Journal of Geographical Sciences ›› 2022, Vol. 32 ›› Issue (10): 1886-1910.doi: 10.1007/s11442-022-2028-z
• Research Articles • Previous Articles Next Articles
ZHANG Li1,2,3(), LEI Jun1,2,*(
), WANG Changjian4, WANG Fei5, GENG Zhifei6, ZHOU Xiaoli7
Received:
2021-11-01
Accepted:
2022-03-14
Online:
2022-10-25
Published:
2022-12-25
Contact:
LEI Jun
E-mail:toby.zl@163.com;leijun@ms.xjb.ac.cn
About author:
Zhang Li (1988-), Senior Engineer, specialized in urban and regional planning, regional sustainable development. E-mail: toby.zl@163.com
Supported by:
ZHANG Li, LEI Jun, WANG Changjian, WANG Fei, GENG Zhifei, ZHOU Xiaoli. Spatio-temporal variations and influencing factors of energy-related carbon emissions for Xinjiang cities in China based on time-series nighttime light data[J].Journal of Geographical Sciences, 2022, 32(10): 1886-1910.
Table 1
Carbon emission factor for different types of fuels
Energy category | Coal | Coke | Crude | Gasoline | Kerosene | Diesel | Fuel oil | Natural gas | Electricity |
---|---|---|---|---|---|---|---|---|---|
Standard coal coefficient Bi (tce/t) | 0.7143 | 0.9714 | 1.4285 | 1.4714 | 1.4714 | 1.4571 | 1.4286 | 1.33 | 0.1229 |
Carbon emission coefficients Ki (t/tce) | 0.7559 | 0.855 | 0.5857 | 0.5538 | 0.5714 | 0.5921 | 0.6185 | 0.4483 | 0.272 |
Table 2
Detection results for influencing factors
Influencing factor | Index | q | |||
---|---|---|---|---|---|
1992 | 2000 | 2010 | 2020 | ||
Economic growth | Gross Domestic Product (GDP) | 0.654*** | 0.685*** | 0.746*** | 0.431* |
The secondary industry output value (SV) | 0.564*** | 0.612*** | 0.672*** | 0.440*** | |
Industrial structure | The ratio of secondary industry output value to GDP (SP) | 0.211** | 0.352*** | 0.215*** | 0.133** |
Population size | Population size (POP) | 0.517** | 0.420 | 0.462* | 0.398* |
Urban population size (UP) | 0.529** | 0.571*** | 0.562*** | 0.391* | |
Urbanization level | Urbanization rate (UR) | 0.115* | 0.313*** | 0.258*** | 0.197** |
Energy consumption intensity | Energy consumption intensity per unit of GDP (EIG) | 0.354*** | 0.205* | 0.268 | 0.378* |
Table 3
Detection results of interaction for influencing factors
Interacting factors | 1992 | Interacting factors | 2000 | Interacting factors | 2010 | Interacting factors | 2020 |
---|---|---|---|---|---|---|---|
GDP∩EIG | 0.990 | SV∩EIG | 0.934 | GDP∩EIG | 0.942 | SV∩EIG | 0.869 |
POP∩EIG | 0.905 | GDP∩EIG | 0.918 | SV∩EIG | 0.857 | GDP∩EIG | 0.860 |
SV∩EIG | 0.879 | POP∩SP | 0.782 | SV∩UR | 0.830 | UP∩ EIG | 0.789 |
UP∩ EIG | 0.866 | UP∩SP | 0.777 | GDP∩UR | 0.816 | POP∩EIG | 0.777 |
POP∩GDP | 0.798 | POP∩SV | 0.768 | SV∩UP | 0.813 | UR∩ EIG | 0.616 |
POP∩SP | 0.714 | UP∩ EIG | 0.768 | POP∩SV | 0.811 | UR∩ POP | 0.602 |
GDP∩SP | 0.709 | SV∩UP | 0.746 | GDP∩SP | 0.794 | POP∩SP | 0.583 |
SV∩SP | 0.701 | SV∩UR | 0.733 | SV∩SP | 0.793 | UR∩ SV | 0.582 |
GDP∩POP | 0.674 | GDP∩SP | 0.749 | GDP∩POP | 0.774 | SV∩UP | 0.544 |
GDP∩SV | 0.673 | GDP∩POP | 0.724 | UR∩POP | 0.757 | SV∩POP | 0.543 |
[1] |
Chen J, Gao M, Cheng S et al., 2020. County-level CO2 emissions and sequestration in China during 1997-2017. Scientific Data, 7(1): 1-12.
doi: 10.1038/s41597-019-0340-y |
[2] |
Cui C, Shan Y, Liu J et al., 2019. CO2 emissions and their spatial patterns of Xinjiang cities in China. Applied Energy, 252: 113473.
doi: 10.1016/j.apenergy.2019.113473 |
[3] |
Elvidge C D, Baugh K, Zhizhin M et al., 2017. VIIRS night-time lights. International Journal of Remote Sensing, 38(21): 5860-5879.
doi: 10.1080/01431161.2017.1342050 |
[4] |
Friedlingstein P, O'Sullivan M, Jones M W et al., 2020. Global Carbon Budget 2020. Earth System Science Data, 12(4): 3269-3340.
doi: 10.5194/essd-12-3269-2020 |
[5] |
Guan D, Hubacek K, Weber C L et al., 2008. The drivers of Chinese CO2 emissions from 1980 to 2030. Global Environmental Change, 18(4): 626-634.
doi: 10.1016/j.gloenvcha.2008.08.001 |
[6] |
Han M, Yao Q, Lao J et al., 2020. China’s intra- and inter-national carbon emission transfers by province: A nested network perspective. Science China Earth Sciences, 63(6): 852-864.
doi: 10.1007/s11430-019-9598-3 |
[7] |
Han M, Yao Q, Liu W et al., 2018. Tracking embodied carbon flows in the Belt and Road regions. Journal of Geographical Sciences, 28(9): 1263-1274.
doi: 10.1007/s11442-018-1524-7 |
[8] |
Hausfather Z, Peters G P, 2020. Emissions: The ‘business as usual’ story is misleading. Nature, 577(7792): 618-620.
doi: 10.1038/d41586-020-00177-3 |
[9] |
Li X, Li D, Xu H et al., 2017. Intercalibration between DMSP/OLS and VIIRS night-time light images to evaluate city light dynamics of Syria’s major human settlement during Syrian Civil War. International Journal of Remote Sensing, 38(21): 5934-5951.
doi: 10.1080/01431161.2017.1331476 |
[10] |
Liu Y, Yan B, Zhou Y, 2016. Urbanization, economic growth, and carbon dioxide emissions in China: A panel cointegration and causality analysis. Journal of Geographical Sciences, 26(2): 131-152.
doi: 10.1007/s11442-016-1259-2 |
[11] |
Liu Z, Guan D, Moore S et al., 2015a. Climate policy: Steps to China’s carbon peak. Nature, 522(7556): 279-281.
doi: 10.1038/522279a |
[12] |
Liu Z, Guan D, Wei W et al., 2015b. Reduced carbon emission estimates from fossil fuel combustion and cement production in China. Nature, 524(7565): 335-338.
doi: 10.1038/nature14677 |
[13] |
Liu Z, He C, Zhang Q et al., 2012. Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008. Landscape and Urban Planning, 106(1): 62-72.
doi: 10.1016/j.landurbplan.2012.02.013 |
[14] |
Luo W, Jasiewicz J, Stepinski T et al., 2016. Spatial association between dissection density and environmental factors over the entire conterminous United States. Geophysical Research Letters, 43(2): 692-700.
doi: 10.1002/2015GL066941 |
[15] |
Ma J, Guo J, Ahmad S et al., 2020. Constructing a new inter-calibration method for DMSP-OLS and NPP-VIIRS nighttime light. Remote Sensing, 12(6): 937.
doi: 10.3390/rs12060937 |
[16] |
Meng L, Graus W, Worrell E et al., 2014. Estimating CO2 (carbon dioxide) emissions at urban scales by DMSP/OLS (Defense Meteorological Satellite Program’s Operational Linescan System) nighttime light imagery: Methodological challenges and a case study for China. Energy, 71: 468-478.
doi: 10.1016/j.energy.2014.04.103 |
[17] |
Mi Z, Meng J, Guan D et al., 2017. Chinese CO2 emission flows have reversed since the global financial crisis. Nature Communications, 8(1): 1-10.
doi: 10.1038/s41467-016-0009-6 |
[18] |
Shan Y, Fang S, Cai B et al., 2021. Chinese cities exhibit varying degrees of decoupling of economic growth and CO2 emissions between 2005 and 2015. One Earth, 4(1): 124-134.
doi: 10.1016/j.oneear.2020.12.004 |
[19] |
Shan Y, Liu J, Liu Z et al., 2019. An emissions-socioeconomic inventory of Chinese cities. Scientific Data, 6: 190027.
doi: 10.1038/sdata.2019.27 |
[20] |
Shi K, Chen Y, Yu B et al., 2016. Detecting spatiotemporal dynamics of global electric power consumption using DMSP-OLS nighttime stable light data. Applied Energy, 184: 450-463.
doi: 10.1016/j.apenergy.2016.10.032 |
[21] |
Su W, Liu Y, Wang S et al., 2018. Regional inequality, spatial spillover effects, and the factors influencing city-level energy-related carbon emissions in China. Journal of Geographical Sciences, 28(4): 495-513.
doi: 10.1007/s11442-018-1486-9 |
[22] |
Su Y, Chen X, Li Y et al., 2014. China’s 19-year city-level carbon emissions of energy consumptions, driving forces and regionalized mitigation guidelines. Renewable and Sustainable Energy Reviews, 35: 231-243.
doi: 10.1016/j.rser.2014.04.015 |
[23] | Su Y, Chen X, Wang C et al., 2015. A new method for extracting built-up urban areas using DMSP-OLS nighttime stable lights: A case study in the Pearl River Delta, southern China. Giscience & Remote Sensing, 52(2): 218-238. |
[24] |
Wang C, Miao Z, Chen X et al., 2021. Factors affecting changes of greenhouse gas emissions in Belt and Road countries. Renewable and Sustainable Energy Reviews, 147: 111220.
doi: 10.1016/j.rser.2021.111220 |
[25] |
Wang C, Wang F, Zhang H et al., 2014. China’s carbon trading scheme is a priority. Environmental Science & Technology, 48(23): 13559-13559.
doi: 10.1021/es505198t |
[26] |
Wang C, Wang F, Zhang X et al., 2017a. Examining the driving factors of energy related carbon emissions using the extended STIRPAT model based on IPAT identity in Xinjiang. Renewable and Sustainable Energy Reviews, 67: 51-61.
doi: 10.1016/j.rser.2016.09.006 |
[27] |
Wang C, Wang F, Zhang X et al., 2017b. Influencing mechanism of energy-related carbon emissions in Xinjiang based on the input-output and structural decomposition analysis. Journal of Geographical Sciences, 27(3): 365-384.
doi: 10.1007/s11442-017-1382-8 |
[28] | Wang C, Zhang H, Wang F et al., 2019a. Slash local emissions to protect Tibetan Plateau. Nature, 566(7745): 455. |
[29] |
Wang J, Lu F, 2021. Modeling the electricity consumption by combining land use types and landscape patterns with nighttime light imagery. Energy, 234: 121305.
doi: 10.1016/j.energy.2021.121305 |
[30] |
Wang J F, Li X H, Christakos G et al., 2010. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region, China. International Journal of Geographical Information Science, 24(1): 107-127.
doi: 10.1080/13658810802443457 |
[31] |
Wang J F, Zhang T L, Fu B J, 2016. A measure of spatial stratified heterogeneity. Ecological Indicators, 67: 250-256.
doi: 10.1016/j.ecolind.2016.02.052 |
[32] |
Wang S, Gao S, Huang Y et al., 2020a. Spatiotemporal evolution of urban carbon emission performance in China and prediction of future trends. Journal of Geographical Sciences, 30(5): 757-774.
doi: 10.1007/s11442-020-1754-3 |
[33] |
Wang S, Huang Y, Zhou Y, 2019b. Spatial spillover effect and driving forces of carbon emission intensity at the city level in China. Journal of Geographical Sciences, 29(2): 231-252.
doi: 10.1007/s11442-019-1594-1 |
[34] |
Wang S, Liu X, 2017. China’s city-level energy-related CO2 emissions: Spatiotemporal patterns and driving forces. Applied Energy, 200: 204-214.
doi: 10.1016/j.apenergy.2017.05.085 |
[35] |
Wang Y, Li G, 2017. Mapping urban CO2 emissions using DMSP/OLS ‘city lights’ satellite data in China. Environment and Planning A: Economy and Space, 49(2): 248-251.
doi: 10.1177/0308518X16656374 |
[36] |
Wang Y, Liu Z, He C et al., 2020b. Quantifying urbanization levels on the Tibetan Plateau with high-resolution nighttime light data. Geography and Sustainability, 1(3): 233-244.
doi: 10.1016/j.geosus.2020.08.004 |
[37] |
Wang Y, Wang S, Li G et al., 2017c. Identifying the determinants of housing prices in China using spatial regression and the geographical detector technique. Applied Geography, 79: 26-36.
doi: 10.1016/j.apgeog.2016.12.003 |
[38] |
Wei Y, Liu H, Song W et al., 2014. Normalization of time series DMSP-OLS nighttime light images for urban growth analysis with pseudo invariant features. Landscape and Urban Planning, 128: 1-13.
doi: 10.1016/j.landurbplan.2014.04.015 |
[39] |
Welsby D, Price J, Pye S et al., 2021. Unextractable fossil fuels in a 1.5 °C world. Nature, 597(7875): 230-234.
doi: 10.1038/s41586-021-03821-8 |
[40] |
Wu K, Wang X, 2019. Aligning pixel values of DMSP and VIIRS nighttime light images to evaluate urban dynamics. Remote Sensing, 11(12): 1463.
doi: 10.3390/rs11121463 |
[41] |
Yang Y, Wu J, Wang Y et al., 2021a. Quantifying spatiotemporal patterns of shrinking cities in urbanizing China: A novel approach based on time-series nighttime light data. Cities, 118: 103346.
doi: 10.1016/j.cities.2021.103346 |
[42] |
Yang Z, Chen Y, Guo G et al., 2021b. Using nighttime light data to identify the structure of polycentric cities and evaluate urban centers. Science of the Total Environment, 780: 146586.
doi: 10.1016/j.scitotenv.2021.146586 |
[43] |
Ye Y, Wu K, Xie Y et al., 2019. How firm heterogeneity affects foreign direct investment location choice: Micro-evidence from new foreign manufacturing firms in the Pearl River Delta. Applied Geography, 106: 11-21.
doi: 10.1016/j.apgeog.2019.03.005 |
[44] |
Zhang Y, Pan J, Zhang Y et al., 2021. Spatial-temporal characteristics and decoupling effects of China’s carbon footprint based on multi-source data. Journal of Geographical Sciences, 31(3): 327-349.
doi: 10.1007/s11442-021-1839-7 |
[1] | LIU Wangbao, LIU Lan. Housing tenure and type choices of urban migrants in China [J]. Journal of Geographical Sciences, 2023, 33(9): 1832-1850. |
[2] | WANG Yazhu, DUAN Xuejun, WANG Lei, WANG Lingqing. Evolution of rural multifunction and its natural and socioeconomic factors in coastal China [J]. Journal of Geographical Sciences, 2023, 33(9): 1791-1814. |
[3] | YU Yingjie, LYU Lachang. Spatial pattern of knowledge innovation function among Chinese cities and its influencing factors [J]. Journal of Geographical Sciences, 2023, 33(6): 1161-1184. |
[4] | SONG Yongyong, XIA Siyou, XUE Dongqian, MA Beibei, LIU Xianfeng. Spatio-temporal differences and influencing factors of carbon emission equity in the Loess Plateau based on major function-oriented zones [J]. Journal of Geographical Sciences, 2023, 33(6): 1245-1270. |
[5] | MA Dujuan, WU Xiaodan, WANG Jingping, MU Cuicui. The spatiotemporal scale effect on vegetation interannual trend estimates based on satellite products over Qinghai-Tibet Plateau [J]. Journal of Geographical Sciences, 2023, 33(5): 924-944. |
[6] | KE Xinli, ZHANG Ying, ZHOU Ting. Spatio-temporal characteristics and typical patterns of eco-efficiency of cultivated land use in the Yangtze River Economic Belt, China [J]. Journal of Geographical Sciences, 2023, 33(2): 357-372. |
[7] | ZHANG Hua, CHEN Mingxing, LIANG Chen. Urbanization of county in China: Spatial patterns and influencing factors [J]. Journal of Geographical Sciences, 2022, 32(7): 1241-1260. |
[8] | CHEN Hongjin, LIU Lin, ZHANG Zhengyong, LIU Ya, TIAN Hao, KANG Ziwei, WANG Tongxia, ZHANG Xueying. Spatio-temporal correlation between human activity intensity and land surface temperature on the north slope of Tianshan Mountains [J]. Journal of Geographical Sciences, 2022, 32(10): 1935-1955. |
[9] | YAO Junqiang, MAO Weiyi, CHEN Jing, DILINUER Tuoliewubieke. Recent signal and impact of wet-to-dry climatic shift in Xinjiang, China [J]. Journal of Geographical Sciences, 2021, 31(9): 1283-1298. |
[10] | CUI Yaoping, LI Nan, FU Yiming, CHEN Liangyu. Carbon neutrality and mitigating contribution of terrestrial carbon sink on anthropogenic climate warming in China, the United States, Russia and Canada [J]. Journal of Geographical Sciences, 2021, 31(7): 925-937. |
[11] | LUO Xiang, AO Xinhe, ZHANG Zuo, WAN Qing, LIU Xingjian. Spatiotemporal variations of cultivated land use efficiency in the Yangtze River Economic Belt based on carbon emission constraints [J]. Journal of Geographical Sciences, 2020, 30(4): 535-552. |
[12] | CAI Jianming, MA Enpu, LIN Jing, LIAO Liuwen, HAN Yan. Exploring global food security pattern from the perspective of spatio-temporal evolution [J]. Journal of Geographical Sciences, 2020, 30(2): 179-196. |
[13] | Huan WANG, Jiangbo GAO, Wenjuan HOU. Quantitative attribution analysis of soil erosion in different geomorphological types in karst areas: Based on the geodetector method [J]. Journal of Geographical Sciences, 2019, 29(2): 271-286. |
[14] | Shaojian WANG, Jieyu WANG, Yang WANG. Effect of land prices on the spatial differentiation of housing prices: Evidence from cross-county analyses in China [J]. Journal of Geographical Sciences, 2018, 28(6): 725-740. |
[15] | Xueyan ZHAO, Weijun WANG, Wenyu WAN. Regional differences in the health status of Chinese residents: 2003-2013 [J]. Journal of Geographical Sciences, 2018, 28(6): 741-758. |
|