Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2603.04345

On the fair abatement of riparian pollution

Ricardo Martinez·Juan D. Moreno-Ternero
View original source ↗Read source document ↗Export metadata ↓

Abstract

We study the design of fair allocation rules for the abatement of riparian pollution. To do so, we consider the so-called river pollution claims model, recently introduced by Yang et al. (2025) to distribute a budget of emissions permits among agents (cities, provinces, or countries) located along a river. In such a model, each agent has a claim reflecting population, emission history, and business-as-usual emissions, and the issue is to allocate among them a budget that is lower (or equal) than the aggregate claim. For environmental reasons, the specific location along the river where pollutants are emitted is an important concern (the more upstream the location is the higher the damage of polluting the river). We characterize a class of geometric rules that adjust proportional allocations to compromise between fairness and environmental concerns. Our class is an alternative to the one proposed by Yang et al. (2025). We compare both alternatives through an axiomatic study, as well as an illustration for the case study of the Tuojiang Basin in China.

Explore related subjects

econ.TH

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ricardo Martinez, Juan D. Moreno-Ternero. 2026-03-04. On the fair abatement of riparian pollution. https://arxiv.org/abs/2603.04345

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Information Greenhouse: Optimal Persuasion for Medical Test-Avoiders

Patients often avoid medical tests because the information they provide, although medically useful, is psychologically painful. This paper studies optimal communication between a doctor and an information-avoidant patient who can refuse testing and treatment. I characterize when optimal communication creates an information greenhouse, a commitment to reward participation with comforting information about the untreated prognosis. When testing is voluntary and the patient is unwilling to be tested under extreme pessimism, an information greenhouse is optimal and takes the form of committed comfort, which provides reassuring information after the test. When the patient can reject the consultation at the outset and the patient's prior belief about the untreated prognosis is intermediate, an information greenhouse is optimal and takes the form of precautionary comfort, which provides reassuring information before the test. In all other cases in which the patient can be persuaded, warning-based policies that trigger pessimism prevail.

econ.TH↗
arXiv2026

Accelerator and Brake: Dynamic Persuasion with Dead Ends

This paper studies dynamic persuasion in a strategic-experimentation relationship in which the principal has a single-peaked preference over the agent's stopping time. Excessive experimentation may end in a dead end. The principal privately observes project quality, which determines the agent's payoff conditional on success, while both parties learn about feasibility only through the agent's experimentation. We show that an optimal policy uses at most two one-shot disclosures: an accelerator before the principal's ideal stopping time and a brake afterward. A local Arrow--Pratt comparison of induced payoffs over stopping time determines whether the accelerator is concentrated or gradual. Under common discounting, the comparison yields a one-shot accelerator. Under heterogeneous discounting, the one-shot result remains robust unless the agent is sufficiently more impatient than the principal, in which case the ranking reverses over an interval and the accelerator can take a one-shot--gradual--one-shot form.

econ.TH↗
arXiv2026

Modeling Human Behavior with Type Vectors Using AI

We introduce a general, easy-to-implement AI-based modeling technique for analyzing human behavior. A key feature of this approach, which contrasts with existing modeling techniques, is that it combines the flexibility and interpretability of natural language with a mathematical structure that can be fitted to data and easily analyzed. We assign a large language model a vector of trait intensities-a type vector-and then ask it to choose actions across settings in which we observe human choices. For instance, the type vector (2,4) could correspond to "You are a player characterized by the following profile: Altruism: 2 out of 5, Risk Aversion: 4 out of 5," after which it is asked to make choices. We can then vary the traits (e.g., Altruism, Fairness, Trust,...) and values (e.g., 1-5) to minimize distance to human choices. We illustrate the method by applying it to model 119,147 decisions made by 78,657 subjects from more than 35 countries across 10 classic economic game roles. We find that human behavior can be closely matched using three dimensions: Risk Aversion, Strategic Sophistication, and Trust. The type vectors needed to fit individuals across games cluster into fewer than a dozen groups, with substantial variation in fit across subjects. Moreover, the individual type vectors can predict behavior in held-out games with different rules and available actions. More broadly, this new modeling method is highly generalizable and interpretable: we can input any vector of traits and use them to model behavior across any setting

econ.TH↗