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Without fundamentally altering how humans generate and utilise energy, there is no effective strategy to safeguard the environment. The motivation behind this study was to analyse the effectiveness of renewable energy in addressing climate change, as it is one of the most pressing global issues. This study involved the analysis of panel data covering 138 nations over a 27 year period, from 1995 to 2021, making it the latest addition to the existing literature. We examined the extent of the impact of renewable energy on carbon dioxide over time using panel, linear, and non-linear regression approaches. The results of our analysis, revealed that the majority of countries with the exception of Canada, exhibited a downward trend, underscoring the potential of increasing renewable energy consumption as an effective method to reduce carbon dioxide emissions and combat climate change. Furthermore, to reduce emissions and combat climate change, it is advisable for nations with the highest carbon dioxide emissions to adopt and successfully transition to renewable energy sources. [ABSTRACT FROM AUTHOR]

Today’s global energy agenda focuses especially on the fields of increasing energy demand, security of supply and climate change. This situation causes the energy efficiency phenomenon to be considered by policymakers seriously, and additionally to be developed strategies by determining targets in this field. In this sense, it is thought that developments in the field of energy efficiency will increase energy savings and reduce emissions caused by high consumption. On the other hand, the expected improvements in energy saving based on consumer behavior are less than anticipated. In measuring the mentioned dimension, one of the important parameters is defined as the rebound effect. This effect is considered as a dilemma that is frequently emphasized, especially in developed countries since there is a prevailing opinion that the developments in energy efficiency may not cause the expected results in savings. Therefore, it is extremely important to accurately measure the dimensions of the said effect in terms of both guiding policymakers in their strategies on energy efficiency and preventing waste of resources. This study tests the validity of the rebound effect for Canada using annual data from 1972 to 2019. In the study, the Fourier Engle-Granger Cointegration Test, which is one of the current econometric methods, was used, and then FMOLS, CCR and DOLS methods were utilized for the estimation of the short- and long-term coefficients. Empirical findings suggest that increases in energy efficiency in Canada increase energy consumption. Thus, it can be said that the rebound effect is valid for Canada. [ABSTRACT FROM AUTHOR]

Across the OECD, Canada's record on CO emissions is particularly poor, with overall emissions up 32% over the 1990-2007 period. The current paper seeks to better understand this situation by making systematic comparisons of Canada with other OECD countries. For Canada overall, the rapid increase in emissions over the 1990-2007 period can be explained by several factors, including major population growth, increased affluence (although to a lesser extent than elsewhere in the OECD), a continued dependence on fossil fuels, while continuing to increase its overall demand for energy. While the energy intensity of Canada's economy has declined somewhat over recent years, it actually lagged behind most OECD countries on this front and remains one of the most energy intense economies in the world (2nd highest in the OECD on our indicator of energy intensity). While there are many factors responsible for this, Canada's particularly energy-intensive industrial structure is certainly relevant, as is the importance of its primary sector relative to most developed nations. [ABSTRACT FROM AUTHOR]

This paper presents a multidisciplinary modeling approach to quickly quantify climate change impacts on energy consumption, peak load, and load composition of residential and commercial buildings. This research focuses on addressing the impact of temperature changes on the building cooling load in ten major cities across the Western United States and Canada. Our results have shown that by the mid-century, building yearly energy consumption and peak load will increase in the Southwest. Moreover, the peak load months will spread out to not only the summer months but also spring and autumn months. The Pacific Northwest will experience more hot days in the summer months. The penetration levels of air-conditioning (a/c) systems in this region are likely to increase significantly over the years. As a result, some locations in the Pacific Northwest may be shifted from winter peaking to summer peaking. Overall, the Western U.S. grid may see more simultaneous peaks across the North and South in summer months. Increased cooling load will result in a significant increase in the motor load, which consumes more reactive power and requires stronger voltage support from the grid. This study suggests an increasing need for the industry to implement new technology to increase the efficiency of temperature-sensitive loads and apply proper protection and control to prevent possible adverse impacts of a/c motor loads. [ABSTRACT FROM AUTHOR]

Due to the alarming rate of climate change, fuel consumption and emission estimates are critical in determining the effects of materials and stringent emission control strategies. In this research, an analytical and predictive study has been conducted using the Government of Canada dataset, containing 4973 light-duty vehicles observed from 2017 to 2021, delivering a comparative view of different brands and vehicle models by their fuel consumption and carbon dioxide emissions. Based on the findings of the statistical data analysis, this study makes evidence-based recommendations to both vehicle users and producers to reduce their environmental impacts. Additionally, Convolutional Neural Networks (CNN) and various regression models have been built to estimate fuel consumption and carbon dioxide emissions for future vehicle designs. This study reveals that the Univariate Polynomial Regression model is the best model for predictions from one vehicle feature input, with up to 98.6% accuracy. Multiple Linear Regression and Multivariate Polynomial Regression are good models for predictions from multiple vehicle feature inputs, with approximately 75% accuracy. Convolutional Neural Network is also a promising method for prediction because of its stable and high accuracy of around 70%. The results contribute to the quantifying process of energy cost and air pollution caused by transportation, followed by proposing relevant recommendations for both vehicle users and producers. Future research should aim towards developing higher performance models and larger datasets for building APIs and applications. [ABSTRACT FROM AUTHOR]

A national sample of Canadian households was used to relate climatic variables, dwelling appliance/vehicle descriptors, demographic characteristics, and attitudinal variables to actual in-home energy consumption and self-reported gasoline consumption. Using split-sample replications, dwelling and vehicle descriptors indicated a strong association with energy consumption, and both male and female attitudes indicated weak associations. [ABSTRACT FROM AUTHOR]