Location, concentration, and performance of economic activity in Brazil

What are the prospects for economic development in lagging sub-national regions? What are the roles of public infrastructure investments and fiscal incentives in influencing the location and performance of industrial activity? To examine these questions, the authors estimate a spatial profit function for industrial activity in Brazil that explicitly incorporates infrastructure improvements and fiscal incentives in the cost structure of individual firms. The authors use firm level data from the 2001 annual industrial survey along with spatially disaggregated regional data and find that there are considerable cost savings from being located in areas with relatively lower transport costs to reach large markets. In comparison, fiscal incentives, such as tax expenditures, have modest effects in terms of influencing firm level costs. Although the results suggest that firms benefit from being in locations with good access to markets, the authors do not suggest that improving interregional connectivity would necessarily assist lagging regions. In the short run, improving interregional connectivity implicitly reduces a natural tariff barrier so firms currently serving large markets and benefiting from economies of scale can more easily expand into new markets in competition with local producers. Therefore, producers in the leading regions can crowd out local producers, which would be detrimental for local production and employment in the lagging region.Decentralization,Economic Theory&Research,Banks&Banking Reform,Environmental Economics&Policies,Water and Industry,Environmental Economics&Policies,Water and Industry,Banks&Banking Reform,Economic Theory&Research,Municipal Financial Management

The Shape-Alignment relation in Λ\LambdaCDM Cosmic Structures

In this paper we study the supercluster - cluster morphological properties using one of the largest ($2\times 512^{3}$ SPH+N-body simulations of large scale structure formation in a $\Lambda$CDM model, based on the publicly available code GADGET. We find that filamentary (prolate-like) shapes are the dominant supercluster and cluster dark matter halo morphological feature, in agreement with previous studies. However, the baryonic gas component of the clusters is predominantly spherical. We investigate the alignment between cluster halos (using either their DM or baryonic components) and their parent supercluster major-axis orientation, finding that clusters show such a preferential alignment. Combining the shape and the alignment statistics, we also find that the amplitude of supercluster - cluster alignment increases although weakly with supercluster filamentariness.Comment: Accepted for puplication in MNRAS, 10 pages, 15 figure

MS-ReRO and D-ROSE methods: Assessing relational uncertainty and evaluating scenarios risks and opportunities on multi-scale infrastructure systems

[EN] There is a growing interest in model-based decision support systems contributing to strategic planning. The application of these in the case of urban infrastructure planning requires methods specifically aimed at addressing the relational uncertainties arising from the complex, multi-scale, nature of this field. This study presents UPSS, a comprehensive urban planning support system integrating the generation of planning alternatives, the evaluation of alternatives under a set of relevant scenarios selected dynamically in a cognitive way, and the proposal of policies to accompany the planning alternative. For this purpose, UPSS integrates two novel methods. These deal respectively with the ex post identification of relevant scenarios for the evaluation of the vulnerability and resilience of the alternatives, and with the assessment of relational uncertainty. According to the risks and opportunities borne by the system, the process makes it possible to select an infrastructure plan to alleviate the problem of urban vulnerability, as well as a set of relational contracts for its proper implementation across the different governmental scales of the infrastructure system. The whole process is tested via a case study, in which USPP first proposes optimal urban infrastructure plans that contribute to ameliorate the problem of urban vulnerability in Spain, then evaluates the risks and opportunities attached to the planning alternatives, and finally presents sets of policy measures to accompany the implementation of the alternative selected.The authors acknowledge the financial support of the Spanish Ministry of Economy and Competitiveness, along with FEDER funding (Project: BIA2017-85098-R).Salas, J.; Yepes, V. (2019). MS-ReRO and D-ROSE methods: Assessing relational uncertainty and evaluating scenarios risks and opportunities on multi-scale infrastructure systems. Journal of Cleaner Production. 216:607-623. https://doi.org/10.1016/j.jclepro.2018.12.083S60762321

Impact of R&D&I on the Performance of Spanish Construction Companies

[EN] Deciding whether certain factors should be considered drivers of innovation in construction firms is crucial in terms of improving their performance and survival in an environment that is changing by leaps and bounds. Throughout the years, construction companies have been considered to be traditional and without the tendency to innovate. However, several studies have confirmed that this perception of the sector is evolving and that successful instruments from other industries are gradually being adapted for the benefit of the industry. The objective of this paper is therefore to investigate the potential factors affecting the performance of these organizations. Eighteen factors related to the individual, group, and organizational levels were identified through a review of the literature and an instrument developed that was validated by experienced professionals. A questionnaire was sent to 103 people working in the sector at the national level to obtain their views. The results of the classification analysis indicate that "technology and equipment" and "software acquisition" are considered the two most significant factors. In addition, these 18 factors can be classified into 7 groups: (i) internal drivers of innovation; (ii) innovation within the organization; (iii) technological innovation; (iv) technological links with the environment; (v) external drivers of innovation; (vi) innovation in processes; (vii) a culture of innovation in the company. Innovation in processes has the highest level of impact. This research deepens the current understanding of the factors at different organizational levels that must be highlighted in the implementation of an R&D system in order for companies to improve their performance and survival in future processes.This research was funded by the Spanish Ministry of Economy and Competitiveness, along with FEDER, Grant no. BIA2017-85098-R.López, S.; Yepes, V. (2020). Impact of R&D&I on the Performance of Spanish Construction Companies. Advances in Civil Engineering. 2020:1-14. https://doi.org/10.1155/2020/7835231S114202

Assessing the Relationship between Landscape and Management within Marinas: The Managers' Perception

[EN] Marinas are maritime features related to nautical tourism. The contemplation of pleasant surroundings acquires great importance in achieving this leisure character. The European Landscape Convention undertakes the necessity of integrating landscape into the planning policies. Thus, the marina¿s management decision-making processes should reflect this awareness of the landscape. However, the landscape evaluation has not been appropriately considered despite its importance. This research attempts to introduce an initial framework to evaluate this influence, highlighting the strengths and weaknesses of the different subjects. For this purpose, the most significant elements of the marina management related to the landscape were rated, both from management and landscape perspectives. Two expert panels from Spain were used: 23 experts evaluated the above elements following the Delphi method, and 17 weighted the main management activities using DHP. Results show that there is a lack of concern for the landscape. Managers tend to consider physical conditions, whereas subjective conditions are relegated to the background. In this respect, this methodology provides the first stage for the landscape/management relationship, helping managers identify the main topics and prioritize related actions.This research was funded by Ministerio de Ciencia e Innovación grant number PID2020-117056RB-I00Martín, R.; Yepes, V. (2022). Assessing the Relationship between Landscape and Management within Marinas: The Managers' Perception. Land. 11(7):1-22. https://doi.org/10.3390/land1107096112211

Urban vulnerability assessment: Advances from the strategic planning outlook

[EN] Urban strategic planning and urban vulnerability assessment have increasingly become important issues in both policy agenda and academia. However, a comprehensive review of the advances made in urban vulnerability, emphasizing their shared aspects, has yet to be performed. The aiming of this paper is to addresses the latter by conducting an evaluation on assessment methods disclosed in this decade. Once their common evolutive pathway is traced, the review follows an analytical framework, based on the above, evaluating the research requirements from both a quantitative and qualitative point of view. Our findings indicate that the robustness, cognitive and participatory research lines are those in which most advancement has been made, while those of urban dynamics and multi-scale progressed the least. Our analysis also demonstrates that methods integrating more lines of research, as well as the employment of comprehensive approaches, promotes advancing the developmental stage. We conclude that the focusing of research lines should be shifted, in order to bridge the qualitative gap identified without demanding an improbable, quantitative increase.The authors acknowledge the financial support of the Spanish Ministry of Economy and Competitiveness, along with FEDER funding (Project: BIA2017-85098-R).Salas, J.; Yepes, V. (2018). Urban vulnerability assessment: Advances from the strategic planning outlook. Journal of Cleaner Production. 179:544-558. https://doi.org/10.1016/j.jclepro.2018.01.088S54455817

VisualUVAM: A Decision Support System Addressing the Curse of Dimensionality for the Multi-Scale Assessment of Urban Vulnerability in Spain

[EN] Many-objective optimization methods have proven successful in the integration of research attributes demanded for urban vulnerability assessment models. However, these techniques suffer from the curse of the dimensionality problem, producing an excessive burden in the decision-making process by compelling decision-makers to select alternatives among a large number of candidates. In other fields, this problem has been alleviated through cluster analysis, but there is still a lack in the application of such methods for urban vulnerability assessment purposes. This work addresses this gap by a novel combination of visual analytics and cluster analysis, enabling the decision-maker to select the set of indicators best representing urban vulnerability accordingly to three criteria: expert¿s preferences, goodness of fit, and robustness. Based on an assessment framework previously developed, VisualUVAM affords an evaluation of urban vulnerability in Spain at regional, provincial, and municipal scales, whose results demonstrate the effect of the governmental structure of a territory over the vulnerability of the assessed entities.This research was funded by the Spanish Ministry of Economy and Competitiveness, along with FEDER, grant number Project: BIA2017-85098-R".Salas, J.; Yepes, V. (2019). VisualUVAM: A Decision Support System Addressing the Curse of Dimensionality for the Multi-Scale Assessment of Urban Vulnerability in Spain. Sustainability. 11(8):2191-01-2191-17. https://doi.org/10.3390/su11082191S2191-012191-17118Rigillo, M., & Cervelli, E. (2014). Mapping Urban Vulnerability: The Case Study of Gran Santo Domingo, Dominican Republic. Advanced Engineering Forum, 11, 142-148. doi:10.4028/www.scientific.net/aef.11.142Malekpour, S., Brown, R. R., & de Haan, F. J. (2015). Strategic planning of urban infrastructure for environmental sustainability: Understanding the past to intervene for the future. Cities, 46, 67-75. doi:10.1016/j.cities.2015.05.003Salas, J., & Yepes, V. (2018). Urban vulnerability assessment: Advances from the strategic planning outlook. Journal of Cleaner Production, 179, 544-558. doi:10.1016/j.jclepro.2018.01.088Moraci, F., Errigo, M., Fazia, C., Burgio, G., & Foresta, S. (2018). Making Less Vulnerable Cities: Resilience as a New Paradigm of Smart Planning. Sustainability, 10(3), 755. doi:10.3390/su10030755De Gregorio Hurtado, S. (2017). Is EU urban policy transforming urban regeneration in Spain? Answers from an analysis of the Iniciativa Urbana (2007–2013). Cities, 60, 402-414. doi:10.1016/j.cities.2016.10.015Salas, J., & Yepes, V. (2019). MS-ReRO and D-ROSE methods: Assessing relational uncertainty and evaluating scenarios’ risks and opportunities on multi-scale infrastructure systems. Journal of Cleaner Production, 216, 607-623. doi:10.1016/j.jclepro.2018.12.083Dor, A., & Kissinger, M. (2017). A multi-year, multi-scale analysis of urban sustainability. Environmental Impact Assessment Review, 62, 115-121. doi:10.1016/j.eiar.2016.05.004Rega, C., Singer, J. P., & Geneletti, D. (2018). Investigating the substantive effectiveness of Strategic Environmental Assessment of urban planning: Evidence from Italy and Spain. Environmental Impact Assessment Review, 73, 60-69. doi:10.1016/j.eiar.2018.07.004Salas, J., & Yepes, V. (2018). A discursive, many-objective approach for selecting more-evolved urban vulnerability assessment models. Journal of Cleaner Production, 176, 1231-1244. doi:10.1016/j.jclepro.2017.11.249Penadés-Plà, V., García-Segura, T., Martí, J., & Yepes, V. (2016). A Review of Multi-Criteria Decision-Making Methods Applied to the Sustainable Bridge Design. Sustainability, 8(12), 1295. doi:10.3390/su8121295Zio, E., & Bazzo, R. (2011). A clustering procedure for reducing the number of representative solutions in the Pareto Front of multiobjective optimization problems. European Journal of Operational Research, 210(3), 624-634. doi:10.1016/j.ejor.2010.10.021Ishibuchi, H., Akedo, N., & Nojima, Y. (2015). Behavior of Multiobjective Evolutionary Algorithms on Many-Objective Knapsack Problems. IEEE Transactions on Evolutionary Computation, 19(2), 264-283. doi:10.1109/tevc.2014.2315442A fast and effective method for pruning of non-dominated solutions in many-objective problems https://www.scopus.com/inward/record.uri?eid=2-s2.0-33750253049&partnerID=40&md5=f46109796025a884fd054d73e71c308eTaboada, H. A., Baheranwala, F., Coit, D. W., & Wattanapongsakorn, N. (2007). Practical solutions for multi-objective optimization: An application to system reliability design problems. Reliability Engineering & System Safety, 92(3), 314-322. doi:10.1016/j.ress.2006.04.014Kasprzyk, J. R., Nataraj, S., Reed, P. M., & Lempert, R. J. (2013). Many objective robust decision making for complex environmental systems undergoing change. Environmental Modelling & Software, 42, 55-71. doi:10.1016/j.envsoft.2012.12.007Adger, W. N. (2006). Vulnerability. Global Environmental Change, 16(3), 268-281. doi:10.1016/j.gloenvcha.2006.02.006A new decision sciences for complex systems http://people.physics.anu.edu.au/~tas110/Teaching/Lectures/L1/Material/Lempert02.pdfThomas, J., & Kielman, J. (2009). Challenges for Visual Analytics. Information Visualization, 8(4), 309-314. doi:10.1057/ivs.2009.26Andrienko, G., Andrienko, N., Demsar, U., Dransch, D., Dykes, J., Fabrikant, S. I., … Tominski, C. (2010). Space, time and visual analytics. International Journal of Geographical Information Science, 24(10), 1577-1600. doi:10.1080/13658816.2010.508043Santos, J., Ferreira, A., & Flintsch, G. (2017). A multi-objective optimization-based pavement management decision-support system for enhancing pavement sustainability. Journal of Cleaner Production, 164, 1380-1393. doi:10.1016/j.jclepro.2017.07.027Análisis urbanístico de barrios vulnerables https://www.fomento.gob.es/MFOM/LANG_CASTELLANO/DIRECCIONES_GENERALES/ARQ_VIVIENDA/SUELO_Y_POLITICAS/OBSERVATORIO/Analisis_urba_Barrios_Vulnerables/Informes_CCAA.htmBirkmann, J., Garschagen, M., & Setiadi, N. (2014). New challenges for adaptive urban governance in highly dynamic environments: Revisiting planning systems and tools for adaptive and strategic planning. Urban Climate, 7, 115-133. doi:10.1016/j.uclim.2014.01.006Besagni, G., & Borgarello, M. (2019). The socio-demographic and geographical dimensions of fuel poverty in Italy. Energy Research & Social Science, 49, 192-203. doi:10.1016/j.erss.2018.11.007Khalil, N., Kamaruzzaman, S. N., & Baharum, M. R. (2016). Ranking the indicators of building performance and the users’ risk via Analytical Hierarchy Process (AHP): Case of Malaysia. Ecological Indicators, 71, 567-576. doi:10.1016/j.ecolind.2016.07.032Pellicer, E., Sierra, L. A., & Yepes, V. (2016). Appraisal of infrastructure sustainability by graduate students using an active-learning method. Journal of Cleaner Production, 113, 884-896. doi:10.1016/j.jclepro.2015.11.010Sierra, L. A., Yepes, V., & Pellicer, E. (2018). A review of multi-criteria assessment of the social sustainability of infrastructures. Journal of Cleaner Production, 187, 496-513. doi:10.1016/j.jclepro.2018.03.022Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9-26. doi:10.1016/0377-2217(90)90057-

Enhancing sustainability and resilience through multi-level infrastructure planning

[EN] Resilient planning demands not only resilient actions, but also resilient implementation, which promotes adaptive capacity for the attainment of the planned objectives. This requires, in the case of multi-level infrastructure systems, the simultaneous pursuit of bottom-up infrastructure planning for the promotion of adaptive capacity, and of top-down approaches for the achievement of global objectives and the reduction of structural vulnerabilities and imbalances. Though several authors have pointed out the need to balance bottom-up flexibility with top-down hierarchical control for better plan implementation, very few methods have yet been developed with this aim, least of all with a multi-objective perspective. This work addressed this lack by including, for the first time, the mitigation of urban vulnerability, the improvement of road network condition, and the minimization of the economic cost as objectives in a resilient planning process in which both actions and their implementation are planned for a controlled, sustainable development. Building on Urban planning support system (UPSS), a previously developed planning tool, the improved planning support system affords a planning alternative over the Spanish road network, with the best multi-objective balance between optimization, risk, and opportunity. The planning process then formalizes local adaptive capacity as the capacity to vary the selected planning alternative within certain limits, and global risk control as the duties that should be achieved in exchange. Finally, by means of multi-objective optimization, the method reveals the multi-objective trade-offs between local opportunity, global risk, and rights and duties at local scale, thus providing deeper understanding for better informed decision-making.This research was funded by the Spanish Ministry of Economy and Competitiveness, along with FEDER, grant number Project: BIA2017-85098-R.Salas, J.; Yepes, V. (2020). Enhancing sustainability and resilience through multi-level infrastructure planning. International Journal of Environmental research and Public Health. 17(3):1-22. https://doi.org/10.3390/ijerph17030962S122173Holling, C. S. (2004). From Complex Regions to Complex Worlds. Ecology and Society, 9(1). doi:10.5751/es-00612-090111Sharifi, A., & Yamagata, Y. (2014). Resilient Urban Planning: Major Principles and Criteria. Energy Procedia, 61, 1491-1495. doi:10.1016/j.egypro.2014.12.154Chen, Z., & Qiu, B. (2015). Resilient Planning Frame for Building Resilient Cities. GeoJournal Library, 33-41. doi:10.1007/978-3-319-14145-9_4Salas, J., & Yepes, V. (2019). MS-ReRO and D-ROSE methods: Assessing relational uncertainty and evaluating scenarios’ risks and opportunities on multi-scale infrastructure systems. Journal of Cleaner Production, 216, 607-623. doi:10.1016/j.jclepro.2018.12.083Schulz, A., Zia, A., & Koliba, C. (2015). Adapting bridge infrastructure to climate change: institutionalizing resilience in intergovernmental transportation planning processes in the Northeastern USA. Mitigation and Adaptation Strategies for Global Change, 22(1), 175-198. doi:10.1007/s11027-015-9672-xSharifi, A., & Yamagata, Y. (2018). Resilience-Oriented Urban Planning. Lecture Notes in Energy, 3-27. doi:10.1007/978-3-319-75798-8_1Gonzales, P., & Ajami, N. K. (2017). An integrative regional resilience framework for the changing urban water paradigm. Sustainable Cities and Society, 30, 128-138. doi:10.1016/j.scs.2017.01.012Leigh, N., & Lee, H. (2019). Sustainable and Resilient Urban Water Systems: The Role of Decentralization and Planning. Sustainability, 11(3), 918. doi:10.3390/su11030918Rogers, C. D. (2018). Engineering future liveable, resilient, sustainable cities using foresight. Proceedings of the Institution of Civil Engineers - Civil Engineering, 171(6), 3-9. doi:10.1680/jcien.17.00031Wagenaar, H., & Wilkinson, C. (2013). Enacting Resilience: A Performative Account of Governing for Urban Resilience. Urban Studies, 52(7), 1265-1284. doi:10.1177/0042098013505655Wei, Y. D., Li, H., & Yue, W. (2017). Urban land expansion and regional inequality in transitional China. Landscape and Urban Planning, 163, 17-31. doi:10.1016/j.landurbplan.2017.02.019France-Mensah, J., & O’Brien, W. J. (2019). Developing a Sustainable Pavement Management Plan: Tradeoffs in Road Condition, User Costs, and Greenhouse Gas Emissions. Journal of Management in Engineering, 35(3), 04019005. doi:10.1061/(asce)me.1943-5479.0000686Mao, X., Wang, J., Yuan, C., Yu, W., & Gan, J. (2018). A Dynamic Traffic Assignment Model for the Sustainability of Pavement Performance. Sustainability, 11(1), 170. doi:10.3390/su11010170Torres-Machi, C., Pellicer, E., Yepes, V., & Chamorro, A. (2017). Towards a sustainable optimization of pavement maintenance programs under budgetary restrictions. Journal of Cleaner Production, 148, 90-102. doi:10.1016/j.jclepro.2017.01.100Torres-Machi, C., Osorio, A., Godoy, P., Chamorro, A., Mourgues, C., & Videla, C. (2018). Sustainable Management Framework for Transportation Assets: Application to Urban Pavement Networks. KSCE Journal of Civil Engineering, 22(10), 4095-4106. doi:10.1007/s12205-018-1314-xOuma, Y. O., Opudo, J., & Nyambenya, S. (2015). Comparison of Fuzzy AHP and Fuzzy TOPSIS for Road Pavement Maintenance Prioritization: Methodological Exposition and Case Study. Advances in Civil Engineering, 2015, 1-17. doi:10.1155/2015/140189Viera Gomes, S., Cardoso, J. L., & Azevedo, C. L. (2018). Portuguese mainland road network safety performance indicator. Case Studies on Transport Policy, 6(3), 416-422. doi:10.1016/j.cstp.2017.10.006Heinitz, F. M. (2018). Consistency of state road network master plan development steps. Case Studies on Transport Policy, 6(3), 400-415. doi:10.1016/j.cstp.2017.08.001Rezaei, A., & Tahsili, S. (2018). Urban Vulnerability Assessment Using AHP. Advances in Civil Engineering, 2018, 1-20. doi:10.1155/2018/2018601Masi, A., Santarsiero, G., & Chiauzzi, L. (2014). Development of a seismic risk mitigation methodology for public buildings applied to the hospitals of Basilicata region (Southern Italy). Soil Dynamics and Earthquake Engineering, 65, 30-42. doi:10.1016/j.soildyn.2014.05.011Beilin, R., & Wilkinson, C. (2015). Introduction: Governing for urban resilience. Urban Studies, 52(7), 1205-1217. doi:10.1177/0042098015574955Cedergren, A., Johansson, J., & Hassel, H. (2018). Challenges to critical infrastructure resilience in an institutionally fragmented setting. Safety Science, 110, 51-58. doi:10.1016/j.ssci.2017.12.025Regmi, B. R., Star, C., & Leal Filho, W. (2014). Effectiveness of the Local Adaptation Plan of Action to support climate change adaptation in Nepal. Mitigation and Adaptation Strategies for Global Change, 21(3), 461-478. doi:10.1007/s11027-014-9610-3Frank, J., & Martinez-Vazquez, J. (Eds.). (2015). Decentralization and Infrastructure in the Global Economy. doi:10.4324/9781315694108Frank, J., & Martinez-Vazquez, J. (Eds.). (2015). Decentralization and Infrastructure in the Global Economy. doi:10.4324/9781315694108Lehmann, P., Brenck, M., Gebhardt, O., Schaller, S., & Süßbauer, E. (2013). Barriers and opportunities for urban adaptation planning: analytical framework and evidence from cities in Latin America and Germany. Mitigation and Adaptation Strategies for Global Change, 20(1), 75-97. doi:10.1007/s11027-013-9480-0Jain, M., & Korzhenevych, A. (2017). Spatial Disparities, Transport Infrastructure, and Decentralization Policy in the Delhi Region. Journal of Urban Planning and Development, 143(3), 05017003. doi:10.1061/(asce)up.1943-5444.0000379De Gregorio Hurtado, S. (2017). Is EU urban policy transforming urban regeneration in Spain? Answers from an analysis of the Iniciativa Urbana (2007–2013). Cities, 60, 402-414. doi:10.1016/j.cities.2016.10.015Newman, J. P., Dandy, G. C., & Maier, H. R. (2014). Multiobjective optimization of cluster-scale urban water systems investigating alternative water sources and level of decentralization. Water Resources Research, 50(10), 7915-7938. doi:10.1002/2013wr015233Gänzle, S., Stead, D., Sielker, F., & Chilla, T. (2018). Macro-regional Strategies, Cohesion Policy and Regional Cooperation in the European Union: Towards a Research Agenda. Political Studies Review, 17(2), 161-174. doi:10.1177/1478929918781982Roozbahani, A., Zahraie, B., & Tabesh, M. (2012). Integrated risk assessment of urban water supply systems from source to tap. Stochastic Environmental Research and Risk Assessment, 27(4), 923-944. doi:10.1007/s00477-012-0614-9Gupta, J., Bergsma, E., Termeer, C. J. A. M., Biesbroek, G. R., van den Brink, M., Jong, P., … Nooteboom, S. (2015). The adaptive capacity of institutions in the spatial planning, water, agriculture and nature sectors in the Netherlands. Mitigation and Adaptation Strategies for Global Change, 21(6), 883-903. doi:10.1007/s11027-014-9630-zRigillo, M., & Cervelli, E. (2014). Mapping Urban Vulnerability: The Case Study of Gran Santo Domingo, Dominican Republic. Advanced Engineering Forum, 11, 142-148. doi:10.4028/www.scientific.net/aef.11.142Salas, J., & Yepes, V. (2018). Urban vulnerability assessment: Advances from the strategic planning outlook. Journal of Cleaner Production, 179, 544-558. doi:10.1016/j.jclepro.2018.01.088Salas, J., & Yepes, V. (2018). A discursive, many-objective approach for selecting more-evolved urban vulnerability assessment models. Journal of Cleaner Production, 176, 1231-1244. doi:10.1016/j.jclepro.2017.11.249Zhao, P., Chapman, R., Randal, E., & Howden-Chapman, P. (2013). Understanding Resilient Urban Futures: A Systemic Modelling Approach. Sustainability, 5(7), 3202-3223. doi:10.3390/su5073202Salas, J., & Yepes, V. (2019). VisualUVAM: A Decision Support System Addressing the Curse of Dimensionality for the Multi-Scale Assessment of Urban Vulnerability in Spain. Sustainability, 11(8), 2191. doi:10.3390/su11082191Saku Kukkonen, & Jouni Lampinen. (2007). Ranking-Dominance and Many-Objective Optimization. 2007 IEEE Congress on Evolutionary Computation. doi:10.1109/cec.2007.4424990Navarro, I. J., Martí, J. V., & Yepes, V. (2019). Reliability-based maintenance optimization of corrosion preventive designs under a life cycle perspective. Environmental Impact Assessment Review, 74, 23-34. doi:10.1016/j.eiar.2018.10.001Adger, W. N. (2006). Vulnerability. Global Environmental Change, 16(3), 268-281. doi:10.1016/j.gloenvcha.2006.02.006Santos, J., Ferreira, A., & Flintsch, G. (2017). A multi-objective optimization-based pavement management decision-support system for enhancing pavement sustainability. Journal of Cleaner Production, 164, 1380-1393. doi:10.1016/j.jclepro.2017.07.027Zhang, Chen, Cai, Gao, Zhang, Liu, … Li. (2019). Analysis of the Spatial Distribution Characteristics of Urban Resilience and Its Influencing Factors: A Case Study of 56 Cities in China. International Journal of Environmental Research and Public Health, 16(22), 4442. doi:10.3390/ijerph16224442Baudrit, C., Taillandier, F., Tran, T. T. P., & Breysse, D. (2018). Uncertainty Processing and Risk Monitoring in Construction Projects Using Hierarchical Probabilistic Relational Models. Computer-Aided Civil and Infrastructure Engineering, 34(2), 97-115. doi:10.1111/mice.12391Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9-26. doi:10.1016/0377-2217(90)90057-iSingh, R. P., & Nachtnebel, H. P. (2016). Analytical hierarchy process (AHP) application for reinforcement of hydropower strategy in Nepal. Renewable and Sustainable Energy Reviews, 55, 43-58. doi:10.1016/j.rser.2015.10.138Convertino, M., Muñoz-Carpena, R., Chu-Agor, M. L., Kiker, G. A., & Linkov, I. (2014). Untangling drivers of species distributions: Global sensitivity and uncertainty analyses of MaxEnt. Environmental Modelling & Software, 51, 296-309. doi:10.1016/j.envsoft.2013.10.001Groen, E. A., Bokkers, E. A. M., Heijungs, R., & de Boer, I. J. M. (2016). Methods for global sensitivity analysis in life cycle assessment. The International Journal of Life Cycle Assessment, 22(7), 1125-1137. doi:10.1007/s11367-016-1217-3Evelyne Groen, Global Sensitivity Analysishttps://evelynegroen.github.io/Code/globalsensitivity.htmlConvertino, M., & Valverde, L. J. (2013). Portfolio Decision Analysis Framework for Value-Focused Ecosystem Management. PLoS ONE, 8(6), e65056. doi:10.1371/journal.pone.0065056García-Segura, T., Penadés-Plà, V., & Yepes, V. (2018). Sustainable bridge design by metamodel-assisted multi-objective optimization and decision-making under uncertainty. Journal of Cleaner Production, 202, 904-915. doi:10.1016/j.jclepro.2018.08.177McGlashan, A., Verrinder, G., & Verhagen, E. (2018). Working towards More Effective Implementation, Dissemination and Scale-Up of Lower-Limb Injury-Prevention Programs: Insights from Community Australian Football Coaches. International Journal of Environmental Research and Public Health, 15(2), 351. doi:10.3390/ijerph15020351YEPES, V., TORRES-MACHI, C., CHAMORRO, A., & PELLICER, E. (2016). OPTIMAL PAVEMENT MAINTENANCE PROGRAMS BASED ON A HYBRID GREEDY RANDOMIZED ADAPTIVE SEARCH PROCEDURE ALGORITHM. JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, 22(4), 540-550. doi:10.3846/13923730.2015.1120770Sierra, L. A., Yepes, V., & Pellicer, E. (2018). A review of multi-criteria assessment of the social sustainability of infrastructures. Journal of Cleaner Production, 187, 496-513. doi:10.1016/j.jclepro.2018.03.022Sierra, L. A., Pellicer, E., & Yepes, V. (2016). Social Sustainability in the Lifecycle of Chilean Public Infrastructure. Journal of Construction Engineering and Management, 142(5), 05015020. doi:10.1061/(asce)co.1943-7862.0001099Sierra, L. A., Yepes, V., García-Segura, T., & Pellicer, E. (2018). Bayesian network method for decision-making about the social sustainability of infrastructure projects. Journal of Cleaner Production, 176, 521-534. doi:10.1016/j.jclepro.2017.12.14