Assessing the benefits of urban consolidation centres: an overview based on a systematic literature review

Figures & data

Figure 1. Illustration of the SLR process.

Table 1. Description of the articles and changes made.

With a baseline	Description	Changed vehicle type
Dupas et al. (2023)	Scenario study of implementing UCC to handle deliveries in the city centre of Bordeaux	No
Katsela et al. (2022)	Three scenarios in the city of Malmö: (1) a micro UCC close to the delivery points, (2) consolidation at LSP terminals, and (3) both a UCC and a micro UCC.	Change to light electric trucks with reduced loading capacity for all scenarios.
Anand et al. (2021)	Simulation study of the distribution to retailers via one UCC in the city of Rotterdam.	Change to electric trucks with reduced loading capacity
Paddeu (2021)	Scenario study of deliveries to local business in Cagliari using a UCC.	Same electric trucks were used in both the baseline and scenario.
Roquel et al. (2018)	Scenario study of implementing 15 UCCs in the city of Manilla	Same vehicle used in both the baseline and scenario. No change in loading capacity.
Veličković et al. (2018)	Seven scenarios for three different locations of one to three UCCs in the city of Novi Sad. The two most extreme scenarios are: (1): one UCC at the “worst” available location, (2): three UCCs.	Change to smaller electric light distribution vehicles with reduced capacity.
Papoutsis et al. (2018)	Scenario study of one retailer’s deliveries to its stores in the City of Antwerp.	Change to slightly smaller trucks with reduced loading capacity.
Nocera and Cavallaro (2017)	Scenario study implementing a UCC and: (1) a few light electric trucks, (2) doubling the use of electric trucks, and (3) increased use of electric trucks by 67% compared to (2).	Change to light electric trucks, no change in loading capacity.
Alessandrini et al. (2012)	Scenario study of deliveries in Rome using a UCC in combination with inbound rail transport.	Same vehicle type for scenario 1. Change to hybrid vehicles in scenario 2. No change in loading capacity.
Browne et al. (2011)	A trial performed on deliveries from one office supply company to retailers in London.	Diesel-driven trucks were combined with electric trucks and electric tricycles, both with reduced loading capacity.
No baseline reported
Settey et al. (2021)	Scenario analysis on UCC locations based on one retailer’s deliveries to stores in Bratislava.	Same type of electric vehicles in all scenarios.
Dupas et al. (2020)	Scenario analysis of three carriers in Tokyo. Coordinated transport from LSP to the UCC, and integration (i.e. sending packages between UCCs) was tested. Two extreme scenarios are: Scenario 1: Integration, no pooling. Scenario 2: Pooling and integration. Study conducted in two parts, Part 1 with homogeneous vehicle fleet and part 2 with heterogeneous vehicle fleet.	Part 1: No change in vehicle or loading capacity Part 2: Vehicles with reduced loading capacity for all scenarios

Table 2. Comparison of the benefits of UCCs in different scenarios. A negative value means an increase compared to the baseline without UCC.

	Vkm/year Baseline	Vkm/year UCC	Change in vkm [%]	Change in CO2 [%]
Alessandrini et al. (2012), Scenario 2	72 037	27 610	62%	75%
Alessandrini et al. (2012) Scenario 1	72 037	27 610	62%	51%
Katsela et al. (2022) Scenario 3	43 700	17 100	61%	74%
Nocera and Cavallaro (2017) Scenario 3	3 600	1 400	61%	68%
Dupas et al. (2020) Part 1 Scenario 2	250 600	124 900	50%	48%
Settey et al. (2021) Scenario 1	397 700	203 900	49%	49%***
Settey et al. (2021) Scenario 2	397 700	232 000	42%	42%***
Dupas et al. (2023)	4 520 200	2 953 300	35%	25%
Nocera and Cavallaro (2017) Scenario 2	3 600	2 400	32%	33%
Dupas et al. (2020) Part 2 Scenario 2	316 900	217 100	31%	32%
Settey et al. (2021) Scenario 3	397 700	300 450	24%	24%***
Katsela et al. (2022) Scenario 2	43 700	34 8900	20%	29%
Browne et al. (2011)	35 000	28 500	19%	54%
Nocera and Cavallaro (2017) (2017) Scenario 1	3 600	3 100	14%	17%
Dupas et al. (2020) Part 1 Scenario 1	250 600	221 200	12%	5%
Dupas et al. (2020) Part 2 Scenario 2	316 900	285 400	10%	4%
Roquel et al. (2018)	829 600	795 000	4%	4%***
Anand et al. (2021)	12 300	11 900	4%	4%***
Papoutsis et al. (2018)	14 500	14 900	−3%	−7%
Veličković et al. (2018) Scenario 2	5 982 400	6 905 600	−15%	24%
Katsela et al. (2022) Scenario 1	43 700	62 700	−44%	−25%**
Veličković et al. (2018) Scenario 1	5 982 400	14 398 400	−141%	−98%**
Paddeu (2021)*	N/D	N/D	Reduction of 60 000 vkm/year

*The article has a baseline, but only reported a reduction in vkm.

**No reported CO₂ data, the number is based on the changes in external costs presented in the article.

***No reported CO₂ data, the number is based on the changes in vkm.

Dupas, R., Deschamps, J.-C., Taniguchi, E., Qureshi, A. G., & Hsu, T. (2023). Optimizing the location selection of urban consolidation centers with sustainability considerations in the city of Bordeaux. Research in Transportation Business & Management, 47, 1–17. https://doi.org/10.1016/j.rtbm.2022.100943

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Katsela, K., Pålsson, H., & Ivernå, J. (2022). Environmental impact and costs of externalities of using urban consolidation centres: A 24-hour observation study with modelling in four scenarios. International Journal of Logistics Research and Applications, 25(12), 1542–1563. https://doi.org/10.1080/13675567.2021.1915261

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Anand, N., van Duin, R., & Tavasszy, L. (2021). Carbon credits and urban freight consolidation: An experiment using agent based simulation. Research in Transportation Economics, 85, 1–10. https://doi.org/10.1016/j.retrec.2019.100797

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Paddeu, D. (2021). The five attribute performance assessment (FAPA) model to evaluate the performance of an urban consolidation centre. Research in Transportation Economics, 90, 1–9. https://doi.org/10.1016/j.retrec.2021.101065

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Roquel, K. I. D., Fillone, A., & Yu, K. D. (2018). Co-Benefit assessment of logistics optimization programs: The case of the philippine greater capital region. International Journal of GEOMATE, 15, 1–8. https://doi.org/10.21660/2018.48.69279

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Veličković, M., Stojanović, Đ, Nikoličić, S., & Maslarić, M. (2018). Different urban consolidation centre scenarios: Impact on external costs of last-mile deliveries. Transport, 33(4), 948–958. https://doi.org/10.3846/16484142.2017.1350995

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Papoutsis, K., Dewulf, W., Vanelslander, T., & Nathanail, E. (2018). Sustainability assessment of retail logistics solutions using external costs analysis: A case-study for the city of Antwerp. European Transport Research Review, 10(2), 1–17. https://doi.org/10.1186/s12544-018-0297-5

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Nocera, S., & Cavallaro, F. (2017). A two-step method to evaluate the well-To-wheel carbon efficiency of urban consolidation centres. Research in Transportation Economics, 65, 44–55. https://doi.org/10.1016/j.retrec.2017.04.001

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Alessandrini, A., Site, P. D., Filippi, F., & Salucci, M. V. (2012). Using rail to make urban freight distributionmore sustainable. European Transport - Trasporti Europei 50, 1–17.

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Browne, M., Allen, J., & Leonardi, J. (2011). Evaluating the use of an urban consolidation centre and electric vehicles in central London. IATSS Research, 35(1), 1–6. https://doi.org/10.1016/j.iatssr.2011.06.002

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Settey, T., Gnap, J., Beňová, D., Pavličko, M., & Blažeková, O. (2021). The growth of E-commerce Due to COVID-19 and the need for urban logistics centers using electric vehicles: Bratislava case study. Sustainability, 13(10), 1–20. https://doi.org/10.3390/su13105357

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Dupas, R., Taniguchi, E., Deschamps, J.-C., & Qureshi, A. G. (2020). A multi-commodity network flow model for sustainable performance evaluation in city logistics: Application to the distribution of multi-tenant buildings in Tokyo. Sustainability, 12(6), 1–18. https://doi.org/10.3390/su12062180

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