The paper introduces a hybrid framework that uses large language models (LLMs) to assist in designing behavioural and scenario specifications for an agent‑based model of solar photovoltaic adoption by Irish dairy farms. It integrates bounded behavioural rubrics—conservative, balanced, and optimistic—with structured scenario specifications into a calibrated ABM, preserving the original techno‑economic adoption mechanism while adding controlled behavioural modulation and scenario‑driven uncertainty analysis. Experiments across various policy settings and Monte Carlo simulations show stable, economically plausible outcomes, with up to a 13% increase in behavioural adoption compared to a logistic baseline, without causing unrealistic saturation dynamics.
By Iias Faiud, Hossein Khaleghy, Michael Schukat, Karl Mason
The paper presents a Monte Carlo-based framework to quantify the green benefits of an AI-driven smart agriculture platform in Hainan. By integrating large-language-model question answering, multimodal pest diagnosis, IoT sensing, satellite remote sensing, and a closed-loop field record system, the study builds a cradle-to-farm-gate carbon accounting model and simulates three crop scenarios (mango, winter vegetable, rice). Results show median reductions of 23.5% in pesticide use, 21.0% in fertilizer, 16.5% in irrigation water, and 21.5% in carbon intensity, with high probabilities for fertilizer and carbon reductions but lower for water savings.
By Zhaoyang Li, Ruijie Zhang, Zhaoji Sun, Lu Zhang
arXiv:2607. 26588v1 Announce Type: new Abstract: The rapid development of large language models (LLMs) has renewed interest in agent-based modeling (ABM).
By Shaopeng Wei, Yufei Cheng, Wenxi Sun, Yepeng Ding, Yu Zhao, Gang Kou
arXiv:2509. 01924v4 Announce Type: replace-cross Abstract: Agricultural decision-making faces a dual challenge: sustaining high yields to meet global food security needs while reducing the environmental impacts of input use, including fertilizer losses and other agrochemical applications such as herbicides, insecticides, and fungicides.
By Sakshi Arya, Wentao Lin
The paper investigates how a single pooled contract offered by an aggregator to heterogeneous smallholder farmers can be designed to maximize profit while addressing private adoption costs and unobserved effort over multiple seasons. Using a POMDP framework and reinforcement learning, the authors find that profit‑maximizing contracts disproportionately favor large farms, achieving 87.7% of possible adoption on large farms versus only 8.2% on smallholdings, largely due to higher measurement, reporting, and verification costs on smaller plots. The study suggests that adjusting MRV cost structures could reduce this disparity and help scale carbon farming to smallholders.
By Rishi Bharadwaj, Yadati Narahari
arXiv:2609.15038v1 Announce Type: new
Abstract: Language-model agents are increasingly used as synthetic people in surveys and social simulations, yet their apparent realism is often judged from popu...
By Zhanliang Zhu, Ziwei Li, Yuchen Liu, Liujun Zhu, Ruiqi Wu, Tongqing Shen, Junliang Jin, Jianyun Zhang
The paper presents a new method for sensitivity analysis of decision rules in an agent‑based agro‑ecological model. It treats the decision structure as a hierarchical design space, using a random‑forest‑supervised HSIC‑ANOVA to rank main effects and interactions across both categorical and numerical variables. The approach is applied to outputs such as nitrogen leaching, soil organic carbon variation, and yield predictions, providing an interpretable, decision‑oriented analysis.
By Benjamin Pianet, Baptiste Lesquoy, Paul Saves, Benoit Gaudou, Jean Villerd, Nicolas Verstaevel, Olivier Therond
The paper evaluates the green benefits of individual AI modules within a smart‑agriculture platform through two Monte Carlo simulations. In Experiment 1, AI diagnosis significantly increases the likelihood of reducing pesticide and fertilizer use compared to traditional extension services, with up to a 49 % probability of a 20 % pesticide reduction when accuracy and adoption are high. Experiment 2 shows that adding AI irrigation scheduling to IoT‑based engineering yields a 5 percentage‑point increase in median water savings and a 30.5 % reduction in paddy methane emissions, while farmer adoption remains the key limiting factor for achieving green targets.
By Zhaoyang Li, Ruijie Zhang, Zhaoji Sun, Lu Zhang
arXiv:2608.23796v1 Announce Type: cross
Abstract: Problem definition: Solar electricity generation is a strategic component of energy portfolios designed to meet growing demand and reduce carbon emis...
By Sebasti\'an Souyris, Jason A. Duan, Anantaram Balakrishnan, Varun Rai
arXiv:2607. 07759v1 Announce Type: new Abstract: Agricultural supply chains are vulnerable to disruptions through linked biophysical and economic systems.
By Joshua R. Waite, Dana Golden, Brett Indelicato, Kevin Camp, Mojdeh Saadati, Shannon Regan, Patrick Schnable, Baskar Ganapathysubramanian, Carlos Messina, Suzanne Thornsbury, Soumik Sarkar
arXiv:2610.34644v1 Announce Type: new
Abstract: AI agents are increasingly proposed as autonomous experimenters, yet the benchmarks that test them mostly offer cheap experiments that answer at once....
By Ningpei Ding, Zai Wen Feng
arXiv:2509. 05364v2 Announce Type: replace-cross Abstract: Residential buildings contribute significantly to energy use, health outcomes, and carbon emissions.
By Abdollah Baghaei Daemei