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Author Zhang, W.; Cui, Y.; Wang, J.; Wang, C.; Streets, D.G. url  doi
openurl 
  Title How does urbanization affect CO2 emissions of central heating systems in China? An assessment of natural gas transition policy based on nighttime light data Type Journal Article
  Year 2020 Publication Journal of Cleaner Production Abbreviated Journal Journal of Cleaner Production  
  Volume 276 Issue Pages (down) 123188  
  Keywords Remote Sensing  
  Abstract Understanding the different impacts of urbanization on sectorial carbon dioxide (CO2) emissions at different spatial scales is of great importance for the evaluation of energy transition policies and reduction of environmental inequality. However, how urbanization affects the CO2 emissions of central heating systems at high spatial resolution in China has not been fully studied before. Based on satellite-observed NPP-VIIRS nighttime light (NTL) data, we develop a 5 km × 5 km annual CO2 emission inventory for coal boilers, thermal power plants (TPPs), and natural gas boilers in China’s central heating systems for the period 2012–2017 by using the geographical and temporally weighted regression (GTWR) model. It is observed that nonurban areas generated 2–4 times the CO2 emissions of coal boilers in urban areas. The largest increments of CO2 emissions of gas boilers are observed in urban areas of the eastern (6.80 times) and central regions (2.86 times) in 2013–2014, due to the clean heating policy in the “2 + 26” cities in China. The effects of urbanization on CO2 emissions from natural gas boilers are approximately 2–3 times those of coal boilers, and the differences are largest in western cities with only minor differences in northeastern cities. Our results will aid in designing low-carbon development goals and provide micro-level information on central heating facilities in urbanized and less developed regions.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0959-6526 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 3072  
Permanent link to this record
 

 
Author Feng, D.; Yang, C.; Fu, M.; Wang, J.; Zhang, M.; Sun, Y.; Bao, W. url  doi
openurl 
  Title Do anthropogenic factors affect the improvement of vegetation cover in resource-based region? Type Journal Article
  Year 2020 Publication Journal of Cleaner Production Abbreviated Journal Journal of Cleaner Production  
  Volume in press Issue Pages (down) 122705  
  Keywords Remote Sensing  
  Abstract Vegetation plays a vital role in ecological systems and, therefore, changes in vegetation reflect the state of the ecological environment. Anthropogenic factors significantly impact vegetation cover. This study investigated variations in vegetation cover and the contribution of anthropogenic factors to these variations using the resourced-based region Shanxi Province as a case study. Theil-Sen median slope analysis and Mann-Kendall tests were used to analyze the vegetation cover change. A series of quantitative and qualitative techniques including spatial econometric modeling and residual analysis modeling were used to assess the effects of anthropogenic factors including ecological policies, urbanization, and coal mining. The results showed that overall, vegetation cover increased in the study area, but parts of the region experienced degradation. Ecological policies have been implemented in Shanxi Province to benefit vegetation cover and have resulted in large-scale human-induced greening. Urbanization had a more significant influence on vegetation cover than did natural factors. The extent of mining areas was not a decisive factor compared to natural factors; however, coal mining did create government revenue and drive economic development. In this manner, government policies could guide anthropogenic factors to create “win-win” scenarios for the environment and economic development. The results of this study promote a deeper understanding of the impact of anthropogenic factors on the ecological environment in resource-based regions.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0959-6526 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 3028  
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Author Chang, S.; Wang, J.; Zhang, F.; Niu, L.; Wang, Y. url  doi
openurl 
  Title A study of the impacts of urban expansion on vegetation primary productivity levels in the Jing-Jin-Ji region, based on nighttime light data Type Journal Article
  Year 2020 Publication Journal of Cleaner Production Abbreviated Journal Journal of Cleaner Production  
  Volume 263 Issue Pages (down) 121490  
  Keywords Remote Sensing  
  Abstract Rapid urbanization has generated enormous pressure on natural resources. This study illustrates urban expansion in the Jing-Jin-Ji region and its influence on vegetation primary productivity. Tempo-spatial correlations between a vegetation index and nighttime light intensity are discussed to assess the urbanization effect quantitatively. The results show that: (1) From 1998 to 2018, urban areas gradually expanded outward from their original conglomerations. (2) In the past 20 years, Beijing and Tianjin have developed in different ways. The surrounding satellite cities have mostly developed concentrically, although some cities in Hebei province have developed more linearly. (3) The average primary productivity of the study area in 1998, 2003, 2008, 2013, and 2018 was generally lower than that of non-urban regions of the same year. (4) During the period from 1998 to 2018, the primary productivity of vegetation in the urban built-up areas increased, and the condition of the plant improved.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0959-6526 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2925  
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Author Li, P.; Zhang, H.; Wang, X.; Song, X.; Shibasaki, R. url  doi
openurl 
  Title A spatial finer electric load estimation method based on night-light satellite image Type Journal Article
  Year 2020 Publication Energy Abbreviated Journal Energy  
  Volume 209 Issue Pages (down) 118475  
  Keywords Remote Sensing  
  Abstract As a fundamental parameter of the electric grid, obtaining spatial electric load distribution is the premise and basis for numerous studies. As a public, world-wide, and spatialized dataset, NPP/VIIRS night-light satellite image has been long used for socio-economic information estimation, including electric consumption, while little attention has been given to the electric load estimation. Additionally, most of the previous studies were performed at a large spatial scale, which could not reflect the electric information inner a city. Therefore, this paper proposes a method to estimate electric load density at a township-level spatial scale based on NPP/VIIRS night-light satellite data. Firstly, we reveal the different fitting relationships between EC (Electric Consumption)-NLS (Night-Light Sum) and EL (Electric Load)-NLI (Night-Light Intensity). Then, we validated the spatial-scale’s influence on the estimation accuracy by experiment via generating a series of simulated datasets. After working out the super-resolution night-light image with the SRCNN (Super-Resolution Convolutional Neural Network) algorithm, we established a finer spatial estimation model. By taking a monthly data of Shanghai as a case study, we validate the model we established. The result shows that estimating electric load at township-level based on night-light satellite data is feasible, and the SRCNN algorithm can improve the performance.  
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  Series Volume Series Issue Edition  
  ISSN 0360-5442 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 3068  
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Author Lu, L.; Weng, Q.; Xie, Y.; Guo, H.; Li, Q. url  doi
openurl 
  Title An assessment of global electric power consumption using the Defense Meteorological Satellite Program-Operational Linescan System nighttime light imagery Type Journal Article
  Year 2019 Publication Energy Abbreviated Journal Energy  
  Volume 189 Issue Pages (down) 116351  
  Keywords Remote Sensing; Energy; electric power consumption; Night lights  
  Abstract Industrialization and urbanization have led to a remarkable increase of electric power consumption (EPC) during the past decades. To assess the changing patterns of EPC at the global scale, this study utilized nighttime lights in conjunction with population and built-up datasets to map EPC at 1 km resolution. Firstly, the inter-calibrated nighttime light data were enhanced using the V4.0 Gridded Population Density data and the Global Human Settlement Layer. Secondly, linear models were calibrated to relate EPC to the enhanced nighttime light data; these models were then employed to estimate per-pixel EPC in 2000 and 2013. Finally, the spatiotemporal patterns of EPC between the periods were analyzed at the country, continental, and global scales. The evaluation of the EPC estimation shows a reasonable accuracy at the provincial scale with R2 of 0.8429. Over 30% of the human settlements in Asia, Europe, and North America showed apparent EPC growth. At the national scale, moderate and high EPC growth was observed in 45% of the built-up areas in East Asia. The spatial clustering patterns revealed that EPC decreased in Russia and the Western Europe. This study provides fresh insight into the spatial pattern and variations of global electric power consumption.  
  Address Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, PR China; qweng(at)indstate.edu  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication Editor  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0360-5442 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2701  
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