In order to promote the adjustment of energy consumption structure and achieve the national emission reduction target, the paper takes China as the research object, collects data from 2007 to 2019, and uses ARDL (autoregressive distributed lag) model and Granger causality test methods to explore the equilibrium relationship of regional renewable energy consumption and carbon dioxide emissions. Conclusions as follows: (1) The boundary test of the ARDL model confirms that when renewable energy consumption is used as the explained variable, the boundary test results are not significant, and when carbon emissions are used as the explained variable, the boundary test results are significant, confirming the impact of China's renewable energy consumption on carbon emissions is significant; (2) The long-term elasticity coefficient of China's renewable energy consumption on carbon emissions is negative, and passed the significance test, indicating that renewable energy consumption has a significant negative effect on carbon emissions in long-term. The short-term elasticity coefficient is also negative, but did not pass the significance test, indicating that the short-term impact on renewable energy consumption is not statistically significant. (3) when the lag period is 1, 2, 3, and 4 years, Chinese renewable energy consumption constitutes the Granger reason for the carbon emission intensity; when the lag period is 3, 4 years, the carbon emission intensity constitutes the Granger reason for the change in renewable energy consumption; and when the lag period is one or two years, the carbon emission intensity does not constitute the Granger reason for the change in renewable energy consumption. Understanding the relationship between regional renewable energy consumption and carbon emissions in China is conducive to balancing regional and alleviating the conflict between regional low-carbon development and instability, helping to achieve nationwide carbon emission reduction commitments in advance, and formulating regional development policies according to local conditions. •We built an autoregressive distributed lag model to test the relationship between the two.•We measured the intensity of carbon emissions.•We analyzed short-term and long-term impact.•We provided corresponding policy recommendations.