A Sustainability Assessment of a Blockchain-Secured Solar Energy Logger for Edge IoT Environments

Abstract

In this paper, we design, implement, and empirically evaluate a tamper-evident, blockchain-secured solar energy logging system for resource-constrained edge Internet of Things (IoT) devices. Using a Merkle tree batching approach in conjunction with threshold-triggered blockchain anchoring, the system combines high-frequency local logging with energy-efficient, cryptographically verifiable submissions to the Ethereum Sepolia testnet, a public Proof-of-Stake (PoS) blockchain. The logger captured and hashed cryptographic chains on a minute-by-minute basis during a continuous 135 h deployment on a Raspberry Pi equipped with an INA219 sensor. Thanks to effective retrial and daily rollover mechanisms, it committed 130 verified Merkle batches to the blockchain without any data loss or unverifiable records, even during internet outages. The system offers robust end-to-end auditability and tamper resistance with low operational and carbon overhead, which was tested with comparative benchmarking against other blockchain logging models and conventional local and cloud-based loggers. The findings illustrate the technical and sustainability feasibility of digital audit trails based on blockchain technology for distributed solar energy systems. These audit trails facilitate scalable environmental, social, and governance (ESG) reporting, automated renewable energy certification, and transparent carbon accounting.

1. Introduction

A major shift toward decentralized renewable energy systems has occurred in the global energy sector during recent years. European cities and regions with advanced governance and digital infrastructure are leading the way in smart and sustainable development, according to integrated multicriteria benchmarking [1]. The installed capacity of distributed photovoltaic (PV) installations increased rapidly, reaching approximately 1865 gigawatts (GW) globally by the end of 2024, according to the International Renewable Energy Agency (IRENA) [2]. According to recent modeling, a globally interconnected solar-wind system could theoretically produce about 237,330 TWh annually—more than 3.1 times the estimated global electricity demand in 2050—by utilizing just 29.4% of the highest technical solar and wind potential in the world [3]. The necessity for context-aware digital auditing solutions is highlighted by the recent benchmarking of more than 1300 European cities, which shows that environmental, socioeconomic, and policy factors have a significant impact on sustainability outcomes [1]. Increased community energy sovereignty, improved grid resilience, and reduced transmission losses are just a few advantages of decentralized energy generation compared to centralized solutions. The importance of distributed solar as a climate mitigation lever is highlighted by recent high-resolution mapping and scenario modeling, which indicate that widespread deployment of rooftop photovoltaic (RPV) systems could reduce global warming by up to 0.13 °C before 2050, especially in urban areas and regions with strong solar resources [4]. However, new difficulties are also brought about by it, especially when it comes to the reliable verification of energy production and consumption records, guaranteeing data transparency and tamper resistance after recording the data [5]. It has been suggested that cutting-edge technologies such as blockchain and smart contracts can solve these issues by using decentralized and verifiable data recording methods. This is crucial, since multiple stakeholders depend on verifiable data, including energy producers seeking compensation, grid operators guaranteeing balance and dependability, regulators keeping an eye on compliance, and consumers or communities claiming carbon offsets or renewable energy credits. These difficulties are particularly noticeable in decentralized energy markets and peer-to-peer (P2P) energy trading platforms, which often rely on centralized operators or market platforms for metering, settlement, or certification, despite their decentralized trading model. For instance, local energy trading is made possible by platforms such as the Brooklyn Microgrid [6]; however, utility-operated smart meters and centralized data registries are usually required to validate actual energy flows and balances [7]. This means that even though the energy exchange might seem to operate P2P, it frequently relies on centralized infrastructure or third-party operators to verify who produced or consumed a certain amount of energy. These systems risk fraud, disputes, or a loss of stakeholder trust if the data is not independently auditable and tamper-resistant, which becomes critical in cases where accurate data is required for regulatory reporting, energy credits, or financial settlements. Simultaneously, ESG principles are becoming part of business strategy and investment worldwide. Empirically, Shmelev and Gilardi (2025) [8] show that this trend is not only changing reporting and investment priorities, but it is also spurring corporate change in the direction of greater social and environmental value.

These issues of transparency and trust are deliberately addressed by blockchain technology, which was initially created for cryptocurrency transactions [9,10]. This technology is recommended for improving the verifiability of distributed energy resources (DERs) due to characteristics including programmable smart contracts, decentralized consensus mechanisms, and data immutability [11,12]. Recent research shows that real world blockchain applications in the energy sector, including P2P trading, microgrid management, and the issuance of renewable energy certificates (RECs), are increasingly being implemented. In rural microgrids, Aoun et al. [13] show that blockchain-based P2P trading performs better than conventional incentives such as net metering. Blockchain-driven demand-side management in islanded microgrids was validated by Umar et al. [14], who demonstrate enhanced energy balancing and transaction transparency. To improve traceability and trust, Liu et al. [15] suggested a safe, privacy-preserving framework for REC trading. Maintaining transparent, tamper-resistant energy logs is especially important for small-scale solar PV systems because it allows automated carbon accounting, promotes verifiable participation in renewable energy markets, and fosters trust in sustainability claims. However, cost, energy, and bandwidth limitations make it impractical to continuously send all recorded energy data to the blockchain, particularly on low-power Internet of Things (IoT) edge devices.

In this paper, we present the design, implementation, and assessment of a tamper-evident blockchain-enhanced solar energy logger that runs on low-power edge devices such as the Raspberry Pi. Our system employs Merkle tree batching with cumulative energy thresholds to decide when to send verified records to the blockchain. That is, it gathers information locally until a predetermined total energy value is reached, at which point it submits a batch of records to the blockchain; this contrasts with existing fallback-based techniques. An auditable chain of custody for all sensor readings is produced by hashing, chaining, and recording each local CSV log in a JSON file. A Merkle root of batched entries is sent to an Ethereum smart contract upon reaching a production threshold of 0.001 Wh, guaranteeing scalable verifiability with low gas costs. This threshold is set based on micro tracking (i.e., documenting energy production in tiny increments or at extremely brief intervals) and can be adjusted to 1 kWh or any other amount depending on the size and capacity of the energy system. Additionally, we measured the system’s resource consumption, blockchain overhead (i.e., the additional computational, storage, and networking costs when using a blockchain-based system), blockchain submission reliability, and energy logging performance. We used Sepolia for the prototype to avoid actual fees and risks. To ensure that the environmental impact assessment accurately reflects real-world network conditions, the cost and sustainability analysis, including energy consumption and carbon emissions, was based on publicly available benchmarks from the Ethereum mainnet. Additionally, we used data on residential solar deployment in Austria to make national-level projections and estimate sustainability impacts, including energy consumption and carbon emissions. In summary, the main goals of this study are:

• Development and implementation of a tamper-resistant blockchain logger for low-power solar IoT systems based on Merkle trees.
• An empirical assessment of resource usage and blockchain overhead in real-world conditions.
• A sustainability evaluation that quantifies blockchain logging’s scalability, energy consumption, including carbon impact, and national-scale feasibility.
• A comparative benchmarking against local-only logging models and traditional cloud models.

The remainder of this article is structured as follows: Section 2 reviews the existing literature on blockchain and solar energy applications, and outlines various research gaps. Section 3 explains the underlying method, focusing on the system architecture and experimental protocols. Section 4 presents the empirical results, outlining optimization methods. Section 5 discusses the implications of the findings from various sustainability angles. Section 6 concludes with a summary and suggestions for future research.

2. Literature Review

2.1. Blockchain Integration in Renewable Energy Systems

P2P energy trading, grid balancing, the issuance of RECs, and decentralized energy governance are examples of how blockchain technology is revolutionizing decentralized renewable energy management [11,16,17,18,19]. The practicality of blockchain in enabling safe, transparent, and decentralized energy exchanges that do not need to rely on centralized utilities has been demonstrated by real-world applications such as the Brooklyn Microgrid [7]. The use of blockchain in P2P energy trading offers several advantages, such as grid balancing, metering and billing, trading platforms, and renewable certificate issuance [11,16]. These functions are further augmented by smart contracts, which are blockchain-based automated agreements that enable load management, dynamic pricing, and real-time settlement without the need for third-party mediation [7,20]. Blockchain integration with Internet of Things (IoT) devices has greatly improved data provenance assurance, automated energy management, and fraud prevention [21,22]. Decentralized, sensor-driven energy markets with cryptographically secured transaction trustworthiness are supported by IoT-blockchain integration. Nonetheless, there are still major integration issues. IoT edge devices frequently experience resource constraints, sporadic connectivity, and bandwidth limitations, making continuous blockchain logging impractical or unaffordable [7,23,24]. According to broader industry analyses, blockchain adoption rates remain low even in industries where security and transparency are crucial; only 14% of surveyed businesses in international trade have successfully integrated blockchain into their operations [25]. The main obstacles mentioned are regulatory ambiguities, high initial implementation costs, and a lack of technical expertise. Recent developments have suggested selective or fallback-triggered logging mechanisms to address these limitations, thereby significantly reducing on-chain overhead.

Based on a systematic review of 390 peer-reviewed studies, Rejeb et al. [26] showed a notable increase in the use of distributed ledgers in energy metering, REC issuance, and solar microgeneration, and noted tamper resistance, improved transaction traceability, and decentralized coordination as the main benefits. Their review also highlighted significant shortcomings, particularly the lack of deployments that have been hardware validated (i.e., tested and verified on real physical devices), the lack of standardization in smart contract templates, and the gaps in data provenance assurance. Noting the dearth of practical applications that combine verified energy data with blockchain infrastructures, they called for “energy-grade verifiability mechanisms” appropriate for limited IoT configurations. Historically, blockchain systems have been criticized for their high energy consumption, especially Proof-of-Work (PoW) models such as Bitcoin [27]. However, the energy efficiency of public blockchain infrastructures has significantly improved since Ethereum switched to Proof-of-Stake (PoS), reducing consumption by more than 99.95% [28]. This change has led to an increased interest in scalable, eco-friendly blockchain-based energy solutions. Although there is increasing theoretical interest in these sustainable models, there are still few empirical validations in actual renewable energy settings. Our study addresses this research gap by focusing on a hardware-validated, functional blockchain deployment and evaluating resource efficiency, network stability, and real-world constraints.

2.2. Blockchain and Sustainability: Environmental and Economic Considerations

The discussion regarding the environmental sustainability of blockchain has mostly been allayed by Ethereum’s switch to PoS, which decreased its annual footprint from 78 TWh to less than 0.01 TWh [28]. According to Andoni et al. [11] and Ferraro et al. [29], blockchain’s advantages in fraud prevention, automation, and transparency outweigh the operational overhead it brings to the energy markets. Case studies have demonstrated that blockchain-based P2P energy markets can increase economic efficiency and reduce transaction costs in comparison to their centralized counterparts [13]. Additionally, Shmelev et al. (2018)’s multidimensional sustainability assessments show that effective urban sustainability transitions frequently depend on strong governance frameworks and focused technological advancements, indicating that blockchain benefits could be maximized by similar governance frameworks [30]. Energy and cost performance are further impacted by smart contract design, as optimized contracts allow for automated, low-overhead energy management [20]. On public blockchains such as Ethereum, however, transaction fee volatility continues to have an impact on economic sustainability [31]. Although permissioned blockchains provide cost stability, their applicability for open ESG reporting is limited because they forgo decentralization and public verifiability.

Beyond the theoretical supply potential, globally optimized solar-wind deployment and interconnection could reduce the required energy storage capacity by 41.6% and initial infrastructure investment by 15.6% compared to isolated national strategies, according to recent high-resolution global modeling [32]. By reducing redundant overcapacity and utilizing geographic complementarity, trans-regional transmission reduces the financial burden of decarbonization while simultaneously boosting renewable penetration and grid resilience, according to their multi-objective optimization framework. These results highlight the systemic advantages of global coordination, which is becoming a more important factor as blockchain-based systems are suggested for grid balancing, cross-border energy trading, and sustainability monitoring.

2.3. Blockchain-Based Solar Energy Applications

A promising application of blockchain technology lies in the P2P trading, fraud-resistant metering, and real-time energy certification of solar energy [6,7,33]. IoT sensors and blockchain can automate billing, validate solar production data, and support carbon credit systems [16,21,34]. To secure IoT-based energy systems, several trust management frameworks (e.g., RETINA [23]) and lightweight blockchain models (e.g., LightBlock [35]) have been proposed. Still, the majority of these solutions remain conceptual or simulation-based and lack empirical testing of deployments under real world hardware limitations. Many theoretical models describe high-frequency or continuous blockchain logging, yet low-power edge devices cannot use these strategies [21]. Frequent on-chain submissions use high amounts of energy and bandwidth, incurring fees, but these serious obstacles are rarely discussed. Just as this issue remains under acknowledged in the academic literature, so too do the technical solutions remain under-explored. We address this gap by testing the practical performance of selective, threshold-based blockchain logging under realistic circumstances. We contribute to discussions on the trade-offs between data reliability imperatives, security concerns, blockchain overheads, system scalability, and sustainability metrics through a data driven performance analysis.

3. Methodology

3.1. Research Design

In this study, we follow the Design Science Research (DSR) methodological approach to create, improve, and empirically validate a blockchain-secured, tamper-evident solar energy logging system for resource-constrained IoT environments [36,37]. This approach is ideally suited for artifact-centric research that tackles real-world information system problems, especially those pertaining to sustainability, verifiability, and trust [21]. The system was created using progressive prototyping in accordance with DSR’s iterative build-evaluate cycles [29]. Every design iteration included cryptographic mechanisms (i.e., SHA-256 [38], chain hashing [39], Merkle root batching [40]), local and blockchain anchoring, and autonomous recovery from network outages [36]. Specifically, we engineered an artifact—a solar logger built on a Raspberry Pi 4B with an INA219 sensor (Raspberry Pi 4 Model B 4GB Desktop Starter Kit (32 GB), smart-home-komponente, Berlin, Germany; INA219 I2C Bidirectional DC Power Supply Sensor Module, Shenzhen, China)—that implements cryptographic chain hashing of sensor logs, Merkle tree construction for batch proofs, and secure batch submission to an Ethereum-based smart contract. This architecture operationalizes key blockchain principles (e.g., decentralization, immutability, and cryptographic verifiability), as well as green IoT and edge-computing principles to minimize resource overhead [41], prioritizing energy efficiency, reduced environmental footprint, and responsible resource use in line with green computing standards [41] and contemporary frameworks for low-carbon digital infrastructure [42]. To assess system resilience, tamper detection, and loss recovery in real-world scenarios, a pilot deployment was incorporated into the research design. Critical features such as day-end rollover and persistent submission queues were introduced in response to feedback from the pilot study, guaranteeing end-to-end data completeness.

Following Hevner et al. [37], we appraised the achievement of both technical and sustainability goals in the evaluation phase. Specifically, we assessed the logger’s operational overhead, energy and cost footprint, end-to-end data integrity, and recovery performance under simulated network disruptions. Digital infrastructures can incorporate auditability and traceability for ESG reporting, as shown by existing blockchain-based environmental accountability systems [34,43,44]. The effectiveness of design science research (DSR) techniques in carbon-conscious digital infrastructure research is demonstrated indirectly by these examples [45,46]. Therefore, the study’s scope includes both rigorous, repeated field evaluation—including resource profiling, data verifiability, and sustainability impact analysis—and artifact design (hardware, cryptographic architecture, software stack). During each step of the iterative DSR process (i.e., build, intervene, and evaluate) we ensured that the environmental (minimizing energy use and carbon footprint of logging), ethical (ensuring data privacy, compliance with data protection standards, and preventing misuse), and practical needs (ensuring system usability, reliability, and cost-effectiveness) of transparent and reliable logging of renewable energy data were met.

3.2. System Architecture

To ensure sustainability, verifiability, and tamper evidence in solar energy data logging, our system architecture integrates edge computing with strong blockchain anchoring (i.e., linking off-chain data to a blockchain) and sophisticated cryptographic assurance. The modular, fault-tolerant design [47] adheres to best practices for edge computing and the green Internet of Things [31,41]. A Raspberry Pi 4 Model B (4 GB RAM) acts as the edge controller. Three 6 V 1 W mini solar panels are connected in parallel to optimize the current supply in the energy harvesting circuit. Two diodes (1N5819 Schottky) are used to ensure safe and dependable energy flow: One is installed immediately after the solar panel array to prevent reverse current from the battery or load back into the panels, and the other is placed before the battery to prevent backflow from the battery toward the panels. This ensures unidirectional charging and protects both components. The sensing subsystem is powered by a TP4056-charged 18650 lithium-ion (Li-ion) battery (18650 Batteries, Leikurvo, Shenzhen, China), which serves as the storage element. The Raspberry Pi itself is powered by a dedicated charger (5 V, 3 A) separate from the solar-battery circuit. This design decouples the computational node from the energy harvesting and storage path, avoiding voltage drops and instability that can occur in direct solar-powered Pi applications, thus enhancing experimental reliability and system resilience.

At one-minute intervals, the INA219 sensor provides high-precision power data by measuring the voltage and current across the solar circuit. Every sensor reading is added to a local CSV log file with a time stamp. The Raspberry Pi is used because it can provide sufficient cryptographic performance for safe logging while maintaining low idle consumption (<3 W) [31]. Best practices for robust, low-power, off-grid IoT deployments are adhered to by the circuit layout and edge device [31,47]. In accordance with the Secure Hash Standard (SHS), every sensor reading is hashed instantly using SHA-256 [38]. In order to provide forward integrity (i.e., a security property that ensures that past data cannot be altered), these hashes are connected in a chain (inspired by the blockchain) so that every new entry includes the hash of the previous record. Any changes made to earlier entries render downstream hashes invalid [39,40,48]. Merkle’s protocols for effective public key verification [40] and cryptographic timestamping [39] are directly applied in this mechanism. Records are grouped for batch anchoring until the total energy generated exceeds a threshold of 0.001 Wh. A Merkle tree is then created from all of the queued hashes [40], and the resulting Merkle root acts as a cryptographic commitment to the entire batch, allowing for proofs that are secure, scalable, and independently verifiable.

A custom smart contract (SolarLoggerV3 ((submitted on Etherscan 5 June 2025, SPDX MIT) Custom-developed (Javad Vasheghani Farahani), Vienna, Austria)), installed on the Ethereum Sepolia testnet (address: https://sepolia.etherscan.io/address/0xc986b14F1d8a26FB46b10D6fdA35C5D4062bDA98 (accessed on 29 August 2025)), realizes the core of the blockchain anchoring process. All on-chain operations are transparent and reproducible due to the contract’s public verification and accessibility. After every Merkle-batched data segment is finished, the Raspberry Pi logger calls its logBatch function. This function saves the Merkle root, a batch hash, a device identifier (in both plaintext and SHA-256 hash), the batch timestamp, and the cumulative energy reading (in Wh) to the blockchain. Additionally, every batch submission generates an event (BatchLogged) with indexed metadata that can be readily queried for regulatory audit requirements, downstream verification, or ESG reporting [34,43,44].

Key read-only functions such as getLog (which retrieves a single batch by index), getAllLogs (which retrieves all batches), and getTotalLogs (which enumerates all entries) are exposed by the contract, allowing for programmatic data collection and robust off-chain analysis. Following security best practices for limited IoT deployments, all state-changing interactions are submitted to Sepolia through the Infura gateway after being cryptographically signed locally [31]. Long-term, unattended deployments of the system are made possible by this workflow, which enables public, tamper-evident anchoring without the costs associated with operating a complete blockchain node on the Raspberry Pi [31]. As written above, the code of the smart contract and the Application Binary Interface (ABI) are publicly available on the Sepolia testnet.

Every Merkle root is documented in a comprehensive proof log, along with the corresponding CSV SHA-256 hash, blockchain transaction hash, row range, and batch size. By reconstructing the Merkle tree from raw logs and verifying integrity against the on-chain root, an independent verification tool offers cryptographic end-to-end auditability [38,39,40]. The logger uses a daily rollover mechanism for log files to effectively manage storage, reduce system load, and facilitate integrity verification. Every time the system logs, it determines if the UTC date has changed from the previous cycle; the rollover process initiates if a new day is detected. The logger specifically loads all unbatched records that are kept in a persistent queue but have not been added to a Merkle batch or sent to the blockchain yet. The system checks if each record is already in the CSV log for the new day to avoid duplication. The new day’s log file is appended with only those records that have not yet been written. In this way, all sensor records are carried forward and eventually included in a verifiable Merkle batch, regardless of whether they were batched and anchored on-chain the previous day. To ensure that no data is ever lost at a file boundary, this migration is atomic and resilient to system disruptions. In the event of a crash during rollover, the process is automatically retried on the subsequent startup. To facilitate forensic traceability, the rollover event is also documented in a specific audit log, which includes information about the target log file and the quantity of records migrated. By preserving the cryptographic chain hash across successive log files and making sure that every record is eventually included in a Merkle tree for batch submission, data integrity across file boundaries is maintained. In addition to maximizing resource utilization on the edge device and avoiding the accumulation of unnecessarily large files that might impair system performance, this procedure is in line with accepted best practices in audit log management, which recommend daily or size-based log rotation for efficient forensic analysis, security, and operational effectiveness [49]. Any log entries that have not been submitted to the blockchain or combined into a Merkle tree are automatically copied by the system at the end of each day. By taking this precaution, all records are preserved and data loss is prevented. We implemented this procedure after learning in the pilot study that entries lacking a Merkle tree could not be verified by an independent verification tool.

The system incorporates an automated retry mechanism to guarantee dependability in the event of internet or blockchain submission failures. The complete submission payload, which includes the Merkle root, batch hash, timestamp, and cumulative energy, is saved to a file for pending submissions in the event that a transaction fails (for example, because of connectivity problems). The system searches for pending data and attempts resubmission at the beginning of each logging cycle. If successful, the transaction hash is recorded and the pending file is removed; if not, the retry is repeated every minute until connectivity is restored. By using deterministic transaction content, this mechanism guarantees data persistence, guards against loss, and guarantees submissions without any duplicates. With an average CPU load of less than 0.02%, RAM of less than 101 MB, and event-driven network activity (uploading only when batches are committed), the logger is designed to minimize the use of system resources. Even in remote, off-grid, or bandwidth-constrained environments, these design decisions enable sustainable IoT operations [41].

The pilot study, conducted in June 2025, aimed to ensure the system’s functionality and efficiency, ultimately leading to the detection and correction of an issue in the rollover mechanism. By combining chain hashing, Merkle tree batch proofs, and transparent blockchain anchoring—all within a robust, low-resource edge computing framework—the resulting system establishes a new standard for cryptographically guaranteed, tamper-evident solar energy monitoring. For digital energy reporting, our suggested architecture facilitates sustainability, open science, and auditability. The entire system architecture is shown in Figure 1 and illustrates the interaction of the Solar system, the Raspberry Pi, acting as a controller, the local data storage, and the smart contract on blockchain. The hashed logged data, which records the generated energy, is first saved on the local storage, patched to a Merkle Tree, and then submitted to the blockchain.

3.3. Cost, Energy, and Emissions Calculation

In this study, we design and employ a transparent and reproducible framework to quantify the economic and environmental impact of blockchain-based energy logging, in accordance with the DSR approach as well as the sustainability and green computing principles outlined in Section 3.1. We created a traceable and verifiable ESG reporting system by using operational benchmarking of the prototype and scenario modeling at the national level. To ensure practical comparability, all economic projections make reference to mainnet Ethereum conditions, even though the logger prototype was deployed on the Ethereum Sepolia testnet for cost effectiveness and speed of prototyping, while avoid risks associated with the loss of real assets or exposure of sensitive credentials. The observed average gas usage per batch (190,823 units) and the average gas price on 18 June 2025 (2.4 Gwei) [50] were used to calculate the cost per transaction. The same-day ETH–EUR exchange rate (€2160.79 per ETH) [51] was used to convert the gas price to EUR, and this transaction fee is used for the calculation of all results. The Crypto Carbon Ratings Institute (CCRI) benchmark of 6.294 Wh/tx was used to model the energy impact of each blockchain transaction [52]. Ethereum’s post-merge energy profile is consistent with this peer-reviewed, bottom-up estimate, which has been widely used in the literature [53]. Because validator energy consumption is not linearly correlated with transaction throughput, we also point out that per-transaction energy calculations in PoS blockchains do not correspond to a strict one-to-one mapping; rather, they represent an allocation of total network energy use across the transaction count [53]. An alternative estimate of 7.2 Wh/tx [53], which is discussed in our main calculations and is discussed in the results as a sensitivity check, is based on typical transaction volume and annual network consumption of 22,601,000 Wh/year.

To determine the climate impact, we used Austria’s national grid carbon intensity (209 g CO₂/kWh), as reported by the Umweltbundesamt CO₂ Monitor [54], and multiplied it by the attributed energy use of each transaction. In line with best practices in green computing and ESG reporting, this strategy guarantees that the outcomes have regional relevance and facilitate the integration into the Austrian and EU carbon accounting systems [8,34,41]. In accordance with official statistics and guarantees of origin procedures, national-scale projections were based on Austria’s total installed PV generation (3980 GWh/year), with logger deployments fictitiously extended to 250,000 PV systems [55]. The threshold for energy logging in our micro-scale simulation was set at 0.001 Wh, and a standardized threshold of 1 megawatt hour (MWh) was used for national-scale modeling and practical assessments of blockchain transaction volume, energy consumption, and cost. These numbers align with previous research [56] and international renewable energy certificate (REC) frameworks [57]. Following established practices in REC systems and guarantees of origin, this threshold also aligns with digital accountability standards and sustainability reporting norms. To enable robust ESG traceability and the adoption of green computing best practices outlined in Section 3.1, this section addresses the DSR methodology’s requirement for artifact evaluation in real-world conditions by outlining our empirical calculations and scenario modeling.

3.4. Practical and Ethical Considerations

In compliance with ethical research guidelines, we prioritized transparency, data integrity, and responsible innovation in the design of this study. Institutional ethical approval was not required because all sensor data came from a specially designed experimental platform and did not involve human subjects or personally identifiable information. We adhered to open science principles when handling data such that all logs, proofs, and code are tamper-evident, reproducible, and open for independent verification, promoting scientific accountability and transparency [34,47]. Reducing operational expenses and system risks (such as potential financial loss, security vulnerabilities, or data loss during the processes of prototyping and deployment) was given top priority in practical deployment decisions. The decision to use the Ethereum Sepolia testnet for live deployments reduced the environmental impact during development and pilot testing and avoided unnecessary financial expenses. To ensure real-world relevance, all economic and environmental impact projections are based on estimated Ethereum mainnet parameters. By reducing hardware overhead, energy consumption, and network resource consumption, the system architecture complies with green computing guidelines [41], bringing practical implementation into line with general sustainability and ESG objectives [34,47]. As outlined in Section 3.2, strong backup, rollover, and retry procedures were used to mitigate any remaining risks of data loss, duplication, or blockchain submission failure.

3.5. System Constraints and Validity Threats

The design of this study includes several constraints and potential validity risks, which are intrinsic to prototype-based research and need to be considered in the interpretation of results:

• Testnet Deployment: For pragmatic reasons, the logger prototype was only deployed on the Ethereum Sepolia testnet, and mainnet conditions were replicated in all cost and energy calculations. Generalizability may be impacted if testnet features (such as network latency and transaction inclusion policies) diverge from mainnet features.
• Hardware Configuration: The Raspberry Pi 4B, INA219 sensor, and particular battery/solar setup are the only hardware configurations on which the results are based. Different performance profiles may result from variations in edge device models, sensor accuracy, and power supply.
• Transaction Energy Attribution: Although in line with current research [53], the use of per-transaction energy allocation ignores the intrinsic non-linearity of PoS validator energy use and fails to account for the entire range of on-chain activity (e.g., mempool contention, contract complexity).
• National-Scale Modeling: Simplified scaling assumptions and national averages are used in projections to 250,000 PV systems [55]. Real-world deployment constraints, system heterogeneity, and local network conditions are not thoroughly documented.
• Threshold Sensitivity: Although the chosen logging thresholds (1 MWh for the national scale and 0.001 Wh for the micro-scale) adhere to industry standards, they might not be representative of all use cases or optimal for all performance goals.
• Security and Tamper Resistance: The system’s security ultimately rests on the hardware’s integrity and the reliable operation of logging scripts, even though cryptographic chaining and Merkle proofs provide powerful tamper evidence. Software compromises or physical assaults are outside the purview of this study.
• External Factors: Even with safeguards in place, network connectivity, blockchain congestion, or unanticipated software bugs may cause brief delays in data submission in real-world deployments.

4. Results

4.1. Deployment Overview and Logging Performance

Deployed on a Raspberry Pi 4B with an INA219 sensor, the blockchain-secured, tamper-evident solar energy logger ran for 5 days, 15 h, and 5 min (135.08 h) between 12 June 2025 18:32:12 and 18 June 2025 09:37:13. During this time, the system functioned flawlessly, exhibiting strong stability in the face of actual circumstances, such as internet failures. A total of 10,268 energy log records were produced by the logger at one-minute intervals throughout the deployment. To prevent tampering, data entries were continuously appended to local CSV files and cryptographically chained using SHA-256 hashing. A Merkle tree was created from all of the queued records once the total energy produced since the last batch surpassed the 0.001 Wh threshold. The Merkle root was then sent to the Ethereum Sepolia blockchain. A blockchain transaction was associated with each batch commitment, resulting in a total of 130 Merkle batches. On average, it took one minute (about 58 s) from a batch’s last record to its blockchain submission. 283.03 min (about 4.7 h) is the minimum batch interval, and 7519.06 min (about 5.2 days) is the maximum. To guarantee end-to-end data integrity across all files, a daily rollover mechanism was put in place to make sure that no data was lost at file boundaries. Records that were not included in a Merkle batch by the end of the day were automatically carried over into the log file for the following day. Figure 2 illustrates Voltage and Power Readings by the INA219 sensor. On the left (a), the voltage time series recorded by the solar logger over a five-day period is shown, illustrating daily charging and discharging cycles. On the right (b), there is the corresponding power output, highlighting periods of solar energy generation and system downtime. These findings provide a solid foundation for verifiable solar energy auditing by confirming that the logger collects energy data continuously, tamper-evidently, and without missing entries or blockchain submission failures.

4.2. Tamper-Evidence and Verification Accuracy

Every Merkle root produced by the logger was independently checked using the verifier tool to guarantee the accuracy and auditability of all energy records. This verification procedure computes the expected Merkle root, reconstructs the Merkle tree from the raw logs, and compares it to the root that was previously submitted on-chain and saved in the proof log. Any disparity (e.g., a missing, changed, or added record) leads to a failed proof, which makes tampering instantly apparent. Every Merkle batch sent to the blockchain during the primary deployment period was successfully validated, proving end-to-end data integrity.

Table 1. Summary of Batches Submitted (Blockchain Proof Log) and Batches Verified (Local Merkle Verifier).

Date	Batches Submitted (Blockchain Proof Log) and Verified (Local Merkle Verifier)
12 June2025	0
13 June 2025	25
14 June 2025	25
15 June 2025	24
16 June 2025	20
17 June 2025	25
18 June 2025	11
Total	130

shows the daily number of energy logging batches that were successfully submitted on-chain by the blockchain-secured logger and recorded locally. The number of batches submitted to the blockchain, which is accessible on a git repository (the git repository is publicly available at: https://github.com/javadvf/solar_logger; accessed on 1 September 2025), and the number of batches validated with the local Merkle verifier tool are contrasted in the table. The information in Table 1 also demonstrates end-to-end integrity, which shows that for each day the logger was in operation, the number of batches verified locally matched the number of batches confirmed on-chain. On 12 June, the system started after 20:30, so no batches were recorded. The system stopped at 09:37:13 on 18 June, and only 11 batches were submitted, making the day incomplete. Rainfall, which limited solar generation, is the reason for the notably low batch count on 16 June (20 batches), while the batch count on 15 June (24 batches) reflects lower solar output on a semi-cloudy day. Since the logger only submits a batch when the minimum energy threshold is met, these environmental factors had a direct impact on how frequently batches were created. A range of weather conditions, including sunny, semi-cloudy, and rainy days, were present during the observation period, enabling a thorough validation of the logger’s performance under actual operating conditions. The reliability and generalizability of the results are further supported by the large number of batches (130) and the multi-day test duration.

Our results show that the system can sustain verifiable, tamper-evident logging integrity across varying solar production levels and is well-suited for deployment in real-world settings. In order to assess the system’s tamper evidence capabilities further, intentional changes were made to the raw log data. Whenever a single record was added, changed, or deleted from any batch, the verifier tool would always fail to reconstruct the correct Merkle root, immediately marking the proof as invalid. Because the resulting Merkle tree will no longer match the root that was previously committed to the blockchain and the proof log, this cryptographic behavior demonstrates that any attempt to alter log entries after they are first recorded will be detected. This system ensures that downstream auditability is maintained and that any malicious or unintentional breaches of data integrity can be quickly detected. Our implementation thus substantiates the system’s fundamental guarantee of end-to-end verifiability, as the Merkle structure and cryptographic chain guarantee that no entry can be changed covertly. One significant enhancement over the initial implementation was the incorporation of a daily rollover mechanism, which was brought about by problems identified during the pilot stage. Initially, records that were not batched at the end of the day ran the risk of being left out of on-chain anchoring and Merkle proofs, which resulted in entries that could not be verified. To ensure that all data is eventually included in a verified Merkle batch, the updated design automatically moves any unbatched records to the log for the following day. Following the implementation of this mechanism, there were no missing entries during the main study period, and every operational day had full batch verification. Even in lengthy or resource-constrained deployments, this method guarantees continuity, thorough audit trails, and adherence to best practices for safe and effective log management. The system therefore satisfies the strict requirements of tamper-evident, auditable solar energy logging, with cryptographically proven end-to-end integrity and resilience to both operational and environmental disturbances.

4.3. System Resource Performance

The blockchain-secured logger showed low and consistent resource consumption across all system parameters that were monitored over the course of the 135 h deployment. A total of 8106 resource monitoring cycles were recorded at regular intervals, recording network activity, CPU utilization, memory consumption, system temperature, and disk usage. As can be seen in

Table 2. System Resource Utilization.

Resource Metric	Mean	Maximum	During Blockchain Submissions
CPU Usage (%)	0.01	12.50	0.10
RAM Usage (MB)	100.56	106.75	100.41
System Temperature (°C)	40.79	43.80	41.01
Disk Usage (%)	5.30	5.30	5.30
Network Upload (MB)	10.12	11.02	10.14
Network Download (MB)	15.16	17.98	15.20
Internet Availability	8001 True (98.6%)/105 (1.4%) False

, almost no significant load was placed on the Raspberry Pi 4B by the logger, even with its constant operation and cryptographic processing. The average CPU usage was 0.01% over the course of 8106 monitoring cycles, while the memory consumption stayed relatively constant at just over 100 MB. Effective log management and rollover mechanisms kept the system temperature well below thermal thresholds, never rising above 43.8 °C, and the disk usage remained steady at 5.3%. Additionally, there was very little network activity other than brief, sporadic spikes during blockchain batch submissions. Notably, the actual blockchain submission events barely affected the total amount of resources used. The average CPU usage during the 130 on-chain submission windows was 0.10%, which was an order of magnitude higher than the idle mean but still very low in absolute terms. Memory and temperature also stayed relatively constant when compared to baseline operation. These events resulted in slight increases in network upload and download (mean upload 10.14 MB compared to 10.12 MB during continuous operation), demonstrating that the system effectively handles the network and cryptographic tasks related to sending batch proofs and Merkle roots to the blockchain without compromising system stability or concurrent processes. With only 105 brief connectivity outages out of over 8100 cycles, the logger was nearly always able to connect to the blockchain network when needed, further evidenced by the consistently high internet availability (98.6%). All submissions were ultimately finished without any data loss or system outages thanks to the integrated retry and recovery mechanisms. These outcomes show the appropriateness of the blockchain-secured logger design for sustainable edge computing or IoT environments with limited resources. During periods of high blockchain activity as well as continuous operation, cryptographic processing, Merkle batching, and periodic blockchain anchoring can all be carried out with negligible system overhead. This confirms that the logger is appropriate for practical solar monitoring applications where resilience, stability, and energy efficiency are crucial.

The entire system resource and network usage of the 135 h deployment of the blockchain-secured solar logging node is illustrated in Figure 3. Temperature, disk, CPU, RAM, and network statistics are displayed every minute. In contrast to the baseline system load, on-chain logging introduces negligible or no discernible resource spikes, as shown by the vertical red lines that represent blockchain batch submission events.

4.4. Blockchain Logging Cost, Energy, and Emissions

In this section, we examine the economic and environmental effects of blockchain-based solar energy logging, using scaling projections and actual deployment data. Scenarios at the national level (Austrian PV fleet) and the micro level (experimental) are assessed. Carbon emissions, energy consumption, and transaction costs are important metrics that are compared to current blockchain research and pertinent sustainability standards. The average gas cost per Merkle-batched submission to the Ethereum Sepolia blockchain was 190,823 units. With the exchange rate of €2160.79 per ETH [51] and the reference gas price of 2.4 Gwei on 18 June 2025 [50], the average transaction fee was €0.9896. During the deployment period, the total blockchain expenditure for 130 transactions was €128.65 (see

Table 3. Blockchain Transaction Cost Analysis.

Metric	Value	Source/Method
Gas Used per Batch (units)	190,823	Empirical (deployment)
Gas Price	2.4 Gwei	See etherscan.io (accessed on 18 June 2025) [50].
ETH–EUR Exchange Rate	€2160.79/ETH	See coinmarketcap.com (accessed on 18 June 2025) [51]
Cost per Transaction	€0.9896	Calculated
Transactions per Year (Austria)	3,980,000	Projected, see above
Annual Blockchain Logging Cost	€3,938,608	3,980,000 × €0.9896

). These expenses were projected to a typical national scenario to evaluate scalability. The total annual transaction count would be roughly 3,980,000 if 250,000 PV systems each submitted an average of 15.92 batches annually (based on Austria’s annual PV output of 3980 GWh [55]). As a result, the estimated cost of blockchain logging is €3,938,608 per year, or roughly €328,217 per month.

Using the energy consumption estimation of the Crypto Carbon Ratings Institute (CCRI) [52], the energy needed for each blockchain batch submission was calculated using the post-Merge Ethereum benchmark of 6.294 Wh/tx, which is consistent with recent peer-reviewed and industry estimates [53]. This value falls well within the Ethereum PoS wider published range of 0.8–14.7 Wh/tx [53] and resembles the sensitivity analysis of the CCRI estimate, as well as a higher estimate of 7.2 Wh/tx [53] that is calculated based on the Ethereum official website, where energy consumption is annualized at 0.0026 terawatt-hours (TWh). The total amount of energy used by blockchain across the 130 batches that were submitted for the experiment was 818.22 Wh (0.818 kWh). Using the CCRI benchmark, a total of 3,980,000 transactions nationwide would use 25,050.12 kWh annually (see

Table 4. Blockchain Energy Consumption (PoS Ethereum).

Scenario	Transactions	Energy per Tx (Wh)	Total Energy (kWh)	Source
Deployment (study)	130	6.294	0.818	See ethereum.org (accessed on 18 June 2025) [52]
Austria (CCRI)	3,980,000	6.294	25,050.12	Projected
Austria (High)	3,980,000	7.2	28,656.00	See etherscan.io (accessed on 18 June 2025) [53]

).

Next, we used the grid emission factor for Austria (0.209 kg CO₂/kWh) to calculate the carbon impact of logging [54]. The total emissions for the experimental deployment were about 0.171 kg CO₂ (818.22 Wh × 0.209 kg/kWh). As shown in

Table 5. Blockchain Logging Emissions (Austrian Grid Factor).

Scenario	Total Energy (kWh)	Emission Factor (kg CO2/kWh)	Total Emissions (kg CO2)	Source
Deployment	0.818	0.209	0.171	See umweltbundesamt.at (accessed on 18 June 2025) [54]
Austria (CCRI)	25,050.12	0.209	5235.48	See umweltbundesamt.at (accessed on 18 June 2025) [54]
Austria (High)	28,656.00	0.209	5992.10	See umweltbundesamt.at (accessed on 18 June 2025) [54]

and based on the CCRI benchmark, the estimate for the annual national emissions from blockchain technology corresponds to 5235.48 kg CO₂ (i.e., 5.2 metric tons CO₂).

In comparison to Austria’s total yearly PV energy production (3980 GWh) [55], the energy overhead associated with blockchain logging is minimal, accounting for only 0.00063% of the measured energy. This indicates that, in a PoS regime, the emission effects of blockchain-based logging are negligible in comparison to the benefits of auditability and transparency [11,52,53]. Although transaction costs are significant at scale, Ethereum’s PoS mainnet fee economics—rather than the inherent energy cost of the blockchain itself—dominates them. A sensitivity analysis was conducted on important cost and energy parameters to guarantee the validity of our results (see

Table 6. Sensitivity analysis.

Scenario	Gas Price (Gwei)	Wh/tx	Annual Cost (€)	Annual Energy (kWh)
Baseline (study)	2.4	6.294	3,938,608	25,050
High Gas Price	6.0	6.294	9,846,520	25,050
High Energy	2.4	7.2	3,938,608	28,656
Both High	6.0	7.2	9,846,520	28,656

). Both the observed average and a higher gas price (e.g., 2.4 Gwei vs. 6.0 Gwei), the CCRI benchmark (6.294 Wh/tx), and a higher published energy benchmark (7.2 Wh/tx) were used to recalculate annualized costs and energy usage. The findings indicate that transaction costs can increase significantly during times of high demand because they are extremely sensitive to Ethereum network conditions, such as congestion and changes in gas prices. Conversely, even with conservative estimates, the energy impact of these transactions is insignificant in relation to Austria’s overall PV output. While the per-transaction energy range (6.294–7.2 Wh/tx) is supported by recent academic studies [53] and justified by CCRI’s post-Merge analysis [53], the gas price range used in the sensitivity analysis (2.4–6.0 Gwei) reflects typical mainnet fluctuations observed in 2024–2025 based on Etherscan’s historical data [58]. This study demonstrates that our suggested blockchain-secured logging system provides real-time auditability that is guaranteed by cryptography while leaving a small environmental impact. Its alignment with green computing and ESG goals is supported by the fact that its operational carbon and energy costs are minimal when compared to the value of reliable, tamper-evident data reporting for national-scale PV systems [41,43,45,52].

Despite using the Sepolia testnet for on-chain submission in our pilot, we based all energy and cost calculations on actual mainnet gas usage and June 2025 ETH/EUR exchange rates (see Table 3). Mainnet gas prices typically ranged between 2 and 10 Gwei, with occasional spikes during times of network congestion or significant contract launches, according to historical data from Etherscan [58]. Both typical and moderately adverse conditions are represented by the 2.4–6.0 Gwei range covered by our sensitivity analysis (see Table 6). Although these factors are not entirely represented in the testnet-based pilot, practitioners should be aware that unexpected spikes in gas prices, mempool congestion, and validator competition may further raise real-world expenses and submission delays.

Although these national-scale estimates show that blockchain-secured energy logging is feasible and scalable, they are predicated on averaged national statistics and uniform deployment assumptions. However, regional differences in network connectivity, solar resource availability, and regulatory frameworks can have a substantial impact on deployment outcomes in real-world scenarios [5,16,17,54]. For example, logging frequency and transaction rates may be impacted by the varying solar yields in the Alpine and eastern Austria regions [5,54,55]; on-chain submissions may be delayed or complicated, and audit trail reliability may be impacted in rural and remote areas due to sporadic or lower-bandwidth network access [16,21]. Additionally, the operational and financial feasibility of blockchain-based audit solutions for PV operators may be impacted by regional variations in regulations, incentive programs, and compliance standards [16,17,33]. While more dispersed, isolated installations may result in higher per-device costs; high deployment densities in urban or industrial zones may allow for shared infrastructure or cost optimization [5,54,55]. Therefore, even though the current projections offer a useful starting point, practitioners should take local conditions into account and modify system parameters, such as logging thresholds, submission frequency, and resilience measures, to best suit each deployment’s geographic and regulatory context [5,16].

To give practitioners a more thorough guidance, we extended our sensitivity analysis to encompass a range of intermediate batch thresholds ranging from 1 kWh to 10 MWh. As the batch threshold rises, the cumulative blockchain energy overhead and the annual blockchain transaction cost both drop off quickly, as

Table 7. Threshold sensitivity analysis.

Batch Threshold	Annual Tx (Austria)	Blockchain Cost (€)	Blockchain Energy (kWh)
1 kWh	3,980,000,000	€3.94 billion	25,050,120
10 kWh	398,000,000	€393.86 million	2,505,012
100 kWh	39,800,000	€39.39 million	250,501
1 MWh	3,980,000	€3,938,608	25,050
10 MWh	398,000	€393,861	2505

illustrates. The number of transactions (and consequently the cost and energy) increases by a factor of ten when the batch size is reduced from 1 MWh to 100 kWh. At the extreme, employing a 1 kWh batch threshold leads to nearly €4 billion in logging costs annually, which is not economically feasible for PV monitoring on a national scale. Conversely, thresholds of 1 MWh or more limit expenses and energy overhead to less than 0.001% of PV’s overall output and income. Since it matched the energy generation of our micro-PV setup, the lowest tested threshold (0.001 Wh) was selected for the prototype experiment. Established energy market practices for digital guarantees of origin and renewable energy certificates are reflected in the national-scale scenario (1 MWh) [56,57].

Under the simplifying assumption of uniform batch thresholds and identical node output, Table 7 presents a national-scale projection; However, actual deployment is much more heterogeneous than this. About 83.7% of systems are rooftop (primarily residential and commercial), 1.3% are building-integrated (BIPV), and 14.9% are utility-scale, ground-mounted buildings, according to Austria’s official 2022 PV statistics [55]. The annual generation per node varies greatly as a result: Typical annual yields for small residential systems (5–10 kWp), commercial/rooftop installations average ~30 MWh, and utility-scale sites can surpass 500 MWh. A residential node may submit five times more annually, a commercial node thirty times, and a utility site over 500 times more for a fixed 1 MWh threshold. Therefore, even with a constant national PV output, deployment scenarios with a high-capacity node (utility, industrial) predominance will generate significantly higher aggregate blockchain overhead than scenarios with a predominantly residential node population. Particularly in remote or bandwidth-constrained deployments, device or network limitations may also limit the maximum feasible logging frequency. Node counts and average system sizes are estimated in

Table 8. Node capacity sensitivity analysis.

Node Type	Avg. Capacity (MWh/Yr)	Submissions/Yr/Node	Nodes (Austria Est.)	Total Submissions/Yr
Small residential	~5	5	200,000	1,000,000
Commercial/rooftop	~30	30	40,000	1,200,000
Utility-scale	>500	500+	1000	500,000+

using the official system shares (rooftop, BIPV, and utility) from [55]. Drawing from Austrian system sizes and reported capacity breakdowns, we round conservatively to 200,000 residential, 40,000 commercial, and 1000 utility-scale nodes.

This table demonstrates that the distribution of system sizes and types, rather than just the quantity of PV systems, has a significant impact on national blockchain traffic. By using the official ratios of rooftop, BIPV, and ground-mounted installations and explicitly modeling the national PV fleet as a size-distributed population, future analyses could improve these projections even more. For policymakers and grid operators preparing for scalable, low-overhead blockchain-based logging, this strategy would allow for more detailed guidance.

The sustainability of the entire system also depends on the environmental effects of key hardware components, such as the Raspberry Pi 4 (4 GB), solar panel (6 V 1 W), lithium-ion battery (18,650, 1200 mAh), and important IoT electronics (INA219, TP4056, 1N5819 diode, wire), even though blockchain logging overhead is low. With an emphasis on carbon footprint, energy and water demand, toxicity, service life, and end-of-life (EoL) prospects, we have compiled the cradle-to-gate and lifecycle metrics for each here.

These LCA findings align with current best practices in digital infrastructure design and green IoT, which prioritize full life-cycle reporting for all hardware components [42]. Although the environmental profile of the hardware that makes up our system is fully quantified by the previous LCA, it is important to place these results in the operational and technological context of Austria’s PV industry. The 2022 IEA PVPS National Survey Report for Austria [55] states that silicon-based modules from both domestic and international manufacturers (Kioto, Energetica, DAS Energy, Ertex, Sunplugged) make up the majority of the country’s PV infrastructure. These modules are supported by extensive subsystems for inverters, mounting, cabling, and energy storage. With a large percentage of grid-connected sites and a growing number of decentralized lithium-ion storage units, system configurations range from residential rooftop arrays (5–10 kW), commercial and industrial building-integrated PV (100–250 kW+), and large centralized or floating PV installations (>20 MWp) [55].

Crucially, the system suggested here does not require or cause any changes to the hardware makeup, energy generation-distribution mechanisms, or physical PV infrastructure that currently exists at the national level. Our method uses permissionless blockchain networks for transparent and unchangeable record-keeping, Merkle tree data structures for scalable batch integrity, and advanced hashing algorithms (SHA-256) to create a tamper-evident, cryptographically secured audit trail for PV energy data [11,15,16,34,38,39,40]. This is the main innovation in the digital and cybersecurity layer. The goal of this architecture is to completely ignore the underlying PV module, inverter, or balance-of-system equipment while superimposing existing metering and data acquisition frameworks with minimal integration at the system controller or edge device level (for example, using a Raspberry Pi or comparable IoT gateways, which can be performed with current hardware devices as well).

Therefore, the environmental impacts computed for our system (

Table 9. LCA metrics for system hardware.

Component	Carbon Footprint	Cumulative Energy Demand	Water Use	Lifetime/Cycles	Toxicity/EoL Risk	Key References
18650 Li-ion Battery (1200 mAh)	0.6–1.4 kg CO2e per cell	30–45 MJ per cell	60–120 L per cell	800–1000 cycles, 3–5 yr	High (Co/Ni); LFP lower; <7% recycled	[59,60,61]
Solar Panel (Poly-Si, 6 V 1 W)	90–120 g CO2e/kWh (lifetime); 10–20 MJ per panel	80–200 MJ/kWh output	8–20 L/panel	5–10 yr	Lead/EVA; poor mini-PV recycling	[55,62]
Raspberry Pi 4 (4 GB)	17 kg CO2e (5 yr, all phases)	100–150 kWh in use phase	Not Quantified in cited reference (NQ)	5+ yr	Low EoL risk, high recyclability	[63]
INA219 (sensor IC)	NQ	2.7–3.1 MJ per chip	NQ	NQ	NQ	[64]
TP4056 (charger IC)	NQ	10–40 MJ per chip	NQ	NQ	NQ	[64]
1N5819 (diode)	NQ	1–2 MJ per piece	NQ	NQ	NQ	[64]
Wire (1 cm, 5 V)	NQ	NQ	NQ	NQ	NQ	[64]

) are strictly additive and only relate to the incremental footprint of the digital assurance layer that has been deployed; they do not include the operational or embodied impacts of Austria’s national PV capacity. This distinction is crucial because known technology and supply chain pathways continue to govern the core PV sectoral impacts, such as module and inverter production, grid integration, and bulk storage [2,5,55]. Our contribution is thus fully compatible with Austria’s current and future PV infrastructure and policy roadmap, as articulated by national and international energy authorities [2,5,55].

Our approach directly addresses new demands for strong auditability, compliance (e.g., carbon accounting, renewable energy certificates), and cybersecurity in the decentralized energy sector by enabling verifiable, tamper-resistant energy logging and reporting [15,16,33,34]. According to recent systematic reviews and regulatory frameworks, this is in line with international best practices for digital trust and ESG integration in energy informatics [11,15,16,45,46]. Ultimately, the suggested solution supports transparency, security, and sustainability at scale without requiring additional material or infrastructure burdens by providing a high-assurance digital overlay that can be flexibly and sustainably adopted within Austria’s PV ecosystem [16,34,55].

4.5. System Auditability and Security

Robust tamper evidence and cryptographically assured end-to-end auditability are given top priority in the system’s design, as these are crucial prerequisites for reliable digital energy reporting [34,49]. Every energy log entry is chained to the previous record after being hashed in real time using SHA-256 [38]. This ensures forward integrity and makes sure that any subsequent modification, whether intentional or unintentional, makes all downstream hashes invalid [39,40]. All queued record hashes are combined into a Merkle tree once a configurable energy threshold is reached [40]. An immutable blockchain transaction serves as the batch’s on-chain anchor, and the resulting Merkle root functions as a cryptographic commitment for the entire batch. The Ethereum Sepolia testnet makes the Merkle root and related metadata publicly accessible for validation. A proof log comprising the Merkle root, blockchain transaction hash, CSV hash, batch size, and record index range is recorded by the logger for every Merkle batch submission. The structure of this log enables any independent auditor to recalculate the root, reconstruct the entire Merkle tree from raw sensor data, and confirm that it corresponds to the value anchored by the blockchain. This procedure was verified during the deployment using the custom verification tool. Every batch submitted on-chain was successfully validated by the verification script in every instance, exhibiting 100% end-to-end integrity throughout the operational period.

Deliberate alterations were made to historical raw logs following deployment to test tamper evidence empirically. Verification failed instantly if one record was added, removed, or edited within a batch; the reconstructed Merkle root no longer matched the blockchain-committed root. This held true for every kind of tampering, such as:

• Change, deletion, or addition of a single entry
• Reordering in batches
• Replacement of a hash or CSV file

The proof verifier identified an inconsistency in each case, maintaining batch auditability. This directly supports the system’s assertion that no data manipulation can evade detection once initial logging has taken place [34,39,40]. Several levels of operational security and redundancy are enforced by the system, including:

• Daily Rollover Mechanism: To guarantee that no data is orphaned and that every entry is eventually included in a Merkle batch, unbatched records at the end of the day are automatically moved to the log file for the following day. This mechanism fixed previous gaps where unbatched entries could be left out of the audit chain. It was put in place in response to findings from the pilot stage.
• Read-Only Enforcement: To reduce the possibility of unintentional or unauthorized changes, logged CSV files are immediately put into read-only mode (for non-logger users) following batch processing [49].
• Pending Submission and Retry Logic: All submission payloads are kept in a pending queue in case a blockchain submission fails (e.g., because of network outages, node congestion, or a low ETH balance). All actions are recorded for auditability, and the system tries submissions again until they are confirmed.
• System Boot Integrity: To prevent loss from unplanned shutdowns, the system automatically reviews the queue and proof logs upon startup, retrying unsuccessful submissions and rolling over any incomplete data.
• Timely Submission and Duplicate Detection: To guarantee that logs are committed to the blockchain as soon as possible, submissions are initiated either at one-minute intervals or when the energy threshold is reached. To strengthen end-to-end traceability and verifiability, the system looks for unexpected or duplicate submissions. Any anomaly is noted and recorded in the audit log.

There are still certain restrictions even though the logger’s operational procedures and cryptographic architecture provide strong tamper evidence and auditability:

• Hardware Trust Assumptions: The system assumes that there is no physical compromise of the INA219 sensor, Raspberry Pi, and related storage. Root-level operating system (OS) attacks, firmware exploits, and physical attacks are still outside the purview [49]. The integrity of the edge device and sensors is a prerequisite for all cryptographic tamper-evidence and auditability guarantees offered by this system. These guarantees may be nullified if the Raspberry Pi, INA219, or their operating system are physically altered or compromised. The system’s cryptography does not defend against physical attacks, such as root-level OS/firmware exploits, power manipulation, sensor spoofing, or removal or modification of storage. We highly advise protecting the device and sensors in a tamper-evident enclosure for real-world deployments, limiting access to authorized personnel only, putting devices in locked or monitored environments, and routinely checking hardware for any indications of unauthorized modification or tampering—steps that were unnecessary in this controlled lab simulation [65].
• Key Management: A locally stored private key is necessary for the blockchain submission procedure. On-chain anchoring would be impacted by the loss or compromise of this key, which is why secure key management procedures are necessary [34]. We suggest encrypting key storage, reducing needless key duplication, backing up keys in safe offline locations, and, if available, utilizing a hardware security module (HSM) or trusted platform module (TPM) for operational security. Rotate keys frequently, and revoke compromised credentials right away. Protect backups from network-based or physical threats and restrict key access to the logger process only [66].
• Operational Security Practices: Update the operating system and dependencies on the device with security patches, restrict and secure all remote access (such as SSH), turn off unused services, implement robust authentication, and, when practical, use disk encryption. setup logging for system and network activity, examine audit logs on a regular basis, and automate safe backups of logs and proofs to other locations. By taking these steps, real-world risks that cannot be resolved by cryptographic methods alone are reduced [67].
• External Threats: While system logs and redundant backups speed up recovery in the event of a partial loss, distributed denial-of-service (DDoS) attacks, blockchain-level vulnerabilities, and catastrophic storage failures are not directly mitigated.

According to recent reviews of blockchain-based energy systems [11,16,34,49] key requirements for secure digital monitoring systems are met by the auditability and tamper evidence shown here. Green IT, ESG accountability, and international audit log management guidelines are all reflected in the logger’s design [41,45,49]. Real-world and adversarial (tampering) scenarios were used to empirically validate the following: complete transparency from sensor to blockchain, no data loss, and no unverifiable batches.

4.6. Comparative Analysis

This section compares the suggested blockchain-secured, tamper-evident solar energy logging system to the most advanced blockchain-based techniques in the energy industry, as well as conventional digital logging techniques. Three dimensions form the structure of the analysis: (1) data integrity and auditability; (2) resource overhead and operational efficiency; and (3) sustainability and scalability. Traditionally, centralized storage and traditional digital signatures or checksums are used for integrity in conventional digital logging systems, such as standard database-backed IoT platforms [34,49]. Such systems are prone to insider threats, single-point failures, and incomplete audit trails, particularly in distributed or multi-stakeholder environments, even though they can provide basic tamper detection [16,49]. Without thorough, resource-intensive backup and monitoring procedures in place, data logs can easily be changed or removed.

The blockchain-secured logger described in this paper uses public blockchain anchoring, Merkle tree batch proofs, and chained SHA-256 hashes to provide robust, cryptographically enforced, end-to-end integrity [40]. The proof chain is irreversibly broken by any change, even a single entry, and every Merkle root can be independently verified on-chain and locally. Thus, it surpasses both centralized and the majority of permissioned DLT-based energy loggers, meeting the most stringent requirements for digital auditability [16,34]. When cryptographic proof verification fails, any changes made to the data are immediately exposed, guaranteeing total transparency and a 100% tamper detection rate. In case continuous cloud backup or third-party certification is necessary for compliance, traditional secure logging solutions for distributed solar or Internet of Things applications frequently impose significant computational, storage, and network overhead [31,47]. These burdens can increase expenses and energy consumption, as well as limit viability in edge environments with limited resources [31,41]. New blockchain-based energy monitoring solutions usually rely on high-frequency on-chain logging or anchor all data records [11,18]. These are unsuitable for real-time or national-scale use because they can result in excessive blockchain fees, storage bloat, and network congestion, even though they maximize data fidelity [11,17]. High operational costs and limited scalability for continuous on-chain approaches have been highlighted in certain studies.

To overcome these problems, our suggested system uses Merkle batch anchoring, sending only threshold-triggered, cryptographically summarized batches to the blockchain. It offers low usage of CPU (<0.02%) and RAM (~100 MB) in combination with minimal network overhead (Section 4.3), even during blockchain submission events. Comparing this design to other blockchain-based approaches and traditional methods, it significantly improves resource efficiency and allows for dependable operation on low-cost, low-power edge devices [31,41]. When it comes to sustainability, traditional IoT loggers frequently externalize these effects to the cloud or service providers, rarely taking into consideration their own carbon footprint or the entire energy costs of remote certification [41,45]. The high transaction energy of many blockchain-based models, such as pre-Ethereum Merge schemes, has drawn criticism [27,28].

Table 10. System Comparison—Sources: Adapted from [11,16,31,34,41,49,52,53].

Criterion	Conventional IoT Logger	Permissioned Blockchain	Proposed System (Public Blockchain)
Tamper Evidence	Weak/Basic (checksums; vulnerable to insider edits)	Strong (but centralized consensus, but trust in operator)	Strong (cryptographic, decentralized, 100% detection of tampering, see Section 4.2)
Auditability	Limited, not public (logs private, limited external access)	Internal, multi-party (permissioned access, limited public audit)	Fully public, end-to-end (on-chain + local proof log, see Section 4.2)
Resource Overhead	Moderate to high (cloud: CPU > 20%, RAM > 500 MB, network depends on backup frequency	Moderate (blockchain node CPU 1–10%, RAM 256–1024 MB)	Minimal (CPU 0.01%, RAM ~100 MB, network upload 10 MB/5 days; see Table 2)
Energy Impact	High (cloud/server, ~5–50 W per node, plus backupcloud)	Moderate (permissioned DLT 0.1–5 W per node, not always reported)	Low (Ethereum PoS, 6.3 Wh/tx; total system 0.82 kWh/135 h, see Section 4.4)
Operational Cost	Cloud fees, infrastructure (AWS/Azure, $5–50/month/device)	Admin & chain fees (license/maintenance, transaction costs variable)	Transparent, controllable (blockchain: €0.99/tx; total: €128.65/135 h, see Section 4.4)
Scalability	Challenging at a national scale (centralized bottlenecks, hard to scale beyond 103 devices)	May scale, but trust limits (permissioned networks to ~104, but less open)	Demonstrated, low cost/energy (scaling to 250,000 systems: 0.00063% of national PV output, see Section 4.4)
Open Verification	Rare (not supported in commercial solutions)	Limited (verifiable only to authorized parties)	Full (on-chain + local verification tool, all logs/proofs public, see git repository)

synthesizes the comparative findings across integrity, efficiency, and sustainability dimensions and lists the key features of conventional IoT loggers, permissioned blockchain solutions, and the proposed system (public blockchain). It allows for a straightforward, multifaceted evaluation by combining the empirical findings of this study with qualitative evidence from the literature.

5. Discussion

In this study, we demonstrate the viability, effectiveness, and sustainability of a tamper-evident, blockchain-secured solar energy logging system for resource-constrained IoT environments. This research surpasses theoretical or simulation-based frameworks by delivering the first fully open, reproducible, and auditable solution in line with contemporary digital sustainability imperatives through the implementation of a hardware-validated prototype on a Raspberry Pi with real-world solar generation data.

A recurring gap between theoretical proposals for blockchain-enabled energy logging and their viable, hardware-validated implementation in actual renewable energy systems has been brought to light by recent systematic reviews and meta-analyses. For instance, both Borkovcová et al. (2022) [16] and Rejeb et al. (2024) [26] stress that although blockchain has great potential for tamper-evidence, traceability, and decentralized auditability in energy metering and certification, there are still few empirical studies showing field-ready, resource-efficient, and fully verifiable implementations. The majority of studies are still in the conceptual or simulation stage, according to a thorough survey by Habibullah et al. (2024) [22], which also notes that there is a “marked absence of deployments that address resource constraints, automated loss recovery, or cryptographically enforced batch verification on edge hardware”. Likewise, Taherdoost [18] and Honari et al. (2023) [20] advocate for the creation and open release of low-overhead, replicable frameworks that balance digital accountability, blockchain transparency, and green computing. By providing a full-stack, open-source prototype that is empirically validated on hardware with limited resources, achieves publicly auditable Merkle batch anchoring, and reports real-world cost, energy, and emissions data, the current work directly addresses these calls. Specifically, this system achieves the transparent, edge-optimized, and resilient logging infrastructure desired by Habibullah et al. [22] and Borkovcová et al. [16] and operationalizes the “energy-grade verifiability” required by Rejeb et al. [26]. In conclusion, by addressing the most important empirical gaps found in the most recent literature, this study raises the bar for safe, sustainable, and verifiable digital energy logging and offers a transparent, repeatable standard.

5.1. Advancing Data Integrity and Auditability

Our findings validate the robust, cryptographically enforced end-to-end data integrity achieved by combining public blockchain anchoring, Merkle tree batching, and chained SHA-256 hashing. Merkle root mismatches highlight attempts at tampering, and the custom verification tool immediately identifies any changes to the data, whether they are insertions, deletions, or modifications. The “energy-grade verifiability” mechanisms recommended in recent systematic reviews [16,22,26], are expanded upon and operationalized in this way, directly addressing the ongoing lack of empirical, field-ready, and cryptographically secure audit solutions in renewable energy logging. The system overcomes trust bottlenecks and lowers the risks associated with centralized data management by achieving public, decentralized, and independently verifiable auditability in contrast to traditional IoT loggers and permissioned blockchains [34,49].

5.2. Key Achievements and Innovations

This study makes several innovative contributions to the domains of sustainable IoT design, blockchain-based auditability, and digital energy monitoring. The following accomplishments demonstrate the work’s scientific and practical importance. A full-stack, Merkle tree–batched, cryptographically chained energy logging architecture for distributed solar systems using resource-constrained edge devices is being implemented and empirically validated for the first time in this study [11,17,34]. Presenting a real-world deployment, the method goes beyond theoretical proposals or simulations by capturing five days of uninterrupted, tamper-evident data with no missed entries, no batches that cannot be verified, and perfect recovery from network outages. The logger system ensures robust cryptographic tamper evidence by combining public blockchain anchoring, Merkle tree batching, and SHA-256 chain hashing. In addition to being theoretical, independent auditability has been empirically verified with a custom verifier, and all logs and proofs are publicly available for verification. This degree of verifiability supports both scientific transparency and regulatory trust by directly filling in the gaps identified in recent reviews of digital energy reporting [34,49]. With a minimal operational footprint, the logger meets its security and auditability goals. The device’s CPU load averaged only 0.01% over 8106 monitoring cycles, its RAM usage stayed consistent at about 100 MB, and its temperature was well below critical limits. No appreciable increases in resource consumption were brought about by blockchain submissions. These results demonstrate that low-power IoT hardware can achieve robust blockchain auditability [31,41], which makes deployment on a national scale technically possible.

The study offers a clear, expandable framework for blockchain-based solar monitoring that considers costs, energy, and emissions. In terms of national energy production (0.00063% of Austria’s PV output), it shows that the blockchain logging overhead is insignificant and well within PoS energy benchmarks [52,53]. In order to establish the system as a model for green digital infrastructure, the results are presented in formats that are appropriate for direct ESG reporting and integration with European carbon accounting standards [41,43,45]. Automated recovery from failures, pending submission/retry logic, and daily log rollover are important practical innovations that guarantee uninterrupted, lossless operation even in challenging circumstances. A system that is both operationally and cryptographically resilient was achieved by iteratively improving these mechanisms in response to pilot results. By open science principles and recent calls for increased transparency in sustainability research, all code, logs, and verification scripts are made available for independent replication and audit [34,43,47]. This dedication boosts trust in the results and makes it possible for researchers and business professionals to modify them. This study, in short, establishes a new benchmark for digital energy monitoring that is safe, sustainable, and verifiable. An open, replicable model for the upcoming generation of green, auditable digital infrastructure is provided by combining resource-efficient edge deployment, blockchain transparency, and cryptographic best practices, bridging the gap between theoretical promise and real-world application.

5.3. Resource Efficiency and Scalability

According to our empirical findings, there was very little system overhead, with CPU usage averaging 0.01%, temperature and RAM profiles remaining constant, and little additional network traffic during blockchain submissions. We contrasted the empirical resource metrics of the suggested blockchain logger with those of local-only and cloud-based logging models in order to better contextualize its resource efficiency. With or without blockchain submission, the cryptographic batching, chain hashing, and local logging features were always operational in our test deployment. As demonstrated in Table 2 and Figure 3, measured CPU and RAM usage for local-only operation was almost the same as those recorded during blockchain-enabled operation, confirming that the extra blockchain anchoring step introduces only a brief, minimal overhead. However, the literature and reported performance metrics [31,41] indicate that traditional cloud-based logging platforms can impose significantly higher network and CPU loads because of continuous data uploads and authentication, frequently surpassing 20% CPU and 500 MB RAM. To accommodate local device, network, and regulatory constraints, real-world deployments will necessitate dynamic adjustments to batch size, submission frequency, and retry logic. Even in the pilot, our system showed strong performance even during prolonged connectivity outages; however, scaling to thousands of nodes might require further optimizations, such as larger batch sizes or local buffering to prevent data loss and reduce network congestion. This confirms that the design is appropriate for IoT devices that are edge-deployed and low-power, addressing important requests for resource-conscious architectures [31,41,47]. Both technical and financial obstacles to national-scale deployment can be removed thanks to the batched, threshold-based logging approach, which performs noticeably better in terms of scalability and operational cost than continuous on-chain or high-frequency anchoring schemes [11,18]. The observed energy and cost requirements are still transparent and manageable, facilitating practical implementation in dispersed PV fleets. On the other hand, rollups or permissioned blockchains might provide less expensive options, but public verifiability would be sacrificed in the process [24].

5.4. Sustainability, ESG, and Broader Implications

The intermittent nature of renewable generation and the requirement for efficient grid balancing with other resources like wind, hydro, or flexible demand present a significant obstacle to the deployment of solar-powered blockchain logging, particularly at the city or national level. Recent developments in sustainability science emphasize how crucial it is to design solar systems that take advantage of techno-ecological synergies in order to enable multipurpose land use and improved ecosystem services. Incorporating solar infrastructure with ecological goals can yield benefits beyond energy production, promoting food systems, biodiversity, and water regulation, according to Hernandez et al. (2019) [3]. When it comes to urban and rural energy transitions, these comprehensive frameworks can help guide the growth of blockchain-secured digital infrastructure by coordinating technical, environmental, and social objectives [3]. For system operators and market participants, real-time data integrity and transparent reporting are even more crucial because intermittency can lead to substantial temporal mismatches between supply and demand [5,13]. Particularly when combined with wind and storage resources, integrating blockchain-secured energy logging with more general smart grid platforms can enhance resilience and enable dynamic balancing [2,14]. Even though this study uses Austria as a case study, the general methodology can be applied to urban and regional energy systems around the world, including those in cities with complicated regulatory frameworks and a variety of renewable energy sources [2,29]. Optimizing the logging architecture to local conditions, including renewable resource mix, grid flexibility, and policy frameworks, is crucial to supporting city-scale energy transition initiatives and optimizing impact.

Globally, the availability of solar energy varies greatly; the highest levels of irradiation (>2000 kWh/m² annually) are found in equatorial and desert regions like the American Southwest, Australia, North Africa, and the Middle East [2,5]. China (>600 GW), the United States (>180 GW), and the European Union (~260 GW) continue to be the leading producers by installed capacity as of 2024 [2]. The early adoption of blockchain-enabled accountability systems for PV logging is most likely to occur in areas with the required digital infrastructure, connectivity, and regulatory incentives, especially the EU, where compliance and ESG reporting frameworks justify additional costs, even though the technical potential is worldwide. As connectivity and governance mechanisms improve, other high-irradiation but less digitally integrated regions might reap the benefits later. These dynamics are contextualized in Figure 4, which displays the potential of the world’s solar resources as well as the top producers.

Utilizing Ethereum’s PoS network, the system achieves an extremely low carbon and energy footprint: blockchain-based audit logging would cost less than 0.00063% of Austria’s annual PV output, with a projected annual emission of ~5.2 tons of carbon dioxide (tCO₂) for 250,000 systems [52,58]. These results are well within recognized PoS efficiency benchmarks and are robust to changes in gas price and transaction throughput. This is especially important, since strong digital accountability is now in line with national and EU-level ESG framework requirements [34,41,43]. In addition, the system’s reproducible, open-source design raises the bar for sustainability reporting transparency and responds to the climate tech literature’s repeated calls for “trustworthy, replicable digital infrastructure” [16,22,26,47]. Additionally, the findings provide a model for extending blockchain-secured logging to distributed energy resources and other renewable sources, such as demand response systems, storage, and wind. However, as Jiang et al. (2025) [32] highlight, strong international cooperation, trans-regional infrastructure investment, and the creation of efficient supranational coordination mechanisms will be essential to the large-scale realization of these systemic sustainability and efficiency gains. The success of large-scale renewable integration is still limited by policy alignment, infrastructure harmonization, and geopolitical stability across interconnected regions, despite the fact that technical solutions, like blockchain-secured logging, can provide operational resilience and transparency. This is further highlighted by their global stress-testing [32].

Regulators’ recognition of blockchain-secured logs as legitimate compliance proof in ESG and renewable reporting frameworks is a key factor in adoption. Although the EU offers a supportive environment with its carbon accounting standards, taxonomy, and guarantees of origin programs [33,46,56], regulatory acceptance is still uneven throughout the world. Holzapfel et al. [56], for example, point out that guarantees of origin are essential to Europe’s renewable energy goals. However, their international counterparts (such as I-RECs [57]) are not as harmonized, which restricts cross-border interoperability. The effectiveness of blockchain-driven accountability systems in supply chains is also shown by Alotaibi et al. [45], who emphasize that institutional recognition is necessary to supplement technical transparency. Governance culture plays a significant role in mediating ESG adoption at the corporate level [8,44], indicating that blockchain-secured auditability will take off most quickly in areas and industries where regulatory incentives coincide with corporate sustainability objectives. On the other hand, even when technical viability has been established, jurisdictions with disjointed energy or digital policies run the risk of postponing adoption. These factors reaffirm that in order to guarantee the systemic impact of blockchain-secured energy logging, policy alignment, standardization, and regulatory adaptation are just as crucial as technical design.

5.5. Practical Applications and System Integration Potential

Even though the presented system’s main goal is to securely and impenetrably log solar energy generation at the edge, its architecture is specifically made to serve as an enabling digital infrastructure for the changing demands of contemporary energy systems. A variety of high-impact use cases can directly benefit from the cryptographically verifiable audit trails generated by this logger, such as but not restricted to:

• Automated Reporting and Regulatory Compliance: The system facilitates transparent, auditable data streams for European and international regulatory framework compliance, REC issuance, and ESG reporting [16,33,43,56].
• Peer-to-Peer and Community Energy Trading: Recent research and microgrid deployments have shown that trustworthy and up-to-date energy data supports dynamic pricing, settlement, and fraud prevention in decentralized energy markets and P2P trading platforms [6,11,13,14,17]. The integrity of claims about production and consumption is guaranteed by secure logging, which promotes trust between market operators and dispersed participants.
• Demand Response and Grid Balancing: Although the current prototype is primarily concerned with logging, the system’s fundamental ability to deliver precise, impenetrable, and real-time telemetry sets it up for integration with grid management platforms. Advanced applications that rely on reliable, high-frequency metering data, like dynamic grid balancing, automated demand response, and the provision of ancillary services, would be made possible by this [5,14,29].
• Integration with Digital Guarantees of Origin: To support both domestic and foreign markets, the logger can act as a reliable metering layer for green energy certification procedures and digital GO [56,57].
• Scalability to Diverse Distributed Energy Resources: The design of this study can be easily adapted to other renewable sources (wind, hydro), storage assets, and multi-vector energy systems, even though its proof-of-concept is centered on solar PV. This supports a comprehensive shift to resilient, flexible smart grids [2,5,18,29].

Overall, this system is an open, extensible infrastructure for verifiable digital energy systems rather than just a proof of concept for blockchain-based telemetry. Its usefulness transcends market, regulatory, and operational contexts, meeting fundamental needs for accountability, transparency, and trust in the shift to decentralized and decarbonized energy environments.

5.6. Socioeconomic Implications

The implementation and expansion of blockchain-secured energy logging systems, such as the one described in this study, could significantly aid in workforce development and job creation in the digital technology and renewable energy industries. There will likely be a greater need for experts in software development, IoT engineering, data science, cybersecurity, and smart grid integration [18,29] as global investment in decentralized and digitally enabled energy infrastructure increases [2,5,11]. In addition, new positions will appear in the fields of energy market operation, audit and verification, blockchain system maintenance, and regulatory compliance [13,33]. In both developed and emerging markets, these changes can promote economic growth and the larger digital and green transition. According to the World Economic Forum, the energy sector’s digital transformation could lead to the creation of millions of new jobs worldwide by 2030, especially as smart grid and peer-to-peer energy solutions are widely implemented [2,5]. In reality, however, automation is likely to have a more immediate effect on labor and transaction costs, especially when it comes to preparing, confirming, and reporting energy data for compliance. In addition, Zhang et al. (2025) [4] stress that to fully realize the mitigation potential of rooftop PV, coordinated policy action, investments in digital and urban infrastructure, and local adaptation are necessary to maximize the volume and intensity of carbon mitigation. According to their analysis, RPV adoption gaps can be filled, especially in developing nations and rapidly populous cities, by combining cutting-edge data systems—like blockchain-secured audit trails—with focused policy interventions [4]. This supports our claim that scalable, transparent, and resilient digital infrastructure promotes socioeconomic and environmental advantages in a range of energy markets. As a result, the deployment of safe, open-source, and scalable digital logging systems supports new opportunities for skilled employment and capacity building throughout the energy value chain, with positive socioeconomic effects that go beyond technical innovation.

6. Conclusions, Limitations, and Future Work

In this paper, we introduce and empirically validate a new tamper-evident, blockchain-secured energy logging system for solar monitoring that is tailored to edge deployment in IoT environments with limited resources. With low overhead and high resilience, the suggested system operationalizes end-to-end data integrity, auditability, and sustainability by addressing the most significant empirical gaps found in the recent literature. The system raises the bar for reliable digital energy reporting by achieving public, cryptographically verifiable audit trails. It showcases the design and open-source implementation of a hardware-blockchain (i.e., implementing physical sensor hardware with blockchain-based logging and verification) full-stack prototype, the demonstration of minimal carbon and operational costs in real-world scenarios, and conformance to current ESG and green IT standards. These developments allow digital renewable energy systems to achieve new levels of trust, accountability, and transparency while also bridging the gap between theoretical promise and real-world implementation. In conclusion, this work presents a model for the next generation of digital infrastructure in the energy sector that is scalable, secure, and sustainable. Its implications span the domains of blockchain, IoT, and climate tech. By making code and data publicly available on the GitHub platform, the scientific community is better able to duplicate, examine, and expand on our results, hastening the development of auditable, sustainable, and future-proof energy systems.

Notwithstanding the prototype’s convincing empirical outcomes, a number of limitations must be noted. First, while the Ethereum Sepolia testnet was used for deployment, later energy and economic calculations simulated mainnet conditions, and some differences in operational dynamics might not be accounted for. Second, the integrity of the hardware and software is ultimately what makes the system secure; advanced denial-of-service attacks, OS-level exploitations, and physical compromise are not considered. Third, cost estimates depend on how Ethereum gas prices and validator economics change in the future. Finally, only solar energy was used to illustrate the solution; additional studies are needed to confirm that our model can be applied to various distributed energy and renewable energy scenarios. Future research needs to concentrate on (1) extending cryptographic audit mechanisms to multi-asset or hybrid renewable environments; (2) integrating with standards for digital guarantees of origin and carbon accounting; (3) testing system robustness through multi-site and national-scale deployments; and (4) investigating integration with privacy-preserving proofs (such as zero-knowledge proofs) for General Data Protection Regulation (GDPR)-compliant energy data sharing.