| Records |
| Author |
Liu, H.; Ma, L.; Xu, L. |
| Title |
Estimating spatiotemporal dynamics of county-level fossil fuel consumption based on integrated nighttime light data |
Type |
Journal Article |
| Year |
2020 |
Publication |
Journal of Cleaner Production |
Abbreviated Journal |
Journal of Cleaner Production |
| Volume |
in press |
Issue |
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Pages  |
123427 |
| Keywords |
Remote Sensing |
| Abstract |
Rapid urbanization has resulted in increased fossil fuel consumption in China, which is closely related to regional sustainable development and CO2 emissions. This study proposed an improved model to estimate fossil fuel consumption on the county scale in Shandong to understand the dynamics of fossil fuel consumption. This improved model is beneficial for establishing energy-saving and CO2 emission reduction policies for both Shandong and other regions in China. Because of the lack of data on a county scale, this study applied panel data analysis from 1992 to 2017 by integrating the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and NPP-VIIRS (Suomi National Polar-Orbiting Partnership satellite with the Visible Infrared Imaging Radiometer Suit) nighttime light data. Results showed that (1) The new method performed better than previous methods on estimating fossil fuel consumption; (2) Fossil fuel consumption has been rapidly growing in all cities and most districts and counties, mainly concentrated in west and east central Shandong; (3) Regions with high fossil fuel consumption shifted from west central Shandong to east central on the city level, and expanded from the east central to the entire central region on the county level; (4) In regions with a lower level of development, fossil fuel consumption tended to grow faster. These results will improve the understanding of fossil fuel consumption over the long term on county scales, and are beneficial for creating policies on regional energy savings and CO2 emission reduction. |
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0959-6526 |
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GFZ @ kyba @ |
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3060 |
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| Author |
Ye, Y.; Xue, X.; Huang, L.; Gan, M.; Tong, C.; Wang, K.; Deng, J. |
| Title |
A new perspective to map the supply and demand of artificial night light based on Loujia1-01 and urban big data |
Type |
Journal Article |
| Year |
2020 |
Publication |
Journal of Cleaner Production |
Abbreviated Journal |
Journal of Cleaner Production |
| Volume |
276 |
Issue |
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Pages |
123244 |
| Keywords |
Remote Sensing |
| Abstract |
The notable increase in artificial night light (ANL) induced by the rapid urbanization process has been widely studied, but a deep understanding of the supply and demand status of ANL is still lacking. This paper attempts to map the supply and demand of ANL from the human perspective by using advanced Loujia1-01 nighttime imagery and social media derived population density (PD) data, which provides a new tool for light regulation in urban management. The bivariate clustering based k-means algorithm and template matching technique are integrated to delineate mismatch regions at the block scale to further analyze the underlying reason for unbalanced status. The results showed that the high supply but low demand (HSLD) ANL status was the leading component in the mismatch regions, occupying more than 650,000 ha and mainly occurring in the city center. The HSLD proportion was considerable in terms of public services (44%), commercial (40%), industrial (39%), transportation (56%), and green space areas (53%). Moreover, the HSLD area notably increased 946 ha over time from 18:00 to 22:00. The measurements for validation obtained by field investigation showed highly linear relationship with ANL (R2 = 0.75) and PD (R2 = 0.62), and the mapping results were consistent with the actual conditions. This study reveals the highly unbalanced ANL status, and appeals to planners for the establishment of optimal lighting regulations to alleviate disruptive effects. |
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0959-6526 |
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GFZ @ kyba @ |
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3070 |
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Zhang, W.; Cui, Y.; Wang, J.; Wang, C.; Streets, D.G. |
| 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 |
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Pages |
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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0959-6526 |
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GFZ @ kyba @ |
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3072 |
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Feng, D.; Yang, C.; Fu, M.; Wang, J.; Zhang, M.; Sun, Y.; Bao, W. |
| 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 |
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Pages |
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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0959-6526 |
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GFZ @ kyba @ |
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3028 |
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| Author |
Chen, H.; Zhang, X.; Wu, R.; Cai, T. |
| Title |
Revisiting the environmental Kuznets curve for city-level CO2 emissions: based on corrected NPP-VIIRS nighttime light data in China |
Type |
Journal Article |
| Year |
2020 |
Publication |
Journal of Cleaner Production |
Abbreviated Journal |
Journal of Cleaner Production |
| Volume |
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Issue |
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Pages |
121575 |
| Keywords |
Remore Sensing; China; carbon emissions; CO2 emissions; night lights; NPP-VIIRS; VIIRS-DNB; VIIRS-DNB; Kuznets curve |
| Abstract |
With the increasing trend of global warming, the Chinese government faces tremendous pressure to reduce CO2 emissions. The purpose of this study is to accurately measure CO2 emissions at the city scale in China and examine the environmental Kuznets curve, thereby providing a reference for decision-making. Corrected NPP-VIIRS nighttime light data were used to accurately estimate carbon dioxide emissions at the provincial and city scales in China. Then, based on the STRIPAT model, 291 cities in China were used to verify the environmental Kuznets curve. Our results show that on the provincial scale, the R2 between the estimated value and the statistical value of carbon dioxide reaches 0.85. Western cities in China emit more CO2, as do economically developed cities and industry- and mining-dominated cities. There are two CO2 emission hot spots in the north and one cold spot in the south. It was found that the environmental Kuznets curve on the city scale exists. This study has practical value in utilizing NPP-VIIRS data for the estimation of city CO2 emissions. The results also have academic value for determining factors that contribute to carbon dioxide emissions and can provide a reference for relevant decision makers. This study could be considered the first to simulate CO2 emissions at the provincial and city levels in China based on a NPP-VIIRS nighttime light model to explore the associated geographical distribution characteristics and potential influencing factors. |
| Address |
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China |
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Elsevier |
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English |
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English |
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0959-6526 |
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IDA @ john @ |
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2917 |
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