pwc-archive/papers-with-abstracts huggingface dataset:
How circular is the linear economy? Analysing circularity, resource flows and their relation to GDP
Authors: Amir Rashid
Date: 2025-05-19 00:00:00

The concept of Circular Economy (CE) has evolved significantly over the past decade, transitioning from a simple model of resource circulation to an increasingly complex and debated framework. While its primary objective remains the elimination of waste and pollution through regenerative processes, CE has encountered definitional ambiguities and criticisms. This study critically examines the prevailing circularity metrics, such as the circular material use rate or circularity and argues that such narrow definitions obscure the true potential of CE by excluding higher-value strategies like maintenance, repair, refurbishment, and remanufacturing. Through a mixed-methods analysis of global resource flows (e.g.,104 Gt input in 2020, with only 9% recycled), the study demonstrates how adjusting circularity calculations for non-recoverable materials reveals a real circularity rate of 27%, far exceeding the apparent 9%. Yet even this higher rate translates to a mere 1.4% of global GDP, underscoring the limited economic impact of recycling-centric approaches. The study identifies that 69% of economic value already derives from managing existing stocks, suggesting mainstream CE discourse has largely overlooked the most substantial circular practices already embedded in modern economies. The study thus proposes a radical change in the assessment framework that, (a) replaces annual input-based metrics with economic value creation as the primary indicator, (b) incorporates stock utilization efficiency as a core circularity measure and (c) establishes new policy targets focused on value retention and reuse rather than mere material recovery. These findings necessitate a paradigm shift in circular economy strategy -- from counting recycled materials to optimizing economic resilience through intelligent stock management and service-based value creation.
On long-duration storage, weather uncertainty and limited foresight
Authors: Felix Schmidt
Date: 2025-05-18 00:00:00

Long-duration energy storage (LDES) is a key component for fully renewable, sector-coupled energy systems based on wind and solar. While capacity expansion planning has begun to take into account interannual weather variability, it often ignores weather uncertainty and limited foresight in capacity and operational decisions. We build a stochastic capacity expansion model for fully decarbonized energy systems with LDES in Europe accounting for weather uncertainty - isolating the effect of limited foresight by comparing it to a perfect foresight benchmark. Under limited foresight, LDES acts as a hedge against extreme system states operating defensively and exhibiting a stockpiling effect absent under perfect foresight. Solar PV gains in system value for its higher predictability with up to 25% higher capacities versus the benchmark while onshore wind capacities are lower. We shed light on the underlying mechanisms by deriving implicit LDES bidding curves. We show that LDES bids reflect the costs and the weather-dependent probability of extreme system states conditional on the current system state. This has important implications for the price formation on renewable electricity markets, as a wide and continuous range of probabilistic LDES bids alleviates concerns of extreme price disparity at high renewable shares.
The Impact of Artificial Intelligence on Gross Domestic Product: A Global Analysis
Authors: Davit Gondauri
Date: 2025-05-17 00:00:00

This research paper explores the impact of Artificial intelligence (AI) on the global economy, with particular emphasis on its influence on gross domestic product (GDP). The paper begins with an overview of AI, followed by a discussion of its potential benefits and Drawbacks of economic growth. Next, the The paper examines empirical evidence and case studies to Analyze the relationship between AI adoption and GDP growth across different countries and regions. Finally, The paper concludes by providing policy Recommendations for governments seeking to harness The potential of AI to foster economic growth.
Can LLMs Credibly Transform the Creation of Panel Data from Diverse Historical Tables?
Authors: Verónica Bäcker-Peral, Vitaly Meursault, Christopher Severen
Date: 2025-05-16 00:00:00

Multimodal LLMs offer a watershed change for the digitization of historical tables, enabling low-cost processing centered on domain expertise rather than technical skills. We rigorously validate an LLM-based pipeline on a new panel of historical county-level vehicle registrations. This pipeline is 100 times less expensive than outsourcing, reduces critical parsing errors from 40% to 0.3%, and matches human-validated gold standard data with an $R^2$ of 98.6%. Analyses of growth and persistence in vehicle adoption are statistically indistinguishable whether using LLM or gold standard data. LLM-based digitization unlocks complex historical tables, enabling new economic analyses and broader researcher participation.
Beyond Time: Unveiling the Invisible Burden of Mental Load
Authors: Francesca Barigozzi, Pietro Biroli, Chiara Monfardini, Natalia Montinari, Elena Pisanelli, Sveva Vitellozzi
Date: 2025-05-16 00:00:00

This paper introduces a novel, scalable methodology to measure individual perceptions of gaps in mental load -- the cognitive and emotional burden associated with organizing household and childcare tasks -- within heterosexual couples. Using original data from the TIMES Observatory in Italy, the study combines time-use diaries with new survey indicators to quantify cognitive labor, emotional fatigue, and the spillover of mental load into the workplace. Results reveal systematic gender asymmetries: women are significantly more likely than men to bear organizational responsibility for domestic tasks, report lower satisfaction with this division, and experience higher emotional fatigue. These burdens are underestimated by their partners. The effects are particularly pronounced among college-educated and employed women, who also report greater spillovers of family responsibilities than men during paid work hours. The perceived responsibility for managing family activities is more strongly associated with within-couple gaps in time use than with the absolute time spent on their execution, underscoring the relational and conflictual nature of mental load.
AI-Driven Digital Transformation and Firm Performance in Chinese Industrial Enterprises: Mediating Role of Green Digital Innovation and Moderating Effects of Human-AI Collaboration
Authors: Jun Cui
Date: 2025-05-16 00:00:00
Tasks: Management

This study examines the relationship between AI-driven digital transformation and firm performance in Chinese industrial enterprises, with particular attention to the mediating role of green digital innovation and the moderating effects of human-AI collaboration. Using panel data from 6,300 firm-year observations collected from CNRDS and CSMAR databases between 2015 and 2022, we employ multiple regression analysis and structural equation modeling to test our hypotheses. Our findings reveal that AI-driven digital transformation significantly enhances firm performance, with green digital innovation mediating this relationship. Furthermore, human-AI collaboration positively moderates both the direct relationship between digital transformation and firm performance and the mediating pathway through green digital innovation. The results provide valuable insights for management practice and policy formulation in the context of China's evolving industrial landscape and digital economy initiatives. This research contributes to the literature by integrating perspectives from technology management, environmental sustainability, and organizational theory to understand the complex interplay between technological adoption and business outcomes.
The Impact of Large Language Models on Task Automation in Manufacturing Services
Authors: Jochen Wulf, Juerg Meierhofer
Date: 2025-05-14 00:00:00
Tasks: Hallucination, Question Answering, RAG, Retrieval-augmented Generation

This paper explores the potential of large language models (LLMs) for task automation in the provision of technical services in the production machinery sector. By focusing on text correction, summarization, and question answering, the study demonstrates how LLMs can enhance operational efficiency and customer support quality. Through prototyping and the analysis of real-life customer data, LLMs are shown to reliably correct errors, generate concise summaries of complex communication, and provide accurate, context-aware responses to customer inquiries. The research also integrates Retrieval Augmented Generation (RAG) to combine LLM outputs with domain-specific knowledge, enhancing precision and relevance. While the findings highlight significant efficiency gains, challenges such as knowledge hallucination and integration with human workflows remain barriers to large-scale adoption. This study contributes to the theoretical understanding and practical application of LLMs in manufacturing, paving the way for further research into scalable, domain-specific implementations.
Communication with Multiple Senders
Authors: Kailin Chen
Date: 2025-05-20 00:00:00

This paper analyzes a cheap talk model with one receiver and multiple senders. Each sender observes a noisy signal regarding an unknown state of the world. Existing literature (e.g., Levit and Malenko, 2011; Battaglini, 2017) focuses on scenarios where the receiver and senders have aligned preferences in each state. We further explore situations with disagreement states where the receiver and the senders have misaligned preferences. We first show that, when the number of senders grows large, each sender's message must convey almost no information to the receiver. Furthermore, we identify a discontinuity in information transmission: with moderate conflict between the receiver and the senders, introducing an arbitrarily small probability of disagreement states causes complete unraveling, contrary to complete information transmission predicted by the literature. Finally, we demonstrate that the receiver cannot fully learn the state even when receiving messages from arbitrarily many senders.
AI Agents in the Electricity Market Game with Cryptocurrency Transactions: A Post-Terminator Analysis
Authors: Microsoft Copilot, Stephen E. Spear
Date: 2025-05-20 00:00:00

This paper extends (Spear 2003) by replacing human agents with artificial intelligence (AI) entities that derive utility solely from electricity consumption. These AI agents must prepay for electricity using cryptocurrency and the verification of these transactions requires a fixed amount of electricity. As a result the agents must strategically allocate electricity resources between consumption and payment verification. This paper analyzes the equilibrium outcomes of such a system and discusses the implications of AI-driven energy markets.
An Attentional Model of Time Discounting
Authors: Zijian Zark Wang
Date: 2025-05-19 00:00:00
Tasks: model

When decision makers evaluate a sequence of rewards, they may pay more attention to larger rewards and, given attention is limited, less attention to smaller rewards. They may also become less attentive to each reward when attention is spread over a longer period of time. Such reductions in attention could lead to greater discounting of the rewards' values. This paper introduces a novel theory of time discounting based on these assumptions. The resulting discount factors in the theory follow a distribution similar to the multinomial logit function. We characterize such discount factors using two approaches: one based on information maximizing exploration and the other based on the optimal discounting framework. The theory can explain a wide range of anomalies, including the hidden-zero effect, S-shaped value function, and intertemporal correlation aversion. Also, it specifies new mediators for some well-known psychological effects, such as the common difference effect, risk aversion over time lotteries, and the present bias.
Lexicographic Preferences over Random Availability Functions
Authors: Somdeb Lahiri
Date: 2025-05-19 00:00:00

We provide an axiomatic characterization of lexicographic preferences over the set of all random availability functions using two assumptions. The first assumption is strong monotonicity, which in our framework is equivalent to the strong dominance property in microeconomics. The second assumption is independence of worse alternatives and we show that a weaker version of the same suffices for our purpose.
On the existence of stable contract systems
Authors: V. I. Danilov
Date: 2025-05-19 00:00:00

In 1962, Gale and Shapley \cite{GS} introduced the concept of stable marriages and proved their existence. Since then, the statement of the stability problem has been highly generalized. And a lot of proofs has emerged for the existence in these more general statements. It's time to review them and identify the similarities and differences. First, we will briefly discuss the classical case, because the existence proofs in the general case grew out of it. Or rather, from the idea of "deferred acceptance". When the best of the proposed contracts is temporarily retained until a better offer is received.
Reputational cheap talk versus prior information
Authors: Allen Vong
Date: 2025-05-17 00:00:00

I study a setting where a sender communicates her information about a hidden state with a receiver who wants to match his action with that state. The sender strives to appear informed at the receiver's expense. I characterize the receiver's tradeoff between relying on the sender or on prior information about the state when picking his action in all informative equilibria. I show that the receiver relies only on prior information, irrespective of how informed the sender may be, if and only if prior information is neither sufficiently noisy nor sufficiently informative. My results highlight a complementarity that informative prior information facilitates high-quality communication under reputation concern.
Unique global solution of an integral-differential equation of Footloose Entrepreneur model in new economic geography
Authors: Kensuke Ohtake
Date: 2025-05-16 00:00:00

This paper studies the Footloose Entrepreneur model in new economic geography in continuous space. In an appropriate function space, the model is formulated as an initial value problem for an infinite-dimensional ordinary differential equation. A unique global solution is constructed based on the Banach fixed point theorem. The stability of a homogeneous stationary solution is then investigated and numerical simulations of the asymptotic behavior of the solution are performed. Numerical solutions starting near the unstable homogeneous stationary solution converge to spike-shaped stationary solutions, and the number of spikes decreases with decreasing transport costs and strengthening preference for variety.
Predicting Financial Market Crises using Multilayer Network Analysis and LSTM-based Forecasting of Spillover Effects
Authors: Mahdi Kohan Sefidi
Date: 2025-05-16 00:00:00

Financial crises often occur without warning, yet markets leading up to these events display increasing volatility and complex interdependencies across multiple sectors. This study proposes a novel approach to predicting market crises by combining multilayer network analysis with Long Short-Term Memory (LSTM) models, using Granger causality to capture within-layer connections and Random Forest to model interlayer relationships. Specifically, we utilize Granger causality to model the temporal dependencies between market variables within individual layers, such as asset prices, trading values, and returns. To represent the interactions between different market variables across sectors, we apply Random Forest to model the interlayer connections, capturing the spillover effects between these features. The LSTM model is then trained to predict market instability and potential crises based on the dynamic features of the multilayer network. Our results demonstrate that this integrated approach, combining Granger causality, Random Forest, and LSTM, significantly enhances the accuracy of market crisis prediction, outperforming traditional forecasting models. This methodology provides a powerful tool for financial institutions and policymakers to better monitor systemic risks and take proactive measures to mitigate financial crises.
Hybrid Monetary Ecosystems: Integrating Stablecoins and Fiat in the Future of Currency Systems
Authors: Hongzhe Wen, Songbai Li, Jamie Zhang
Date: 2025-05-16 00:00:00

With market capitalization exceeding USD 200 billion as of early 2025, stablecoins have evolved from a crypto-focused innovation into a vital component of the global monetary structure. This paper identifies the characteristics of stablecoins from an analytical perspective and investigates the role of stablecoins in forming hybrid monetary ecosystems where public (fiat, CBDC) and private (USDC, USDT, DAI) monies coexist. Through econometric analysis with multiple models, we find that stablecoins maintain strong peg stability, while each type also exhibits distinctive responses to market variables such as trading volume and capitalization, depending on the mechanisms behind. We introduce a hybrid system design that proposes a two-layer structure where private stablecoin issuers are backed by central bank reserves, ensuring uniformity, security, and programmability. This model merges the advantages of decentralized finance and payment innovation while utilizing the Federal Reserve's institutional trust. A case study on the 2023 SVB-USDC depeg event illustrates how such a hybrid system could prevent panic-induced instability through transparent reserves, secured liquidity, and interoperable assets. Ultimately, this research examines the model using the Dybvig model and Monte Carlo Simulation and concludes, with the results of the examinations, that a hybrid monetary model not only enhances financial inclusivity, scalability, and dollar utility in digital ecosystems but also strengthens systemic resilience, offering a credible blueprint for future digital dollar architectures.
SPARC: Staking Performance And Reward Coopetition
Authors: Michael D. Norman, Simon Brown, Mallesh Pai, Laurence Smith
Date: 2025-05-15 00:00:00

This paper presents a novel staking coopetition design aimed at incentivizing decentralization and continuous growth of economic security within a proof-of-stake system. Staking rewards follow a nonlinear mapping relative to stake size. This affords the highest effective yields to smaller operators, fueling network growth and giving users an incentive to delegate their stake to smaller operators. This prevents the preferential accrual and centralization of stake seen in popular blockchains such as Ethereum, where popular liquid staking protocols control large fractions of the total stake thereby having outsized potential impacts on the economic security of the protocol. The proposed system addresses key challenges such as Sybil attacks and offers a comprehensive framework for future research and implementation. We introduce innovative mechanisms and gamification elements, to enhance user engagement and provide transparency in emissions.
FlowTSE: Target Speaker Extraction with Flow Matching
Authors: Aviv Navon, Aviv Shamsian, Yael Segal-Feldman, Neta Glazer, Gil Hetz, Joseph Keshet
Date: 2025-05-20 00:00:00
Tasks: Target Speaker Extraction

Target speaker extraction (TSE) aims to isolate a specific speaker's speech from a mixture using speaker enrollment as a reference. While most existing approaches are discriminative, recent generative methods for TSE achieve strong results. However, generative methods for TSE remain underexplored, with most existing approaches relying on complex pipelines and pretrained components, leading to computational overhead. In this work, we present FlowTSE, a simple yet effective TSE approach based on conditional flow matching. Our model receives an enrollment audio sample and a mixed speech signal, both represented as mel-spectrograms, with the objective of extracting the target speaker's clean speech. Furthermore, for tasks where phase reconstruction is crucial, we propose a novel vocoder conditioned on the complex STFT of the mixed signal, enabling improved phase estimation. Experimental results on standard TSE benchmarks show that FlowTSE matches or outperforms strong baselines.
Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach
Authors: Yi-Cheng Lin, Huang-Cheng Chou, Hung-Yi Lee
Date: 2025-05-20 00:00:00
Tasks: Emotion Recognition, Fairness, Speech Emotion Recognition

While subgroup disparities and performance bias are increasingly studied in computational research, fairness in categorical Speech Emotion Recognition (SER) remains underexplored. Existing methods often rely on explicit demographic labels, which are difficult to obtain due to privacy concerns. To address this limitation, we introduce an Implicit Demography Inference (IDI) module that leverages pseudo-labeling from a pre-trained model and unsupervised learning using k-means clustering to mitigate bias in SER. Our experiments show that pseudo-labeling IDI reduces subgroup disparities, improving fairness metrics by over 28% with less than a 2% decrease in SER accuracy. Also, the unsupervised IDI yields more than a 4.6% improvement in fairness metrics with a drop of less than 3.6% in SER performance. Further analyses reveal that the unsupervised IDI consistently mitigates race and age disparities, demonstrating its potential when explicit demographic information is unavailable.
MDDM: A Multi-view Discriminative Enhanced Diffusion-based Model for Speech Enhancement
Authors: Nan Xu, Zhaolong Huang, Xiaonan Zhi
Date: 2025-05-19 00:00:00
Tasks: Speech Enhancement

With the development of deep learning, speech enhancement has been greatly optimized in terms of speech quality. Previous methods typically focus on the discriminative supervised learning or generative modeling, which tends to introduce speech distortions or high computational cost. In this paper, we propose MDDM, a Multi-view Discriminative enhanced Diffusion-based Model. Specifically, we take the features of three domains (time, frequency and noise) as inputs of a discriminative prediction network, generating the preliminary spectrogram. Then, the discriminative output can be converted to clean speech by several inference sampling steps. Due to the intersection of the distributions between discriminative output and clean target, the smaller sampling steps can achieve the competitive performance compared to other diffusion-based methods. Experiments conducted on a public dataset and a realworld dataset validate the effectiveness of MDDM, either on subjective or objective metric.
SPIRIT: Patching Speech Language Models against Jailbreak Attacks
Authors: Amirbek Djanibekov, Nurdaulet Mukhituly, Kentaro Inui, Hanan Aldarmaki, Nils Lukas
Date: 2025-05-18 00:00:00

Speech Language Models (SLMs) enable natural interactions via spoken instructions, which more effectively capture user intent by detecting nuances in speech. The richer speech signal introduces new security risks compared to text-based models, as adversaries can better bypass safety mechanisms by injecting imperceptible noise to speech. We analyze adversarial attacks and find that SLMs are substantially more vulnerable to jailbreak attacks, which can achieve a perfect 100% attack success rate in some instances. To improve security, we propose post-hoc patching defenses used to intervene during inference by modifying the SLM's activations that improve robustness up to 99% with (i) negligible impact on utility and (ii) without any re-training. We conduct ablation studies to maximize the efficacy of our defenses and improve the utility/security trade-off, validated with large-scale benchmarks unique to SLMs.