Abstract
With the speedy growth of economic development, the imbalance of energy supply and demand pose a critical challenge for the energy security of our country. Meanwhile, the increasing and excessive energy consumption lead to the greenhouse effect and atmospheric pollution, greatly threatening the survival and development of human beings. This study integrated two nighttime light remote sensing datasets, namely Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) data and Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) data, to extend the temporal coverage of the study. Then, the distributions of China’s energy consumption from 1995 to 2016 at a 1-km resolution were estimated using different models and the spatiotemporal variations of energy consumption were explored on the basis of the best estimated results. Next, the factors influencing China’s energy intensity on the provincial level were investigated based on the spatial econometric model. The results show that: (1) The integrated nighttime light datasets can be successfully applied to estimate the dynamic changes of energy consumption. Moreover, the panel data model established in our research performed better than the quadratic polynomial model. (2) During the observation period, the energy consumption in China significantly increased, especially in the Yangtze River Delta, the Pearl River Delta, the Beijing–Tianjin–Hebei region, eastern coastal cities, and provincial capitals. (3) Different from the random spatial distribution pattern of energy consumption on the provincial level, the spatial distribution of energy consumption on the prefectural level has significant clusters, and its spatial agglomeration was strengthened year by year during the research period. (4) The spatial Durbin model (SDM) with a spatial fixed effect has been proved to be more suitable to explore the impact mechanism of China’s energy consumption. Among the four socio-economic factors, industrial structure has the greatest impact on the provincial energy intensity in China. Moreover, the changes in industrial structure and foreign direct investment (FDI) can not only influence the local energy intensity but also affect the energy intensity of the neighboring provinces.
Highlights
With the mushroom growth of the economy, China’s energy consumption has grown rapidly in the past 40 years since the reforms and opening up of the country
The estimation result of energy consumption based on the oTphteimTliahnleemaliorn,deqealur,waqdausraaadtdricaotppictoepdlyo. nlyonmomiaila,l,lologgaarriitthhmm, aanndd eexxppoonneenntitailarlerleatliaotniosnhisphsipbsetbweetewnepernovpirnocviailncial energeynecrognyscuomnsputmiopntiaonndanthdethTeDTNDNofotfhtehennigighhtttitmimeelliigghhtt ddaattaawweerereesetsatbalbislhisehdeidn ienxceexlcreelspreescptievcetliyv. ely
There exist errors in the energy consumption estimation based on nighttime light data, for the nighttime light can only represent a portion of energy consumption while a large portion of energy consumption occurs in daytime by industrial and other activities
Summary
With the mushroom growth of the economy, China’s energy consumption has grown rapidly in the past 40 years since the reforms and opening up of the country. The proportion of China’s primary energy consumption in the world increased from 6.10% in 1980 to 23.6% in 2018, ranking first in global energy growth for 18 consecutive years [1]. Excessive energy consumption, especially the large share of fossil fuels, is the main driver of climate change [5,6]. Energy consumption, especially fossil energy consumption, is the main cause of air pollution [8,9]. Studies have confirmed that energy consumption has a significant positive effect on haze, and measures such as controlling total energy consumption and optimizing energy structure will effectively improve air quality [10,11,12]. Due to the significant impacts of energy consumption on energy security, climate change, and atmospheric pollution, it is imperative to control and manage energy consumption in China
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