Abstract
Motor vehicles have been identified as a growing contributor to air pollution, such that analyzing the traffic policies on energy and environment systems (EES) has become a main concern for governments. This study developed a dual robust stochastic fuzzy optimization - energy and environmental systems (DRSFO-EES) model for sustainable planning EES, while considering the traffic sector through integrating two-stage stochastic programming, robust two-stage stochastic optimization, fuzzy possibilistic programming, and robust fuzzy possibilistic programming methods into a framework, which can be used to effectively tackle fuzzy and stochastic uncertainties as well as their combinations, capture the associated risks from fuzzy and stochastic uncertainties, and thoroughly analyze the trade-offs between system costs and reliability. The proposed model can: (i) generate robust optimized solutions for energy allocation, coking processing, oil refining, heat processing, electricity generation, electricity power expansion, electricity importation, energy production, as well as emission mitigation under multiple uncertainties; (ii) explore the impacts of different vehicle policies on vehicular emission mitigation; (iii) identify the study of regional atmospheric pollution contributions of different energy activities. The proposed DRSFO-EES model was applied to the EES of the Beijing-Tianjin-Hebei (BTH) region in China. Results generated from the proposed model disclose that: (i) limitation of the number of light-duty passenger vehicles and heavy-duty trucks can effectively reduce vehicular emissions; (ii) an electric cars’ policy is enhanced by increasing the ratio of its power generated from renewable sources; and (iii) the air-pollutant 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. The DRSFO-EES model can not only provide scientific support for the sustainable managing of EES by cost-effective ways, but also analyze the desired policies for mitigating pollutant emissions impacts with a risk adverse attitude under multiple uncertainties.
Highlights
Energy-related activities are the dominant sources of air pollution [3], with the amounts of carbon dioxide (CO2 ) and air pollutant emitted from electricity generation plants accounting for approximately 40% and 30% of the total CO2 and air pollutant emissions, respectively [2,4]
The robust two-stage stochastic optimization (RTSO) method can deal with the stochastic uncertainties of real-world management problems, analyze the policy scenarios associated with economic penalties when the predefined policies of the first-stage are violated, capture the variability of the second-stage costs that are greater than the expected values, and evaluate trade-offs between system economy and risk [23,24]
Five scenarios labeled S1–S5 were designed to analyze the potentials of different emission mitigation strategies and policies for reducing vehicular emissions (NOx, hydrocarbon compound (HC), carbon monoxide (CO), and particulate matter (PM)) in the BTH region
Summary
China has experienced severe environmental pollution in recent years, which pose a critical threat to public health and sustainable development [1,2]. Energy-related activities are the dominant sources of air pollution [3], with the amounts of carbon dioxide (CO2 ) and air pollutant emitted from electricity generation plants accounting for approximately 40% and 30% of the total CO2 and air pollutant emissions, respectively [2,4]. Motor vehicles have been identified as growing contributors to air pollution due to the rapid growth of transportation, accounting for approximately. There is currently a severe conflict between increasing energy demand, excessive vehicle population, and “high coal” energy mix on the one hand, and the imperative of mitigating air pollution on the other hand [7]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
