Luigi Ranieri

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).

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 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.

A mixed integer linear programming model for optimisation of organics management in an integrated solid waste system

In this paper, the authors propose a mixed integer linear programming model for designing an Integrated Solid Waste Management System (ISWMS) to meet specific economic goals. The model refers to a set of municipalities, known as ‘local basin’, which have to share a common waste management system. At the municipal level the model allows for an identification of the optimal collection service option; at the local basin level, the model provides the optimal waste flow appropriate to the collection service option of each municipality. The model has been applied to a full-scale case study of an area located in southeast Italy. A scenario analysis was carried out to investigate alternative municipal solid waste management options, which fundamentally differ in the organic flow mass rate to be either collected and composted or landfilled. Findings show that an increase in the cost of landfilling determines the optimal collection scenario and the configuration plants tend to recover higher rates of organics in separate collection and thus higher refuse derived fuel productions. The results obtained validate the application of the model in both the strategic planning and operational phases, by supporting public administrators at both municipality and local basin level in decision making and evaluation of technical and economic performances of ISWMSs.

Using multi-objective optimization for the integrated energy efficiency improvement of a smart city public buildings' portfolio

The paper presents a multi-objective optimization algorithm to improve in an integrated and holistic way the building stock energy efficiency, sustainability, and comfort, while efficiently allocating the available budget to the buildings. The developed algorithm determines a set of optimal energy retrofit plans for a portfolio of public buildings in a smart city. An existing stock of public buildings located in the municipality of Bari, Italy is used as case study. The application results demonstrate that the developed algorithm is an effective support tool for the smart city governance in enhancing the energy efficiency performance of a stock of public buildings.

Increasing public healthcare network performance by de‐hospitalization

Purpose – The aim of this paper is to support public decision‐makers in a local healthcare agency (LHA) in evaluating the effects of different de‐hospitalization strategies and the potential for outsourcing clinical services.Design/methodology/approach – The approach adopted is based on the “patient pathway” perspective. Starting from the identification of specific care pathways, all the feasible care paths in a given LHA in Italy are investigated in order to evaluate the practicability of the de‐hospitalization of some phases with a particular focus on co‐ordination of hospitals and territorial services. A heuristic approach based on discrete‐event simulation modelling is proposed. The methodology and the simulation model have been validated with reference to field data derived from a full‐scale case study carried out within a LHA in southern Italy.Findings – The results show where, in terms of care pathways, de‐hospitalization is practicable, valuable in terms of better resource utilization, and eligibl...

A real options–based approach in guaranteed energy savings contracting

The full development of the energy services company (ESCO) industry is still inhibited by the difficulties during the negotiation between customers and ESCOs. Contractual arrangements are traditionally based on energy performance contracting (EPC). Improvements on EPC schemes are required in order to achieve the success of the negotiation. This work focuses on a particular type of EPC, named guaranteed savings (GS) contract, where a minimum energy saving is guaranteed to the customer by the ESCO. A model based on real options theory to share risks among contractual parties is proposed in order to estimate the fair value of main contractual parameters. A Monte Carlo simulation is adopted for evaluating the most critical factors influencing the overall risk sharing. A numerical example concerning a cogeneration plant of a paper mill is presented. A two level full factorial design of experiments (DOE) analysis is carried out in order to estimate single and compound effects of model parameters.