Land Use & Planning Archives

Do inclusionary zoning policies affect local housing markets? An empirical study in the United States

Ruoniu (Vince) Wang, Wei Kang, Xinyu Fu, Do inclusionary zoning policies affect local housing markets? An empirical study in the United States, Cities, Volume 158, 2025, 105736, ISSN 0264-2751, https://doi.org/10.1016/j.cities.2025.105736.

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Abstract

In the face of a housing affordability crisis, many cities have adopted inclusionary zoning (IZ) policies to increase the supply of affordable housing. Yet, IZ remains a controversial local policy due to its varied and inconclusive effects on housing market outcomes. This study investigates this debate by adopting a quasi-experimental design with a national dataset of IZ policies in the United States. We find that, on average, IZ policies did not affect municipality-wide housing permits or rents. However, the implementation of IZ resulted in an average of 2.1 % increase in home prices. Our results also underscore the connection between IZ policy design and market outcomes: more stringent IZ policies (i.e., those that are mandatory and apply to the entire jurisdiction) led to a higher impact on home prices while mitigating the rent effect. Additionally, IZ's market effects varied based on market conditions and the time elapsed since policy adoption. We discuss these findings in terms of implications for policy design and planning practice.

Keywords

Inclusionary zoning; Housing market outcome; Policy effect; United States; Staggered Difference-in-Differences

Urban landscape affects scaling of transportation carbon emissions across geographic scales

Jung, M. C., Wang, T., Kang, M., Dyson, K., Dawwas, E. B., & Alberti, M. (2024). Urban landscape affects scaling of transportation carbon emissions across geographic scales. Sustainable Cities and Society, 113, 105656-. https://doi.org/10.1016/j.scs.2024.105656

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Abstract

Understanding the carbon dynamics of the transportation sector is necessary to mitigate global climate change. While urban scaling laws have been used to understand the impact of urban population size on carbon efficiency, the instability of these scaling relationships raises additional questions. Here, we examined the scaling of on-road transportation carbon emissions across 378 US metropolitan statistical areas (MSAs) using diverse urban landscape patterns and spatial units, from the MSA level down to 1 km grid cells. Beginning with a baseline scaling model that uses only population size, we expanded the model to include landscape metrics at each spatial scale based on correlation results. We found that: (1) urban landscape characteristics provide insights into carbon mechanisms not fully captured by population size alone, (2) the impact of population size on on-road carbon emissions transitions from linear to sub-linear scaling relationships as the geographic scale of analysis decreases, and (3) clustered urban developments can form carbon-efficient landscapes, while fragmented urban areas tend to be carbon-inefficient. Based on empirical evidence, this research advocates for hierarchical spatial planning and supports the implementation of policy measures aligned with smart growth principles to mitigate carbon pollution.

Key performance indicators for hospital planning and construction: a systematic review and meta-analysis

Liu, W., Chan, A.P.C., Chan, M.W., Darko, A. and Oppong, G.D. (2024), “Key performance indicators for hospital planning and construction: a systematic review and meta-analysis”, Engineering, Construction and Architectural Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/ECAM-10-2023-1060

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Abstract

Purpose
The successful implementation of hospital projects (HPs) tends to confront sundry challenges in the planning and construction (P&C) phases due to their complexity and particularity. Employing key performance indicators (KPIs) facilitates the monitoring of HPs to advance their successful delivery. This study aims to comprehensively investigate the KPIs for hospital planning and construction (HPC).

Design/methodology/approach
The KPIs for HPC were identified through a systematic review. Then a comprehensive assessment of these KPIs was performed utilizing a meta-analysis method. In this process, basic statistical analysis, subgroup analysis, sensitive analysis and publication bias analysis were performed.

Findings
Results indicate that all 27 KPIs identified from the literature are significant for executing HPs in P&C phases. Also, some unconventional performance indicators are crucial for implementing HPs, such as “Project monitoring effectiveness” and “Industry innovation and synergy,” as their high significance is reflected in this study. Despite the fact that the findings of meta-analysis are more trustworthy than those of individual studies, a high heterogeneity still exists in the findings. It highlights the inherent uncertainty in the construction industry. Hence, this study applied subgroup analysis to explore the underlying factors causing the high level of heterogeneity and used sensitive analysis to assess the robustness of the findings.

Originality/value
There is no consensus among the prior studies on KPIs for HPC specifically and their degree of significance. Additionally, few reviews in this field have focused on the reliability of the results. This study comprehensively assesses the KPIs for HPC and explores the variability and robustness of the results, which provides a multi-dimensional perspective for practitioners and the research community to investigate the performance of HPs during the P&C stages.

Keywords

Key performance indicators; hospital projects; planning and construction; systematic review; meta-analysis; project monitoring effectiveness; industry innovation and synergy

Challenges to energy retrofitting of existing office buildings in high-rise high-density cities: The case of Hong Kong

Linyan Chen, Amos Darko, Mayowa I. Adegoriola, Albert P.C. Chan, Yang Yang, Mershack O. Tetteh, “Challenges to energy retrofitting of existing office buildings in high-rise high-density cities: The case of Hong Kong,” Energy and Buildings, Volume 312, 2024, 114220, ISSN 0378-7788, https://doi.org/10.1016/j.enbuild.2024.114220.

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Abstract

Achieving carbon neutrality by 2050 has become a global goal, sparking concerns regarding energy consumption and carbon emissions in building operations. Office buildings in high-rise high-density cities serve as central business districts, contributing significantly to the city’s economic activity and consuming a lot of energy. The process of retrofitting existing office buildings for energy efficiency in high-rise high-density cities tends to be challenging. However, there is a lack of comprehensive understanding of the challenges involved in office buildings’ energy retrofitting, as they have not been thoroughly explored. This study aims to investigate the challenges to the existing office building energy retrofitting (EOBER) in high-rise high-density cities with real cases in Hong Kong. Initially, a systematic literature review was undertaken to identify 49 potential EOBER challenges and categorized into seven groups: technical, financial, institutional, social, environmental, regulatory, and other categories. Afterward, 23 EOBER challenges were identified through 24 semi-structured interviews with 36 real office building energy retrofitting cases in Hong Kong. Moreover, these challenges were quantified by the Z-numbers-based Delphi survey and analysis. Results show that regulatory challenges are the primary challenges, followed by financial challenges. The lack of government incentives, policies, legislation and regulations significantly hinders practitioners’ ability to engage in energy retrofitting initiatives. The long payback period of building energy retrofitting poses a critical financial concern for practitioners embracing such initiatives. In the end, this research proposed integrated strategies to tackle these challenges and increase building energy efficiency, including launching financial and regulatory incentives, shortening the interval for mandatory energy audits, disseminating knowledge, and diversifying finance channels of building energy retrofitting. The findings contribute to the body of knowledge by employing systems thinking to identify and evaluate EOBER challenges in high-rise high-density cities through empirical methodologies. Moreover, this study provides valuable references for practitioners in navigating these challenges and minimizing risks associated with the retrofitting process.

Interactions between climate change and urbanization will shape the future of biodiversity

Urban, M.C., Alberti, M., De Meester, L. et al. Interactions between climate change and urbanization will shape the future of biodiversity. Nat. Clim. Chang. (2024). https://doi.org/10.1038/s41558-024-01996-2

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Abstract

Climate change and urbanization are two of the most prominent global drivers of biodiversity and ecosystem change. Fully understanding, predicting and mitigating the biological impacts of climate change and urbanization are not possible in isolation, especially given their growing importance in shaping human society. Here we develop an integrated framework for understanding and predicting the joint effects of climate change and urbanization on ecology, evolution and their eco-evolutionary interactions. We review five examples of interactions and then present five hypotheses that offer opportunities for predicting biodiversity and its interaction with human social and cultural systems under future scenarios. We also discuss research opportunities and ways to design resilient landscapes that address both biological and societal concerns.

Assessing the equity and evolution of urban visual perceptual quality with time series street view imagery

Wang, Z., Ito, K., & Biljecki, F. (2024). Assessing the equity and evolution of urban visual perceptual quality with time series street view imagery. Cities, 145, 104704-. https://doi.org/10.1016/j.cities.2023.104704
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Abstract

The well-being of residents is considerably influenced by the quality of their environment. However, due to the lack of large-scale quantitative and longitudinal evaluation methods, it has been challenging to assess residents' satisfaction and achieve social inclusion goals in neighborhoods. We develop a novel cost-effective method that utilizes time series street view imagery for evaluating and monitoring visual environmental quality in neighborhoods. Unlike most research that relies on site visits or surveys, this study trains a deep learning model with a large-scale dataset to analyze six perception indicators' scores in neighborhoods in different geographies and does so longitudinally thanks to imagery taken over a period of a decade, a novelty in the body of knowledge. Implementing the approach, we examine public housing neighborhoods in Singapore and New York City as case studies. The results demonstrated that temporal imagery can effectively assess spatial equity and monitor the visual environmental qualities of neighborhoods over time, providing a new, comprehensive, and scalable workflow. It can help governments improve policies and make informed decisions on enhancing the design and living standards of urban residential areas, including public housing communities, which may be affected by social stigmatization, and monitor the effectiveness of their policies and actions.

Keywords

Residential quality; Public housing; Environmental quality; Spatial equity; Street view imagery; Visual environment

Identifying recurrent and persistent landslides using satellite imagery and deep learning: A 30-year analysis of the Himalaya

Tzu-Hsin Karen Chen, Mark E. Kincey, Nick J. Rosser, Karen C. Seto, Identifying recurrent and persistent landslides using satellite imagery and deep learning: A 30-year analysis of the Himalaya, Science of The Total Environment, Volume 922, 2024, 171161, ISSN 0048-9697, https://doi.org/10.1016/j.scitotenv.2024.171161.

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Abstract

This paper presents a remote sensing-based method to efficiently generate multi-temporal landslide inventories and identify recurrent and persistent landslides. We used free data from Landsat, nighttime lights, digital elevation models, and a convolutional neural network model to develop the first multi-decadal inventory of landslides across the Himalaya, spanning from 1992 to 2021. The model successfully delineated >265,000 landslides, accurately identifying 83 % of manually mapped landslide areas and 94 % of reported landslide events in the region. Surprisingly, only 14 % of landslide areas each year were first occurrences, 55–83 % of landslide areas were persistent and 3–24 % had reactivated. On average, a landslide-affected pixel persisted for 4.7 years before recovery, a duration shorter than findings from small-scale studies following a major earthquake event. Among the recovered areas, 50 % of them experienced recurrent landslides after an average of five years. In fact, 22 % of landslide areas in the Himalaya experienced at least three episodes of landslides within 30 years. Disparities in landslide persistence across the Himalaya were pronounced, with an average recovery time of 6 years for Western India and Nepal, compared to 3 years for Bhutan and Eastern India. Slope and elevation emerged as significant controls of persistent and recurrent landslides. Road construction, afforestation policies, and seismic and monsoon activities were related to changes in landslide patterns in the Himalaya.

Keywords

Landslide inventory; Landslide evolution; Vegetation recovery; Multi-temporalSpatiotemporal analysis; Machine learning

Unraveling energy justice in NYC urban buildings through social media sentiment analysis and transformer deep learning

Ashayeri, M., & Abbasabadi, N. (2024). Unraveling energy justice in NYC urban buildings through social media sentiment analysis and transformer deep learning. Energy and Buildings, 306, 113914-. https://doi.org/10.1016/j.enbuild.2024.113914
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Abstract

This study explores the intricate relationship between human sentiment on social media data, herein tweet posts on X platform, urban building characteristics, and the socio-spatial dynamics of New York City (NYC) boroughs. Leveraging Natural Language Processing (NLP) techniques, particularly sentiment analysis, augmented by the capabilities of transformer deep learning models, RoBERTa, the study places particular emphasis on the term ‘Stay-at-Home’ to encapsulate the pronounced shift in building occupancy during the pandemic's inaugural year. This focus intertwines with pivotal terms like ‘Energy Bill’ and ‘HVAC’, shedding light on their interconnected implications. The sentiment analysis leverages data from New York City's PLUTO and the Department of Energy's LEAD databases to emotional disparities connected to urban building characteristics as well as demographic and socioeconomic factors. This analytical approach unravels prevailing public emotions and extends the discussion to include energy justice concerns, viewing them through the lens of the city's built infrastructure. The research uncovers profound disparities in the built environment and the allocation of resources in NYC, highlighting the critical need to embrace a spatial justice framework for a sustainable future. This research can aid designers, planners, and policymakers in their efforts to promote equitable and inclusive urban development.

Legacies of redlining lead to unequal cooling effects of urban tree canopy

Jung, M. C., Yost, M. G., Dannenberg, A. L., Dyson, K., & Alberti, M. (2024). Legacies of redlining lead to unequal cooling effects of urban tree canopy. Landscape and Urban Planning, 246. https://doi.org/10.1016/j.landurbplan.2024.105028
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Abstract

Redlining—a racially discriminatory policy of systematic disinvestment established by the Home Owners’ Loan Corporation (HOLC) in the 1930s and continued until the late 1960s—still influences the contemporary landscape of cities in the US. While the heterogeneous distribution of land surface temperature and tree canopy cover between neighborhoods with different HOLC grades have been recently examined, the development of long-term and city-specific heat management strategies is still limited. Here, we explored the effect of redlining in Portland, Oregon, and Philadelphia, Pennsylvania, to assess its contemporary impact on climate equity. We performed a change analysis of land surface temperature and tree canopy area over the past and introduced mixed-effects models to test the intra- and inter-city differences in canopy cooling effects between the different HOLC grades. We found that (1) persistent temporal patterns of lower land surface temperatures and larger tree canopy areas are observed in higher HOLC grades, (2) greater green equity was achieved through contrasting temporal changes in tree canopy areas across HOLC grades in Portland and Philadelphia, and (3) opposite patterns exist between these cities, with stronger canopy cooling effects in neighborhoods with a Low HOLC grade in Portland and those with a High HOLC grade in Philadelphia. Differences in tree canopy change between the two cities over the past decade highlight potential influences of city-specific tree planting practices. Local planners should back tree planting initiatives to equitably mitigate urban heat exposure, considering historical redlining contexts and contemporary landscape features.

Keywords

Redlining; HOLC grade; Tree canopy; Land surface temperature; Tree equity

UW researchers issue state-level policy recommendations for transit-oriented development

CBE Researchers developed a report “Finding Common Ground: Best Practices for Policies Supporting Transit-Oriented Development,” with the Mobility Innovation Center and led by the Washington Center for Real Estate Research. Project Team: Mason Virant, Associate Director, Washington Center for Real Estate Research Christian Phillips, Urban Design and Planning PhD Program Steven C. Bourassa, PhD Director, Washington Center for Real Estate Research Arthur Acolin, Associate Professor, Runstad Department of Real Estate Visit the project page here.