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Artificial Intelligence and the Institutional Efficiency of Environmental Policy: A Transaction Cost Perspective 认领 引用
1
作者 Valeriia Tarasenko 《Proceedings of Business and Economic Studies》 2026年第4期80-87,共8页
Environmental policy effectiveness is often constrained not by instrument design itself, but by implementation frictions, information asymmetry, high monitoring and enforcement costs, and slow policy adjustment under ... Environmental policy effectiveness is often constrained not by instrument design itself, but by implementation frictions, information asymmetry, high monitoring and enforcement costs, and slow policy adjustment under rapidly changing climate and market conditions. This conceptual article develops a transaction cost perspective on how artificial intelligence (AI) can function as a governance infrastructure that strengthens environmental regulation. It highlights three channels: (1) intelligent monitoring that upgrades Monitoring, Reporting, and Verification (MRV) from periodic reporting to continuous data-driven oversight;(2) predictive modeling that supports adaptive recalibration of policy parameters through a policy feedback loop;and (3) improved integrity of carbon pricing and emissions trading systems (ETS) via anomaly detection and cross-validation of emissions claims. The analysis outlines expected economic effects, such as lower administrative and compliance costs, more reliable price signals, and stronger incentives for green investment, while emphasizing that benefits depend on institutional safeguards. A governance risk framework is proposed to address algorithmic bias, digital inequality, AI energy use, and technological dependency through measurable KPIs. Overall, AI complements rather than replaces environmental policy by expanding institutional capacity for effective regulation. 展开更多
关键词 Green AI Environmental governance Transaction costs MRV Policy feedback loop ETS Carbon pricing Regulatory compliance Algorithmic governance
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A Study on Algorithmic Oversight and the Protection of Rights in Tax Administration in the Age of Artificial Intelligence 认领 引用
2
作者 Wenzhou Long Yueqi Xiong Peng Xiong 《LEGAL》 2026年第2期16-27,共12页
As a core driver of industrial transformation and digital governance,artificial intelligence is propelling China’s tax governance from digitalization toward intelligentization.Currently,algorithmic technologies have ... As a core driver of industrial transformation and digital governance,artificial intelligence is propelling China’s tax governance from digitalization toward intelligentization.Currently,algorithmic technologies have been widely applied in tax administration scenarios such as intelligent consultation,personalized services,and tax risk identification,playing a significant role in enhancing administrative efficiency,optimizing taxpayer services,and accurately preventing tax revenue leakage.However,issues inherent to algorithms—such as their“black-box”nature and data bias—present a series of challenges for tax governance.China’s regulation of tax algorithms faces difficulties such as insufficient legislative provisions,a lack of end-to-end oversight,and unclear boundaries of responsibility;taxpa yers’rights to information,privacy,and remedies are difficult to fully safeguard due to the opacity of algorithmic processes,data misuse,and difficulties in providing evidence.To address these issues,China can draw on the experiences of the European Union,Germany,the United States,and other countries in algorithm regulation and rights protection.This involves improving legislation related to digital tax collection,establishing full-lifecycle regulation of algorithms,clarifying the auxiliary role of algorithms in tax administration,and building a system to safeguard taxpayers’rights. 展开更多
关键词 Smart Taxation Algorithm Governance Rule of Law in Taxation Taxpayer Rights
A synergistic governance framework for algorithmic transparency obligations and intellectual property protection 认领 引用
3
作者 Mohong Liu 《Advances in Social Behavior Research》 2025年第7期121-125,共5页
Regulatory initiatives on algorithmic accountability now oblige organisations to reveal decision logic,data provenance,and safety controls,yet the same systems embody proprietary assets whose exposure can nullify comp... Regulatory initiatives on algorithmic accountability now oblige organisations to reveal decision logic,data provenance,and safety controls,yet the same systems embody proprietary assets whose exposure can nullify competitive advantage.This study engineers and empirically validates a governance framework that reconciles those apparently contradictory imperatives.A three-tier disclosure architecture is combined with lattice-based cryptographic watermarking and policy-driven smart-contract gates,then exercised in two high-stakes domains:a regulated credit-scoring engine and a 1.1-billion-parameter text-to-video generator.Across 18,000 simulated disclosure transactions,the framework achieves a statutory-coverage score of 0.927±0.018 while suppressing parameter-exfiltration entropy to 1.37 bits·kg-1,a 63.8%reduction relative to a full-disclosure baseline.Median inference latency rises only 3.6 ms,and predictive accuracy remains statistically unchanged(ΔAUC=0.0007,p=0.746;ΔFID=0.09,p=0.532).Sensitivity analyses confirm that compliance quality varies by less than 2.4%under±20%weight perturbations,evidencing robustness.Findings demonstrate that calibrated transparency and vigorous IP protection are jointly attainable,providing quantitative benchmarks for emerging legislation and standardisation efforts. 展开更多
关键词 algorithmic governance transparency compliance intellectual property protection risk-tiered disclosure cryptographic watermarking compliance metrics
Challenges and Realization Paths of the Rule-of-Law in China’s Technology-Enabled Off-site Law Enforcement 认领 引用
4
作者 LIU Feihu YANG Qiangqiang 《US-China Law Review》 2022年第9期408-415,共8页
Under the development of internet technology and the trend of administrative automation,off-site law enforcement in China has evolved from low artificial intelligence to high artificial intelligence,while its applicat... Under the development of internet technology and the trend of administrative automation,off-site law enforcement in China has evolved from low artificial intelligence to high artificial intelligence,while its application scenarios have been expanded from simple ones to complex ones.Although it can improve the efficiency of China’s administrative law enforcement,it also raises legal issues such as the legality of data collection and algorithm rules,the credibility of data quality and electronic evidence,as well as the due process of law enforcement.To meet the demand for off-site law enforcement through the integration of technology and law,and to achieve a balance between efficiency and the rule-of-law,China’s legal supervision system should respond as follows:fulfill the obligation of informing and confidentiality in remote evidence collection;meanwhile carry out algorithm registration and supervision.Verify the authenticity of electronic evidence through multiple ways;complete the rules of the burden of proof in data quality disputes.Clarify the applicable limits of off-site law enforcement;incorporate due process into the law enforcement process appropriately. 展开更多
关键词 off-site law enforcement administrative automation algorithmic governance
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Platform Intelligence in Agriculture 认领 引用 被引量：1
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作者 Sébastien Grappe Stéphane Grumbach 《The International Journal of Intelligent Control and Systems》 2026年第1期29-41,共13页
This paper considers the current challenges of agriculture in historical retrospect.As in all activities,agriculture is orchestrated by information governance systems.They have evolved along history following a rather... This paper considers the current challenges of agriculture in historical retrospect.As in all activities,agriculture is orchestrated by information governance systems.They have evolved along history following a rather regular trend of disembedding and concentration,disembedding of information from its origin in the physical world and concentration under the control of increasingly powerful market actors.The current challenges of agriculture,whether environmental,health,or culture-related,can be shown to relate to blind spots of the information systems.We consider the potential of intermediation platforms,that are increasingly in control of multi-sided markets in increasingly more sectors,to reshape the view systems have,making them more holistic,able to deal with what was previously neglected as externalities.We first consider the issue theoretically,and then illustrate with the claims of real actors towards a systemic intelligence of agriculture. 展开更多
关键词 Platform intelligence two-sided markets epistemic infrastructure cybernetic regulation intermediation platforms algorithmic governance
Functional Reconstruction of the Safe Harbour Rule for E-commerce Platforms in the Context of Artificial Intelligence and Cloud Computing:A Comparative Study of China and the European Union 认领 引用
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作者 Wuling Ying Xiaolong He 《LEGAL》 2026年第2期1-15,共15页
The safe harbour rule is a fundamental rule in the liability regime for online platforms.Its basic function is to prevent platforms from bearing excessive liability for third-party content,product information,or trans... The safe harbour rule is a fundamental rule in the liability regime for online platforms.Its basic function is to prevent platforms from bearing excessive liability for third-party content,product information,or transactional conduct,while requiring them to take necessary measures once they have knowledge of infringement or receive a valid notice.This rule was justified in the early Internet environment.At that stage,platforms mainly provided information storage,transmission,and transactional channels.It was neither realistic nor desirable to require them to conduct comprehensive ex ante review of massive amounts of content and transactions.However,artificial intelligence(AI)and cloud computing are changing the factual basis on which the safe harbour rule applies to e-commerce platforms.Modern e-commerce platforms do not merely store product information.They also influence transactions through cloud-based data processing,algorithmic recommendation,search ranking,advertising placement,automated review,and dispute resolution mechanisms.Platforms can centralize and process merchant qualifications,transaction records,consumer reviews,complaint data,and abnormal transaction signals.They may also participate in the display,optimization,and dissemination of product information through recommender systems and generative AI tools.Against this background,it is increasingly difficult to describe e-commerce platforms simply as passive intermediaries.From a comparative perspective between China and the European Union(EU),this article examines the functional reconstruction of the safe harbour rule for e-commerce platforms in the context of AI and cloud computing.EU law retains intermediary liability exemptions,while strengthening platform duties concerning notice and action,statements of reasons,internal complaint handling,trader traceability,recommender-system transparency,and systemic risk governance under the Digital Services Act(DSA).Chinese law has developed a more fragmented framework through the Civil Code,the E-Commerce Law,rules on algorithmic recommendation,and generative AI regulation.This article argues that the future application of the safe harbour rule to e-commerce platforms should not depend solely on whether the relevant information originates from a third-party merchant.Instead,it should be assessed by reference to the platform’s technical capacity,degree of transactional control,algorithmic amplification,commercial benefit,and procedural safeguards. 展开更多
关键词 safe harbour rule e-commerce platforms artificial intelligence(AI) cloud computing platform liability algorithmic governance China-EU comparison
Risk Categories as Boundary Work:How Classification Regimes Reorder Innovation and Inequality 认领 引用
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作者 Yahan LI 《数字社会与虚拟治理》 2025年第2期61-78,共18页
Risk-based governance has become the dominant regulatory paradigm for artificial intelligence,yet existing scholarship largely treats risk categories as neutral assessments of technological harm that precede regulator... Risk-based governance has become the dominant regulatory paradigm for artificial intelligence,yet existing scholarship largely treats risk categories as neutral assessments of technological harm that precede regulatory intervention.This article challenges that assumption by arguing that risk categories function less as assessments of harm than as social architectures—institutional boundary-making devices that reorganize innovation capacity,compliance burdens,and inequality across jurisdictions and organizational types.Drawing on Beck’s risk society thesis,Jasanoff’s co-production framework,and Gieryn’s concept of boundary work,the article develops a diagnostic framework—Risk as Social Architecture—and formalizes a causal loop mechanism linking category definition,innovation channeling,compliance burden,perception lock-in,and inequality reproduction.A Cobb–Douglas formalization and three testable propositions operationalize the framework’s core variables.Applying this framework to three jurisdictional cases—the European Union’s AI Act(2021–2024),China’s Algorithmic Governance Provisions(2022–2023),and Singapore’s Model AI Governance Framework—demonstrates that identical AI systems receive fundamentally different risk classifications,with variance explained by political negotiation rather than technical properties.The article contributes to governance scholarship by reframing risk classification as a primary site of institutional power rather than a secondary policy tool.More broadly,it demonstrates how governance through categories reshapes inequality not only in artificial intelligence but across regulatory domains—including biotechnology,finance,and climate risk—wherever classification systems mediate access to markets,legitimacy,and innovation. 展开更多
关键词 boundary work classification politics AI risk categories EU AI Act innovation inequality coproduction risk-based regulation regulatory competition algorithmic governance algorithmic political economy
Single Window for International Trade:Intelligent Optimization and Computational Social Science Methodological Exploration 认领 引用
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作者 Sophia LI 《计算社会科学》 2025年第1期68-76,共9页
The rapid evolution of international trade necessitates the adoption of intelligent digital solutions to enhance trade facilitation.The Single Window System(SWS)has emerged as a key mechanism for streamlining trade do... The rapid evolution of international trade necessitates the adoption of intelligent digital solutions to enhance trade facilitation.The Single Window System(SWS)has emerged as a key mechanism for streamlining trade documentation,customs clearance,and regulatory compliance.However,traditional SWS implementations face challenges such as data fragmentation,inefficient processing,and limited real-time intelligence.This study proposes a computational social science framework that integrates artificial intelligence(AI),machine learning,network analytics,and blockchain to optimize SWS operations.By employing predictive modeling,agentbased simulations,and algorithmic governance,this research demonstrates how computational methodologies improve trade efficiency,enhance regulatory compliance,and reduce transaction costs.Empirical case studies on AI-driven customs clearance,blockchain-enabled trade transparency,and network-based trade policy simulation illustrate the practical applications of these techniques.The study concludes that interdisciplinary collaboration and algorithmic governance are essential for advancing digital trade facilitation,ensuring resilience,transparency,and adaptability in global trade ecosystems. 展开更多
关键词 Computational Social Science Single Window System(SWS) Trade Facilitation Artificial Intelligence Machine Learning Blockchain Network Analytics Algorithmic Governance
Multidimensional Perspectives and Pathways of AI-Empowered Modern Management Research 认领 引用
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作者 Jing Ning Jun Tan 《Journal of Frontier in Economic and Management Research》 2025年第1期469-478,共10页
Artificial intelligence(AI)technology is profoundly reshaping the global management ecosystem,transforming its role from a tool for efficiency to a structural force driving organizational change.This study,grounded in... Artificial intelligence(AI)technology is profoundly reshaping the global management ecosystem,transforming its role from a tool for efficiency to a structural force driving organizational change.This study,grounded in the context of China's modernization,systematically explores the multidimensional applications of AI technology in management research and the challenges it faces.The study finds core challenges in the current management field,including a crisis of adaptability between the industrial-era paradigm and the intelligent ecosystem,the dissipation of governance effectiveness caused by algorithmic black boxes,and cognitive barriers to human-machine collaboration.These issues stem from the conflict between mechanistic cognition and complex systems,the imbalance between instrumental and value rationality,and the paradigmatic differences between biological and machine intelligence.To address these challenges,the study proposes three solutions:building an AI-enabled distributed dynamic knowledge network,establishing a hierarchical and transparent governance system,and developing cognitive coupling interfaces.This research not only provides new perspectives for innovation in management theory but also offers practical paths for AI management practice in the Chinese context. 展开更多
关键词 AI management research algorithmic governance human-machine collaboration knowledge networks
Transformative Impacts of Big Data Technologies on the Credit Reporting Industry:Drivers,Challenges,and Future Trajectories 认领 引用
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作者 Zhiming Song Huilin Mo 《Journal of Frontier in Economic and Management Research》 2025年第1期284-306,共23页
Amid the rapid evolution of the digital economy,big data technologies are reshaping the foundations of traditional credit reporting by expanding data sources,refining modeling methods,and enhancing risk response capac... Amid the rapid evolution of the digital economy,big data technologies are reshaping the foundations of traditional credit reporting by expanding data sources,refining modeling methods,and enhancing risk response capacity.From the integrated perspective of the“technology–institution–ethics”triad,this paper systematically reviews 33 studies published between 2012 and 2025,supplemented by representative case analyses.The review follows PRISMA 2020 guidelines,covering both international literature and China-specific practices.The analysis shows that while big data enables more dynamic,precise,and intelligent credit evaluation,it also generates systemic risks,including privacy infringement,algorithmic bias,model opacity,and regulatory lag.To address these dilemmas,a comprehensive governance framework is proposed that combines explainable artificial intelligence,privacy-preserving computation,cross-sector regulatory coordination,and ethical algorithmic norms.The study acknowledges its limitations as a review-based work—particularly in terms of proprietary data accessibility,interpretability of complex models,and empirical cross-platform validation-and suggests future research directions involving realworld experimentation,interpretable deep models,and multi-institutional governance mechanisms.Overall,this research aims to provide theoretical foundations and policy insights for building an open,transparent,and sustainable digital credit ecosystem. 展开更多
关键词 Big data credit reporting explainable AI algorithmic governance regulatory coordination digital credit ethics
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