Carbon Emission Inversion Model from Provincial to Municipal Scale Based on Nighttime Light Remote Sensing and Improved STIRPAT
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Data Sources
3. Methods
3.1. Extension of the STIRPAT Model
3.2. Construction of City Development Element Based on Nighttime Lighting Data
3.3. An Improved STIRPAT Carbon Emission Inversion Model (ISTIRPAT)
4. Results and Discussion
4.1. Model Verification and Comparative Analysis
4.1.1. Verification of ISTIRPAT Model
4.1.2. Comparative Analysis of the Original STIRPAT Model and ISTIRPAT Model
4.2. Analysis of Influencing Factors of Carbon Emissions in Hubei Province
4.3. Spatial and Temporal Pattern of Carbon Emissions in Hubei Province
5. Conclusions
- (1)
- The improved STIRPAT carbon emission inversion model has a carbon emission inversion accuracy of 0.96 at the municipal level, accurately reflecting the carbon emissions of 17 cities and prefectures in Hubei Province from 2012 to 2018. This verifies that the carbon emission inversion model proposed in this paper has high inversion accuracy and a high degree of model fitting.
- (2)
- Since 2012, the carbon emissions in Hubei Province have increased steadily, forming a circular pattern radiating from Wuhan, Xiangyang, Yichang, and Jingzhou as the main carbon-emitting cities, with low carbon emissions in the central region of Hubei Province and high carbon emissions in the marginal cities. The formation of this pattern was related to the distribution of the residential population and regional industrial structure in Hubei Province.
- (3)
- From 2012 to 2018, carbon emissions increased in cities and prefectures in Hubei Province, and the annual growth rate in the province was highest in Wuhan. Despite the increase in overall carbon emissions, the carbon emissions per unit of GDP for 17 cities in Hubei Province show a downward trend. This trend reflects the positive effects of the adjustment of industrial structure and technological upgrades in the whole province. As a result, many carbon emission enterprises are gradually improving their energy efficiency, resulting in improved economic gains while reducing environmental pollution.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Levin–Lin–Chu (LLC) | Fisher–ADF | |
---|---|---|
LnCE | 0.000 | 0.3419 |
LnDNQ | 0.000 * | 0.0478 * |
LnGDP | 0.000 * | 0.2960 * |
0.000 * | 0.4379 * | |
LnSeGDP | 0.000 | 0.9566 |
LnPeople | 0.000 * | 0.0262 * |
LnCI | 0.000 | 0.0602 |
LnMDN | 0.000 * | 0.0478 * |
LnSUML | 0.000 * | 0.0478 |
Coefficient | Std. Error | T-Statistic | Prob | |
---|---|---|---|---|
Lne | −15.41744 | 3.750565 | 3.750565 | 0.0001 |
LnDNQ | −2.18689 | 0.783035 | 0.783035 | 0.0057 |
LnGDP | 0.642356 | 0.297547 | 0.297547 | 0.0320 |
−0.031573 | 0.014857 | 0.014857 | 0.0348 | |
LnSeGDP | 0.215767 | 0.089851 | 0.089851 | 0.0172 |
LnPeople | 0.544228 | 0.096255 | 0.096255 | 0.0001 |
LnCI | −0.084016 | 0.03568 | 0.03568 | 0.0195 |
LnMDN | 2.613019 | 0.696867 | 0.696867 | 0.0002 |
LnSUML | 0.201933 | 0.087698 | 0.087698 | 0.0223 |
Names of Cities | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 |
---|---|---|---|---|---|---|---|
Wuhan | 75.0389 | 77.5471 | 78.9226 | 81.3334 | 82.4333 | 84.7197 | 87.1527 |
Huangshi | 15.9004 | 16.3843 | 16.4143 | 16.2563 | 16.5632 | 17.3197 | 17.8310 |
Shiyan | 19.4268 | 19.8797 | 20.4274 | 20.8971 | 21.6169 | 22.6866 | 23.0859 |
Yichang | 30.1725 | 31.4959 | 32.6366 | 33.1453 | 33.7219 | 33.6357 | 33.8096 |
Xiangyang | 37.3170 | 38.8334 | 39.9226 | 40.3882 | 41.2933 | 42.1599 | 42.4789 |
Ezhou | 7.9296 | 8.2292 | 8.3260 | 8.5095 | 8.6989 | 9.0854 | 9.5228 |
Jingmen | 19.6024 | 19.9613 | 20.2448 | 20.4081 | 20.9560 | 21.5850 | 22.2350 |
Xiaogan | 23.8605 | 24.7058 | 25.2081 | 25.7521 | 26.7239 | 27.9861 | 28.7937 |
Jingzhou | 27.5535 | 28.5658 | 29.4673 | 29.9263 | 30.8244 | 31.9443 | 32.7407 |
Huanggang | 26.4084 | 27.0936 | 28.2458 | 28.4311 | 29.2768 | 30.5183 | 31.3569 |
Xianning | 13.7475 | 14.4315 | 14.9280 | 15.2032 | 15.6828 | 16.2592 | 16.7669 |
Suizhou | 12.2445 | 12.6784 | 12.9589 | 13.3449 | 13.8208 | 14.2214 | 14.6367 |
Enshi | 13.0509 | 13.7939 | 14.2352 | 14.6681 | 15.1793 | 15.9643 | 16.4902 |
Xiantao | 7.0488 | 7.5067 | 7.4682 | 7.5378 | 7.6410 | 7.6515 | 7.8367 |
Qianjiang | 5.5641 | 5.9254 | 6.0643 | 6.1805 | 6.2921 | 6.6201 | 6.9085 |
Tianmen | 5.9643 | 6.2330 | 6.3437 | 6.4864 | 6.5634 | 6.7876 | 7.0186 |
Shennongjia | 0.2054 | 0.2187 | 0.2242 | 0.2300 | 0.3252 | 0.2579 | 0.2818 |
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Wang, Q.; Huang, J.; Zhou, H.; Sun, J.; Yao, M. Carbon Emission Inversion Model from Provincial to Municipal Scale Based on Nighttime Light Remote Sensing and Improved STIRPAT. Sustainability 2022, 14, 6813. https://doi.org/10.3390/su14116813
Wang Q, Huang J, Zhou H, Sun J, Yao M. Carbon Emission Inversion Model from Provincial to Municipal Scale Based on Nighttime Light Remote Sensing and Improved STIRPAT. Sustainability. 2022; 14(11):6813. https://doi.org/10.3390/su14116813
Chicago/Turabian StyleWang, Qi, Jiejun Huang, Han Zhou, Jiaqi Sun, and Mingkun Yao. 2022. "Carbon Emission Inversion Model from Provincial to Municipal Scale Based on Nighttime Light Remote Sensing and Improved STIRPAT" Sustainability 14, no. 11: 6813. https://doi.org/10.3390/su14116813