Assessing the Spatio-Temporal Dynamics of Land Use Carbon Emissions and Multiple Driving Factors in the Guanzhong Area of Shaanxi Province
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Research Framework
2.3. Data Sources and Processing
2.4. Calculation of LCE
2.5. Theil-Sen Median Trend Analysis and Mann–Kendall Test
2.6. Spatial Auto-Correlation Analysis
2.7. Analysis of the Driving Factors Affecting Carbon Emissions
2.7.1. Correlation Analysis
2.7.2. The Multi-Scale Geographically Weighted Regression (MGWR) Model
3. Results
3.1. The Land Use Cover Change in the GZA
3.2. Spatiotemporal Changes of LCE
3.3. Spatial Autocorrelation of LCE
3.4. The Response of LCE Changes to Different Driving Factors
3.4.1. Analysis of the Driving Factors Affecting LCE Changes at the Global Dimension
3.4.2. Analysis of the Driving Factors Affecting LCE Changes at the Local Dimension
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
References
- Liu, J.; Peng, K.; Zuo, C.; Li, Q. Spatiotemporal variation of land-use carbon emissions and its implications for low carbon and ecological civilization strategies: Evidence from Xiamen-Zhangzhou-Quanzhou metropolitan circle, China. Sustain. Cities Soc. 2022, 86, 104083. [Google Scholar] [CrossRef]
- Huang, Z.; Li, S.; Gao, F.; Wang, F.; Lin, J.; Tan, Z. Evaluating the performance of LBSM data to estimate the gross domestic product of China at multiple scales: A comparison with NPP-VIIRS nighttime light data. J. Clean. Prod. 2021, 328, 129558. [Google Scholar] [CrossRef]
- Lin, B.; Zhou, Y. How do economic growth targets affect energy consumption? The role of Chinese-style fiscal decentralization. Process Saf. Environ. Prot. 2023, 169, 736–745. [Google Scholar] [CrossRef]
- Jahanger, A.; Usman, M.; Murshed, M.; Mahmood, H.; Balsalobre-Lorente, D. The linkages between natural resources, human capital, globalization, economic growth, financial development, and ecological footprint: The moderating role of technological innovations. Resour. Policy 2022, 76, 102569. [Google Scholar] [CrossRef]
- Xiang, Y.; Cui, H.; Bi, Y. The impact and channel effects of banking competition and government intervention on carbon emissions: Evidence from China. Energy Policy 2023, 175, 113476. [Google Scholar] [CrossRef]
- Gao, F.; Wu, J.; Xiao, J.; Li, X.; Liao, S.; Chen, W. Spatially explicit carbon emissions by remote sensing and social sensing. Environ. Res. 2023, 221, 115257. [Google Scholar] [CrossRef]
- Cao, M.; Tian, Y.; Wu, K.; Chen, M.; Chen, Y.; Hu, X.; Sun, Z.; Zuo, L.; Lin, J.; Luo, L.; et al. Future land-use change and its impact on terrestrial ecosystem carbon pool evolution along the Silk Road under SDG scenarios. Sci. Bull. 2023, 68, 740–749. [Google Scholar] [CrossRef]
- Chang, X.; Xing, Y.; Wang, J.; Yang, H.; Gong, W. Effects of land use and cover change (LUCC) on terrestrial carbon stocks in China between 2000 and 2018. Resour. Conserv. Recycl. 2022, 182, 106333. [Google Scholar] [CrossRef]
- Yang, Y.; Li, H. Monitoring spatiotemporal characteristics of land-use carbon emissions and their driving mechanisms in the Yellow River Delta: A grid-scale analysis. Environ. Res. 2022, 214, 114151. [Google Scholar] [CrossRef]
- Yu, Z.; Chen, L.; Tong, H.; Chen, L.; Zhang, T.; Li, L.; Yuan, L.; Xiao, J.; Wu, R.; Bai, L.; et al. Spatial correlations of land-use carbon emissions in the Yangtze River Delta region: A perspective from social network analysis. Ecol. Indic. 2022, 142, 109147. [Google Scholar] [CrossRef]
- Zhang, C.; Tian, Y.-X.; Han, M.-H. Recycling mode selection and carbon emission reduction decisions for a multi-channel closed-loop supply chain of electric vehicle power battery under cap-and-trade policy. J. Clean. Prod. 2022, 375, 134060. [Google Scholar] [CrossRef]
- Wang, B.; Liu, Q.; Wang, L.; Chen, Y.; Wang, J. A review of the port carbon emission sources and related emission reduction technical measures. Environ. Pollut. 2023, 320, 121000. [Google Scholar] [CrossRef]
- Wu, H.; Deng, K.; Dong, Z.; Meng, X.; Zhang, L.; Jiang, S.; Yang, L.; Xu, Y. Comprehensive assessment of land use carbon emissions of a coal resource-based city, China. J. Clean. Prod. 2022, 379, 134706. [Google Scholar] [CrossRef]
- Xia, C.; Zhang, J.; Zhao, J.; Xue, F.; Li, Q.; Fang, K.; Shao, Z.; Zhang, J.; Li, S.; Zhou, J. Exploring potential of urban land-use management on carbon emissions—A case of Hangzhou, China. Ecol. Indic. 2023, 146, 109902. [Google Scholar] [CrossRef]
- Yuan, J.; Li, R.; Huang, K. Driving factors of the variation of ecosystem service and the trade-off and synergistic relationships in typical karst basin. Ecol. Indic. 2022, 142, 109253. [Google Scholar] [CrossRef]
- Zhang, C.-y.; Zhao, L.; Zhang, H.; Chen, M.-n.; Fang, R.-y.; Yao, Y.; Zhang, Q.-p.; Wang, Q. Spatial-temporal characteristics of carbon emissions from land use change in Yellow River Delta region, China. Ecol. Indic. 2022, 136, 108623. [Google Scholar] [CrossRef]
- Zhang, Y.; Chen, N.; Wang, S.; Wen, M.; Chen, Z. Will carbon trading reduce spatial inequality? A spatial analysis of 200 cities in China. J. Environ. Manag. 2023, 325, 116402. [Google Scholar] [CrossRef]
- Liu, X.-J.; Jin, X.-B.; Luo, X.-L.; Zhou, Y.-K. Multi-scale variations and impact factors of carbon emission intensity in China. Sci. Total Environ. 2023, 857, 159403. [Google Scholar] [CrossRef]
- Huang, Y.; Shen, L.; Liu, H. Grey relational analysis, principal component analysis and forecasting of carbon emissions based on long short-term memory in China. J. Clean. Prod. 2019, 209, 415–423. [Google Scholar] [CrossRef]
- Luo, F.; Guo, Y.; Yao, M.; Cai, W.; Wang, M.; Wei, W. Carbon emissions and driving forces of China’s power sector: Input-output model based on the disaggregated power sector. J. Clean. Prod. 2020, 268, 121925. [Google Scholar] [CrossRef]
- Wei, W.; Zhang, X.; Cao, X.; Zhou, L.; Xie, B.; Zhou, J.; Li, C. Spatiotemporal dynamics of energy-related CO2 emissions in China based on nighttime imagery and land use data. Ecol. Indic. 2021, 131, 108132. [Google Scholar] [CrossRef]
- Zhang, D.; Yang, W.; Kang, D.; Zhang, H. Spatial-temporal characteristics and policy implication for non-grain production of cultivated land in Guanzhong Region. Land Use Policy 2023, 125, 106466. [Google Scholar] [CrossRef]
- Lin, B.; Tan, R. Sustainable development of China’s energy intensive industries: From the aspect of carbon dioxide emissions reduction. Renew. Sustain. Energy Rev. 2017, 77, 386–394. [Google Scholar] [CrossRef]
- Li, W.; Xie, S.; Wang, Y.; Huang, J.; Cheng, X. Effects of urban expansion on ecosystem health in Southwest China from a multi-perspective analysis. J. Clean. Prod. 2021, 294, 126341. [Google Scholar] [CrossRef]
- Liu, L.; Zong, H.; Zhao, E.; Chen, C.; Wang, J. Can China realize its carbon emission reduction goal in 2020: From the perspective of thermal power development. Appl. Energy 2014, 124, 199–212. [Google Scholar] [CrossRef]
- Zhao, H.; Niu, Z.; Feng, X. Factors influencing improvements in air quality in Guanzhong cities of China, and variations therein for 2014–2020. Urban Clim. 2021, 38, 100877. [Google Scholar] [CrossRef]
- Zhang, R.; Li, P.; Xu, L. Evaluation and analysis of ecological security based on the improved three-dimensional ecological footprint in Shaanxi Province, China. Ecol. Indic. 2022, 144, 109483. [Google Scholar] [CrossRef]
- Huang, S.; Yu, L.; Cai, D.; Zhu, J.; Liu, Z.; Zhang, Z.; Nie, Y.; Fraedrich, K. Driving mechanisms of urbanization: Evidence from geographical, climatic, social-economic and nighttime light data. Ecol. Indic. 2023, 148, 110046. [Google Scholar] [CrossRef]
- Yang, Y.; Li, Y.; Guo, Y. Impact of the differences in carbon footprint driving factors on carbon emission reduction of urban agglomerations given SDGs: A case study of the Guanzhong in China. Sustain. Cities Soc. 2022, 85, 104024. [Google Scholar] [CrossRef]
- Wu, S.; Zhou, W.; Cheng, P.; Xiong, X.; Zhou, J.; Feng, T.; Hou, Y.; Chen, N.; Wang, P.; Du, H.; et al. Tracing fossil fuel CO2 by 14C in maize leaves in Guanzhong Basin of China. J. Environ. Manag. 2022, 323, 116286. [Google Scholar] [CrossRef]
- Zhao, J.; Ji, G.; Yue, Y.; Lai, Z.; Chen, Y.; Yang, D.; Yang, X.; Wang, Z. Spatio-temporal dynamics of urban residential CO2 emissions and their driving forces in China using the integrated two nighttime light datasets. Appl. Energy 2019, 235, 612–624. [Google Scholar] [CrossRef]
- Liang, X.; Min, F.; Xiao, Y.; Yao, J. Temporal-spatial characteristics of energy-based carbon dioxide emissions and driving factors during 2004–2019, China. Energy 2022, 261, 124965. [Google Scholar] [CrossRef]
- Zhu, B.; Liao, J.; Shen, G. Combining time series and land cover data for analyzing spatio-temporal changes in mangrove forests: A case study of Qinglangang Nature Reserve, Hainan, China. Ecol. Indic. 2021, 131, 108135. [Google Scholar] [CrossRef]
- Wan, H.; Xie, Y.; Li, B.; Cai, Y.; Yang, Z. An integrated method to identify and evaluate the impact of hydropower development on terrestrial ecosystem. Environ. Impact Assess. Rev. 2023, 99, 107042. [Google Scholar] [CrossRef]
- Liu, Q.; Song, J.; Dai, T.; Shi, A.; Xu, J.; Wang, E. Spatio-temporal dynamic evolution of carbon emission intensity and the effectiveness of carbon emission reduction at county level based on nighttime light data. J. Clean. Prod. 2022, 362, 132301. [Google Scholar] [CrossRef]
- Ma, J.-J. Who shapes the embodied carbon dioxide emissions of interconnected power grids in China? A seasonal perspective. J. Environ. Manag. 2022, 324, 116422. [Google Scholar] [CrossRef]
- Jiang, W.; Sun, Y. Which is the more important factor of carbon emission, coal consumption or industrial structure? Energy Policy 2023, 176, 113508. [Google Scholar] [CrossRef]
- Baumann, M.; Gasparri, I.; Piquer-Rodríguez, M.; Pizarro, G.G.; Griffiths, P.; Hostert, P.; Kuemmerle, T. Carbon emissions from agricultural expansion and intensification in the Chaco. Glob. Chang. Biol. 2016, 23, 1902–1906. [Google Scholar] [CrossRef]
- Kuze, A.; Nakamura, Y.; Oda, T.; Yoshida, J.; Kikuchi, N.; Kataoka, F.; Suto, H.; Shiomi, K. Examining partial-column density retrieval of lower-tropospheric CO2 from GOSAT target observations over global megacities. Remote Sens. Environ. 2022, 273, 112966. [Google Scholar] [CrossRef]
- Ionov, D.V.; Makarova, M.V.; Kostsov, V.S.; Foka, S.C. Assessment of the NOx integral emission from the St.Petersburg megacity by means of mobile DOAS measurements combined with dispersion modelling. Atmos. Pollut. Res. 2022, 13, 101598. [Google Scholar] [CrossRef]
- Zhang, X.; Cai, Z.; Song, W.; Yang, D. Mapping the spatial-temporal changes in energy consumption-related carbon emissions in the Beijing-Tianjin-Hebei region via nighttime light data. Sustain. Cities Soc. 2023, 94, 104476. [Google Scholar] [CrossRef]
- Irfan, M.; Ullah, S.; Razzaq, A.; Cai, J.; Adebayo, T.S. Unleashing the dynamic impact of tourism industry on energy consumption, economic output, and environmental quality in China: A way forward towards environmental sustainability. J. Clean. Prod. 2023, 387, 135778. [Google Scholar] [CrossRef]
- Liang, D.; Lu, H.; Guan, Y.; Feng, L.; He, L.; Qiu, L.; Lu, J. Population density regulation may mitigate the imbalance between anthropogenic carbon emissions and vegetation carbon sequestration. Sustain. Cities Soc. 2023, 92, 104502. [Google Scholar] [CrossRef]
- Sinha, S.K.; Padalia, H.; Patel, N.R.; Chauhan, P. Modelling sun-induced fluorescence for improved evaluation of forest carbon flux (GPP): Case study of tropical deciduous forest, India. Ecol. Model. 2021, 449, 109552. [Google Scholar] [CrossRef]
- Liu, C.; Sun, W.; Li, P.; Zhang, L.; Li, M. Differential characteristics of carbon emission efficiency and coordinated emission reduction pathways under different stages of economic development: Evidence from the Yangtze River Delta, China. J. Environ. Manag. 2023, 330, 117018. [Google Scholar] [CrossRef] [PubMed]
- Zhu, H.; Zhang, D.; Goh, H.H.; Wang, S.; Ahmad, T.; Mao, D.; Liu, T.; Zhao, H.; Wu, T. Future data center energy-conservation and emission-reduction technologies in the context of smart and low-carbon city construction. Sustain. Cities Soc. 2023, 89, 104322. [Google Scholar] [CrossRef]
- Li, Y.; Xiong, J.; Ma, W.; Ma, H.; Yuan, Z. Environmental decoupling, eco-efficiency improvement, and industrial network optimization: Insights from 44 sectors in China. J. Clean. Prod. 2022, 376, 134374. [Google Scholar] [CrossRef]
- Wang, Y.; Niu, Y.; Li, M.; Yu, Q.; Chen, W. Spatial structure and carbon emission of urban agglomerations: Spatiotemporal characteristics and driving forces. Sustain. Cities Soc. 2022, 78, 103600. [Google Scholar] [CrossRef]
- Fang, G.; Gao, Z.; Tian, L.; Fu, M. What drives urban carbon emission efficiency?—Spatial analysis based on nighttime light data. Appl. Energy 2022, 312, 118772. [Google Scholar] [CrossRef]
Data Type | Spatial Resolution/Formula | Source |
---|---|---|
Land Use/Cover | 300 m | European Space Agency (http://maps.elie.ucl.ac.be/CCI/viewer/, accessed on 1 June 2022) |
Carbon Emission | 1 km | Odiac—Fossil fuel CO2 emission data product (http://db.cger.nies.go.jp/dataset/ODIAC/data_policy.html, accessed on 1 May 2022) |
Nighttime Light | 1 km | NOAA website NGDC Data Center (https://www.ngdc.noaa.gov/eog/download.html, accessed on 4 August 2022) |
Population Grid | 1 km | Open Spatial Demographic Data and Research—WorldPop (https://www.worldpop.org/project/list, accessed on 13 August 2022) |
Gross Primary Productivity | 1 km | National Tibetan Plateau/Third Pole Environment Data Center (https://data.tpdc.ac.cn/zh-hans/data/582663f5-3be7-4f26-bc45-b56a3c4fc3b7, accessed on 13 September 2022) |
Digital Elevation Model (DEM) | 90 m | China Geospatial Data Cloud (https://www.gscloud.cn/sources/accessdata/305?pid=302, accessed on 10 August 2022) |
Land Use Type | Carbon Emission Factor |
---|---|
Cropland | 0.422 |
Grassland | −0.021 |
Forestland | −0.644 |
Wetland | −0.0006132 |
Unused land | −0.005 |
Water | −0.253 |
Change Type | Definition |
---|---|
Significantly increasing | s > 0, |z| > 1.96 |
Slightly significantly increasing | s > 0, |z| < 1.96 |
No-significantly changing | s = 0 |
Slightly significantly decreasing | s < 0, |z| > 1.96 |
Significantly decreasing | s < 0, |z| < 1.96 |
2000 | Cropland | Forestland | Grassland | Wetland | Construction Land | Water | Unused Land | Total in 2019 | Transfer in 2019 | |
---|---|---|---|---|---|---|---|---|---|---|
2019 | ||||||||||
Cropland | 33,275.07 | 30.51 | 48.96 | 0 | 0 | 0 | 0 | 33,354.54 | 79.47 | |
Forestland | 84.6 | 23,160.6 | 105.57 | 0 | 0 | 0 | 0 | 23,350.77 | 190.17 | |
Grassland | 99.27 | 209.52 | 3696.84 | 0.18 | 0 | 0 | 0 | 4005.81 | 308.97 | |
Wetland | 0 | 0 | 0 | 93.78 | 0 | 0 | 0 | 93.78 | 0 | |
Construction land | 2218.05 | 1.62 | 101.43 | 2.88 | 440.19 | 0.99 | 1.26 | 2766.42 | 2326.23 | |
Water | 0 | 0 | 0 | 0 | 0 | 104.58 | 0 | 104.58 | 0 | |
Total in 2000 | 35,676.99 | 23,402.25 | 3952.8 | 96.84 | 440.19 | 105.57 | 1.26 | 63675.9 | ||
Transfer out in 2000 | 2401.92 | 241.65 | 255.96 | 3.06 | 0 | 0.99 | 1.26 |
Year | Moran’s I | Z Value | p Value | Year | Moran’s I | Z Value | p Value |
---|---|---|---|---|---|---|---|
2000 | 0.1117 | 3.9563 | 0.001 | 2010 | 0.1244 | 4.4225 | 0.002 |
2001 | 0.1073 | 3.7998 | 0.001 | 2011 | 0.1566 | 5.6595 | 0.003 |
2002 | 0.1191 | 4.2129 | 0.001 | 2012 | 0.1566 | 5.6581 | 0.005 |
2003 | 0.1174 | 4.1606 | 0.001 | 2013 | 0.1565 | 5.6545 | 0.004 |
2004 | 0.1442 | 5.1692 | 0.002 | 2014 | 0.1565 | 5.6574 | 0.004 |
2005 | 0.1227 | 4.4282 | 0.002 | 2015 | 0.1562 | 5.6471 | 0.005 |
2006 | 0.1153 | 4.1100 | 0.002 | 2016 | 0.1570 | 5.6746 | 0.004 |
2007 | 0.1147 | 4.0877 | 0.002 | 2017 | 0.1755 | 6.3689 | 0.004 |
2008 | 0.1151 | 4.1009 | 0.002 | 2018 | 0.1413 | 5.2622 | 0.004 |
2009 | 0.1153 | 4.1091 | 0.002 | 2019 | 0.1413 | 5.2618 | 0.005 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Wang, Y.; Liu, Y.; Wang, Z.; Zhang, Y.; Fang, B.; Jiang, S.; Yang, Y.; Wen, Z.; Zhang, W.; Zhang, Z.; et al. Assessing the Spatio-Temporal Dynamics of Land Use Carbon Emissions and Multiple Driving Factors in the Guanzhong Area of Shaanxi Province. Sustainability 2023, 15, 7730. https://doi.org/10.3390/su15097730
Wang Y, Liu Y, Wang Z, Zhang Y, Fang B, Jiang S, Yang Y, Wen Z, Zhang W, Zhang Z, et al. Assessing the Spatio-Temporal Dynamics of Land Use Carbon Emissions and Multiple Driving Factors in the Guanzhong Area of Shaanxi Province. Sustainability. 2023; 15(9):7730. https://doi.org/10.3390/su15097730
Chicago/Turabian StyleWang, Yali, Yangyang Liu, Zijun Wang, Yan Zhang, Bo Fang, Shengnan Jiang, Yijia Yang, Zhongming Wen, Wei Zhang, Zhixin Zhang, and et al. 2023. "Assessing the Spatio-Temporal Dynamics of Land Use Carbon Emissions and Multiple Driving Factors in the Guanzhong Area of Shaanxi Province" Sustainability 15, no. 9: 7730. https://doi.org/10.3390/su15097730