Climate Risks to IoT Devices in Kazakhstan: Projections and Adaptation Strategies

1. Introduction

The Internet of Things (IoT) is a transformative technology that connects networked devices capable of collecting and exchanging data, enabling real-time monitoring and control across all industries. IoT is being applied in agriculture, healthcare, energy and urban infrastructure, offering efficiency improvements and automation on a global scale (Atzori et al., 2010a) (Xu et al., 2014), there are several types of outdoor IoT devices, such as cameras, sensors, routers, actuators, Pos terminals and Sim cards, which are widely used in sectors such as agriculture, transport and infrastructure. These devices are crucial for real-time monitoring but are vulnerable to extreme weather conditions. Their importance is emphasised in this research paper on the ability of IoT devices to withstand weather threats and prevent failures in harsh conditions. By connecting devices to central systems, IoT enables data-driven decision-making and has the potential to revolutionise sectors that rely heavily on environmental monitoring and resource optimisation. In Kazakhstan, a country known for its vast territory, diverse topography and extreme weather conditions, IoT systems play a critical role in monitoring environmental parameters, managing urban infrastructure, optimising agricultural activities and energy consumption (Pandiyan et al., 2024, Kazhydromet.kz, 2023).

However, the reliability and sustainability of these IoT systems face significant challenges in the context of climate change, especially in regions with extreme weather conditions. Kazakhstan's climate is characterised by strong seasonal and regional temperature fluctuations: temperatures can drop to -44 °C in the northern regions in winter and reach +45 °C in the southern desert regions in summer (World Bank, 2021). Such extreme temperature ranges can pose operational risks for IoT devices as most of them, such as sensors, cameras, actuators, routers and SIM cards, are designed for certain acceptable environmental conditions. Constant exposure to prolonged summer heat, winter frost and unpredictable temperature fluctuations can lead to device malfunctions, inaccurate sensors and shortened device lifetimes, jeopardising the reliability of these critical systems (Rahmani et al., 2023). In arid and desert climates, overheating and dust accumulation have been identified as key risks for sensor reliability (Algarni and Nutter, 2015).

Climate projections for Kazakhstan indicate not only an increase in average temperatures, but also an increase in the frequency of extreme events, including increased heat waves in summer, severe cold in winter, and unpredictable seasonal transitions. These predictions highlight the urgent need to consider how IoT systems will be able to withstand these conditions. Without proper adaptation, IoT infrastructure can fail anywhere, disrupting critical functions such as agricultural monitoring, power grid management, and urban traffic management (Othmane Friha, 2021). This potential vulnerability emphasises the need for a robust climate risk assessment for IoT infrastructure in Kazakhstan.

This paper addresses the need to quantify climate risks for IoT devices by developing risk assessment curves and climate risk models. While IoT technologies offer promising solutions to improve efficiency and automation, their resilience to climate extremes - especially in vulnerable and unstable regions such as Kazakhstan - is still poorly understood. A deep understanding of climate impacts on IoT systems is critical to building a robust infrastructure that can withstand expected climate stresses, ensuring the efficiency and durability of IoT-enabled services in Kazakhstan.

The Representative Concentration Pathways (RCPs) are widely used climate models developed by the Intergovernmental Panel on Climate Change (IPCC) to represent different greenhouse gas concentration pathways and their potential impacts on global climate (IPCC, 2021). These pathways range from RCP2.6, which involves high mitigation efforts and low greenhouse gas emissions, to RCP8.5, a scenario with high emissions and minimal mitigation. These pathways are important for understanding future climate projections and developing adaptation strategies such as resilience of Internet of Things (IoT) devices.

This study considers RCP4.5 and RCP8.5 scenarios with medium and high emissions. These scenarios are particularly relevant for Kazakhstan and the Central Asia region, as they contain projections of changes in temperature and precipitation that may affect the functionality and resilience of IoT systems. Climate projections derived from these scenarios are the basis for assessing potential environmental risks and planning necessary adjustments to IoT infrastructure.

To model these risks, we used the historical climate data from the Climate Data Store, in particular monthly average ERA5 data (from 1940 to present) and monthly average land ERA5 data (from 1950 to present). Using this data, we developed the risk curves based on the failure rate of IoT devices as a function of temperature deviation from the normal range. These risk assessments will help identify critical thresholds at which the performance of IoT devices may degrade or fail, enabling the development of adaptation strategies that support the resilience of IoT systems in the face of climate variability (Copernicus Climate and Service, 2021). By establishing these thresholds, the study provides recommendations for adaptation measures and resilience planning necessary to ensure the continuity and functionality of IoT systems under future climate projections.

2.1. Global and Regional Perspectives on IoT Resilience to Climate Risks

This section provides an overview of key areas of research on climate risks to IoT systems, measures to increase the resilience of IoT devices in extreme climatic conditions, and specific challenges associated with IoT deployment in Kazakhstan. These studies serve as a basis for understanding the specific climate vulnerability of IoT devices and identifying necessary adaptation measures.

Globally, IoT systems are increasingly exposed to climate risks such as extreme heat, storms, and humidity fluctuations. Studies in Europe, North America, and arid regions demonstrate that IoT devices are vulnerable when environmental conditions exceed their operational thresholds (Salam, 2020). These findings highlight the importance of resilience planning, particularly in regions with climatic extremes.

2.2. Climate Change and IoT Vulnerability: Global Trends and Specific Risks in Kazakhstan

The impact of climate change on IoT devices is attracting attention worldwide, but vulnerability is particularly pronounced in regions with significant seasonal and climatic extremes, such as Kazakhstan. IoT devices based on real-time data collection, monitoring and transmission are widely used in sectors such as agriculture, energy, urban infrastructure and transport. However, extreme weather events caused by climate change, such as heat waves, sudden cooling and seasonal variations, pose a significant risk to IoT devices, as highlighted in studies Atzori and Luigi (Atzori et al., 2010b). Studies conducted in arid regions show that exposure to extreme temperatures can lead to degradation of electronic components, changes in sensor accuracy, and even device failure (Soussi et al., 2024).

Kazakhstan, with its varied topography and strong climatic differences in different regions, presents a serious challenge for devices. Winter temperatures in northern regions can fall below -40 °C, while summer temperatures in southern regions exceed 40 °C. Such variations can subject devices to mechanical stresses that affect power consumption and functionality (Salnikov et al., 2023). Studies in Central Asia show that the increased frequency of extreme weather events in the region due to climate change may put more stress on IoT devices, increasing the risk of failure (World Bank, 2021). In addition, climate data projections for Kazakhstan indicate a trend towards warmer summers and colder winters with changing seasonal patterns, which could increase the strain on IoT systems if appropriate resilience measures are not taken.

The vulnerability of IoT devices to climate change in Kazakhstan is exacerbated by the region's unique environmental challenges, including extreme temperatures and an increase in the frequency and intensity of extreme weather events such as storms, heavy snowfall and droughts. Such environmental stresses disrupt the normal operation of IoT devices, especially those that rely on external sensors and power sources (Atzori et al., 2010b). For example, high temperatures can overheat and damage sensitive electronic components, while low temperatures can cause batteries to fail or sensors and communication systems to malfunction. In addition, seasonal variations, such as prolonged periods of extreme heat or cold, are an increasing challenge for IoT systems that must operate consistently throughout the year. In addition, the infrastructure that supports these devices, such as power grids and communication networks, can also be susceptible to weather fluctuations, further increasing the likelihood of device failures. Given the growing dependence of IoT systems on mission-critical operations in industries such as agriculture, transport and energy, robust adaptation strategies are needed to increase the resilience of these devices and ensure their reliability even in the most extreme weather conditions (Rastegari et al., 2023).

2.3. Climate-Related Specialties of Kazakhstan

Kazakhstan’s climate is characterized by exceptional variability: winter temperatures can fall below −44 °C in the north, while summer temperatures often exceed +45 °C in southern deserts. Seasonal transitions are abrupt, with rapid shifts between hot summers and cold winters. In addition, the country faces frequent droughts, dust storms in arid regions, strong winds across the steppes, and heavy snow accumulation in the north and east (Kazhydromet, 2023). These environmental conditions create unique challenges for IoT systems, increasing the risk of failure due to freezing, overheating, moisture intrusion, or physical damage from storms.

2.4. Strategies for IoT Resilience to Harsh and Variable Environmental Conditions

Research on resilience strategies for IoT systems in harsh environments has focused on both technical and operational adaptation. Effective resilience strategies often include designing devices with components that can withstand a wider range of temperatures, collecting real-time data to dynamically adapt operations, and developing predictive maintenance protocols to ensure continuous functionality in variable environments. Raza and Saleem, for example, point out in their research the need for heat-resistant materials and adaptive power management to maintain device functionality at extreme temperatures (Raza et al., 2019). Other studies recommend implementing algorithms that adjust device settings based on environmental data to reduce power consumption and extend device life (Himeur et al., 2021).

In Kazakhstan, resilience strategies should consider not only temperature extremes but also other unique climatic factors such as early frosts, dry winds, and the rapid transition from summer to winter temperatures. For example, provisions to support the agricultural sector must consider the challenges of temperature fluctuations, droughts, and soil moisture fluctuations that affect crop health and resource allocation (Xenarios et al., 2018). IoT devices are also used in urban areas to monitor traffic and air quality, which requires robust sensor networks that can operate efficiently despite temperature fluctuations and seasonal changes in air quality (Gil et al., 2016). Studies in Central Asia show that without proactive measures to improve the resilience of IoT infrastructure, IoT may not provide accurate data, jeopardising public safety and economic stability (Riaz et al., 2023). Therefore, implementing adaptive algorithms, investing in heat-resistant devices, and utilising data-driven maintenance strategies are necessary to maintain IoT functionality in Kazakhstan's variable climate. In addition to technical solutions such as heat-resistant materials and adaptive algorithms, operational strategies in Kazakhstan should focus on integrating local weather data to predict environmental changes and adjust device settings in advance. For example, real-time monitoring of moistness and weather conditions can lead to automatic adjustment of irrigation systems, while predictive analytics can help predict extreme weather events so that IoT devices can go into energy-saving mode or shut down when weather conditions are not suitable (Pwavodi et al., 2024). Additionally, ensuring reliable connectivity between devices through redundant networks and backup systems is necessary to prevent data loss in the circumstance of extreme weather conditions (Gonçalves et al., 2024). By combining these adaptive approaches with a robust infrastructure, organisations can improve the overall resilience of IoT systems so that they can operate efficiently and provide accurate, real-time data despite the challenges posed by dynamic and extreme weather conditions.

2.5. Risk Modelling and Predictive Reliability Analysis of IoT Devices in Extreme Climatic Conditions

Quantifying climate-related risks for IoT devices is critical for predicting and mitigating potential failures. Climate risk models typically use environmental data to create risk curves that represent the probability of device failure under certain climatic conditions. However, while risk modelling is typically applied to physical infrastructure, research on risk assessment for IoT devices is relatively recent. Cote, Jean-Nicolas describe the use of risk assessment curves to identify critical temperature thresholds at which device performance may degrade. These models can be used to make operational decisions and design changes to improve device resilience (Cote et al., 2024).

For Kazakhstan, this approach is particularly relevant given the extreme and varying climatic conditions in different regions. The use of historical climate data, such as monthly averages of ERA5, allows risk modelling based on temperature and precipitation trends over decades (Copernicus Climate and Service, 2021). Studies focusing on Central Asia emphasise that risk curves can help identify device-specific thresholds at which performance problems are likely to occur, allowing for necessary changes in IoT deployment strategies (Parsons et al., 2023). By integrating real-time monitoring data and predictive analytics, these models allow for timely adjustments to IoT devices before environmental factors reach critical levels, thereby enhancing business continuity.

The potential for climate-related IoT disruptions also has implications for Kazakhstan's economy and public safety. For example, in agriculture, IoT devices play a key role in monitoring soil health, water consumption and pest control. Predictive risk models can help farmers optimise the use of devices, reducing the likelihood of failures during critical growing seasons. In urban infrastructure, air quality sensors, traffic management systems and energy monitoring devices depend on uninterrupted operation to maintain services and ensure safety. By predicting and mitigating risks by adapting to weather conditions, risk prediction models help create a more resilient IoT infrastructure that can cope with climate challenges in Kazakhstan. Additionally, incorporating predictive reliability analysis into IoT risk models allows organisations to take proactive actions, allowing them to anticipate and mitigate failures before they occur. For example, machine learning algorithms can be used to continuously update risk curves based on real-time data, improving the accuracy of predictions and providing valuable insights into long-term trends (Penalva, 2023). By taking into account local environmental variables such as wind speed, humidity, and snow level, these models can also provide a more complete picture of the complex factors affecting the performance of IoT devices. This approach not only improves the performance of IoT systems in Kazakhstan's harsh climate, but also facilitates cost-effective decision-making by optimising maintenance schedules, device replacement and resource allocation in industries such as agriculture, transport and energy. By continuously improving these models, Kazakhstan will be able to build a more robust and adaptable IoT infrastructure that can withstand the challenges of an extreme climate.

3. Methodology

This study employs a mixed-methods approach, integrating climate projection data with qualitative insights from interviews to develop a climate risk model for IoT device resilience in Kazakhstan. The methodology is organized into four key steps: data collection, device selection, climate risk modelling and data processing and visualization. These steps are designed to quantify the impact of extreme weather on IoT devices and propose adaptation strategies based on identified vulnerabilities.

3.1. Climate Data Collection

We used two main datasets:

ERA5 Reanalysis (Copernicus Climate Data Store):

Resolution: 0.25° × 0.25° (approx. 30 km), hourly to monthly.

Period: 1940–2023.

Variables: near-surface temperature, relative humidity, precipitation, solar radiation, snow depth, and wind speed at 10 m.

Format: NetCDF (float32 datatype, gridded time series).

Processing: Bias correction was applied by comparing with national meteorological station data.

CMIP6 Climate Projections:

Scenarios: RCP4.5 (stabilization) and RCP8.5 (high emissions), with SSP5-8.5 used for extreme scenario analysis.

Period: 1950–2100.

Resolution: 0.5° × 0.5°, monthly means, downscaled for Kazakhstan.

Variables: same as ERA5 for consistency.

Data Type: NetCDF multidimensional arrays.

These datasets provided the quantitative basis for estimating the resilience of IoT devices to projected climate extremes.

We used climate projection data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to estimate the resilience of IoT devices to future climate extremes in Kazakhstan. CMIP6, the core dataset underpinning the 6th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), is an extended collection of global climate simulations. These simulations are the result of collaboration between leading climate research institutes using state-of-the-art general circulation models (GCMs). CMIP6 data allow us to explore important scientific questions such as the predictability of the climate system, quantification of future climate change impacts, and uncertainties associated with different socioeconomic and emissions scenarios.

In particular, we used data from the SSP5-8.5 scenario, a high-emissions scenario characterised by rapid economic growth, high dependence on fossil fuels and minimal climate change mitigation. This ‘business-as-usual’ scenario is particularly useful for studying the extreme impacts of climate change because it predicts large increases in global temperature, more frequent and intense heat waves, more intense cold waves, and greater seasonal variability. These factors overlap with critical stressors for IoT devices in Kazakhstan, a region already exposed to extreme climate variability. All figures were created using Python, specifically using libraries like Matplotlib and NumPy. First, loaded the climate data from a NetCDF file with netCDF4, then extracted key variables like temperature, latitude, and longitude. Then selected a specific time period from the data and used a contour plot to visualize the temperature distribution.

The CMIP6 dataset used in this study includes two-dimensional and three-dimensional climate variables with high spatial and temporal resolution. These variables can be adapted to specific regions and periods to focus on different climate zones in Kazakhstan, from dry steppes to cold mountainous regions. SSP5-8.5 data were integrated into our climate risk models to estimate the probability of IoT device failure under different environmental conditions. For example, extreme temperatures exceeding operating thresholds can cause sensors, cameras and other IoT devices to malfunction or even fail completely. Similarly, high humidity or extreme temperature fluctuations can lead to condensation and degradation of materials, further limiting the functionality of devices.

To evaluate the potential impact of climate change on IoT devices in Kazakhstan, climate projections from CMIP6 models based on the RCP4.5 and RCP8.5 scenarios were used (IPCC, 2021). The Table 1 below summarises the key climate data, including changes in temperature and precipitation, used in the modelling to predict future conditions.

Risk curves help identify critical thresholds, for example points where the probability of failure increases dramatically if climate conditions differ from the normal range. For IoT sensors used for agricultural monitoring, for example, a temperature deviation of ±10 °C from the design range can significantly increase the probability of failure and require additional protective measures such as isolation mechanisms or cooling.

3.2. IoT Device Selection and Priorities

The selection of IoT devices was based on their relevance to Kazakhstan’s critical sectors: agriculture, energy, transport, and urban infrastructure. We prioritized devices widely deployed outdoors, where exposure to climate stress is highest.

Selection priorities included: relevance to national infrastructure (agricultural sensors, energy monitoring devices, POS terminals); exposure to harsh outdoor conditions (routers, cameras, actuators); sectoral representativity (covering at least one device type from each critical sector); comparability with international studies to allow benchmarking of resilience strategies.

Regarding connectivity devices, we initially included SIM cards because they remain widely used in Kazakhstan. However, we acknowledge the transition toward e-SIM technology, which may provide improved durability and flexibility. This aspect is now discussed in the revised manuscript to reflect technological trends and alternatives.

3.3. IoT Device Operating Conditions

Based on the climate risk projections described above, it is important to determine the operating conditions and performance thresholds of the IoT devices used in this study. The Table 2 below summarises the typical operating ranges of these devices, including cameras, sensors, actuators and others, to determine risk assessment thresholds and identify environmental factors that may affect device functionality in future climate conditions. The operating temperature ranges are based on manufacturer specifications for each IoT device type. Differences between ranges reflect design considerations: devices intended for outdoor use (e.g., sensors, cameras) are manufactured with wider tolerances to withstand environmental stress, while devices primarily used in sheltered settings (e.g., POS terminals) have narrower ranges.

By combining high-resolution CMIP6 climate projections, we extended these risk models to estimate the future probability of failure based on predicted conditions, such as the increase in intensity of heat waves by mid-century. The integration of CMIP6 data also allowed us to create a spatial map of these risks across Kazakhstan, highlighting the region’s most prone to IoT device failures. Such maps are essential for policymakers and stakeholders to prioritise resilience building in critical sectors such as agriculture, energy and transport.

Overall, the use of CMIP6 data, particularly the SSP5-8.5 scenario, provided a solid basis for understanding the potential impacts of extreme climate scenarios on IoT devices in Kazakhstan. The combination of climate projections, modelling of device failure probability and regional mapping provides practical insights to help industry adapt IoT systems to the challenges of climate change.

Another data collection method was used in this research is the qualitative data from interviews. To contextualize the quantitative data, we conducted interviews with related organizations, including IoT developers, climate scientists, and professionals in sectors reliant on IoT devices, such as transportation, logistics, agriculture. These interviews provide insights into the specific operational challenges faced by IoT devices in Kazakhstan’s climate, as well as current adaptation strategies and potential areas for improvement. Interview responses were conducted thematically, with a focus on identifying common concerns related to temperature fluctuations, device maintenance, and reliability in extreme weather conditions.

The second step involves data analysis. We will first discuss statistical methods for analysing climate trends, followed by a thematic analysis of interview data.

The historical and projected climate data from ERA5 are subjected to statistical analysis to identify trends in temperature extremes, seasonal shifts, and precipitation patterns. This analysis includes calculating mean, minimum, and maximum values, as well as examining variability across different regions (Yilmaz, 2023). Given Kazakhstan’s distinct climate zones, regional analysis is essential for capturing localized impacts. Statistical tests, such as t-tests or Mann-Kendall trend tests, are used to verify the significance of observed changes in climate variables over time.

The thematic analysis method was used to analyse the interview data.

Interview data is analysed using thematic coding to extract recurrent themes regarding IoT resilience issues. Key themes include temperature thresholds for device failure, challenges related to power supply in remote areas, and maintenance difficulties during extreme weather events. This qualitative analysis supports the quantitative findings by offering a grounded perspective on how climate conditions impact IoT functionality in the organisations among Kazakhstan.

3.4. Climate Risk Modelling

The next important step is to conduct climate risk modelling. How was the development of risk estimate curves carried out? The core of this study’s analysis involves developing risk estimate curves, which quantify the probability of IoT device failure under varying climate conditions. Using historical climate data, we define threshold values for environmental variables, such as temperature and humidity, beyond which device performance is likely to degrade. These thresholds are determined based on both literature (Yilmaz, 2023) and (Copernicus Climate and Service, 2021) the operational parameters of IoT devices obtained from industry reports.

We developed risk curves based on device operating ranges and climate variables. Probability of failure was calculated as:

where f(x) is the kernel density estimation (KDE)-based probability density function of temperature or humidity, and R is the device’s operating range. Sensitivity tests were conducted with threshold shifts of ±5 °C and ±10 % humidity.

The risk estimate curves are constructed by mapping device performance data against temperature and humidity thresholds. For example, sensors may operate optimally within a specified temperature range, but performance may decline or fail entirely as temperatures exceed these limits. By using failure probability distributions, we create curves that illustrate the likelihood of device failure as climate variables deviate from baseline operational ranges.

How did we conduct the validation and sensitivity testing? To ensure the accuracy of the risk estimate curves, we conduct sensitivity testing by adjusting input variables and assessing the resulting changes in failure probability. This step allows us to understand the robustness of our model and identify the environmental conditions that most significantly affect device reliability. Validation of the model is performed using cross-validation techniques, where data subsets are analysed separately to compare the consistency of risk estimates.

Next is the time for the Integration with Climate Projections, let us discuss this. Finally, to forecast the future impact of climate change on IoT devices, we integrate ERA5 climate projections into the model. By applying projected climate scenarios to the risk estimate curves, we assess how the likelihood of device failure may change under predicted climate conditions. This integration enables a forward-looking analysis, providing estimates of IoT reliability under potential future scenarios, such as increased frequency of heatwaves or colder winter extremes.

3.5. Data Processing and Visualization

The final important step is mapping and visualization. To effectively display the spatial distribution of climate risks, data analysis and visualization, Python was used. Beyond Matplotlib and NumPy, the following libraries were employed:

Pandas (data handling, time series analysis).

netCDF4 (extraction of ERA5 and CMIP6 NetCDF files).

Seaborn (statistical plotting, KDE).

SciPy (statistical tests: t-test, Mann–Kendall trend analysis).

Cartopy (geospatial mapping with correct projections and scale bars).

All figures were revised to include error bars, improved legends, probability density function (PDF) definitions, and professional cartographic standards for maps. By leveraging Python’s data visualization libraries, we overlaid risk data onto regional maps, illustrating which areas are most likely to experience operational challenges with IoT devices. These visualizations emphasize regions vulnerable to climate impacts, helping policymakers and industry stakeholders prioritize areas for resilience planning and adaptation investments.

5. Conclusion

In conclusion, this study assessed the vulnerability of Internet of Things (IoT) devices in Kazakhstan to climate extremes by integrating ERA5 and CMIP6 climate projections with device operating thresholds and stakeholder interviews. The results demonstrate that IoT systems face increasing risks from temperature extremes, humidity fluctuations, and additional stressors such as solar radiation, windstorms, heavy snowfall, and droughts. Our findings show that northern Kazakhstan is most vulnerable to severe cold events, while southern and central regions face heightened risks from prolonged heatwaves. These climatic pressures significantly increase the probability of device malfunctions, particularly in sensors, cameras, routers, and POS systems. Interviews confirmed that stakeholders are concerned about maintenance difficulties, calibration errors under extreme conditions, and unreliable power supply in remote areas. Several resilience strategies emerged from this research. Technical measures include designing hardware with extended operating ranges, protective casings, and adaptive energy management. Operational measures involve predictive maintenance, redundancy in communication networks, and climate-informed deployment planning. Together, these approaches can enhance device performance and reduce operational downtime under extreme conditions. The integration of risk modelling, probability density functions, and spatial risk mapping provides policymakers and industry stakeholders with a practical framework to anticipate IoT failures and prioritize adaptation investments. By identifying regional “hotspots” of vulnerability, decision-makers can allocate resources more effectively to safeguard critical sectors such as agriculture, energy, and urban infrastructure. This study has three important implications: Policy and planning: National IoT strategies should include climate resilience standards, informed by risk projections. Research and development: Device design must incorporate tolerance to multiple stressors, including radiation, wind, and snow, not just temperature and humidity. Future studies: Expanding validation with real-world device failure data and comparing resilience strategies across Central Asia will enhance model accuracy and regional relevance. In conclusion, IoT devices in Kazakhstan are highly vulnerable to projected climate extremes, but with targeted technical and operational adaptations, resilience can be significantly improved. The methodology developed here offers a transferable framework for other Central Asian countries facing similar challenges, contributing to the broader agenda of building climate-resilient digital infrastructures.