Pollution and the Efficiency of Urban Growth

We analyze the efficiency of urbanization patterns in a dynamic model of endogenous urban growth with two sectors of production. Production exhibits increasing returns to scale on aggregate. Urban environmental pollution, as a force that discourages agglomeration, is caused by domestic production. We show that cities are too large and too few in number in equilibrium, compared to the efficient urbanization path, if economic growth implies increasing aggregate emissions. If, on the other hand, production becomes cleaner over time (`quality growth') the urbanization path approximates the efficient outcome after finite time.Cities, Urbanisation, Pollution, Growth, Migration, Sustainable Development

Biodiversity Conservation on Private Lands: Information Problems and Regulatory Choices

This survey paper examines various information insufficiencies in biodiversity conservation and their impact of regulatory choices. We surveyed the literature in the field and identified four major types of informational insufficiencies in making efficient biodiversity conservation decisions: 1) biological uncertainty 2) natural uncertainty 3) individual information, and 4) monitoring problem. The consequences of these four types of information insufficiencies on the choice of regulatory tools are explored. We discuss in this context three types of regulatory tools: land takings, environmental fees/charges, and contracts. The efficiency of each type of regulatory tools is shown dependent on the specific informational constraints that the regulatory faces.Biodiversity conservation, Information, Regulatory tools

Oil Security Short- and Long-Term Policies

Increasing oil security represents one of the most important policy actions, especially within IEA countries. Short and long term mechanisms could help such goal. On the short term side, revision of IEA emergency response oil stock system has been discussed. The attention is mainly focused on three issues: the high costs of stock management for private industries, the possible use of strategic reserves to smooth price when no high supply disruption has taken, the extension of IEA emergency system to non-OECD countries. The main actions specifically proposed by the European Commission are: an harmonisation of national storage systems, with the institution of public and private agency, a wider co-ordinated use of security stocks, and an increase in the physical amount of oil stocks. Long term measures for enhancing oil supply security can be seen on the demand-side and the supply-side. Main demand-side policies could be the following: energy saving and efficiency, investments in research and technology, and reduction of oil price inelasticity especially for transport sector. Main supply-side policies can be summarized into co-operation and institutional promotion for supply diversification of suppliers/routes. Main factors that could affect described policies could be the liberalization of international trade even in the energy sector and the increasing role of oil demand from developing countries.Oil, Security, Energy

Three essays in applied market design

The market design approach to economics recognizes that markets do not arise naturally but are rather an amalgamation of various rules and norms. From this perspective, an economist can reverse engineer the rules that are consequential to a functioning market and evaluate the effects of those rules on market outcomes, with an eye toward potentially re-engineering certain components to achieve some objective. In this thesis, I present three market settings inspired by real-world applications, viewed broadly from the lens of market design. The first chapter, joint work with Chia-Ling Hsu, explores the consequences of various market details on equilibrium outcomes. Specifically, we consider a situation in which a matching problem between two sets of agents is solved by a platform serving as an intermediary. For instance, an artist who wants to find donors and a backer who wants to find artists to support can find each other on a crowdfunding platform like Kickstarter. Existing models of platform markets restrict agent heterogeneity and so the matching problem is secondary. However, it is possible that different artists target different types of backers and even likely that backers differ in their preferences for artists. In this chapter, we introduce agent heterogeneity by proposing a matching model of platform markets. In such markets, stability eliminates the possibility of an individual or group of agents switching in equilibrium, thus ensuring successful coordination. The model allows exploration into the properties of equilibrium with heterogeneous agents, offering a new approach to studying platform markets. In the second chapter, I empirically quantify the value of public school choice. Traditionally, public school assignment is determined by a family’s residence in the district. An alternative policy is to allow families to apply to any school in the district. Such school choice programs provide families with more options, but it is unclear how much families value these options over ii having a guaranteed school. In this chapter, I exploit a natural experiment in Champaign-Urbana, IL: in 1998, Champaign school district adopted school choice while the neighboring district of Urbana did not. Using variation in housing prices in each district, before and after the policy change, I estimate the marginal willingness to pay for school choice relative to residence-based assignment. I find that, on average, households are willing to pay between 5- 7% more for school choice relative to residence-based assignment. The results are robust to regularization and alternative model specifications. The third chapter, joint work with Blake Riley, is motivated by decentralized matching: the process by which agents find matches on their own. We show that, without revealing information to a centralized matchmaker and without coordination, agents can find stable matches on their own. Existing work on uncoordinated matching, based on the random better reply dynamics of Roth and Vande Vate (1990), shows that agents do find stable matches but that in the worst case it could take exponentially long. We introduce a new process that, in various numerical experiments, appears to converge in polynomial time. The key to our proposal process is mitigating a major bottleneck in uncoordinated matching: the possibility that an agent is single for a very long time before finding a match. In the worst case, our process converges in O(n^3 ) time in moderate sized balanced markets with n agents on each side. We also consider unblanaced markets, in which there are more agents on one side of the market. While convergence to stability is not guaranteed in polynomial time, we show numerically that typical outcomes of our proposal process are more egalitarian than stable outcomes. This chapter thus sheds some light on the value of centralizing a matching market, as opposed to allowing the market to clear on its own. The common thread in all three chapters is that, while markets should not be taken as given, it is important to evaluate the relative importance of particular design elements. The first chapter characterizes equilibrium outcomes under various designs; the second considers the relative value of two particular designs; and the third questions the value of designing at all. In the spirit of market design, each application is driven by actual markets and a variety of methodologies

Linkage of Tradable Permit Systems in International Climate Policy Architecture

Cap-and-trade systems have emerged as the preferred national and regional instrument for reducing emissions of greenhouse gases throughout the industrialized world, and the Clean Development Mechanism — an international emission-reduction-credit system — has developed a substantial constituency, despite some concerns about its performance. Because linkage between tradable permit systems can reduce compliance costs and improve market liquidity, there is great interest in linking cap-and-trade systems to each other, as well as to the CDM and other credit systems. We examine the benefits and concerns associated with various types of linkages, and analyze the near-term and long-term role that linkage may play in a future international climate policy architecture. In particular, we evaluate linkage in three potential roles: as an independent bottom-up architecture, as a step in the evolution of a top-down architecture, and as an ongoing element of a larger climate policy agreement. We also assess how the policy elements of climate negotiations can facilitate or impede linkages. Our analysis throughout is both positive and normative.Linkage, Cap-and-Trade, Tradable Permits, Global Climate Change

Multi-agent reinforcement learning for the coordination of residential energy flexibility

This thesis investigates whether residential energy flexibility can be coordinated without sharing personal data at scale to achieve a positive impact on energy users and the grid. To tackle climate change, energy uses are being electrified at pace, just as electricity is increasingly provided by non-dispatchable renewable energy sources. These shifts increase the requirements for demand-side flexibility. Despite the potential of residential energy to provide such flexibility, it has remained largely untapped due to cost, social acceptance, and technical barriers. This thesis investigates the use of multi-agent reinforcement learning to overcome these challenges. This thesis presents a novel testing environment, which models electric vehicles, space heating, and flexible household loads in a distribution network. Additionally, a generative adversarial network-based data generator is developed to obtain realistic training and testing data. Experiments conducted in this environment showed that standard independent learners fail to coordinate in the partially observable stochastic environment. To address this, additional coordination mechanisms are proposed for agents to practise coordination in a centralised simulated rehearsal, ahead of fully decentralised implementation. Two such coordination mechanisms are proposed: optimisation-informed independent learning, and a centralised but factored critic network. In the former, agents lean from omniscient convex optimisation results ahead of fully decentralised coordination. This enables cooperation at scale where standard independent learners under partial observability could not be coordinated. In the latter, agents employ a deep neural factorisation network to learn to assess their impact on global rewards. This approach delivers comparable performance for four agents and more, with a 34-fold speed improvement for 30 agents and only first-order computational time growth. Finally, the impacts of implementing implicit coordination using these multi- agent reinforcement learning methodologies are modelled. It is observed that even without explicit grid constraint management, cooperating energy users reduce the likelihood of voltage deviations. The cooperative management of voltage constraints could be further promoted by the MARL policies, whereby their likelihood could be reduced by 43.08% relative to an uncoordinated baseline, albeit with trade-offs in other costs. However, while this thesis demonstrates the technical feasibility of MARL-based cooperation, further market mechanisms are required to reward all participants for their cooperation

Packing, Scheduling and Covering Problems in a Game-Theoretic Perspective

Many packing, scheduling and covering problems that were previously considered by computer science literature in the context of various transportation and production problems, appear also suitable for describing and modeling various fundamental aspects in networks optimization such as routing, resource allocation, congestion control, etc. Various combinatorial problems were already studied from the game theoretic standpoint, and we attempt to complement to this body of research. Specifically, we consider the bin packing problem both in the classic and parametric versions, the job scheduling problem and the machine covering problem in various machine models. We suggest new interpretations of such problems in the context of modern networks and study these problems from a game theoretic perspective by modeling them as games, and then concerning various game theoretic concepts in these games by combining tools from game theory and the traditional combinatorial optimization. In the framework of this research we introduce and study models that were not considered before, and also improve upon previously known results.Comment: PhD thesi