The manufacture of products in the industrial sector is the principal source of carbon emissions. To slow the progression of global warming and advance low-carbon economic development, it is essential to develop methods for accurately predicting carbon emissions from industrial sources and imposing reasonable controls on those emissions. We select a support vector machine to predict industrial carbon emissions from 2021 to 2040 by comparing the predictive power of the BP (backpropagation) neural network and the support vector machine. To reduce noise in the input variables for BP neural network and support vector machine models, we use a random forest technique to filter the factors affecting industrial carbon emissions. The statistical results suggest that BP’s neural network is insufficiently adaptable to small sample sizes, has a relatively high error rate, and produces inconsistent predictions of industrial carbon emissions. The support vector machine produces excellent fitting results for tiny sample data, with projected values of industrial carbon dioxide emissions that are astonishingly close to the actual values. In 2030, carbon emissions from the industrial sector will have reached their maximum level.

With the gradual increase of social awareness of environmental protection, environmental information disclosure has become the key for enterprises to accept social supervision and fulfill their social responsibility. This study examines the high-polluting enterprises that were listed on Chinese A-shares between 2008 and 2021. The influence of environmental information disclosure quality on green innovation is examined using ordinary least squares (OLS) as a benchmark model. The results show that the improvement of environmental information disclosure quality of high-polluting enterprises can significantly improve the quantity and quality of green innovation of enterprises and are mediated by alleviating financing constraints and enhancing cash reserves. Moreover, improving the quality of environmental information disclosure of highly polluting enterprises has a more significant contribution to the quantity and quality of green patents of non-state-owned enterprises, enterprises located in central and eastern China, and large enterprises. The findings of this paper provide theoretical support for achieving a “win-win” situation of environmental protection and green innovation.