Open AccessArticle A Simple Tool to Estimate Transport GHGs Mitigated from Compact Urban Form by Scott Baker Scott Baker SciProfiles Scilit Preprints.org Google Scholar 1,*, Rashika Mittal Rashika Mittal SciProfiles Scilit Preprints.org Google Scholar 1, Stephen Kovacs Stephen Kovacs SciProfiles Scilit Preprints.org Google Scholar 2 and Peter Newman Peter Newman SciProfiles Scilit Preprints.org Google Scholar 2 1 Climatebase Fellowship, 2908 Bush Street, San Francisco, CA 94115, USA 2 Sustainability Policy Institute, School of Design and the Built Environment, Curtin University, Kent Street, Bentley 6102, Australia * Author to whom correspondence should be addressed. Appl. Sci. 2026, 16(12), 5828; https://doi.org/10.3390/app16125828 (registering DOI) Submission received: 8 May 2026 / Revised: 27 May 2026 / Accepted: 2 June 2026 / Published: 9 June 2026 Featured Application The simple model allows for a preliminary assessment of the potential benefits from implementing compact urban form before investing in more sophisticated modeling. The model is best suited for car-centric municipalities from wealthier nations. Abstract Compact urban form can reduce road transportation GHG emissions and mitigate resource supply bottlenecks associated with mass EV adoption. Global databases from Climate TRACE and the Global Human Settlement Layer are utilized to develop the Compact Urban Form Estimation Tool or CUFET for calculating the reduction in VKT and road transportation GHGs from shifting toward CUF. The CUFET does not explicitly account for mechanistic changes in driving (e.g., modal shift) but rather uses settlement density as a coarse proxy for walking and transit urban fabrics. VKT was modeled using weighted least squares regression from the independent variables settlement population, settlement population density, and country fixed effects. Population size banding was introduced to the model to improve explanatory power. The model was developed using 10,495 settlements in the 2021 Climate TRACE dataset. The CUFET VKT model was able to explain 78% ( p 329,480) settlements. Higher variability was observed in VKT estimates of small settlements. The CUFET VKT was validated by backcasting historical VKT data from 1960 to 2000. The backcasting exercise used historical administrative boundaries and only included high economic output nations (GDP per capita above $20,000 in 2021 USD). Despite these limitations, backcasting achieved a % difference of ~20% for settlements after 1990, suggesting the model can make useful estimates within 30 years of the model calibration year for high economic output nations. The VKT model was used to calculate emissions using a settlement-specific emissions factor. Settlements with annual road transportation emissions per capita greater than 2 t CO 2eq have the lowest population densities relative to their populations and are mostly located in the United States, Japan, Canada, and Australia. The nations with the highest transportation emissions are also nations where the CUFET provides the most accurate VKT estimates. The CUFET aims to bridge the gap between academic consensus and local decision-making practice by reducing the barriers to estimate VKT and transportation GHG reduction from shifting to compact urban form. Keywords: road transportation; GHG emissions; population density 1. Introduction Electric vehicles (EVs) will be an important technology to reduce emissions; however, treating EVs as a silver bullet to road transportation emissions may lead to missed mitigation targets [ 4]. Potential barriers to EVs replacing internal combustion engine (ICE) vehicles by 2050 include a risk of elevated electricity prices due to a range of supply-and-demand-related factors. Supply impediments include lagging transmission and distribution infrastructure development [ 5], while increased demand is expected from vehicle fleets transitioning to EVs [ 4], increased air conditioning in a warmer world [ 6], artificial intelligence (AI) data centers [ 7], and atmospheric CO 2 removal [ 8]. Additionally, supply bottlenecks of key metals and minerals may hinder the scale of EV production required to replace ICEs by 2050 [ 9, 10, 11]. Countries across the globe, and especially autocentric countries, can reduce their risk of missing GHG mitigation targets by reducing their dependence on private vehicles for road transportation alongside EV adoption. Compact urban form (CUF) results in reduced road transportation, often measured as vehicle kilometers traveled (VKT), relative to sprawling urban form [ 12, 13, 14, 15, 16, 17, 18, 19]. Additionally, CUF uses infrastructure such as roads, wastewater, drinking water, and electrical distribution more efficiently, reducing the resources and cost required per person to provide urban services [ 20, 21]. CUF has been described both theoretically as walking and transit urban fabrics [ 22] and quantitatively through the optimization of the ‘D variables’—density, diversity, design, destination accessibility, and distance to transit [ 14]. CUF reduces VKT by reducing trip length [ 13, 14] and inducing modal shift from private transportation to transit and/or active transportation [ 23, 24]. These trends in modal shift can become self-reinforcing; for example, safety perceptions can improve if more people use active transportation modes [ 25], while additional pedestrian footfall can attract local businesses, thereby increasing destination accessibility [ 26]. Additionally, transit operators may experience reduced operating deficits from higher patronage [ 27], enabling provision of higher frequency off-peak services. 2. Materials and Methods 2.1. Model Data Detailed methods for describing how VKT and GHG emissions factors were calculated in the Climate TRACE dataset are described elsewhere [ 29] and will be briefly summarized here. The annual VKT of a settlement was calculated in the Climate TRACE dataset by summing the modeled VKT of all road segments by segment type (i.e., highway arterial, local) in a settlement area. The VKT of road segments was calculated as the capacity (total length of road network) multiplied by a capacity factor (average daily traffic for the year) which estimated the use of the road segment. The data for calculating road capacity and capacity factor came from open databases (e.g., OpenStreetMap), and data gaps were filled using satellite imagery (Sentinel-2) and machine learning algorithms. GHG emissions were calculated by multiplying the VKT by a settlement-specific emissions factor that considered the combined 100-year GHG warming potential of CO 2, N 2O, and CH 4. Emissions factors were based on vehicle fleet mix, fuel type, and nominal fuel efficiency (adjusted by monthly temperatures). Emissions factors were calculated for each type of road segment for each municipality and multiplied by the activity of each road segment type. Calculated road transportation emissions were compared to the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) v2025 international emissions inventory. Climate TRACE data compared well to the EDGAR values at mid- and high-emission settlements but contained a high amount of variability and predicted higher than actual emissions for low-emission settlements ( 4 n −1) 4.79% Cook’s Distance (D > 1) 0% CUFET (WLS + FE + Size Band) Training N 8396 FE Countries 99 R 20.68 Adjusted R 20.67 F 164.45 ( p 4 n −1) 3.08% Cook’s Distance (D > 1) 0% CUFET (WLS + FE + Size Band) Testing N 2099 R 20.78 Aggregate Bias +2.28% Root Mean Square Error (log-scale) 0.793 Table 2. Population and density elasticities, standard errors and significance for CUFET VKT model with small, medium and large population settlements. Table 2. Population and density elasticities, standard errors and significance for CUFET VKT model with small, medium and large population settlements. Parameter Size Band Elasticity SE (HC3) p-Value Population (β 1) Small (50,000 to 88,335) 1.583 0.110 329,480) 1.036 0.039 329,480) −0.206 0.077 0.007 Table 3. Percent difference in modeled vs. measured settlement VKT stratified by 2021 country GDP per capita decile. Table 3. Percent difference in modeled vs. measured settlement VKT stratified by 2021 country GDP per capita decile. Country 2021 GDP per Capita Decile Number of Settlements Decile Range GDP per Capita (2021 USD) Mean % Difference Data Unavailable 218 NA 62.4 1 494 356–885 74.1 2 455 893–1243 50.4 3 534 1245–2061 49.0 4 2302 2138–2735 82.4 5 992 2787–4183 50.6 6 784 4287–6061 40.1 7 807 6223–9982 30.0 8 2645 9984–18,636 40.9 9 509 19,031–44,119 23.9 10 755 47,691–93,665 19.0 Table 4. A summary of the percent difference in settlement VKT measured by Newman, Kenworthy, and Laube [ 18, 38] and settlement VKT predicted by the CUFET by year. Table 4. A summary of the percent difference in settlement VKT measured by Newman, Kenworthy, and Laube [ 18, 38] and settlement VKT predicted by the CUFET by year. Year Number of Settlements Mean % Difference Paired t p-Value 1960 18 82.0 −2.37 0.030 1970 17 53.9 −2.31 0.034 1980 24 35.1 −2.39 0.025 1990 24 20.0 −1.66 0.110 1995/96 31 16.1 −0.66 0.512 2000 24 20.1 −1.02 0.320 Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Share and Cite MDPI and ACS Style Baker, S.; Mittal, R.; Kovacs, S.; Newman, P. A Simple Tool to Estimate Transport GHGs Mitigated from Compact Urban Form. Appl. Sci. 2026, 16, 5828. https://doi.org/10.3390/app16125828 AMA Style Baker S, Mittal R, Kovacs S, Newman P. A Simple Tool to Estimate Transport GHGs Mitigated from Compact Urban Form. Applied Sciences. 2026; 16(12):5828. https://doi.org/10.3390/app16125828 Chicago/Turabian Style Baker, Scott, Rashika Mittal, Stephen Kovacs, and Peter Newman. 2026. "A Simple Tool to Estimate Transport GHGs Mitigated from Compact Urban Form" Applied Sciences 16, no. 12: 5828. https://doi.org/10.3390/app16125828 APA Style Baker, S., Mittal, R., Kovacs, S., & Newman, P. (2026). A Simple Tool to Estimate Transport GHGs Mitigated from Compact Urban Form. 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A Simple Tool to Estimate Transport GHGs Mitigated from Compact Urban Form
