TY - JOUR
T1 - Using comparative socio-ecological modeling to support Climate Action Planning (CAP)
AU - Pan, Haozhi
AU - Page, Jessica
AU - Zhang, Le
AU - Chen, Si
AU - Cong, Cong
AU - Destouni, Georgia
AU - Kalantari, Zahra
AU - Deal, Brian
N1 - Funding Information:
In this work we used the ROGER supercomputer, which is supported by NSF under grant number 1429699. We thank Shaowen Wang and Anand Padmanabhan for their assistance with high-performance computing jobs, which was made possible through the CyberGIS Center Help Desk. This work is also supported by LS 2018-0736 from Stockholm County (in Swedish: Stockholm läns landsting). We are also very grateful for the anonymous reviewers and editor for their constructive comments, which helped improve the quality of the paper.
Funding Information:
In this work we used the ROGER supercomputer, which is supported by NSF under grant number 1429699 . We thank Shaowen Wang and Anand Padmanabhan for their assistance with high-performance computing jobs, which was made possible through the CyberGIS Center Help Desk. This work is also supported by LS 2018-0736 from Stockholm County (in Swedish: Stockholm läns landsting). We are also very grateful for the anonymous reviewers and editor for their constructive comments, which helped improve the quality of the paper.
Publisher Copyright:
© 2019 The Authors
PY - 2019/9/20
Y1 - 2019/9/20
N2 - We present a comparative socio-ecological modeling approach to identify possible improvement opportunities for Climate Action Plans (CAPs), focusing on two cities, Chicago and Stockholm. The aim is to provide a tool for capturing and addressing deep-rooted behavioral and institutional preferences that may aggravate greenhouse gas (GHG) emissions in cities. Socio-economic activities, land use change, and future urban forms are considered and forecast to the year 2040 on 30m × 30m spatial grids. GHG emissions associated with these urban development aspects are calculated and compared between the cities. Innovative policy instruments for growth control and zoning (GCZ) are simulated and tested through the socio-ecological model, to determine their effectiveness when added to other interventions included in the CAPs. Our findings show that behavioral/institutional preference for sprawl, its low-density form, and resultant carbon sink losses are main factors driving current and future residential and transportation GHG emissions in Chicago. GCZ policies are shown to counteract and mitigate around 20% of these factors in the form of future GHG emissions.
AB - We present a comparative socio-ecological modeling approach to identify possible improvement opportunities for Climate Action Plans (CAPs), focusing on two cities, Chicago and Stockholm. The aim is to provide a tool for capturing and addressing deep-rooted behavioral and institutional preferences that may aggravate greenhouse gas (GHG) emissions in cities. Socio-economic activities, land use change, and future urban forms are considered and forecast to the year 2040 on 30m × 30m spatial grids. GHG emissions associated with these urban development aspects are calculated and compared between the cities. Innovative policy instruments for growth control and zoning (GCZ) are simulated and tested through the socio-ecological model, to determine their effectiveness when added to other interventions included in the CAPs. Our findings show that behavioral/institutional preference for sprawl, its low-density form, and resultant carbon sink losses are main factors driving current and future residential and transportation GHG emissions in Chicago. GCZ policies are shown to counteract and mitigate around 20% of these factors in the form of future GHG emissions.
KW - Climate action planning
KW - Comparative urban study
KW - Greenhouse gas emissions
KW - Land use
KW - Socio-ecological modeling
KW - Urban planning
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U2 - 10.1016/j.jclepro.2019.05.274
DO - 10.1016/j.jclepro.2019.05.274
M3 - Article
AN - SCOPUS:85066984037
SN - 0959-6526
VL - 232
SP - 30
EP - 42
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
ER -