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The electric power industry is not only facing the pressure from the reduction of industrial and commercial electricity prices to stimulate the significant growth of demand, but also facing the increasingly serious pressure of unreasonable consumption caused by cross-subsidies; the cross-subsidy mitigation effect and energy-saving effect of the current tiered electricity price policy have basically disappeared. This article examines the main variables that affect the electricity demand and carbon emissions in order to develop a better tiered electricity pricing scheme that can effectively manage cross-subsidies in electricity prices while simultaneously saving energy and lowering carbon emissions. The China Statistical Yearbook’s electricity balance sheets for 2013–2020 are used in this article, along with pertinent data from the State Grid Corporation of China and the China Statistical Yearbook for 2006–2016. It builds an electricity demand model for classified users by using the time series analysis method and multiple statistical regression method. The variables are then subjected to a Granger causality test and a cointegration test in this article. The analysis shows that the adjustment of the electricity price policy has a significant impact on energy-saving and carbon reduction, and for residential electricity consumption, the income variable is the main factor affecting the electricity demand. We take residents’ affordability as the constraint condition for dividing the range of electricity and determining the beneficiary group, take the carbon emission responsibility target and the cross-subsidy degree alleviation target as constraints in determining the tiered price difference, propose an improvement scheme for the tiered electricity price, and carry out the sensitivity analysis of the influence between the parameters. The results show that the optimization improves the precision of the cross-subsidy treatment and significantly improves the effects of energy conservation and emission reduction.
Liu et al. (Mon,) studied this question.