The accurate quantification of grid carbon emission factors (CEFs) is foundational for robust carbon accounting, effective climate policy, and credible corporate sustainability reporting. Traditional national-average CEFs are fundamentally inadequate, perpetuating the “copper plate” fallacy by ignoring profound spatial and temporal heterogeneity within interconnected power systems. This review critically evaluates the emerging paradigm of “grid hierarchical and zonal division” as a necessary response to this challenge. We systematically analyze the limitations of existing methodologies, highlighting the significant gap between top-down administrative calculations and bottom-up physical flow tracing. As our central contribution, we propose an integrated three-layer framework that synthesizes established but previously siloed concepts, physical flow modeling, policy boundary definition, and data architecture, into a unified structure. The novelty lies in their explicit integration and the modeling of interdependencies across layers: a Physical Flow Layer (‘engine’), a Policy Boundary Layer (‘rulebook’), and a Data and Calculation Layer (‘nervous system’). Our comparative analysis demonstrates that no single methodology is universally superior; a strategic, hybrid application across the hierarchy is essential. A structured case-based analysis applying the framework to China’s West-East Electricity Transfer corridor demonstrates its practical utility, with illustrative estimates from published comparative analyses suggesting that different accounting choices for cross-border electricity can result in differences of 15%–30% in an importing region’s reported Scope 2 emissions. The review identifies critical challenges data transparency, treatment of electricity imports, and lack of standardization, and proposes actionable pathways. Future research frontiers include dynamic real-time CEFs, artificial intelligence for forecasting and zoning, and blockchain for data integrity. This framework provides an essential blueprint for next-generation grid CEFs indispensable for guiding a precise and efficient energy transition.
John Alexander Taborda, Cesar Enrique Polo Castro, Alexander Armando Bustamante, Holman Dario Bustos
The transition toward decentralized renewable energy systems creates monitoring problems that current digital infrastructures do not solve: sustainability claims are produced by the same actors they evaluate, environmental evidence is reported periodically rather than observed continuously, and the communities most affected by deployment cannot inspect the data used to represent their territories. Existing integrated platforms combine subsets of the blockchain, Internet of Things (IoT) sensing and life cycle assessment (LCA) at the data layer, but they do not organize that integration through an explicit governance structure. This paper contributes a cybernetic governance framework in which the Viable System Model (VSM) supplies the organizing structure of a blockchain–IoT–LCA monitoring architecture, so that sensing, distributed trust, strategic intelligence and participatory governance are recursively coupled rather than sequentially chained. The framework was developed and evaluated under the Design Science Research paradigm, and instantiated in the IMPACT Energy.CO platform across two technology routes, wind and solar, in La Guajira, Cesar, Atlántico and Magdalena, Colombia. Evaluation against six pre-declared criteria reports 45 executed test cases with a 100% pass rate, 90% unit and 87% integration code coverage, load tests up to 5000 concurrent users with zero errors and sub-second mean response, an operating hash-chained provenance layer issuing verifiable LCA certificates, 14 participatory validation workshops, 199 users trained and 166 technicians certified. We use traceability in a deliberately narrow sense throughout: the property whereby a committed record can be linked to the ingested data series, model version and computation that produced it, and its integrity and ordering checked by a party that does not trust the producer. It is provenance and integrity traceability from the point of ingestion onward, and it is not metrological traceability: the architecture cannot verify that an original sensor measurement corresponds to the physical quantity it purports to represent. We accordingly make explicit what the architecture does not guarantee: a ledger protects records after commitment but cannot certify measurement at the point of capture, and we present a threat model, a set of implemented controls and the residual risk that remains. This study contributes an architecture, a reproducible development and evaluation method, and a calibrated account of what verifiable environmental monitoring can and cannot deliver in contested Global-South territories.
Under China's strategic commitment to peak carbon emissions by 2030 and achieve carbon neutrality by 2060 (the "Dual Carbon Goals"), renewable energy enterprises face unprecedented pressure to simultaneously expand capacity, reduce costs, enhance supply chain resilience, and minimize carbon footprints. This study systematically investigates supply chain synergy optimization for wind and solar power enterprises within the Dual Carbon policy framework. Employing a multi-method approach integrating literature review, system analysis, and a case study of LONGi Green Energy, this research identifies three core synergy barriers: geographic fragmentation and policy decoupling, carbon traceability credibility crises, and inherent conflicts among efficiency, decarbonization, and resilience objectives. A three-tier collaborative optimization framework is proposed, comprising: (1) an information synergy layer based on blockchain-enabled carbon data pools; (2) an operational synergy engine integrating multi-objective optimization models with dynamic carbon taxation and shared warehousing; and (3) a carbon synergy mechanism incorporating tiered supplier incentives and green transition funds. Empirical validation through the LONGi case demonstrates significant improvements: total supply chain costs reduced by 15.3%, lifecycle carbon emissions per watt decreased by 39.6%, and disruption recovery time shortened by 58.3%. This research contributes a "policy-geography-technology" three-dimensional synergy blockage theory, a tri-objective dynamic equilibrium model, and a responsibility-sharing carbon governance framework, offering both theoretical advancements and practical pathways for sustainable energy supply chain management. Keywords: Dual Carbon Goals, Renewable Energy, Supply Chain Synergy, Carbon Traceability, Supply Chain Resilience, Blockchain, Multi-Objective Optimization, Green Supply Chain.
C. N. He, H. D. Chen, H. J. Tian, J. Zhang · 5 authors
This study investigates low-carbon investment strategies in power supply chains under the combined influence of carbon quota mechanisms (CQM) and blockchain technology (BCT). A two-echelon system consisting of a power generator and an electricity retailer is modeled, and four decision scenarios are constructed by considering blockchain adoption under both the grandfathering method (GFM) and benchmarking method (BMM). A Stackelberg game framework is employed to analyze the interactions among low-carbon technology investment, low-carbon electricity promotion, market demand, and enterprise profitability. Results show that the BMM consistently induces higher low-carbon investment levels, stronger market demand, and greater retailer profitability than the GFM, regardless of blockchain adoption. Furthermore, blockchain-enabled information traceability exhibits a significant threshold effect: when implementation costs remain below a critical level, trusted information transmission enhances consumer green trust, stimulates demand for low-carbon electricity, and improves the economic performance of supply-chain participants. Sensitivity analysis further demonstrates that consumer green trust, low-carbon preference, and responsiveness to low-carbon promotion positively influence both emissionreduction efforts and enterprise profitability, whereas excessive blockchain deployment costs weaken these benefits. The proposed framework provides a quantitative methodology for analyzing information-enabled lowcarbon decision making and coordinated investment strategies in modern power systems.
Stephen Oko Gyan Torto, Rupendra Kumar Pachauri, Jai Govind Singh, Shubham Tiwari · 7 authors
Global projects are mobilizing technologies to fight power generation curtailment and smooth demand by exploiting excess energy via transactive energy management and control. Sharing and transferring energy between microgrids helps manufacturers and businesses create energy autonomously. The transition to Multi-Vector Multi-Agent Energy Systems (MMV-ES) demands a paradigm shift from traditional centralized control to decentralized, market-based coordination. Transactive Energy Management (TEM) has emerged as a key enabler in this context, supporting local flexibility, peer-to-peer (P2P) trading, and integrated energy vectors across distributed assets. This review systematically decomposes and classifies the existing state of TEM from several perspectives: the market topology, the interaction of the agent, game-theoretic models and the real deployment challenges. Moreover, two game-theory formulations (cooperative and non-cooperative) were given special attention and a detailed comparison between Shapley value and Nucleolus was provided as approaches for fair cost allocation. To enhance the adaptability of the market and the overall efficiency of the system, we introduce the Transactive Energy Reformulation Model (TE-RM), a hybrid model combining AI-powered congestion pricing with coalition formation and fairness-based incentives. The comparative tables in this paper summarize TEM and TE-RM's strengths and weaknesses and compare it to the centralized and conventional DSM methodologies. Lastly, key research gaps including scalability, regulatory fit, and AI model interpretability are reviewed, and future directions are proposed for the integration of future advanced technologies (e.g., reinforcement learning, blockchain, IoT) to enable stable, fair and interoperable energy markets.
The modern energy transition has outpaced the control and optimization frameworks built to govern it. As power and energy systems fragment into webs of renewable generators, storage operators, flexible loads, and carbon-constrained firms, the deterministic, single-optimizer models that once sufficed buckle against nonlinearity, bounded rationality, and strategic conflict among parties who learn and revise as they go. Evolutionary game theory (EGT), which traces how strategies propagate through populations by imitation and selection rather than instantaneous optimization, offers a route through this difficulty—one this review develops across three scales of low-carbon coordination central to cleaner production: enterprise-level industrial symbiosis, system-level smart energy operation, and market-level carbon governance. We synthesize three decades of theory alongside the recent fusion of EGT with artificial intelligence, where deep reinforcement learning approximates high-dimensional payoffs, federated learning lets rival firms co-train models without surrendering proprietary data, and blockchain underwrites decentralized mechanism execution. The synthesis is accompanied by two illustrative numerical case studies, constructed for this review rather than drawn from the surveyed literature, whose quantitative outputs are reported below as demonstrations of modeled behavior rather than as empirical measurements. In the first of these, cooperative emergence in industrial symbiosis hinges on critical thresholds that travel from 0.15 to 0.75 as subsidies and transaction costs vary, with anchor-enterprise targeting accelerating cooperation 2.4-fold while cutting outcome variance 3-fold. In smart energy coordination, AI-enhanced learning buys 32 to 41% faster convergence, yet pays 25 to 39% larger oscillations—a speed–stability tension whose resolution lives in a narrow learning-rate band near 0.08 to 0.12, outside which either sluggishness or instability takes hold. Carbon-market behavior turns on price thresholds: emitters switch abruptly from buying quotas toward investing in abatement once the clearing price clears firm-specific triggers, a discrete state switch that smooth equilibrium analysis misses entirely. Across all three domains, fragmented data, path dependence, and regime-switching dynamics recur as the binding constraints on modeling and on governance alike. Four mechanisms prove invariant to scale—the decisive weight of initial conditions, the catalytic leverage of well-positioned anchor agents, the equilibrium-shaping force of institutional design, and the computational reach added by AI integration—which suggests that insight earned in one domain transfers to the others. We close by mapping open problems in heterogeneity modeling, verification under deep uncertainty, and the still-unrealized coupling of digital twins with privacy-preserving learning. EGT emerges not as retrospective description but as prospective guidance for the cooperative transitions on which credible decarbonization depends.
Against the background of the global "dual carbon" goal and the EU Carbon Border Adjustment Mechanism (CBAM), targeting problems such as missing trust in emission reduction and insufficient technological collaboration in cross-border low-carbon supply chains, this paper incorporates blockchain technology, vertical spillover of emission reduction and consumer low-carbon preference into a unified analytical framework. It constructs a two-echelon cross-border supply chain model consisting of a single supplier and a single manufacturer, builds Stackelberg game models under centralized decision-making and decentralized decision-making respectively, comparatively analyzes the optimal emission reduction levels, pricing strategies and profit distributions under two scenarios with and without vertical spillover, and verifies the conclusions through numerical simulation. The research shows that the EU CBAM carbon tax, vertical spillover of emission reduction and consumer low-carbon preference form a positive synergistic incentive, which significantly lifts the supply chain's emission reduction level and overall profit, and the synergistic effect is more prominent under centralized decision-making. A rising emission reduction cost coefficient will restrain enterprises' investment in emission reduction, and vertical spillover will aggravate this restraining effect. Whether vertical spillover is considered or not, centralized decision-making outperforms decentralized decision-making in both emission reduction efficiency and total supply chain profit; the higher the carbon tax rate and vertical spillover rate, the wider the gap between the two. This paper further puts forward management insights from the aspects of enterprise technology sharing, decision-making mode selection and government policy guidance, so as to provide theoretical reference and decision support for cross-border supply chains to respond to CBAM regulations and realize low-carbon transformation.
ComputeGrid is a concept paper and feasibility framework for a utility-integrated network of provider-owned compute nodes hosted at commercial buildings, public facilities, and — through a heat-recovery variant — homes. Hosts receive full metered reimbursement of node electricity plus a separate credit; nodes are aggregated by an orchestration and settlement platform (ComputeGrid OS) into one secure, grid-aware, dispatchable compute layer beneath hyperscale data centers. Version 3.0 evaluates the concept against two structural facts: residential retail electricity is the most expensive power a compute operator can buy, and a decade of distributed-compute ventures (spot marketplaces, deploy-first token networks, and heat-recovery operators) shows which configurations survive. The paper therefore leads with commercial and public-building deployment, admits residential nodes only where waste heat displaces heating the host would otherwise purchase, and requires every site to pass at least one of four economic qualifiers — near-commercial power price, monetizable heat, a priced locality premium, or measurable grid-flexibility value — before hardware is committed. The paper includes a two-sided illustrative unit ledger, host consumer-protection and property-rights baselines, rules preventing host-purchased “income” hardware, a staged offtake-first pilot plan with published kill criteria, and a claim framework that treats all economics as illustrative pending Stage 0 diligence. It is an open concept paper, not peer reviewed; no field deployment or empirical dataset is reported. Version 1.0 drafting was assisted by OpenAI ChatGPT; Versions 2.0 and 3.0 critique and revision were assisted by Anthropic Claude under the author’s direction. The author is responsible for the final claims and release.
Mario Mihetec, Goran Stunjek, Goran Krajačić, Gordana Mikulčić Krnjaja
ABSTRACT Suburban areas with dispersed buildings and low heat flux densities present distinct challenges for the decarbonization of heating systems. While district heating is often promoted in dense urban cores, its economic viability in suburban zones remains questionable due to high network costs and thermal losses. This study investigates whether decentralized, household level solutions combining high-temperature air source heat pumps with photovoltaics can outperform centralized district heating in such contexts. Using a case study of four peripheral settlements in Croatia, the research employs a dual-scale techno-economic optimization framework: a mixed-integer linear programming model for district heating and a prosumer-level model for individual heat pump-photovoltaic–battery systems. Three building renovation scenarios (no, partial, and full renovation) are evaluated alongside a mixed-financing scheme involving grants, household equity, and energy service company participation. Results show that decentralized heat pump-photovoltaic–battery systems under full renovation deliver the highest energy savings (75% reduction in household energy costs), the greatest carbon dioxide reduction (2,207 tonnes annually), with a net present value of 1.49 million EUR and an internal rate of return of 7.21%. When external costs of air pollution and carbon are internalized, the economic net present value rises to 68.39 million EUR. The results suggest that, under the assumptions and boundary conditions defined in this study, decentralized renewable heating systems are both technically viable and economically favorable compared to district heating in low-heat-density suburban contexts. This work provides a replicable decision-support framework for policymakers and planners seeking to accelerate the clean heating transition in dispersed residential areas.
We estimate the causal price elasticity of gas demand on Ethereum mainnet (L1) and Arbitrum One (L2), a quantity necessary for calibrating fee mechanism simulations, evaluating resource pricing reforms, and explaining observed usage patterns. A two-way fixed effects panel regression instrumented by each wallet's own lagged base fee removes the congestion-driven endogeneity that causes naive regressions to substantially underestimate demand sensitivity. On Ethereum mainnet (full year 2025), the pooled IV elasticity is -0.006***, near-inelastic: a 10% fee increase reduces total gas demand by approximately 0.06%. On Arbitrum One (October 2025--April 2026), the pooled IV elasticity is -0.036**. Both chains are inelastic in the aggregate, with L2 measurably more responsive than L1. A per-resource decomposition of L2 demand reveals elasticities ranging from modestly elastic computation (-0.027*) to -0.27*** for refunds, with storage growth (-0.15***) and calldata (-0.06*) in between. Behavioral clustering identifies always-on protocol wallets as near-inelastic and high-volume operators as substantially more responsive, with cluster-level elasticities up to roughly 6x the pooled estimate. These results establish an empirical foundation for downstream simulations and for evaluating fee mechanism designs.
This paper assesses the role of green hydrogen and green ammonia in the low-carbon reconstruction of Ukraine’s energy sector. The country, severely affected by war, has more than 70% of its energy infrastructure damaged or destroyed, which calls for novel solutions for not only reconstructing but also rethinking Ukraine’s energy sector shaped by the Soviet-era planning. In this context, decentralized and renewable energy solutions appear to be one of the best options to achieve this goal. This study combines four novel and mutually reinforcing methods: a Scopus-based literature review of highly cited green hydrogen publications, natural language processing (NLP) and bibliometric network analysis of Ukraine-related hydrogen research, a SWOT assessment, and a geospatial hydrogen production cost model (GEOH2). The novelty of this research lies in this integrated Ukraine-specific framework, which links research trends, wartime reconstruction constraints, hub-level policy choices, and financing risk-sensitive cost modeling. Therefore, the quantitative part of GEOH2 estimates the levelized cost of green hydrogen, while ammonia is treated as a downstream screening-level conversion and export pathway rather than as a full plant-level ammonia model. Our results show that Ukrainian green hydrogen research is concentrated on renewable-energy strategy, wind and solar electrolysis, water and desalination constraints, gas grid blending, underground storage, ammonia derivatives, and decentralized energy systems. The GEOH2 results indicate that southern Ukraine has strong physical potential for competitive green hydrogen production under de-risked financing, while war risk financing can make even resource-rich areas economically unattractive. Odesa and Dnipro emerge as important export-oriented and industrial hubs, whereas Zakarpattia remains strategically relevant as a safer western corridor linked to European markets. Our findings demonstrate that Ukraine’s hydrogen and ammonia development needs to follow a phased pathway: domestic renewable build-out and grid repair, pilot electrolysis projects and screening-level ammonia conversion pathways, targeted de-risking and insurance mechanisms, and only then broader export corridor development. This pathway can support decarbonization, energy security, industrial modernization, and Ukraine’s long-term integration into European clean energy value chains.
Energy demand in urban and metropolitan regions has been growing rapidly, often exceeding production capacity, leading to imbalances in energy distribution. Existing peer-to-peer (P2P) energy trading models, along with classical algorithms like FCFS and best-fit frameworks in smart cities, address some of these issues. However, they often face challenges such as limited transaction success percentage, inefficiencies in price matching, and privacy concerns during trades. To overcome these limitations, a framework is proposed that integrates game-theoretic pricing-based collaborative trading with Nash equilibrium and an additional pricing mechanism (CoGap) to enhance fairness and transaction success percentage in decentralized energy markets. The proposed framework is implemented on an Ethereum-based blockchain using Solidity smart contracts, incorporating cryptographic security through the Keccak-256 hash function and privacy-preserving zero-knowledge proofs (ZKPs). Moreover, it ensures security and price negotiations while maximizing transaction efficiency. Simulation results demonstrate that CoGap consistently achieves higher transaction success rates compared to four state-of-the-art collaborative energy trading schemes.
The built environment is a critical frontier for climate change mitigation and adaptation, with residential buildings accounting for a substantial portion of global energy consumption and greenhouse gas emissions. This paper presents a critical review of contemporary literature (2020-2025) synthesizing advancements in climate-resilient housing through integrated architectural and renewable energy solutions. A systematic analysis of 51 studies examines three core areas: passive and active architectural design for thermal resilience; the role of decentralized renewable energy in enhancing autonomy; and the socio-technical, policy, and governance dimensions of implementation. The present review identifies a paradigm shift from static efficiency toward dynamic, adaptive building systems, highlighting the efficacy of bioclimatic design, smart materials, and AI-driven management. Decentralized solar energy is underscored as fundamental for decarbonization and energy security, though its success depends on supportive policies, community engagement, and equitable finance. Persistent gaps are noted, including the need for holistic lifecycle assessments, scalable models for low-income contexts, and stronger integration of technical and social equity approaches. The review concludes by advocating for a transformative shift toward adaptive, regenerative, and just residential environments.
Abdullah Umar, Prashant K. Jamwal, Deepak Kumar, Nitin Gupta · 6 authors
Renewable-driven microgrids require transparent and adaptive coordination mechanisms to manage variability in distributed generation and flexible demand. Conventional pricing schemes and centralized demand-side programs are often insufficient to regulate real-time imbalances, leading to inefficient renewable utilization and limited prosumer participation. This work proposes a blockchain-integrated Stackelberg pricing model that combines real-time price regulation, optimal demand-side management, and peer-to-peer energy exchange within a unified operational framework. The Microgrid Energy Management System (MEMS) acts as the Stackelberg leader, setting hourly prices and demand response incentives, while prosumers and consumers respond through optimal export and load-shifting decisions derived from quadratic cost models. A distributed supply–demand balancing algorithm iteratively updates prices to reach the Stackelberg equilibrium, ensuring system-level feasibility. To enable trust and tamper-proof execution, smart-contract architecture is deployed on the Polygon Proof-of-Stake network, supporting participant registration, day-ahead commitments, real-time measurement logging, demand-response validation, and automated settlement with negligible transaction fees. Experimental evaluation using real-world demand and PV profiles shows improved peak-load reduction, higher renewable utilization, and increased user participation. Results demonstrate that the proposed framework enhances operational reliability while enabling transparent and verifiable microgrid energy transactions.
Sustainable energy communities (ECs) are rapidly expanding in scale and heterogeneity, making fully centralized energy management increasingly impractical due to computational burden and privacy concerns. In this context, this review synthesizes distributed optimization (DO) as a practical management paradigm for ECs, identifies key application areas (demand response, distributed generation and storage management, and microgrid or smart-grid integration) and profiles scalability, privacy, and resilience characteristics. The survey follows a systematic protocol: records are sourced from Scopus, filtered with iteratively refined keyword sets, and screened following a PRISMA flow. Key technological enablers, such as blockchain/distributed ledgers, artificial intelligence, and game-theoretic constructs, are assessed and analyzed for how they support secure data exchange, real-time coordination, and incentive compatibility across multi-agent energy networks. The analysis highlights persistent challenges for DO at EC scale, including convergence under heterogeneity, time-varying conditions, communication delays, cybersecurity and privacy guarantees, while recent advances (e.g., ADMM) partially mitigate these issues without sacrificing local autonomy. Across representative studies, DO achieves near-centralized optimality with 0.0029% gap. Overall, we present an integrative framework that maps DO families to EC use cases and outlines research directions toward robust, privacy-preserving, and scalable EC optimization. • Recent advances in Distributed Optimization Methods. • Technological Enablers for Sustainable Energy Communities. • Technology innovations for distributed optimization in energy systems. • Distributed Optimization Challenges in energy systems
The construction industry is a major global consumer of energy and a leading source of greenhouse gas emissions, underscoring the need for transparent, data-driven, and energy-efficient supply chain strategies. This study develops an integrated mixed-integer linear programming (MILP) model for a multi-echelon, multi-product construction supply chain that explicitly incorporates differentiated building energy efficiency levels ( A +, A ++, A +++) as exogenous determinants of material requirements, production processes, and logistics flows. By embedding blockchain-enabled smart contracts, the model automates supplier governance and ensures compliance with delivery reliability, quality standards, and CO 2 performance through predefined incentives and penalties, thereby enhancing transparency and accountability. The framework jointly optimizes facility location, material and product flows, supplier selection, and reverse logistics operations under a CO₂ emission cap, while simultaneously capturing the implications of greenfield and brownfield project conditions. A real-scale numerical case study demonstrates the model’s ability to evaluate the economic–environmental trade-offs arising from increasingly stringent sustainability requirements. The results reveal that although higher energy efficiency levels incur greater initial supply chain costs due to advanced materials and more complex logistics, they lead to substantial reductions in long-term operational energy consumption, rendering the A +++ option the most economically favorable from a lifecycle perspective. Furthermore, the integration of blockchain-enabled smart contracts partially offsets cost escalations by penalizing non-compliant suppliers and rewarding high-performing ones. Overall, the proposed model provides a rigorous and transparent decision-support framework that enables contractors to align supply chain design with energy-efficiency targets, CO 2 -reduction policies, and circular-economy objectives while preserving operational feasibility and supply reliability.
In the present times, Transformation finance has become a prominent approach for a systematic financial channel to facilitate the step-by-step decarbonization of carbon-intensive sectors. Such mechanisms rely on the accuracy of carbon emissions data to measure environmental performance and to inform capital decisions. The current carbon accounting methods are limited by inadequate data-collection provisions, slow verification processes, and low auditability, which undermine the reliability of emission-reduction claims and constrain the effectiveness of carbon asset markets. In the present research work, a blockchain-based framework is proposed that will create reliable carbon data accounting and facilitate structured carbon asset circulation within ecosystems of transformation finance. The framework establishes a single carbon lifecycle for data, integrating real-time emission tracking, multi-step verification, a secure registry, and computer-generated assets. The datasets of industrial emissions used to test the operation of the proposed system under multi-sector conditions include energy systems and manufacturing activities, logistics networks, and urban service infrastructure. The objective of the proposed framework is to measure the reliability of carbon accounting by normalizing emission intensities, estimating verification confidence, and scoring trust with uncertainty. In addition, a circulation model is proposed to describe the liquidity of carbon assets, the efficiency of their utilization, and the stability of decentralized transactions. The outcome of the present research is to regulate the creation and transfer of tokenized carbon assets, which guarantees the consistency of environmental performance and financial representation. The review shows a quantifiable increase in the visibility of emission records, a decrease in verification delays, and greater visibility into asset circulation processes compared with traditional centralized systems. The suggested framework establishes a logical link between verifiable carbon-reduction results and decentralized financial mechanisms, enhancing the operational feasibility of transformation finance.
Christian Kaps, Serguei Netessine, Vishrut Rana, Ömer Karaduman
Achieving SDG 7 requires closing persistent energy access gaps while simultaneously scaling renewable generation and reliably integrating intermittent supply into electricity systems. In 2024, 655 million people remain without access to electricity, and about 2 billion still rely on polluting cooking fuels. Simultaneously, clean energy technologies are increasingly cost-competitive and deployment is accelerating. Still, adoption and scaling remain constrained by affordability, supply-chain and infrastructure bottlenecks, coordination failures among decentralized actors, financing, and institutional frictions. We highlight the grand challenges behind these operational frictions and discuss how the operations management research community can contribute to the progress towards SDG 7 targets.
Decentralized bio-hydrogen systems, integrating localized biomass gasification, hydrogen cogeneration, and HVAC absorption cooling, offer a viable pathway for decarbonizing commercial real estate. However, existing control strategies fail to co-optimize second-law thermodynamic exergy efficiency with dynamic grid carbon intensity and fluctuating carbon-tax pricing, a gap consistent with the broader finding that carbon-pricing frameworks only translate into emission reductions when paired with dispatchable technology able to respond to the price signal. This paper formulates a nonlinear thermo-economic optimization model for building-integrated bio-hydrogen systems operating under regional emission-trading schemes, together with an explicit capital-recovery-factor-based definition of the Levelized Cost of Building Energy (LCOBE) tying the dispatch objective to the building's actual 20-year capital and financing profile. Evaluated across three distinct climate zones (Cold, Mixed, Tropical), the proposed Carbon-Exergy Co-Optimization (CECO) algorithm increases annual exergy efficiency by an average of 21% relative to conventional heat-led operation, and reduces LCOBE by up to 21.4% under a strict $130/ton carbon-tax framework.
Open access
Integrated Energy Systems Optimization
Thermodynamic and Exergetic Analyses of Power and Cooling Systems
Michele Fabi, Viraj Nadkarni, Leonardo Leone, Matheus V. X. Ferreira
<div> We develop an axiomatic theory for Automated Market Makers (AMMs) in local energy sharing markets and analyze the Markov Perfect Equilibrium of the resulting economy with a Mean-Field Game. In this game, heterogeneous prosumers solve a Bellman equation to optimize energy consumption, storage, and exchanges. Our axioms identify a class of mechanisms with linear, Lipschitz continuous payment functions, where prices decrease with the aggregate supply-to-demand ratio of energy. We prove that implementing batch execution and concentrated liquidity allows standard design conditions from decentralized finance-quasi-concavity, monotonicity, and homotheticity-to construct AMMs that satisfy our axioms. The resulting AMMs are budget-balanced and achieve ex-ante efficiency, contrasting with the strategy-proof, expost optimal VCG mechanism. Since the AMM implements a Potential Game, we solve its equilibrium by first computing the social planner's optimum and then decentralizing the allocation. Numerical experiments using data from the Paris administrative region suggest that the prosumer community can achieve gains from trade up to 40% relative to the grid-only benchmark. </div>
Adama Sarr, Aldo Bischi, Umberto Desideri, Cheikh Mouhamed Fadel Kébé
Achieving universal electricity access in Senegal by 2030 remains a major policy challenge due to persistent spatial disparities in infrastructure, population density, and resource availability. This study conducts a nationwide, spatially explicit assessment of least-cost electrification pathways using OnSSET. The analysis develops context-specific scenarios to plan optimal technology mixes across rural and peri‑urban areas, based on differentiated tiers of electricity access. By integrating high-resolution geospatial, demographic, and techno-economic data, the model identifies the most economically viable solutions for achieving universal access. Results indicate that grid extension is the least-cost option for approximately 93.7 % of the population, largely concentrated in peri‑urban areas with high population density and proximity to existing grid infrastructure. In contrast, solar PV mini-grids (MG PV) and stand-alone PV (SA PV) systems are optimal for 0.7 % and 5.6 % of the population, respectively, mainly in remote, sparsely populated rural settlements. The total investment required to achieve universal electricity access by 2030 is estimated at USD 269.8 million, corresponding to 116.1 MW of additional installed capacity. Beyond quantifying cost-optimal solutions, the study demonstrates the potential of open-source geospatial models like OnSSET to support transparent, data-driven planning in developing country contexts. It also highlights key policy implications, emphasizing the need for integrated national electrification strategies that combine centralized and decentralized systems to address regional disparities. Limitations of the study include uncertainties in input data quality, static demand assumptions, and the exclusion of non-technical barriers such as institutional capacity and financing constraints. Nonetheless, the findings provide a valuable decision-support basis for Senegal’s ongoing energy transition and broader Sustainable Development Goal 7 (SDG7) objectives.
Virtual Power Plant (VPP) trading mechanisms confront unprecedented challenges from behavioral complexities and technological uncertainties that conventional rational choice models inadequately address. This research develops an integrated framework combining prospect theory-driven decision modeling with evolutionary smart contracts and multi-stage negotiation protocols to enhance trading effectiveness in cross-regional energy markets. We establish mathematical foundations incorporating loss aversion, probability distortion, and reference-dependent preferences into VPP decision-making, while developing adaptive contracts capable of autonomous evolution responding to market changes. Through composite game-theoretic analysis examining nested interactions between contract evolution and negotiation dynamics, we validate the framework across three comprehensive scenarios: emergency dispatch under extreme weather, renewable energy integration, and cross-regional collaboration. Simulation results demonstrate 15–25% negotiation efficiency improvements compared to traditional mechanisms, with behavioral models capturing significant heterogeneity in loss aversion coefficients (2.1–3.4) across VPP configurations. The evolutionary contracts successfully adapt within 72-hour windows to policy changes and technological developments, while maintaining system stability. Cross-regional analysis reveals how cultural distance and information asymmetries influence trading outcomes, with the framework achieving superior market integration despite these barriers. These findings establish new paradigms for behaviorally-informed energy market design, offering transformative implications for renewable integration and decentralized electricity systems.
Nan Geng, Can Zhou, Jiafeng Feng, Xin Zhang · 7 authors
The advancing integration of Cyber-Physical-Social Systems (CPSS) within the modern power industry has highlighted the need for enhanced data integrity and multi-entity coordination. In this context, the pursuit of secure and trustworthy lifecycle management for power materials, regarded as a foundational component in ensuring system stability and operational efficiency, has attracted increasing attention. However, existing systems often face limitations such as information opacity, insufficient data accuracy, and the absence of a secure trust mechanism, hindering intelligent development and long-term sustainability. Blockchain technology, distinguished by its distributed ledger, transparency, immutability, and smart contract capabilities, offers a promising solution by enhancing data security and ensuring information reliability. This study introduces a blockchain-based framework for the secure and trustworthy lifecycle management of power materials within CPSS environments, which ensures lifecycle traceability, real-time monitoring, and trustworthy information exchange. By integrating key application scenarios, such as refined equipment management and paperless execution of contracts, the proposed approach addresses crucial operational needs. A multidimensional analysis with conventional systems reveals its advantages in improving management efficiency, optimizing resource allocation, enhancing data security, and reducing operational costs. The proposed framework thus provides both theoretical foundations and practical pathways for leveraging blockchain in power material lifecycle management, enabling digital transformation, managerial innovation, and collaborative industry development.
Anis Ur Rehman, M. J. Sanjari, Rajvikram Madurai Elavarasan, Taskin Jamal
Transformation of the energy sector is necessary to meet climate targets and ensure universal access to reliable and affordable energy. Despite progress, more than 675 million people still lack electricity and 770 million face an unreliable power supply. Renewable energy now provides nearly 30 % of global electricity generation and represents approximately 17.9 % of total final energy consumption. This amount is insufficient for the 1.5 ∘ C pathway and requires a tripling of renewable capacity by 2030. Energy efficiency also lags with average annual gains of 1.6 % compared with the 4 % required for climate-aligned energy scenarios. Therefore, this paper reviews pathways toward decentralized low-carbon solutions that can accelerate global energy transformation. The review paper examines how technologies such as microgrids, virtual power plants, energy storage systems, and vehicle-to-grid (V2G) solutions are reshaping modern energy systems. It highlights that digitalization, smart grids, and sector integration are key to building flexible and consumer-focused networks. However, achieving sustainable energy access requires more than new technologies. Strong governance, fair financing, and social inclusion are equally important to ensure a just and balanced energy transition. Case studies from Asia, Africa, and Latin America show how policy, innovative financing, and regional cooperation can drive progress despite challenges such as underinvestment, fossil fuel dependency, and energy poverty. The review demonstrates that an integrated approach, combining technological innovation, financial mechanisms, and inclusive policies, can collectively build low-carbon, resilient, and equitable energy systems. • Research gaps in sustainable energy supply on technology, policy, and equity are identified. • Sustainability-aligned pathways toward decentralized low-carbon solutions are reviewed. • Governance and planning are key for sustainable energy transitions. • A comprehensive framework of technical, economic, and social insights for sustainable transition is introduced.