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This paper bridges the gap between the theory and practice by developing a life cycle sustainability tracker (LCST). The study is seeking to proffer solutions to an observed shortcoming of conventional life cycle sustainability assessment (LCSA) communication platforms. Notably, the static nature of the information provided on such platforms has made it difficult for them to be used for real-time decision-making and predictions. The main aim of this paper is to develop a LCST that facilitates a dynamic visualisation of life cycle sustainability results and allows for an integrated benchmark across the dimensions of sustainability.

The study leverages the model development capabilities of the design science research strategy in accomplishing a dynamic and novel communication platform. A life cycle thinking methodology and appropriate multicriteria decision approach (MCDA) is applied to accomplish a comprehensive, streamlined and replicable approach in mapping and tracking the progress of sustainable development goals (SDGs) in the National Infrastructure Pipeline (NIP) projects in India.

It was found that: (1) The use of the LCST tracker provides a dynamic and holistic insight into the key LCSA indicators with clearly defined benchmarks to assess the impact on the SDG 11, (2) The NIP projects achieve an upward trend across all the regions, and the percentage of opportunities ranges from 11 to 24%, with the South experiencing the highest growth and the North having the minimal increase in percentage and (3) The assessment score (52–58%) provides performance metrics that align well with the LCST – which ranges between “Fair” and “Average” for all the regions in India.

The novelty of this research is that the LCST provides a transparent and harmonised approach to reporting on the LCSA results. The LCST utilises heat maps and radial mapping to achieve an intuitive display of large amounts of highly heterogeneous data, thus allowing the synthesis of large sets of information compactly and with coherence. Progress towards the SDGs change on a yearly basis; hence, a dynamic LCSA tool provides a timely and the valuable context to map and track performance across different regions and contexts.

Administrators in higher-education settings routinely create planning documents that help steer the organization in mission-centric ways. In the area of sustainability planning, strategic plans, sustainability plans and climate action plans are the most common methods used. The purpose of this study is to evaluate if specific forms of planning predict sustainability outcomes.

This question was evaluated via an empirical archival study of the AASHE STARS database in relation to planning, administration and governance credits and criteria to determine if specific forms of planning were predictive of sustainability implementation outcomes in the categories of Education and Research, Operations, Diversity and Affordability, Human Resources, Investment, Public Engagement and Innovation.

Findings support the notion that climate action plans were most predictive of achieving sustainability outcomes, and strategic plans were best able to predict educational outcomes.

These findings have important implications for the design and execution of sustainability planning processes in higher-education institutions.

The academic literature contains relatively few empirical studies that demonstrate the capacity of planning on the realization of sustainability outcomes.

Higher education institutions (HEIs) face a number of challenges in effectively managing and reporting on sustainability information, such as siloes of data and a limited distribution of information. Business intelligence (BI) can assist in addressing the challenges faced by organisations. The purpose of this study was to propose a BI framework for strategic sustainability information management (the Sustainable BI Framework) that can be used in HEIs.

The research applied the design science research methodology whilst using a South African HEI as a case study. The problems with sustainability information management were identified, and a theoretical framework was proposed. In addition, a practical BI software tool was developed as proof of concept to address these problems and to assist with the management of strategic sustainability information in an HEI.

The proposed sustainability BI tool was evaluated through heuristic and usability evaluations with senior management. The results indicated that the usability of the BI tool was positively rated and that the framework can assist in overcoming the constraints that HEIs face in effectively managing sustainability information.

The research was limited to a single case. However, the theoretical framework was derived from and expanded on existing stakeholder theory, sustainability reporting theory and literature on BI dashboard development. The framework was implemented successfully in the Sustainable BI Tool prototype at the case study, and the results reveal in-depth information regarding information management for sustainability reporting in higher education.

The Sustainable BI Tool is a solution that integrates data from multiple areas of sustainability and provides a single integrated view of the information to stakeholders. The information is provided through performance dashboards, which provide predictive capabilities to enable management to report on sustainability and determine if the institution is meeting its strategic goals. The lessons learnt can also assist other HEIs considering implementing BI for sustainability reporting.

Improved sustainability reporting for HEIs provided by the BI framework can improve the environmental and social impact of the educational community.

This study provides the most comprehensive framework for guiding the design of a BI tool to assist in effectively managing sustainability information in HEIs.

Urban logistics pooling is seen as a serious alternative to imposed urban consolidation centers. However, such strategies are quite new in urban distribution and merit to be evaluated using adapted methods that take into account the group decision nature of resource pooling. This chapter aims to propose, via an experimental collaborative decision support method, to define a grid of indicators and a reference situation database to measure the sustainable performance of urban logistics pooling systems.

The proposed methodology combines a systematic literature analysis of Key Performance Indicators and a group decision support method to choose a suitable set to define a dashboard. First, we identify the main sustainability indicators from an overview of the literature, and class them into the categories of the 4As Sustainable Transport vision (i.e., Awareness, Act and shift, Avoidance, and Anticipation). Then, a group of 20 experts is solicited for an iterative experimental group decision-making method to converge to the concordance of a set of indicators.

The method allowed us to define a hierarchic dashboard agreed by all experts with seven main indicators and nine secondary indicators. Moreover, the experts signaled the need of defining a unified basis of comparison to estimate initial situations. To do this, we proposed a database of urban routes from the French Surveys on Urban Goods Transport.

The proposed dashboard is an example, and to provide a more unified one, the experience has to be iterated using different groups of decision-makers.

This method has the advantage of proposing a dashboard agreed by all involved stakeholders. Therefore, this chapter shows the patterns to reproduce it since the method is able to be replicated in any context of group decision in urban logistics.

The originality of the chapter arises on the use of an experimental group decision method using a group with a majority of practitioners, and to validate it by consensus.

This research navigates the confluence of data analytics, machine learning, and artificial intelligence to revolutionize the management of urban services in smart cities. The study thoroughly investigated with advanced tools to scrutinize key performance indicators integral to the functioning of smart cities, thereby enhancing leadership and decision-making strategies. Our work involves the implementation of various machine learning models such as Logistic Regression, Support Vector Machine, Decision Tree, Naive Bayes, and Artificial Neural Networks (ANN), to the data. Notably, the Support Vector Machine and Bernoulli Naive Bayes models exhibit robust performance with an accuracy rate of 70% precision score. In particular, the study underscores the employment of an ANN model on our existing dataset, optimized using the Adam optimizer. Although the model yields an overall accuracy of 61% and a precision score of 58%, implying correct predictions for the positive class 58% of the time, a comprehensive performance assessment using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) metrics was necessary. This evaluation results in a score of 0.475 at a threshold of 0.5, indicating that there's room for model enhancement. These models and their performance metrics serve as a key cog in our data analytics pipeline, providing decision-makers and city leaders with actionable insights that can steer urban service management decisions. Through real-time data availability and intuitive visualization dashboards, these leaders can promptly comprehend the current state of their services, pinpoint areas requiring improvement, and make informed decisions to bolster these services. This research illuminates the potential for data analytics, machine learning, and AI to significantly upgrade urban service management in smart cities, fostering sustainable and livable communities. Moreover, our findings contribute valuable knowledge to other cities aiming to adopt similar strategies, thus aiding the continued development of smart cities globally.

The purpose of this paper is to propose a sustainable dashboard for evaluating the sustainable performance of urban delivery systems from the perspective of operational logistics managers, one of the categories of stakeholders given less consideration by public authorities in their quest for consensus.

First, a synthesis of the main works on the subject is proposed to provide a common grid of economic, environmental and social/societal indicators for sustainable supply chain management (SSCM), after which the method for defining the dashboard is presented. This method is derived from a collaborative decision-support approach and applied to a panel of operational logistics managers. Using a co-constructive method, a group of experts is consulted first separately, then by small groups and then a group restitution and consensus search process is made to find an agreed-upon set of indicators.

The results show a difference between the indicators chosen in the individual phase and those defined in small groups. They also show a gap between classical expert-obtained indicators (mainly made by one or a small group of non-operational experts) and the proposed dashboard, made by and for operational managers.

The originality of the paper is that it addresses two issues (urban logistics evaluation and consensus search) by using methods of natural and active pedagogy and shows by an experimental method the interests and opportunities of collaboration in defining sets of indicators for urban logistics evaluation.

This paper aims to assist higher education institutions (HEIs) in their decision-making process to define initiatives and foster research projects contributing to sustainable development (SD) and minimizing the deficits found in the municipalities.

A documental analysis was performed to select HEIs and Brazilian regional development indicators. Then, the assessment of the sustainable and institutional performance of Brazilian municipalities that have HEIs consisted of three parts: clustering with an unsupervised machine learning model, ranking with a hybrid multi-criteria decision making method and visualization of sustainability performance with the dashboard.

The critical analysis of institutional and sustainability indicators contributes to a more active role of HEIs in matters of social responsibility, with a more holistic view of the performance and quality of municipal education. Furthermore, this critical analysis creates a scenario where HEIs can develop public policy proposals in partnership with the government to mitigate the main issues identified.

With this study, HEIs will be able to direct their actions to minimize the deficits found in the municipalities, consolidating their social responsibility.

This study proposes a new decision-support tool with a dashboard of indicators so that HEIs can foster research projects with a focus on regional SD.

Introduction and Purpose: Sustainable development is a concept that plays an essential part in the business and industry of the twenty-first century. At present, the authors observe a wide range of models to analyse and implement sustainable development measures, which use various methodologies to address sustainability goals and monitor and assess implementation progress-wise. These models are based on a systemic vision and the complexity of the interconnected component elements. This chapter presents two fundamental concepts (systemic and related) through a comparison made between two sustainability assessment systems, the first one focussing on evaluating the sustainable development goals (SDG) implementation, and the second one focussing on environmental, social, and governance (ESG) criteria assessment.

Methodology: Then, using cluster analysis, the authors grouped European Union (EU) member states from a sustainability assessment point of view.

Findings: Results show a congruent and complementary evolution of EU member states that use both sustainability assessment systems. As an outcome of this research, the authors suggest integrating the two sustainability assessment systems to better understand the phenomenon and the evolutionary trends in the field of sustainability.

Significance and Originality of Findings: Using the comparative approach and the cluster analysis method, this chapter showed that the two systems are consistent and offer compatible and complementary views, which led at necessity of integration of the two visions into a unitary systemic concept consisting of inputs (ESG scores) and outputs (SDG index).

The primary aim of this paper is to develop a reference model for measuring corporate sustainability that can be used by organizations to integrate sustainability measures into their current performance measurement system, helping them to embed sustainability into daily activities and to forge a sustainability culture. A secondary intent is to present a critical analysis of some well‐known sustainability measurement initiatives, showing their strengths and shortcomings.

The approach employed to develop the reference model described in this paper is a qualitative analysis of the complementarity, shortcomings and strengths of eight well‐known sustainability measurement initiatives alongside an extant corporate sustainability literature review.

The research carried out has found that there is not a single initiative analyzed that tackles all sustainability issues and in fact there is no consensus around what should be measured and how. The main divergences are related to the following aspects: different criteria are applied by the initiatives to classify issues between dimensions; same impacts are evaluated at different levels of a cause‐effect relationship continuum by the same initiative; disagreement about the groups of stakeholders a company should engage and assessing the company impacts that should be taken into account (direct only or those of its whole value chain). Moreover, the way in which most initiatives measure sustainability performance is not the most adequate to embed it into the performance measurement systems, since they evaluate sustainability via presence of management practice and employ absolute values indicators rather than result‐oriented measures and ratio indicators that are more adequate for internal decision making. In this context, a sustainability measurement model was developed that is more comprehensive, objective and value‐oriented, constituting an attempt to shed light on these problems.

The major limitation is the fact that the proposed model does not provide any guidance to select the sustainability key issues for an organization to be integrated into its current performance measurement system. It mainly provides a very comprehensive set of sustainability issues and measures that could be used.

This paper sheds light on some sustainability measurement current challenges – lack of consensus of what should be measured and how – and sustainability embedment into daily activities. Academics will find it useful in their research efforts since it presents a broad review of sustainability concepts as well as an analysis of strengths and shortcomings of all and each sustainability initiative focused. Practitioners will also find it useful as a tool to better understand the sustainability concept, to start measuring sustainability performance, to integrate it in, as well as to evaluate, their current performance measurement systems.