TY - JOUR
T1 - Understanding location decisions of energy multinational enterprises within the European smart cities’ context
T2 - An integrated AHP and extended fuzzy linguistic TOPSIS method
AU - Porro, Olga
AU - Pardo-Bosch, Francesc
AU - Agell, N.
AU - Sánchez, Mónica
N1 - Funding Information:
Funding: The authors acknowledge the support from the European Union “Horizon 2020 Research and Innovation Programme” under the grant agreements No 731297. Also, this research has been partially supported by the INVITE Research Project (TIN2016-80049-C2-1-R and TIN2016-80049-C2-2-R (AEI/FEDER, UE)), funded by the Spanish Ministry of Science and Information Technology.
Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution
PY - 2020/5/1
Y1 - 2020/5/1
N2 - Becoming a smart city is one of the top priorities in the urban agenda of many European cities. Among the various strategies in the transition path, local governments seek to bring innovation to their cities by encouraging multinational enterprises to deploy their green energy services and products in their municipalities. Knowing how to attract these enterprises implies that political leaders understand the multi-criteria decision problem that the energy sector enterprises face when deciding whether to expand to one city or another. To this end, the purpose of this study is to design a new manageable and controllable framework oriented to European cities’ public managers, based on the assessment of criteria and sub-criteria governing the strategic location decision made by these enterprises. A decision support framework is developed based on the AHP technique combined with an extended version of the hesitant fuzzy linguistic TOPSIS method. The main results indicate the higher relative importance of government policies, such as degree of transparency or bureaucracy level, as compared to market conditions or economic aspects of the city’s host country. These results can be great assets to current European leaders, they show the feasibility of the method and open up the possibility to replicate the proposed framework to other sectors or geographical areas.
AB - Becoming a smart city is one of the top priorities in the urban agenda of many European cities. Among the various strategies in the transition path, local governments seek to bring innovation to their cities by encouraging multinational enterprises to deploy their green energy services and products in their municipalities. Knowing how to attract these enterprises implies that political leaders understand the multi-criteria decision problem that the energy sector enterprises face when deciding whether to expand to one city or another. To this end, the purpose of this study is to design a new manageable and controllable framework oriented to European cities’ public managers, based on the assessment of criteria and sub-criteria governing the strategic location decision made by these enterprises. A decision support framework is developed based on the AHP technique combined with an extended version of the hesitant fuzzy linguistic TOPSIS method. The main results indicate the higher relative importance of government policies, such as degree of transparency or bureaucracy level, as compared to market conditions or economic aspects of the city’s host country. These results can be great assets to current European leaders, they show the feasibility of the method and open up the possibility to replicate the proposed framework to other sectors or geographical areas.
KW - AHP
KW - Fuzzy TOPSIS
KW - Group decision-making
KW - Hesitant fuzzy linguistic term sets
KW - Location factors
KW - Proportional hesitant fuzzy linguistic term sets
KW - Smart cities
UR - https://www.scopus.com/pages/publications/85085214886
UR - http://hdl.handle.net/20.500.14342/5078
U2 - 10.3390/en13102415
DO - 10.3390/en13102415
M3 - Article
AN - SCOPUS:85085214886
SN - 1996-1073
VL - 13
JO - Energies
JF - Energies
IS - 10
M1 - 2415
ER -