Data required to analyze causal relationships between residential electricity consumption and its explanatory factors.
Description
In order to contribute to the enrichment of studies on causal relationships linked to electricity consumption in the residential sector, four annual frequency explanatory factors are used, covering the period 1994-2019. These are GDP per capita, an indicator of individual income, and the ability of each person to take out an ongoing electricity subscription. CO2 emissions are also among the factors with a causal relationship to electricity consumption. Although it is assumed that there is no causality between CO2 emissions and national electricity consumption in Cameroon, it would be interesting to carry out a targeted assessment within the residential sector. As in the Talbi et al. 2020 analyses, the urbanization factor is also taken into account in this study. However, the price factor is not taken into account. Prices per kWh are stable and depend only on consumption ranges. For consumption below 110 kWh, the price is set at 0.09 USD/kWh; between 111 kWh and 400 kWh, the price is 0.145 USD/kWh; between 401 kWh and 800 kWh, the price is 0.170 USD/kWh, while between 801 kWh and 2000 kWh, the price is set at 0.180 USD/kWh. This segmented distribution of electricity prices within the residential sector makes it difficult to take into account any price trends that could be compared with trends in electricity consumption. The number of subscribers is also included in the list of factors explaining electricity consumption. This is based on the assumption of a fairly close link with electricity consumption. The databases of the International Energy Agency provide information on electricity consumption in Cameroon's residential sector (REC in GWh). Data on GDP (in current US dollars), CO2 emissions from thermal power generation (in kilotons of CO2) and urban population (UR) are available in the World Bank database. Finally, data on the number of subscribers (NS) are provided by electricity distributor ENEO Cameroon. These variables cover the period 1994-2019, during which the country's economy and demographic factors are growing overall. Some descriptive statistics based on these data are calculated. Mean, maximum and minimum values, standard deviation, skewness and kurtosis are all included. In theory, if the skewness of a given time series is between [-3,3] and its kurtosis is between [ -10,10], the series has a normal distribution and is representative of the physical process it describes. Our calculations show that the kurtosis and skewness of each time series meet the aforementioned reliability conditions. Consequently, the associated data are reliable representations of the real fluctuations of each variable. Statistical tests suggest unidirectional causality, from all explanatory factors to electricity consumption. Indeed, any growth in GDP per capita leads to growth in electricity consumption without feedback. Urbanization and growth in the number of subscribers are intensifying electricity consumption.
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Steps to reproduce
All the data sources used are described in the published article describing the causal relationships highlighted ( https://doi.org/10.1016/j.esr.2023.101155 ). The steps required to study these causal relationships are also described in this article.
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Funding
Haute école Spécialisée de Suisse Occidentale
2022.0694/Kamerun/OP