Search

With the continuous development of the social giant system, the independence of subsystem boundaries has decreased under the drive of complex relationships and multiple uncertainties. Decision makers are required to consider the interactions between different systems to develop precise policy guidance. Given these challenges, an integrated optimization and multi-scale input–output (OMIO) model is proposed by integrating interval programming, chance-constrained programming, and integer programming into the input–output framework to provide comprehensive policy analysis. The optimized decision variables that carry uncertain information from energy system are identified as connection points to induce changes in economic-environmental effects. A typical fossil-energy-dependent region that mainly relies on fossil fuels, Shanxi Province, China, was used as a case study. It was found that the joint effects of energy system optimization and socioeconomic rebalance will promote the rise of the petroleum product sector, the logistics industry, and tertiary industries, as well as the popularity of electric vehicles. The increase in the use of clean energy and electric vehicles will stimulate GDP growth in the short term, and the emissions of carbon dioxide and air pollutants will be reduced to varying degrees. The study findings can provide comprehensive energy allocation strategies, socioeconomic effect analysis, and climate mitigation results to help fossil-energy-dependent regions better adapt to the requirements of sustainable development under multiple systematic uncertainties.

With the continuous development of the social giant system, the independence of subsystem boundaries has decreased under the drive of complex relationships and multiple uncertainties. Decision makers are required to consider the interactions between different systems to develop precise policy guidance. Given these challenges, an integrated optimization and multi-scale input–output (OMIO) model is proposed by integrating interval programming, chance-constrained programming, and integer programming into the input–output framework to provide comprehensive policy analysis. The optimized decision variables that carry uncertain information from energy system are identified as connection points to induce changes in economic-environmental effects. A typical fossil-energy-dependent region that mainly relies on fossil fuels, Shanxi Province, China, was used as a case study. It was found that the joint effects of energy system optimization and socioeconomic rebalance will promote the rise of the petroleum product sector, the logistics industry, and tertiary industries, as well as the popularity of electric vehicles. The increase in the use of clean energy and electric vehicles will stimulate GDP growth in the short term, and the emissions of carbon dioxide and air pollutants will be reduced to varying degrees. The study findings can provide comprehensive energy allocation strategies, socioeconomic effect analysis, and climate mitigation results to help fossil-energy-dependent regions better adapt to the requirements of sustainable development under multiple systematic uncertainties. [Display omitted] • An integrated optimization and multi-scale input-output model is developed. • We explore the interaction mechanism between systems under multiple uncertainties. • Energy optimization strategies and the induced environ-economic effects are revealed. • We achieve an ensemble of bottom-up and top-down models through decision variables. • This study is apt to guide sustainable transition for fossil-energy-dependent regions. [ABSTRACT FROM AUTHOR]