{"title":"加拿大分解能源利用与环境健康的相互作用:协整与反事实分析方法","authors":"Md. Idris Ali, Md. Monirul Islam, Brian Ceh","doi":"10.1155/er/1173970","DOIUrl":null,"url":null,"abstract":"<p>Despite the substantial impact of various energy consumption parameters on the generation of nitrous oxide and methane emissions, which contribute to environmental degradation, most prior research has primarily concentrated on carbon emissions and ecological footprint metrics. This study seeks to address this void by examining the intricate link between total energy usage and its disaggregated components, including natural gas, oil, coal, renewable, and nuclear energy sources, and their role in four critical aspects of the environment in Canada. Incorporating time series data spanning from 1990 to 2022, we employ the dynamic autoregressive distributed lag (DARDL) approach, which reveals that total energy consumption, particularly from nonrenewable sources, such as coal, natural gas, and oil, contributes to environmental degradation by accelerating ecological footprint, carbon, nitrous oxide, and methane emissions. Conversely, renewable and nuclear energy sources have the opposite effect, reducing environmental decay. Additionally, counterfactual analysis examines the effects of (±) 1% and (±) 5% shocks from the predictors to predicted variables. Moreover, the study evaluates the robustness of the findings derived from the DARDL estimation technique by employing the Kernel-based Regularized Least Squares (KRLSs) machine learning algorithm. While the environmental impacts of various energy sources are well-documented, this study offers a novel contribution by analyzing the differentiated effects of both renewable and nonrenewable energy consumption on CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O emissions in a single framework, with a specific focus on the Canadian context. Unlike previous studies, this research integrates economic policy uncertainty and technological innovation as vibrant variables, revealing their distinct roles in amplifying or mitigating environmental degradation. Finally, important policy implications are discussed.</p>","PeriodicalId":14051,"journal":{"name":"International Journal of Energy Research","volume":"2025 1","pages":""},"PeriodicalIF":4.3000,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/er/1173970","citationCount":"0","resultStr":"{\"title\":\"Interaction Between Decomposed Energy Utilization and Environmental Health in Canada: A Cointegration and Counterfactual Analysis Approach\",\"authors\":\"Md. Idris Ali, Md. Monirul Islam, Brian Ceh\",\"doi\":\"10.1155/er/1173970\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Despite the substantial impact of various energy consumption parameters on the generation of nitrous oxide and methane emissions, which contribute to environmental degradation, most prior research has primarily concentrated on carbon emissions and ecological footprint metrics. This study seeks to address this void by examining the intricate link between total energy usage and its disaggregated components, including natural gas, oil, coal, renewable, and nuclear energy sources, and their role in four critical aspects of the environment in Canada. Incorporating time series data spanning from 1990 to 2022, we employ the dynamic autoregressive distributed lag (DARDL) approach, which reveals that total energy consumption, particularly from nonrenewable sources, such as coal, natural gas, and oil, contributes to environmental degradation by accelerating ecological footprint, carbon, nitrous oxide, and methane emissions. Conversely, renewable and nuclear energy sources have the opposite effect, reducing environmental decay. Additionally, counterfactual analysis examines the effects of (±) 1% and (±) 5% shocks from the predictors to predicted variables. Moreover, the study evaluates the robustness of the findings derived from the DARDL estimation technique by employing the Kernel-based Regularized Least Squares (KRLSs) machine learning algorithm. While the environmental impacts of various energy sources are well-documented, this study offers a novel contribution by analyzing the differentiated effects of both renewable and nonrenewable energy consumption on CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O emissions in a single framework, with a specific focus on the Canadian context. Unlike previous studies, this research integrates economic policy uncertainty and technological innovation as vibrant variables, revealing their distinct roles in amplifying or mitigating environmental degradation. 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Interaction Between Decomposed Energy Utilization and Environmental Health in Canada: A Cointegration and Counterfactual Analysis Approach
Despite the substantial impact of various energy consumption parameters on the generation of nitrous oxide and methane emissions, which contribute to environmental degradation, most prior research has primarily concentrated on carbon emissions and ecological footprint metrics. This study seeks to address this void by examining the intricate link between total energy usage and its disaggregated components, including natural gas, oil, coal, renewable, and nuclear energy sources, and their role in four critical aspects of the environment in Canada. Incorporating time series data spanning from 1990 to 2022, we employ the dynamic autoregressive distributed lag (DARDL) approach, which reveals that total energy consumption, particularly from nonrenewable sources, such as coal, natural gas, and oil, contributes to environmental degradation by accelerating ecological footprint, carbon, nitrous oxide, and methane emissions. Conversely, renewable and nuclear energy sources have the opposite effect, reducing environmental decay. Additionally, counterfactual analysis examines the effects of (±) 1% and (±) 5% shocks from the predictors to predicted variables. Moreover, the study evaluates the robustness of the findings derived from the DARDL estimation technique by employing the Kernel-based Regularized Least Squares (KRLSs) machine learning algorithm. While the environmental impacts of various energy sources are well-documented, this study offers a novel contribution by analyzing the differentiated effects of both renewable and nonrenewable energy consumption on CO2, CH4, and N2O emissions in a single framework, with a specific focus on the Canadian context. Unlike previous studies, this research integrates economic policy uncertainty and technological innovation as vibrant variables, revealing their distinct roles in amplifying or mitigating environmental degradation. Finally, important policy implications are discussed.
期刊介绍:
The International Journal of Energy Research (IJER) is dedicated to providing a multidisciplinary, unique platform for researchers, scientists, engineers, technology developers, planners, and policy makers to present their research results and findings in a compelling manner on novel energy systems and applications. IJER covers the entire spectrum of energy from production to conversion, conservation, management, systems, technologies, etc. We encourage papers submissions aiming at better efficiency, cost improvements, more effective resource use, improved design and analysis, reduced environmental impact, and hence leading to better sustainability.
IJER is concerned with the development and exploitation of both advanced traditional and new energy sources, systems, technologies and applications. Interdisciplinary subjects in the area of novel energy systems and applications are also encouraged. High-quality research papers are solicited in, but are not limited to, the following areas with innovative and novel contents:
-Biofuels and alternatives
-Carbon capturing and storage technologies
-Clean coal technologies
-Energy conversion, conservation and management
-Energy storage
-Energy systems
-Hybrid/combined/integrated energy systems for multi-generation
-Hydrogen energy and fuel cells
-Hydrogen production technologies
-Micro- and nano-energy systems and technologies
-Nuclear energy
-Renewable energies (e.g. geothermal, solar, wind, hydro, tidal, wave, biomass)
-Smart energy system