Loading...
2 results
Search Results
Now showing 1 - 2 of 2
- Modeling the relationship between economic complexity and environmental degradation: evidence from top seven economic complexity countriesPublication . Moleiro Martins, José; Adebayo, Tomiwa Sunday; Mata, Mário Nuno; Oladipupo, Seun Damola; Adeshola, Ibrahim; Ahmed, Zahoor; Correira, Anabela BatistaThe continuous growth in CO2 emissions of nations around the globe has made achieving the aim of sustainable development extremely challenging. Therefore, the current research assesses the connection between CO2 emissions and economic complexity in the top 7 economic complexity countries while taking into account the role of economic growth, renewable energy consumption, and globalization for the period between 1993 and 2018. The research aims to answer the following questions: 1) What is the association between CO2 and the regressors in the long-run? 2) What are the effects of renewable energy consumption, economic growth, economic complexity, and globalization on CO2 emissions? The research utilized the CS-ARDL, CCEMG and panel causality approaches to investigate these interconnections. The empirical outcomes revealed that economic growth and economic complexity increase CO2 emissions while renewable energy consumption and globalization mitigate CO2 emissions. The outcomes of the causality test revealed a feedback causal connection between economic growth and CO2, while a unidirectional causality was established from economic complexity, globalization and renewable energy consumption to CO2 emissions in the top 7 economic complexity countries.
- Another look into the relationship between economic growth, carbon emissions, agriculture and urbanization in Thailand: a frequency domain analysisPublication . Mata, Mário Nuno; Oladipupo, Seun Damola; Rjoub, Husam; Ferrão, Joaquim; Altuntaş, Mehmet; Martins, Jessica Nunes; Kirikkaleli, Dervis; Dantas, Rui; Lourenco, AntonioThis empirical study assesses the effect of CO2 emissions, urbanization, energy consumption, and agriculture on Thailand’s economic growth using a dataset between 1970 and 2018. The ARDL and the frequency domain causality (FDC) approaches were applied to assess these interconnections. The outcome of the bounds test suggested a long-term association among the variables of investigation. The ARDL outcomes reveal that urbanization, agriculture, energy consumption, and CO2 emissions positively trigger Thailand’s economic growth. Additionally, the frequency domain causality test was used to detect a causal connection between the series. The main benefit of this technique is that it can detect a causal connection between series at different frequencies. To the understanding of the authors, this is the first study in the case of Thailand that will apply the FDC approach to capture the causal linkage between GDP and the regressors. The outcomes of the causality test suggested that CO2 emissions, urbanization, energy consumption, and agriculture can predict Thailand’s economic growth in the long term. These outcomes have far-reaching implications for economic performance and Thailand’s macroeconomic indicators.