Established Deterministic Investment Appraisal versus Uncertainty in Investment When it comes to investing in an infrastructure project, the conventional approach is to evaluate risk through a deterministic approach. Infrastructure Investment: An Engineering Perspective, however, takes on uncertainty in investment. Of interest to engineering consultants, government departments,...
Read moreEstablished Deterministic Investment Appraisal versus Uncertainty in Investment
When it comes to investing in an infrastructure project, the conventional approach is to evaluate risk through a deterministic approach. Infrastructure Investment: An Engineering Perspective, however, takes on uncertainty in investment. Of interest to engineering consultants, government departments, financial institutions, or anyone involved in investment in infrastructure, this text provides the necessary tools for the analysis and appraisal of investment in infrastructure and other assets with uncertain futures.
It factors in the finance and engineering of assets such as roads, buildings, bridges, dams, pipelines, railways, ports, seawalls, wastewater treatment facilities, and addresses future demand, operating costs, maintenance costs, and other lifetime and investment parameters in both financial and non-financial terms. It considers the impact of climate change and the possible use of adaptive and flexible solutions capable of responding to changed futures, as well as how such uncertainty affects the future performance of these investments. The book also incorporates illustrated case studies and Markov chains to model an investment.
A pivotal work containing 11 chapters, this text provides:
- An original contribution to feasibility analysis under uncertainty
- A systematic and ordered treatment of capital investment in infrastructure
- A structured flow, from a systematic treatment of conventional deterministic approaches through to a complete treatment incorporating uncertainty
Infrastructure Investment: An Engineering Perspective
details investment analysis in the presence of uncertainty, and is beneficial to students, academics, and practitioners dealing with decision
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