RESEARCH
RESEARCH INTERESTS
- E-governance
- Smart city
- Social equity
- Coproduction
- Big data analytics
- Public service
- Spatial econometrics
- Text analysis and social media
- Energy demand side management
- Sustainability policy
- Technology policy
- Administrative behavior
- Emergency management
- Interdisciplinary methodology
WORK IN PROGRESS
- Xu, Corey Kewei, and Tian Tang, Toward Individual Equity in the Era of Big Data: Estimating Individual Level of Needs Using Machine Learning.
- Xu, Corey Kewei, Tian Tang, Michael Olson, and Frances Berry, The Interconnections of Decisionsmaking Tools in the Era of Smart Governance.
- Xu, Corey Kewei, Does Goal Setting Improve Responsivenss of Technology-Enabled Coproduction: Evidence From 311 Systems in 13 U.S. Cities.
PUBLICATIONS
- Gupta, Jayant, Alexander Long, Corey Kewei Xu, Tian Tang, and Shashi Shekhar. 2021.“Spatial Dimensions of Algorithmic Transparency: A Summary.” In 17th International Symposium on Spatial and Temporal Databases, pp. 116-125.
- Spatial data brings an important dimension to AI’s quest for algorithmic transparency. For example, data driven computer-aided policy-decisions use measures of segregation (e.g., dissimilarity index) or income-inequality (e.g., Gini index), and these measures are affected by space partitioning choice. This may lead policymakers to underestimate the level of inequality or segregation within a region. The problem stems from the fact that many segregation based analyses use aggregated census data but do not report result sensitivity to choice of spatial partitioning (e.g., census block, tract). Beyond the well-known Modifiable Areal Unit Problem, this paper shows (via mathematical proofs as well as case studies with census data and census based synthetic micro-population data) that values of many measures (e.g., Gini index, dissimilarity index) diminish monotonically with increasing spatial-unit size in a hierarchical space partitioning (e.g., block, block-group, tract), however the ranking based on spatially aggregated measures remain sensitive to the scale of spatial partitions (e.g., block, block group). This paper highlights the need for social scientists to report how rankings of inequality are affected by the choice of spatial partitions.
- Spatial data brings an important dimension to AI’s quest for algorithmic transparency. For example, data driven computer-aided policy-decisions use measures of segregation (e.g., dissimilarity index) or income-inequality (e.g., Gini index), and these measures are affected by space partitioning choice. This may lead policymakers to underestimate the level of inequality or segregation within a region. The problem stems from the fact that many segregation based analyses use aggregated census data but do not report result sensitivity to choice of spatial partitioning (e.g., census block, tract). Beyond the well-known Modifiable Areal Unit Problem, this paper shows (via mathematical proofs as well as case studies with census data and census based synthetic micro-population data) that values of many measures (e.g., Gini index, dissimilarity index) diminish monotonically with increasing spatial-unit size in a hierarchical space partitioning (e.g., block, block-group, tract), however the ranking based on spatially aggregated measures remain sensitive to the scale of spatial partitions (e.g., block, block group). This paper highlights the need for social scientists to report how rankings of inequality are affected by the choice of spatial partitions.
- Clark, Lara, Samuel Tabory, Kangkang Tong, Joe Servadio, Kelsey Kappler, Corey Kewei Xu, Abi Lawal, Peter Wiringa, Len Kne, Rick Feiock, Julian Marshall, Ted Russell, Anu Ramaswami*. 2021. A Data Framework for Assessing Social Inequality and Inequity in Multi-Sector Social-Ecological Infrastructural Urban Systems (SEIUS): Focus on Fine-Spatial Scales, forthcoming in Journal of Industrial Ecology.
- Cities are increasingly advancing multiple societal goals related to environmental sustainability, health, wellbeing, and equity. However, there are few comprehensive data sets that address social inequality and equity across multiple infrastructure sectors, determinants, and outcomes, particularly at fine intra-urban spatial scales. This paper: (1) Offers an overarching conceptualization of inequality and equity in multi-sector urban systems; (2) Introduces a broad data framework to assess inequality and equity across social (S), ecological (E), infrastructural (I), and urban (U) form determinants (SEIU), and environment (E), health (H), wellbeing (W), and economy and security (E) outcomes (EHWE), identifying a universe of >110 SEIU-EHWE data layers (variables) of interest; (3) Provides an illustrative data case study of a US city that synthesizes publicly available sources of the associated SEIU-EHWE data attributes, noting their availability/gaps at fine spatial scales, important to inform social inequality; (4) Discusses analytic methods to quantify inequality and spatial correlates across of SEIU determinants and EHWE outcomes; and, (5) Demonstrates several use-cases of the data framework and companion analytic methods through real-world applied case studies that inform equity planning in applications ranging from energy sector investments to air pollution and health. The US data case study reveals data availability (covering 41 of 113 data layers) as well as major gaps associated with EHWE outcomes at fine spatial scales, while the application examples demonstrate practical use. Overall, the SEIU-EHWE data framework provides an anchor for systematically gathering, analyzing, and informing multiple dimensions of inequality and equity in sustainable urban systems.
- Cities are increasingly advancing multiple societal goals related to environmental sustainability, health, wellbeing, and equity. However, there are few comprehensive data sets that address social inequality and equity across multiple infrastructure sectors, determinants, and outcomes, particularly at fine intra-urban spatial scales. This paper: (1) Offers an overarching conceptualization of inequality and equity in multi-sector urban systems; (2) Introduces a broad data framework to assess inequality and equity across social (S), ecological (E), infrastructural (I), and urban (U) form determinants (SEIU), and environment (E), health (H), wellbeing (W), and economy and security (E) outcomes (EHWE), identifying a universe of >110 SEIU-EHWE data layers (variables) of interest; (3) Provides an illustrative data case study of a US city that synthesizes publicly available sources of the associated SEIU-EHWE data attributes, noting their availability/gaps at fine spatial scales, important to inform social inequality; (4) Discusses analytic methods to quantify inequality and spatial correlates across of SEIU determinants and EHWE outcomes; and, (5) Demonstrates several use-cases of the data framework and companion analytic methods through real-world applied case studies that inform equity planning in applications ranging from energy sector investments to air pollution and health. The US data case study reveals data availability (covering 41 of 113 data layers) as well as major gaps associated with EHWE outcomes at fine spatial scales, while the application examples demonstrate practical use. Overall, the SEIU-EHWE data framework provides an anchor for systematically gathering, analyzing, and informing multiple dimensions of inequality and equity in sustainable urban systems.
- Tong, Kangkang, Anu Ramaswami, Corey Kewei Xu, Richard Feiock, Patrick Schmitz, and Michael Ohlsen. “Measuring social equity in urban energy use and interventions using fine-scale data.” Proceedings of the National Academy of Sciences (PNAS) 118, no. 24 (2021).
- Cities seek nuanced understanding of intraurban inequality in energy use, addressing both income and race, to inform equitable investment in climate actions. However, nationwide energy consumption surveys are limited (<6,000 samples in the United States), and utility-provided data are highly aggregated. Limited prior analyses suggest disparity in energy use intensity (EUI) by income is ∼25%, while racial disparities are not quantified nor unpacked from income. This paper, using new empirical fine spatial scale data covering all 200,000 households in two US cities, along with separating temperature-sensitive EUI, reveals intraurban EUI disparities up to a factor of five greater than previously known. We find 1) annual EUI disparity ratios of 1.27 and 1.66, comparing lowest- versus highest-income block groups (i.e., 27 and 66% higher), while previous literature indicated only ∼25% difference; 2) a racial effect distinct from income, wherein non-White block groups (highest quintile non-White percentage) in the lowest-income stratum reported up to a further ∼40% higher annual EUI than less diverse block groups, providing an empirical estimate of racial disparities; 3) separating temperature-sensitive EUI unmasked larger disparities, with heating–cooling electricity EUI of lowest-income block groups up to 2.67 times (167% greater) that of highest income, and high racial disparity within lowest-income strata wherein high non-White (>75%) population block groups report EUI up to 2.56 times (156% larger) that of majority White block groups; and 4) spatial scales of data aggregation impact inequality measures. Quadrant analyses are developed to guide spatial prioritization of energy investment for carbon mitigation and equity. These methods are potentially translatable to other cities and utilities.
- Cities seek nuanced understanding of intraurban inequality in energy use, addressing both income and race, to inform equitable investment in climate actions. However, nationwide energy consumption surveys are limited (<6,000 samples in the United States), and utility-provided data are highly aggregated. Limited prior analyses suggest disparity in energy use intensity (EUI) by income is ∼25%, while racial disparities are not quantified nor unpacked from income. This paper, using new empirical fine spatial scale data covering all 200,000 households in two US cities, along with separating temperature-sensitive EUI, reveals intraurban EUI disparities up to a factor of five greater than previously known. We find 1) annual EUI disparity ratios of 1.27 and 1.66, comparing lowest- versus highest-income block groups (i.e., 27 and 66% higher), while previous literature indicated only ∼25% difference; 2) a racial effect distinct from income, wherein non-White block groups (highest quintile non-White percentage) in the lowest-income stratum reported up to a further ∼40% higher annual EUI than less diverse block groups, providing an empirical estimate of racial disparities; 3) separating temperature-sensitive EUI unmasked larger disparities, with heating–cooling electricity EUI of lowest-income block groups up to 2.67 times (167% greater) that of highest income, and high racial disparity within lowest-income strata wherein high non-White (>75%) population block groups report EUI up to 2.56 times (156% larger) that of majority White block groups; and 4) spatial scales of data aggregation impact inequality measures. Quadrant analyses are developed to guide spatial prioritization of energy investment for carbon mitigation and equity. These methods are potentially translatable to other cities and utilities.
- Xu, Corey Kewei, and Tian Tang 2020. Closing the Gap or Widening the Divide: The Impacts of Technology-Enabled Coproduction on Equity in Public Service Delivery. Public Administration Review.
- This article investigates how 311 systems affect distributional equity in public service delivery. Many local governments in the United States have adopted interactive 311 platforms to engage citizens in coproduction. Using a novel household-level data set on 311 service requests and power service restoration in the City of Tallahassee, Florida, after Hurricane Michael in 2018, the authors examine possible disparities between racial minority groups and nonminorities in making power service restoration requests via 311. The article further analyzes how coproduction participation through 311 affects distributional equity in power restoration. The findings show that minority groups are more likely to utilize these smart technologies to submit requests for essential services after disasters, as they may have greater needs but less political capital to reach out to the government. Their utilization of e-governance technologies has helped them gain more attention from the government, which narrows the equity gap in service delivery.
- This article investigates how 311 systems affect distributional equity in public service delivery. Many local governments in the United States have adopted interactive 311 platforms to engage citizens in coproduction. Using a novel household-level data set on 311 service requests and power service restoration in the City of Tallahassee, Florida, after Hurricane Michael in 2018, the authors examine possible disparities between racial minority groups and nonminorities in making power service restoration requests via 311. The article further analyzes how coproduction participation through 311 affects distributional equity in power restoration. The findings show that minority groups are more likely to utilize these smart technologies to submit requests for essential services after disasters, as they may have greater needs but less political capital to reach out to the government. Their utilization of e-governance technologies has helped them gain more attention from the government, which narrows the equity gap in service delivery.
- Curley, Cali, Nicky Harrison, Corey Kewei Xu, and Shan Zhou. 2020. Collaboration Mitigates Barriers of Utility Ownership on Policy Adoption: Evidence from the United States. Journal of Environmental Planning and Management. Routledge: 1–21.
- Informed by literature on collaboration, policy adoption, and utility governance, this paper develops and empirically tests hypotheses addressing the implications of collaboration, utility ownership and city-level commitment to sustainability on city-level clean energy policy decisions. This paper offers an answer to the question, “to what extent does collaboration between utilities and local governments influence policy adoption.” We utilize cross-sectional data from the United States focused Integrated City Sustainability Database (ICSD) to perform an ordinary least squares regression analysis that investigates the degree that specific city attributes and state policy influence the creation of city-scale policy. This analysis shows that cities with a Publicly Owned Utility (POU) adopt close to two energy policies more than similar cities served by an IOU. Higher levels of collaboration among cities and with an Investor Owned Utility (IOU) can offset a portion of the adoption gap for community-oriented energy policy, but has little to no impact on governmental or renewable policies.
- Informed by literature on collaboration, policy adoption, and utility governance, this paper develops and empirically tests hypotheses addressing the implications of collaboration, utility ownership and city-level commitment to sustainability on city-level clean energy policy decisions. This paper offers an answer to the question, “to what extent does collaboration between utilities and local governments influence policy adoption.” We utilize cross-sectional data from the United States focused Integrated City Sustainability Database (ICSD) to perform an ordinary least squares regression analysis that investigates the degree that specific city attributes and state policy influence the creation of city-scale policy. This analysis shows that cities with a Publicly Owned Utility (POU) adopt close to two energy policies more than similar cities served by an IOU. Higher levels of collaboration among cities and with an Investor Owned Utility (IOU) can offset a portion of the adoption gap for community-oriented energy policy, but has little to no impact on governmental or renewable policies.
- Curley, Cali, Richard Feiock, and Kewei Xu. 2020. Policy Analysis of Instrument Design: How Policy Design Affects Policy Constituency. Journal of Comparative Policy Analysis: Research and Practice. Taylor & Francis, 1–22.
- This research investigates the extent to which the design of different policy instruments directed towards the same goal shape the constituencies of residents that participate in the program, and discusses the implications of these relationships for policy making and policy design. The theoretical focus is on the constituencies of policy takers that respond to differing policy designs. Based on this framing, participation in energy efficiency programs is empirically estimated, including loan, rebate, and audit, in Tallahassee, Florida, at the household level. Examining community characteristics such as racial, economic and demographic characteristics of residents, the study identifies systematic differences in participation across these policy instruments. In conclusion, the article discusses how the characteristics of potential policy takers might align with policy makers’ motivations to shape instrument design.
- This research investigates the extent to which the design of different policy instruments directed towards the same goal shape the constituencies of residents that participate in the program, and discusses the implications of these relationships for policy making and policy design. The theoretical focus is on the constituencies of policy takers that respond to differing policy designs. Based on this framing, participation in energy efficiency programs is empirically estimated, including loan, rebate, and audit, in Tallahassee, Florida, at the household level. Examining community characteristics such as racial, economic and demographic characteristics of residents, the study identifies systematic differences in participation across these policy instruments. In conclusion, the article discusses how the characteristics of potential policy takers might align with policy makers’ motivations to shape instrument design.
- Yuan, Shengxi, Wendell Stainsby, Mo Li, Kewei Xu, Michael Waite, Dan Zimmerle, Richard Feiock, Anu Ramaswami, and Vijay Modi. 2019. “Future Energy Scenarios with Distributed Technology Options for Residential City Blocks in Three Climate Regions of the United States.” Applied Energy 237: 60–69.
- To reduce greenhouse gas emissions, the electricity sector is going through two main transitions. First, the electric grid is integrating variable renewable generation, such as wind and solar. Second, demands are changing as heating systems are shifting from gas-based to high efficiency electric heat pumps. This paper provides a comparative analysis of future energy scenarios with distributed technology options including (1) wind and solar generation; (2) heat pumps for heating and cooling; and (3) battery and thermal storage in representative residential blocks in four cities, including New York City, New York; Minneapolis, Minnesota; Tallahassee, Florida; and Fort Collins, Colorado. These cities are located in three climate regions with different weather patterns which result in different demand profiles and different local renewable resources. Future energy demand scenarios with 100% penetration of air source or ground source heat pumps for heating and cooling are estimated for the four residential city blocks. Under a future scenario with all electric demand with air source heat pumps and high renewable energy penetration, this study finds that (1) the optimal wind and solar generation mix varies with location and amount of storage and (2) battery storage is more cost effective than thermal storage, ground source heat pumps, and overbuilt renewable generation.
- To reduce greenhouse gas emissions, the electricity sector is going through two main transitions. First, the electric grid is integrating variable renewable generation, such as wind and solar. Second, demands are changing as heating systems are shifting from gas-based to high efficiency electric heat pumps. This paper provides a comparative analysis of future energy scenarios with distributed technology options including (1) wind and solar generation; (2) heat pumps for heating and cooling; and (3) battery and thermal storage in representative residential blocks in four cities, including New York City, New York; Minneapolis, Minnesota; Tallahassee, Florida; and Fort Collins, Colorado. These cities are located in three climate regions with different weather patterns which result in different demand profiles and different local renewable resources. Future energy demand scenarios with 100% penetration of air source or ground source heat pumps for heating and cooling are estimated for the four residential city blocks. Under a future scenario with all electric demand with air source heat pumps and high renewable energy penetration, this study finds that (1) the optimal wind and solar generation mix varies with location and amount of storage and (2) battery storage is more cost effective than thermal storage, ground source heat pumps, and overbuilt renewable generation.
Corey Kewei XU 