David Hsu

Massachusetts Institute of Technology (MIT), Department of Urban Studies and Planning (DUSP), Faculty Member

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I study how environmental policy is implemented in cities through systems of infrastructure, buildings, behavior, institutions, and finance. Topics of particular interest include energy and water, green buildings and building codes, consumer behavior, and data analysis.

Prior to academia, I worked in city government in New York and Seattle on urban redevelopment, energy conservation, and smart grid initiatives; as a financial analyst for real estate equities; and as a structural engineer in London on green buildings and bridges.

My current research develops predictive models for water and energy demand, and analyzes how to link conservation outcomes to the implementation of specific policies in complex environments. Current working papers focus on classical and Bayesian statistical models for water demand; causal analysis of a specific water pricing policy change in Seattle; new federal government regulations for stormwater management; data analysis of building disclosure policies; and infrastructure finance in developing countries.
Address: 77 Massachusetts Ave, Cambridge, MA 02139

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Papers by David Hsu

Straight out of Cape Cod: The origin of community choice aggregation and its spread to other states

Energy Research & Social Science, 2022

This policy history describes how community choice aggregation was created in Massachusetts by a ... more This policy history describes how community choice aggregation was created in Massachusetts by a small group of advocates and subsequently spread across the US. Twenty-one interviews with key participants, primary materials from government and personal archives, and newspaper articles were used to attribute and corroborate these events. A new finding is that community choice aggregation was created as part of electric sector restructuring efforts in Massachusetts in 1997, but that this new policy was barely perceived by many stakeholders in the larger restructuring process, and was included by legislators in response to advocates who organized local governments through direct democracy strategies. Kingdon's multiple streams approach provides a useful framework to understand how organizing by advocates led to successful passage of legislation in Massachusetts. The spread of community choice aggregation to other states occurred through organizing that combined advocacy with policy learning and emulation. CCA has since been adopted by more than 1800 local governments that represent more than 36 million people in six states. This article concludes by discussing the early outcomes, current status, and some prospective implications of community choice aggregation.

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Stated preferences for smart green infrastructure in stormwater management

Landscape and Urban Planning, 2019

Many smart technologies have been proposed to improve existing or new infrastructures by adding s... more Many smart technologies have been proposed to improve existing or new infrastructures by adding sensing, controls, communications and computing, but it is not well understood which actual functions and capabilities are desired by potential users. Since water systems in the United States are largely run by public sector agencies, this study conducted a national survey with officials in water utilities and agencies to understand what kinds and capabilities of smart green infrastructure are desired for stormwater management. Analyzing the stated pre- ferences of these users using a discrete choice model, our results indicate that these officials are willing to invest more upfront in smart technologies if they lower the costs associated over time with construction, maintenance, and labor. For example, in a typical rain garden, water agencies are willing to pay 12.1% more for construction to reduce maintenance costs by 20% and would pay 12.9% more to add self-irrigating capabilities. Preferences for smart green infrastructure among agencies are also affected by their characteristics. Agencies with large service areas or prior experience with green infrastructure are more likely to adopt smart green infrastructure. These results should assist in the further design and development of smart technologies and green infrastructure in stormwater management.

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Green and Smart: Perspectives of City and Water Agency Officials in Pennsylvania toward Adopting New Infrastructure Technologies for Stormwater Management

by David Hsu and Ting Meng

Stormwater runoff associated with urbanization is considered to be a major factor in slowing prog... more Stormwater runoff associated with urbanization is considered to be a major factor in slowing progress in attaining clean water quality over the past forty years. Pennsylvania is a state with both ageing, existing infrastructures for stormwater management and many water quality problems. These existing infrastructures could be upgraded by implementing green infrastructure and/or by deploying “smart” infrastructure, which adds technology including sensors, controls, and communication networks to existing or new infrastructure. This paper examines how water agencies perceive or understand green infrastructure and/or smart services as solutions to their stormwater management problems, particularly after construction and for operation and maintenance. Semi-structured interviews were conducted with city and water utility officials from five cities and towns throughout Pennsylvania. Responses indicate that the main concerns with green infrastructure are its uncertain performance and labor-intensive maintenance; they hold mostly positive views about smart infrastructure but want more information on actual performance and costs to reduce runoff. The conclusion is that city and utility officials are willing to consider new tools but want systems for stormwater management that are cost-effective, user-friendly and robust.

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Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data

of recent work identifying subgroups of energy consumption in buildings. Clusterwise (or latent c... more of recent work identifying subgroups of energy consumption in buildings. Clusterwise (or latent class) regression gives superior prediction accuracy. K-means gives more stable clusters when the correct number of clusters is chosen. A tradeoff between prediction accuracy and cluster stability seems to exist. a b s t r a c t Clustering methods are often used to model energy consumption for two reasons. First, clustering is often used to process data and to improve the predictive accuracy of subsequent energy models. Second, stable clusters that are reproducible with respect to non-essential changes can be used to group, target, and interpret observed subjects. However, it is well known that clustering methods are highly sensitive to the choice of algorithms and variables. This can lead to misleading assessments of predictive accuracy and misinterpretation of clusters in policymaking. This paper therefore introduces two methods to the modeling of energy consumption in buildings: clusterwise regression, also known as latent class regression, which integrates clustering and regression simultaneously; and cluster validation methods to measure stability. Using a large dataset of multifamily buildings in New York City, clusterwise regression is compared to common two-stage algorithms that use K-means and model-based clustering with linear regression. Predictive accuracy is evaluated using 20-fold cross validation, and the stability of the perturbed clusters is measured using the Jaccard coefficient. These results show that there seems to be an inherent tradeoff between prediction accuracy and cluster stability. This paper concludes by discussing which clustering methods may be appropriate for different analytical purposes.

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Identifying key variables and interactions in statistical models of building energy consumption using regularization

Energy, Mar 5, 2015

Statistical models can only be as good as the data put into them. Data about energy consumption c... more Statistical models can only be as good as the data put into them. Data about energy consumption continues to grow, particularly its non-technical aspects, but these variables are often interpreted differently among disciplines, datasets, and contexts. Selecting key variables and interactions is therefore an important step in achieving more accurate predictions, better interpretation, and identification of key subgroups for further analysis.

This paper therefore makes two main contributions to the modeling and analysis of energy consumption of buildings. First, it introduces regularization, also known as penalized regression, for principled selection of variables and interactions. Second, this approach is demonstrated by application to a comprehensive dataset of energy consumption for commercial office and multifamily buildings in New York City. Using cross-validation, this paper finds that a newly-developed method, hierarchical group-lasso regularization, significantly outperforms ridge, lasso, elastic net and ordinary least squares approaches in terms of prediction accuracy; develops a parsimonious model for large New York City buildings; and identifies several interactions between technical and non-technical parameters for further analysis, policy development and targeting. This method is generalizable to other local contexts, and is likely to be useful for the modeling of other sectors of energy consumption as well.

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Information and communication technologies and climate change adaptation in Latin America and the Caribbean: a framework for action

by David Hsu and Tracy-Ann Hyman

Climate & Development, Sep 3, 2014

Despite ongoing interest in deploying information and communication technologies (ICTs) for susta... more Despite ongoing interest in deploying information and communication technologies (ICTs) for sustainable development, their use in climate change adaptation remains understudied. Based on the integration of adaptation theory and the existing literature on the use of ICTs in development, we present an analytical model for conceptualizing the contribution of existing ICTs to adaptation, and a framework for evaluating ICT success. We apply the framework to four case studies of ICTs in use for early warning systems and managing extreme events in the Latin American and the Caribbean countries. We propose that existing ICTs can support adaptation through enabling access to critical information for decision-making, coordinating actors and building social capital. ICTs also allow actors to communicate and disseminate their decision experience, thus enhancing opportunities for collective learning and continual improvements in adaptation processes. In this way, ICTs can both communicate the current and potential impacts of climate change, as well as engage populations in the development of viable adaptation strategies.

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How much information disclosure of building energy performance is necessary?

Energy Policy, Jan 2014

Many different governments have begun to require disclosure of building energy performance, in or... more Many different governments have begun to require disclosure of building energy performance, in order to allow owners and prospective buyers to incorporate this information into their investment decisions. These policies, known as disclosure or information policies, require owners to benchmark their buildings and sometimes conduct engineering audits. However, given substantial variation in the cost to disclose different types of information, it is natural to ask: how much and what kind of information about building energy performance should be disclosed, and for what purposes? To answer this question, this paper assembles and cleans a comprehensive panel dataset of New York City multifamily buildings, and analyzes its predictive power using a Bayesian multilevel regression model. Analysis of variance (ANOVA) reveals that building-level variation is the most important factor in explaining building energy use, and that there are few, if any, relationships of building systems to observed energy use. This indicates that disclosure laws requiring benchmarking data may be relatively more useful than engineering audits in explaining the observed energy performance of existing buildings. These results should inform the further development of information disclosure laws.

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Improving energy benchmarking with self-reported data

Building Research & Information, 2014

Energy benchmarking for buildings has become increasingly important in government policy and indu... more Energy benchmarking for buildings has become increasingly important in government policy and industry practice for energy efficiency. The questions of how energy benchmarking is currently conducted, and how it might be improved using rapidly growing quantities of self-reported data, are examined. A case study of commercial office buildings in New York City demonstrates how the rapid growth in self-reported data presents both new opportunities and challenges for energy benchmarking for buildings. A critique is presented for the scoring methodology and data sources for Energy Star, one of the largest and most successful benchmarking certification schemes. Findings from recent studies are examined to illustrate how this certification currently works in the marketplace. Self-reported building energy data are rapidly growing in Portfolio Manager (the user interface to Energy Star) due to mandatory energy benchmarking laws, and can be used to improve Energy Star's current scoring methods. These self-reported data are tested and improved for analysis by applying theories and methods of data quality developed in computer science, statistics and data management. These new data constitute a critical building block for the development of energy efficiency policies, and will affect how government, consultants, and owners measure and compare building energy use.

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