Sustainable Agricultural Development in Indonesia: A Quantile Regression Analysis for The Role of Credit and Green Technologies in Food Production and Emission Reduction

The key objective of the study is to explore the direct and indirect effects of agricultural credit (AGCR) and technological innovations (TCI) on food production (FOOP) and agricultural greenhouse gas emissions (AGHGS) in Indonesia across lower, medium, and higher quantiles. This study fills the gap by analyzing how agricultural credit and technological innovations jointly influence food production and emissions, providing new insights into the productivity–environment trade-off and sustainable agriculture. Using quarterly data from 2000 to 2022, the Quantile Autoregressive Distributed Lag (QARDL) model is employed to capture short- and long-run dynamics and asymmetries. The findings show that AGCR significantly enhances FOOP at medium and high quantiles but also increases AGHGS across all quantiles, highlighting the environmental trade-offs of agricultural finance. Although the direct impact of TCI on FOOP is primarily inconsequential, TCI significantly increases AGHGS at medium and high quantiles. Notably, the interaction term (TCIxAGCR) reveals that TCI enhances the positive effects of AGCR on FOOP, especially at lower and middle quantiles, and simultaneously reduces AGHGS across almost all quantiles. Control variables such as fertilizer use (FTU) and transport infrastructure (TRINF) also show significant roles—FTU boosts FOOP, while TRINF contributes to higher AGHGS. The Wald test supports the non-linearity and justifies the QARDL model. The study highlights the trade-off between agricultural credit’s role in boosting food production and increasing emissions, while showing that integrating technological innovations with credit can foster sustainable, low-carbon agriculture in Indonesia.