monitor clusters evolution in data streams. Monitoring the changes in clustering solutions in streaming datasets plays a vital role in policy-making and future prediction. Of course, it has a wide range of applications that cannot be covered in a single study, but some of the most common are highlighted in this article.