Revisit economic growth and CO2 emission nexus in G7 countries: mixed frequency VAR model

Jia, Linyu; Chang, Tsangyao; Wang, Mei-Chih

doi:10.1007/s11356-022-24080-8

Cited by 4 publications

(1 citation statement)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another study examined the associations between CO 2 emissions and the GDP per capita employing a mixed frequency vector autoregressive (MF-VAR) methodology across G7 nations during the period spanning from the first quarter of 1970 to the fourth quarter of 2019 [54]. The results of the MF-VAR model also demonstrated that among the G7 nations, there existed a unidirectional causal relationship between the GDP and CO 2 emissions in Canada, the United Kingdom, and the United States.…”

Section: Gdp: Evaluating the Direct Relationship Between Gdp And Co 2...mentioning

confidence: 99%

Investigating the Impact of Multiple Factors on CO2 Emissions: Insights from Quantile Analysis

Sobirov,

Makhmudov,

Saibniyazov

et al. 2024

Sustainability

View full text Add to dashboard Cite

This study investigates the impacts of alternative energy use, urbanization, GDP, agriculture, ICT development, and FDI on carbon dioxide (CO2) emissions in the 14 leading CO2-emitting countries in Asia. This research comprises various econometric techniques, including MMQR, FMOLS, DOLS, and Driscoll–Kraay, to extend the data analysis from 1996 to 2020. The findings provide significant support for an inverted U-shaped link between economic expansion and environmental deterioration, known as the environmental Kuznets curve. Moreover, this paper verifies that the GDP square, renewable energy use, and agriculture are shown to help to decrease pollution, as indicated by the research findings. On the contrary, urbanization and the GDP are demonstrated to be variables that contribute to carbon emissions. Furthermore, the panel quantile regression models validate that the impacts of each explanatory variable on CO2 emissions vary across various quantiles. Finally, this analysis provides valuable suggestions to scholars, environmentalists, politicians, and authorities for identifying and mitigating the main cause of emissions.

show abstract

Section: Gdp: Evaluating the Direct Relationship Between Gdp And Co 2...mentioning

confidence: 99%

Investigating the Impact of Multiple Factors on CO2 Emissions: Insights from Quantile Analysis

Sobirov,

Makhmudov,

Saibniyazov

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

The synergistic effect of green innovation and governance in carbon neutrality: Insights from Japanese companies

Keerthana,

Wu,

Kokulnathan

et al. 2024

Bus Strat Env

View full text Add to dashboard Cite

Japan is firmly committed to transitioning to a low‐carbon economy to address climate change. This research focuses on the role of innovation and governance in achieving carbon neutrality among Japanese companies listed on the Tokyo Stock Exchange from 2017 to 2023, particularly in all carbon dioxide‐contributing sectors. Through multivariate statistics, regression modeling, and quantitative literature analysis, we provide robust evidence of the relationship among innovation, governance, and carbon neutrality. The findings highlight the importance of fostering a sustainability‐oriented culture to drive sustainable innovation and achieve carbon neutrality. We compared the environmental, social, and governance (ESG) and governance to expand our scope. Its results underscore the significance of strong corporate governance in facilitating sustainable initiatives. A robustness test is included to finalize that the samples have avoided endogeneity. These findings demonstrate a positive outlook for Japan's future emissions reduction.

show abstract

Multi-step prediction of carbon emissions based on a secondary decomposition framework coupled with stacking ensemble strategy

Zhang

Ling

Zeng

et al. 2023

Environ Sci Pollut Res

View full text Add to dashboard Cite

Accurate prediction of carbon emissions is vital to achieving carbon neutrality, which is one of the major goals of the global effort to protect the ecological environment. However, due to the high complexity and volatility of carbon emission time series, it is hard to forecast carbon emissions effectively. This research offers a novel decomposition-ensemble framework for multi-step prediction of short-term carbon emissions. The proposed framework involves three main steps: (i) data decomposition. A secondary decomposition method, which is a combination of empirical wavelet transform (EWT) and variational modal decomposition (VMD), is used to process the original data. (ii) Prediction and selection: ten models are used to forecast the processed data. Then, neighborhood mutual information (NMI) is used to select suitable sub-models from candidate models. (iii) Stacking ensemble: the stacking ensemble learning method is innovatively introduced to integrate the selected sub-models and output the final prediction results. For illustration and verification, the carbon emissions of three representative EU countries are used as our sample data. The empirical results show that the proposed framework is superior to other benchmark models in predictions 1, 15, and 30 steps ahead, with the mean absolute percentage error (MAPE) of the proposed framework being as low as 5.4475% in Italy dataset, 7.3159% in France dataset, and 8.6821% in Germany dataset.

show abstract

Revisit economic growth and CO2 emission nexus in G7 countries: mixed frequency VAR model

Cited by 4 publications

References 52 publications

Investigating the Impact of Multiple Factors on CO2 Emissions: Insights from Quantile Analysis

Investigating the Impact of Multiple Factors on CO2 Emissions: Insights from Quantile Analysis

The synergistic effect of green innovation and governance in carbon neutrality: Insights from Japanese companies

Multi-step prediction of carbon emissions based on a secondary decomposition framework coupled with stacking ensemble strategy

Contact Info

Product

Resources

About