Economy-energy-environment nexus: the potential of agricultural value-added toward achieving China’s dream of carbon neutrality

Agriculture is one of the major sources of global emissions that cause climate change while agricultural value added helps to boost the economy in developing countries like China. Therefore, this study aims to investigate the long- and short-term influences of agricultural value added, economic growth (GDP), and energy use on carbon dioxide (CO 2 ) emissions in China. The autoregressive distributed lag (ARDL) method was used by using annual time series data from 1990 to 2021. The empirical outcomes revealed that a 1% increase in the agricultural value added would cut CO 2 emissions by 1.37% in the long-run and 0.65% in the short-run. However, this study found that both GDP and energy consumption have a positive and statistically significant effect on CO 2 emissions. Furthermore, an inverted U-shaped association between economic growth and environmental pollution was discovered by spotting the positive coefficient of GDP and the negative coefficient of GDP squared, which proved the validity of the environmental Kuznets curve (EKC) hypothesis. The robustness of the ARDL outcomes was verified by using the fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and canonical cointegration regression (CCR) approaches. This study offers a comprehensive set of policy recommendations aimed at enhancing agricultural value added in China. These suggestions focus on the promotion of climate-smart agriculture, the integration of renewable energy sources in agricultural production, and the adoption of advanced technologies within agricultural systems. Implementing these measures would contribute to the achievement of China’s goal of carbon neutrality. Graphical Abstract Highlights • Agriculture is a key sector of economic development in China. • This research investigated the effects of agricultural value added on carbon emissions. • The autoregressive distributed lag approach was employed by utilizing time series data. • The results shed new light on the emission reduction potential of agricultural value added. • This article offers policy suggestions to achieve sustainable and climate-smart agriculture.