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Uncertainty management (UM) in projects has been a point of attention for researchers for many years. Research on UM has mainly been aimed at uncertainty analyses in the front-end and managing uncertainty in the construction phase. In contrast, UM components in the design phase have received less attention. This research aims to improve knowledge about the key components of UM in the design phase of large road projects.

This study adopted a literature review and case study. The literature review was used to identify relevant criteria for UM. These criteria helped to design the interview guide. Multiple case study research was conducted, and data were collected through document study and interviews with project stakeholders in two road projects. Each case's owners, contractors and consultants were interviewed individually.

The data analysis obtained helpful information on the involved parties, process and exploit tools and techniques during the design phase. Johansen's (2015) framework [(a) human and organisation, (b) process and (c) tools and techniques)] was completed and developed by identifying relevant criteria (such as risk averse or risk-taker, culture and documentation level) for each component. These criteria help to measure UM performance. The authors found that owners and contractors are major formal UM actors, not consultants. Empirical data showed the effectiveness of Web-based tools in UM.

The studied cases were Norwegian, and this study focussed on uncertainties in the project's design phase. Relevant criteria did not cover all the criteria for evaluating the performance of UM. Qualitative evaluation of criteria allows further quantitative analysis in the future.

This paper gave project owners and managers a better understanding of relevant criteria for measuring UM in the owners and managers' projects. The paper provides policy-makers with a deeper understanding of creating rigorous project criteria for UM during the design phase. This paper also provides a guideline for UM in road projects.

This research gives a holistic evaluation of UM by noticing relevant criteria and criteria's interconnection in the design phase.

The purpose of this paper is to estimate energy efficiency of 132 countries from 2007 to 2014 according to their performance, categorizing the nations into similar groups.

Data envelopment analysis model based on Goal Programming and then K-Means clustering algorithm are used to determine the efficiency and clustering the nations based on their efficiency performances.

The results of the study reveal that developing low-income countries could lead to high energy-efficiency scores, and countries with different development and income levels can become efficient in the field of energy consumption. Following the nations during a seven-year period also indicates that the changes in energy-related indicators such as renewable energy consumption and energy productivity are the main drivers to move a country between clusters.

The present study aimed to investigate whether similar nations with similar energy efficiency level in a cluster are similar in their development and income level, and changing the energy consumption pattern during the seven-year period could move the countries from a cluster to another one.