Global 1 km × 1 km gridded revised real gross domestic product and electricity consumption during 1992–2019 based on calibrated nighttime light data
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M. Song | Shulei Cheng | M. Gao | Jiandong Chen | W. Hou | Xin Liu | Yu Liu
[1] Burak Güneralp,et al. Industrial electricity consumption and economic growth: A spatio-temporal analysis across prefecture-level cities in China from 1999 to 2014 , 2021 .
[2] Yu-Feng Zhao,et al. Forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach , 2021, Energy.
[3] Kun Guo,et al. Research on the economic development pattern of Chinese counties based on electricity consumption , 2020 .
[4] M. Song,et al. County-level CO2 emissions and sequestration in China during 1997–2017 , 2020, Scientific data.
[5] C. Jin,et al. A novel classification regression method for gridded electric power consumption estimation in China , 2020, Scientific reports.
[6] Yuyu Zhou,et al. An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration , 2020, Earth System Science Data.
[7] Lu Qiao,et al. Modeling and spatio-temporal analysis of city-level carbon emissions based on nighttime light satellite imagery , 2020, Applied Energy.
[8] Yuyu Zhou,et al. A harmonized global nighttime light dataset 1992–2018 , 2020, Scientific Data.
[9] M. Mildner,et al. Re-epithelialization and immune cell behaviour in an ex vivo human skin model , 2020, Scientific Reports.
[10] Célia Michotey,et al. The GenTree Dendroecological Collection, tree-ring and wood density data from seven tree species across Europe , 2020, Scientific Data.
[11] Paul Sutton,et al. Global Mapping of GDP at 1 km2 Using VIIRS Nighttime Satellite Imagery , 2019, ISPRS Int. J. Geo Inf..
[12] Y. Shang,et al. Multiscale analysis on spatiotemporal dynamics of energy consumption CO2 emissions in China: Utilizing the integrated of DMSP-OLS and NPP-VIIRS nighttime light datasets. , 2019, The Science of the total environment.
[13] M. Santamouris,et al. Socio-economic status and residential energy consumption: A latent variable approach , 2019, Energy and Buildings.
[14] Yingyao Hu,et al. Illuminating Economic Growth , 2019, Journal of Econometrics.
[15] Zhizhu Lai,et al. Spatio-temporal dynamics of urban residential CO2 emissions and their driving forces in China using the integrated two nighttime light datasets , 2019, Applied Energy.
[16] Guy Stecklov,et al. Can incentives improve survey data quality in developing countries?: results from a field experiment in India , 2018, Journal of the Royal Statistical Society. Series A,.
[17] Juan A. Mendoza,et al. On measuring economic growth from outer space: a single country approach , 2018, Empirical Economics.
[18] Matti Kummu,et al. Gridded global datasets for Gross Domestic Product and Human Development Index over 1990–2015 , 2018, Scientific Data.
[19] Bui Hoang Ngoc,et al. The Relationship between Foreign Direct Investment, Electricity Consumption and Economic Growth in Vietnam , 2018 .
[20] J. Boussemart,et al. Worldwide carbon shadow prices during 1990–2011 , 2017 .
[21] Zhengyan Shao,et al. On electricity consumption and economic growth in China , 2017 .
[22] Guofeng Cao,et al. Forecasting China’s GDP at the pixel level using nighttime lights time series and population images , 2017 .
[23] Bailang Yu,et al. Detecting spatiotemporal dynamics of global electric power consumption using DMSP-OLS nighttime stable light data , 2016 .
[24] Shuwen Niu,et al. Does electricity consumption improve residential living status in less developed regions? An empirical analysis using the quantile regression approach , 2016 .
[25] N. Fantom,et al. The World Bank's Classification of Countries by Income , 2016 .
[26] A. Glassman,et al. The Political Economy of Bad Data: Evidence from African Survey and Administrative Statistics , 2015 .
[27] Ola Hall,et al. Monitoring economic development from space : using nighttime light and land cover data to measure economic growth , 2015 .
[28] 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 .
[29] Yaoqiu Kuang,et al. China׳s 19-year city-level carbon emissions of energy consumptions, driving forces and regionalized mitigation guidelines , 2014 .
[30] Feng Shi,et al. Spatialization of electricity consumption of China using saturation-corrected DMSP-OLS data , 2014, Int. J. Appl. Earth Obs. Geoinformation.
[31] T. Pei,et al. Responses of Suomi-NPP VIIRS-derived nighttime lights to socioeconomic activity in China’s cities , 2014 .
[32] Carsten A. Holz. The Quality of China's GDP Statistics , 2013 .
[33] C. Tang,et al. Sectoral analysis of the causal relationship between electricity consumption and real output in Pakistan , 2013 .
[34] Donald W. Hillger,et al. First-Light Imagery from Suomi NPP VIIRS , 2013 .
[35] Zhifeng Liu,et al. Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008 , 2012 .
[36] J. Henderson,et al. Measuring Economic Growth from Outer Space , 2009, The American economic review.
[37] W. Nordhaus,et al. Using luminosity data as a proxy for economic statistics , 2011, Proceedings of the National Academy of Sciences.
[38] Yadong Ning,et al. Changes in industrial electricity consumption in china from 1998 to 2007 , 2010 .
[39] Kelvin K. W. Yau,et al. Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks , 2007 .
[40] C. Elvidge,et al. Night-time lights of the world: 1994–1995 , 2001 .
[41] Thomas G. Rawski. What is happening to China's GDP statistics? , 2001 .
[42] V. Jain,et al. Modelling of electrical energy consumption in Delhi , 1999 .
[43] C. Elvidge,et al. Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption , 1997 .