Does sectoral energy consumption depend on trade, monetary, and fiscal policy uncertainty? Policy recommendations using novel bootstrap ARDL approach

Bhowmik, Roni; Durani, Farah; Sarfraz, Muddassar; Syed, Qasim Raza; Nasseif, Ghadah

doi:10.1007/s11356-022-22869-1

Cited by 22 publications

(13 citation statements)

References 84 publications

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“…The level of renewable energy development has no effect on trade liberation. The outcome of the study is supported by the finding of Bhowmik et al [ 107 ] study that revealed that trade openness does not have any significant effect on energy use. However, the result is contradicted by the study of Najarzadeh et al [ 108 ] whose result indicated a negative correlation between trade and energy use.…”

Section: Resultssupporting

confidence: 67%

Exploring the impact of renewable energy on economic growth and carbon emissions: Evidence from partial least squares structural equation modeling

Gyimah,

Fiati,

Nwigwe

et al. 2023

PLoS ONE

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The concern for environmental sustainability comes along with sustainable energy for consumption. Therefore, this study aims to explore the direct and indirect effects of renewable energy on economic growth and carbon emissions by employing Partial Least Square Structure Equation Modeling and Granger Causality Test and the data for this study is from 1990 to 2021. The results from the Partial Least Squares Structure Equation Modeling indicate that renewable energy consumption causes carbon emissions and has no effect on economic growth. Financial inclusion and foreign direct investment have positive effects on carbon emissions. However, renewable energy has an indirect negative effect on carbon emissions through economic growth. Foreign direct investment affects economic growth positively. Furthermore, the results from the Granger causality test indicate that renewable energy has a unidirectional causality relationship with financial inclusion and foreign direct investment and has a feedback causality relationship with economic growth. In addition, there is a feedback causal effect between financial inclusion and carbon emissions, a unidirectional effect running from carbon emissions to foreign direct investment, and a causal effect from economic growth to foreign direct investment. This study has suggested comprehensive policy recommendations for policymakers based on the findings.

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Section: Resultssupporting

confidence: 67%

Exploring the impact of renewable energy on economic growth and carbon emissions: Evidence from partial least squares structural equation modeling

Gyimah,

Fiati,

Nwigwe

et al. 2023

PLoS ONE

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“…Depending on which training set is present, a covariate can be twice as important to the final result of the model. This result highlights the need for multiple different “seeds” to be set prior to model training when splitting the training and test sets in order to avoid potential training-set biases and to have the model at least be representative of the cohort it is being trained and tested on (if not representative of the population the cohort is a sample of) [ 16 , 30 , 53 ]. Similar to the model accuracy statistics, this also highlights the difficulty in replication of results in machine-learning models from study to study [ 1 , 54 , 55 ].…”

Section: Discussionmentioning

confidence: 99%

“…Provision of explanations about how model predictions are researched and providing accurate summary statistics for model accuracy metrics (e.g., AUROC, Sensitivity, Specificity, F1, Balanced Accuracy) will increase the transparency of machine learning methods and increase confidence when using their predictions [8,9,26,27]. Potential solutions to these weaknesses in machine learning that have been applied within the field of computer science are SHapely Additive exPlanations (SHAP) for model interpretability and bootstrap simulation for quantifying the statistical distribution of model accuracy metrics [28][29][30]. However, little is known about the efficacy of SHAP and Bootstrap in evaluating machine-learning methods for medical outcomes such as heart disease.…”

Section: Introductionmentioning

confidence: 99%

Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations

Huang

2023

PLoS ONE

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Machine learning methods are widely used within the medical field. However, the reliability and efficacy of these models is difficult to assess, making it difficult for researchers to identify which machine-learning model to apply to their dataset. We assessed whether variance calculations of model metrics (e.g., AUROC, Sensitivity, Specificity) through bootstrap simulation and SHapely Additive exPlanations (SHAP) could increase model transparency and improve model selection. Data from the England National Health Services Heart Disease Prediction Cohort was used. After comparison of model metrics for XGBoost, Random Forest, Artificial Neural Network, and Adaptive Boosting, XGBoost was used as the machine-learning model of choice in this study. Boost-strap simulation (N = 10,000) was used to empirically derive the distribution of model metrics and covariate Gain statistics. SHapely Additive exPlanations (SHAP) to provide explanations to machine-learning output and simulation to evaluate the variance of model accuracy metrics. For the XGBoost modeling method, we observed (through 10,000 completed simulations) that the AUROC ranged from 0.771 to 0.947, a difference of 0.176, the balanced accuracy ranged from 0.688 to 0.894, a 0.205 difference, the sensitivity ranged from 0.632 to 0.939, a 0.307 difference, and the specificity ranged from 0.595 to 0.944, a 0.394 difference. Among 10,000 simulations completed, we observed that the gain for Angina ranged from 0.225 to 0.456, a difference of 0.231, for Cholesterol ranged from 0.148 to 0.326, a difference of 0.178, for maximum heart rate (MaxHR) ranged from 0.081 to 0.200, a range of 0.119, and for Age ranged from 0.059 to 0.157, difference of 0.098. Use of simulations to empirically evaluate the variability of model metrics and explanatory algorithms to observe if covariates match the literature are necessary for increased transparency, reliability, and utility of machine learning methods. These variance statistics, combined with model accuracy statistics can help researchers identify the best model for a given dataset.

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“…Energy sources mainly consist of fossil fuels that contain high-carbon components, resulting in high levels of COE. Many studies recently use ECN (see, for example, Mesagan and Olunkwa, 2022 ; Bhowmik et al, 2023 ). Hence, we follow the empirical model of Hashmi et al ( 2022 ), who include ECN in the EKC framework to discern the impact of geopolitical risk on global COE.…”

Section: Model and Datamentioning

confidence: 99%

Testing the environmental Kuznets curve (EKC) hypothesis amidst climate policy uncertainty: sectoral analysis using the novel Fourier ARDL approach

Hashmi

Syed

et al. 2023

Environ Dev Sustain

Self Cite

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In recent years, climate policy has experienced several episodes of crest and trough in the US, which has induced profound uncertainty. This climate policy uncertainty (CPU) may exert economic, social, and environmental impacts. Therefore, using the environmental Kuznets curve (EKC) framework, this study targets to probe whether CPU affects sectoral carbon dioxide emissions (COE) in the US. We make use of advanced econometric procedures such as the novel SOR unit root test (to probe the order of integration of the entire dataset) and the novel Fourier ARDL approach (to retrieve the long- and short-run estimates). The findings delineate that the EKC holds for the industrial, electric power, commercial, and residential sectors. In addition, CPU escalates COE in the residential, commercial, and electric power sectors in both the long- and short-run. Parallel to this, CPU affects industrial COE neither in the short-run nor in the long-run. Keeping in view the key findings, we propose a set of sector-specific policy implications to curb COE.

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Does sectoral energy consumption depend on trade, monetary, and fiscal policy uncertainty? Policy recommendations using novel bootstrap ARDL approach

Cited by 22 publications

References 84 publications

Exploring the impact of renewable energy on economic growth and carbon emissions: Evidence from partial least squares structural equation modeling

Exploring the impact of renewable energy on economic growth and carbon emissions: Evidence from partial least squares structural equation modeling

Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations

Testing the environmental Kuznets curve (EKC) hypothesis amidst climate policy uncertainty: sectoral analysis using the novel Fourier ARDL approach

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