scholarly journals The Relationship between Economic Complexity, Energy Consumption Structure and Greenhouse Gas Emission: Heterogeneous Panel Evidence from the EU Countries

2019 ◽  
Vol 11 (2) ◽  
pp. 497 ◽  
Author(s):  
Olimpia Neagu ◽  
Mircea Teodoru
Keyword(s):  
Energy Consumption ◽  
Least Squares ◽  
Greenhouse Gas ◽  
Greenhouse Gas Emission ◽  
Gas Emission ◽  
Consumption Structure ◽  
Economic Complexity ◽  
European Economies ◽  
Heterogeneous Panel

The aim of the paper is to examine the long-term relationship between economic complexity, energy consumption structure, and greenhouse gas emission, within a panel of European Union countries and two subpanels: (i) European economies with higher economic complexity and (ii) European economies with a lower level of economic complexity. Taking into consideration the heterogeneity among European countries, the heterogeneous panel technique is used, including panel estimation through fully modified least squares (FMOLS) and dynamic ordinary least squares (DOLS). The empirical findings indicate a long-term equilibrium relationship between economic complexity, energy consumption structure and greenhouse gas emission within all three panels. Economic complexity and energy consumption structure have a statistically significant impact on greenhouse gas emission within all panels, but the influence is higher within the subpanel of countries with a lower level of economic complexity, suggesting a higher risk of pollution as the economic complexity grows and as the energy balance inclines in favor of non-renewable energy consumption. Our paper suggests that the economic complexity is a variable that must be taken into consideration when national economic and energy policies are shaped. Finally, policy implications for each panel of countries are discussed.

Energy Consumption and Greenhouse Gas Emission of Korean Offshore Fisheries

2018 ◽  
Vol 17 (3) ◽  
pp. 675-682 ◽  
Author(s):  
Jihoon Lee ◽  
Taeho Kim ◽  
Harald Ellingsen ◽  
Erik Skontorp Hognes ◽  
Bokyu Hwang
Keyword(s):  
Energy Consumption ◽  
Greenhouse Gas ◽  
Greenhouse Gas Emission ◽  
Gas Emission

External Insulation Design Impacts on the Energy Consumption and Greenhouse Gas Emission of Building

2016 ◽  
Vol 847 ◽  
pp. 381-390 ◽  
Author(s):  
Yao Li ◽  
Xian Zheng Gong ◽  
Zhi Hong Wang ◽  
Hao Li ◽  
Miao Miao Fan
Keyword(s):  
Life Cycle ◽  
Energy Consumption ◽  
Greenhouse Gas ◽  
Greenhouse Gas Emission ◽  
Gas Emission ◽  
Production Phase ◽  
External Insulation ◽  
Insulation Thickness ◽  
Building Life Cycle ◽  
Operation Phase

In order to determine the optimal parameters of the external insulation system and guide the energy saving and greenhouse gas emission reduction of building, a typical student dormitory building in Beijing was chosen as research object. The life cycle thinking and dynamic simulation method were used in the present investigation. The relationship between the expandable polystyrene (EPS) external insulation system design parameters and building energy consumption and greenhouse gas emission in each phase of materials production phase, operation phase and the whole life cycle was studied, systematically . The results show that the consumption of clay brick, concrete and cement mortar account for 98.1% of the total materials consumption, where concrete contributes most to both energy consumption (36.6%) and greenhouse gas emission (35.9%). Regarding the contribution to energy consumption and greenhouse gas emission for building life cycle, materials production phase accounts for 5.6%-18.8% and building operation phase takes up 80.6%-93.4%. With the increase of EPS insulation thickness, the energy consumption and greenhouse gas emission increase linearly in materials production phase, decrease in building operation phase, and have an optimization value in the building life cycle to reach the minimum when the heat transfer coefficient (K) is 0.3W / (m2 • K) equivalent to the EPS insulation thickness is 130mm. Building heating load reduces with the increases of insulation thickness, but the envelope thermal insulation performance has no significant influence on cooling load.

GREENHOUSE GAS EMISSION FROM COMBUSTION OF ASSOCIATED PETROLEUM GAS IN RUSSIA

2021 ◽  
pp. 54-61
Author(s):  
N. V. Popov ◽  
◽  
I. L. Govor ◽  
M. L. Gitarskii ◽  
◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Greenhouse Gases ◽  
Greenhouse Gas ◽  
Greenhouse Gas Emission ◽  
Component Composition ◽  
Emission Factors ◽  
Gas Emission ◽  
Associated Petroleum Gas ◽  
Carbon Dioxide Co2

The average weighted long-term component composition of associated petroleum gas burned at the fields in Russia is obtained, where the volume fractions of carbon dioxide (CO2) and methane (CH4) make up 0.8 and 66.4%, respectively. Based on it, the national emission factors of greenhouse gases from the flaring of associated petroleum gas are developed: the values are equal to 2.76 103 t CO2 and 0.0155 103 t CH4 per 1 106 m3 of the gas burnt. The calculations based on the emission factors led to the 37% increase in total equivalent emission of CO2 and CH4 as compared to the calculations based on the IPCC emission factors. The use of the national emission factors increases the reliability of the estimates of greenhouse gas emissions and the evaluation of their impact on climate.

scholarly journals Future Predictions of Rainfall and Temperature Using GCM and ANN for Arid Regions: A Case Study for the Qassim Region, Saudi Arabia

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1260 ◽  
Author(s):  
Khalid Alotaibi ◽  
Abdul Ghumman ◽  
Husnain Haider ◽  
Yousry Ghazaw ◽  
Md. Shafiquzzaman
Keyword(s):  
Saudi Arabia ◽  
Greenhouse Gas ◽  
Greenhouse Gas Emission ◽  
Data Driven ◽  
Emission Scenarios ◽  
Gas Emission ◽  
The Future ◽  
Emissions Scenarios ◽  
The Impact

Future predictions of rainfall patterns in water-scarce regions are highly important for effective water resource management. Global circulation models (GCMs) are commonly used to make such predictions, but these models are highly complex and expensive. Furthermore, their results are associated with uncertainties and variations for different GCMs for various greenhouse gas emission scenarios. Data-driven models including artificial neural networks (ANNs) and adaptive neuro fuzzy inference systems (ANFISs) can be used to predict long-term future changes in rainfall and temperature, which is a challenging task and has limitations including the impact of greenhouse gas emission scenarios. Therefore, in this research, results from various GCMs and data-driven models were investigated to study the changes in temperature and rainfall of the Qassim region in Saudi Arabia. Thirty years of monthly climatic data were used for trend analysis using Mann–Kendall test and simulating the changes in temperature and rainfall using three GCMs (namely, HADCM3, INCM3, and MPEH5) for the A1B, A2, and B1 emissions scenarios as well as two data-driven models (ANN: feed-forward-multilayer, perceptron and ANFIS) without the impact of any emissions scenario. The results of the GCM were downscaled for the Qassim region using the Long Ashton Research Station’s Weather Generator 5.5. The coefficient of determination (R2) and Akaike’s information criterion (AIC) were used to compare the performance of the models. Results showed that the ANNs could outperform the ANFIS for predicting long-term future temperature and rainfall with acceptable accuracy. All nine GCM predictions (three models with three emissions scenarios) differed significantly from one another. Overall, the future predictions showed that the temperatures of the Qassim region will increase with a specified pattern from 2011 to 2099, whereas the changes in rainfall will differ over various spans of the future.

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