Impact of land-use regulations on logistics facility distribution

Transcription

1 International Urban Freight Conference (I-NUF) October 21-23, Long Beach, California Impact of land-use regulations on logistics facility distribution October 23, 2015 Takanori SAKAI, University of Illinois at Chicago Kazuya KAWAMURA, University of Illinois at Chicago Tetsuro HYODO, Tokyo University of Marine Science and Technology

2 Background The distribution of logistics facilities has been changing along with the evolution in logistics practices and urban environments. In North American and European cities, suburbanization of logistics facilities has been observed. Logistics sprawl (Dablanc et al., 2014), the movement of logistics facilities away from urban centers, is an emerging concern because of the associated negative externalities. 2

3 Literature Review Several research measure logistics sprawl in cities around the world. Atlanta (Dablanc & Ross, 2012), L.A. (Dablanc, et al., 2014), U.S. metropolitan areas (Cidell, 2010), Paris (Dablanc and Rakotonarivo, 2010), Toronto (Woudsma et al., 2015 ), Tokyo (Sakai et al., 2015) etc. Sakai et al. (2015) observed the increase in the spatial mismatch between facility locations and shipping demands as the facilities locate farther from the urban center. 3

4 Literature Review (2) Limited number of location choice models have been developed for logistics facilities (Hagino, 2007; Hagino et al., 2011; Cao and Sano, 2010). Some research focuses on the factors of location choice (land rent, accessibility and proximity indicators, agglomerations, etc. (Verhetsel et al., 2005; Woudsma et al., 2008; Van den Heuvel et al., 2013) The relationship between land-use policy logistics facility locations traffic impacts has not been addressed well in the past. 4

5 Objectives Objectives To analyze the mechanism of logistics facility location choice in the Tokyo Metropolitan Area (TMA) using statistical models (discrete choice model) To evaluate effects of land-use policies on the distribution of logistics facilities and traffic impacts We focus on the impacts of land-use zoning under the assumption that the location choice for a logistics facility can be influenced by the supply of the land with specific zoning codes. 5

6 Outline for the Rest 1. Land-use regulations in Japan 2. Modelling location choice for logistics facilities 2.1 Data samples for location choice model 2.2. Model structure and variables 2.3. Estimated model 3. Scenario Simulation 3.1 Zoning Scenarios 3.2 Changes in probability on location choice 3.3 Performance of the scenarios 3.4 Interpretation 4. Conclusion 6

7 Land-use Regulations in Japan Land-use regulations Tokyo Metropolitan Area Area Divisions (two categories): Urbanization Promotion Area (UPA) Urbanization Control Area (UCA) Zoning Districts (16 categories): Most lands in UPAs are specified as Land-use Zoning District (12 sub-categories that include seven residential uses, two commercial uses and three industrial use categories) 7

8 Data samples for location choice model 2013 Tokyo Metropolitan Freight Survey (TMFS): Urban freight survey for the TMA that covers the area of 23 thou. km 2 & the population of 42 mil. 4,646 logistics facilities (DCs, truck terminals, warehouses, intermodal facilities and oil terminals) in the sample set. 1,350 of them have been developed in 2003 or later. 601 of them provided complete information. Data for 601 logistics facilities are used for the analyses. 8

9 Model Structure Unit of analysis: 1 km-by-1 km GIS polygon Utility: U i,j = V i,j + ε i,j (Eq. 1) V i,j : an observed deterministic component ε i,j : a randomly distributed unobserved component V i,j = αd i,j + k β k L j,k (Eq. 2) D i,j : the Eucledean distance between location j and the optimum location for facility i L j,k : the measure of the characteristics k of location j α, β k : the parameters to be estimated Note: The optimum location is the location where the total travel distance of inbound/outbound trucks is minimized for a facility. Probability: P i,j = exp (V i,j+lna j ) h exp (V i,h +lna h ) (Eq.3) A j : the available area for development at location j 9

10 Data Variables Accessibility Population density Distance from the optimum location (D i,j ) ln(accessibility to employments) Accessibility to employment = D j exp ( μ log d ij ) D j : no. of employments in location j d ij : network distance between locations i and j μ: impedance factor (=0.5) ln(distance from nearest expressway IC) Land Characteristics Along Ring Road 3 (dummy) Port Area (dummy) ln(average land price) Zoning (Share) Residential zone Commercial zone Quasi-industrial zone Industrial zone Exclusively industrial zone Urbanization control zone Miscellaneous land use Non-urban planning zone 10

11 Floor Area Medium Small facilities facilities Variables tend have Have to more be more located variety limit on (standardized closer excl. available dummy) in to demands demands Not locations willing to Accessibility locate in high density area Small <=400 m 2 Medium 400 m 2 <, <=3000 m 2 Large > 3000 m 2 (n=130) (n=228) (n=243) exp(beta) t-value exp(beta) t-value exp(beta) t-value Distance from the optimum location * * * ln(accessibility to employment) * * * ln(dist. from nearest expressway IC) * Land Characteristics Population Density * * * Along Ring Road 3 (dummy) * Port area (dummy) Only for large facilities, all industrial zones are significant Ln(average land price) * Port area ln(average land price) * * Zoning (Share) Commercial zone * * Quasi-industrial zone * * * Industrial zone * Exclusively industrial zone * Urbanization control zone Miscellaneous land use * Non-urban planning zone Adjusted Rho-squared

12 Scenario Simulation Calculate the changes in the choice probability for each 1 km-by-1 km polygon under different land-use zoning scenarios. Conduct simulations of land-use zoning scenarios to evaluate the reproducibility of the model and estimate the impacts of the scenario (Monte Carlo method). Measure the following indicators: Avg. distance from the urban center Avg. distance from the optimum locations Avg. shipping distance (Euclidean distance) 12

13 Zoning Scenarios (1) Focusing on Quasi-Industrial Zone (QIZ) Base Scenario (QIZ: 414 km 2 ): Actual zoning in the area. Share: the percentage of QIZ in each 1 km x 1 km polygon. 13

14 Zoning Scenarios (2) Focusing on Quasi-Industrial Zone (QIZ) Scenario A (QIZ: 414 km 2 ): Relocation of QIZ into the polygons that are within 1.5 km of any optimum facility locations. Share: the percentage of QIZ in each 1 km x 1 km polygon. 14

15 Zoning Scenarios (3) Focusing on Quasi-Industrial Zone (QIZ) Scenario B (QIZ: 626 km 2 ): While actual QIZs remain, 25% of the residential zones are converted to QIZs in the area that are within 1.5 km of any optimum facility locations. Share: the percentage of QIZ in each 1 km x 1 km polygon. 15

16 Zoning Scenarios (4) Focusing on Quasi-Industrial Zone (QIZ) Scenario C (QIZ: 839 km 2 ): While actual QIZs remain, 50% of the residential zones are converted to QIZs in the area that are within 1.5 km of any optimum facility locations. Share: the percentage of QIZ in each 1 km x 1 km polygon. 16

17 Changes in Probability In Scn B & C, the decreases in probability are defused around the urban center. 17

18 Large Medium Small Result of the Scenario Simulation Dist. from the urban center (km) Dist. from the optimum locations (km) Ave. shipping distance (Euclidean dist., km) Mean S.D. Change from Base Scn. (%) Mean S.D. Change from Base Scn. (%) Mean S.D. Change from Base Scn. (%) Actual Base Scn Scn. A Scn. B Scn. C Actual Base Scn Scn. A Scn. B Scn. C Actual Base Scn Scn. A Scn. B Scn. C

19 Interpretation The simulation using virtual scenarios shows only marginal effects of zoning. Factors that are not considered, such as land availability and the nuances in the conditions of the available sites, may have much larger influence. Many of the optimum locations are situated in the areas that area extremely expensive and/or dense or suffer from other conditions that make them not viable. (Zoning with the influence to price and other factors has to be tested) There are zonings other than the QIZ that are open to logistics facilities (49.6% of the 601 samples are located in non-industrial zones), especially for smaller facilities. The greater sensitivity of large facilities to the scenarios may be explained by much less flexibility with the zoning (& available sites). 19

20 Conclusion Modelling analysis - highlights the heterogeneity in the locational preference for logistics facilities across facilities sizes and the shipment origins and destinations. (Polices should fit with specific needs) - indicate that applying general accessibility indicators may have limited explanatory powers of location models. (Heterogeneity should be considered in modeling.) The impacts of zoning regulation alone seems limited for promoting efficient logistics facility distribution in terms of truck travel distance. (In case of the TMA, it is difficult to control the facility locations through zoning alone, especially for smaller facilities.) 20

21 Fin thank you for listening

22 Only for large facilities, all industrial zones are significant Elasticity Floor Medium Small Area facilities Small Medium Large facilities tend have to more be located <=400 m Have more variety limit on 400 m 2 <, <=3000 m 2 > 3000 m 2 (n=130) (n=228) (n=243) Variables (normalized) closer available in to demands demands Not locations willing to Beta t-value Beta t-value Beta t-value Accessibility locate in high density area Distance from the optimum location * * * ln(accessibility to employment) * * * ln(dist. from nearest expressway IC) * Land Characteristics Population Density * * * Along Ring Road 3 (dummy) * Port area (dummy) Ln(average land price) * Port area ln(average land price) * * Zoning (Share) Commercial zone * * Quasi-industrial zone * * * Industrial zone * Exclusively industrial zone * Urbanization control zone Miscellaneous land use * Non-urban planning zone Adjusted Rho-squared