Controlling Pollution with Fixed Inspection Capacity

In this paper I model the optimal monitoring and enforcement strategy when inspection capacity is fixed by budget or manpower constraints. I adopt a leverage enforcement structure that classifies firms into two groups with different enforcement intensities. Optimal monitoring and enforcement requires effective allocation of the fixed number of inspections to the two groups. In each period, a fixed number of firms are selected from each group for inspection, and those with the highest emissions are placed in the targeted group in which the inspection probability is higher. This transition structure induces rankorder tournaments among inspected firms. Once selected for inspection, the emissions of each firm are subject to a standard above which the firm pays a fixed penalty. I find that a regulator facing inspection capacity constraints should leverage the limited inspections by allocating more inspections to the targeted group. In addition, I show that targeting enforcement is generally superior to static enforcement. This is in accordance with findings in the literature. These results are consistent over different ranges of regulatory parameters.

Essays on Environmental Policies Under Incomplete Enforcement

Essay 1In this paper I model the optimal monitoring and enforcement strategy when inspection capacity is fixed by budget or manpower constraints. I adopt a leverage enforcement structure that classifies firms into two groups with different enforcement intensities. Optimal monitoring and enforcement requires effective allocation of the fixed number of inspections to the two groups. In each period, a fixed number of firms are selected from each group for inspection, and those with the highest emissions are placed in the targeted group in which the inspection probability is higher. This transition structure induces rankorder tournaments among inspected firms. Once selected for inspection, the emissions of each firm are subject to a standard above which the firm pays a fixed penalty. I find that a regulator facing inspection capacity constraints should leverage the limited inspections by allocating more inspections to the targeted group. In addition, I show that targeting enforcement is generally superior to static enforcement. This is in accordance with findings in the literature. These results are consistent over different ranges of regulatory parameters. Essay 2We model the optimal design of programs requiring firms to disclose harmful emissions when disclosure yields both direct and indirect benefits. The indirect benefit arises from the internalization of social costs and resulting reduction in emissions. The direct benefit results from the disclosure of previously private information which is valuable to potentially harmed parties. Previous theoretical and empirical analyses of such programs restrict attention to the former benefit while the stated motivation for such programs highlights the latter benefit. When disclosure yields both direct and indirect benefits, policymakers face a tradeoff between inducing truthful self-reporting and deterring emissions. Internalizing the social costs of emissions, such as through a Pigovian tax, will deter emissions, but may also reduce incentives for firms to truthfully report their emissions. Essay 3This paper investigates the compliance behavior of firms simultaneously regulated under multiple environmental programs. Three possible relationships among regulatory programs are considered: complementarity, substitution and independence. I develop a theoretical model of firm decision making that shows the potential for interrelationships among regulations. I propose an indirect test of the theoretical results and implement the empirical model using data on compliance with Resource Conservation and Recovery Act (RCRA) for facilities in Michigan that are regulated under both RCRA and Clean Air Act (CAA). Results show evidence of positive cross program effects such that an increase in measures of CAA enforcement intensity lead to increased firm compliance with RCRA; the empirical results are consistent with a complementary relationship between the two programs. Thus coordination is required for optimal monitoring and enforcement strategies

Pressure dependence of the Grüneisen parameter and thermal expansion coefficient of solids

Following the assumption of Jeanloz and Anderson, a computing model for the pressure dependence of the Grüneisen parameter and the thermal expansion coefficient has been proposed. Applying them to alkali metals and NaCl in different pressure ranges, the calculated results are found in good agreement with the experimental data. Finally, the flaw appears in other literatures have been corrected in this study

Smoothed Differential Privacy

Differential privacy (DP) is a widely-accepted and widely-applied notion of privacy based on worst-case analysis. Often, DP classifies most mechanisms without additive noise as non-private (Dwork et al., 2014). Thus, additive noises are added to improve privacy (to achieve DP). However, in many real-world applications, adding additive noise is undesirable (Bagdasaryan et al., 2019) and sometimes prohibited (Liu et al., 2020). In this paper, we propose a natural extension of DP following the worst average-case idea behind the celebrated smoothed analysis (Spielman & Teng, May 2004). Our notion, smoothed DP, can effectively measure the privacy leakage of mechanisms without additive noises under realistic settings. We prove that any discrete mechanism with sampling procedures is more private than what DP predicts, while many continuous mechanisms with sampling procedures are still non-private under smoothed DP. In addition, we prove several desirable properties of smoothed DP, including composition, robustness to post-processing, and distribution reduction. Based on those properties, we propose an efficient algorithm to calculate the privacy parameters for smoothed DP. Experimentally, we verify that, according to smoothed DP, the discrete sampling mechanisms are private in real-world elections, and some discrete neural networks can be private without adding any additive noise. We believe that these results contribute to the theoretical foundation of realistic privacy measures beyond worst-case analysis.Comment: 16 Page main text + Appendi

Regulation with Direct Benefits of Information Disclosure and Imperfect Monitoring

We model the optimal design of programs requiring heterogeneous firms to disclose harmful emissions when disclosure yields both direct and indirect benefits. The indirect benefit arises from the internalization of social costs and resulting reduction in emissions. The direct benefit results from the disclosure of previously private information which is valuable to potentially harmed parties. Previous theoretical and empirical analyses of such programs restrict attention to the former benefit while the stated motivation for such programs highlights the latter benefit. When disclosure yields both direct and indirect benefits, policymakers face a tradeoff between inducing truthful self-reporting and deterring emissions. Internalizing the social costs of emissions, such as through an emissions tax, will deter emissions, but may also reduce incentives for firms to truthfully report their emissions

How Private Are Commonly-Used Voting Rules?

Differential privacy has been widely applied to provide privacy guarantees by adding random noise to the function output. However, it inevitably fails in many high-stakes voting scenarios, where voting rules are required to be deterministic. In this work, we present the first framework for answering the question: "How private are commonly-used voting rules?" Our answers are two-fold. First, we show that deterministic voting rules provide sufficient privacy in the sense of distributional differential privacy (DDP). We show that assuming the adversarial observer has uncertainty about individual votes, even publishing the histogram of votes achieves good DDP. Second, we introduce the notion of exact privacy to compare the privacy preserved in various commonly-studied voting rules, and obtain dichotomy theorems of exact DDP within a large subset of voting rules called generalized scoring rules