Climate Uncertainty Research Articles

Artificial Intelligence (AI) to Build Climate Models to Improve Weather Forecasting as Torrential Rains, Floods and Droughts Proliferate Across the Vast Country

Global warming has triggered more intense clashes of weather systems in India in recent years, increasing extreme weather events, which the independent Centre for Science and Environment estimates have killed nearly 3,000 people this year. Weather agencies around the world are focussing on AI, which can bring down cost and improve speed, and with a recent Google-funded model found to have outperformed conventional methods. Accurate weather forecasting is particularly crucial in India, a country of 1.4 billion people, many impoverished, and the world's second-largest producer of rice, wheat and sugar Using AI with an expanded observation network could help generate higher-quality forecast data at lower cost. The increasing frequency and intensity of torrential rains, floods, and droughts due to climate change present significant challenges to weather forecasting and disaster preparedness. Artificial Intelligence (AI) offers transformative potential in building advanced climate models to address these challenges. By integrating vast datasets, including satellite imagery, historical weather records, and real-time sensor data, AI enhances the accuracy and resolution of climate predictions. Machine learning algorithms can identify complex patterns, simulate localized weather phenomena, and predict extreme weather events with greater precision. This paper explores the role of AI-driven climate models in improving weather forecasting, enabling proactive responses to mitigate the impacts of climate-induced disasters across diverse regions. Enhanced forecasting through AI not only safeguards lives and livelihoods but also supports sustainable resource management in the face of escalating climate uncertainties

Weathering the Risk: How Climate Uncertainty Fuels Corporate Fraud

Weathering the Risk: How Climate Uncertainty Fuels Corporate Fraud

Unpacking the progression of climate uncertainty into precarity in the urban context of drylands: the case of floods in Lodwar, Turkana

ABSTRACT Climate uncertainty has always existed both as a socio-ecological reality for pastoralists living with climate variability in drylands and as a component within climate modelling, yet there is little consideration as to the experiences of poor people in the urban drylands living with intensified hazards. This paper discusses an emerging conceptual nexus of uncertainty and precarity, using the example of flood disaster governance in Lodwar, a small yet rapidly growing urban centre in the Arid and Semi-Arid Lands of Kenya. The paper is grounded within feminist political ecology and is based on multiple predominantly qualitative research methods, mainly semi-structured interviews. We conclude that precarity unfolds through a combination of climate uncertainty, arbitrary vertical (institutional support) and horizontal (social network) disaster governance, and vulnerability defined by structural causes. It manifests through stressful living in suspension waiting for governmental support, as well as unpredictability in relation to support within social networks that can be both crucial but also unpredictable and exclusionary. Finally, precarity is intersectional and subjective; vulnerable people experience high unpredictability and suffering and have a low ability for strategic adaptation even if they manage to be resourceful during the disaster.

Exploring Optimal Complexity for Water Stress Representation in Terrestrial Carbon Models: A Hybrid‐Machine Learning Model Approach

AbstractTerrestrial biosphere models offer a comprehensive view of the global carbon cycle by integrating ecological processes across scales, yet they introduce significant uncertainties in climate and biogeochemical projections due to diverse process representations and parameter variations. For instance, different soil water limitation functions lead to wide productivity ranges across models. To address this, we propose the Differentiable Land Model (DifferLand), a novel hybrid machine learning approach replacing unknown water limitation functions in models with neural networks (NNs) to learn from data. Using automatic differentiation, we calibrated the embedded NN and the physical model parameters against daily observations of evapotranspiration, gross primary productivity, ecosystem respiration, and leaf area index across 16 FLUXNET sites. We evaluated six model configurations where NNs simulate increasingly complex soil water and photosynthesis interactions against test data sets to find the optimal structure‐performance tradeoff. Our findings show that a simple hybrid model with a univariate NN effectively captures site‐level water and carbon fluxes on a monthly timescale. Across a global aridity gradient, the magnitude of water stress limitation varies, but its functional form consistently converges to a piecewise linear relationship with saturation at high water levels. While models incorporating more interactions between soil water and meteorological drivers better fit observations at finer time scales, they risk overfitting and equifinality issues. Our study demonstrates that hybrid models have great potential in learning unknown parameterizations and testing ecological hypotheses. Nevertheless, careful structure‐performance tradeoffs are warranted in light of observational constraints to translate the retrieved relationships into robust process understanding.

Climate Change Education in Curriculum Documents by The Norwegian Directorate for Education and Training: A Content Analysis

Abstract The education sector has increasingly been involved in informing learners about the issue of climate change (Reid, 2019), given that this issue represents an existential threat to young people and, generally, to the human race (Sears, 2020). The education sector has responded to the urgency of climate change by proposing the notion of “climate change education”. It can be defined as the process of learning in the face of climate change-related risks and uncertainty (Stevenson et al., 2017). Climate change education is argued to aim at raising primary and secondary school students’ awareness of climate change and its consequences (Tang, 2024). Climate change education is embraced by the education sector in a number of Nordic countries, in particular, Norway (Seikkula-Leino et al., 2021). In Norway, for instance, climate change education seems to fall within the scope of The Norwegian Directorate for Education and Training (henceforth – The Directorate), which is responsible for the governance of the education sector from kindergarten to secondary school (Kapranov, 2021). To-date, however, there is no published research on how climate change education is represented in The Directorate’s curriculum documents that pertain to pre-primary, primary, and secondary school curricula. Seeking to bridge the current research gap, the article presents a study that looks into this underresearched aspect by means of analysing a corpus of The Directorate’s curriculum documents available on its official homepage https://www.udir.no/. The corpus of The Directorate’s curriculum documents is investigated in the study by applying an approach to content analysis developed by Krippendorff (2004). The results of the content analysis indicate that climate change education appears to be represented in the corpus in a rather disproportionate manner. Specifically, climate change education is addressed, predominantly, in the curriculum documents in sciences in upper secondary school, whereas it is substantially underrepresented in curriculum documents on pre-primary and primary school levels. These and other findings are further discussed in the article. The article concludes with a range of practical suggestions that arise out of the present findings.

Assessing the vulnerability of empirical infrastructure networks to natural catastrophes

Human-made infrastructures are complex systems continually exposed to events that threat their function, such as cascading failures, occurring when the flow of physical quantities is redistributed within the network as a consequence of localized disruptions. Nevertheless, the role played by exogenous catastrophic events and internal failures on the robustness of critical infrastructures is usually addressed independently, under simplifying assumptions and lacking a unified picture for realistic risk assessments. Here, we fill this gap by introducing the Operational-Affected-Dismantled (OAD) model that captures both local and nonlocal failure propagation mechanisms. The model combines reaction–diffusion processes for local spreading with a global field effect for long-range interactions, allowing us to quantitatively characterize the cascade dynamics in infrastructure networks. Moreover, we include information on external stressors to assess the robustness of empirical network infrastructures and build spatial risk maps. By using data from severe storms (2009–2016) and from earthquakes (2000–2023) as stressors of the North American power grid and the worldwide airline transportation system, respectively, we offer a quantitative way to rank events by their potential to trigger systemic effects. By analyzing the response of the European power grid to simulated severe storms, we find that it can show high levels of systemic risk. Uncertainty in global climate and the accelerating frequency of extreme events all over the globe call for novel strategies to quantify, adapt to and mitigate systemic risk. Our framework provides a suitable starting point to assess the robustness of empirical systems in realistic and what-if scenarios.

Advanced AI, Machine Learning and Deep Learning Techniques for Climate Change Studies: A Review

Climate models have become an important tool in understanding as well as projecting future climate conditions. They thus become very critical to decision-making processes, especially in mitigation and adaptation strategies. This paper provides a general overview of methodologies that are applied in climate modeling, its validation, and uncertainty mapping, notably their strengths, limitations, and challenges. The paper takes the reader through all the intricacies associated with the modeling of climate systems: global circulation models, regional climate models, and statistical downscaling. Much emphasis is given to the issue of model choice depending on the research objectives and spatial scales. Thorough model validation is considered, emphasizing that observational data forms the core and good quality and availability are issues. Climate modeling should incorporate uncertainty quantification as a core activity. This paper debates estimation and representation techniques of model uncertainties applied, such as probabilistic approaches, sensitivity analysis, and ensemble modeling. Introduce an idea called here uncertainty mapping, as a means of communicating the climate-related risks. This paper sets this tone by asserting that uncertainties in climate modeling have to be communicated as clearly as possible to decision-makers. No matter how intrinsically complex and limited they are, climate models represent, by themselves, very valuable means for the task of estimating the probable futures of climates. This quest ranges from enhancing model accuracy to ever-lowering estimates of uncertainties, all impelled by new advances made in computational powers and deeper insights into the climate systems.

Polar ice sheets are decisive contributors to uncertainty in climate tipping projections

The Earth’s climate is a complex system including key components such as the Arctic Summer Sea Ice and the El Niño Southern Oscillation alongside climate tipping elements including polar ice sheets, the Atlantic Meridional Overturning Circulation, and the Amazon rainforest. Crossing thresholds of these elements can lead to a qualitatively different climate state, endangering human societies. The cryosphere elements are vulnerable at current levels of global warming (1.3 °C) while also having long response times and large uncertainties. We assess the impact of interacting Earth system components on tipping risks using an established conceptual network model of these components. Polar ice sheets (Greenland and West Antarctic ice sheets) are most decisive for tipping likelihoods and cascading effects within our model. At a global warming level of 1.5 °C, neglecting the polar ice sheets can alter the expected number of tipped elements by more than a factor of 2. This is concerning as overshooting 1.5 °C of global warming is becoming inevitable, while current state-of-the-art IPCC-type models do not (yet) include dynamic ice sheets. Our results suggest that polar ice sheets are critical to improving understanding of tipping risks and cascading effects. Therefore, improved observations and integrated model development are crucial.

Unwrapping the triptych of climatic, social and energy-market uncertainties in the operation of multipurpose hydropower reservoirs

The ongoing debate about hydropower and its role in the transition towards renewable energy development is strongly associated with its sustainable character, social and environmental footprint, and potential benefits. Multipurpose hydropower reservoirs play a significant dual role as they serve both water and energy uses. However, their operation and management are subject to inherently uncertain processes, future challenges, and externalities originating from climate, society and the energy market. In this context, we contribute an uncertainty-aware optimization methodology that supports operators in accounting for the cascade effects of three main uncertain drivers, i.e., rainfall, water demands, and energy scheduling. To describe climatic and energy-market uncertainties, stochastic approaches are followed to generate 2000 years of daily synthetic rainfall and electricity price data, respectively. To account for human-oriented procedures, i.e., water and energy targets, statistical analyses of historical abstractions are employed to fit copula-based relationships, in which the desirable releases for energy production depend on day-ahead electricity prices. Finally, a toolbox is established, offering insights for decision-making regarding the estimated profits, their expected changes, and the associated risk due to climate or market-oriented shifts. Our approach is demonstrated in a multipurpose reservoir in Greece, Plastiras, which is affected by rapidly increasing socioeconomic conflicts. Our findings indicate that an improved understanding of uncertainty can lead to more efficient operation policies and shield operator from misleading perceptions and their often-detrimental effects. Specifically, in the high electricity price era the expected profits are substantially unstable, since a suboptimal trade-off between irrigation and energy production may be risky and costly.

Asymmetric Impact of Exchange Rate on Balance of Payment in Nigeria

Maintaining a stable exchange rate is key to attaining macroeconomic objectives for any emerging economy like Nigeria. Thus, an unstable exchange rate may lead to economic instability and uncertainty in the investment climate of an economy like Nigeria. Over time Nigeria has been faced with unstable exchange rates. persistent economic challenges and an unfavourable balance of payments. In the pursuit of these macroeconomic goals, Nigeria has implemented various exchange rate regimes, including fixed, floating, and a unified exchange rate system. However, these goals remain a challenge to the economy. It is against this backdrop that this study employed the Nonlinear Autoregressive Distributed Lag estimation technique to analyze the asymmetric impact of exchange rate fluctuations on Nigeria’s balance of payments. Additionally, it considers the impact of control variables such as real interest rates, gross fixed capital formation, trade openness, trade policies, financial deepening, natural resources, and trade balance. Findings from this study revealed that the exchange rate has an asymmetric impact on the balance of payments in Nigeria. Other control variables were statistically insignificant but contributed positively to the balance of payment in Nigeria. Therefore, the research suggested that to improve the balance of payments of the country, Nigeria’s monetary authorities; the Central Bank of Nigeria (CBN), should implement exchange rate policies that will promote domestic investment and encourage export activities. Interest rate policies that would encourage domestic investments are necessary. This can be achieved through reducing the monetary policy rate which is a driver of banks’ interest rate. This will further encourage loanable funds for investment, hence a favourable balance of payment in Nigeria. Furthermore, the Federal Government through the Ministry of Petroleum and Natural Resources should leverage international oil prices to enhance export activities and strengthen the balance of payments.

Climate policy uncertainty, market volatility and sustainable investing: new insights from a TVP-VAR framework

ABSTRACT Under a TVP-VAR framework, this study investigates not only the role of climate-transition stocks (CS) and green bonds (GB) in combating climate policy uncertainty and market volatility but also the association between CS and GB in the context of climate and market uncertainties. Findings reveal that both CS and GB are resistant to climate and market risks from a medium- to long-term perspective. However, from a short-term perspective, extreme market turmoil caused by the COVID-19 pandemic has weakened the ability of CS and GB to withstand market risks. Furthermore, risk spillovers between CS and GB are consistently positive and tend to be medium-lived, providing new evidence for co-movements between sustainable equity and bond markets.

Hidden semi-Markov models for rainfall-related insurance claims

We analyze the temporal structure of a novel insurance dataset about home insurance claims related to rainfall-induced damage in Norway and employ a hidden semi-Markov model (HSMM) to capture the non-Gaussian nature and temporal dynamics of these claims. By examining a broad range of candidate sojourn and emission distributions and assessing the goodness-of-fit and commonly used risk measures of the corresponding HSMM, we identify an appropriate model for effectively representing insurance losses caused by rainfall-related incidents. Our findings highlight the importance of considering the temporal aspects of weather-related insurance claims and demonstrate that the proposed HSMM adeptly captures this feature. Moreover, the model estimates reveal a concerning trend: the risks associated with heavy rain in the context of home insurance have exhibited an upward trajectory between 2004 and 2020, aligning with the evidence of a changing climate. This insight has significant implications for insurance companies, providing them with valuable information for accurate and robust modeling in the face of climate uncertainties. By shedding light on the evolving risks related to heavy rain and their impact on home insurance, our study offers essential insights for insurance companies to adapt their strategies and effectively manage these emerging challenges. It underscores the necessity of incorporating climate change considerations into insurance models and emphasizes the importance of continuously monitoring and reassessing risk levels associated with rainfall-induced damage. Ultimately, our research contributes to the broader understanding of climate risk in the insurance industry and supports the development of resilient and sustainable insurance practices.

Sustainable land and irrigation management to limit loss of hydropower in the Andes-Amazon headwaters

Water plays a critical role in adapting food and energy systems to climate change in the Andes. However, robust climate solutions that work across sectors have not been explored for these working landscapes. Here we develop a framework consisting of high-fidelity catchment and hydropower models while considering climate and other uncertainties in a robust, bottom-up manner for the upper Huallaga River in Peru. We then explore adaptation strategies across sectors. Our results highlight that irrigation will be critical for maintaining food production under climate change. Yet, an unplanned expansion of irrigation could lead to a near total loss of firm hydropower downstream. Exploring 10,080 possible landuse/management adaptation pathways, we find limited opportunities for win-win solutions under climate change. In the light of such challenges, our results highlight the need for navigating structural trade-offs between upstream land and water users and downstream interests in the Andes in a strategic and equitable manner.

Venture Capital and Vulnerability: Navigating Natural Disasters and Investment Resilience

ABSTRACTThis study examines the impact of natural disasters on venture capital (VC) investment decisions. Using 47 catastrophic natural disasters occurred in the United States from 1990 to 2019, our empirical analysis reveals a significant reduction in VC investments in disaster zones. Additionally, natural disasters negatively influence VC exit strategies, reducing the likelihood and extending the time to successful exits via IPOs. However, we find that green VCs are more likely to invest in disaster‐affected areas, indicating potential resilience through green technological innovation. Our findings emphasize sustainability and disaster mitigation, and offer valuable insights for policymakers and investors amidst rising climate uncertainties.

Scalable, data-assimilated models predict large-scale shoreline response to waves and sea-level rise

Coastal change is a complex combination of multi-scale processes (e.g., wave-driven cross-shore and longshore transport; dune, bluff, and cliff erosion; overwash; fluvial and inlet sediment supply; and sea-level-driven recession). Historical sea-level-driven coastal recession on open ocean coasts is often outpaced by wave-driven change. However, future sea-level-driven coastal recession is expected to increase significantly in tandem with accelerating rates of global sea-level rise. Few models of coastal sediment transport can resolve the multitude of coastal-change processes at a given beach, and fewer still are computationally efficient enough to achieve large-scale, long-term simulations, while accounting for historical behavior and uncertainties in future climate. Here, we show that a scalable, data-assimilated shoreline-change model can achieve realistic simulations of long-term coastal change and uncertainty across large coastal regions. As part of the modeling case study of the U.S. South Atlantic Coast (Miami, Florida to Delaware Bay) presented here, we apply historical, satellite-derived observations of shoreline position combined with daily hindcasted and projected wave and sea-level conditions to estimate long-term coastal change by 2100. We find that 63 to 94% of the shorelines on the U.S. South Atlantic Coast are projected to retreat past the present-day extent of sandy beach under 1.0 to 2.0 m of sea-level rise, respectively, without large-scale interventions.

The Impacts of Beaver Dams on Groundwater Regime and Habitat 6510

Changes in land usage, increasing climatic uncertainty, and dynamic development of the rate of natural population growth of the Eurasian beaver will lead to increasing benefits and disadvantages from beaver activity. During three growing seasons from 2020 to 2022, four cross-sections were marked on unused sub-irrigation systems with the periodic occurrence of beaver dams, located on organic soils in parts of the facility protected by the Habitats Directive (natural habitat 6510) in Central Poland. Periodic water table measurements in wells, the beds of adjacent ditches, and the riverbed were carried out. Identification of the states and structures of plant communities was done using the botanical-weight analysis of several samples with an area of 1 m2. Beaver dams increased water levels in the river, ditches, and groundwater depth in over 78% of events in 2020–2022 years. A large impact of precipitation on the hydraulic conditions in the meadow was observed. In the studied area, since a moderately moist habitat (6510) is protected within the Natura 2000 network, phenomena increasing soil moisture, in the absence of mowing of meadows and the occurrence of expansive herbaceous vegetation that tolerates increased moisture, may lead to the disappearance of these habitats, especially in the zone near the riverbed.

Effect of Extreme Weather Conditions on Delivery Services of India Post and Private Couriers in Uttar Pradesh

The impact of extreme weather conditions on delivery services, including India Post and private couriers, is increasingly becoming a concern in Uttar Pradesh, India. This paper explores how severe weather events such as heavy rains, floods, heat waves, and storms disrupt the logistical operations of these services. The study highlights that extreme weather can lead to delays, damaged goods, and operational inefficiencies due to road blockages, infrastructure damage, and health risks for delivery personnel as per Pekka L. et-al (2014). India Post, with its extensive rural reach, and private couriers, which focus on urban areas, both face challenges in maintaining timely deliveries. While technological interventions like GPS tracking and route optimization are being used, infrastructure improvements and weather-resilient planning are needed to mitigate these disruptions. The study is an attempt to understand the probable impacts on delivery service and the company of India Post and private courier companies due to various weather conditions such as delay in delivery, concern about safety of shipment, and delivery person, financial loses, risk of accidents, blockage of roads, time sensitivity of parcel etc. These challenges may be faced by both India Post and private courier service companies. Apart from ascertaining the effects and impacts, this study also emphasizes the necessity for adaptive strategies to ensure the continuity of delivery services and to safeguard the safety of parcel and staff involved in delivery amidst climate uncertainties in Uttar Pradesh.

Impact of Climate Risks and Global Uncertainties on Technology and Environmental Protection Stocks: A Study on China

This study aims to examine the relationship between CUM, CPU, CU, and UCT with the FTSE China Technology Index (FTXIN410) and SZSE Environmental Protection Index (SZEPI) using ARDL and NARDL methods. To test the robustness of the model, three additional parametric methods are employed: FMOLS, DOLS, and CCR. The technology and environmental protection sectors in China are crucial for the country's sustainable economic future. Understanding the impact of global and local uncertainties on these sectors is critical for predicting sectoral trends and future market dynamics. Climate uncertainties and global uncertainties can impact investors' returns and market behaviors. In critical sectors such as technology and environmental protection, uncertainties need to be closely monitored for their implications on investment decisions and market stability. The results of the study indicate a long-term relationship between China’s climate uncertainties and global uncertainties with technology and environmental protection indices.

Local Perception of The Effects of Climate Uncertainty on Cattle Farming in Burkina Faso: A Case Study of The Mouhoun Province

Climate change represents a threat to pastoralists in the Mouhoun Province, whose main source of income is the exploitation of natural and animal resources. The problem of this study is based on the good perception of breeders which was necessary for them to adapt to the climatic crises that have already occurred. The objective of this study is to analyze the perception of the effects of climate uncertainty on ruminant breeding in the Mouhoun province. For this purpose, 286 breeders, including agro-breeders and agro-breeders, or 22 breeders per village, were surveyed. In addition, a focus group was organized in each village, for a total of thirteen (13). Phinks software was used to process the individual survey data. The results indicate a deterioration in the forage supply of natural rangelands (100%), an increase in the mobility of animals (69.31%), an increase in morbidity (69.3%) and a drop in the frequency of twin births (56.0%). In summary, it appears that the population has a good perception of the effects of the local climate on animals and forage resources. Furthermore, although it is difficult to integrate research evidence into development policies and practices, the results of this research could guide policy decision-making. This study leads to the conclusion that the changes perceived by breeders are relevant for the development of adaptation strategies to climate change.

Segregated Supply of Sustainable Aviation Fuel to reduce Contrail Energy Forcing – Demonstration and Potentials

Aviation contributes about 4 % to net anthropogenic climate forcing, with contrails being the largest individual contributor to radiative forcing from aviation. One option to mitigate contrail-related climate impacts is using kerosene containing fewer or no aromatic components and thus showing a higher hydrogen content compared to conventional kerosene (i.e., fossil fuel-based). Such “low contrail” kerosene can be provided as a blend of conventional (crude oil-based) and synthetic kerosene or from hydroprocessing conventional kerosene.Low contrail kerosene reduces contrail lifetime and optical thickness and thus the magnitude of contrail climate forcing. However, market shares of such kerosene are presently very low. Simultaneously, a small fraction (< 10 %) of all flights globally accounts for the majority (> 80 %) of global warming contrail climate forcing. Hence, the targeted use of low contrail kerosene on those flights appears promising. But, such an approach would require additional operational efforts, such as a duplication of supply lines and storage tanks.This study evaluates the feasibility and operational efforts of a segregated supply of a 35 m-% SAF-blend (14.34 m-% hydrogen content) to 84 winter time demonstration flights to reduce contrail climate forcing. Between 17th January 2023 and 2nd February 2023, low contrail kerosene was supplied to commercial A320 type aircraft flights on the route between Stockholm and Copenhagen in northern Europe. The operational feasibility and related efforts to target flights with the highest contrail energy forcing as well as a large-scale application are described. The evolution of contrails is tracked using data from the Meteosat Second Generation (MSG) satellite. The contrail energy forcing is calculated for the corresponding flight trajectories assuming another, well-validated engine model (CFM56-5B4 for the simulations instead of LEAP1A-26 for the demonstration flights) using the Contrail Cirrus Prediction (CoCiP) model with meteorological input fields from European Reanalysis data (ERA5).For the first time, the experiment demonstrates the operational feasibility for a segregated supply of low contrail kerosene to medium range aircraft at Stockholm airport. The segregated supply of low contrail kerosene can be realized for short to medium range flights, which can be fueled by a refueller truck. Targeting individual flights via single line hydrant fueling systems seems impractical as of now. Operational efforts to target single flights with highest contrail energy forcing are almost identical to the efforts in this demonstration experiment.Simulations estimate that the segregated supply of the medium blend kerosene (14.34 m-% hydrogen content) can reduce contrail energy forcing by about 11 % assuming the use of a “Rich-Quench-Lean” (RQL) engine (CFM56-5B4). The contrail climate benefit increases to >20 % for a 50 % blend ratio (14.7 m-% hydrogen content). Also, the location and evolution of the demonstration flights’ 28 contrails calculated with CoCiP was tracked with satellite data.The uncertainty of absolute contrail climate forcing estimates is mainly limited due to meteorological data input and also by lacking information on fuel composition in terms of cycloalkane, mono- and polycyclic aromatics content. Contrarily, the uncertainty of relative changes in contrail climate forcing is subject to low uncertainty, since it compares the use of different fuels for an identical fleet and identical weather conditions.