Evaluating the impact of renewable energy, agriculture, and livestock on CO₂ and GDP in OECD countries using an extended STIRPAT framework

This study examines how renewable energy, agriculture, and livestock affect CO₂ emissions and economic growth in 26 OECD countries between 1970 and 2021. Using an extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) framework, the analysis applies a broad set of panel estimators, including CS-ARDL, CS-DL, AMG, CCEMG, FMOLS, DOLS, CCR, and GMM-based PVAR, to ensure robustness and test for causality. Results show that renewable energy and agriculture are significantly associated with lower CO₂ emissions, while GDP, coal use, and energy intensity increase emissions. At the same time, renewable energy, agriculture, and livestock contribute positively to GDP growth, whereas energy intensity has a negative effect. Granger causality tests reveal unidirectional causality running from renewable energy and agriculture to CO₂ emissions and GDP, and bidirectional causality between livestock and GDP. Overall, the findings indicate that (i) the agricultural sector is more effective than the livestock sector in reducing CO₂ emissions, while both contribute equally to economic growth in OECD countries, and (ii) renewable energy not only reduces emissions but also plays a significant role in supporting long-term economic growth. These insights support the case for integrated policies that scale up investment in renewable energy and agriculture to accelerate the transition toward sustainability in high-income economies.