The Pricing Formula of Carbon Emissions Cost Based on STIRPAT Model

2014 ◽  
Vol 3 (6) ◽  
Keyword(s):  
Carbon Emissions ◽  
Stirpat Model ◽  
Pricing Formula

scholarly journals Examining the Driving Factors of the Direct Carbon Emissions of Households in the Ebinur Lake Basin Using the Extended STIRPAT Model

2021 ◽  
Vol 13 (3) ◽  
pp. 1339
Author(s):  
Ziyuan Chai ◽  
Zibibula Simayi ◽  
Zhihan Yang ◽  
Shengtian Yang
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Driving Factors ◽  
Series Data ◽  
Lake Basin ◽  
Stirpat Model ◽  
Consumption Structure ◽  
Ebinur Lake ◽  
Ebinur Lake Basin

In order to achieve the carbon emission reduction targets in Xinjiang, it has become a necessary condition to study the carbon emission of households in small and medium-sized cities in Xinjiang. This paper studies the direct carbon emissions of households (DCEH) in the Ebinur Lake Basin, and based on the extended STIRPAT model, using the 1987–2017 annual time series data of the Ebinur Lake Basin in Xinjiang to analyze the driving factors. The results indicate that DCEH in the Ebinur Lake Basin during the 31 years from 1987 to 2017 has generally increased and the energy structure of DCEH has undergone tremendous changes. The proportion of coal continues to decline, while the proportion of natural gas, gasoline and diesel is growing rapidly. The main positive driving factors affecting its carbon emissions are urbanization, vehicle ownership and GDP per capita, while the secondary driving factor is residents’ year-end savings. Population, carbon intensity and energy consumption structure have negative effects on carbon emissions, of which energy consumption structure is the main factor. In addition, there is an environmental Kuznets curve between DCEH and economic development, but it has not yet reached the inflection point.

scholarly journals The Scale, Structure and Influencing Factors of Total Carbon Emissions from Households in 30 Provinces of China—Based on the Extended STIRPAT Model

Energies ◽  
2018 ◽  
Vol 11 (5) ◽  
pp. 1125 ◽  
Author(s):  
Yong Wang ◽  
Guangchun Yang ◽  
Ying Dong ◽  
Yu Cheng ◽  
Peipei Shang
Keyword(s):  
Influencing Factors ◽  
Carbon Emissions ◽  
Scale Structure ◽  
Total Carbon ◽  
Stirpat Model

scholarly journals Technology Innovation, Economic Growth and Carbon Emissions in the Context of Carbon Neutrality: Evidence from BRICS

2021 ◽  
Vol 13 (20) ◽  
pp. 11138
Author(s):  
Huan Zhang
Keyword(s):  
Economic Growth ◽  
South Africa ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Test Results ◽  
Stirpat Model ◽  
Causality Test ◽  
Carbon Neutrality ◽  
Brics Countries ◽  
The Impact

This study selects the panel data of five BRICS nations (Brazil, Russia, India, China, South Africa) from 1990 to 2019 to empirically explore the impact of technological innovation and economic growth on carbon emissions under the context of carbon neutrality. Granger causality test results signify that there exists a one-way causality from technology patent to carbon emission and from economic growth to carbon emission. We also constructed an improved Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. The regression results manifest that technology patents contribute to the realization of carbon emission reduction and carbon neutralization, while the economic growth of emerging economies represented by BRICS countries significantly improves carbon emissions, but every single BRICS country shows differentiated carbon emissions conditions with their economic development stages. The impact of the interaction term on carbon emissions for the five BRICS countries also presents country-specific heterogeneity. Moreover, the Environmental Kuznets Curve (EKC) test results show that only Russia and South Africa have an inverted U-shaped curve relationship between economic growth and carbon emissions, whereas Brazil, India and China have a U-shaped curve relationship. There exists no EKC relationship when considering BRICS nations as a whole. Further robustness tests also verify that the conclusions obtained in this paper are consistent and stable. Finally, the paper puts forward relevant policy suggestions based on the research findings.

scholarly journals Effects of straw’s comprehensive utilization-technology on agricultural carbon emission in Jiangsu Province

2021 ◽  
Vol 267 ◽  
pp. 01014
Author(s):  
Xue Qin ◽  
Jun Yan ◽  
G.Y. Zhu
Keyword(s):  
Influencing Factors ◽  
Carbon Emissions ◽  
Emission Reduction ◽  
Carbon Emission ◽  
Explanatory Variable ◽  
Inhibitory Effect ◽  
Jiangsu Province ◽  
Stirpat Model ◽  
Comprehensive Utilization ◽  
The Core

Straw resources are abundant in Jiangsu province, the utilization and burning of straw is an important problem in agriculture carbon emission reduction. In order to analyze the effect of straw’s comprehensive utilization technology on agricultural carbon emission, the STIRPAT model is introduced, which takes straw utilization technology as the core explanatory variable while other influencing factors as control variables, and the ridge regression is adopted to conduct an empirical analysis on the influencing factors of agricultural carbon emission in Jiangsu province from 2008 to 2018. The results demonstrate that for every 1% increasing of straw’s comprehensive utilization technology, agriculture carbon emission will be reduced by 0.17%; the labor force is the biggest driver of agriculture carbon emissions; agriculture economic development, energy consumption takes a certain inhibitory effect on agriculture carbon emissions, but not very great.

scholarly journals Driving Factor Analysis of Carbon Emissions in China’s Power Sector for Low-Carbon Economy

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Dan Yan ◽  
Yalin Lei ◽  
Li Li
Keyword(s):  
Power Generation ◽  
Carbon Emissions ◽  
Electricity Consumption ◽  
Least Squares Regression ◽  
Low Carbon ◽  
Power Sector ◽  
Stirpat Model ◽  
Coal Consumption ◽  
Low Carbon Economy ◽  
Generation Structure

The largest percentage of China’s total coal consumption is used for coal-fired power generation, which has resulted in the power sector becoming China’s largest carbon emissions emitter. Most of the previous studies concerning the driving factors of carbon emissions changes lacked considerations of different socioeconomic factors. This study examines the impacts of eight factors from different aspects on carbon emissions within power sector from 1981 to 2013 by using the extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model; in addition, the regression coefficients are effectively determined by a partial least squares regression (PLS) method. The empirical results show that (1) the degree of influence of various factors from strong to weak is urbanization level (UL) > technology level (T1) > population (P) > GDP per capita (A) > line loss (T2) > power generation structure (T3) > energy intensity (T4) > industry structure (IS); (2) economic activity is no longer the most important contributing factor; the strong correlation between electricity consumption and economic growth is weakening; and (3) the coal consumption rate of power generation had the most obvious inhibitory effect, indicating that technological progress is still a vital means of achieving emissions reductions.

scholarly journals Impact of Economic Policy Uncertainty on Carbon Emissions: Evidence from 137 Multinational Countries

2021 ◽  
Vol 19 (1) ◽  
pp. 4
Author(s):  
Hai-Jie Wang ◽  
Yong Geng ◽  
Xi-Qiang Xia ◽  
Quan-Jing Wang
Keyword(s):  
Economic Policy ◽  
World Bank ◽  
Carbon Emissions ◽  
Policy Implications ◽  
Policy Uncertainty ◽  
International Treaties ◽  
Stirpat Model ◽  
Economic Policy Uncertainty ◽  
Cross Country ◽  
The Relationship

With growing economic policy uncertainty (EPU) and the importance of protecting the natural environment worldwide, the relationship between EPU and carbon emissions should be investigated further. However, conclusions in the existing literature on the relationship between EPU and carbon emission are inconclusive. This paper aims to examine the influence of EPU on carbon emissions according to the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model. To investigate such essential issues, we conduct GMM estimations by utilizing cross-country data covering 137 countries during the period 1970–2018, obtained from World Bank and OECD statistics. Our empirical estimations support that EPU would bring about more carbon emissions, while we conduct empirical analysis by changing the system of measurement, employing alternative estimation and constructing new samples. Our study provides substantial policy implications for government participation in international treaties on environmental protection to mitigate environmental degradation.

Analysis the Impact of Urbanization on Carbon Emissions Using the Stirpat Model in Tianjin, China

2013 ◽  
Vol 13 (21) ◽  
pp. 4608-4611 ◽  
Author(s):  
Xu Li-na ◽  
Zhao Tao ◽  
Yang Xiao-feng ◽  
Zhang Yan-dong
Keyword(s):  
Carbon Emissions ◽  
Stirpat Model ◽  
Impact Of Urbanization ◽  
The Impact
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