scholarly journals The Peak Value of Carbon Emissions in the Beijing-Tianjin-Hebei Region Based on the STIRPAT Model and Scenario Design

2016 ◽  
Vol 25 (2) ◽  
pp. 823-834 ◽  
Author(s):  
Yanjun Liu ◽  
Lei Wen
Keyword(s):  
Carbon Emissions ◽  
Stirpat Model ◽  
Scenario Design ◽  
Peak Value

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 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 Energy Demand and Carbon Emission Peak Forecasting of Beijing Based on Leap Energy Simulation Method

2020 ◽  
Keyword(s):  
Sustainable Development ◽  
Energy Consumption ◽  
Carbon Emissions ◽  
Energy Demand ◽  
Carbon Emission ◽  
Simulation Method ◽  
Negative Influence ◽  
Low Carbon ◽  
Peak Value

<p>The long-term forecasting of the energy demand is an important issue of an area’s sustainable development, especially for mega cities such as Beijing. Beijing is changing its energy supply strategy to depend on energy imports from other provinces due to the city’s long-term low carbon sustainable development plan. Beijing has promised that it will reach the peak value of energy consumption by 2050 and the peak value of the carbon emissions by 2030. To understand whether this can be achieved, this study built an energy demand simulation model using the LEAP with different development scenarios. The results show that, the peak value of Beijing’s energy demand is between 108.25 and 131.74 Mtce during the period of 2044 to 2048, while the peak value of carbon emissions is between 134 and 139.38 million tons in 2025. We also find that adjusting the industry structure and improving the tertiary industry’s energy usage efficiency can be efficient ways to reduce energy consumption. These approaches not only reduce the negative influence of the economic development, but also achieve the energy saving and carbon emission reducing requirements. This study provides an interpretation of the implications for the future energy and climate policies of Beijing.</p>

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.

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