Urban Residential CO2 Emissions and Its Determinants: A Case Study of Central Plains Economic Region

ZHAO Jin-cai, ZHONG Zhang-qi, LU He-li, WU Le-ying, CHEN Yu-long

JOURNAL OF NATURAL RESOURCES ›› 2017, Vol. 32 ›› Issue (12) : 2100-2114.

PDF(1253 KB)
PDF(1253 KB)
JOURNAL OF NATURAL RESOURCES ›› 2017, Vol. 32 ›› Issue (12) : 2100-2114. DOI: 10.11849/zrzyxb.20161068
Resource Evaluation

Urban Residential CO2 Emissions and Its Determinants: A Case Study of Central Plains Economic Region

  • ZHAO Jin-cai1, ZHONG Zhang-qi2, LU He-li3a, WU Le-ying3b, CHEN Yu-long1
Author information +
History +

Abstract

Urban residential energy consumption and CO2 emissions have a major impact on regional carbon reduction policy. Due to lack of data and limitation of methods, spatial characteristics of urban residential CO2 emissions and its influencing factors at county scale have rarely been discussed. Therefore, taking the Central Plains Economic Region as a case, this paper used the DMSP-OLS nighttime light imageries corrected by enhanced saturation correction model to estimate the spatial distribution of carbon emissions at the 1 km resolution and analyze its influencing factors at the county scale with geographical weighted regression model. The rationality of our method was confirmed in the process of estimating carbon emissions. The linear regression model between the estimated CO2 emissions and the statistical CO2 emissions of urban residents with R2 of 0.837 1 demonstrated that the inversion model based on the nighttime light imageries has strong feasibility and suitability. Based on the carbon emissions of urban residents at 1 km resolution, we can calculate carbon emissions across administrative boundaries. In terms of spatial characteristics, CO2 emissions in the northwest Central Plains Economic Region were significantly higher than those in the southeast. Moreover, Zhengzhou District held the first place of CO2 emissions with total amount of 2.47 ×106 t, accounting for 6.58% of total CO2 emissions. However, Xingtai, Huixian and Xiangyuan were the regions with high CO2 emissions per capita, and these regions with higher carbon emissions per capita should be paid more attention. With respect to influencing factors, per capita GDP, carbon intensity, proportion of secondary industry and HDD (Heating Degree Days) all have the positive effects on urban residential carbon emissions at the county scale. However, urbanization rate presented a negative effect. Furthermore, it's important to note that CDD (Cooling Degree Days) has positive impact on urban residential carbon emissions in some cities while has negative impact in other cities. However, the maximum coefficient of its negative impact was 0.046 6, which could be ignored compared with the coefficient of positive impact. Therefore, CDD would be regarded as a positive influencing factor as a whole. Overall, the analysis of influencing factors in this paper provides an important theory basis for policy-makers to carry out more feasible policy on regional carbon emissions in the Central Plains Economic Region.

Key words

Central Plains Economic Region / CO2 emissions / nighttime light / urban resident

Cite this article

Download Citations
ZHAO Jin-cai, ZHONG Zhang-qi, LU He-li, WU Le-ying, CHEN Yu-long. Urban Residential CO2 Emissions and Its Determinants: A Case Study of Central Plains Economic Region[J]. JOURNAL OF NATURAL RESOURCES, 2017, 32(12): 2100-2114 https://doi.org/10.11849/zrzyxb.20161068

References

[1] FAN J L, LIAO H, LIANG Q M, et al. Residential carbon emission evolutions in urban-rural divided China: An end-use and behavior analysis [J]. Applied Energy, 2013, 101(1): 323-332.
[2] ZHAO X L, LI N, MA C B. Residential energy consumption in urban China: A decomposition analysis [J]. Energy Policy, 2012, 41(1): 644-653.
[3] 国家统计局. 中国统计年鉴 [M]. 北京: 中国统计出版社, 2015. [National Bureau of Statistics of China. China Statistical Yearbook. Beijing: China Statistics Press, 2015.]
[4] ZHOU N, MCNEIL M A, LEVINE M. Energy for 500 million homes: Drivers and outlook for residential energy consumption in China [R]. Environmental Energy Technologies Division, Ernest Orlando Lawrence Berkeley National Laboratory-2417E, 2009.
[5] LIU L C, WU G, WANG J N, et al. China's carbon emissions from urban and rural households during 1992-2007 [J]. Journal of Cleaner Production, 2011, 19(15): 1754-1762.
[6] 张艳, 秦耀辰, 闫卫阳, 等. 我国城市居民直接能耗的碳排放类型及影响因素 [J]. 地理研究, 2012, 31(2): 345-356. [ZHANG Y, QIN Y C, YAN W Y, et al. Urban types and impact factors on carbon emissions from direct energy consumption of residents in China. Geographical Research, 2012, 31(2): 345-356. ]
[7] 刘晔, 刘丹, 张林秀. 中国省域城镇居民碳排放驱动因素分析 [J]. 地理科学, 2016, 36(5): 691-696. [LIU Y, LIU D, ZHANG L X. Driving factors analysis of carbon emissions in Chinese provincial urban households. Scientia Geographica Sinica, 2016, 36(5): 691-696. ]
[8] 叶红, 潘玲阳, 陈峰, 等. 城市家庭能耗直接碳排放影响因素——以厦门岛区为例 [J]. 生态学报, 2010, 30(14): 3802-3811. [YE H, PAN L Y, CHEN F, et al. Direct carbon emission from urban residential energy consumption: A case study of Xiamen, China. Acta Ecologica Sinca, 2010, 30(14): 3802-3811. ]
[9] 王丹寅, 唐明方, 仁引. 丽江市家庭能耗碳排放特征及影响因素 [J]. 生态学报, 2012, 32(24): 7716-7721. [WANG D Y, TANG M F, REN Y. The characteristics and influential factors of direct carbon emissions from residential energy consumption: A case study of Lijiang city, China. Acta Ecologica Sinca, 2012, 32(24): 7716-7721. ]
[10] TSO G K F, GUAN J. A multilevel regression approach to understand effects of environment indicators and household features on residential energy consumption [J]. Energy, 2014, 66(2): 722-731.
[11] GUAN D, LIU Z, GENG Y, et al. The gigatonne gap in China's carbon dioxide inventories [J]. Nature Climate Change, 2012, 2(9): 672-675.
[12] XIE Y H, WENG Q H. Detecting urban-scale dynamics of electricity consumption at Chinese cities using time-series DMSP-OLS (Defense Meteorological Satellite Program-Operational Linescan System) nighttime light imageries [J]. Energy, 2016, 100: 177-189.
[13] 李治, 李培, 郭菊娥, 等. 城市家庭碳排放影响因素与跨城市差异分析 [J]. 中国人口·资源与环境, 2013, 23(10): 87-94. [LI Z, LI P, GUO J E, et al. Impact factors estimation and research on the differences across cities of residential CO 2 emissions in Chinese major cities. China Population, Resources and Environment, 2013, 23(10): 87-94. ]
[14] ZHU D, TAO S, WANG R, et al. Temporal and spatial trends of residential energy consumption and air pollutant emissions in China [J]. Applied Energy, 2013, 106(11): 17-24.
[15] 卓莉, 陈晋, 史培军, 等. 基于夜间灯光数据的中国人口密度模拟 [J]. 地理学报, 2005, 60(2): 266-276. [ZHUO L, CHEN J, SHI P J, et al. Modeling population density of China in 1998 based on DMSP/OLS nighttime light image. Acta Geographica Sinica, 2005, 60(2): 266-276. ]
[16] YAO Y L. Correlation of human activities with population and GDP in Chinese cities—Based on the data of DMSP-OLS [J]. International Journal of Economics and Management Engineering, 2012, 2(3): 125-128.
[17] CHAND T R K, BADARINATH K V S, ELVIDGE C D, et al. Spatial characterization of electrical power consumption patterns over India using temporal DMSP-OLS night-time satellite data [J]. International Journal of Remote Sensing, 2009, 30(3): 647-661.
[18] HE C Y, MA Q, LI T, et al. Modeling the spatiotemporal dynamics of electric power consumption in Mainland China using saturation-corrected DMSP/OLS nighttime stable light data [J]. International Journal of Digital Earth, 2014, 7(12): 993-1014.
[19] SU Y X, CHEN X Z, LI Y, et al. China's 19-year city-level carbon emissions of energy consumptions, driving forces and regionalized mitigation guidelines [J]. Renewable & Sustainable Energy Reviews, 2014, 35: 231-243.
[20] CHEN H, HUANG Y, SHEN H Z, et al. Modeling temporal variations in global residential energy consumption and pollutant emissions [J]. Applied Energy, 2015, 20(2): 327-340.
[21] MENG L N, GRAUS W, WORRELL E, et al. Estimating CO 2 (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 [J]. Energy, 2014, 71: 468-478.
[22] LU H L, LIU G F. Spatial effects of carbon dioxide emissions from residential energy consumption: A county-level study using enhanced nocturnal lighting [J]. Applied Energy, 2014, 131(9): 297-306.
[23] XU Y, DIETZENBACHER E. A structural decomposition analysis of the emissions embodied in trade [J]. Ecological Economics, 2014, 101: 10-20.
[24] 赵荣钦, 张帅, 黄贤金, 等. 中原经济区县域碳收支空间分异及碳平衡分区 [J]. 地理学报, 2014, 69(10): 1425-1437. [ZHAO R Q, ZHANG S, HUANG X J, et al. Spatial variation of carbon budget and carbon balance zoning of Central Plains Economic Region at county-level. Acta Geographica Sinica, 2014, 69(10): 1425-1437. ]
[25] 李亚婷, 潘少奇, 苗长虹. 中原经济区县际经济联系网络结构及其演化特征 [J]. 地理研究, 2014, 33(7): 1239-1250. [LI Y T, PAN S Q, MIAO C H. Structure and evolution of economic linkage network at county level in Central Plains Economic Zone. Geographical Research, 2014, 33(7): 1239-1250. ]
[26] 国家统计局. 中国能源统计年鉴 [M]. 北京: 中国统计出版社, 2012. [National Bureau of Statistics of China. China Energy Statistical Yearbook. Beijing: China Statistics Press, 2012. ]
[27] ZHANG Q. Residential energy consumption in China and its comparison with Japan, Canada, and USA [J]. Energy and Buildings, 2004, 36(12): 1217-1225.
[28] LETU H, HARA M, YAGI H, et al. Estimating energy consumption from night-time DMPS/OLS imagery after correcting for saturation effects [J]. International Journal of Remote Sensing, 2010, 31(16): 4443-4458.
[29] 卓莉, 张晓帆, 郑璟, 等. 基于EVI指数的DMSP/OLS夜间灯光数据去饱和方法 [J]. 地理学报, 2015, 70(8): 1339-1350. [ZHUO L, ZHANG X F, ZHENG J, et al. An EVI-based method to reduce saturation of DMSP/OLS nighttime light data. Acta Geographica Sinica, 2015, 70(8): 1339-1350. ]
[30] SHI K, CHEN Y, YU B, et al. Modeling spatiotemporal CO 2 (carbon dioxide) emission dynamics in China from DMSP/OLS nighttime stable light data using panel data analysis [J]. Applied Energy, 2016, 168: 523-533.
[31] WANG S J, FANG C L, MA H T, et al. Spatial differences and multi-mechanism of carbon footprint based on GWR model in provincial China [J]. Journal of Geographical Sciences, 2014, 24(4): 612-630.
[32] FAN J L, YU H, WEI Y M. Residential energy-related carbon emissions in urban and rural China during 1996-2012: From the perspective of five end-use activities [J]. Energy and Buildings, 2015, 96: 201-209.
[33] GU Z, SUN Q, WENNERSTEN R. Impact of urban residences on energy consumption and carbon emissions: An investigation in Nanjing, China [J]. Sustainable Cities and Society, 2013, 7: 52-61.
[34] 陈琦, 郑一新, 陈云波, 等. 昆明市城镇家庭消费碳排放特征及影响因素分析 [J]. 环境科学导刊, 2010, 29(5): 14-17. [CHEN Q, ZHENG Y X, CHEN Y B, et al. Analysis of characteristics and influencing factors of carbon emissions with urban family consumption in Kunming City. Environmental Sciences Survey, 2010, 29(5): 14-17. ]
[35] 冯玲, 吝涛, 赵千钧. 城镇居民生活能耗与碳排放动态特征分析 [J]. 中国人口·资源与环境, 2011, 21(5): 93-100. [FENG L, LIN T, ZHAO Q J. Analysis of the dynamic characteristics of urban household energy use and carbon emissions in China. China Population, Resources and Environment, 2011, 21(5): 93-100. ]
[36] 汪东, 汲奕君, 田丽丽, 等. 中国居民生活能源消费CO 2 排放的影响因素研究 [J]. 环境污染与防治, 2012, 34(4): 101-105. [WANG D, JI Y J, TIAN L L, et al. Study on influencing factors of CO 2 emissions from residential energy consumption in China. Environmental Pollution and Control, 2012, 34(4): 101-105. ]
[37] 张艳, 陈太政, 秦耀辰. 中国城市居民直接能耗碳排放的空间格局及影响因素 [J]. 河南大学学报(自然科学版), 2013, 43(2): 161-167. [ZHANG Y, CHEN T Z, QIN Y C. Spatial pattern and the influencing factors of CO 2 emissions from urban residents direct energy consumption. Journal of Henan University (Natural Science Edition), 2013, 43(2): 161-167. ]
[38] 曲建升, 张志强, 曾静静, 等. 西北地区居民生活碳排放结构及其影响因素 [J]. 科学通报, 2013, 58(3): 260-266. [QU J S, ZHANG Z Q, ZENG J J, et al. Household carbon emission differences and their driving factors in northwestern China. Chinese Science Bulletin, 2013, 58(3): 260-266. ]
[39] FORTHERINGHAM A S, BRUNSDON C, CHARLTON M. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships [M]. Wiley, New Work, 2002.
[40] ZHANG C, LIN Y. Panel estimation for urbanization, energy consumption and CO 2 emissions: A regional analysis in China [J]. Energy Policy, 2012, 49: 488-498.
[41] ZHU Q, PENG X. The impacts of population change on carbon emissions in China during 1978-2008 [J]. Environmental Impact Assessment Review, 2012, 36: 1-8.
[42] 万文玉, 赵雪雁, 王伟军. 中国城市居民生活能源碳排放的时空格局及影响因素分析 [J]. 环境科学学报, 2016, 36(9): 3445-3455. [WAN W Y, ZHAO X Y, WANG W J. Spatial-temporal patterns and impact factors analysis on carbon emissions from energy consumption of urbanresidents in China. Acta Scientiae Circumstantiae, 2016, 36(9): 3445-3455. ]
[43] 杜威, 樊胜岳. 城镇化进程中居民生活碳排放动态特征分析 [J]. 生态经济, 2016, 32(5): 48-52. [DU W, FAN S Y. Dynamic analysis of carbon emissions for urban and rural residents in the process ofurbanization. Ecological Economy, 2016, 32(5): 48-52. ]
[44] 刘莉娜, 曲建升, 黄雨生, 等. 中国居民生活碳排放的区域差异及影响因素分析 [J]. 自然资源学报, 2016, 31(8): 1364-1377. [LIU L N, QU J S, HUANG Y S, et al. Analyze on the spatial-temporal pattern and influence factors of China's per capita household carbon emissions. Journal of Natural Resources, 2016, 31(8): 1364-1377. ]

Funding

Science & Technology Innovation Talents in Universities of Henan Province, No.16HASTIT022; Zhejiang Provincial Social Science Planning Fund Program, No.18NDJC149YB; National Natural Science Foundation of China, No.71742001
PDF(1253 KB)

2026

Accesses

0

Citation

Detail

Sections
Recommended

/