## 6 Conclusions  
In this study, a DRSFO-EES model was developed for planning an EES while considering the traffic sector, which integrates the TSP, FPP, RTSO, and RFPP methods in a single framework for the effective handling of EES uncertainties expressed as fuzzy sets and stochastic uncertainties as well as their combinations, capturing of the associated risks from the stochastic and fuzzy uncertainties, and analyzing the trade-offs between system costs and reliability. Four confidence levels (\(\alpha=0.5\), \(\alpha=0.6\), \(\alpha=0.8\), and \(\alpha=0.9\)) and four robust levels (\(\rho=0.2\), \(\rho=0.6\), \(\rho=0.8\), and \(\rho=1.0\)) were used for examining the impacts of uncertainties on the objective function, constraints and optimized solutions of the DRSFO-EES model. The proposed model was applied to the EES of the BTH region in China. Following is a summary of the findings and the identified policy implications:  
1. Limiting the numbers of LDVs and HDTs could effectively reduce vehicular emissions. LDVs are expected to be the major contributors of CO and HC emissions, and HDTs are expected to be the major contributors of NO\({}_{x}\) and PM emissions. 2. A EVs policy would be enhanced by increasing the ratio of power generated for EVs from renewable sources. The emission reduction effect of an EVs policy would thus be limited, especially with regard to NO\({}_{x}\) and PM emissions, if the EVs power source was entirely coal-based. 3. Optimizing the energy mix and developing the renewable energy can effectively reduce air-pollutant and CO\({}_{2}\) emissions. Air-pollutant amounts of NO\({}_{x}\), SO\({}_{2}\), and PM emissions in the BTH region are expected to peak around 2030, because the energy mix of the study region would be transformed from one dominated by coal to one with a cleaner pattern, with vigorous development of the utilization of natural gas and renewable energy. 4. Enhancement of the energy utilization efficiencies of coal-based power generation, oil refining, and coke processing would effectively reduce CO\({}_{2}\) and air-pollutant emissions. Coal-based power generation and coke processing are expected to be the major contributors of air-pollutant  
Figure 11: Weighted values of the expected deviations under different robust levels. missions, while oil refining, coal-based power generation and coke processing would be the chief sources of CO\({}_{2}\) emissions. Although the DRSFO method was the first attempt for planning an EES while considering the traffic sector of the BTH region, results indicated that DRSFO-EES could: (i) explore the impacts of different vehicle policies (i.e., EVs deployment, EVs power source for EVs, and vehicular emission standards) on vehicular emissions; (ii) generate robust optimized solutions for energy allocation, oil refining, cooking processing, heat processing, electricity generation and expansion, electricity importation, as well as emission mitigation under multiple uncertainties; (iii) identify the atmospheric pollution contributions of different energy activities such as coke processing, electricity generation, heat processing, oil refining, and motor vehicle operation. The proposed model could help to balance the contradiction between increasing energy demands and an increasing vehicle population, \"high coal\" energy systems, and the pressures of emission mitigation. Moreover, the proposed model could be applied at both city and regional scales, which would support policymakers adjusting current energy and environmental strategies in sustainable and robust ways. However, the DRSFO-EES also has potential limitations and extensions should be addressed in future study. Firstly, the developed model, based on historical data of annual electricity demand to predict future electricity demands, does not consider the specific parameters such as hourly or seasonal electricity load curves, which may result in significant deviations from the optimized decision schemes, even in the event of electricity shortage.