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Carbon emissions and energy consumption have serious impacts on humans and ecosystems. This paper investigates the effects of industrial reconstructuring on energy conservation and emission reduction. A multi-objective optimization model was established. The model classifies industrial sectors into four groups according to their carbon emission levels and contributions to economic growth, then non dominated sorting genetic algorithm is applied to solve the model and the Pareto frontier is obtained. The best solution is selected from the Pareto frontier by using a super data envelopment analysis model which measures the degree of coordination between economy and environment. The model is used to analyze the effects of industrial reconstructuring in Beijing during 2018–2020. The results show that industrial reconstructuring enabled economic growth to reach the government's planned rate while the carbon intensity and energy intensity surpassed the goal of the 13th Five-Year Plan. This case can provide decision-making basis for the sustainable development of ecology and economy in other regions. Fig. 6. Pareto front in Beijing region in 2020 The optimal Pareto solution for carbon emissions, energy consumptions and GDP targets after industrial structure reconstructuring. Image 1 • Classifying industrial sectors into four groups according to carbon emission levels and economic growth contributions. • A multi-objective optimization model was established and solved by the genetic algorithm. • A super DEA model evaluates the final plan which reflects the coordination of the economy and environment. • The analysis of each group can help achieving maximum benefits of economic and environment. [ABSTRACT FROM AUTHOR]