The strategy of a low-carbon economy based on the STIRPAT and SD models

Fu, Bi; Wu, Meng; Che, Yue; Wang, Min; Huang, Yu-Chi; Bai, Yang

doi:10.1016/j.chnaes.2015.06.008

Cited by 43 publications

(19 citation statements)

References 6 publications

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“…Thus, it is essential to change the consumption patterns and improve the quality of economic growth to reduce CO 2 emissions in Beijing. Under the background of economic development transformation, an effective way to reduce CO 2 emissions is to promote the upgrading and optimization of industrial structure through technology progress (Garbaccio et al, 1999;Zhou et al, 2013). In Beijing, the government should compel the enterprises to close down the backward facilities and develop the innovative business model, and this is extremely urgent (Geng et al, 2013).…”

Section: Co 2 Emissions Of Representative Sectors In Beijingmentioning

confidence: 99%

“…Frequently used methods include the IPCC method (IPCC, 2006), the IPAT method (Di et al, 2011), the STIRPAT method (Fu et al, 2015;York et al, 2003), the LMDI method (Ang, 2005;Cansino et al, 2015), the GFI method (Wang et al, 2012;Ze et al, 2006) the Kaya Index method (Mavromatidis et al, 2016;Zhang and Zhou, 2007), etc. These methods strive to examine the differentiated impact factors on energy-related CO 2 emission and argue that differentiated measures should be adopted according to regions (Wang and Zhao, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Driving forces analysis of energy-related carbon dioxide (CO 2 ) emissions in Beijing: an input–output structural decomposition analysis

Wei

Huang

Yang³

et al. 2017

Journal of Cleaner Production

156

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Section: Co 2 Emissions Of Representative Sectors In Beijingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Driving forces analysis of energy-related carbon dioxide (CO 2 ) emissions in Beijing: an input–output structural decomposition analysis

Wei

Huang

Yang³

et al. 2017

Journal of Cleaner Production

156

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“…The results showed that the 40-45% reduction target might be achieved. Wang et al [21], Wang et al [22], Fu et al [23], Li et al [24], and Wang et al [25] utilized the STIRPAT model to find out the factors which affected CO 2 emissions in different areas.…”

Section: Literature Reviewmentioning

confidence: 99%

“…(5) Energy Consumption Structure. China's energy consumption structure in 2015 was 64.0% [23]. The scenarios were set according to BP [38] (as shown in Figure 12).…”

Section: The Scenariosmentioning

confidence: 99%

Prediction on the Peak of the CO₂ Emissions in China Using the STIRPAT Model

Lei

et al. 2016

Advances in Meteorology

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Climate change has threatened our economic, environmental, and social sustainability seriously. The world has taken active measures in dealing with climate change to mitigate carbon emissions. Predicting the carbon emissions peak has become a global focus, as well as a leading target for China’s low carbon development. China has promised its carbon emissions will have peaked by around 2030, with the intention of peaking earlier. Scholars generally have studied the influencing factors of carbon emissions. However, research on carbon emissions peaks is not extensive. Therefore, by setting a low scenario, a middle scenario, and a high scenario, this paper predicts China’s carbon emissions peak from 2015 to 2035 based on the data from 1998 to 2014 using the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. The results show that in the low, middle, and high scenarios China will reach its carbon emissions peak in 2024, 2027, and 2030, respectively. Thus, this paper puts forward the large-scale application of technology innovation to improve energy efficiency and optimize energy structure and supply and demand. China should use industrial policy and human capital investment to stimulate the rapid development of low carbon industries and modern agriculture and service industries to help China to reach its carbon emissions peak by around 2030 or earlier.

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“…Subsequently, the STIRPAT model was further transformed by York et al [64], and merged with other accessional factors, such as the nonfarm payrolls ratio and urbanization. Hence, the STIRPAT model combined social, economic and technical factors can be widely used to solve practical problems [65]. The general STIRPAT model can be expressed as below:…”

Section: Stirpat Modelmentioning

confidence: 99%

Temporal-Spatial Variations and Regional Disparities in Land-Use Efficiency, and the Response to Demographic Transition

Wang

Yang

et al. 2019

Sustainability

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China has undergone rapid industrialization and urbanization over the past 40 years. In this process, as a large country with a vast territory and a large population, China's population development and land utilization have been greatly affected and undergone dramatic changes. In this paper, we mainly discuss the temporal and spatial variation characteristics of land-use efficiency in China from 1991 to 2016 and the regional disparities and explore the impacts of demographic transition on land-use efficiency by employing a STIRPAT model. In terms of space, China's land-use efficiency has significant agglomeration distribution characteristics and regional inequality, and the degrees of agglomeration and differentiation have gradually become enhanced over time. Our study on the influences of demographic transition on land-use efficiency found a Kuznets curve relationship between the transition of population size and land-use efficiency, as well as between the income level transition and land-use efficiency. Especially, land-use efficiency first increases up to the population threshold of 10,611.877 × 10 4 , then efficiency decreases as the population grows. The overall average population in the whole country is 4117.753 × 10 4 , which is smaller than the identified threshold. Interestingly, the factors influencing land-use efficiency also showed very significant regional disparities. In the eastern region, there is a U-curve relationship between the population employed in secondary industries (ES2) and land-use efficiency. Land-use efficiency decreases down to the ES2 threshold of 343.674 × 10 4 for the eastern region, whereas the overall average ES2 is 874.976 × 10 4 , indicating that this region has reached the turning point where land-use efficiency will improve as the population employed in secondary industries increases. Meanwhile, the increase in the human capital level was significantly positively correlated with land-use efficiency in the eastern region. For the central region, the transition of the urban-rural population structure (measured by the urbanization rate) significantly increased land-use efficiency. In addition, the results of panel estimation showed a Kuznets relationship between the population employed in tertiary industries (ES3) and land-use efficiency in the western region. Land-use efficiency increases up to the ES3 threshold of 455.545 × 10 4 , and then decreases with an increasing population employed in tertiary industries, whereas the overall average ES3 in the western region is 415.97 × 10 4 , which is smaller than the identified threshold. Policymakers could use these findings to inform rational suggestions with a sound scientific basis regarding the promotion of land-use transition.

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The strategy of a low-carbon economy based on the STIRPAT and SD models

Cited by 43 publications

References 6 publications

Driving forces analysis of energy-related carbon dioxide (CO 2 ) emissions in Beijing: an input–output structural decomposition analysis

Driving forces analysis of energy-related carbon dioxide (CO 2 ) emissions in Beijing: an input–output structural decomposition analysis

Prediction on the Peak of the CO₂ Emissions in China Using the STIRPAT Model

Temporal-Spatial Variations and Regional Disparities in Land-Use Efficiency, and the Response to Demographic Transition

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The strategy of a low-carbon economy based on the STIRPAT and SD models

Cited by 43 publications

References 6 publications

Driving forces analysis of energy-related carbon dioxide (CO 2 ) emissions in Beijing: an input–output structural decomposition analysis

Driving forces analysis of energy-related carbon dioxide (CO 2 ) emissions in Beijing: an input–output structural decomposition analysis

Prediction on the Peak of the CO2 Emissions in China Using the STIRPAT Model

Temporal-Spatial Variations and Regional Disparities in Land-Use Efficiency, and the Response to Demographic Transition

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Prediction on the Peak of the CO₂ Emissions in China Using the STIRPAT Model