Xiao, H;
Ma, Z;
Mi, Z;
Kelsey, J;
Zheng, J;
Yin, W;
Yan, M;
(2018)
Spatio-temporal simulation of energy consumption in China's provinces based on satellite night-time light data.
Applied Energy
, 231
pp. 1070-1078.
10.1016/j.apenergy.2018.09.200.
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Abstract
Delay in publication of energy statistics prevents a timely assessment of progress towards meeting targets for energy saving and emission reduction in China. This makes it difficult to meet the requirements to rapidly monitor and evaluate energy consumption for each province. In this study, an alternative approach is provided to estimate the energy consumption by using satellite remote sensing data. We develop spatio-temporal geographically weighted regression models to simulate energy consumption of provinces in China based on the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) global stable night-time light data. The models simulate China's energy consumption accurately with the goodness of fit higher than 99%. Generally, the national average annual energy consumption is 2.8 billion tonnes of coal equivalent in China between 2000 and 2013, which is close to the actual value with errors smaller than 0.1%. From both temporal and spatial dimensions, the relative errors are smaller than 5.5% at the provincial level. Therefore, the use of satellite night-time light data provides a useful reference in monitoring and assessing provincial energy consumption in China.
| Type: | Article |
|---|---|
| Title: | Spatio-temporal simulation of energy consumption in China's provinces based on satellite night-time light data |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.apenergy.2018.09.200 |
| Publisher version: | https://doi.org/10.1016/j.apenergy.2018.09.200 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Night-time light data, Energy consumption, Spatio-temporal geographically weighted regression, China, Simulation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10059076 |
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