We generated a total of 3000 encoded blocks, of which 2962 blocks were filled with raw information and the rest were filled with zeros. Since the zero-filled blocks can be derived from the information blocks, we did not synthesise the sequence of zero-filled blocks (i.e., non-synthesis redundant data).

Zero Trust Segmentation for Warehouse Networks Market Outlook

According to our latest research, the global Zero Trust Segmentation for Warehouse Networks market size reached USD 1.47 billion in 2024, with a robust compound annual growth rate (CAGR) of 19.6% projected through 2033. By 2033, the market is forecasted to achieve a value of USD 6.89 billion. This rapid expansion is primarily fueled by the escalating need for robust cyber defense strategies in warehouse environments, driven by the digital transformation of supply chains and heightened threats targeting critical warehouse network infrastructures.

The primary growth factor propelling the Zero Trust Segmentation for Warehouse Networks market is the surge in cyberattacks targeting warehousing and logistics operations. As warehouses increasingly adopt IoT devices, robotics, and automated inventory management systems, their network perimeters become more complex and vulnerable. Traditional security models that rely on perimeter-based defenses are proving inadequate in the face of sophisticated cyber threats, making Zero Trust Segmentation essential. This security approach ensures that every user, device, and application within the network is continuously authenticated, authorized, and monitored, significantly reducing the risk of lateral movement by malicious actors. The widespread adoption of cloud-based warehouse management systems and the integration of third-party logistics providers further amplify the necessity for granular access controls and micro-segmentation, strengthening the case for Zero Trust architectures.

Another significant driver is the tightening of regulatory requirements and industry standards across sectors such as logistics, retail, and manufacturing. Governments and regulatory bodies worldwide are mandating stricter data privacy and cybersecurity measures, compelling warehouse operators to implement advanced security frameworks. The Zero Trust Segmentation model aligns perfectly with compliance frameworks like GDPR, CCPA, and industry-specific standards such as ISO 27001. By segmenting warehouse networks and enforcing least-privilege access, organizations not only safeguard sensitive operational and customer data but also demonstrate proactive compliance, reducing the risk of hefty fines and reputational damage. This regulatory push is particularly pronounced in regions like North America and Europe, where compliance-driven adoption is accelerating market growth.

Technological advancements in artificial intelligence (AI), machine learning, and network analytics are also catalyzing the adoption of Zero Trust Segmentation in warehouse networks. Modern solutions leverage AI-powered anomaly detection, real-time threat intelligence, and automated policy enforcement to provide dynamic, context-aware security. These innovations enable warehouse operators to adapt quickly to evolving threats and operational changes, ensuring uninterrupted business continuity. The integration of Zero Trust principles into warehouse automation platforms and enterprise resource planning (ERP) systems is further streamlining implementation, making advanced security accessible to organizations of all sizes. As digital transformation initiatives continue to reshape the warehousing landscape, the demand for scalable, intelligent, and automated security solutions is expected to soar.

Regionally, North America dominates the Zero Trust Segmentation for Warehouse Networks market, accounting for the largest share in 2024. This leadership position is underpinned by the presence of major logistics hubs, advanced supply chain infrastructure, and early adoption of digital and cloud technologies. Europe follows closely, driven by stringent regulatory frameworks and a strong focus on data privacy. The Asia Pacific region is emerging as a high-growth market, propelled by rapid industrialization, e-commerce expansion, and increasing investments in smart warehousing solutions. Latin America and the Middle East & Africa are also witnessing rising adoption, albeit at a slower pace, as organizations in these regions gradually modernize their warehouse operations and prioritize cybersecurity.

Zero Trust Segmentation for Warehouse Networks Market Outlook

According to our latest research, the global Zero Trust Segmentation for Warehouse Networks market size reached USD 1.35 billion in 2024, driven by the surging need for robust cybersecurity frameworks within the warehouse and logistics sectors. The market is expected to grow at a CAGR of 18.7% during the forecast period, reaching a projected value of USD 6.27 billion by 2033. This remarkable growth is primarily fueled by the increasing adoption of digital warehouse management systems, heightened regulatory compliance requirements, and a rapidly evolving threat landscape targeting supply chain and warehouse infrastructures.

The rapid digitization of warehouse operations, including the integration of IoT devices, automated inventory management, and cloud-based platforms, has significantly expanded the attack surface for cyber threats. As warehouses become more connected, the risk of lateral movement by malicious actors within networks has increased, necessitating a paradigm shift toward Zero Trust Segmentation. This approach, which assumes that no user or device inside or outside the network can be trusted by default, enables organizations to isolate critical assets, enforce granular access controls, and minimize the blast radius of any potential breach. The growing sophistication of ransomware and supply chain attacks has made Zero Trust Segmentation a strategic priority for warehouse operators, particularly those managing sensitive customer data and high-value inventories.

Another key growth factor is the escalating regulatory scrutiny across industries such as logistics, retail, and manufacturing. Governments and industry bodies are mandating stricter compliance with data protection, privacy, and cybersecurity standards. Zero Trust Segmentation solutions provide a robust framework for meeting these requirements by ensuring continuous verification, least-privilege access, and comprehensive visibility into network activity. This not only helps organizations avoid costly penalties but also enhances their reputation as secure and trustworthy partners within the supply chain ecosystem. The increasing frequency of audits and the rising cost of non-compliance are compelling warehouse operators to invest in advanced segmentation technologies.

Furthermore, the proliferation of remote and hybrid work models within warehouse environments has introduced new vulnerabilities. Employees, contractors, and third-party vendors often require access to critical systems from disparate locations, making traditional perimeter-based security models obsolete. Zero Trust Segmentation for Warehouse Networks addresses this challenge by dynamically authenticating users and devices, regardless of their location, and restricting access based on real-time risk assessments. This ensures that only authorized personnel can interact with sensitive assets, thereby reducing the likelihood of insider threats and unauthorized lateral movement. The convergence of operational technology (OT) and information technology (IT) in modern warehouses is also driving demand for unified, scalable segmentation solutions.

From a regional perspective, North America dominated the Zero Trust Segmentation for Warehouse Networks market in 2024, accounting for approximately 38% of the global revenue. The region’s leadership is attributed to its advanced logistics infrastructure, high adoption of digital technologies, and stringent regulatory environment. Europe and Asia Pacific are rapidly emerging as significant growth markets, driven by expanding e-commerce sectors, increasing cyberattacks on supply chains, and supportive government initiatives. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as warehouse operators in these regions prioritize digital transformation and risk mitigation strategies.

Component Analysis

The Zero Trust Segmentation for Warehouse Networks market is segmented by component into Software, Hardware, and Services. The software segment forms the backbone of Zero Trust Segmentation, offering solutions such as micro-segmentation platforms, identity and access management, and network monitoring tools. These software offerings are essential for implementing granular security policies, automating threat detection, and providing real-time visibility into warehouse network activity. The increasing integration of artificial intelligence and machine lear

The size of the Zero Footprint Storage (ZFS) System market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX% during the forecast period.

Zero Trust OT Security for Warehouses Market Outlook

According to our latest research, the Zero Trust OT Security for Warehouses market size reached USD 1.82 billion globally in 2024, and is expected to grow at a robust CAGR of 14.6% from 2025 to 2033, reaching a forecasted market size of USD 6.31 billion by 2033. The primary growth driver for this market is the rapid digitalization and automation of warehouse operations, which is increasing the need for advanced security frameworks to safeguard operational technology (OT) systems from escalating cyber threats and vulnerabilities.

The growth of the Zero Trust OT Security for Warehouses market is being propelled by the intensifying sophistication of cyberattacks targeting warehouse OT environments. As warehouses adopt IoT devices, robotics, and interconnected management systems, their attack surface expands, making them lucrative targets for ransomware, data breaches, and operational disruptions. This has led organizations to shift from traditional perimeter-based security models to the Zero Trust approach, which mandates continuous authentication, strict access controls, and comprehensive monitoring of all network traffic. The increasing frequency and impact of cyber incidents in the logistics and supply chain sectors have made Zero Trust OT Security a critical investment for warehouse operators seeking to ensure business continuity, protect sensitive data, and comply with tightening regulatory standards.

Another key factor fueling the adoption of Zero Trust OT Security in warehouses is the growing convergence of IT and OT systems. As warehouses integrate enterprise resource planning (ERP) solutions, advanced analytics, and cloud-based platforms with their operational infrastructure, the risk of lateral movement by malicious actors rises substantially. Zero Trust frameworks, with their granular segmentation and least-privilege principles, enable organizations to isolate critical assets, prevent unauthorized access, and mitigate the risk of insider threats. This convergence trend is particularly pronounced in sectors such as retail, manufacturing, and e-commerce, where the seamless flow of information between IT and OT is essential for real-time inventory management, order fulfillment, and supply chain optimization.

Furthermore, the regulatory landscape is becoming increasingly stringent, with governments and industry bodies introducing guidelines and mandates for OT security in warehouse environments. Compliance with standards such as NIST, IEC 62443, and GDPR is driving warehouse operators to adopt Zero Trust architectures that provide comprehensive visibility, auditability, and control over their OT networks. The need to demonstrate security best practices to customers, business partners, and regulators is accelerating investments in Zero Trust OT Security solutions and services, creating a fertile environment for market expansion across diverse end-user industries.

From a regional perspective, North America currently leads the Zero Trust OT Security for Warehouses market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The high concentration of technologically advanced warehouses, stringent regulatory requirements, and a mature cybersecurity ecosystem in the United States and Canada are driving early adoption. However, Asia Pacific is expected to witness the fastest growth over the forecast period, fueled by rapid industrialization, increasing warehouse automation, and a rising awareness of OT security risks among enterprises in China, Japan, and India.

Component Analysis

The Zero Trust OT Security for Warehouses market is segmented by component into Solutions and Services, each playing a pivotal role in the overall security ecosystem. Solutions comprise software and hardware offerings such as firewalls, secure gateways, endpoint protection platforms, and identity management systems specifically designed for OT environments. The demand for these solutions is being driven by warehouse operators’ need for real-time threat detection, network segmentation, and access control, as they strive to protect critical assets and ensure uninterrupted operations. The proliferation of IoT devices and connected equipment in warehouses necessitates robust security solutions that can adapt to dynamic environments and evolving threat landscapes.

On the other hand, the S

Zero Trust OT Security for Warehouses Market Outlook

According to our latest research, the global Zero Trust OT Security for Warehouses market size reached USD 2.47 billion in 2024, reflecting a robust demand across logistics, retail, manufacturing, and e-commerce sectors. The market is experiencing a strong growth momentum, with a compound annual growth rate (CAGR) of 17.8% projected from 2025 to 2033. By the end of 2033, the market is forecasted to reach USD 8.17 billion, driven by the escalating need for advanced cybersecurity frameworks in operational technology (OT) environments. The critical growth factor is the increasing digitization of warehouses and the persistent threat landscape targeting OT infrastructure, compelling organizations to adopt Zero Trust security models to safeguard assets and ensure operational continuity.

A significant driver for the Zero Trust OT Security for Warehouses market is the rapid convergence of information technology (IT) and operational technology (OT) within modern warehouse environments. As warehouses adopt automation, robotics, and IoT-enabled devices, the attack surface expands, making traditional perimeter-based security approaches obsolete. Zero Trust principles, which operate on the "never trust, always verify" philosophy, are being widely implemented to address these vulnerabilities. Organizations are prioritizing segmentation, continuous authentication, and least-privilege access controls to protect critical OT assets from sophisticated cyber threats. This evolution in security posture is further accelerated by regulatory mandates and industry standards, which require robust protection of sensitive data and operational integrity, especially in sectors handling large volumes of goods and customer information.

Another key factor propelling market growth is the proliferation of ransomware and targeted cyberattacks specifically designed to disrupt warehouse operations. The financial and reputational damage caused by such incidents has heightened awareness among warehouse operators about the importance of proactive security measures. Zero Trust OT security solutions offer granular visibility and control over network traffic, enabling real-time threat detection and rapid incident response. The adoption of AI-driven analytics and machine learning within these solutions further enhances their ability to identify anomalous behavior and mitigate risks before they escalate. As cybercriminals continue to innovate, the demand for adaptive and resilient security frameworks is expected to surge, reinforcing the market's upward trajectory.

Furthermore, the increasing adoption of cloud-based warehouse management systems and the rise of remote workforces have introduced new security challenges. Zero Trust OT security frameworks are uniquely positioned to address these issues by ensuring secure access to warehouse resources regardless of user location or device. The scalability and flexibility offered by Zero Trust architectures make them ideal for dynamic warehouse environments, where assets and endpoints are constantly changing. This adaptability is particularly valuable for organizations with distributed warehouse networks, as it enables centralized policy management and consistent enforcement of security protocols across all sites. As supply chains become more interconnected and globalized, the need for unified and robust OT security solutions will continue to drive market expansion.

From a regional perspective, North America currently leads the Zero Trust OT Security for Warehouses market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The U.S. and Canada are at the forefront due to their advanced logistics infrastructure and early adoption of digital transformation initiatives. Europe is witnessing significant investments in smart warehouses, particularly in Germany, the UK, and France, where regulatory compliance and data privacy concerns are driving security upgrades. Meanwhile, Asia Pacific is emerging as a high-growth region, propelled by rapid industrialization, expanding e-commerce, and increasing awareness of OT cybersecurity risks in countries like China, Japan, and India. The Middle East & Africa and Latin America are also experiencing steady growth, albeit at a slower pace, as organizations in these regions gradually modernize their warehouse operations and prioritize security investments.

Overview

Repository to share the data and code associated with the scientific article Istrate et al. One-tenth of EU’s biomethane potential combined with carbon capture and storage can shift the region’s ammonia production to net-zero. One Earth (2024). The repository contains data files and code to import the life cycle inventories (LCIs), reproduce the results, and generate the figures presented in the article.

The data folder includes:

• inventories.xlsx contains the LCI datasets for biomethane and ammonia production formatted for use with Brightway.
• sustainable_biomethane_potential_Europe.xlsx contains data on the sustainable biomethane potential in Europe disaggregated by feedstock and country.
• ammonia_production_europe.xlsx contains ammonia production levels in the EU in 2021.
• SA_methane leakage_for presample.xlsx contains data to perform sensitivity analysis on the methane leakage with presamples
• SA_upgrading technology_presamples.xlsx contains data to perform sensitivity analysis on upgrading technologies with presamples
• results folder within data contains csv files with the results, which are used in 05_visualization.ipynb for analysis and visualization purposes.

The notebooks folder includes:

• 01_project_setup.ipynb sets up a new Brightway project and imports the ecoinvent database.
• 02_lci.ipynb imports the LCIs and regionalize some datasets (e.g., biomethane supply based on the bimethane potential).
• 03_lcia.ipynb calculates life cycle impacts and all the additional results presented in the paper (e.g., calculation of blending ratios).
• 04_sensitivity_analysis.ipynb performs the sensitivity analysis.
• 05_visualization.ipynb imports all results and generates the figures presented in the scientific article.

The src folder contains supporting functions required to regionalize LCIs and perform the calculations.

Some of the LCI datasets in the inventories.xlsx file are partially based on data from the ecoinvent LCI database. To comply with licensing requirements, the file shared in this repository does not include these data points. If you hold a valid ecoinvent license, please contact me directly to receive the full input files containing all ecoinvent data points.

Contact

Robert Istrate: i.r.istrate@cml.leidenuniv.nl

Offshore Geological Storage of Hydrogen: Is This Our Best Option to Achieve Net-Zero?

Main data and code for the article "A net-zero emissions strategy for China’s power sector using carbon capture utilization and storage".

This dataset contains the common Map Unit attributes for each polygon within the gSSURGO database plus NRCS derived attributes from a data summary table called the National Valu Added Look Up (valu) Table #1. It is comprised of 57 pre-summarized or "ready to map" derived soil survey geographic database attributes including soil organic carbon, available water storage, crop productivity indices, crop root zone depths, available water storage within crop root zone depths, drought vulnerable soil landscapes, and potential wetland soil landscapes. Related metadata values for themes are included. These attribute data are pre-summarized to the map unit level using best practice generalization methods intended to meet the needs of most users. The generalization methods include map unit component weighted averages and percent of the map unit meeting a given criteria. These themes were prepared to better meet the mapping needs of users of soil survey information and can be used with both SSURGO and Gridded SSURGO (gSSURGO) datasets. Gridded SSURGO (gSSURGO) Database is derived from the official Soil Survey Geographic (SSURGO) Database. SSURGO is generally the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes, and are derived from properties and characteristics stored in the National Soil Information System (NASIS). The gSSURGO data were prepared by merging traditional SSURGO digital vector map and tabular data into State-wide extents, and adding a State-wide gridded map layer derived from the vector, plus a new value added look up (valu) table containing "ready to map" attributes. The gridded map layer is offered in an ArcGIS file geodatabase raster format. The raster and vector map data have a State-wide extent. The raster map data have a 10 meter cell size that approximates the vector polygons in an Albers Equal Area projection. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link to raster cells and polygons to attribute tables, including the new value added look up (valu) table that contains additional derived data.VALU Table Content:The map unit average Soil Organic Carbon (SOC) values are given in units of g C per square meter for eleven standard layer or zone depths. The average thickness of soil map unit component horizons used in these layer/zone calcuations is also included. The standard layers include: 0-5cm, 5-20cm, 20-50cm, 50-100cm, 100-150cm, and 150-150+cm (maximum reported soil depth). The standard zones include: 0-5cm (also a standard layer), o-20cm, 0-30cm, 0-100cm, and 0-150+cm (full reported soil depth). Zero cm represents the soil surface.The map unit average Available Water Storage (AWS) values are given in units of millimeters for eleven standard layer or zone depths. The average thickness of soil map unit component horizons used in these layer/zone calcuations is also included. The standard layers include: 0-5cm, 5-20cm, 20-50cm, 50-100cm, 100-150cm, and 150-150+cm (maximum reported soil depth). The standard zones include: 0-5cm (also a standard layer), 0-20cm, 0-30cm, 0-100cm, and 0-150+cm (full reported soil depth). Zero cm represents the soil surface.The map unit average National Commodity Crop Productivity Index (NCCPI) values (low index values indicate low productivity and high index values indicate high productivity) are provided for major earthy components. NCCPI values are included for corn/soybeans, small grains, and cotton crops. Of these crops, the highest overall NCCPI value is also identified. Earthy components are those soil series or higher level taxa components that can support crop growth. Major components are those soil components where the majorcompflag = 'Yes' in the SSURGO component table. A map unit percent composition for earthy major components is provided. See Dobos, R. R., H. R. Sinclair, Jr, and M. P. Robotham. 2012. National Commodity Crop Productivity Index (NCCPI) User Guide, Version 2. USDA-NRCS. Available at: ftp://ftp-fc.sc.egov.usda.gov/NSSC/NCCPI/NCCPI_user_guide.pdfThe map unit average root zone depth values for commodity crops are given in centimeters for major earthy components. Criteria for root-limiting soil depth include: presence of hard bedrock, soft bedrock, a fragipan, a duripan, sulfuric material, a dense layer, a layer having a pH of less than 3.5, or a layer having an electrical conductivity of more than 12 within the component soil profile. If no root-restricting zone is identified, a depth of 150 cm is used to approximate the root zone depth (Dobos et al., 2012). The map unit average available water storage within the root zone depth for major earthy components value is given in millimeters.Drought vulnerable soil landscapes comprise those map units that have available water storage within the root zone for commodity crops that is less than or equal to 6 inches (152 mm) expressed as "1" for a drought vulnerable soil landscape map unit or "0" for a nondroughty soil landscape map unit or NULL for miscellaneous areas (includes water bodies).The potential wetland soil landscapes (PWSL version 1) information is given as the percentage of the map unit (all components) that meet the criteria for a potential wetland soil landscape. See table column (field) description for criteria details. If water was determined to account for 80 or greater percent of a map unit, a value of 999 was used to indicate a water body. This is not a perfect solution, but is helpful to identifying a general water body class for mapping.The map unit sum of the component percentage representative values is also provided as useful metadata. For all valu table columns, NULL values are presented where data are incomplete or not available. How NoData or NULL values and incomplete data were handled during VALU table SOC and AWS calculations:The gSSURGO calculations for SOC and AWS as reported in the VALU table use the following data checking and summarization rules. The guiding principle was to only use the official data in the SSURGO database, and not to make assumptions in case there were some data entry errors. However, there were a few exceptions to this principle if there was a good reason for a Null value in a critical variable, or to accommodate the data coding conventions used in some soil surveys.Horizon depths considerations:If the depth to the top of the surface horizon was missing, but otherwise the horizon depths were all okay, then the depth to the top of the surface horizon (hzdept_r) was set to zero.If the depth to the bottom of the last horizon was missing, and the horizon represented bedrock or had missing bulk density, the depth to the bottom was set to equal to the depth to the top of the same horizon (hzdepb_r = hzdept_r), effectively giving the horizon zero thickness (and thus zero SOC or AWS), but not blocking calculation of other horizons in the profile due to horizon depth errors.Other types of horizon depth errors were considered uncorrectable, and led to all horizon depths for the component being set to a NoData value, effectively eliminating the component from the analysis. The errors included gaps or overlaps in the horizon depths of the soil profile, other cases of missing data for horizon depths, including missing data for the bottom depth of the last horizon if the soil texture information did not indicate bedrock and a bulk density value was coded. The SOC or AWS values were effectively set to zero for components eliminated in this way, so the values at the map unit level could be an underestimate for some soils.Horizon rock fragment considerations:Part of the algorithm for calculating the SOC requires finding the volume of soil that is not rock. This requires three SSURGO variables that indicate rock fragments (fraggt10_r, frag3to10_r, and sieveno10_r). If the soil is not organic, and any of these are missing, then the ratio of the volume of soil fines to the total soil volume was set to “NoData†, and the SOC results were coded as “NoData†and effectively set to zero for the horizon. If the soil is organic, then it may be logical that no measurement of rock fragments was made, and default values for the “zero rock†situation was assumed for these variables (i.e., fraggt10_r = 0, frag3to10_r = 0, sieveno10_r = 100). Organic soils were identified by an “O†in the horizon designator or the texture code represented “Peat†, “Muck†or “Decomposed Plant Material†. If all three of the fragment variables were present, but indicated more than 100% rock, then 100% rock was assumed (zero volume of soil and thus zero for SOC). The rock fragment variables do not influence the AWS calculation because rock content is already accounted for in the available water capacity (awc_r) variable at the horizon level.Horizon to component summary:To summarize data from the horizon level to the component level, the evaluation proceeded downward from the surface. If a valid value for AWS could not be calculated for any horizon, then the result for that horizon and all deeper horizons was set to NoData. The same rule was separately applied to the SOC calculation, so it was possible to have results for SOC but not AWS, or vice versa.Component to mapunit summary:To summarize data from the component level to the map unit level, the component percentages must be valid. There are tests both of the individual component percentage (comppct_r) data, and also of the sum of the component percentages at the map unit level (mu_sum_comppct_r). For the gSSURGO VALU table, the following rules were applied for the individual components: 1) The comppct_r must be in the range from 0 to 100, inclusive. 2) Individual components with a comppct_r that was Null (nothing coded) were ignored. A zero comppct_r value excludes

This dataset is about books. It has 1 row and is filtered where the book is Net zero and beyond : what role for bioenergy with carbon capture and storage?. It features 7 columns including author, publication date, language, and book publisher.

Transportation technologies over human historyDate of first appearance, average speed, capacity, and energy source for various air, land, and water transport vessels used for food and other transportation over human historySupplemental_3.docxFood storage technologiesFood storage technologies with historical dates of appearance, energy requirements, and shelf life of various food items with and without storage technologySupplemental2_revised.docxGrowth rates of food spoiling microbesThe temperature-dependent growth rates of various microbes causing food spoilageSupplemental_1.xlsx

Renewable energy systems are at the core of global efforts to reduce greenhouse gas (GHG) emissions and to combat climate change. Focusing on the role of energy storage in enhancing dependability and efficiency, this paper investigates the design and optimization of a completely sustainable hybrid energy system. Furthermore, hybrid storage systems have been used to evaluate their viability and cost-benefits. Examined under a 100% renewable energy microgrid framework, three setup configurations are as follows: (1) photovoltaic (PV) and Battery Storage System (BSS), (2) Hybrid PV/Wind Turbine (WT)/BSS, and (3) Integrated PV/WT/BSS/Electrolyzer/Hydrogen Tank/Fuel Cell (FC). Using its geographical solar irradiance and wind speed data, this paper inspires on an industrial community in Neom, Saudi Arabia. HOMER software evaluates technical and economic aspects, net present cost (NPC), levelized cost of energy (COE), and operating costs. The results indicate that the PV/BSS configuration offers the most sustainable solution, with a net present cost (NPC) of $2.42M and a levelized cost of electricity (LCOE) of $0.112/kWh, achieving zero emissions. However, it has lower reliability, as validated by the provided LPSP. In contrast, the PV/WT/BSS/Elec/FC system, with a higher NPC of $2.30M and LCOE of $0.106/kWh, provides improved energy dependability. The PV/WT/BSS system, with an NPC of $2.11M and LCOE of $0.0968/kWh, offers a slightly lower cost but does not provide the same level of reliability. The surplus energy has been implemented for hydrogen production. A sensitivity analysis was performed to evaluate the impact of uncertainties in renewable resource availability and economic parameters. The results demonstrate significant variability in system performance across different scenarios.