Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 2 de 2
Filter
Add more filters










Database
Language
Publication year range
1.
Environ Sci Pollut Res Int ; 31(1): 948-965, 2024 Jan.
Article in English | MEDLINE | ID: mdl-38030839

ABSTRACT

The Belt and Road Initiative (BRI) represents a comprehensive developmental blueprint that has been deployed across numerous Asian, European, and African nations, aimed at fostering economic growth and enhanced regional connectivity. However, concerns have been raised about its potential impact on the environment, specifically in the context of carbon dioxide (CO2) emissions. Employing non-parametric analytical techniques, this research undertakes an empirical investigation into the relationship between economic growth (GDP), renewable energy consumption (REC), and CO2 emissions within the context of BRI participant countries, spanning the years from 2000 to 2018. The findings of this study reveal that REC exerts a pronounced and statistically significant mitigating effect on CO2 emissions, implying that an increase in REC corresponds to a reduction in CO2 emissions. In contrast, trade openness (TRADE) exhibits a positive and statistically significant influence on CO2 emissions, signifying that greater trade openness is associated with heightened CO2 emissions. However, the observed effects of GDP, fixed telephone subscriptions (FTS), and mobile cellular subscriptions (MCS) on CO2 emissions remain inconclusive, as their impact lacks statistical significance. The effect estimates of covariates on CO2 emissions using various models reveal that REC and TRADE significantly affect CO2 emissions, while GDP, FTS, and MCS still yield uncertain results. The outcomes draw attention to the necessity of implementing policies that encourage the use of REC and reducing trade openness as an efficient way of neutralizing CO2 emissions. This research provides valuable insights into the impact of the BRI on CO2 emissions and emphasizes the importance of addressing the environmental implications of this initiative. Policymakers should carefully consider these findings and develop effective strategies to foster sustainable development.


Subject(s)
Carbon Dioxide , Renewable Energy , Humans , Economic Development , Social Conditions , Policy
2.
Environ Sci Pollut Res Int ; 30(47): 104432-104449, 2023 Oct.
Article in English | MEDLINE | ID: mdl-37700135

ABSTRACT

Green innovation is crucial for reducing carbon dioxide (CO2) emissions and promoting environmental efficiency worldwide. However, there is a lack of scholarly research investigating the relationship between environmentally friendly innovations and improved environmental performance. To fill this knowledge gap, a comprehensive study was conducted using data from 64 countries spanning 2010 to 2018. The study employed a hybrid approach, combining fuzzy DEA, structural equation modeling (SEM), and artificial neural networks (ANN) to analyze the nexus between green innovation and environmental efficiency. The SEM analysis revealed that green innovation, green trade, green employment, and green investment significantly impact environmental efficiency. The ANN model achieves a perfect prediction rate for environmental efficiency and green growth, emphasizing the importance of incorporating various sources of green innovation to achieve long-term environmental goals. The study's findings have significant implications for policymakers and governments, highlighting the value of environmentally friendly technologies and the need to allocate resources toward their development. Regional collaboration and integrating green innovation throughout the development process are essential for achieving environmental efficiency. By embracing green innovation, nations can capitalize on its potential benefits while mitigating pollution and promoting sustainable development. Overall, this research serves as a cornerstone for decision-makers, providing insights into the importance of green innovation and guiding efforts to foster environmentally conscious technologies globally.


Subject(s)
Environmental Pollution , Government , Latent Class Analysis , Investments , Neural Networks, Computer , Economic Development , Carbon Dioxide , China , Efficiency
SELECTION OF CITATIONS
SEARCH DETAIL
...