Environmental pollution caused by energy consumption is a global problem. Optimization of the energy system will contribute to the sustainable development of city, especially of the industrial cities. Based on the Long-term Energy Alternative Planning System (LEAP) model, the LEAP-Suzhou model was established to explore the energy system optimization and emission reduction path of Suzhou to 2050. By accounting for current energy consumption and carbon emissions, the baseline scenario (BAU) was established. According to the different methods and intensities of energy transformation, an industrial structure optimization scenario (ISO), an energy structure optimization scenario (ESO), and an energy transformation optimization scenario (ETD) were created. Combined with the energy flow diagram, the energy structure and the direction of optimization were analyzed. The results showed that the baseline scenario will consume 259.954 million tons of standard coal by 2050, and the carbon emission will be 677.6 Mt. Compared with BAU, the ISO, ESO, and ETD scenarios will reduce energy consumption by 37.9%, 37.4%, and 74.8%, respectively, by 2050. ETD had the best carbon dioxide reduction, followed by ESO, and finally ISO. Among them, the carbon emission of ETD will reach its peak around 2030 and decrease to 73.8 Mt in 2050, resulting in the best emission reduction effect. This scenario is the best path for Suzhou to achieve the goal of “carbon peak and neutrality” and sustainable development. The LEAP-Suzhou model successfully explores the low carbon path of Suzhou, provides policy guidance for the optimization of energy transition and carbon neutrality of industrial cities, In the future, the energy structure should be further optimized in Suzhou, and advanced energy technologies should be introduced to improve energy efficiency, especially for the power generation sector, and the proportion of clean energy such as gas should be further expanded.

In the pursuit of China’s dual carbon goals, identifying spatio-temporal changes in industrial carbon emission efficiency and their influencing factors in cities at different stages of development is the key to effective formulation of countermeasures to promote the low-carbon transformation of Chinese national industry and achieve high-quality economic development. In this study, we used balanced panel data of 270 Chinese cities from 2005 to 2020 as a research object: (1) to show spatio-temporal evolution patterns in urban industrial carbon emission efficiency; (2) to analyze the aggregation characteristics of industrial carbon emission efficiency in Chinese cities using Global Moran’s I statistics; and (3) to use the hierarchical regression model for panel data to assess the non-linear impact of the digital economy on the industrial carbon emission efficiency of cities. The results show the following: (1) the industrial carbon emission efficiency of Chinese cities exhibited an upward trend from 2005 to 2020, with a spatial distribution pattern of high in the south and low in the north; (2) China’s urban industrial carbon emission efficiency is characterized by significant spatial autocorrelation, with increasing and stabilizing correlation, and a relatively fixed pattern of spatial agglomeration; (3) there is a significant inverted-U-shaped relationship between the digital economy and the industrial carbon emission efficiency of cities. The digital economy increases carbon emissions and inhibits industrial carbon emission efficiency in the early stages of development but inhibits carbon emissions and promotes industrial carbon emission efficiency in mature developmental stages. Therefore, cities at all levels should reduce pollution and carbon emissions from high-energy-consuming and high-polluting enterprises, gradually reduce carbon-intensive industries, and accelerate the digital transformation and upgrading of enterprises. Western, central, and eastern regions especially should seek to promote the sharing of innovation resources, strengthen exchanges and interactions relating to scientific and technological innovation, and jointly explore coordinated development routes for the digital economy.

In the pursuit of China’s dual carbon goals, identifying spatio-temporal changes in industrial carbon emission efficiency and their influencing factors in cities at different stages of development is the key to effective formulation of countermeasures to promote the low-carbon transformation of Chinese national industry and achieve high-quality economic development. In this study, we used balanced panel data of 270 Chinese cities from 2005 to 2020 as a research object: (1) to show spatio-temporal evolution patterns in urban industrial carbon emission efficiency; (2) to analyze the aggregation characteristics of industrial carbon emission efficiency in Chinese cities using Global Moran's I statistics; and (3) to use the hierarchical regression model for panel data to assess the non-linear impact of the digital economy on the industrial carbon emission efficiency of cities. The results show the following: (1) the industrial carbon emission efficiency of Chinese cities exhibited an upward trend from 2005 to 2020, with a spatial distribution pattern of high in the south and low in the north; (2) China's urban industrial carbon emission efficiency is characterized by significant spatial autocorrelation, with increasing and stabilizing correlation, and a relatively fixed pattern of spatial agglomeration; (3) there is a significant inverted-U-shaped relationship between the digital economy and the industrial carbon emission efficiency of cities. The digital economy increases carbon emissions and inhibits industrial carbon-emission efficiency in the early stages of development, but inhibits carbon emissions and promotes industrial carbon emission efficiency in mature developmental stages. Therefore, cities at all levels should reduce pollution and carbon emissions from high-energy-consuming and high-polluting enterprises, gradually reduce carbon-intensive industries, and accelerate the digital transformation and upgrading of enterprises. Western, central and eastern regions especially should seek to promote the sharing of innovation resources, strengthen exchanges and interactions relating to scientific and technological innovation, and jointly explore coordinated development routes for the digital economy.