Comment on “An extended STIRPAT model-based methodology for evaluating the driving forces affecting carbon emissions in existing public building sector: evidence from China in 2000–2015” by Ma et al. (2017)

2019 ◽  
Vol 99 (1) ◽  
pp. 609-610
Author(s):  
Catalina García García ◽  
Román Salmerón Gómez ◽  
José García García
Keyword(s):  
Carbon Emissions ◽  
Driving Forces ◽  
Building Sector ◽  
Stirpat Model ◽  
Public Building ◽  
Model Based

scholarly journals Carbon emissions and driving forces of China’s power sector: Input-output model based on the disaggregated power sector

2020 ◽  
Vol 268 ◽  
pp. 121925 ◽  
Author(s):  
Fang Luo ◽  
Yi Guo ◽  
Mingtao Yao ◽  
Wenqiu Cai ◽  
Meng Wang ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Driving Forces ◽  
Input Output ◽  
Power Sector ◽  
Model Based

scholarly journals Operational Carbon Change in Commercial Buildings under the Carbon Neutral Goal: A LASSO–WOA Approach

Buildings ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 54
Author(s):  
Xiwang Xiang ◽  
Xin Ma ◽  
Zhili Ma ◽  
Minda Ma
Keyword(s):  
Carbon Emissions ◽  
Emission Reduction ◽  
Driving Forces ◽  
Commercial Buildings ◽  
Low Carbon ◽  
Lasso Regression ◽  
Commercial Building ◽  
Building Sector ◽  
Whale Optimization ◽  
New Perspective

The rapid growth of energy consumption in commercial building operations has hindered the pace of carbon emission reduction in the building sector in China. This study used historical data to model the carbon emissions of commercial building operations, the LASSO regression was applied to estimate the model results, and the whale optimization algorithm was used to optimize the nonlinear parameter. The key findings show the following: (1) The major driving forces of carbon emissions from commercial buildings in China were found to be the population size and energy intensity of carbon emissions, and their elastic coefficients were 0.6346 and 0.2487, respectively. (2) The peak emissions of the commercial building sector were 1264.81 MtCO2, and the peak year was estimated to be 2030. Overall, this study analyzed the historical emission reduction levels and prospective peaks of carbon emissions from China’s commercial buildings from a new perspective. The research results are helpful for governments and decision makers to formulate effective emission reduction policies and can also provide references for the low-carbon development of other countries and regions.

scholarly journals Trends and Driving Forces of Carbon Emissions from Energy Consumption: A Case Study of Nanjing, China

2018 ◽  
Vol 10 (12) ◽  
pp. 4348 ◽  
Author(s):  
Kong-Qing Li ◽  
Ran Lu ◽  
Rui-Wen Chu ◽  
Dou-Dou Ma ◽  
Li-Qun Zhu
Keyword(s):  
Growth Rate ◽  
Energy Consumption ◽  
Carbon Emissions ◽  
Driving Forces ◽  
Positive Impact ◽  
High Growth ◽  
Future Trend ◽  
Gdp Per Capita ◽  
Stirpat Model ◽  
Per Capita

Based on the scientific calculation of carbon emissions from energy consumption in Nanjing, this paper analyzed the driving forces of carbon emissions from 2000 to 2016 by using the stochastic impacts by regression on population, affluence and technology (STIRPAT) model. The results show that from 2000 to 2016, the energy carbon emissions of Nanjing were on the rise; the urbanization rate, population, GDP per capita, and energy intensity had a significant positive impact on the growth of carbon emissions in Nanjing, China. Based on this, we presented five development scenarios to analyze the future trend of carbon emissions of the city. By contrast, the growth rate of carbon emissions from energy consumption is the slowest when the population maintains a low growth rate and the GDP per capita and technical level maintain high growth. This indicates a better urban development strategy in which industrial restructuring must be associated with talent structure adjustment to decarbonize the urban economy, and the extensive urban sprawl development approach might need to be changed.

Examining the driving factors of energy related carbon emissions using the extended STIRPAT model based on IPAT identity in Xinjiang

2017 ◽  
Vol 67 ◽  
pp. 51-61 ◽  
Author(s):  
Changjian Wang ◽  
Fei Wang ◽  
Xinlin Zhang ◽  
Yu Yang ◽  
Yongxian Su ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Driving Factors ◽  
Stirpat Model ◽  
Model Based

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 Heterogeneity of consumption-based carbon emissions and driving forces in Indian states

2021 ◽  
pp. 100039
Author(s):  
Qi Huang ◽  
Heran Zheng ◽  
Jiashuo Li ◽  
Jing Meng ◽  
Yunhui Liu ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Driving Forces ◽  
Indian States
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