arXiv subjects

Sudeep Shukla

Publications and source records attributed to Sudeep Shukla.

6 recordsLinked to original sources

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↗

arXiv2025

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↗

arXiv2025

Causal Links Between Anthropogenic Emissions and Air Pollution Dynamics in Delhi

Air pollution poses significant health and environmental challenges, particularly in rapidly urbanizing regions. Delhi-National Capital Region experiences air pollution episodes due to complex interactions between anthropogenic emissions and meteorological conditions. Understanding the causal drivers of key pollutants such as $PM_{2.5}$ and ground $O_3$ is crucial for developing effective mitigation strategies. This study investigates the causal links of anthropogenic emissions on $PM_{2.5}$ and $O_3$ concentrations using predictive modeling and causal inference techniques. Integrating high-resolution air quality data from Jan 2018 to Aug 2023 across 32 monitoring stations, we develop predictive regression models that incorporate meteorological variables (temperature and relative humidity), pollutant concentrations ($NO_2, SO_2, CO$), and seasonal harmonic components to capture both diurnal and annual cycles. Here, we show that reductions in anthropogenic emissions lead to significant decreases in $PM_{2.5}$ levels, whereas their effect on $O_3$ remains marginal and statistically insignificant. To address spatial heterogeneity, we employ Gaussian Process modeling. Further, we use Granger causality analysis and counterfactual simulation to establish direct causal links. Validation using real-world data from the COVID-19 lockdown confirms that reduced emissions led to a substantial drop in $PM_{2.5}$ but only a slight, insignificant change in $O_3$. The findings highlight the necessity of targeted emission reduction policies while emphasizing the need for integrated strategies addressing both particulate and ozone pollution. These insights are crucial for policymakers designing air pollution interventions in other megacities, and offer a scalable methodology for tackling complex urban air pollution through data-driven decision-making.

stat.AP↗

arXiv2025

Arctic teleconnection on climate and ozone pollution in the polar jet stream path of eastern US

Arctic sea-ice loss is a defining feature of climate change and offers insight into its impact on mid-latitude air quality. Here, we investigate how variability in Arctic sea-ice extent (ASI) affects ground-level ozone ($O_3$) across eastern US states through physically and chemically mediated atmospheric pathways. Using observations and causal-inference methods grounded in atmospheric dynamics, we show that ASI drives wintertime ozone variability primarily via indirect meteorological mechanisms, including changes in humidity, temperature, and atmospheric circulation along the polar and subtropical jet streams. Inland regions exhibit the strongest sensitivity, while coastal areas are modulated by marine boundary-layer processes. Seasonal contrasts reveal that Arctic-driven dynamics suppress ozone in winter but can enhance accumulation under certain summer conditions. These findings highlight the importance of Arctic-midlatitude teleconnections in shaping regional air quality and highlight the need to integrate large-scale climate processes into ozone management and climate adaptation strategies.

physics.ao-ph↗

arXiv2024

Understanding North Atlantic Climate Instabilities and Complex Interactions using Data Science

The North Atlantic Oscillation (NAO) index, a measure of sea-level atmospheric pressure variability, holds significant influence over weather patterns in North America and Northern Europe. A negative (positive) NAO value signifies increased cold air outbreaks and storm occurrences (reduced occurrences) in these regions. NAO, a product of multiple climate factors, demonstrates intricate dynamics with sea surface temperature (SST) and sea ice extent (SIE). In this study, we adopt a data-driven approach to explore the complex interplay between NAO, SST, and SIE, revealing a critical instability rooted in positive feedback loops among these climate variables. Our statistical machine learning methodology examines the impacts of melting Arctic SIE and rising SST on NAO, thereby understanding the weather patterns across the North Atlantic region. The skewness analysis yields a negative skewness in NAO across various time intervals -- daily, weekly, and monthly. This skewness, coupled with NAO's mean zero stationary nature, accentuates system instability. To capture these dynamics, we formulate a Bayesian Granger-causal dynamic linear model, which effectively updates the predictor-dependent variable relationship over time. The findings underscore an impending critical instability, indicative of more frequent occurrences of intensely cold climates in eastern North America and northern Europe, theory signifies a notable climate shift. By delving into the intricate feedback mechanisms of NAO, SST, and SIE, our study enhances our comprehension of climate variability, fostering a more informed perspective on the imminent climate changes that lie ahead.

stat.AP↗