arXiv subjects

Abbinav Sankar Kailasam

Publications and source records attributed to Abbinav Sankar Kailasam.

3 recordsLinked to original sources

arXiv2026

Mitigating Financial Risk from Climate-Induced Agricultural Price Volatility

Agricultural price volatility, driven by market dynamics and meteorological factors such as temperature and precipitation, poses challenges for sustainable finance, planning, and policy. This study analyzes the impact of climate on crop price volatility for soybean in Madhya Pradesh (India) and Illinois (US), rice in Assam (India), wheat in North Dakota (US), cotton in Gujarat (India), and corn in Iowa (US). Using CMIP6 climate projections from the Copernicus Climate Change Service, we examine historical climate patterns and evaluate two future scenarios: SSP2-4.5 (moderate) and SSP5-8.5 (severe). We estimate conditional price volatility using the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model, and forecast this volatility with a Seasonal Autoregressive Integrated Moving Average with Exogenous Regressors (SARIMAX) model that incorporates meteorological variables. Finally, we apply the Black-Scholes framework to evaluate the cost of put-option-based insurance, which provides protection to farmers against adverse price drops linked to climate change. Our results highlight the role of meteorological data in improving agricultural risk modelling, enabling better design of insurance mechanisms, price stabilization tools, and sustainable policy interventions under climate uncertainty.

stat.AP↗

arXiv2026

Heatwave-Related Mortality Across Indian Cities Under Future Climate Scenarios

Heatwaves are intensifying as a major climate extreme and have emerged as a growing public health threat in rapidly urbanizing regions such as India. In this study, we integrate long-term heat-related mortality records (1970-2023) with bias-corrected CMIP6 climate projections to quantify future heatwave-related mortality across 67 Indian cities under intermediate (SSP2-4.5) and high-emission (SSP5-8.5) scenarios. A time-series forecasting framework was applied using summer mean temperature as the primary climate driver to project mortality trajectories through the end of the 21st century. Results indicate a strong and sustained increase in heat-related mortality under both scenarios, with multi-fold amplification under SSP5-8.5 relative to SSP2-4.5, reflecting the high sensitivity of health outcomes to emission pathways. Spatial analysis reveals increasing regional divergence under high-emission conditions, with urban regions in the Deccan Plateau, western India, and parts of eastern and northeastern India exhibiting disproportionately higher mortality growth. Multidimensional scaling further highlights emerging clustering of state-level mortality behavior under extreme warming, indicating structurally different regional responses to future heat stress. In contrast, the intermediate mitigation pathway produces more moderate and spatially uniform mortality trends. These findings demonstrate that climate mitigation can substantially reduce both the magnitude and inequality of future urban heat-health burdens. By linking updated climate projections with long-term mortality data at national and sub-national scales, this study provides policy-relevant evidence to support heat adaptation planning and climate-resilient urban development in one of the world's most heat-vulnerable regions.

physics.soc-ph↗

arXiv2025

The Impact of Meteorological Factors on Crop Price Volatility in India: Case studies of Soybean and Brinjal

Climate is an evolving complex system with dynamic interactions and non-linear feedback mechanisms, shaping environmental and socio-economic outcomes. Crop production is highly sensitive to climatic fluctuations (and many other environmental, social and governance factors). This paper studies the price volatility of agricultural crops as influenced by meteorological variables, which is critical for agricultural planning, sustainable finance and policy-making. As case studies, we choose the two Indian states: Madhya Pradesh (for Soybean) and Odisha (for Brinjal/Eggplant). We employ an Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model to estimate the conditional volatility of the log returns from 2012 to 2024. We further explore the cross-correlations between price volatility and the meteorological variables followed by a Granger-causal test to analyze the causal effect of meteorological variables on the volatility. The Seasonal Auto-Regressive Integrated Moving Average with Exogenous Regressors (SARIMAX) and Long Short-Term Memory (LSTM) models are implemented as simple machine learning models of price volatility with meteorological factors as exogenous variables. Finally, to capture spatial dependencies in volatility across districts, we extend the analysis using a Conditional Autoregressive (CAR) model to construct monthly volatility surfaces that reflect both local price risk as well as geographic dependence. We believe, this paper will illustrate the usefulness of simple machine learning models in agricultural finance, and help the farmers to make informed decisions by considering climate patterns and making beneficial decisions with regard to crop rotation or allocations. In general, incorporating meteorological factors to assess agricultural performance could help to understand and reduce price volatility and possibly lead to economic stability.

stat.AP↗