Estimating the non-linear effects of urban built environment at residence and workplace on carbon dioxide emissions from commuting

Understanding the relationship between CO2 emissions from commuting (CEC) and the built environment is crucial for sustainable transportation and land-use policymaking during the process of constructing a low carbon city. Previous studies usually assume that the relationship is linear, which may lead to inaccurate CEC prediction and ineffective policy. Using daily travel survey data of residents in the central city of Jinan, this study adopted a gradient boosting decision tree model to explore the threshold effect and the non-linear relationship between built environments and CEC. Our findings suggest that 40% of CEC is related to the workplace environment, which is higher than the residential environment and other socioeconomic variables. The five most important variables are road density within 1 km radius of the workplace (13.493%), distance to the center at workplace and residence (10.908%, 10.530%), population density at workplace (9.097%) and distance to bus stop from the residence (8.399%). Distance to city center plays the most important role and its non-linear relationship reflects the influence of the urban spatial structure of Jinan on CEC. Furthermore, the thresholds and non-linear relationships provide planning guidelines to support urban planning development policies for low carbon city.

[1]  Nikola Milojevic-Dupont,et al.  Using explainable machine learning to understand how urban form shapes sustainable mobility , 2022, Transportation Research Part D: Transport and Environment.

[2]  Wenjia Zhang,et al.  Land use densification revisited: Nonlinear mediation relationships with car ownership and use , 2021 .

[3]  Jie Yin,et al.  Threshold and moderating effects of land use on metro ridership in Shenzhen: Implications for TOD planning , 2020 .

[4]  Y. Chai,et al.  Nonlinear effect of accessibility on car ownership in Beijing: Pedestrian-scale neighborhood planning , 2020 .

[5]  Tao Feng,et al.  Examining the relationship between built environment and metro ridership at station-to-station level , 2020, Transportation Research Part D: Transport and Environment.

[6]  Sadegh Sabouri,et al.  Guidelines for a Polycentric Region to Reduce Vehicle Use and Increase Walking and Transit Use , 2020 .

[7]  Yingling Fan,et al.  Examining threshold effects of built environment elements on travel-related carbon-dioxide emissions , 2019, Transportation Research Part D: Transport and Environment.

[8]  Arefeh A. Nasri,et al.  How Urban Form Characteristics at Both Trip Ends Influence Mode Choice: Evidence from TOD vs. Non-TOD Zones of the Washington, D.C. Metropolitan Area , 2019, Sustainability.

[9]  Chuan Ding,et al.  How does the built environment at residential and work locations affect car ownership? An application of cross-classified multilevel model , 2019, Journal of Transport Geography.

[10]  Hai-long Ma,et al.  Structural contribution and scenario simulation of highway passenger transit carbon emissions in the Beijing-Tianjin-Hebei metropolitan region, China , 2019, Resources, Conservation and Recycling.

[11]  Xiao-shu Cao,et al.  Examining the effects of the neighborhood built environment on CO2 emissions from different residential trip purposes: A case study in Guangzhou, China , 2018, Cities.

[12]  Shenghui Cui,et al.  Investigating the comparative roles of multi-source factors influencing urban residents' transportation greenhouse gas emissions. , 2018, The Science of the total environment.

[13]  Petter Næss,et al.  Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo , 2018 .

[14]  Ye Liu,et al.  The impact of urban characteristics and residents’ income on commuting in China , 2017 .

[15]  Douglas Houston,et al.  Can New Light Rail Reduce Personal Vehicle Carbon Emissions? A Before‐After, Experimental‐Control Evaluation in Los Angeles , 2017, 2206.12610.

[16]  D. He,et al.  Influence of land use and street characteristics on car ownership and use: Evidence from Jinan, China , 2017 .

[17]  Xiao-shu Cao,et al.  Examining the effects of the built environment and residential self-selection on commuting trips and the related CO2 emissions: An empirical study in Guangzhou, China , 2017 .

[18]  M. Kwan,et al.  The impact of the uncertain geographic context on the space-time behavior analysis: A case study of Xining, China , 2017 .

[19]  Q. Shen,et al.  Factors affecting car ownership and mode choice in rail transit-supported suburbs of a large Chinese city , 2016 .

[20]  Daniel S. Cohan,et al.  Net greenhouse gas emissions savings from natural gas substitutions in vehicles, furnaces, and power plants , 2016 .

[21]  Xiao-shu Cao,et al.  Commuting carbon emission characteristics of community residents of three spheres: A case study of three communities in Guangzhou city , 2015 .

[22]  R. Ewing,et al.  Quantifying Transit’s Impact on GHG Emissions and Energy Use—The Land Use Component , 2015 .

[23]  Anne Aguiléra,et al.  Urban form, commuting patterns and CO2 emissions: What differences between the municipality’s residents and its jobs? , 2014 .

[24]  A. Khattak,et al.  Is Smart Growth Associated with Reductions in Carbon Dioxide Emissions? , 2013 .

[25]  Arefeh A. Nasri,et al.  How built environment affects travel behavior: A comparative analysis of the connections between land use and vehicle miles traveled in US cities , 2012 .

[26]  Lawrence D. Frank,et al.  An assessment of urban form and pedestrian and transit improvements as an integrated GHG reduction strategy. , 2011 .

[27]  Reid Ewing,et al.  Travel and the Built Environment , 2010 .

[28]  Harry Timmermans,et al.  Influence of the residential and work environment on car use in dual-earner households , 2009 .

[29]  J Elith,et al.  A working guide to boosted regression trees. , 2008, The Journal of animal ecology.

[30]  F. Schmidt.  Meta-Analysis , 2008 .

[31]  Chandra R. Bhat,et al.  A Comprehensive Analysis of Built Environment Characteristics on Household Residential Choice and Auto Ownership Levels , 2007 .

[32]  T. Litman,et al.  Land Use Impacts on Transport , 2005 .

[33]  Rongfang Liu,et al.  Mode Biases of Urban Transportation Policies in China and Their Implications , 2005 .

[34]  Robert Cervero,et al.  Built environments and mode choice: toward a normative framework , 2002 .

[35]  William S. Curran,et al.  A/I: a synthesis , 1982, ACM-SE 20.

[36]  Ming Zhang,et al.  The net effects of the built environment on household vehicle emissions: A case study of Austin, TX , 2017 .

[37]  Jinhyun Hong,et al.  Non-linear influences of the built environment on transportation emissions: Focusing on densities , 2017 .

[38]  H. Jingnan,et al.  The Effect of Traffic Facilities Accessibility on Household Commuting Caused Carbon Emission: A Case Study of Wuhan City, China , 2015 .

[39]  Dang Yun-xia.  Impact of land-use mixed degree on resident's home-work separation in Beijing , 2015 .

[40]  Sun Bin-don.  Impact of urban built environment on residential choice of commuting mode in Shanghai , 2015 .

[41]  L. Zhi-lin.  Low-carbon optimization strategies based on CO_2 emission mechanism of household daily travels:A case study of Beijing , 2012 .

[42]  J. Preston,et al.  ‘60-20 emission’—The unequal distribution of greenhouse gas emissions from personal, non-business travel in the UK , 2010 .

[43]  Antonio Páez,et al.  Determinants of distance traveled with a focus on the elderly: a multilevel analysis in the Hamilton CMA, Canada , 2009 .

[44]  Christian Brand,et al.  “Hockey Sticks” Made of Carbon , 2009 .

[45]  P. Mahadevan,et al.  An overview , 2007, Journal of Biosciences.

[46]  Daniel G. Chatman,et al.  How Density and Mixed Uses at the Workplace Affect Personal Commercial Travel and Commute Mode Choice , 2003 .

[47]  Reid Ewing,et al.  Travel and the Built Environment: A Synthesis , 2001 .

[48]  P. Newman,et al.  The land use—transport connection: An overview , 1996 .