Cities have become primary actors on climate change and are increasingly setting goals aimed at net-zero emissions. The rapid proliferation of subnational governments "racing to zero" emissions and articulating their own climate mitigation plans warrants closer examination to understand how these actors intend to meet these goals. The scattered, incomplete and heterogeneous nature of city climate policy documents, however, has made their systemic analysis challenging. We analyze 318 climate action documents from cities that have pledged net-zero targets or joined a transnational climate initiative with this goal using machine learning-based natural language processing (NLP) techniques. We use these approaches to accomplish two primary goals: 1) determine text patterns that predict "ambitious" net-zero targets, where we define an ambitious target as one that encompasses a subnational government's economy-wide emissions; and 2) perform a sectoral analysis to identify patterns and trade-offs in climate action themes (i.e., land-use, industry, buildings, etc.). We find that cities that have defined ambitious climate actions tend to emphasize quantitative metrics and specific high-emitt
Our main contribution is a strongly polynomial algorithm for computing an equilibrium for the Arctic Auction, which is the quasi-linear extension of the linear Fisher market model. We build directly on Orlin's strongly polynomial algorithm for the linear Fisher market (Orlin, 2010). The first combinatorial polynomial algorithm for the linear Fisher market was based on the primal-dual paradigm (Devanur et al., 2008). This was followed by Orlin's scaling-based algorithms. The Arctic Auction (Klemperer 2018) was developed for the Government of Iceland to allow individuals to exchange blocked offshore assets. It is a variant of the product-mix auction (Klemperer 2008, 2010, 2018) that was designed for, and used by, the Bank of England, to allocate liquidity efficiently across banks pledging heterogeneous collateral of varying quality. Our work was motivated by the fact that banks often need to run Arctic Auctions under many different settings of the parameters in order to home in on the right one, making it essential to find a time-efficient algorithm for Arctic Auction.
Methane emissions by livestock have a negligible effect on Earth's temperature. For example, killing all of the approximately 1.6 billion cattle on Earth in the year 2025, when this paper was written, would only reduce atmospheric methane concentrations enough to change the temperature by -0.04 C. Killing all 1.3 billion sheep would lead to a temperature change of -0.004 C. New Zealand's pledge to reduce methane emissions of their livestock by 14% to 24% from those in the year 2017 would change the temperature by -0.000005 to -0.000008 C, far too small to measure. These are maximum temperature savings where methane emissions from domestic livestock are not replaced by other sources (such as wild ruminants and termites) during the inevitable rewilding of managed grasslands and rangelands.
This paper builds a finite-horizon model to study the role of physical collateral in a model of strategic defaults, when the borrower can develop reputation for honesty. Asset ownership increases attractiveness of the reputational channel: the borrower who would prefer to remain in autarky in the absence of the asset prefers to apply for collateralized debt. Pledging the asset as collateral facilitates reputation building, which is especially successful at the times of asset price drops, because these are the times when default is most tempting. The complementarity between secured and unsecured lending is especially pronounced when the ratio of the borrower's financial to non-financial income is neither too large nor too small.
Costly pre-play messages can deter unnecessary wars - but the same messages can also entrench stalemates once violence begins. We develop an overlapping-generations model of a security dilemma with persistent group types (normal vs bad), one-sided private signaling by the current old to the current young, and noisy private memory of the last encounter. We characterize a stationary equilibrium in which, for an intermediate band of signal costs, normal old agents mix on sending a costly reassurance only after an alarming private history; the signal is kept marginally persuasive by endogenous receiver cutoffs and strategic mimicking by bad types. Signaling strictly reduces the hazard of conflict onset; conditional on onset, duration is unchanged in the private model but increases once a small probability of publicity (leaks) creates a public record of failed reconciliation. With publicity, play generically absorbs in a peace trap or a conflict trap. We discuss welfare and policy: when to prefer back-channels versus public pledges.
Outer space exploration is one of the most prominent domains of earth-space governance. In this context, multiple policy documents by the UN, NASA, or Committee on Space Research (COSPAR) pledge to protect extraterrestrial environments from harmful human influence under the framework of planetary protection or planetary stewardship, understood primarily as the isolation of other celestial bodies from possible biological contamination. This paper analyses justifications of this framework that rely on analogies with the protection of terrestrial wilderness and nature's intrinsic value, portraying them as representative of a conservationist paradigm of earth-space governance. After presenting this paradigm, the paper builds an alternative constructivist paradigm, grounded in recent findings about the evolution of the solar system, planets, and life. Ultimately, the paper argues that conservation is not the opposite of construction but one of its modalities: a conclusion that encourages the development of pragmatic protocols for space exploration instead of absolute imperatives.
Political pledges reflect candidates' policy commitments, but tracking their fulfilment requires reasoning over incremental evidence distributed across multiple, dynamically updated sources. Existing methods simplify this task into a document classification task, overlooking its dynamic, temporal and multi-document nature. To address this issue, we introduce \textsc{PledgeTracker}, a system that reformulates pledge verification into structured event timeline construction. PledgeTracker consists of three core components: (1) a multi-step evidence retrieval module; (2) a timeline construction module and; (3) a fulfilment filtering module, allowing the capture of the evolving nature of pledge fulfilment and producing interpretable and structured timelines. We evaluate PledgeTracker in collaboration with professional fact-checkers in real-world workflows, demonstrating its effectiveness in retrieving relevant evidence and reducing human verification effort.
I develop a tractable adverse-selection model comparing secured bank loans and bonds when both pledge collateral but differ in effective liquidation efficiency. A small wedge in recovery rates generates coexistence, a sharp bank-bond cutoff, and distinctive comparative statics in issuance, pricing, collateral, and default. Changes in insolvency regimes or creditor coordination shift the composition of external finance and welfare, with clear implications for bank-based versus market-based intermediation and financial stability.
This paper studies bargaining when buyers can continue searching for alternative sellers while negotiating, which limits their commitment to complete a transaction. Using transaction level data from a Japanese online marketplace, I document frequent post-agreement nonpurchase and show that buyers who explicitly pledge immediate payment are more likely to have their offers accepted, renege less often, and complete transactions faster. I develop and estimate a dynamic bargaining model with buyer search and limited commitment. Counterfactuals that restrict search during bargaining show that increased buyer commitment can reduce total welfare. Sellers especially those with higher valuations benefit from the elimination of delays and walkaways and respond by raising list prices. This reduces buyer welfare by lowering the option value of search and increasing expected list prices. Platform revenue also declines because buyer behavior shifts away from counteroffers and negotiated prices fall.
Climate change has increased demands for transparent and comparable corporate climate disclosures, yet imitation and symbolic reporting often undermine their value. This paper develops a multidimensional framework to assess disclosure maturity among 828 U.S.listed firms using large language models (LLMs) fine-tuned for climate communication. Four classifiers-sentiment, commitment, specificity, and target ambition-extract narrative indicators from sustainability and annual reports, which are linked to firm attributes such as emissions, market capitalization, and sector. Analyses reveal three insights: (1) risk-focused narratives often align with explicit commitments, but quantitative targets (e.g., net-zero pledges) remain decoupled from tone; (2) larger and higher-emitting firms disclose more commitments and actions than peers, though inconsistently with quantitative targets; and (3) widespread similarity in disclosure styles suggests mimetic behavior, reducing differentiation and decision usefulness. These results highlight the value of LLMs for ESG narrative analysis and the need for stronger regulation to connect commitments with verifiable transition strategies.
We explore the integration of climate action and Sustainable Development Goals (SDGs) in nationally determined contributions (NDCs), revealing persistent synergies and trade-offs across income groups. While high-income countries emphasize systemic challenges like health (SDG3) and inequality (SDG10), low-income nations prioritize the water-energy-food nexus (SDGs 6-7-12) and natural resource management (SDG15) due to vulnerabilities to climate impacts. Harnessing an innovative artificial intelligence routine, we discuss what these diverging development trajectories imply for the Paris Agreement and the 2030 Agenda for sustainable development in terms of global inequality, the climate and sustainable finance flows and multilateral governance.
We examine normal-form games in which players may \emph{pre-commit} to outcome-contingent transfers before choosing their actions. In the one-shot version of this model, Jackson and Wilkie showed that side contracting can backfire: even a game with a Pareto-optimal Nash equilibrium can devolve into inefficient equilibria once unbounded, simultaneous commitments are allowed. The root cause is a prisoner's dilemma effect, where each player can exploit her commitment power to reshape the equilibrium in her favor, harming overall welfare. To circumvent this problem we introduce a \emph{staged-commitment} protocol. Players may pledge transfers only in small, capped increments over multiple rounds, and the phase continues only with unanimous consent. We prove that, starting from any finite game $Γ$ with a non-degenerate Nash equilibrium $\vecσ$, this protocol implements every welfare-maximizing payoff profile that \emph{strictly} Pareto-improves $\vecσ$. Thus, gradual and bounded commitments restore the full efficiency potential of side payments while avoiding the inefficiencies identified by Jackson and Wilkie.
Companies that develop foundation models publish behavioral guidelines they pledge their models will follow, but it remains unclear if models actually do so. While providers such as OpenAI, Anthropic, and Google have published detailed specifications describing both desired safety constraints and qualitative traits for their models, there has been no systematic audit of adherence to these guidelines. We introduce an automated framework that audits models against their providers specifications by parsing behavioral statements, generating targeted prompts, and using models to judge adherence. Our central focus is on three way consistency between a provider specification, its model outputs, and its own models as judges; an extension of prior two way generator validator consistency. This establishes a necessary baseline: at minimum, a foundation model should consistently satisfy the developer behavioral specifications when judged by the developer evaluator models. We apply our framework to 16 models from six developers across more than 100 behavioral statements, finding systematic inconsistencies including compliance gaps of up to 20 percent across providers.
The Global Methane Pledge and other methane measures may potentially undermine CO2 mitigation in certain countries, unless they are considered as additional to the existing Nationally Determined Contributions to strengthen overall greenhouse gas emission targets. Maintaining the progress on CO2 mitigation in the revision of Nationally Determined Contributions after the first Global Stocktake, while pursuing the immediate benefits from methane mitigation, is necessary to address climate change in the long-term.
Sandboxing mechanisms allow developers to limit how much access applications have to resources, following the least-privilege principle. However, it's not clear how much and in what ways developers are using these mechanisms. This study looks at the use of Seccomp, Landlock, Capsicum, Pledge, and Unveil in all packages of four open-source operating systems. We found that less than 1% of packages directly use these mechanisms, but many more indirectly use them. Examining how developers apply these mechanisms reveals interesting usage patterns, such as cases where developers simplify their sandbox implementation. It also highlights challenges that may be hindering the widespread adoption of sandboxing mechanisms.
In our previous publication [{\em Calc. Var. Partial Differential Equations}, 60(1):Paper No. 16, 27, 2021], we delved into examining a critical Sobolev-type embedding of a Sobolev weighted space into an exponential weighted Orlicz space. We specifically determined the optimal Moser-type constant for this embedding, utilizing the monomial weight introduced by Cabré and Ros-Oton [{\em J. Differential Equations}, 255(11):4312--4336, 2013]. Towards the conclusion of that paper, we pledged to explore the existence of an extremal function within this framework. In this current work, we not only provide a positive affirmation to this inquiry but extend it to a broader range of weights known as \emph{$α$-homogeneous weights}.
The ATLAS Google Project was established as part of an ongoing evaluation of the use of commercial clouds by the ATLAS Collaboration, in anticipation of the potential future adoption of such resources by WLCG grid sites to fulfil or complement their computing pledges. Seamless integration of Google cloud resources into the worldwide ATLAS distributed computing infrastructure was achieved at large scale and for an extended period of time, and hence cloud resources are shown to be an effective mechanism to provide additional, flexible computing capacity to ATLAS. For the first time a total cost of ownership analysis has been performed, to identify the dominant cost drivers and explore effective mechanisms for cost control. Network usage significantly impacts the costs of certain ATLAS workflows, underscoring the importance of implementing such mechanisms. Resource bursting has been successfully demonstrated, whilst exposing the true cost of this type of activity. A follow-up to the project is underway to investigate methods for improving the integration of cloud resources in data-intensive distributed computing environments and reducing costs related to network connectivity, which re
As coin-based rewards dwindle, transaction fees play an important role as mining incentives in Bitcoin. In this paper, we propose a novel mechanism called Efficient Dynamic Transaction Storage (EDTS) for dynamically allocating transactions among blocks to achieve efficient storage utilization. By leveraging a combination of Cuckoo Filter and Dynamic Transaction Storage (DTS) strategies, EDTS is able to improve the scalability while remaining sustainable even after the Bitcoin enters a transaction-fee regime. In addition to preventing deviant mining behaviors under the transaction-fee regime, EDTS can also provide differentiated transmission priorities based on transaction fees while allowing the investors to engage in pledging more transaction fees. In EDTS, we applied the multi-objective optimization algorithm U-NSGA-III to find the best DTS strategy and its corresponding attributes. Experimental results show that the EDTS mechanism together with the optimized DTS strategy can achieve a throughput of 325.3 TPS. The experimental results reveal that the scalability improvement of EDTS is superior to the performance of Bitcoin NG, which is the best known on-chain scaling solution, wh
Filecoin is the largest storage-based open-source blockchain, both by storage capacity (>11EiB) and market capitalization. This paper provides the first formal security analysis of Filecoin's consensus (ordering) protocol, Expected Consensus (EC). Specifically, we show that EC is secure against an arbitrary adversary that controls a fraction $β$ of the total storage for $βm< 1- e^{-(1-β)m}$, where $m$ is a parameter that corresponds to the expected number of blocks per round, currently $m=5$ in Filecoin. We then present an attack, the $n$-split attack, where an adversary splits the honest miners between multiple chains, and show that it is successful for $βm \ge 1- e^{-(1-β)m}$, thus proving that $βm= 1- e^{-(1-β)m}$ is the tight security threshold of EC. This corresponds roughly to an adversary with $20\%$ of the total storage pledged to the chain. Finally, we propose two improvements to EC security that would increase this threshold. One of these two fixes is being implemented as a Filecoin Improvement Proposal (FIP).