Roberta Pellegrino

Measuring and Managing the Smartness of Cities: A Framework for Classifying Performance Indicators

Due to the continuous increase of the world population living in cities, it is crucial to identify strategic plans and perform associated actions to make cities smarter, i.e., more operationally efficient, socially friendly, and environmentally sustainable, in a cost effective manner. To achieve these goals, emerging smart cities need to be optimally and intelligently measured, monitored, and managed. In this context the paper proposes the development of a framework for classifying performance indicators of a smart city. It is based on two dimensions: the degree of objectivity of observed variables and the level of technological advancement for data collection. The paper shows an application of the presented framework to the case of the Bari municipality (Italy).

Revenue guarantee in public-private partnerships: a fair risk allocation model

Public-private partnerships (PPPs) are adopted throughout the world for delivering public infrastructure. Despite the attractiveness of the PPP structure, its implementation has not been without trouble due to multiple uncertainties embedded with PPP projects. Private investors often require some mitigation of these risks through government support. One of the most common forms of government support is minimum revenue guarantee (MRG). A real option-based model is developed that uses a new mechanism for setting the revenue guarantee level secured by the government, which balances the private sector’s profitability needs and the public sector’s fiscal management interests and uses the concept of fairness for structuring MRGs. The model uses Monte Carlo simulation to take into account the uncertainty. The model is applied to the projected 1 kilometre long ‘Camionale di Bari’ toll road that will link the port of Bari (located in Puglia, Southern Italy) with the existing road network without affecting the urban traffic. It was found that government support is often needed to make the project attractive to private investors and that the developed model can be, for both public and private sectors, a valid tool for defining the fair value of the minimum amount of revenue secured by the government.

Risk Management in Motorway PPP Projects: Empirical-based Guidelines

Abstract This paper deals with the topic of risk management in Public Private Partnership (PPP). The analysis of the related literature reveals that risks must be analyzed and managed on a context-specific approach, and that there is a lack of a comprehensive study on the appropriate risk mitigation strategies for each risk embedded in PPP projects. Focusing on the transport sector, based on the results of a Delphi survey, the paper provides guidelines for both public and private parties in defining a list of significant risks in PPP motorway projects, and identifying for them both the effective allocation and the suitable mitigation strategies. Results of the Delphi survey have been compared with the common practices on risk management applied in eight real motorway PPP projects.

A real options-based model to supporting risk allocation in price cap regulation approach for public utilities

Price cap regulation of public utilities is based on an incentive mechanism to prevent monopolistic infrastructure firms from charging excessive prices. The challenge of this regulation mechanism is to define incentives able to avoid abnormal profits of firms and simultaneously increase quality of service and promote investment projects. A new risk-based approach to support the definition of the fair incentive mechanism as between the regulator, the community and the firm is proposed. The methodology is based on the combined use of real options theory and Monte Carlo simulation. The methodology is then applied to the Italian water market where the regulator adopts a ‘hybrid’ price cap mechanism that gives monopolistic firms the incentive to implement investment projects for reducing the actual infrastructural gap in the water supply system. The results reveal the capability of the proposed model to support public decision makers at the negotiation stage to define the incentive scheme and investment plan able to increase the quality of service allowing a fair risk allocation among parties.

A Hierarchical Decision-Making Strategy for the Energy Management of Smart Cities

This paper presents a hierarchical decision-making strategy for the energy management of a smart city. The proposed decision process supports the city energy manager and local policy makers in taking energy retrofit decisions on different urban sectors by an integrated, structured, and transparent management. To this aim, in the proposed decision strategy, a bilevel programming model integrates several local decision-making units, each focusing on the energy retrofit optimization of a specific urban subsystem, and a central decision unit. We solve the hierarchical decision problem by a game theoretic distributed algorithm. We apply the developed decision model to the case study of the city of Bari (Italy), where a smart city program has recently been launched.

A Decision Making Technique to Optimize a Buildings’ Stock Energy Efficiency

This paper focuses on applying multicriteria decision making tools to determine an optimal energy retrofit plan for a portfolio of buildings. We present a two-step decision making technique employing a multiobjective optimization algorithm followed by a multiattribute ranking procedure. The method aims at deciding, in an integrated way, the optimal energy retrofit plan for a whole stock of buildings, optimizing efficiency, sustainability, and comfort, while effectively allocating the available financial resources to the buildings. The proposed methodology is applied to a real stock of public buildings in Bari, Italy. The obtained results demonstrate that the approach effectively supports the city governance in making decisions for the optimal management of the buildings’ energy efficiency.

An urban control center for the energy governance of a smart city

The paper addresses the emerging need of providing urban managers with tools for energy governance of smart cities. We present the architecture of a decision support system called Urban Control Center (UCC). The UCC measures the city energy performance and supports the decision maker in determining the optimal action plan for implementing smartness strategies in the city energy governance. To this aim, the UCC relies on a two-level decentralized programming model that integrates several decision making units (decision panels), each focusing on the energy optimization of a specific urban subsystem.

Real options approach to evaluate postponement as supply chain disruptions mitigation strategy

The purpose of this paper is to assess the value of postponement as strategy for mitigating supply chain disruptions. To accomplish this objective, we develop a real option computational model that quantifies the value of postponement in mitigating both supply and demand disruptions by taking into account the value of managerial flexibility to decide whether exploiting or not the strategy, if and when disruptions occur, and whenever product differentiation proves valuable based on information available at that time. Numerical experiments show the importance of incorporating an option valuation method when pricing the value of postponement. This ensures managers implement postponement only when it is valuable, thus avoiding burdening the company with its initial sunk costs. By modelling the postponement implementation under different conditions, we identify the situations in which postponement performs better as supply chain disruptions mitigation strategy. We derive the operational configurations, in terms of decoupling point position, and external conditions, in terms of riskiness of the environment, which make the postponement an effective mitigation strategy.

ICT and optimization for the energy management of smart cities: The street lighting decision panel

The paper addresses the emerging need for tools devoted to the energy governance of smart cities. We propose a hierarchical decision process that supports the energy manager in governing the smart city while addressing different urban sectors with an integrated, structured, and transparent planning. Starting from the urban control center proposed in a previous contribution for the urban energy management, a hierarchical strategic decision structure is proposed. More in detail, a two-level decentralized programming model integrates several decision making units (decision panels), each focusing on the energy optimization of a specific urban subsystem. We focus on the presentation of the street lighting decision panel and on its application to the energy management of the public lighting of the city of Bari (Italy), where a smart city program has recently been launched.

Construction of multi-storey concrete structures in Italy: patterns of productivity and learning curves

The achievement of expected site productivity is one of the main characteristics of successfully completed projects. The productivity rates of concrete construction according to the learning curve theory are discussed in this paper. The study builds upon the records of variable productivity rates achieved in the erection of 15 multi-storey concrete structures in Southern Italy and the discussion of the factors behind such variability. In this last regard, a multilevel regression analysis identifies the most important factors. The repetitive work that characterizes these structures provides distinct opportunities for productivity enhancement. Learning curve theory is applied to quantify such an improvement by using a straight-line model. The quantification of learning rates, ranging from 85% to 95%, is useful for the labour cost and time planning of future concrete structures in the region. In the case of Italian sites, the application of the learning curve would be more beneficial if more effort were spent in the planning and control of the initial construction site operations and in the constructability analysis of design documents.