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.