scholarly journals Examining the link between carbon emission, carbon emission intensity and financial development in Nigeria. A Dynamic ARDL Simulation

2021 ◽  
Author(s):  
Kingsley Ikechukwu Okere ◽  
Maxwell Onyemachi Ogbulu ◽  
Obumneke Bob Muoneke ◽  
Favour Chidinma Onuoha ◽  
Agbede Moses Oyeyemi
Keyword(s):  
Emission Intensity ◽  
Co2 Emission ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Economic Globalization ◽  
Bank Credit ◽  
Fossil Energy ◽  
Carbon Emission Intensity ◽  
Long Run ◽  
Short Run

Abstract The need for adequate and consistent policies to mitigate the continuous rise of carbon emission have motivated the energy economist in the past decades to actively involved and explore common economic agents that are driving the rising pattern in the environmental pollution. This study is positioned towards contributing to the on-going debates on this issue by exploring the impact of bank credit to the private sector on aggregate carbon emissions and carbon emission intensity in Nigeria over the period 1971– 2016 using dynamic ARDL simulations. Controlling for the influence of fossil energy intensity of consumption and economic globalization, the study found that bank credit to the private sector has a positive significant long-run increasing effect on aggregate CO2 emission and carbon emission intensity in the economy. Second, the estimated coefficients show that fossil energy intensity of consumption and economic globalization have a significant long-run and short-run increasing impact on aggregate CO2 emission and carbon emission intensity in the economy. In contrast, the population has a significant long-run and short-run reducing effect on aggregate CO2 emission and only the long run reducing effect on carbon emission intensity. Third, economic growth has significant short-run and increasing long-run effects on aggregate CO2 emission and a long run increasing effect on carbon emission intensity. In sum, the results show that the economy is yet to transient to renewable energy.

Energy technological innovation and carbon emissions mitigation: evidence from China

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shuping Cheng ◽  
Lingjie Meng ◽  
Lu Xing
Keyword(s):  
Technological Innovation ◽  
Carbon Emissions ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Fossil Energy ◽  
Content Type ◽  
Carbon Emission Intensity ◽  
Emissions Mitigation ◽  
The Impact ◽  
Positive Effect

PurposeThe purpose of this paper is to examine the effects of energy technological innovation on carbon emissions in China from 2001 to 2016.Design/methodology/approachConditional mean (CM) methods are first applied to implement our investigation. Then, considering the tremendous heterogeneity in China, quantile regression is further employed to comprehensively investigate the potential heterogeneous effect between energy technological innovation and carbon emission intensity.FindingsThe results suggest that renewable energy technological innovation has a significantly positive effect on carbon emission intensity in lower quantile areas and a negative effect in higher quantile areas. Contrarily, fossil energy technological innovation exerts a negative correlation with carbon emission intensity in lower quantile areas and a positive effect on carbon emission intensity in higher quantiles areas.Originality/valueConsidering that energy consumption is the main source of CO2 emissions, it is of great importance to study the impact of energy technological innovation on carbon emissions. However, the previous studies mainly focus on the impact of integrated technological innovation on carbon emissions, ignoring the impact of energy technological innovation on carbon emissions mitigation. To fill this gap, we construct an extended STIRPAT model to examine the effects of renewable energy technological innovation and fossil energy technological innovation on carbon emissions in this paper. The results can provide a reference for the government to formulate carbon mitigation policies.

scholarly journals Construction and Application Analysis of Carbon Emission Influence Factor Model of Energy Consumption in Mining Industry

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Lili Wei ◽  
Xiwen Feng ◽  
Guangyu Jia
Keyword(s):  
Energy Consumption ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Mining Industry ◽  
Energy Structure ◽  
Low Carbon ◽  
Greenhouse Gas Inventory ◽  
Carbon Emission Intensity ◽  
Low Carbon Development

With the proposal of China’s “double carbon goal,” as a high energy-consuming industry, it is urgent for the mining industry to adopt a low-carbon development strategy. Therefore, in order to better provide reasonable suggestions and references for the low-carbon development of mining industry, referring to the methods and parameters of the 2006 IPCC National Greenhouse Gas Inventory Guidelines and China’s Provincial Greenhouse Gas Inventory Preparation Guidelines (Trial), a carbon emission estimation model is established to estimate the carbon emission of energy consumption of China's mining industry from 2000 to 2020. Then, using the extended Kaya identity, the influencing factors of carbon emission in mining industry are decomposed into energy carbon emission intensity, energy structure, energy intensity, industrial structure, and output value. On this basis, an LMDI model is constructed to analyze the impact of five factors on carbon emission from mining industry. The research shows that the carbon emission and carbon emission intensity of energy consumption in China’s mining industry first rise and then fall and then rise slightly. The carbon emission intensity in recent three years is about 2 tons/10000 yuan. The increase in output value is the main factor to increase carbon emission. The reduction in energy intensity is the initiative of carbon emission reduction. The current energy structure of mining industry is not conducive to carbon emission reduction.

scholarly journals The Energy Conservation and Emission Reduction Effects of Economic Agglomeration: A Spatial Perspective Based on China's Province-level Data

2021 ◽  
Author(s):  
Tianyu Luo ◽  
Hongmin Chen
Keyword(s):  
Energy Conservation ◽  
Emission Intensity ◽  
Emission Reduction ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Collaborative Governance ◽  
Mediating Effect ◽  
Spatial Data Analysis ◽  
Carbon Emission Intensity ◽  
Economic Agglomeration

Abstract Based on the data of 30 provinces in China from 1995 to 2017, this paper combines exploratory spatial data analysis method, dynamic spatial Durbin model, and intermediary effect model to explore the spatial influence mechanism between economic agglomeration, energy intensity, and carbon emission intensity. The research results provide a basis for China's early realization of energy conservation and emission reduction goals, economical green development, and regional development strategy selection. Firstly, the results show that China's carbon emission intensity has apparent spatial agglomeration and path dependence characteristics. Secondly, the economic agglomeration has the dual effect of energy saving and emission reduction. Furthermore, there is a significant inverted N-curve relationship between economic agglomeration and carbon emission intensity and carbon emissions, and a significant U-shaped curve relationship exists between economic agglomeration and energy intensity. Finally, economic agglomeration can indirectly affect carbon emission intensity through the mediating effect of energy intensity, and there is a significant inverted U-shaped curve relationship between energy intensity and carbon emission intensity. Therefore, promoting mutual coordination of environmental policies and building a regional collaborative governance mechanism is an effective way to achieve a win-win situation for the environment and economy of Beautiful China.

Time Series Analysis of Energy Consumption and Carbon Emission for Binhai New Area of Tianjin

2012 ◽  
Vol 174-177 ◽  
pp. 3571-3575 ◽  
Author(s):  
Chun Li Chu ◽  
Yi Fang Yang ◽  
Xue Bai ◽  
Qian Peng ◽  
Mei Ting Ju
Keyword(s):  
Time Series ◽  
Energy Consumption ◽  
Emission Intensity ◽  
Co2 Emission ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Carbon Intensity ◽  
Per Capita ◽  
Tianjin City ◽  
Co2 Emission Intensity

With the rapid development of industrialization and urbanization, cities become the centers to address the problem of climate change for China. Binhai New Area of Tianjin city plays an important role to boost the economy of North China according to the long-term development planning of China. It is essential for Binhai New Area of Tianjin to promote energy efficiency and reduce the CO2 emission intensity. The study explores the characteristics of the energy consumption, energy intensity, carbon emission and carbon intensity of Binhai New Area through time series analysis. We conclude that the consumption of energy has increased with an annual growth rate of 17.9% from 2000 to 2009. The energy consumption per capita increases from 4.32 tons of SCE per capita in 2000 to 12.37 tons of SCE per capita in 2009, which is much higher than that of Tianjin city and also China as a whole. The energy intensity has declined from 0.79 tons of SCE/104Y in 2000 to 0.38 tons of SCE/104Y in 2009. But it is lower than that of Tianjin. Total carbon emission has increased by 225% from 2000 to 2009. The carbon emission per capita increases from 10.8 tons per capita in 2000 to 30.8 tons per capita in 2009. The carbon intensity has declined from 1.97 tons /104Y in2000 to 0.96 tons/104Y in 2009. Thus, we suggest that the composition of energy consumption should be optimized and more clean energy should be used to reduce the total CO2 emission and CO2 emission intensity.

scholarly journals The Changes of Carbon Emission in China’s Industrial Sectors from 2002 to 2010: A Structural Decomposition Analysis and Input-Output Subsystem

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Guoxing Zhang ◽  
Mingxing Liu
Keyword(s):  
Carbon Emissions ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Decomposition Analysis ◽  
Final Demand ◽  
Good Effect ◽  
Input Output ◽  
Carbon Emission Intensity ◽  
Industrial Sectors ◽  
Three Components

Based on 2002–2010 comparable price input-output tables, this paper first calculates the carbon emissions of China’s industrial sectors with three components by input-output subsystems; next, we decompose the three components into effect of carbon emission intensity, effect of social technology, and effect of final demand separately by structure decomposition analysis; at last, we analyze the contribution of every effect to the total emissions by sectors, thus finding the key sectors and key factors which induce the changes of carbon emissions in China’s industrial sectors. Our results show that in the latest 8 years five departments have gotten the greatest increase in the changes of carbon emissions compare with other departments and the effect of final demand is the key factor leading to the increase of industrial total carbon emissions. The decomposed effects show a decrease in carbon emission due to the changes of carbon emission intensity between 2002 and 2010 compensated by an increase in carbon emissions caused by the rise in final demand of industrial sectors. And social technological changes on the reduction of carbon emissions did not play a very good effect and need further improvement.

Agricultural land usage and tourism impact on renewable energy consumption among Coastline Mediterranean Countries

2018 ◽  
Vol 29 (8) ◽  
pp. 1438-1454 ◽  
Author(s):  
Andrew Adewale Alola ◽  
Uju Violet Alola
Keyword(s):  
Renewable Energy ◽  
Energy Consumption ◽  
Carbon Emission ◽  
Agricultural Land ◽  
Tourism Development ◽  
Renewable Energy Consumption ◽  
Land Usage ◽  
Mediterranean Countries ◽  
Long Run ◽  
Short Run

Abstact This empirical study aims to investigate the dynamic response of renewable energy consumption to long-run disequilibrium and short-run impact of tourism development and agricultural land usage for the period of 1995 to 2014 in 16 Coastline Mediterranean Countries. For this reason, a dynamic Autoregressive Distributed Lag approach is employed in a multivariate and two-model framework such that carbon emission and gross domestic product are being controlled for in the models. Significantly, there is evidence of a joint impact of tourism development and agricultural land usage on renewable energy consumption. With a speed of adjustment of 21.6% from short-run disequilibrium to long run, their respective panel elasticities are 0.33 and negative 1.60 in the long run. Significant evidence shows that nine of the Coastline Mediterranean Countries have tourism development as a short-run factor while Slovenia and Cyprus exhibit a short-run common factor. Also, Granger causality evidences from carbon emission, gross domestic product and tourism development to renewable energy are all with feedbacks. However, Granger causality from agricultural land usage to renewable energy is without feedback. In the region, effective policy implementations through the collaborative effort of stakeholders will ensure a sustainable renewable energy development amidst agricultural and tourism activities.

The Association Between Bank Credit Terms and Conditions and the Business Cycle in the Euro Area

2019 ◽  
Vol 65 (4) ◽  
pp. 247-256
Author(s):  
Dimitrios Anastasiou ◽  
Konstantinos Drakos
Keyword(s):  
Business Cycle ◽  
Euro Area ◽  
Bank Loan ◽  
Bank Credit ◽  
Monetary Policies ◽  
Policy Makers ◽  
Long Run ◽  
Loan Terms ◽  
Short Run ◽  
The Business Cycle

Abstract We explored the trajectory of bank loan terms and conditions over the business cycle, where the latter was decomposed into its long-run (trend) and short-run (cyclical) components. We found that deterioration of each business cycle component leads to a significant tightening of credit terms and conditions. We found mixed results concerning the symmetry of impacts of the short and long run components. Symmetry was found between the terms and conditions on loans for small vs. large enterprises. Our findings provide very useful information to policy makers and should be taken into consideration when monetary policies are designed.

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