Second Best Energy Policies

The paper considers the problem of resource allocation when factor groups attempt to obtain a share of real income which is greater than what would be imputed by classical economies. A formulation stressing the Divvy nature of the problem is given both in theoretical terms and with a framework which is susceptible to empirical estimation. Policy questions resulting from the formation of OPEC are discussed and a framework for policy analysis is given

A General Equilibrium Framework for the Divvy Economy

In a recent paper, G. Dantzig has formulated a model for resource allocation in the so called "Divvy Institutional Economy." The author proves the existence of a general equilibrium solution to the economic problem (in terms of prices and quantities of input factors and final goods) which at the same time satisfies agreed upon shares of monetary flows allocated to input resource groups and to output consumer groups. The agreement upon the share values is carried out by a political process, while the market mechanisms adjust the prices of primary resource inputs and the relative sizes of the consumer groups until those shares are satisfied. The inputs and outputs and the production and transformation technology are presented in an Input-Output format. The formalization of the resource allocation problem takes into account the presence of institutionalized forces together with the market mechanism. Examples can be taken from empirical observation (collective bargaining, Congressional Budget Approval, indexed prices of raw material) is per se a major innovation with respect to more classical results. In the following sections we we will try to view the Divvy results in relation to the classic economic formulation of the problem and study possible implications of it

A discursive, many-objective approach for selecting more-evolved urban vulnerability assessment models

[EN] The development of more-evolved urban vulnerability assessment (UVA) models has become an increasingly important issue for both policy agendas and academia. Several requirements have already been set for this goal; they should be pursued simultaneously. However, methods with such integration are yet to be developed. The present paper addresses this integration via a discursive process in which interactions between decision makers and the method contribute to the selection of a model fulfilling these requirements. That model yields a UVA built upon both qualitative information and quantitative data from indicators selected for the neighbourhood, city, province, region and country political-administrative scales. The characteristics demanded are encoded both into the UVA assessment model and in the optimization and control modules governing the process. While the optimization produces compromise solutions, the control module supervises the process, provides dynamic control and enables the interactions. Interactions are informed with knowledge derived from the cognitive approach entailed by the method and afford a better understanding of the process dynamics. We conclude that the goodness of fit and time dynamics objectives are aligned. Therefore, UVA methods performing well for these objectives are available, although at the expense of a medium to poor performance in preferences and robustnessSalas, J.; Yepes, V. (2018). A discursive, many-objective approach for selecting more-evolved urban vulnerability assessment models. Journal of Cleaner Production. 176:1231-1244. https://doi.org/10.1016/j.jclepro.2017.11.249S1231124417

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

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. 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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. 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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). 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Canonical Equivalence of a Generic 2D Dilaton Gravity Model and a Bosonic String Theory

We show that a canonical tranformation converts, up to a boundary term, a generic 2d dilaton gravity model into a bosonic string theory with a Minkowskian target space.Comment: LaTeX file, 9 pages, no figure