The Hardening of the American Landscape

References

• Aguiar, F. C., Bentz, J., Silva, J. M. N., Fonseca, A. L., Swart, R., Santos, F. D., & Penha-Lopes, G. (2018). Adaptation to climate change at local level in Europe: An overview. Environmental Science & Policy, 86(April), 38–63. https://doi.org/10.1016/j.envsci.2018.04.010
   Google Scholar
• Aichele, S. S., & Andresen, J. A. (2013). Spatial and temporal variations in land development and impervious surface creation in Oakland County, Michigan, 1945–2005. Journal of Hydrology, 485(April), 96–102. https://doi.org/10.1016/j.jhydrol.2012.12.049
   Google Scholar
• Alberti, M., Booth, D., Hill, K., Coburn, B., Avolio, C., Coe, S., & Spirandelli, D. (2007). The impact of urban patterns on aquatic ecosystems: An empirical analysis in Puget lowland sub-basins. Landscape and Urban Planning, 80(4), 345–361. https://doi.org/10.1016/j.landurbplan.2006.08.001
   Web of Science ®Google Scholar
• AnyLogic. (2016). Developing disruptive business strategies with simulation: White paper. https://www.anylogic.com/resources/white-papers/developing-disruptive-business-strategies-with-simulation/
   Google Scholar
• Arnold, C. L., Gibbons, C. J., Arnold, C. L., & Gibbons, C. J. (1996). Impervious surface coverage: The emergence of a key environmental indicator. Journal of the American Planning Association, 62(2), 243–258. https://doi.org/10.1080/01944369608975688
   Web of Science ®Google Scholar
• Artmann, M., Inostroza, L., & Fan, P. (2019). Urban sprawl, compact urban development and green cities. How much do we know, how much do we agree? Ecological Indicators, 96, 3–9. https://doi.org/10.1016/j.ecolind.2018.10.059
   Web of Science ®Google Scholar
• Avin, U., & Goodspeed, R. (2020). Using exploratory scenarios in planning practice: A spectrum of approaches. Journal of the American Planning Association, 86(4), 403–416. https://doi.org/10.1080/01944363.2020.1746688
   Web of Science ®Google Scholar
• Balwant, H., Baier, K., Jha, R., & Azzam, R. (2018). Impact of urbanization on groundwater recharge and urban water balance for the city of Hyderabad, India. International Soil and Water Conservation Research, 6(1), 51–62. https://doi.org/10.1016/j.iswcr.2017.10.003
   Web of Science ®Google Scholar
• Becker, J., & Kaza, N. (2022). Tale of two sprawls: Energy planning and challenges for smart growth. In G.-J. Knaap, R. Lewis, A. Chakraborty, and K. June-Friesen (Eds.), Handbook on smart growth (pp. 291–306). Edward Elgar.
   Google Scholar
• Birdshire, K. R., Carper, A. L., & Briles, C. E. (2020). Bee community response to local and landscape factors along an urban-rural gradient. Urban Ecosystems, 23(4), 689–702. https://doi.org/10.1007/s11252-020-00956-w
   Web of Science ®Google Scholar
• Blum, A. G., Ferraro, P. J., Archfield, S. A., & Ryberg, K. R. (2020). Causal effect of impervious cover on annual flood magnitude for the United States. Geophysical Research Letters, 47(5), 480. https://doi.org/10.1029/2019GL086480
   Web of Science ®Google Scholar
• Brabec, E., Ann, M., Schulte, S., Ann, M., Schulte, S. (2000). Fragmentation, impervious surfaces and water quality: Quantifying the effects of density and spatial arrangement. Paper presented at 3rd International Workshop on Sustainable Land Use Planning, Wageningen, 2000.
   Google Scholar
• Bradshaw, T. K., & Muller, B. (2010). Impacts of rapid urban growth on farmland conversion: Application of new regional land use policy models and geographical information systems. Rural Sociology, 63(1), 1–25. https://doi.org/10.1111/j.1549-0831.1998.tb00662.x
   Google Scholar
• Brinig, M. F., & Garnett, N. S. (2013). A room of one’s own? Accessory dwelling unit reforms and local parochialism. Urban Lawyer, 45(3), 519–569. https://www.jstor.org/stable/24392672
   Web of Science ®Google Scholar
• Buechler, S., Niu, D., & Kinsella Thompson, A. (2021). Predicting urban growth with machine learning. SSRN Electronic Journal, 21, 3784787. https://doi.org/10.2139/ssrn.3784787
   Google Scholar
• Carruthers, J. I., Wei, H., & Wostenholme, L. (2022). Urban containment as smart growth. In G.-J. Knaap, R. Lewis, A. Chakraborty, and K. June-Friesen (Eds.), Handbook on smart growth (pp. 60–74). Edward Elgar.
   Google Scholar
• Carter, J. G., Cavan, G., Connelly, A., Guy, S., Handley, J., & Kazmierczak, A. (2015). Climate change and the city: Building capacity for urban adaptation. Progress in Planning, 95, 1–66. https://doi.org/10.1016/j.progress.2013.08.001
   Web of Science ®Google Scholar
• Cervero, R., & Kockelman, K. (1997). Travel demand and The 3Ds. Transportation Research Part D, 2(3), 199–219. https://doi.org/10.1016/S1361-9209(97)00009-6
   Web of Science ®Google Scholar
• Chakraborty, A., Kaza, N., Knaap, G. J., & Deal, B. (2011). Robust plans and contingent plans. Journal of the American Planning Association, 77(3), 251–266. https://doi.org/10.1080/01944363.2011.582394
   Web of Science ®Google Scholar
• Chakraborty, A., & McMillan, A. (2015). Scenario planning for urban planners: Toward a practitioner’s guide. Journal of the American Planning Association, 81(1), 18–29. https://doi.org/10.1080/01944363.2015.1038576
   Web of Science ®Google Scholar
• Chakraborty, A., & McMillan, A. (2017). GIS and scenario analysis: Tools for better urban planning. Comprehensive Geographic Information Systems, 3(3), 371–380. https://doi.org/10.1016/B978-0-12-409548-9.09649-4
   Google Scholar
• City and County of Denver. (2023). City and County of Denver zoning code. https://test.denvergov.org/Government/Agencies-Departments-Offices/Agencies-Departments-Offices-Directory/Community-Planning-and-Development/Denver-Zoning-Code
   Google Scholar
• City of Denver. (2019). Denver blueprint: A blueprint for an inclusive city. https://test.denvergov.org/Government/Agencies-Departments-Offices/Agencies-Departments-Offices-Directory/Community-Planning-and-Development/Planning/Blueprint-Denver
   Google Scholar
• City of Denver Public Works. (2018). City of Denver green infrastructure implementation strategy. https://www.denvergov.org/Government/Agencies-Departments-Offices/Agencies-Departments-Offices-Directory/Department-of-Transportation-and-Infrastructure/Programs-Services/Green-Infrastructure/Implementation-Strategy?lang_update=638212252138930551
   Google Scholar
• Coutts, C. (2016). Green infrastructure and public health. Routledge. https://doi.org/10.4324/9781315647623
   Google Scholar
• Cutter, W. B., & Franco, S. F. (2012). Do parking requirements significantly increase the area dedicated to parking? A test of the effect of parking requirements values in Los Angeles County. Transportation Research Part A, 46(6), 901–925. https://doi.org/10.1016/j.tra.2012.02.012
   Google Scholar
• Deng, X., Houghton, N., Li, H., Weiler, J. (2014). Cost analysis for the MS4 permits [Worcester Polytechnic Institute]. https://web.wpi.edu/Pubs/E-project/Available/E-project-050614-115834/unrestricted/COST_ANALYSIS_FOR_THE_MS4_PERMITS_WPI_STUDENT_PROJECT.pdf%0Ahttps://www.cwp.org/archived-webcasts-for-members/
   Google Scholar
• De la Sota, C., Ruffato-Ferreira, V. J., Ruiz-García, L., & Alvarez, S. (2019). Urban green infrastructure as a strategy of climate change mitigation: A case study in northern Spain. Urban Forestry & Urban Greening, 40(August 2018), 145–151. https://doi.org/10.1016/j.ufug.2018.09.004
   Google Scholar
• Denver Regional Council of Governments. (2002). Denver regional aerial photography project. Denver Regional Council of Governments.
   Google Scholar
• Ding, C. (2013). Building height restrictions, land development and economic costs. Land Use Policy, 30(1), 485–495. https://doi.org/10.1016/j.landusepol.2012.2.04.016
   Web of Science ®Google Scholar
• Dou, Y., & Kuang, W. (2020). Science of the total environment: A comparative analysis of urban impervious surface and green space and their dynamics among 318 different size cities in China in the past 25 years. The Science of the Total Environment, 706, 135828. https://doi.org/10.1016/j.scitotenv.2019.135828
   PubMed Web of Science ®Google Scholar
• Durance, P., & Godet, M. (2010). Scenario building: Uses and abuses. Technological Forecasting and Social Change, 77(9), 1488–1492. https://doi.org/10.1016/j.techfore.2010.06.007
   Web of Science ®Google Scholar
• EPA-Administered Pollutant Permit Programs, 40 C.F.R., Part 122. (2018). The National Pollutant Discharge Elimination System. https://www.ecfr.gov/current/title-40/chapter-I/subchapter-D/
   Google Scholar
• Epps, T. H., & Hathaway, J. M. (2019). Using spatially-identified effective impervious area to target green infrastructure retrofits: A modeling study in Knoxville, TN. Journal of Hydrology, 575(May), 442–453. https://doi.org/10.1016/j.jhydrol.2019.05.062
   Google Scholar
• Ewing, R., & Cervero, R. (2010). Travel and the built environment. Journal of the American Planning Association, 76(3), 265–294. https://doi.org/10.1080/01944361003766766
   Web of Science ®Google Scholar
• Falcone, J., & Pearson, J. (2006). Land-cover and imperviousness data for regional areas near Denver, Colorado; Dallas-Fort Worth, Texas; and Milwaukee-Green Bay, Wisconsin 2001. US Geological Survey Data Series.
   Google Scholar
• Fattorini, S. (2011). Insect extinction by urbanization: A long term study in Rome. Biological Conservation, 144(1), 370–375. https://doi.org/10.1016/j.biocon.2010.09.014
   Web of Science ®Google Scholar
• Fedele, G., Donatti, C. I., Harvey, C. A., Hannah, L., & Hole, D. G. (2019). Transformative adaptation to climate change for sustainable social-ecological systems. Environmental Science & Policy, 101(August), 116–125. https://doi.org/10.1016/j.envsci.2019.07.001
   Google Scholar
• Feng, D., Bao, W., Yang, Y., & Fu, M. (2021). Land use policy: How do government policies promote greening? Evidence from China. Land Use Policy, 104(March), 105389. https://doi.org/10.1016/j.landusepol.2021.105389
   Google Scholar
• Finley, B. (2019, January 13). As development eats away at Denver’s green space, the “city within a park” is becoming a concrete metropolis. Denver Post. https://www.denverpost.com/2019/01/13/denver-green-space-urban-density/
   Google Scholar
• Gallo, E. M., Bell, C. D., Panos, C. L., Smith, S. M., & Hogue, T. S. (2020). Investigating tradeoffs of green to grey stormwater infrastructure using a planning-level decision support tool. Water, 12(7), 2005. https://doi.org/10.3390/w12072005
   Web of Science ®Google Scholar
• Garvin, D. A., & Levesque, L. C. (2005). A note on scenario planning. Harvard Business School Cases, 10(1991), 1. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=22138577&site=ehost-live%5Cnhttp://harvardbusinessonline.hbsp.harvard.edu/relay.jhtml?name=itemdetail&id=306003
   Google Scholar
• Gill, S. E., Handley, J. F., Ennos, A. R., Pauleit, S., Theuray, N., & Lindley, S. J. (2008). Characterising the urban environment of UK cities and towns: A template for landscape planning. Landscape and Urban Planning, 87(3), 210–222. https://doi.org/10.1016/j.landurbplan.2008.06.008
   Web of Science ®Google Scholar
• Gironás, J., Roesner, L. A., Rossman, L. A., & Davis, J. (2010). A new applications manual for the Storm Water Management Model (SWMM). Environmental Modelling & Software, 25(6), 813–814. https://doi.org/10.1016/j.envsoft.2009.11.009
   Web of Science ®Google Scholar
• Glick, R. H. (2009). Impacts of impervious cover and other factors on storm-water quality in Austin, Tex. Journal of Hydrologic Engineering, 14(4), 316–323. https://doi.org/10.1061/(ASCE)1084-0699(2009)14
   Web of Science ®Google Scholar
• Gómez, J. A., Patiño, J. E., Duque, J. C., & Passos, S. (2019). Spatiotemporal modeling of urban growth using machine learning. Remote Sensing, 12(1), 109. https://doi.org/10.3390/rs12010109
   Web of Science ®Google Scholar
• Goodspeed, R., Liu, R., Gounaridis, D., Lizundia, C., & Newell, J. (2022). A regional spatial planning model for multifunctional green infrastructure. Environment and Planning B, 49(3), 815–833. https://doi.org/10.1177/23998083211033610
   Google Scholar
• Gounaridis, D., Newell, J. P., & Goodspeed, R. (2020). The impact of urban sprawl on forest landscapes in Southeast Michigan, 1985–2015. Landscape Ecology, 35(9), 1975–1993. https://doi.org/10.1007/s10980-020-01075-9
   Web of Science ®Google Scholar
• Gustafson, K. (2019). Quantifying the effects of residential infill development [Master’s thesis]. Colorado School of Mines. http://hdl.handle.net/11124/173300
   Google Scholar
• Hall, D. M., Camilo, G. R., Tonietto, R. K., Ollerton, J., Ahrné, K., Arduser, M., Ascher, J. S., Baldock, K. C. R., Fowler, R., Frankie, G., Goulson, D., Gunnarsson, B., Hanley, M. E., Jackson, J. I., Langellotto, G., Lowenstein, D., Minor, E. S., Philpott, S. M., Potts, S. G., … Threlfall, C. G. (2017). The city as a refuge for insect pollinators. Conservation Biology, 31(1), 24–29. https://doi.org/10.1111/cobi.12840
   PubMed Web of Science ®Google Scholar
• Heintzman, L. J., & McIntyre, N. E. (2019). Quantifying the effects of projected urban growth on connectivity among wetlands in the Great Plains (USA). Landscape and Urban Planning, 186(October 2018), 1–12. https://doi.org/10.1016/j.landurbplan.2019.02.007
   Google Scholar
• Hendricks, M., & Berke, P. (2022). Community resilience to environmental hazards and climate change: Can smart growth make a difference? In G.-J. Knaap, R. Lewis, A. Chakraborty, and K. June-Friesen (Eds.), Handbook on smart growth (pp. 277–290). Edward Elgar.
   Google Scholar
• Heris, M. P., Middel, A., & Muller, B. (2020). Impacts of form and design policies on urban microclimate: Assessment of zoning and design guideline choices in urban redevelopment projects. Landscape and Urban Planning, 202, 103870. https://doi.org/10.1016/j.landurbplan.2020.103870
   Web of Science ®Google Scholar
• Isaksson, C. (2018). Impact of urbanization on birds. In D. T. Tietze (Ed.), Bird species: How they arise, modify and vanish (pp. 235–258). Springer Open. https://doi.org/10.1007/978-3-319-91689-7_13
   Google Scholar
• Ishimatsu, K., Ito, K., Mitani, Y., Tanaka, Y., Sugahara, T., & Naka, Y. (2017). Use of rain gardens for stormwater management in urban design and planning. Landscape and Ecological Engineering, 13(1), 205–212. https://doi.org/10.1007/s11355-016-0309-3
   Web of Science ®Google Scholar
• Johnston, R. A., Gao, S., & Clay, M. J. (2005). Modeling long-range transportation and land use scenarios with citizen-generated policies in the Sacramento, California, region. Transportation Research Record, 1902(1), 99–106. https://doi.org/10.3141/1902-12
   Google Scholar
• Just, M. G., Frank, S. D., & Dale, A. G. (2018). Impervious surface thresholds for urban tree site selection. Urban Forestry & Urban Greening, 34(July 2017), 141–146. https://doi.org/10.1016/j.ufug.2018.06.008
   Google Scholar
• Khoshkar, S., Balfors, B., & Wärnbäck, A. (2018). Planning for green qualities in the densification of suburban Stockholm: Opportunities and challenges. Journal of Environmental Planning and Management, 61(14), 2613–2635. https://doi.org/10.1080/09640568.2017.1406342
   Web of Science ®Google Scholar
• Kilman, S. (2023, April 7). Polis’ sweeping effort to reform Colorado zoning set for changes. Denver Post. https://www.denverpost.com/2023/04/07/colorado-housing-zoning-reform-affordability-polis/
   Google Scholar
• Knaap, G. J., Engelberg, D., Avin, U., Erdogan, S., Ducca, F., Welch, T. F., Finio, N., Moeckel, R., & Shahumyan, H. (2020). Modeling sustainability scenarios in the Baltimore–Washington (DC) region: Implications for methodology and policy. Journal of the American Planning Association, 86(2), 250–263. https://doi.org/10.1080/01944363.2019.1680311
   Web of Science ®Google Scholar
• Knaap, G.-J., Lewis, R., Chakraborty, A., & June-Friesen, K. (Eds.) (2022). Handbook on smart growth. Edward Elgar.
   Google Scholar
• Landis, J., & Zhang, M. (1998). The second generation of the California urban futures model. Part 1: Model logic and theory. Environment and Planning B, 25(5), 657–666. https://doi.org/10.1068/b250657
   Web of Science ®Google Scholar
• Law, N. L., Cappiella, K., & Novotney, M. E. (2009). The need for improved pervious land cover characterization in urban watersheds. Journal of Hydrologic Engineering, 14(4), 305–308. https://doi.org/10.1061/(ASCE)1084-0699(2009)14
   Web of Science ®Google Scholar
• Lee, S., & French, S. P. (2009). Regional impervious surface estimation: An urban heat island application. Journal of Environmental Planning and Management, 52(4), 477–496. https://doi.org/10.1080/09640560902868207
   Web of Science ®Google Scholar
• Li, H., Sharkey, L. J., Hunt W. F., & Davis, A. P. (2009). Mitigation of impervious surface hydrology using bioretention in North Carolina and Maryland. Journal of Hydrologic Engineering, 14(April), pp. 407–415. https://doi.org/10.1061/(ASCE)1084-0699(2009)14:4(407)
   Google Scholar
• Li, X., Gong, P., Yu, L., & Hu, T. (2017). A segment derived patch-based logistic cellular automata for urban growth modeling with heuristic rules. Computers, Environment and Urban Systems, 65, 140–149. https://doi.org/10.1016/j.compenvurbsys.2017.06.001
   Web of Science ®Google Scholar
• Manville, M., Monkkonen, P., & Lens, M. (2020). It’s time to end single-family zoning. Journal of the American Planning Association, 86(1), 106–112. https://doi.org/10.1080/01944363.2019.1651216
   Web of Science ®Google Scholar
• Marselle, M. R., Turbe, A., Shwartz, A., Bonn, A., & Colléony, A. (2021). Addressing behavior in pollinator conservation policies to combat the implementation gap. Conservation Biology, 35(2), 610–622. https://doi.org/10.1111/cobi.13581
   PubMed Web of Science ®Google Scholar
• Matthews, T., Lo, A. Y., & Byrne, J. A. (2015). Reconceptualizing green infrastructure for climate change adaptation: Barriers to adoption and drivers for uptake by spatial planners. Landscape and Urban Planning, 138, 155–163. https://doi.org/10.1016/j.landurbplan.2015.02.010
   Web of Science ®Google Scholar
• McGlinchy, J., Muller, B., Johnson, B., Joseph, M., & Diaz, J. (2021). Fully convolutional neural network for impervious surface segmentation in mixed urban environment. Photogrammetric Engineering & Remote Sensing, 87(2), 117–123. https://doi.org/10.14358/PERS.87.2.117
   Web of Science ®Google Scholar
• Montalto, F. A., Bartrand, T. A., Waldman, A. M., Travaline, K. A., Loomis, C. H., McAfee, C., Geldi, J. M., Riggall, G. J., & Boles, L. M. (2013). Decentralised green infrastructure: The importance of stakeholder behaviour in determining spatial and temporal outcomes. Structure and Infrastructure Engineering, 9(12), 1187–1205. https://doi.org/10.1080/15732479.2012.671834
   Web of Science ®Google Scholar
• Moore, T. L., Gulliver, J. S., Stack, L., & Simpson, M. H. (2016). Stormwater management and climate change: Vulnerability and capacity for adaptation in urban and suburban contexts. Climatic Change, 138(3–4), 491–504. https://doi.org/10.1007/s10584-016-1766-2
   Web of Science ®Google Scholar
• Norton, R. K., Buckman, S., Meadows, G. A., & Rable, Z. (2019). Using simple, decision-centered, scenario-based planning to improve local coastal management. Journal of the American Planning Association, 85(4), 405–423. https://doi.org/10.1080/01944363.2019.1627237
   Web of Science ®Google Scholar
• Nowak, D. J., & Green, E. J. (2018). Declining urban and community tree cover in the United States. Urban Forestry & Urban Greening, 32(February), 32–55. https://doi.org/10.1016/j.ufug.2018.03.006
   Google Scholar
• Onaran, K. (2019). Crafting form-based codes: Resilient design, policy and regulation. Routledge.
   Google Scholar
• Park Hill Residents Association. (2023). Park Hill residents association. https://www.parkhillyonkers.org/
   Google Scholar
• Pettit, C. J. (2005). Use of a collaborative GIS-based planning-support system to assist in formulating a sustainable-development scenario for Hervey Bay, Australia. Environment and Planning B, 32(4), 523–545. https://doi.org/10.1068/b31109
   Web of Science ®Google Scholar
• Qureshi, S., & Haase, D. (2014). Compact, eco-, hybrid or teleconnected? Novel aspects of urban ecological research seeking compatible solutions to socio-ecological complexities. Ecological Indicators, 42, 1–5. https://doi.org/10.1016/j.ecolind.2014.04.017
   Web of Science ®Google Scholar
• Ramsey, K., & Poresky, A. (2013). A place-based tool for assessing cumulative impervious surface outcomes of proposed development scenarios. URISA Journal, 25(2), 25–38.
   Google Scholar
• Reginster, I., & Rounsevell, M. (2006). Scenarios of Future Urban Land Use in Europe. Environment and Planning B: Planning and Design, 33, pp. 619–636.
   Google Scholar
• Reilly, J., Maggio, P., & Karp, S. (2004). A model to predict impervious surface for regional and municipal land use planning purposes. Environmental Impact Assessment Review, 24(3), 363–382. https://doi.org/10.1016/j.eiar.2003.10.022
   Web of Science ®Google Scholar
• Richter, B., & Behnisch, M. (2019). Integrated evaluation framework for environmental planning in the context of compact cities. Ecological Indicators, 96(2), 38–53. https://doi.org/10.1016/j.ecolind.2018.05.025
   Google Scholar
• Ridd, M. K. (1995). Exploring a V-I-S model for urban ecosystem analysis through remote sensing: Comparative anatomy for cities. International Journal of Remote Sensing, 16(12), 2165–2185. https://doi.org/10.1080/01431169508954549
   Web of Science ®Google Scholar
• Rocky Mountain Real Estate Law. (2018). Blueprint Denver 2.0: R.I.P. areas of stability. https://www.rockymountainrealestatelaw.com/2018/09/blueprint-denver-2-0-r-i-p-areas-of-stability/
   Google Scholar
• Schoemaker, P. J. H. (1995). Scenario planning: A tool for strategic thinking. Sloan Management Review, 36(2), 25–40. https://doi.org/10.1016/0024-6301(95)91604-0
   Google Scholar
• Schueler, T. R. (2018). The impervious cover model, revisited (again): Assumptions of the ICM progress in measuring IC. https://cwp.org/wp-content/uploads/2018/04/ICM-Revisited.pdf
   Google Scholar
• Schueler, T. R., Fraley-Mcneal, L., & Cappiella, K. (2009). Is impervious cover still important? Review of recent research. Journal of Hydrologic Engineering, 14(4), 309–315. https://doi.org/10.1061/ASCE1084-0699200914:4309
   Web of Science ®Google Scholar
• Sexton, J. O., Song, X., Huang, C., Channan, S., Baker, M. E., & Townshend, J. R. (2013). Remote sensing of environment urban growth of the Washington, D.C.–Baltimore, MD metropolitan region from 1984 to 2010 by annual, Landsat-based estimates of impervious cover. Remote Sensing of Environment, 129, 42–53. https://doi.org/10.1016/j.rse.2012.10.025
   Web of Science ®Google Scholar
• Shahtahmassebi, A. R., Pan, Y., Lin, L., Shortridge, A., Wang, K., Wu, J. X., Wu, D., & Zhang, J. (2014). Implications of land use policy on impervious surface cover change in Cixi County, Zhejiang Province, China. Cities, 39, 21–36. https://doi.org/10.1016/j.cities.2014.02.002
   Web of Science ®Google Scholar
• Shahtahmassebi, A. R., Wu, C., Blackburn, G. A., Zheng, Q., Huang, L., Shortridge, A., Shahtahmassebi, G., Jiang, R., He, S., Wang, K., Lin, Y., Clarke, K. C., Su, Y., Lin, L., Wu, J., Zheng, Q., Xu, H., Xue, X., Deng, J., & Shen, Z. (2018). How do modern transportation projects impact on development of impervious surfaces via new urban area and urban intensification? Evidence from Hangzhou Bay Bridge, China. Land Use Policy, 77(June), 479–497. https://doi.org/10.1016/j.landusepol.2018.05.059
   Google Scholar
• Sharifi, A. (2019). Urban form resilience: A meso-scale analysis. Cities, 93(October), 238–252. https://doi.org/10.1016/j.cities.2019.05.010
   Google Scholar
• Song, X., Sexton, J. O., Huang, C., Channan, S., & Townshend, J. R. (2016). Remote sensing of environment characterizing the magnitude, timing and duration of urban growth from time series of Landsat-based estimates of impervious cover. Remote Sensing of Environment, 175, 1–13. https://doi.org/10.1016/j.rse.2015.12.027
   Web of Science ®Google Scholar
• Stone, B. (2004). Paving over paradise: How land use regulations promote residential imperviousness. Landscape and Urban Planning, 69(1), 101–113. https://doi.org/10.1016/j.landurbplan.2003.10.028
   Web of Science ®Google Scholar
• Thorn, A. M., Wake, C. P., Grimm, C. D., Mitchell, C. R., Mineau, M. M., & Ollinger, S. V. (2017). Development of scenarios for land cover, population density, impervious cover, and conservation in New Hampshire, 2010–2100. Ecology and Society, 22(4), 220419. https://doi.org/10.5751/ES-09733-220419
   Web of Science ®Google Scholar
• Thuy, P. T., Moeliono, M., Locatelli, B., Brockhaus, M., Di Gregorio, M., & Mardiah, S. (2014). Integration of adaptation and mitigation in climate change and forest policies in Indonesia and Vietnam. Forests, 5(8), pp. 2016–2036. https://doi.org/10.3390/f5082016
   Google Scholar
• Tian, P., Li, J., Cao, L., Pu, R., Wang, Z., Zhang, H., Chen, H., & Gong, H. (2021). Assessing spatiotemporal characteristics of urban heat islands from the perspective of an urban expansion and green infrastructure. Sustainable Cities and Society, 74(July), 103208. https://doi.org/10.1016/j.scs.2021.103208
   Google Scholar
• Titeux, N., Henle, K., Mihoub, J. B., Regos, A., Geijzendorffer, I. R., Cramer, W., Verburg, P. H., & Brotons, L. (2016). Biodiversity scenarios neglect future land-use changes. Global Change Biology, 22(7), 2505–2515. https://doi.org/10.1111/gcb.13272
   PubMed Web of Science ®Google Scholar
• Trust for Public Lands. (2018). Westwood via verde: A vision for multimodal access and green infrastructure for Westwood. https://www.denvergov.org/content/dam/denvergov/Portals/711/documents/greeninfrastructure/Westwood-Via-Verde-final-vision.pdf
   Google Scholar
• Tzoulas, K., Korpela, K., Venn, S., Yli-Pelkonen, V., Kaźmierczak, A., Niemela, J., & James, P. (2007). Promoting ecosystem and human health in urban areas using green infrastructure: A literature review. Landscape and Urban Planning, 81(3), 167–178. https://doi.org/10.1016/j.landurbplan.2007.02.001
   Web of Science ®Google Scholar
• Ürge-Vorsatz, D., Rosenzweig, C., Dawson, R. J., Sanchez Rodriguez, R., Bai, X., Salisu Barau, A., Seto, K. C., & Dhakal, S. (2018). Locking in positive climate responses in cities: Adaptation-mitigation interdependencies. Nature Climate Change, 8(3), 174–177. www.nature.com/natureclimatechange https://doi.org/10.1038/s41558-018-0100-6
   Web of Science ®Google Scholar
• U.S. Environmental Protection Agency (EPA). (2006). Protecting water resources with high density development. EPA.
   Google Scholar
• U.S. EPA Office of Environmental Protection. (2010). Methodology to calculate baseline estimates of impervious area (IA) and directly connected impervious area (DCIA) for Massachusetts communities. http://www.mass.gov/mgis/eot_layers.htm.
   Google Scholar
• Volkery, A., & Ribeiro, T. (2009). Scenario planning in public policy: Understanding use, impacts and the role of institutional context factors. Technological Forecasting and Social Change, 76(9), 1198–1207. https://doi.org/10.1016/j.techfore.2009.07.009
   Web of Science ®Google Scholar
• Waddell, P. (2002). Urbansim: Modeling urban development for land use, transportation, and environmental planning. Journal of the American Planning Association, 68(3), 297–314. https://doi.org/10.1080/01944360208976274
   Web of Science ®Google Scholar
• Wang, Q., Roß-Nickoll, M., Wu, D., Deng, W., Wang, Z., Yuan, X., & Zhang, Y. (2018). Impervious area percentage predicated influence of rapid urbanization on macroinvertebrate communities in a southwest China river system. The Science of the Total Environment, 627, 104–117. https://doi.org/10.1016/j.scitotenv.2018.01.231
   PubMed Web of Science ®Google Scholar
• Washburn, B., Yancey, K., & Mendoza, J. (2010). Users guide for the California impervious surface coefficients. State of California. Ecotoxicology Program Integrated Risk Assessment Branch Office of Environmental Health Hazard Assessment.
   Google Scholar
• Wegmann, J. (2020). Death to single-family zoning…and new life to the missing middle. Journal of the American Planning Association, 86(1), 113–119. https://doi.org/10.1080/01944363.2019.1651217
   Web of Science ®Google Scholar
• Westerhoff, L., Sheppard, S. R. J., Mathew Iype, D., Cote, S., & Salter, J. (2018). Social mobilization on climate change and energy: An evaluation of research projects in British Columbia, Canada. Energy Research & Social Science, 46(July), 368–380. https://doi.org/10.1016/j.erss.2018.07.022
   Google Scholar
• White, R., & Engelen, G. (1997). Cellular automata as the basis of integrated dynamic regional modelling. Environment and Planning B, 24(2), 235–246. https://doi.org/10.1068/b240235
   Google Scholar
• Wilson, M. T., Fischbach, J. R., Siler-Evans, K., & Tierney, D. (2020). Modeling the uncertainty of potential impacts on robust stormwater management from neighborhood-scale impervious cover change: A case study of population-based scenarios in Pittsburgh, Pennsylvania modeling the uncertainty of potential impacts on robust. Urban Water Journal, 17(7), 628–641. https://doi.org/10.1080/1573062X.2020.1804594
   Web of Science ®Google Scholar
• Wu, W., Li, C., Liu, M., Hu, Y., & Xiu, C. (2020). Change of impervious surface area and its impacts on urban landscape: An example of Shenyang between 2010 and 2017. Ecosystem Health and Sustainability, 6(1), 1767511. https://doi.org/10.1080/20964129.2020.1767511
   Web of Science ®Google Scholar
• Zabcik, B. (2017). Texas stormwater scorecard: Evaluating municipal policies for green stormwater infrastructure and low-impact development. Environment Texas Research & Policy Center.
   Google Scholar
• Zeng, Q., Xie, Y., & Liu, K. (2019). Assessment of the patterns of urban land covers and impervious surface areas: A case study of Shenzhen, China. Physics and Chemistry of the Earth, 110(January), 1–7. https://doi.org/10.1016/j.pce.2019.04.002
   Google Scholar
• Zhang, N., Luo, Y. J., Chen, X. Y., Li, Q., Jing, Y. C., Wang, X., & Feng, C. H. (2018). Understanding the effects of composition and configuration of land covers on surface runoff in a highly urbanized area. Ecological Engineering, 125(October), 11–25. https://doi.org/10.1016/j.ecoleng.2018.10.008
   Google Scholar
• Zhang, Y., & Sun, L. (2019). Spatial-temporal impacts of urban land use land cover on land surface temperature: Case studies of two Canadian urban areas. International Journal of Applied Earth Observation and Geoinformation, 75(October 2018), 171–181. https://doi.org/10.1016/j.jag.2018.10.005
   Google Scholar
• Zhao, S. M., Ma, Y. F., Wang, J. L., & You, X. Y. (2019). Landscape pattern analysis and ecological network planning of Tianjin City. Urban Forestry & Urban Greening, 46, 126479. https://doi.org/10.1016/j.ufug.2019.126479
   Web of Science ®Google Scholar
• Zheng, Q., Hao, L., Huang, X., Sun, L., & Sun, G. (2020). Effects of urbanization on watershed evapotranspiration and its components in southern China. Water. Water, 12(3), 645. https://doi.org/10.3390/w12030645
   Web of Science ®Google Scholar