Optimal low-carbon energy system planning from direct and indirect perspectives under multiple uncertainties: an advanced model applied in Fujian (China)

Carbon emission intensity (COI) reduction considering direct and indirect relationships among multiple energy sources, economic sectors, mitigation measures, and uncertainties is critical for energy sustainable development. Previous researches cannot quantify indirect energy consumption derived by trades among sectors, or capture the optimal energy consumption structure directly caused by final demand. To fill the gap, a fractional dual-interval chance-constrained optimization-embedded input-output (FDC-EIO) model is proposed for low-carbon energy system planning with minimizing the COI subjecting to various constraints under uncertainties. Through applying the model in Fujian, China (2024–2035), results disclose that: (i) The COI would be in the range of [[12.43, 15.59], [16.04, 19.76]] g/RMB¥, revealing effects of the uncertainties on system performance cannot be ignored. (ii) Fujian's economic output would keep growing, annually [[3.61, 3.79], [3.82, 3.93]]%. (iii) Energy consumption would increase by [13.68, 14.37] % (direct) and [[25.98, 27.22], [28.82, 31.58]] % (indirect). (iv) Using the five measures, direct carbon emissions would continuously decrease, by [18.91, 21.96], [31.13, 36.69]] %; indirect carbon emissions would reach peak (increase by [[6.93, 10.19], [12.64, 14.83]] %) around 2030, and then decrease by [[10.03, 11.04], [11.17, 11.66]] %. Authorities could select appropriate schemes based on their risk acceptance and data vagueness levels.