scholarly journals Testing the Role of Trade on Carbon Dioxide Emissions in Portugal

Economies ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 22 ◽  
Author(s):  
Nuno Carlos Leitão
Keyword(s):  
Carbon Dioxide ◽  
Energy Consumption ◽  
Least Squares ◽  
Carbon Dioxide Emissions ◽  
Monopolistic Competition ◽  
Positive Impact ◽  
Unit Root Test ◽  
Ordinary Least Squares ◽  
Trade Intensity ◽  
Distributed Lag

This article considers the relationship between trade intensity, energy consumption, income per capita, and carbon dioxide emissions from 1970–2016 for the Portuguese economy. Considering the arguments of monopolistic competition, the article tests the hypotheses of trade and energy consumption on climate change. We use the autoregressive distributed lag-ARDL model, quantile regression, and cointegration models such as fully modified ordinary least squares (FMOLS), canonical cointegration regression, and dynamic ordinary least squares (DOLS) as an econometric strategy. The econometric results have support with the literature review. The variables used in this research are integrated with the first differences, as indicated by the unit root test. The empirical study proves that trade intensity contributes to environmental improvements. However, energy consumption presents a positive impact on CO2 emissions. The econometric results also demonstrated that a sustainable environmental system exists in the long run.

scholarly journals The Effects of Corruption, Renewable Energy, Trade and CO2 Emissions

Economies ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 62
Author(s):  
Nuno Carlos Leitão
Keyword(s):  
Economic Growth ◽  
Carbon Dioxide ◽  
International Trade ◽  
Renewable Energy ◽  
Least Squares ◽  
Carbon Dioxide Emissions ◽  
Positive Impact ◽  
Unit Root Test ◽  
Renewable Energies ◽  
Long Run

Corruption reflects a set of illegal activities that jeopardize the smooth functioning of economies, society, and climate and environmental issues. This article tests the relationships between economic growth, corruption, renewable energies, international trade, and carbon dioxide emissions using panel data for European countries, namely Portugal, Spain, Italy, Ireland, and Greece, from 1995–2015. As an econometric strategy, this research uses the panel fully modified least squares (FMOLS), panel dynamic least squares (DOLS), and panel two-stage least squares estimator (TSLS). Considering the variables utilized in the research and the panel unit root test, we observed that the variables are integrated I (1) in the first difference. The variables of corruption, economic growth, renewable energies, international trade, and carbon dioxide emissions are cointegrated in the long run, using the Pedroni and Kao residual cointegration test arguments. The methodology of Dumitrescu–Hurlin to test the causality between carbon dioxide emissions, corruption, economic growth, and renewable energy shows that there is unidirectional causality between carbon dioxide emissions and corruption and economic growth and corruption. The results suggest that the corruption index and economic growth have a statistically significant positive impact on carbon dioxide emissions. However, renewable energies and international trade reduce climate change and improve the environmental quality.

scholarly journals The Impact of Industrialization, Trade Openness, Financial Development, and Energy Consumption on Economic Growth in Indonesia

Economies ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 174
Author(s):  
Khalid Eltayeb Elfaki ◽  
Rossanto Dwi Handoyo ◽  
Kabiru Hannafi Ibrahim
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Least Squares ◽  
Financial Development ◽  
Correction Term ◽  
Trade Openness ◽  
Ordinary Least Squares ◽  
Distributed Lag ◽  
Long Run ◽  
The Impact

This study aimed to scrutinize the impact of financial development, energy consumption, industrialization, and trade openness on economic growth in Indonesia over the period 1984–2018. To do so, the study employed the autoregressive distributed lag (ARDL) model to estimate the long-run and short-run nexus among the variables. Furthermore, fully modified ordinary least squares (FMOLS), dynamic least squares (DOLS), and canonical cointegrating regression (CCR) were used for a more robust examination of the empirical findings. The result of cointegration confirms the presence of cointegration among the variables. Findings from the ARDL indicate that industrialization, energy consumption, and financial development (measured by domestic credit) positively influence economic growth in the long run. However, financial development (measured by money supply) and trade openness demonstrate a negative effect on economic growth. The positive nexus among industrialization, financial development, energy consumption, and economic growth explains that these variables were stimulating growth in Indonesia. The error correction term indicates a 68% annual adjustment from any deviation in the previous period’s long-run equilibrium economic growth. These findings provide a strong testimony that industrialization and financial development are key to sustained long-run economic growth in Indonesia.

scholarly journals The Impacts of Monetary and Fiscal Policies on Economic Growth in Malaysia, Singapore and Thailand

2020 ◽  
Vol 9 (1) ◽  
pp. 114-130
Author(s):  
Chai-Thing Tan ◽  
Azali Mohamed ◽  
Muzafar Shah Habibullah ◽  
Lee Chin
Keyword(s):  
Economic Growth ◽  
Least Squares ◽  
Least Squares Method ◽  
Negative Impact ◽  
Positive Impact ◽  
Ordinary Least Squares ◽  
Jel Classification ◽  
Fiscal Policies ◽  
Distributed Lag ◽  
The Impact

This article analyses the impact of monetary and fiscal policies on economic growth in Malaysia, Singapore and Thailand from 1980:Q1 to 2017:Q1. Autoregressive distributed lag (ARDL) approach is employed to determine the long-run relationship. Further, a range of econometric models, such as fully modified least squares method (FMOLS), canonical cointegration regression (CCR) and dynamic ordinary least squares method (DOLS), are applied to check the robustness. The results are stable and robust as all the models yield consistency result. The main findings in this study demonstrate that: (a) interest rate had a negative impact on economic growth in three selected countries. (b) Government spending had a negative impact on economic growth in Malaysia and Singapore, but had a positive impact in Thailand. (c) Monetary policy is more effective in Malaysia and Singapore, while fiscal policy is more effective in Thailand. JEL Classification: E52, E58, E62, C01

scholarly journals Analysis of carbon dioxide emissions, energy consumption, and economic growth in China

2020 ◽  
Vol 66 (No. 4) ◽  
pp. 183-192 ◽  
Author(s):  
Abrham Tezera Gessesse ◽  
Ge He
Keyword(s):  
Carbon Dioxide ◽  
Energy Consumption ◽  
Co2 Emissions ◽  
Carbon Dioxide Emissions ◽  
Economic Structure ◽  
Energy Utilization ◽  
Kuznets Curve ◽  
Distributed Lag ◽  
Long Run ◽  
The Relationship

This study examines the nexus of carbon dioxide (CO2) emissions, energy consumption (EC) and gross domestic products (GDP), using an Autoregressive Distributed Lag (ARDL) bounds test approach of co-integration and error-correction model (ECM) for the period 1971–2015. The aim of the research is to i) examine the relationship between CO2 and GDP as “cross-coupling, relative decoupling, or absolute decoupling,” and validate the existence of the Environmental Kuznets Curve (EKC) hypothesis; ii) detect causality between CO2 emissions, EC, and GDP, and scrutinize their impacts. The ARDL results confirm a long-run and short-run co-integration relationship between the variables. The relationship between CO2 emissions and GDP is “relatively decoupling,” and the EKC exists in China. Its CO2 emissions are more explained by EC and contribute twofold of GDP. In the long run, there was significant negative causality from CO2 emission and GDP to EC. This indicates Chinese economic development structure should be re-designed towards energy-saving and decarbonized economic structure. Moreover, the central and provincial governments of China should synchronize optimal energy utilization and green economic structure to mitigate environmental deterioration and climate change.

scholarly journals Energy Consumption and Economic Growth Nexus in China: Autoregressive Distributed Lag (ARDL)

2020 ◽  
Vol 10 (2) ◽  
pp. 194
Author(s):  
Wan-Lin Yong ◽  
Jerome Kueh ◽  
Yong Sze Wei ◽  
Jang-Haw Tiang
Keyword(s):  
Economic Growth ◽  
Carbon Dioxide ◽  
Energy Consumption ◽  
Carbon Dioxide Emissions ◽  
Carbon Dioxide Emission ◽  
Sustainable Growth ◽  
Total Export ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Long Run

This paper intends to investigate the nexus between energy consumption, carbon dioxide emission, total export and economic growth of China from 1971 to 2014. This study adopted Autoregressive Distributed Lag (ARDL) bounds test to examine the existence of short-run and long-run relationships among the variables. Empirical findings indicated that energy consumption contribute to economic growth while carbon dioxide emission is impeding the growth. There is a positive long-run relationship between both energy consumption and total export with economic growth of China. However, a negative relationship is observed between carbon dioxide emissions and economic growth. Hence, in terms of policy recommendation, policymakers can implement a balance environment-economic policy; reduce the carbon dioxide emission by imposing carbon tax; promote renewable energy among the industries and households and promoting reserves forest policy is needed for aspiration of sustainable growth for both environmental and economic.

Carbon Dioxide Emissions, Energy Consumption in Agriculture: A Causality Analysis for India

2018 ◽  
Vol 17 (2) ◽  
pp. 183-207 ◽  
Author(s):  
Sudeshna Ghosh
Keyword(s):  
Carbon Dioxide ◽  
Energy Consumption ◽  
Granger Causality ◽  
Financial Development ◽  
Carbon Dioxide Emissions ◽  
Value Added ◽  
Causality Analysis ◽  
Distributed Lag ◽  
Long Run ◽  
Short Run

This article utilises the vector error correction model (VECM) and Granger causality tests to explore short-run and long-run relationships, in India, across carbon dioxide (CO2) emissions, energy consumption, agriculture value added (AV), trade liberalisation and financial development over the time period 1971–2013. The study adopts the autoregressive distributed lag (ARDL)-bound testing approach and Johansen–Juselius maximum likelihood procedure to find out the cointegrating relation among the variables. Both ARDL approach and Johansen–Juselius cointegration approach show that the concerned variables under study are cointegrated. Short-run Granger causality results indicate the existence of bidirectional causality between AV and CO2 emissions, and energy used and CO2 emissions. In the long-run trade, financial development, energy consumption and AV affect CO2 emissions. The results put thrust on the need to utilise energy-efficient technologies in agriculture to save the damage of the environment. JEL: C32, O53, Q43

scholarly journals Gasoline to Diesel Consumption Ratio: A New Socioeconomic Indicator of Carbon Dioxide Emissions in China

2020 ◽  
Vol 12 (14) ◽  
pp. 5608 ◽  
Author(s):  
Zhe Li ◽  
Renjin Sun ◽  
Manman Qin ◽  
Dongou Hu
Keyword(s):  
Carbon Dioxide ◽  
Growth Rate ◽  
Least Squares ◽  
Co2 Emissions ◽  
Carbon Dioxide Emissions ◽  
Economic Transition ◽  
Ordinary Least Squares ◽  
Ekc Hypothesis ◽  
Socioeconomic Indicator ◽  
Consumption Ratio

In recent years, gross domestic product (GDP) has grown rapidly in China, but the growth rate of carbon dioxide (CO2) emissions has begun to decline. Some scholars have put forward the environmental Kuznets curve (EKC) hypothesis for CO2 emissions in China. This paper utilized the panel data of 30 provinces in China from 1997 to 2016 to verify the EKC hypothesis. To explore the real reasons behind the EKC, the index gasoline to diesel consumption ratio (GDCR) was introduced in this paper. The regression results showed that CO2 emissions and GDP form an inverted U-shaped curve. This means that the EKC hypothesis holds. The regression results also showed that a 1% GDCR increase was coupled with a 0.118186% or 0.114056% CO2 emission decrease with the panel fully modified ordinary least squares or panel dynamic ordinary least squares method, respectively. This means that CO2 emissions negatively correlate with GDCR. From the discussion of this paper, the growth rate reduction of CO2 emissions is caused by the economic transition in China. As changes of GDCR can, from a special perspective, reflect the economic transition, and as GDCR is negatively correlated with CO2 emissions, GDCR can sometimes be used as a new socioeconomic indicator of carbon dioxide emissions in China.

scholarly journals THE IMPACT OF RENEWABLE ENERGY CONSUMPTION ON CARBON DIOXIDE EMISSIONS - THE CASE OF SOUTH AMERICAN COUNTRIES

2018 ◽  
Vol 7 (2) ◽  
Author(s):  
Matheus Da Costa Koengkan ◽  
José Alberto Fuinhas
Keyword(s):  
Carbon Dioxide ◽  
Renewable Energy ◽  
Energy Consumption ◽  
Carbon Dioxide Emissions ◽  
Fossil Fuels ◽  
South American ◽  
Renewable Energy Consumption ◽  
Distributed Lag ◽  
Long Run ◽  
The Impact

The impact of renewable energy consumption on the carbon dioxide emissions was analyzed for a panel of ten South American countries in a period from 1980 to 2012. The Autoregressive Distributed Lag Methodology was used in order to decompose the total effect of renewable energy consumption on the carbon dioxide emissions in its short- and long-run components. The results indicate that the consumption of renewable energy reduce the carbon dioxide emissions in -0.0420 % when the consumption of alternative sources increases in 1% in short-run. The empirical evidence shows that the renewable consumption plays an important role in reducing CO2 emissions and that the economic growth and energy consumption in the South American countries are still based on fossil fuels.  Keywords: Environmental, Energy economics, Econometric.

scholarly journals The Nexus between Technological Innovation and Carbon Dioxide Emissions: Evidence from China

2018 ◽  
pp. 181-193
Author(s):  
Sarfaraz Ahmed Shaikh ◽  
Zainab Taiyyeba ◽  
Khalid Khan
Keyword(s):  
Economic Growth ◽  
Carbon Dioxide ◽  
Technological Innovation ◽  
Environmental Quality ◽  
Carbon Dioxide Emissions ◽  
Positive Impact ◽  
Dynamic Effect ◽  
Development Economic ◽  
Kuznets Curve ◽  
Distributed Lag

This study inspects the dynamic effect of technological innovation, financial development, economic growth, and energy consumption on carbon dioxide (CO2) emissions. We applied the auto-regressive distributed lag (ARDL) model technique for the period from 1980 to 2017. The quantitative outcomes show a negative and insignificant relationship between technological innovation and environmental pollution in China during the said period. Furthermore, the long-run assessment results disclose that economic growth; boost-up the environmental quality of China. Thus, the results support the Environmental Kuznets Curve (EKC) hypothesis, which means that environmental degradation can be resolved inevitably by economic growth. Similarly, results show that the financial sector development exerts a positive impact on environmental quality.

scholarly journals Validating and Forecasting Carbon Emissions in the Framework of the Environmental Kuznets Curve: The Case of Vietnam

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3144
Author(s):  
Anh-Tu Nguyen ◽  
Shih-Hao Lu ◽  
Phuc Thanh Thien Nguyen
Keyword(s):  
Economic Growth ◽  
Carbon Dioxide ◽  
Energy Consumption ◽  
Carbon Dioxide Emissions ◽  
Environmental Kuznets Curve ◽  
Back Propagation ◽  
Kuznets Curve ◽  
Causality Test ◽  
Distributed Lag ◽  
Long Run

This paper examines the environmental Kuznets curve (EKC) in Vietnam between 1977 and 2019. Using the autoregressive distributed lag (ARDL) approach, we find an inverted N-shaped relation between economic growth and carbon dioxide emissions in both the long- and short-run. The econometric results also reveal that energy consumption and urbanization statistically positively impact pollution. The long-run Granger causality test shows a unidirectional causality from energy consumption and economic growth to pollution while there is no causal relationship between energy consumption and economic growth. These suggest some crucial policies for curtailing emissions without harming economic development. In the second step, we also employed the back-propagation neural networks (BPN) to compare the work of econometrics in carbon dioxide emissions forecasting. A 5-4-1 multi-layer perceptron with BPN and learning rate was set at 0.1, which outperforms the ARDL’s outputs. Our findings suggest the potential application of machine learning to notably improve the econometric method’s forecasting results in the literature.

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