Life cycle carbon accounting and waste valorisation in litchi supply chains for climate-resilient food systems

Abstract

The growing environmental concerns necessitate sustainability assessment in agricultural food systems. The present study quantified carbon emissions in litchi supply chains using a circular tiered hybrid-life cycle assessment (CrTH-LCA) framework, accompanied by carbon emission forecasting, using statistical (ARIMA and Prophet) and deep learning (LSTM and GRU) models, along with an economic feasibility assessment of waste valorisation. This study estimated a total carbon footprint of 7305.17 kgCO₂-e ha⁻¹, with household waste (40.44%) and cultivation (31.96%) identified as the major contributors. Carbon absorption and waste valorisation contributed to mitigation strategies, offering an economic potential of $2607.02 per hectare, with waste valorisation alone accounting for 63.06% of the total carbon emissions offset. The Monte Carlo simulation confirmed fertilizer and household wastages as the key uncertainty drivers. Forecasting using deep learning (LSTM) models achieved a high predictive accuracy (MAE = 34.92; RMSE = 35.62), projecting an upward trend in future emissions and emphasizing the need for adaptive mitigation strategies. Based on the six-year forecast trend, a community-based biogas model at the farmer-level demonstrated strong financial feasibility, achieving a high return on investment of 164.01% with a payback period of 28 months. Overall, the study offers a replicable and data-driven framework, linking life cycle assessment, circular waste management, and forecasting for a climate resilient decision-making. Aligning with SDGs 2, 12, and 13, the findings emphasize policy shifts via strengthening carbon crediting and targeted financial incentives, such as leveraging government subsidies and carbon finance, to enhance farmers' income, promote waste-to-energy valorisation, and accelerate India's transition to a low-carbon, circular agri-food system.