Spatio-Temporal Variations and Influencing Factors of Country-Level Carbon Emissions for Northeast China Based on VIIRS Nighttime Lighting Data

This paper constructs a county-level carbon emission inversion model in Northeast China. We first fit the nighttime light data of the Visible Infrared Imaging Radiometer Suite (VIIRS) with local energy consumption statistics and carbon emissions data. We analyze the temporal and spatial characteristics of county-level energy-related carbon emissions in Northeast China from 2012 to 2020. At the same time, we use the geographic detector method to analyze the impact of various socio-economic factors on county carbon emissions under the single effect and interaction. The main results are as follows: (1) The county-level carbon emission model in Northeast China is relatively more accurate. The regression coefficient is 0.1217 and the determination coefficient R2 of the regression equation is 0.7722. More than 80% of the provinces have an error of less than 25%, meeting the estimation accuracy requirements. (2) From 2012 to 2020, the carbon emissions of county-level towns in Northeast China showed a trend of increasing first and then decreasing from 461.1159 million tons in 2012 to 405.752 million tons in 2020. It reached a peak of 486.325 million tons in 2014. (3) The regions with higher carbon emission growth rates are concentrated in the northern and coastal areas of Northeast China. The areas with low carbon emission growth rates are mainly distributed in some underdeveloped areas in the south and north in Northeast China. (4) Under the effect of the single factor urbanization rate, the added values of the secondary industry and public finance income have higher explanatory power to regional emissions. These factors promote the increase of county carbon emissions. When fiscal revenue and expenditure and the added value of the secondary industry and per capita GDP interact with the urbanization rate, respectively, the explanatory power of these factors on regional carbon emissions will be enhanced and the promotion of carbon emissions will be strengthened. The research results are helpful for exploring the changing rules and influencing factors of county carbon emissions in Northeast China and for providing data support for low-carbon development and decision making in Northeast China.

[1]  Hong Li,et al.  Spatiotemporal Evolution of County-Level Land Use Structure in the Context of Urban Shrinkage: Evidence from Northeast China , 2022, Land.

[2]  Kaifang Shi,et al.  Exploring the effect of urban spatial development pattern on carbon dioxide emissions in China: A socioeconomic density distribution approach based on remotely sensed nighttime light data , 2022, Comput. Environ. Urban Syst..

[3]  Zhifei Geng,et al.  Assessing e-commerce impacts on China’s CO2 emissions: testing the CKC hypothesis , 2021, Environmental Science and Pollution Research.

[4]  Wei Liu,et al.  Understanding patterns and multilevel influencing factors of small town shrinkage in Northeast China , 2021 .

[5]  Jay Taneja,et al.  Annual Time Series of Global VIIRS Nighttime Lights Derived from Monthly Averages: 2012 to 2019 , 2021, Remote. Sens..

[6]  L. Zanna,et al.  Heat and carbon coupling reveals ocean warming due to circulation changes , 2020, Nature.

[7]  Ying Long,et al.  Shrinking Cities in China: The Other Facet of Urbanization , 2019, The Urban Book Series.

[8]  Ying Long,et al.  Shrinking Cities in China: The Overall Profile and Paradox in Planning , 2019, The Urban Book Series.

[9]  Bin Yang,et al.  Generating Global Products of LAI and FPAR From SNPP-VIIRS Data: Theoretical Background and Implementation , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[10]  C. Elvidge,et al.  VIIRS night-time lights , 2017, Remote Sensing of Night-time Light.

[11]  Jiansheng Wu,et al.  The Uncertainty of Nighttime Light Data in Estimating Carbon Dioxide Emissions in China: A Comparison between DMSP-OLS and NPP-VIIRS , 2017, Remote. Sens..

[12]  Yan Wang,et al.  Mapping urban CO2 emissions using DMSP/OLS ‘city lights’ satellite data in China , 2017 .

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

[14]  Xiaodong Zhu,et al.  Does urbanization lead to more carbon emission? Evidence from a panel of BRICS countries , 2016 .

[15]  Yun Chen,et al.  Modeling spatiotemporal CO2 (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis , 2016 .

[16]  Ning Zhang,et al.  Carbon emissions reductions and technology gaps in the world's factory, 1990–2012 , 2016 .

[17]  Nicklas Forsell,et al.  Greenhouse gas emissions from current and enhanced policies of China until 2030: Can emissions peak before 2030? , 2016 .

[18]  Chuanglin Fang,et al.  Changing urban forms and carbon dioxide emissions in China: A case study of 30 provincial capital cities , 2015 .

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

[20]  Xiao-wei Song,et al.  Carbon Emission of Guangxi’s Major Industries and Measures for Low-carbon Economic Development , 2015 .

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

[22]  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 .

[23]  C. Choong,et al.  Investigation of the environmental Kuznets curve for carbon emissions in Malaysia: Do foreign direct investment and trade matter? , 2014 .

[24]  Manfred Lenzen,et al.  Drivers of change in Brazil’s carbon dioxide emissions , 2013, Climatic Change.

[25]  Xia Li,et al.  Quantifying the relationship between urban forms and carbon emissions using panel data analysis , 2013, Landscape Ecology.

[26]  C. Justice,et al.  Land and cryosphere products from Suomi NPP VIIRS: Overview and status , 2013, Journal of geophysical research. Atmospheres : JGR.

[27]  Tadhg O' Mahony,et al.  Decomposition of Ireland's carbon emissions from 1990 to 2010: An extended Kaya identity , 2013 .

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

[29]  Bangzhu Zhu,et al.  Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China , 2013 .

[30]  Jung Wan Lee,et al.  Investigating the influence of tourism on economic growth and carbon emissions: Evidence from panel analysis of the European Union , 2013 .

[31]  Jeroen Buysse,et al.  Energy consumption, carbon emissions and economic growth nexus in Bangladesh: Cointegration and dynamic causality analysis , 2012 .

[32]  Hailin Mu,et al.  Analysis of regional difference on impact factors of China’s energy – Related CO2 emissions , 2012 .

[33]  Shamil Maksyutov,et al.  A very high-resolution (1 km×1 km) global fossil fuel CO2 emission inventory derived using a point source database and satellite observations of nighttime lights , 2011 .

[34]  P. Sutton,et al.  Creating a Global Grid of Distributed Fossil Fuel CO 2 Emissions from Nighttime Satellite Imagery , 2010 .

[35]  Peter J. Rayner,et al.  Regional variations in spatial structure of nightlights, population density and fossil-fuel CO2 emissions , 2010 .

[36]  Chonggang Xu,et al.  Uncertainties in the response of a forest landscape to global climatic change , 2009 .

[37]  Ranjith Perera,et al.  Understanding energy consumption pattern of households in different urban development forms: A comparative study in Bandung City, Indonesia , 2008 .

[38]  J. Rogan,et al.  Remote sensing technology for mapping and monitoring land-cover and land-use change , 2004 .

[39]  R. Lucas,et al.  A review of remote sensing technology in support of the Kyoto Protocol , 2003 .

[40]  C. Elvidge,et al.  Mapping City Lights With Nighttime Data from the DMSP Operational Linescan System , 1997 .