The global AI inference market—reaching approximately $90–100 billion annually and growing at 18% CAGR—operates without organized exchange infrastructure. We document three market failures: resource misallocation (80% of China’s newly built compute capacity sits idle), price opacity (100-fold price dispersion across providers of equivalent quality), and unhedged risk exposure (85% of enterprises miss AI cost forecasts by more than 10%). Following the market design tradition of Roth [2002] and Budish et al. [2015], we propose the AI Service Right (ASR) as a transferable property right on AI compute and the AI Service Unit (ASU) as a quality-adjusted, cross-platform unit of account grounded in hedonic price theory [Rosen, 1974]. The ASU is modality-neutral: billing prices across text, image, video, and speech modalities are unified via eq-token conversion factors (κimg ≈ 2,667 eq-tokens per image; κvid ≈ 2,667 per second of video; κspc ≈ 7 per second of audio), and modality-appropriate benchmark sets (MMLU/HumanEval for language; FID/CLIP Score for image; FVD/CLIPSIM for video; MMBench for multimodal) supply the quality in dex via PCA. We design a hybrid secondary market architecture synthesizing mechanisms from four orthogonal market traditions: foreign exchange markets (cross-platform exchange rates and PPP-analog arbitrage via the ASU); equity markets (Central Limit Order Book, market making, clearing); electricity markets (Compute Locational Marginal Pricing for spatial scarcity signals); and decentralized finance (Automated Market Maker for long-tail liquidity). We establish nine formal propositions: bilateral trading is generically inefficient; Compute Locational Marginal Pricing decomposes nodal prices into system marginal cost, capacity congestion, and bandwidth premia; no-arbitrage equi librium holds with capital constraints (extending Shleifer and Vishny 1997); the ASR market Pareto-improves over bilateral trading; market prices are more in formative under ASR; the hybrid CLOB-AMM architecture weakly dominates either mechanism alone; platform adoption admits multiple equilibria with a coordination trap; financialization may improve or reduce price informativeness depending on speculator-hedger composition; and a hedonic micro-foundation justifies the ASU definition. Calibrated agent-based simulation (500 steps, 30 Monte Carlo runs) provides computational validation: the hybrid architecture reduces price dispersion by 90% relative to bilateral trading, and order-of-magnitude welfare estimates suggest enterprise procurement cost savings of 0.2–20% (net of ASR transaction costs; see Table 7) and potential TFP gains from compute reallocation of up to $29.9 billion annually. We propose a phased implementation roadmap from shadow ledger to full financialization, and we engage critically with the concern that financialization may not reduce intermediation costs [Philippon, 2015].

This paper exploits China’s first default of state-owned enterprises to study the implicit government guarantee’s effect on SOEs’ trade credit financing. It finds that SOEs increase trade credit by 2.3% of total liabilities, on average, relative to non-SOEs after the first SOE default in China’s bond markets in 2015. The additional reliance on suppliers’ credit is more prominent among SOEs with higher information opacity. It is consistent with the literature where trade credit advantage lies in the suppliers’ superior information, as they can observe their clients through daily transactions. The current paper also finds that trade credits positively affect SOEs when IGG weakens. Overall, the results suggest that the reduction in IGG significantly affects Chinese firms’ financing decisions, highlighting the trade credit advantage against the backdrop of imperfect market institutions.

With growing awareness of sustainability, the field of Environmental, Social and Governance (ESG), has been attracting mainstream investors and researchers. Many previous studies have found inconclusive or mixed results on the relationship between ESG ratings and firms’ financial performance, which are mainly attributed to their varied markets, time horizons, and sources of ESG rating. Based on evidence from an emerging market, namely China, this paper examines whether ESG is an adequate indicator for firms’ future financial performance. Given the divergence in ESG rating methodologies, we use ESG data from two ESG rating agencies, one based in China (SynTao) and the other based in Switzerland (RepRisk), for robustness. Specifically, we investigate 377 China A-share companies covered by both agencies and find that ESG rating, albeit divergent due to disparate methodologies, is instrumental in predicting the trend of corporate financial performance (CFP). This work verifies that the forward-looking nature of ESG makes it crucial for firms’ long-term valuation and financial performance in emerging markets. Throughout the research, we observe four issues in the current ESG rating process: the opacity and inaccessibility of source data, the obscurity of ESG rating methodologies adopted by rating agencies, the lack of automated pipeline, and the unannounced historical data rewriting. We believe that the public blockchain ecosystem is promising to address these issues, and we propose future research on the ESG framework for blockchain to call for sustainability focus on this emerging technology.

Opacity (the converse of transparency) has only recently received attention as it has been considered to be linked to a series of financial crises. This study utilizes Price Waterhouse Cooper’s 2001 opacity indices and capital flow data from the World Bank and Bank for International Settlement. Capital flows are disaggregated into categories of foreign direct investment flows by multinational enterprises, portfolio capital flows and international bank lending. Regression analysis supports the idea that higher opacity leads to a reduction in capital flows, in general. The results have policy-relevant implications as countries wishing to enhance capital inflows need to reduce the level of opacity in decision-making. More interestingly, however, the investigation with opacity sub- indices shows higher capital flows, in general, were associated with higher opacity in corruption and regulatory indices corroborating some existing evidence in the FDI literature that opacity will influence the choice of entry mode rather than the actual level of flows. In addition, the paper supports the notion that Bank Assurance mechanisms are highly desirable with regard to international bank lending, as the nature of these flows means that they are more influenced by general levels of opacity and are less responsive than FDI and portfolio flows.