Blockchain Papers

Yanxun Gu, Kun Yang, Jian Cao, Mingqian Feng

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.

Jiachang Yuan

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.

Guorui Wang, Liang Zhong, Yixuan Zeng

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.

Caixuan Zhan

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.