scholarly journals Can Nighttime Light Data Be Used to Estimate Electric Power Consumption? New Evidence from Causal-Effect Inference

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3154
Author(s):  
Yongguang Zhu ◽  
Deyi Xu ◽  
Saleem H. Ali ◽  
Ruiyang Ma ◽  
Jinhua Cheng

Nighttime light data are often used to estimate some socioeconomic indicators, such as energy consumption, GDP, population, etc. However, whether there is a causal relationship between them needs further study. In this paper, we propose a causal-effect inference method to test whether nighttime light data are suitable for estimating socioeconomic indicators. Data on electric power consumption and nighttime light intensity in 77 countries were used for the empirical research. The main conclusions are as follows: First, nighttime light data are more appropriate for estimating electric power consumption in developing countries, such as China, India, and others. Second, more latent factors need to be added into the model when estimating the power consumption of developed countries using nighttime light data. Third, the light spillover effect is relatively strong, which is not suitable for estimating socioeconomic indicators in the contiguous regions between developed countries and developing countries, such as Spain, Turkey, and others. Finally, we suggest that more attention should be paid in the future to the intrinsic logical relationship between nighttime light data and socioeconomic indicators.

Author(s):  
Wondimu Sigo

ABSTRACT Modern energy in the form of electricity is vital for economic activities such as for getting clean water, and healthcare, for getting stable and effective lighting, heating, and cooking. However, in developing countries there is a huge shortage of electricity and big gaps in access, while access to electricity in developed countries almost reached a hundred percent. Hence, the purpose of this paper is to shows the possible responsiveness of FDI to electric power consumption in different income-level. Hence, this study aims to address questions, including how is the relationship between economic activities such as FDI and electricity consumption in different income-level. For motivating the research, 131 countries data have been collected from WDI and US-EIA from 1992 to 2016 and both quantitative and qualitative methods used. The cross-country regression result shows that there exists an inverse-U shaped relationship between EPC and FDI net inflow because most high-income countries have a high level of EPC and therefore EPC becomes less important for them to attract FDI. But when we separate the total sample into two, EPC can significantly increase net FDI inflow for middle & low-income countries because for these countries, especially for low-income countries, sufficient electricity supply is important for FDI inflow.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mulin Chen ◽  
Hongyan Cai ◽  
Xiaohuan Yang ◽  
Cui Jin

Abstract Spatially explicit information on electric power consumption (EPC) is crucial for effective electricity allocation and utilization. Many studies have estimated fine-scale spatial EPC based on remotely sensed nighttime light (NTL). However, the spatial non-stationary relationship between EPC and NTL at prefectural level tends to be overlooked in existing literature. In this study, a classification regression method to estimate the gridded EPC in China based on imaging NTL via a Visible Infrared Imaging Radiometer Suite (VIIRS) was described. In addition, owing to some inherent omissions in the VIIRS NTL data, the study has employed the cubic Hermite interpolation to produce a more appropriate NTL dataset for estimation. The proposed method was compared with ordinary least squares (OLS) and geographically weighted regression (GWR) approaches. The results showed that our proposed method outperformed OLS and GWR in relative error (RE) and mean absolute percentage error (MAPE). The desirable results benefited mainly from a reasonable classification scheme that fully considered the spatial non-stationary relationship between EPC and NTL. Thus, the analysis suggested that the proposed classification regression method would enhance the accuracy of the gridded EPC estimation and provide a valuable reference predictive model for electricity consumption.


2020 ◽  
Vol 9 (1) ◽  
pp. 32 ◽  
Author(s):  
Jintang Lin ◽  
Wenzhong Shi

The nighttime light (NTL) imagery acquired from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) enables feasibility of investigating socioeconomic activities at monthly scale, compared with annual study using nighttime light data acquired from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS). This paper is the first attempt to discuss the quantitative correlation between monthly composite VIIRS DNB NTL data and monthly statistical data of electric power consumption (EPC), using 14 provinces of southern China as study area. Two types of regressions (linear regression and polynomial regression) and nine kinds of NTL with different treatments are employed and compared in experiments. The study demonstrates that: (1) polynomial regressions acquire higher reliability, whose average R square is 0.8816, compared with linear regressions, whose average R square is 0.8727; (2) regressions between denoised NTL with threshold of 0.3 nW/(cm2·sr) and EPC steadily exhibit the strongest reliability among the nine kinds of processed NTL data. In addition, the polynomial regressions for 12 months between denoised NTL with threshold of 0.3 nW/(cm2·sr) and EPC are constructed, whose average values of R square and mean absolute relative error are 0.8906 and 16.02%, respectively. These established optimal regression equations can be used to accurately estimate monthly EPC of each province, produce thematic maps of EPC, and analyze their spatial distribution characteristics.


2011 ◽  
Vol 8 (1) ◽  
pp. 233-238
Author(s):  
R.M. Bogdanov ◽  
S.V. Lukin

Oil and petroleum products transportation is characterized by a significant cost of electric power. Correct oil and petroleum products accounting and forecasting requires knowledge of many factors. The software for norms of electric power consumption analysis for the planned period was developed at the Ufa Scientific Center of the Russian Academy of Sciences. Based on the principles of the relational data model, a schematic diagram/arrangement for the main oil transportation objects was developed, which allows to hold the initial data and calculated parameters in a structured manner.


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