Spatiotemporal Dynamic Evolution and Its Driving Mechanism of Carbon Emissions in Hunan Province in the Last 20 Years
Abstract
:1. Introduction
2. Methodology and Data
2.1. Overview of the Study Region
2.2. Data Sources
2.3. Calculation Method of Carbon Emissions
- (1)
- Carbon emissions from land use
- (2)
- Carbon emissions from construction land
- (3)
- Carbon emission of each city
3. Results and ESTDA Analysis
3.1. Time Characteristics of Carbon Emission
3.2. Global Spatial Correlation of Carbon Emissions
3.3. The Migration of Gravity Center and SDE Analysis of Carbon Emissions
3.4. Analysis of LISA Time Path
3.4.1. Geometric Feature Analysis
3.4.2. Average Moving Direction Analysis
3.5. Analysis of LISA Spatiotemporal Transition
4. Analysis of Influencing Factors
4.1. Data Verification
4.2. Spatiotemporal Heterogeneity of Carbon Emission Influencing Factors
5. Conclusions
6. Policy Suggestions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Jiang, Z.-R.; Jin, H.-H.; Wang, C.-J.; Ye, S.-L.; Huang, Y.-H. Measurement of traffic carbon emissions and pattern of efficiency in the Yangtze river economic belt (1985~2016). Environ. Sci. 2020, 41, 2972–2980. [Google Scholar] [CrossRef]
- Wang, X.-F.; Yuan, X.-Z.; Chen, H.; He, Y.-X.; Luo, Z.; Liu, L.; He, Z.-Y. Review of CO2 and CH4 emissions from rivers. Environ. Sci. 2017, 38, 5352–5366. [Google Scholar] [CrossRef]
- Liu, T.-T.; Wang, X.-F.; Yuan, X.-Z.; Gong, X.-J.; Hou, C.-L. Spatial-temporal characteristics and driving factors of greenhouse gas emissions from rivers in a rapidly urbanizing area. Environ. Sci. 2019, 40, 2827–2839. [Google Scholar] [CrossRef]
- Wang, S.-J.; Tian, S.-S.; Cai, Q.-N.; Wu, H.; Wu, C. Driving factors and carbon transfer of industrial carbon emissions in Guangdong province under the background of industrial transfer. Geogr. Res. 2021, 40, 2606–2622. [Google Scholar]
- Du, H.-B.; Wei, W.; Zhang, X.-Y.; Ji, X. Spatiotemporal evolution and influencing factors of energy-related carbon emissions in the Yellow River Basin: Based on the DMSP/OLS and NPP/VIIRS nighttime light data. Geogr. Res. 2021, 40, 2051–2065. [Google Scholar]
- Zhang, W.-J.; Yan, S.-J.; Zhang, J.; Jiang, Y.; Deng, A. Win-win strategy for national food security and agriculture dual-carbon goals. Sci. Agric. Sin. 2021, 54, 3892–3902. [Google Scholar]
- Shi, L.-Y.; Xu, T.; Gao, L.-J.; Han, L.W.; Li, Q.Y. Low carbon community research under sustainability context. Acta Ecol. Sin. 2018, 38, 5170–5177. [Google Scholar]
- Li, J.; Li, S. Energy investment, economic growth and carbon emissions in China-Empirical analysis based on spatial Durbin model. Energy Policy 2020, 140, 111425. [Google Scholar] [CrossRef]
- Su, B.; Ang, B.W. Input–output analysis of CO2 emissions embodied in trade: The effects of spatial aggregation. Ecol. Econ. 2010, 70, 10–18. [Google Scholar] [CrossRef]
- Wu, F.; Huang, N.; Zhang, Q.; Qiao, Z.; Zhan, N. Multi-province comparison and typology of China’s CO2 emission: A spatial–temporal decomposition approach. Energy 2020, 190, 116312. [Google Scholar] [CrossRef]
- Cheng, Z.; Li, L.; Liu, J.; Zhang, H. Total-factor carbon emission efficiency of China’s provincial industrial sector and its dynamic evolution. Renew. Sustain. Energy Rev. 2018, 94, 330–339. [Google Scholar] [CrossRef]
- Wang, Q.; Hang, Y.; Zhou, P.; Wang, Y. Decoupling and attribution analysis of industrial carbon emissions in Taiwan. Energy 2016, 113, 728–738. [Google Scholar] [CrossRef]
- Li, L.; Hong, X.; Peng, K. A spatial panel analysis of carbon emissions, economic growth and high-technology industry in China. Struct. Chang. Econ. Dyn. 2019, 49, 83–92. [Google Scholar] [CrossRef]
- Zhang, F.; Deng, X.; Phillips, F.; Fang, C.; Wang, C. Impacts of industrial structure and technical progress on carbon emission intensity: Evidence from 281 cities in China. Technol. Forecast. Soc. Chang. 2020, 154, 119949. [Google Scholar] [CrossRef]
- Chen, L.; Xu, L.; Yang, Z. Inequality of industrial carbon emissions of the urban agglomeration and its peripheral cities: A case in the Pearl River Delta, China. Renew. Sustain. Energy Rev. 2019, 109, 438–447. [Google Scholar] [CrossRef]
- Li, H.; Zhao, Y.; Wang, S.; Liu, Y. Spatial-temporal characteristics and drivers of the regional residential CO2 emissions in China during 2000–2017. J. Clean. Prod. 2020, 276, 124116. [Google Scholar] [CrossRef]
- Liu, H.; Song, Y. Financial development and carbon emissions in China since the recent world financial crisis: Evidence from a spatial-temporal analysis and a spatial Durbin model. Sci. Total Environ. 2020, 715, 136771. [Google Scholar] [CrossRef]
- Zhang, F.; Zhang, Q.; Li, F.; Fu, H.; Yang, X. The spatial correlation pattern of water footprint intensity and its driving factors in China. J. Nat. Resour. 2019, 34, 934–944. [Google Scholar] [CrossRef]
- Wu, G.; Sun, X.; Yu, F.; Yang, L. Spatial correlation pattern and influencing factors of China’s crop production carbon productivity. Chin. J. Popul. Resour. Environ. 2020, 30, 46–57. [Google Scholar] [CrossRef]
- Chen, L.; Xu, L.; Cai, Y.; Yang, Z. Spatiotemporal patterns of industrial carbon emissions at the city level. Resour. Conserv. Recycl. 2021, 169, 105499. [Google Scholar] [CrossRef]
- Zhao, G.; Zhao, G.; Chen, L.; Sun, H. Research on spatial and temporal evolution of carbon emission intensity and its transition mechanism in China. J. Popul. Resour. Environ. 2017, 27, 84–93. [Google Scholar]
- Lv, Q.; Liu, H.-B. Spatial and temporal evolution characteristics of carbon emissions at county scale in Beijing–Tianjin–Hebei—Based on DMSP/OLS nighttime light data. J. Beijing Inst. Technol. 2019, 21, 41–50. [Google Scholar]
- Li, J.-B.; Huang, X.-J.; Chuai, X.-W.; Sun, S. Spatiotemporal characteristics and influencing factors of carbon emission efficiency in the Yangtze River Delta region. Resour. Environ. Yangtze Basin 2020, 29, 1486–1496. [Google Scholar]
- Guo, Y.; Cao, X.-Z.; Wei, W.-D.; Zeng, G. The impact of regional integration in the Yangtze River Delta on urban carbon emissions. Geogr. Res. 2022, 41, 181–192. [Google Scholar]
- Zhang, L.-G.; Wang, Z.-Q.; Li, B.-Q. Carbon effect accounting and analysis of land consolidation projects in Hubei Province. J. Nat. Resour. 2018, 33, 2006–2019. [Google Scholar]
- Yang, G.-Q.; Zhu, W.-R.; Wen, Y.; Lin, Y. Spatial differentiation in the intensity and efficiency of carbon emission from land use in Guangdong province in past two decades. Ecol. Environ. Sci. 2019, 28, 332–340. [Google Scholar]
- Wei, Y.-R.; Chen, S.-L. Spatial correlation and carbon balance zoning of land use carbon emissions in Fujian Province. Acta Ecol. Sin. 2021, 41, 5814–5824. [Google Scholar]
- Li, X.; Li, Y.-P. A multi-scenario ensemble simulation and environmental input-output model for identifying optimal pollutant-and CO2-emission mitigation scheme of Guangdong province. J. Clean. Prod. 2020, 262, 121413. [Google Scholar] [CrossRef]
- Quan, C.; Cheng, X.; Yu, S.; Ye, X. Analysis on the influencing factors of carbon emission in China’s logistics industry based on LMDI method. Sci. Total Environ. 2020, 734, 138473. [Google Scholar] [CrossRef]
- Shahbaz, M.; Loganathan, N.; Muzaffar, A.T.; Ahmed, K.; Jabran, M.A. How urbanization affects CO2, emissions in Malaysia? The application of STIRPAT model. Renew. Sustain. Energy Rev. 2016, 57, 83–93. [Google Scholar] [CrossRef]
- Yang, L.; Zhang, X.-L. Study on Measurement and Influencing Factors of Household Carbon Emission in Jiangsu Province: An Empirical Analysis Based on GTWR Model. Ecol. Econ. 2020, 36, 31–38. [Google Scholar]
- Huang, B.; Wu, B.; Barry, M. Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. Int. J. Geogr. Inf. Sci. 2010, 24, 383–401. [Google Scholar] [CrossRef]
- Wang, Z.-F.; Du, Y.-Y. Spatial-temporal Differences and Influencing Factors of Carbon Emission Efficiency in Hunan Province Based on SBM-DEA Model. Sci. Geogr. Sin. 2019, 39, 797–806. [Google Scholar]
- Zhou, S.-Y.; Xi, F.-M.; Yin, Y. Accounting and drivers of carbon emission from cultivated land utilization in Northeast China. Chin. J. Appl. Ecol. 2021, 32, 3865–3871. [Google Scholar]
- Cai, Z.-C.; Kang, G.-D.; Tsuruta, H.; Mosier, A. Estimate of CH4 Emissions from Year-Round Flooded Rice Fields During Rice Growing Season in China. Pedosphere 2005, 15, 66–71. [Google Scholar] [CrossRef]
- He, Y. Research on China’s Climate and Terrestrial Ecosystem Carbon Cycle; China Meteorological Press: Beijing, China, 2006.
- Shi, H.-X.; Mu, X.-M.; Zhang, Y.-L.; Lu, M.-Q. Effects of different land use patterns on carbon emission in Guangyuan city of Sichuan Province. Bull. Soil Water Conserv. 2012, 32, 101–106. [Google Scholar]
- Xiao, H.-Y.; Yuan, X.-Z.; Li, B.; Yan, W. Effects of land use change on the carbon emission—A case study of Chongqing. J. Chongqing Norm. Univ. 2012, 29, 38–42+115. [Google Scholar]
- Fang, J.-Y.; Guo, Z.-D.; Piao, S.-L.; Chen, A. Terrestrial vegetation carbon sink in China from 1981–2000. Sci. China Ser. D Earth Sci. 2007, 6, 804–812. [Google Scholar]
- Li, L.; Dong, J.; Xu, L.; Zhang, J.-F. Spatial variation of land use carbon budget and carbon compensation zoning in functional areas: A case study of Wuhan urban agglomeration. J. Nat. Resour. 2019, 34, 1003–1015. [Google Scholar]
- Lai, L. Carbon Emission Effect of Land Use in China; Nanjing University: Nanjing, China, 2010. [Google Scholar]
- Duan, X.-N.; Wang, X.-K.; Lu, F.; Ouyang, Z.-Y. Carbon sequestration and its potential by wetland ecosystems in China. Acta Ecol. Sin. 2008, 463–469. [Google Scholar]
- Fan, J.-S.; Yu, X.-F.; Zhou, L. Carbon emission efficiency growth of land use structure and its spatial correlation: A case study of Nanjing city. Geogr. Res. 2018, 37, 2177–2192. [Google Scholar]
- Liu, X.-Y.; Wang, Y.-F.; Song, Q.-J.; Qi, Y. Study on Carbon Emissions Sources in China’s Urbanization Process. China Popul. Resour. Environ. 2015, 25, 61–66. [Google Scholar]
- Yu, G.-H.; Liu, D.; Liao, X.-Y.; Wang, T.; Tian, Q.-J.; Liao, Y. Quantitative Research on Regional Ecological Compensation from the Perspective of Carbon-Neutral: The Case of Hunan Province, China. Sustainability 2017, 9, 1095. [Google Scholar] [CrossRef]
- National Bureau of Statistics. China Energy Statistics Yearbook 2021; China Statistics Press: Beijing, China, 2021.
- Eggleston, H.-S.; Buendia, L.; Miwa, K.; Ngara, T.; Tanabe, K. 2006 IPCC Guidelines for National Greenhouse Gas Inventories; Preparation of the National Greenhouse Gas Inventories Plan: Tokyo, Japan, 2006. [Google Scholar]
- Bai, B.; Li, X.-C.; Liu, Y.-F.; Zhang, Y. Preliminary study on CO2 industrial point sources and their distribution in China. Chin. J. Rock Mech. Eng. 2006, 25, 2918–2923. [Google Scholar]
- Zhao, J.-N.; Wei, D.-Q. Carbon Emission Factors for Cement Plants in China. Resour. Sci. 2013, 35, 800–808. [Google Scholar]
- Guidelines for the Preparation of Provincial Greenhouse Gas Inventories; Climate Department of National Development and Reform Commission of China: Beijing, China, 2011.
- Xiao, H.-J.; Kuang, Y.-Q.; Huang, N.-S.; Zhu, Z.-Y.; Liu, Y.; Tang, J.-L. Variation of the carbon budget in Guangzhou during its rapid industrialization course. Ecol. Environ. 2006, 15, 1209–1215. [Google Scholar]
- Gao, C.-C.; Liu, X.-Z.; Li, C.-K.; Zhang, Y.; Yu, G.-H.; Qing, S.; Tian, Y. Spatiotemporal dynamics of carbon emissions by energy consumption in China from 1995 to 2014. Prog. Geogr. 2016, 35, 747–757. [Google Scholar]
- Cui, P.-P.; Zhao, Y.; Zhang, L.-J.; Xia, S.; Xu, X. Spatiotemporal evolution and driving mechanism of per capita indirect carbon emissions based on different demand levels from urban residents’ consumption in China. Acta Ecol. Sin. 2020, 40, 1424–1435. [Google Scholar] [CrossRef]
- Rey, S.-J. Spatial empirics for economic growth and convergence. Geogr. Anal. 2001, 33, 195–214. [Google Scholar] [CrossRef]
- Fang, G.-C.; Gao, Z.-Y.; Tian, L.-X.; Fu, M. What drives urban carbon emission efficiency?—Spatial analysis based on nighttime light data. Appl. Energ. 2022, 312, 118772. [Google Scholar] [CrossRef]
- Rey, S.-J.; Janikas, M.V. STARS: Space-time analysis of regional systems. Geogr. Anal. 2006, 38, 67–86. [Google Scholar] [CrossRef]
- Ye, X.-Y.; Rey, S.-J. A framework for exploratory space-time analysis of economic data. Ann. Reg. Sci. 2013, 50, 315–339. [Google Scholar] [CrossRef]
- Rey, S.-J. Space–time patterns of rank concordance: Local indicators of mobility association with application to spatial income inequality dynamics. Ann. Am. Assoc. Geogr. 2016, 106, 788–803. [Google Scholar] [CrossRef]
- Anselin, L. Local indicators of spatial association—LISA. Geogr. Anal. 1995, 27, 93–115. [Google Scholar] [CrossRef]
- Rey, S.-J.; Murray, A.T.; Anselin, L. Visualizing regional income distribution dynamics. Lett. Spat. Resour. Sci. 2011, 4, 81–90. [Google Scholar] [CrossRef]
- Ye, X.-Y.; Carroll, M.-C. Exploratory space-time analysis of local economic development. Appl. Geogr. 2011, 31, 1049–1058. [Google Scholar] [CrossRef]
- Shi, Y.; Matsunaga, T.; Yamaguchi, Y.; Zhao, A.; Li, Z.; Gu, X. Long-term trends and spatial patterns of PM2.5-induced premature mortality in South and Southeast Asia from 1999 to 2014. Sci. Total Environ. 2018, 631, 1504–1514. [Google Scholar] [CrossRef]
Land Use Type | Carbon Emission Coefficients (t·hm−2) | Reference |
---|---|---|
cropland | 0.497 | [34,35,36] |
forest | −0.581 | [37,38] |
grassland | −0.021 | [39,40] |
water | −0.253 | [41,42] |
unused land | −0.005 | [27,40,43,44] |
Energy Type | Standard Coal Conversion Coefficient | Carbon Emission Coefficient (in C, t·t−1) | Energy Type | Standard Coal Conversion Coefficient | Carbon Emission Coefficient (in C, t·t−1) | Energy Type | Standard Coal Conversion Coefficient | Carbon Emission Coefficient (in C, t·t−1) |
---|---|---|---|---|---|---|---|---|
Raw coal (kg·kg−1) | 0.7143 | 0.7559 | Other gas (kg·m−3) | 0.1786 | 0.3548 | Fuel oil (kg·kg−1) | 1.4286 | 0.6185 |
Clean coal (kg·kg−1) | 0.9000 | 0.7559 | Clean Other coking products (kg·kg−1) | 1.1000 | 0.6449 | Liquefied petroleum gas (kg·kg−1) | 1.7143 | 0.5042 |
Other washed coal (kg·kg−1) | 0.2857 | 0.7559 | Crude (kg·kg−1) | 1.4286 | 0.5857 | Other petroleum products (kg·kg−1) | 1.4286 | 0.586 |
Coke (kg·kg−1) | 0.9714 | 0.855 | Gasoline (kg·kg−1) | 1.4714 | 0.5538 | natural gas (kg·m−3) | 1.2143 | 0.4483 |
Coke over gas (kg·m−3) | 0.5714 | 0.3548 | Kerosene (kg·kg−1) | 1.4714 | 0.5714 | Heat power (kg·MJ−1) | 0.0341 | 0.26 |
Blast furnace gas | 0.1286 | 0.4602 | Diesel oil (kg·kg−1) | 1.4571 | 0.5921 | Electricity (kg·(kW·h)−1) | 0.1229 | 2.5255 |
Year | Coordinates of Gravity Center | Shift of Gravity Center | Long Axis/km | Minor Axis/km | Azimuth Angle/° | |||
---|---|---|---|---|---|---|---|---|
Longitude | Latitude | Direction | Distance/km | Speed/km·a−1 | ||||
2000 | 112°23′59″ E | 27°43′18″ N | —— | —— | —— | 143.01 | 199.24 | 173.39 |
2005 | 112°25′43″ E | 27°47′36″ N | Northeast | 9.55 | 1.91 | 146.50 | 185.12 | 175.96 |
2010 | 112°18′6″ E | 27°43′13″ N | Southwest | 16.85 | 3.37 | 144.06 | 184.62 | 168.33 |
2015 | 112°15′57″ E | 27°48′38″ N | Northwest | 11.99 | 2.4 | 142.84 | 180.76 | 0.23 |
2020 | 112°21′51″ E | 27°49′21″ N | Northeast | 11.05 | 2.21 | 136.58 | 184.39 | 3.74 |
Period | t~t+1 | HH | LH | LL | HL | Type | Number | Proportion | SF | SC |
---|---|---|---|---|---|---|---|---|---|---|
2000~2005 | HH | 50 | 0 | 0 | 50 | I | 7 | 50.00 | 50 | 50 |
LH | 33.33 | 33.33 | 33.33 | 0 | II | 2 | 14.29 | |||
LL | 0 | 25 | 75 | 0 | III | 5 | 35.71 | |||
HL | 33.33 | 0 | 33.33 | 33.33 | IV | 0 | 0.00 | |||
2005~2010 | HH | 75 | 25 | 0 | 0 | I | 11 | 78.57 | 21.43 | 78.57 |
LH | 0 | 100 | 0 | 0 | II | 2 | 14.29 | |||
LL | 0 | 20 | 60 | 20 | III | 1 | 7.14 | |||
HL | 0 | 0 | 0 | 100 | IV | 0 | 0.00 | |||
2010~2015 | HH | 66.67 | 33.33 | 0 | 0 | I | 12 | 85.71 | 14.28 | 85.71 |
LH | 0 | 75 | 25 | 0 | II | 1 | 7.14 | |||
LL | 0 | 0 | 100 | 0 | III | 1 | 7.14 | |||
HL | 0 | 0 | 0 | 100 | IV | 0 | 0.00 | |||
2015~2020 | HH | 100 | 0 | 0 | 0 | I | 13 | 92.86 | 7.14 | 92.86 |
LH | 25 | 75 | 0 | 0 | II | 1 | 7.14 | |||
LL | 0 | 0 | 100 | 0 | III | 0 | 0.00 | |||
HL | 0 | 0 | 0 | 100 | IV | 0 | 0.00 |
Variables | Description | Unit | Average | Standard Deviation | Minimum | Maximum | VIF |
---|---|---|---|---|---|---|---|
Population (POP) | Total population (T-POP) | Ten thousand people | 476.00 | 174.66 | 147.81 | 1006.39 | 2.745 |
Economic development level (PGDP) | GDP per capita | RMB/person | 28,022.08 | 26,053.41 | 2534 | 123,296.81 | 4.309 |
Industrial structure (IS) | Secondary industry/tertiated industry | % | 100 | 36 | 23 | 178 | 1.456 |
Technological progress (EI) | Energy consumption per unit GDP | t/10,000RMB | 0.94 | 0.64 | 0.04 | 2.88 | 2.739 |
Per capita energy consumption (PEC) | Per capita energy consumption | t/person | 1.33 | 1.41 | 0.16 | 7.03 | 2.031 |
Land use (CON) | Built-up area | km2 | 288.35 | 155.82 | 34.86 | 932.88 | 5.460 |
Ecological environment (ES) | Forest coverage | % | 60 | 10 | 40 | 77 | 1.206 |
Model Parameter | Bandwidth | Sigma | Residual Squares | AICc | R2 | Adjusted R2 | Spatiotemporal Distance Ratio |
---|---|---|---|---|---|---|---|
Value | 0.150444 | 81.2702 | 462,339 | 954.456 | 0.969799 | 0.966389 | 4.89398 |
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Gu, H.; Liu, Y.; Xia, H.; Tan, X.; Zeng, Y.; Zhao, X. Spatiotemporal Dynamic Evolution and Its Driving Mechanism of Carbon Emissions in Hunan Province in the Last 20 Years. Int. J. Environ. Res. Public Health 2023, 20, 3062. https://doi.org/10.3390/ijerph20043062
Gu H, Liu Y, Xia H, Tan X, Zeng Y, Zhao X. Spatiotemporal Dynamic Evolution and Its Driving Mechanism of Carbon Emissions in Hunan Province in the Last 20 Years. International Journal of Environmental Research and Public Health. 2023; 20(4):3062. https://doi.org/10.3390/ijerph20043062
Chicago/Turabian StyleGu, Huangling, Yan Liu, Hao Xia, Xiao Tan, Yanjia Zeng, and Xianchao Zhao. 2023. "Spatiotemporal Dynamic Evolution and Its Driving Mechanism of Carbon Emissions in Hunan Province in the Last 20 Years" International Journal of Environmental Research and Public Health 20, no. 4: 3062. https://doi.org/10.3390/ijerph20043062