The regional energy ecological footprint is an important evaluation index which can reveal the energy consumption on regional environmental pressure and sustainable development. First, the study relied on EEF (energy ecological footprint) method to calculate the ecological footprint, the energy ecological footprint and the ecological capacity. While STIRPAT model was applied to examine the relationship between the regional populations scale, the economic level, the industrial structure, the energy utilization technology and the energy ecological footprint. Grey prediction model was used to predict the development tendency of the energy ecological footprint in the next 10 years. The data were elicited from statistical data of regional energy consumption. The energy ecological footprint was increased to 0.3437ghm2/person from 0.1234ghm2/person during 2006-2015 in Xiangtan region. Though the energy capacity per capita increased slightly, the energy ecological footprint was kept in deficit. The level was increased to 0.2504ghm2/person from 0.073ghm2/person. The ecological pressure of the energy ecological footprint was very large. Among the influencing factors, the industrial structure contributes the most to explain the energy ecological footprint, followed by the population scale and the GDP per capita. The influence of the energy strength was minimal. The indices of energy ecological footprint, energy capacity and ecological pressure increased to 1.1205, 0.1246 and 8.9013ghm2/person, respectively. The dynamic scale of energy ecological footprint and the analysis of the influencing factors can provide a theory for sustainable development of society-economy-resources and environment.
| DOI | 10.11648/j.ijepp.20190701.13 |
| Published in | International Journal of Environmental Protection and Policy (Volume 7, Issue 1, January 2019) |
| Page(s) | 17-23 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Energy Ecological Footprint, Ecological Capacity, Dynamic Evaluation, Grey Prediction Model, Xiangtan
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APA Style
Luyun Liu, Jian Zheng, Guo Li, Yan Wang. (2019). Regional Scale Dynamic Prediction of Energy Ecological Footprint and Its Influencing Factors. International Journal of Environmental Protection and Policy, 7(1), 17-23. https://doi.org/10.11648/j.ijepp.20190701.13
ACS Style
Luyun Liu; Jian Zheng; Guo Li; Yan Wang. Regional Scale Dynamic Prediction of Energy Ecological Footprint and Its Influencing Factors. Int. J. Environ. Prot. Policy 2019, 7(1), 17-23. doi: 10.11648/j.ijepp.20190701.13
AMA Style
Luyun Liu, Jian Zheng, Guo Li, Yan Wang. Regional Scale Dynamic Prediction of Energy Ecological Footprint and Its Influencing Factors. Int J Environ Prot Policy. 2019;7(1):17-23. doi: 10.11648/j.ijepp.20190701.13
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Download@article{10.11648/j.ijepp.20190701.13,
author = {Luyun Liu and Jian Zheng and Guo Li and Yan Wang},
title = {Regional Scale Dynamic Prediction of Energy Ecological Footprint and Its Influencing Factors},
journal = {International Journal of Environmental Protection and Policy},
volume = {7},
number = {1},
pages = {17-23},
doi = {10.11648/j.ijepp.20190701.13},
url = {https://doi.org/10.11648/j.ijepp.20190701.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepp.20190701.13},
abstract = {The regional energy ecological footprint is an important evaluation index which can reveal the energy consumption on regional environmental pressure and sustainable development. First, the study relied on EEF (energy ecological footprint) method to calculate the ecological footprint, the energy ecological footprint and the ecological capacity. While STIRPAT model was applied to examine the relationship between the regional populations scale, the economic level, the industrial structure, the energy utilization technology and the energy ecological footprint. Grey prediction model was used to predict the development tendency of the energy ecological footprint in the next 10 years. The data were elicited from statistical data of regional energy consumption. The energy ecological footprint was increased to 0.3437ghm2/person from 0.1234ghm2/person during 2006-2015 in Xiangtan region. Though the energy capacity per capita increased slightly, the energy ecological footprint was kept in deficit. The level was increased to 0.2504ghm2/person from 0.073ghm2/person. The ecological pressure of the energy ecological footprint was very large. Among the influencing factors, the industrial structure contributes the most to explain the energy ecological footprint, followed by the population scale and the GDP per capita. The influence of the energy strength was minimal. The indices of energy ecological footprint, energy capacity and ecological pressure increased to 1.1205, 0.1246 and 8.9013ghm2/person, respectively. The dynamic scale of energy ecological footprint and the analysis of the influencing factors can provide a theory for sustainable development of society-economy-resources and environment.},
year = {2019}
}
TY - JOUR T1 - Regional Scale Dynamic Prediction of Energy Ecological Footprint and Its Influencing Factors AU - Luyun Liu AU - Jian Zheng AU - Guo Li AU - Yan Wang Y1 - 2019/03/08 PY - 2019 N1 - https://doi.org/10.11648/j.ijepp.20190701.13 DO - 10.11648/j.ijepp.20190701.13 T2 - International Journal of Environmental Protection and Policy JF - International Journal of Environmental Protection and Policy JO - International Journal of Environmental Protection and Policy SP - 17 EP - 23 PB - Science Publishing Group SN - 2330-7536 UR - https://doi.org/10.11648/j.ijepp.20190701.13 AB - The regional energy ecological footprint is an important evaluation index which can reveal the energy consumption on regional environmental pressure and sustainable development. First, the study relied on EEF (energy ecological footprint) method to calculate the ecological footprint, the energy ecological footprint and the ecological capacity. While STIRPAT model was applied to examine the relationship between the regional populations scale, the economic level, the industrial structure, the energy utilization technology and the energy ecological footprint. Grey prediction model was used to predict the development tendency of the energy ecological footprint in the next 10 years. The data were elicited from statistical data of regional energy consumption. The energy ecological footprint was increased to 0.3437ghm2/person from 0.1234ghm2/person during 2006-2015 in Xiangtan region. Though the energy capacity per capita increased slightly, the energy ecological footprint was kept in deficit. The level was increased to 0.2504ghm2/person from 0.073ghm2/person. The ecological pressure of the energy ecological footprint was very large. Among the influencing factors, the industrial structure contributes the most to explain the energy ecological footprint, followed by the population scale and the GDP per capita. The influence of the energy strength was minimal. The indices of energy ecological footprint, energy capacity and ecological pressure increased to 1.1205, 0.1246 and 8.9013ghm2/person, respectively. The dynamic scale of energy ecological footprint and the analysis of the influencing factors can provide a theory for sustainable development of society-economy-resources and environment. VL - 7 IS - 1 ER -
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