The government-led Chinese economic development system determines that local government competition is a significant factor affecting the economic low-carbon transition. Driving an economic development mode with green technology innovation as the core is the critical path to realizing an economic low-carbon transition. Consequently, it is of significant practical relevance to investigate the impact of local government competition and green technology innovation on the economic low-carbon transition under the government-led Chinese economic development system. This paper systematically explores the nexus between green technology innovation and economic low-carbon transition in terms of local government competition perspective using the system generalized method of moments, panel threshold model, and geographically weighted regression on the basis of a dataset of 30 provincial administrative areas in China from a period of 2006–2019. The results indicate that green technology innovation significantly promotes the economic low-carbon transition. Local government competition not only significantly dampens the economic low-carbon transition but also considerably inhibits the positive effect of green technology innovation on the economic low-carbon transition. A significant N-shaped association is evident between green technology innovation and the economic low-carbon transition when green technology innovation is applied as a threshold, while such association is insignificant when local government competition is used as a threshold. Compared with high-competition intensity areas, green technology innovation promotes economic low-carbon transition weaker in low- competition intensity areas, while local government competition inhibits economic low-carbon transition stronger. However, local government competition significantly inhibits the positive effect of green technology innovation on the economic low-carbon transition in low-competition intensity areas, while insignificant in high-competition intensity areas. The geographically weighted regression technique as a whole also verified the above results. Therefore, policymakers should not only increase research and development investment in green technologies, but also develop a regionally linked low-carbon emission reduction system to avoid ineffective competition among governments to facilitate the earlier fulfillment of the “dual carbon” goal.

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The digital economy has introduced far-reaching innovations in the fields of government governance, enterprise production, and social operation. How to motivate the economic development mode towards a low-carbon and greenway transformation through the digital economy is a major issue concerning the Chinese government. However, there is scarce evidence to interpret the role mechanism of the digital economy on carbon emission efficiency from the factor misallocation scenario. Taking a database from 30 provincial-level administrative regions for the period from 2011 to 2019 in China as an example, the paper examines the effect of the digital economy on carbon emission efficiency, as well as explores its role mechanism deeply in terms of factor misallocation (capital misallocation and labor misallocation). The results suggest that there is a significant potential for the digital economy to contribute to carbon emission efficiency, as well as this finding, is valid when considering both the endogeneity issue and a series of robustness checks. Also, the digital economy can significantly contribute to carbon efficiency in both southern and northern regions, but more strongly in the northern region. Besides, the digital economy can inhibit the factor misallocation (labor misallocation and capital misallocation) level which ultimately improves carbon emission efficiency. Finally, as a digital economy, it can positively impact carbon efficiency in the long run by mitigating factor misallocation (labor misallocation and capital misallocation).

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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.