Residential carbon dioxide emissions at the urban scale for county-level cities in China: A comparative study of nighttime light data

Abstract Carbon emissions from residential energy consumption in urban areas play a pivotal role in meeting emission targets. It is more beneficial to decomposing the emission reduction target to subnational units. This study aims to map urban residential carbon emissions at a finer spatial resolution to offer reference to disaggregating the carbon-reduction targets down to each sub-unit. With the launch of Suomi National Polar-orbiting Partnership (NPP) satellite, the day-night band of Visible Infrared Imaging Radiometer Suite (VIIRS) onboard represents a major advancement compared with Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) in terms of radiometric accuracy, spatial resolution and geometric quality. Therefore, the useful proxy was utilized to model the spatial distribution of residential carbon emissions at the urban scale for county-level cities in China. Firstly, the dynamic optimal threshold method was used to extract urban built-up areas based on NPP-VIIRS data and the statistical data. Secondly, comparison was conducted between the NPP-VIIRS data and both original and saturation-corrected DMSP-OLS data to explore the performance of NPP-VIIRS data. Finally, the spatial characteristics were analyzed in detail at the regional, provincial and prefectural level, respectively. The results show that the NPP-VIIRS data performed better in both statistic regression and spatial comparison. The spatial patterns indicated that there was an obvious north-south differentiation, especially the carbon emission density (defined as carbon emissions per grid), which was significantly higher in Northeastern China than that of other regions. The climate elements positively contributed to the increase of carbon emissions in residential sector, especially stimulating emissions related to building heating.

[1]  Bo Huang,et al.  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 , 2014 .

[2]  C. Mellander,et al.  Night-Time Light Data: A Good Proxy Measure for Economic Activity? , 2015, PloS one.

[3]  Jinhe Jiang.  China's urban residential carbon emission and energy efficiency policy , 2016 .

[4]  Jinpei Ou,et al.  Evaluation of NPP-VIIRS Nighttime Light Data for Mapping Global Fossil Fuel Combustion CO2 Emissions: A Comparison with DMSP-OLS Nighttime Light Data , 2015, PloS one.

[5]  Yi-Ming Wei,et al.  China’s carbon emissions from urban and rural households during 1992–2007 , 2011 .

[6]  Zhou De-qun,et al.  Driving forces of residential CO2 emissions in urban and rural China: An index decomposition analysis , 2010 .

[7]  Yun Chen,et al.  Modeling and mapping total freight traffic in China using NPP-VIIRS nighttime light composite data , 2015 .

[8]  Muhd Zaimi Abd Majid,et al.  A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries) , 2015 .

[9]  William Chung,et al.  A study of residential energy use in Hong Kong by decomposition analysis, 1990-2007 , 2011 .

[10]  Qingyuan Zhang,et al.  Residential energy consumption in China and its comparison with Japan, Canada, and USA , 2004 .

[11]  T. Pei,et al.  Responses of Suomi-NPP VIIRS-derived nighttime lights to socioeconomic activity in China’s cities , 2014 .

[12]  Wenbin Liu,et al.  Carbon emission performance evaluation and allocation in Chinese cities , 2018 .

[13]  Mingxing Sun,et al.  Greenhouse gas emissions estimation and ways to mitigate emissions in the Yellow River Delta High-efficient Eco-economic Zone, China , 2014 .

[14]  中華人民共和国国家統計局.  China statistical yearbook , 1988 .

[15]  Jianping Wu,et al.  Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas , 2014 .

[16]  Lu Miao.  Examining the impact factors of urban residential energy consumption and CO2 emissions in China – Evidence from city-level data , 2017 .

[17]  Guifang Liu,et al.  Spatial effects of carbon dioxide emissions from residential energy consumption: A county-level study using enhanced nocturnal lighting , 2014 .

[18]  Juan Wang,et al.  The effects of urbanization and household-related factors on residential direct CO2 emissions in Shanxi, China from 1995 to 2014: A decomposition analysis , 2017 .

[19]  Yi-Ming Wei,et al.  Residential energy-related carbon emissions in urban and rural China during 1996–2012: From the perspective of five end-use activities , 2015 .

[20]  Jianfeng Zhao,et al.  Does urbanization lead to more direct and indirect household carbon dioxide emissions? Evidence from China during 1996–2012 , 2015 .

[21]  Xia Linlin,et al.  Analyzing spatial patterns of urban carbon metabolism: A case study in Beijing, China , 2014 .

[22]  Yi-Ming Wei,et al.  Residential carbon emission evolutions in urban-rural divided China: An end-use and behavior analysis , 2013 .

[23]  Da Yan,et al.  Urban residential heating in hot summer and cold winter zones of China—Status, modeling, and scenarios to 2030 , 2016 .

[24]  Lei Yan,et al.  Comparison between the Suomi-NPP Day-Night Band and DMSP-OLS for Correlating Socio-Economic Variables at the Provincial Level in China , 2015, Remote. Sens..

[25]  Jianping Wu,et al.  Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data , 2014, Remote. Sens..

[26]  Zhihua Zhou,et al.  Heating energy saving potential from building envelope design and operation optimization in residential buildings: A case study in northern China , 2018 .

[27]  Qihao Weng,et al.  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 , 2016 .

[28]  Yaoqiu Kuang,et al.  China׳s 19-year city-level carbon emissions of energy consumptions, driving forces and regionalized mitigation guidelines , 2014 .

[29]  Feng Shi,et al.  Spatialization of electricity consumption of China using saturation-corrected DMSP-OLS data , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[30]  Weizhong Su,et al.  Urban Household Carbon Emission and Contributing Factors in the Yangtze River Delta, China , 2015, PloS one.

[31]  Zhanming Chen,et al.  Characteristics of residential energy consumption in China: Findings from a household survey , 2014 .

[32]  Yong Geng,et al.  The gigatonne gap in China’s carbon dioxide inventories , 2012 .

[33]  Qie Sun,et al.  Impact of urban residences on energy consumption and carbon emissions: An investigation in Nanjing, China , 2013 .

[34]  Guofeng Shen,et al.  Temporal and spatial trends of residential energy consumption and air pollutant emissions in China , 2013 .

[35]  Xi Li,et al.  Potential of NPP-VIIRS Nighttime Light Imagery for Modeling the Regional Economy of China , 2013, Remote. Sens..

[36]  Yi-Ming Wei,et al.  Exploring the regional characteristics of inter-provincial CO2 emissions in China: An improved fuzzy clustering analysis based on particle swarm optimization , 2012 .

[37]  P. Crompton,et al.  Energy consumption in China: past trends and future directions , 2004 .

[38]  Jing-Li Fan,et al.  The impact of urbanization on residential energy consumption in China: An aggregated and disaggregated analysis , 2017 .

[39]  Li Wang,et al.  Mode selection of China's urban heating and its potential for reducing energy consumption and CO2 emission , 2014 .

[40]  Xiaoli Zhao,et al.  Residential energy consumption in urban China: A decomposition analysis , 2012 .

[41]  Bailang Yu,et al.  Detecting spatiotemporal dynamics of global electric power consumption using DMSP-OLS nighttime stable light data , 2016 .

[42]  R. Kemp,et al.  Drivers of urban and rural residential energy consumption in China from the perspectives of climate and economic effects , 2018 .

[43]  S. Pachauri.  An analysis of cross-sectional variations in total household energy requirements in India using micro survey data , 2004 .

[44]  Shu Tao,et al.  Modeling temporal variations in global residential energy consumption and pollutant emissions , 2016 .

[45]  K. Seto,et al.  The Vegetation Adjusted NTL Urban Index: A new approach to reduce saturation and increase variation in nighttime luminosity , 2013 .

[46]  Lingyun He,et al.  Geographic sources and the structural decomposition of emissions embodied in trade by Chinese megacities: The case of Beijing, Tianjin, Shanghai, and Chongqing , 2017 .