Spatial effects of carbon dioxide emissions from residential energy consumption: A county-level study using enhanced nocturnal lighting
Introduction
The Intergovernmental Panel on Climate Change (IPCC) has reported that global surface temperatures have increased by 0.74 ± 0.18 °C (1.33 ± 0.32 °F) during the 20th century. This is likely the result of increasing concentrations of greenhouse gases (GHGs) [1]. Although human activities, including the burning of fossil fuels and deforestation, were only responsible for 3% of carbon dioxide (CO2) emissions worldwide in the 1990s, the increase in emissions was large enough to exceed the absorptive capacity of natural processes (e.g., photosynthesis). Energy use for power generation in the industrial [2], [3], residential [4], [5] and transportation sectors [6], [7] has been the largest contributor of GHG emissions. In particular, approximately 10–35% of national energy consumption was from the residential sector worldwide in the late 1990s and 2000s [8], and this figure will likely increase as the average person’s income, standard of living, and associated access to home appliances, housing and personal transportation increase. Accordingly, great attention has recently been placed on the residential sector’s role in generating GHGs [9].
In China, the residential sector is now the second greatest energy consumer behind the industrial sector; total energy consumption in the residential sector grew by 8.43% since 2008 compared to an average increase of 2.58%. The increase in energy consumption by this sector has been driven by three major factors. First, the Chinese government has refocused its economy towards domestic consumption, resulting in a household consumption expenditure that was 38.2% of the Gross Domestic Product (GDP) [10]. If the goal of shifting the Chinese economy towards consumption-led growth in the 11th Five Year Plan is met, it is very probable that residential energy consumption (REC) will continue to rise in the future [11]. Secondly, the lifestyles and consumer preferences of Chinese residents have changed as the country continues to develop, with greater individual access to high quality food, comfortable living environments, health care, personal hygiene products and higher education. Finally, the annual per capita energy consumption of urban Chinese residents is 3.5 times that of rural residents, and, with the urban population expected to grow by 20 million per year, the rapid growth of REC is likely to continue.
Given recent increases in REC, estimating associated CO2 emissions from this source in a spatially explicit manner is critical for combating processes like global climate change. Estimating emissions from REC is usually dependent on statistical data [12], making it difficult to produce a spatially realistic representation of emissions. However, because artificial lighting is a unique indicator of residential activity, datasets derived from satellite imagery of nighttime lights have been used to map phenomena which would otherwise be difficult to map through ground surveys. There have been many studies examining the use of these datasets for determining spatiotemporal dimensions of socio-economic factors, including GDP, urban sprawl, impervious surfaces, and ex-urban development. More recently, these datasets of nighttime lights have been used as proxies for estimating CO2 emissions. The first global map of CO2 emissions had a spatial resolution of 1 degree and was developed by combining the lighted area of a city (using imagery of nighttime lights acquired between October 1994 and March 1995) with country-level ancillary statistical information [13]. Oda and Maksyutov [14] created a high resolution global inventory of annual CO2 emissions for the years 1980–2007 by combining a worldwide point source database with satellite observations of global nighttime lights distribution. Finally, Ghosh et al. [15] developed a method of mapping spatially distributed CO2 emissions from the burning of fossil fuels (excluding electric power utilities) at 30 arc-seconds (approximately 1 km2 resolution) using regression models of nighttime lights images.
In this paper, we present a new approach that uses the human activities index (HAI) as auxiliary data to correct saturated nighttime lights and to fill values in areas lacking nighttime lights. Using our methodology, satellite images of nighttime lights may serve as a useful proxy for the distribution of CO2 emissions from REC. Finally we used the emissions distribution to test the hypothesis that counties with similar CO2 emissions from REC are more spatially clustered than would be expected by chance.
Section snippets
Data and methodology
We used images of nighttime lights collected by the U.S. Air Force Defense Meteorological Satellite Program’s (DMSP) Operational Linescan System (OLS), which has been cited as a remarkable example of a global earth observing satellite sensor for detecting human activity [16]. This low orbiting satellite uses the visible/near infrared waveband (0.4–1.1 μm) for detecting lights and the thermal infrared (10.5–12.6 μm) band to filter cloud cover [17]. The satellite typically makes passes a study area
Urban–rural spatial pattern
According to the nighttime light imagery, we mapped distinct regions for urban areas with nighttime lights as well as rural areas with and without nighttime lights. Fig. 2 shows original DMSP–OLS nighttime lights satellite imagery (a), from which patterns (b) for urban areas with nighttime lights, rural areas with nighttime lights and rural areas without nighttime lights were derived. Here, we only present the examples for Beijing and Hebei while this analysis was also conducted for other
Discussion
In this study, we used a new methodological approach that combined DMSP–OLS satellite imagery of nighttime lights and the HAI. The novel use of HAI served as an important proxy for human activity in regions where nighttime lights were unavailable. It also demonstrated the role played by the integration of socio-economic and environmental factors, such as GDP, digital DEMs, temperature, humidity, NDVI and land cover types. We ultimately found that the resulting index was statistically correlated
Conclusions
In this paper, we discuss a new approach for using enhanced datasets of nighttime lights to estimate CO2 emissions from REC at county-level. With recent advances in economic development and extremely high human population densities, China has become the largest emitter of GHGs worldwide, and a large volume of those emissions have come from REC. Although most estimates of CO2 emissions from REC are given by coarse-grained statistics [45], [46], fine-scale evaluations of emissions at a regional
Acknowledgements
The authors thank the anonymous reviewers whose comments and suggestions were very helpful in improving the quality of this paper. The authors also thank the editor for helpful suggestions. This project is funded with support from National Natural Science Foundation of China under Project 41371525, National Basic Research Program of China (973 Program) 2012CB955800 (2012CB955804), China Postdoctoral Science Foundation funded Project (2012M521390 and 2013T60696), Scientific Research Foundation
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