Keen, Kevin

This comprehensive study delves into the intricate connections between economic and financial factors and carbon dioxide (CO2) emissions across G20 nations (excluding the European Union) spanning 1994 to 2021. our investigation, utilizing a multiple linear regression model, meticulously examines diverse energy consumption types, financial institutions, life insurance premiums, economic factors, and the aftermath of the 2008 financial crisis. Our preliminary findings reveal robust links between various energy sources, financial institutions, life insurance volumes, and CO2 emissions. Notably, the Financial Institutions Index and Life Insurance Premium Volume unveil novel insights that can add new visions to conventional perspectives. Recognizing the influential role of the G20 on a global scale, our research aspires to inform and guide sustainable policy decisions. Methodologically, after a comparative evaluation of various data transformation methods, we employ a cube root transformation to enhance analytical precision. Also, Principal Component Analysis (PCA) reveals underlying patterns in the data. Granger causality tests shed light on temporal relationships, complementing the robust quantification of each variable's impact on CO2 emissions derived from the linear regression model. Rigorous validation, including Durbin-Watson, Breusch-Pagan, Shapiro-Wilk, RESET, Bonferroni Outlier test, and ADF stationarity tests, ensures the reliability of our results. Our linear model enhances interpretability and provides clear insights into the determinants of CO2 emissions. This research significantly contributes to the field by extending our knowledge of the complex factors influencing CO2 emissions. It unveils unexpected relationships, underscores the pivotal role of financial institutions, explores the repercussions of economic crises, and provides practical policy implications. Methodologically, our study stands out for its advanced statistical analyses. This research yields a valuable understanding of the sustainability framework, presenting a nuanced view for policymakers, researchers, and practitioners alike. This study enhances the academic speech by thoroughly addressing the factors influencing CO2 emissions and delivering a foundation for informed decisionmaking in pursuing a more sustainable future.

Glaciers hold 1.7% of the Earth's total water supply, but they contain 68.7% of its freshwater. Given the global warming trend, accurate and recent inventory is necessary to assess glacial changes over time. However, frequent cloud and debris cover often make it difficult to determine the glacier's exact edge. Multispectral Landsat 8 imagery along with data from the Global Land Ice Velocity Extraction (GoLIVE) project are combined to to create a Bayesian multivariate general additive model of the glaciers surrounding Mount Rainier, with Autoregressive Moving Average (ARMA) and Gaussian processes used to model the temporal and spatial autocorrelations. Using root mean square error and Watanabe-Akaike information criterion, all 42 combinations of ARMA models up to 4 total parameters and exponential, Matérn 1/2 and spherical covariance kernels were compared. The ARMA(3,1) processes with the exponential Gaussian process kernel was determined to be the best fit model. Gaussian mixture models, hierarchical clustering, hard and soft K-means clustering, and support vector machines are used to classify the posterior distribution. The hard K-means algorithm was the best classifier, and it accurately predicted 85.1% of the glaciers, compared to 68.8% from a univariate classification on the Red/SWIR band ratio.