Smallholder rice production in Uganda is increasingly affected by the combined effects of climate variability and structural production constraints, yet adaptation decisions remain only weakly informed by empirical evidence. This study developed and evaluated an integrated decision-support framework for prioritising climate adaptation strategies across four major rice-growing districts representing Uganda’s principal rice production systems. District-level climate and yield data (1995–2024), household survey data from 384 farmers (2025), and institutional indicators were analysed using panel regression, stochastic frontier analysis (SFA), and a composite Adaptation Priority Index (API). Climate-related moisture stress increased yield variability, while variance decomposition showed that persistent structural differences accounted for a larger share of yield variability than interannual climate fluctuations. SFA further identified substantial recoverable productivity gaps. Integrating climate-risk reduction, efficiency improvement, implementation feasibility, and system co-benefits, the API ranked water control (drainage and supplemental irrigation), stress-tolerant varieties, and soil fertility restoration as the highest-priority adaptation strategies. Monte Carlo simulation and weighting-sensitivity analyses confirmed that these rankings remained robust under plausible parameter and weighting uncertainty. The proposed framework provides a transparent, reproducible, and operational approach for translating climate-impact evidence into adaptation priorities, thereby supporting evidence-based adaptation planning in data-constrained smallholder agricultural systems.
Quantifying national contributions to present-day warming helps clarify how historical emissions from different countries have shaped global temperature change. This is particularly relevant for India, where rising energy demand and emissions make the country’s future mitigation pathway increasingly important for global efforts to limit warming. Understanding India’s contribution to warming to date, and how it varies across greenhouse gases and emitting sectors, can therefore provide useful context for its future mitigation priorities. Here, we quantify India’s contribution to the 2024 global mean surface temperature change from historical territorial emissions of CO _2 , CH _4 and N _2 O over 1750–2024 using PRIMAP-hist emissions and the FaIR climate model. India contributes approximately 0.047 °C–0.05 °C to 2024 warming, less than the United States, China, the EU27 and Russia among the major emitters considered, but more than several other industrial and emerging economies. Much of India’s contribution has accumulated in recent decades, with the strongest increase occurring after 1990. This rise is driven primarily by CO _2 emissions from the energy sector, while agriculture remains the main source of non-CO _2 warming through CH _4 and N _2 O. Overall, CH _4 and N _2 O account for roughly one-third to two-fifths of India’s total contribution, giving India a more multi-gas profile than many early industrialised emitters. Although India’s cumulative contribution to present-day warming remains comparatively modest, its rapid recent growth highlights the increasing importance of energy system decarbonisation. Reducing fossil fuel dependence in the energy sector will therefore be central to limiting further growth in India’s warming contribution and supporting progress towards its long-term net-zero target for 2070.
Abstract A growing body of research suggests that pro-environmental behaviour (PEB) and mental well-being (MWB) might influence each other, but the mechanisms, directions and conditions under which this relationship is most likely to manifest remain unclear. Based on a review of theories and empirical evidence, we identified eight pathways via which PEB and MWB might influence each other. We propose that PEB and MWB are likely to mutually influence each other over time. We identified four cycles between PEB and MWB; virtuous cycle, perseverance cycle, vicious cycle and comfort cycle. By recognising these mutual influences, interventions that are likely to promote both PEB and MWB can be developed. We propose future research directions to better understand and unpack the mutual relationships between PEB and MWB.
Abstract Salt-affected soils constrain agricultural productivity, particularly in Ethiopia’s irrigated lowlands, where salinity and sodicity severely reduce crop yields. Effective reclamation strategies are therefore essential. This study evaluated the effects of phosphogypsum on soil properties, wheat morphophysiology, yield, and phosphorus-use efficiency under saline–sodic conditions at Kessem Sugar Estate. A factorial pot experiment was conducted during the 2022/23 and 2023/24 cropping seasons using different phosphogypsum rates and wheat varieties. Soil analysis before amendment and after harvest showed that phosphogypsum application markedly improved soil properties. The highest phosphogypsum rate (200% GR) reduced soil pH from 8.45 to 7.93 and exchangeable sodium percentage from 26.5% to 6.9%, while increasing exchangeable Ca, available P, and sulfate by 60.2%, 74.1%, and 159.8%, respectively. Combining 150% or 200% GR phosphogypsum with the salt-tolerant wheat variety ETBW-5879 produced the highest panicle length, seeds per spike, relative-water-content, grain yield, transpiration rate, P uptake, and P-use efficiency. Similarly, phosphogypsum at 150% and 200% GR and the ETBW-5879 variety significantly improved plant height, leaf area, chlorophyll content, biomass yield, photosynthetic rate, and water-use efficiency. Grain yield of ETBW-5879 was substantially higher than that of Gambo and Dande’a under untreated soil conditions, by 294% and 536% at 200% GR, 273% and 502% at 150% GR, and 184% and 358% at 100% GR, respectively. Even at 100% GR, phosphogypsum with ETBW-5879 produced 28% and 87% higher grain yields than phosphogypsum with Gambo and Dande’a, respectively. Overall, ETBW-5879 consistently outperformed the other varieties across phosphogypsum rates. The combination of phosphogypsum at 150% GR with ETBW-5879 was the most effective treatment for improving wheat yield under saline–sodic conditions. However, field validation across diverse saline–sodic environments, including assessment of potential phosphogypsum impurities, is recommended before large-scale adoption.
Abstract Gross primary productivity (GPP) regulates terrestrial carbon uptake and is strongly constrained by temperature and water availability, particularly under extreme climatic conditions. While increasing evidence shows that heat and drought can substantially suppress productivity, most studies rely on mean-based analyses and univariate frameworks, obscuring heterogeneous and compound impacts. Here we investigate how temperature and soil moisture anomalies influence the full distribution of GPP in tropical deciduous and evergreen forests within Peninsular India, a climatically heterogeneous and highly seasonal region. Using satellite-derived GPP and a quantile regression framework, we quantify productivity responses across multiple quantiles, thereby examining heterogeneous sensitivities beyond the mean. We further identify probabilistic thresholds of productivity loss under individual temperature and moisture stressors and evaluate how the joint occurrence of hot–dry events amplifies the risk of extreme GPP reductions. Results reveal differential responses across forest types, where seasonally dry deciduous forests exhibit responses that are highly heterogeneous across quantiles and strongly seasonally modulated compared to evergreen forests, highlighting strong functional contrasts among ecosystems. We find region- and plant functional type–specific thresholds, with seasonally dry deciduous forests exhibiting higher probabilities of productivity loss under relatively small temperature and soil moisture anomalies, while evergreen forests show greater resistance and require larger anomalies to trigger comparable declines. In addition, compound hot–dry conditions substantially increase the probability of extreme productivity losses compared to individual stressors alone. These results highlight the need to resolve distributional sensitivities and risks under individual and compound dry-hot conditions, to improve understanding of ecosystem responses to climate stressors and our capacity to anticipate vegetation responses in a warming and increasingly variable climate.
This study reports a synchronized indoor–outdoor field campaign of atmospheric microplastics (MPs) in the coastal urban setting of Dalian, China. Eight synchronized samples were collected using active total suspended particles samplers. Nile Red fluorescence microscopy was used for screening, and 14.6% of the detected particles were verified using micro-Raman spectroscopy. Airborne microplastic concentrations were 4.17–35.28 n m ^−3 indoors and 5.83–45.28 n m ^−3 outdoors, with a bulk indoor-to-outdoor (I/O) ratio of 0.82 ± 1.07. Under a sensitivity-test configuration (excluding an episodic peak on May 8), the mean outdoor and indoor concentrations were 10.37 ± 6.25 n m ^−3 and 9.07 ± 6.25 n m ^−3 , respectively, yielding an I/O ratio of 0.88 ± 0.80. Morphologically, fragments predominated in both environments. Polymer identification revealed a traffic-dominated signature mixed with residential activities, characterized by a dominance of rubber particles alongside polyethylene terephthalate (PET, 19.09%), polyurethane (PU, 4.55%), polyacrylonitrile (PAN, 3.64%), and cotton (3.64%). The results show that the closed building envelope reduced microplastic loading by 12%–18%, demonstrating a physical attenuation effect. Furthermore, size-resolved analysis revealed size-selective infiltration, with smaller MPs (<20 μ m) exhibiting higher cross-barrier permeability. Rather than attempting to establish generalized standard, this pilot study offering preliminary empirical insights into microplastic fate in the built environment, highlighting infiltration dynamics in exposure models and forming a baseline for future multi-season, multi-typology research.
Assessing how a warmer climate alters tropical cyclone-related extreme rainfall is key to strengthening resilience on the Gulf Coast. We use storyline simulations with the Community Atmosphere Model (CAM), configured at ∼28 km over the North Atlantic and initialized from NOAA GFS and Hadley OI SSTs. We examine how rainfall from Hurricane Helene (2024) changes under the current climate (actual) conditions and under potential future warming of +2, +3, and +4 K above preindustrial (counterfactuals), generated by thermodynamic perturbations to temperature, humidity, and sea surface temperature fields. Analysis of track error (calculated as a function of forecast hours) for the 20-member ensemble shows that the median error remains below 50 km during the first 24 h (when initialized on 25 September 2024, at 0Z) for the actual simulation. Mean landfall timing varies modestly among the experiments, occurring near 51 h in the actual and 4 K ensembles, but shifting slightly earlier to about 49 h in 2 K and 48 h in 3 K. Median accumulated rainfall from 26 to 28 September agrees well with satellite and radar estimates under current climate, while counterfactuals show enhanced rainfall near the Florida coast and substantially increased probabilities of intense 3-hourly rainfall over land. Peak rainfall lies west of Helene’s track over Florida and shifts to the east of the track as the storm moves into the southern Appalachians. Changes in 3-hourly accumulated rainfall are sensitive to the storm’s location and the 4 K produces the largest rainfall rates at both near-landfall and inland regions. For the 99.9 ^th percentile, the percentage change in the inland region is 10%–30% in the 4 K scenario compared with the actual scenario, while near landfall it is 32%–44%. Results show that storyline analysis is more challenging inland, where longer lead times can produce non-monotonic changes in 3-hourly rainfall with each 1 K increase in warming.
Subsistence agriculture is highly vulnerable to climate change, and its resilience is constrained by local conditions and networks, negatively impacting livelihoods, food security, and local culture. In the Biobío region of Chile, subsistence farming is crucial for food security; however, it is expected to be significantly affected by climate change and requires a territorial instrument that systematizes agricultural, environmental, and social information for management and decision-making. This study evaluates the vulnerability and resilience of subsistence agriculture at the municipal level using composite indices. The vulnerability index was developed following the Intergovernmental Panel on Climate Change framework, integrating indicators of exposure, sensitivity, and adaptive capacity. The resilience index considered territorial multifunctionality, institutional interaction, and local territorial management and response capacity. Data were sourced from official records and scientific literature on climate, agricultural production, and territorial characteristics. Nineteen indicators were standardized on a scale ranging from 0 to 1, where higher values represented greater vulnerability or resilience, depending on the index considered. The indices were applied to evaluate 33 municipalities in the Biobío region, and the results were spatially represented. The Biobío region exhibited medium levels of vulnerability (0.594) and resilience (0.418). Coastal municipalities showed the highest vulnerability due to their reliance on rainfed agriculture, low crop diversification, and severe soil erosion. In contrast, mountain municipalities, such as Alto Biobío, exhibited higher resilience, reflecting comparatively greater territorial resilience capacities associated with diversified production systems, irrigation infrastructure, crop rotation practices, and stronger institutional linkages. The resulting indices offer a replicable and integrative methodological framework. Our findings highlight the need for targeted public policies on soil restoration, irrigation modernization, crop diversification, and strengthening local networks to enhance resilience. This approach provides a strategic tool for climate risk planning in rural agricultural territories.
The aim of this study is to evaluate diesel, Waste cooking oil biodiesel (WCO-BD), B20 (20 vol.% WCO-BD + 80 vol.% diesel) and B20 doped with Solketal additive (2, 6, 8 and 10 vol.%) and to identify a blend-speed combination that optimizes brake thermal efficiency (BTE), fuel economy and regulated emissions under realistic speed variability. The novelty lies in treating the tested engine speeds as discrete operating cases and applying a speed-wise robust desirability methodology integrated with regret analysis, non-parametric significance tests and bootstrap confidence intervals to rank blends on both average and worst-case performance. Experimentally, a single cylinder diesel engine was fueled with seven blends (diesel, WCO-BD, B20, B20-SK2/6/8/10) over 1200–2400 rpm, measuring combustion, performance and emission characteristics. These responses were mapped to Derringer–Suich desirability, aggregated into an overall index and then summarized via mean desirability, worst-case desirability and a composite robust index. WCO-BD production achieved a 92% mass yield, while B20-SK8 delivered 37.8% BTE at 2000 rpm versus 36.3% for B20 and 35.6% for WCO-BD, reduced NO _x by 12%–20% and smoke by 28%–35% relative to diesel and WCO-BD. B20-SK8 attained the highest scenario-aggregated desirability (0.626) with a zero-regret profile and a high desirability band over 1600–2000 rpm. Practically, the results indicate that B20 doped by 8 vol.% Solketal offers a speed-robust, multi-pollutant improvement for retrofitting small diesel engines on B20. The scenario-based methodology is generalizable to other fuels derived from waste and dual fuel concepts, supporting future robust calibration of low-carbon diesel engines.
Global mean surface temperature is the primary target used to design and compare solar geoengineering simulations and it is a useful first-order predictor of the climate response. Its limits as a measure of performance, however, remain largely untested. Studies of how solar geoengineering alters climate extremes exist yet remain inadequately explored. We argue that extreme-event metrics should become a formal part of performance assessment, so that success is judged by how interventions alter the risks of floods, heatwaves and droughts especially for climate vulnerable regions.
Artificial light at night (ALAN) is increasingly recognized as an emerging environmental stressor with potential implications for agricultural systems, particularly for photoperiod-sensitive crops such as rice. However, large-scale and spatially explicit assessments of potential ALAN exposure in agricultural landscapes remain limited. In this study, we present a reproducible geospatial framework for screening potential ALAN exposure in rice-growing landscapes by integrating high-resolution rice cultivation maps and road-network-based proxies representing public lighting infrastructure, with calibrated Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime radiance data providing a common spatial reference. To evaluate the sensitivity of road-based estimates, multiple road-network scenarios and nominal road-proximity masks were incorporated into the analysis, and rice–road intersections were aggregated to a common VIIRS-scale grid for national comparisons. The framework was demonstrated through a nationwide case study in Thailand, one of the world’s major rice-producing countries. Our results show that national estimates are highly sensitive to road-network selection and that local-access roads contribute substantially to the final proximity estimates. Under the complete road-network scenario, estimated rice–road proximity areas increased from 11 175 km $ ^2$ (11.80%) to 16 697 km $ ^2$ (17.63%) for major-rice systems and from 1 527 km $ ^2$ (10.12%) to 2 598 km $ ^2$ (17.20%) for minor-rice systems when moving from one- to two-pixel proximity masks. These findings demonstrate that road proximity should be interpreted as a structural proxy rather than direct evidence of ALAN exposure or agricultural impacts. Consequently, the proposed approach should be regarded as a first-order screening tool rather than a direct assessment of crop damage or yield loss. Overall, this study provides a transparent, reproducible, and transferable approach for screening potential ALAN exposure in agricultural systems and highlights the importance of considering nighttime lighting as an emerging environmental factor in agroecosystems. The proposed framework can support future field-validation studies, light-aware agricultural management, and environmental monitoring in rapidly developing regions.
The identification and distribution analysis of salt marsh vegetation in Yangtze Estuary are essential for conservation, management and ecological sustainable development of coastal wetlands in this region. However, the accuracy of identification and analysis depends on the model performance and the number of collected sample points used for training. Therefore, this study develops a Bayesian-optimized random forest (BoRF) classification workflow for salt marsh vegetation mapping and combines it with an unmanned aerial vehicle (UAV)-assisted sample collection strategy. After obtaining the optimal feature combination through the comparative analysis, the vegetation classification and precision evaluation were carried out using multi-temporal Sentinel-2A imagery and the sample points collected by a DJI Mavic 3 M drone, in four representative areas of Yangtze Estuary: Chongming East Beach, Jiu Duan Sha, Nanhui East Beach, and Nanhui Coastal Beach. The experiment results indicate that, among the four compared methods, BoRF achieves the best overall performance, with an overall accuracy of 0.88 and a Kappa coefficient of 0.85, and reduces misclassification among vegetation types with similar spectral characteristics. In addition, the UAV-assisted sample construction strategy improves classification accuracy by approximately $3\%$ compared with conventional field-survey-based sampling. These results demonstrate the practical value of the proposed workflow for salt marsh vegetation classification in complex coastal wetland environments.
Sea-level rise (SLR) poses escalating risks to the U.S. Gulf Coast, where critical industries, infrastructure, and populations are concentrated in low-lying areas. This study estimates the short- and long-run economic effects of SLR using a panel autoregressive distributed lag-pooled mean group framework applied to county-level data from 26 coastal counties across five Gulf Coast states from 2005 to 2021. Over this period, mean sea level increased by approximately 11 cm on average across counties, with cumulative changes ranging from about 4 cm to over 24 cm and substantial cross-county variation, providing a meaningful empirical setting for identifying economic adjustment. We analyze sectoral outcomes in employment, business establishments, gross domestic product, and wages. Long-run estimates suggest persistent economic declines associated with rising sea levels, particularly in leisure and hospitality, natural resources and mining, and trade, transportation, and utilities. Construction exhibits mixed dynamics, with short-run disruptions followed by increases in establishments consistent with adaptation-related investment. Although some short-run gains emerge following disaster-related activity, these effects do not offset longer-term structural contraction. Wetland coverage is associated with more heterogeneous longer-run adjustment, suggesting that coastal land-cover conditions may shape economic vulnerability. Descriptive evidence indicates more variable exposure and less stable economic performance in disadvantaged and disaster-prone counties. These findings underscore the need for regionally tailored adaptation strategies that integrate ecosystem preservation with climate-resilient infrastructure and long-run economic planning.
Causal inference plays a central role in sustainability studies, providing the foundation for evidence-based scientific discovery and policy evaluation. However, uncovering credible causal relationships in complex real-world settings often relies heavily on the judgment of domain experts and extensive contextual knowledge. With the emergence of large language models (LLMs), an open question arises as to whether these models can meaningfully support causal reasoning for sustainability research. This study systematically evaluates the capacity of LLM to infer causal hypotheses and methodological structures from contextual descriptions of empirical research. We construct a benchmark based on five high-quality, peer-reviewed sustainability studies and design structured prompts that replicate the informational context available to human researchers prior to estimation. LLM outputs are evaluated against expert-validated ground truth in terms of causal edge recovery, scope expansion, and alignment with identification strategies, allowing for a quantitative assessment of conceptual causal reasoning rather than numerical estimation. Our findings indicate that GPT-5 recovers core causal edges with moderate accuracy (0.33–0.85), and assigns causal direction with high reliability (0.87–1.00). However, the scope expansion rate achieves roughly 0.58–0.93 of the causal edges proposed by the model, indicating a strong tendency toward over-connection between variables. Overall, our findings contribute to a deeper understanding of the potential and limitations of LLMs as tools for causal reasoning and methodological support in empirical sustainability research.
Using satellite observations from Atmospheric Infrared Sounder (AIRS) and Global Navigation Satellite System (GNSS) Radio Occultation (RO), this study evaluates air-specific humidity (QA) biases in CMIP6 models and Community Earth System Model version 2 (CESM2) sensitivity experiments with and without falling-ice radiative effects (FIREs). CMIP6 models are classified by their treatment of radiative effects of frozen hydrometeors: SON2 separates cloud and falling ice, SON1 combines them, and NOS includes only cloud ice. SON1 and NOS exhibit larger upper-tropospheric humidity biases than SON2, indicating that neglecting or simplifying FIREs enhances vertical moisture transport and moistening aloft. The explicit treatment in SON2 produces a more realistic humidity structure, closer to observations. CESM2 sensitivity experiments confirm these results: disabling FIREs reduces surface wind stress, warms sea surface temperatures, and intensifies convection over trade-wind regions, generating upper-tropospheric moistening patterns resembling SON1 and NOS (subgroups in CMIP6). These findings highlight the critical role of FIREs in cloud–radiation–circulation coupling and support broader adoption of separate frozen-hydrometeor radiative treatments in future CMIP frameworks.
Heat exposure is associated with mortality and several adverse health outcomes, including heat strokes and heart diseases. The availability of accurate and high spatial temporal resolution of meteorological information is crucial to evaluate these associations. In this study, we use data from personal weather stations (PWS) to bias correct the recent 1 km high-resolution urban meteorology for impacts dataset (HUMID) daily temperature in the Atlanta metropolitan area for three summer periods from 2016 to 2018. We first developed a 4-step quality control scheme to remove potential erroneous data from PWS. Then, we assessed several bias correction models, a geostatistical Bayesian downscaler modeling approach, a random forest model, and a hybrid approach that uses residuals from the random forest model as the outcome for the Bayesian regression to account for spatial-temporal dependency not captured by random forest. Our results show that the Bayesian downscaler combined with random forest was the best predictive model with root mean square errors (RMSE) of 1.577 °C for maximum temperature and 0.696 °C for minimum temperature. In addition, our models resulted in bias-corrected temperatures that are, on average, lower than HUMID, suggesting that HUMID tends to overestimate temperature in most areas.
In recent years, the global community has aimed to mitigate the loss of biodiversity due to climate change and has focused on sustainability. Habitat suitability (HS) studies play a crucial role in biodiversity conservation and sustainable development practices. In this study, we explored the insights of 38 746 HS-related documents recorded in the Scopus database between 1947 and 2024, applying bibliometric analysis coupled with the latent dirichlet allocation (LDA) unsupervised machine learning model. We examined the publication trends, cooccurrence and co-authorship networks, investigated species and their kingdoms and the thematic LDA topics. As a result, we observed an increasing trend that followed Benford distribution ( P -value > 0.308), suggesting a natural growth pattern rather than any systematic irregularity in publications over time. Keywords such as habitat quality, ecosystems and biodiversity were highly interconnected. The United States of America authored the highest number of publications, followed by the People’s Republic of China and the United Kingdom. While mapping the scientific names, we observed HS research was mainly centered on the Animalia kingdom, followed by plants, fungi, and chromista. In HS studies, species such as Salmo trutta (Animalia), Phragmites australis (Plantae), E. coli (Bacteria), Plasmodium falciparum (Chromista), Trypanosoma cruzi (Protista), and Batrachochytrium dendrobatidis (Fungi) were identified as dominant representatives within their respective biological domains. From the LDA model, six thematic topics: ecological modeling, HS, bioactivity and integrity, species interactions, habitat quality, and taxonomy environmental modeling were observed. The comprehensive approaches and findings of this research offer insights into future researchers, conservationists, and policymakers, supporting environment and conservation management practices.
Understanding the climate-driven variations in sediment transport is critical for water and soil conservation. This study explores the driving mechanisms of sediment transport in the Qingshui River Basin between 1982–1998 and 1999–2015, using wavelet analysis, structural equation modeling (SEM), and moisture budget analysis. Our results show that sediment transport exhibited a significant increase during 1982–1998 and a notable decline during 1999–2015. SEM-based results indicate that precipitation and runoff are the dominant drivers of sediment transport in two periods, with runoff’s contribution (>60%) intensifying markedly in the later period. Further analysis of large-scale climate teleconnections reveals that Pacific decadal oscillation (PDO) phase transitions may be a key factor in regulating basin-scale sediment transport, with a correlation coefficient of 0.26 ( p < 0.1). During the positive PDO phase (1982–1998), tropospheric low-pressure anomalies induced southeasterly winds and enhanced moisture advection, boosting precipitation/runoff and as a consequence, increasing sediment transport. Conversely, the negative PDO phase (1999–2015) triggered high-pressure dominance and stable atmospheric stratification, suppressing precipitation and sediment transport. Moisture budget analysis confirms that horizontal dynamic processes dominated precipitation anomalies in both periods, with contribution rates of 49.18% and 70.91%, respectively, highlighting horizontal moisture advection in modulating hydro-sediment responses. This study clarifies the coupling mechanism of local hydroclimatic factors and large-scale climate teleconnections for sediment transport variations, providing new scientific insights for sustainable soil conservation and water resource management at the basin scale.
India’s pledge to achieve net-zero greenhouse gas emissions by 2070 requires a clean cooking transition that simultaneously addresses household air pollution (HAP), malnutrition, and energy poverty. Despite widespread adoption of liquified-petroleum gas (LPG) stoves, sustained and exclusive LPG use remains limited, particularly in rural and low-income households, due to recurring refill costs, fuel stacking with biomass, and continued reliance on an imported fossil fuel. We propose that pairing induction cooking (IC) with iron cookware offers an efficient and equitable pathway to cut HAP and iron-deficiency anaemia, while aligning cooking energy use with India’s net-zero trajectory. We present a holistic framework spanning economic, environmental, health, and social dimensions to guide policy design and further implementation. A scaled transition to induction-plus-iron has the potential to deliver climate mitigation and adaptation, reduce the burden of air pollution-related disease, alleviate women’s time poverty, and align household energy use with India’s net-zero and planetary health commitments.
