بررسی تأثیر شدت کاربری زمین بر پایداری زیست‌محیطی حمل‌ونقل در شهر مشهد

نویسندگان

دانشگاه فردوسی مشهد

چکیده

شدت کاربری زمین که انواع تراکم‌های شهری را شامل می‌شود، در بسیاری از پژوهش‌ها به‌عنوان عاملی تأثیرگذار بر الگوهای سفر ساکنین شناخته شده است؛ به‌طوری‌که افزایش انواع تراکم‌های جمعیتی، شغلی و مسکونی و کاربری باعث فشردگی بیش‌تر فرم شهر و کوتاه‌تر مسافت طی‌شده با خودروهای شخصی (Vehicle Kilometer Travelled) می‌شود و در نتیجه کاهش انتشار آلاینده‌های کربنی بخش حمل‌ونقل را در پی خواهد داشت. هدف اول مقاله‌ی حاضر بررسی تأثیرگذاری انواع تراکم بر مسافت سفر خودروهای شخصی است. بدین منظور 4 نوع تراکم (جمعیت، اشتغال، تراکم مسکونی و تراکم کاربری‌ها) در نظر گرفته شد. سپس با استفاده از ضرایب آنتروپی، جینی، موران و گری میزان تعادل در شدت کاربری زمین بین تراکم‌های نام‌برده مشخص گردید و همبستگی آن‌ها با متغیر وابسته VKT از طریق آزمون همبستگی پیرسون و مدل رگرسیون چندمتغیره تعیین شد. در مرحله‌ی سوم میزان انتشار CO2 و CO2e خودروهای شخصی به تفکیک مناطق شهر مشهد محاسبه شد و درنهایت رابطه‌ی همبستگی و رگرسیونی بین تراکم‌های شهری هر منطقه‌ موردبررسی قرار گرفت. نتایج نشان می‌دهد رابطه‌ی معناداری بین افزایش شدت کاربری زمین (تراکم) و کاهش VKT برقرار است و بین VKT و انتشار آلاینده‌های کربنی نیز همبستگی معناداری برابر با 0.845 برقرار است. در این میان، تراکم‌های شغلی بیش‌تر از سایر انواع تراکم بر الگوهای سفر ساکنین مشهد نقش دارد. تراکم شغلی دارای ضریب همبستگی 0/790- با VKT است و در مدل رگرسیون چندمتغیره نیز با R2=0.751 بیش‌ترین تأثیر را بر انتشار CO2 داشته است؛ بنابراین می‌توان با توزیع متعادل‌تر تراکم‌های شغلی در سطح مناطق، مسافت سفرهای با خودرو شخصی را کاهش داد و جهت تقویت الگوهای کم‌کربن تر حمل‌ونقل برنامه‌ریزی کرد.

کلیدواژه‌ها


عنوان مقاله [English]

Investigating the Effect of Land Use Intensity on Environmental Sustainability of Transportation in Mashhad

نویسندگان [English]

  • Fahime Ebadinia
  • Mohammad Ajza Shokouhi
  • Mohammad Rahim Rahnama
  • OmidAli Kharazmi
چکیده [English]

Introduction
Urban density includes population density, density of residential areas and employment density In addition, net and gross density are one of the most important determinants of the travel patterns of residents, which can be determined the type and extent of using personal and public transport and consequently the amount of CO2 emission. Kockelman (1997) states that the significance of the compaction index in relation to travel behavior can be as large as the density index as representative, and in the top of the other indicators of the urban form. On the other hand, Ewing and Cervero (2010) studies show that the VMT or VKT is the best indicator for measuring the effect of different types of densities as independent variables on energy consumption in daily trips. And its impact is so high that the per-capita correlation coefficient (VMT) and CO2 emissions are approximately equal to 1, and the per-capita correlation coefficient (VMT) and nitroxid emission are equal to 0.74.
The main goals of this paper are to find out what statistical relations and correlations exist in Mashhad between the main types of urban densities and the per capita of VKT. And which types of density can have the greatest effect on reducing VKT? What is the situation of 15th traffic areas in Mashhad in terms of carbon emission of private vehicles? How much the urban densities can impact on carbon emission reduction?
Materials and Methods
The research method is based on quantitative strategy and descriptive-analytical method. This paper has been used to analyze the behavior of residents' travel and its impact on the emission of pollutants from the dependent variable VKT. Also, the urban densities variables such as population density, employment density and non-residential employment density residential blocks density and land use density, which indicate the intensity of land use, were considered as independent variables.
The data needed to measure VKT was conducted from the results of origin-destination census analysis in Mashhad, 2011. By accessing the results of this survey and measuring the average distance between the origin and destination of private cars in each region, VKT was obtained for each region.
Discussion and Results
The findings show that urban areas with a density of more than 100 hectares of VKT are 2.5 to 4 km less than densities below 100 hectares. By reducing density from the central regions to the margin, the average travel distance increases. In the case of population density variable, the correlation coefficient was -0.557 and P-value = 0.02 which suggests that "there is a significant indirect relationship between increasing the density of regions and reducing the distance of personal car journeys at a significant level of 0.05". In addition to population density, employment density is also very effective in reducing VKT. In addition to population density, employment density is also very effective in reducing VKT and the correlation coefficient was -0.79 (P-value = 0.00) between these two variables at a significant level of 0.01. "
There is also a significant relationship (correlation coefficient = 0.656 and P-value = 0.008), between the density of residential blocks in each region and the vehicle kilometer travel of the residents in each area.
The fourth type of density is the application of mixed land use density .The results of the correlation between this variable and VKT shows that there is a significant relationship between the increase in the diversity of land use and the reduction of VKT. This correlation coefficient for the city of Mashhad is equal to 0.552 (p-value =0.03). Finally, we calculated the amount of carbon released by the each traffic zone and by using the influence coefficients which obtained from the multiple regression equation; we calculated the carbon reduction in the case of increasing density. The highest emissions of carbon contaminants are in areas that are not in a desirable situation of land use intensity. These areas, meanwhile, are more dependent on automobiles
Conclusions
In this paper, four types of densities including population density, employment densities, residential block densities, and mix land use density were considered. And finally, it was revealed that although all four types of densities have a linear relationship with the vehicle kilometer traveled (VKTs). However, only 2 variables including non-resident employment density and density of residential blocks are significant in regression model. Also, by calculating the amount of carbon released by the segments of the 15 traffic areas, it was found that the low carbon approach to transportation of the city of Mashhad in the area of urban densities should be emphasized by employment densities, because business trips have a larger share of personal trips than other travel destinations.

کلیدواژه‌ها [English]

  • Land use intensity
  • VKT
  • spatial correlation coefficients
  • Carbon Emission
  • Mashhad City
Barrett, J., Scott, A., & Vallack, H. (2001). The ecological footprint of passenger transport in Merseyside. Stockholm Environment Institute, York, available from www. mersyeside. org/pdf/EFofPassengerTransport.pdf. Bartholomew, K. (2005). Integrating Land Use Issues into Transportation Planning: Scenario Planning-Summary Report. Bartholomew, K. (2007). Land use-transportation scenario planning: promise and reality. Transportation, 34(4), 397-412. Breheny, M. (1996). Centrists, decentrists and compromisers: views on the future of urban form. The compact city: A sustainable urban form, 13-35. Cervero, R., & Kockelman, K. (1997). Travel demand and the 3Ds: density, diversity, and design. Transportation Research Part D: Transport and Environment, 2(3), 199-219. Chambers, N., & Lewis, K. (2001). Ecological footprint analysis: towards a sustainability indicator for business. Association of Chartered Certified Accountants. Chi, G., & Stone Jr, B. (2005). Sustainable transport planning: estimating the ecological footprint of vehicle travel in future years. Journal of urban planning and development, 131(3), 170-180. Commission of the European Communities. (1990). Green paper on the urban environment (Vol. 12902). Office for Official Publications of the European Communities. e Silva, J. D. A., & da Silva, F. N. (2005). New insights about the relation between modal split and urban density: the Lisbon Metropolitan Area case study revisited. WIT Transactions on The Built Environment, 77. Elkin, T., McLaren, D., & Hillman, M. (1991). Reviving the City: towards sustainable urban development. Friends of the Earth Trust. Ewing, R., & Cervero, R. (2010). Travel and the built environment: a meta-analysis. Journal of the American planning association, 76(3), 265-294. Fouchier, V. (2000). The case of Paris Region, and its urban density and mobility: What do we know? What can we do?. Compact Cities and Sustainable Development: A critical assessment of policies and plans from an international point perspective, 241-250. Frank, L. D., & Pivo, G. (1994). Impacts of mixed use and density on utilization of three modes of travel: single-occupant vehicle, transit, and walking. Transportation research record, 1466, 44-52. Glaeser, E. L., & Kahn, M. E. (2010). The greenness of cities: carbon dioxide emissions and urban development. Journal of urban economics, 67(3), 404-418. Haughton, G. (1997). Developing sustainable urban development models. Cities, 14(4), 189-195. Hillman, M. (1996) In Favour of the Compact City. In Jenks, M, Burton, E. & Williams, K. (Eds.) the Compact City: a sustainable Urban Form? London, E & FN Spon, 36-44. Holden, E. (2004). Ecological footprints and sustainable urban form. Journal of Housing and the Built Environment, 19(1), 91-109. Holtzclaw, J. (1994). Using residential patterns and transit to decrease auto dependence and costs (Vol. 11). San Francisco, CA: Natural Resources Defense Council. IEA (2009) World Energy Outlook - 2009. Paris: International Energy Agency. International Energy Agency (IEA). 2011. Transport energy and CO2- Moving toward Jacobs, J. (1961) The Death and Life of Great American Cities, New York, Vintage Books/ Random House. Knörr, W., & Reuter, C. (2008). EcoTransIT: Ecological transport information tool. Environmental Methodology and Data. Kockelman, K. (1997). Travel behavior as function of accessibility, land use mixing, and land use balance: evidence from San Francisco Bay Area. Transportation Research Record: Journal of the Transportation Research Board, (1607), 116-125. Levinson, D. M., & Kumar, A. (1997). Density and the journey to work. Growth and change, 28(2), 147-172. Naess, P. (2005). Residential location affects travel behavior—but how and why? The case of Copenhagen metropolitan area. Progress in Planning, 63(2), 167-257. Nakamura, K., & Hayashi, Y. (2013). Strategies and instruments for low-carbon urban transport: an international review on trends and effects. Transport Policy, 29, 264-274. Newman, M. (2005) the Compact City Fallacy. Planning Education and Research, 25 (1), 11-26. Newman, P. W., & Kenworthy, J. R. (1989). Gasoline consumption and cities: a comparison of US cities with a global survey. Journal of the american planning association, 55(1), 24-37. Stead, D. (2001). Relationships between land use, socioeconomic factors, and travel patterns in Britain. Environment and Planning B: Planning and Design, 28(4), 499-528. sustainability. Retrieved from www.iea.org /workshop/ cop/ cop15/ Fulton_ IEA_Day.pdf. Thomas, L., & Cousins, W. (1996). The compact city: a successful, desirable and achievable urban form. The compact city: A sustainable urban form, 53-65. Wackernagel, M., & Rees, W. (1998). Our ecological footprint: reducing human impact on the earth (No. 9). New Society Publishers. Yan, X., & Crookes, R. J. (2009). Reduction potentials of energy demand and GHG emissions in China's road transport sector. Energy Policy, 37(2), 658-668. Zamba, A., & Hadjibiros, K. (2007, September). Estimating the ecological footprint of vehicles in the city of Athens. In Proc. 10th Int. Conf. on Environmental Science and Technology, University of the Aegean, Rhodes (pp. 1638-1645). Zhang, Y., & Guindon, B. (2006). Using satellite remote sensing to survey transport-related urban sustainability: Part 1: Methodologies for indicator quantification. International Journal of Applied Earth Observation and Geoinformation, 8(3), 149-164