Annual Review of Resource Economics - Volume 13, 2021

This survey distills recent work on the price elasticity of demand for urban and agricultural water and outlines how it can inform the design of market-based approaches to manage increasingly scarce water resources. We offer a brief description of the water sector, including the primary users, main water sources, and market failures in the allocation and use of surface water and groundwater. A review of recent empirical research on the price elasticities of agricultural and urban water demand shows the progress made in our understanding of user response to prices and reveals substantial heterogeneity in the price response. We apply what we have learned about elasticities to surface water markets and price-based groundwater management. Heterogeneity in price elasticities suggests that water transfers may lead to large efficiency gains, but that their magnitude is site specific. Groundwater pricing may cost-effectively manage groundwater and fund the development of alternative water supplies, but heterogeneity in elasticity estimates highlights that the conservation and revenue generated are basin specific.

The transformation of the electricity sector is a central element of the transition to a decarbonized economy. Conventional generators powered by fossil fuels have to be replaced by variable renewable energy (VRE) sources in combination with electricity storage and other options for providing temporal flexibility. We discuss the market dynamics of increasing VRE penetration and its integration in the electricity system. We describe the merit-order effect (the decline of wholesale electricity prices as VRE penetration increases) and the cannibalization effect (the decline of VRE value as its penetration increases). We further review the role of electricity storage and other flexibility options for integrating variable renewables and how storage can contribute to mitigating the two mentioned effects. We also use a stylized open-source model to provide some graphical intuition on these issues. While relatively high shares of VRE are achievable with moderate amounts of electricity storage, the role of long-duration storage increases as the VRE share approaches 100%.

This article reviews recent endeavors to incorporate big data and machine learning techniques into energy and environmental economics research. We find that novel datasets, from high frequency smart meter data to satellite images and social media data, are already used by researchers. At the same time most of the analyses rely on traditional econometric techniques. Nevertheless, we find applications of machine learning models that address the high dimensionality of the data and seek out new and better strategies for estimating heterogenous treatment effects. We provide an introduction to the main themes in machine learning, which are likely to be of use to economists in energy and environmental economics, and illustrate them using a real data example derived from an energy efficiency program evaluation. We provide the data and code in order to stimulate further research in this area.