Campus Sustainability Appraisal in Nigeria: Setting up Sustainable Attributes for Higher Educational Institutions

Sustainable campus development has gained the attention of several policymakers and urban planners within the past decades with different campuses across the world claiming to be sustainable or have adopted initiatives of becoming sustainable. The different tools for assessing sustainability in higher education cannot be utilised in all institutions across the globe due to factors such as regional variation. This paper established and formalised a systematic approach to comprehensively review sustainability indicators identified in 13 campus sustainability assessment tools. Thereafter, Twitter social media and an online big data analysis tool were utilised in selecting environmental-based sustainability indicators for higher educational institutions in Nigeria. The rise in the use of social media amongst tertiary institution stakeholders ensures that a better understanding of environmental challenges can be derived from the perspectives of these stakeholders. The findings from the comprehensive review of the selected 13 tools reveal that there are variations in the sets of their sustainability indicators and selection process. None of the tools have compatible indicators for campus sustainability appraisal and none of the tools utilised social media and big data technology to arrive at the adopted set of indicators for their appraisal framework, threshold, and rating. We identified energy, environment, transport, infrastructure, waste, and water as the major categories for sustainability indicators in Nigeria. The current research gap identified from literature strongly justifies the purpose of this study that setup sustainability indicators that are peculiar to tertiary institutions in Nigeria that will bring about an appraisal framework and also give room for campuses to compare their sustainability performance and interchange of standard practices

Spatial Estimation and Visualization of CO2 Emissions for Campus Sustainability: The Case of King Abdullah University of Science and Technology (KAUST), Saudi Arabia

A total of 21 metric tons of CO2 per person in terms of per capita emissions from consumption of energy was recorded in Saudi Arabia in 2011 and forecasts have shown that this emission of CO2 is increasing. This poses the threat of climate change and global warming and therefore the need for the sustainability of the country. The Kingdom of Saudi Arabia’s Vision for 2030 addresses environmental sustainability that includes a reduction in CO2 emissions as well as diversified economic growth. Universities have been regarded as institutions with significant responsibilities to resolve the issues of sustainability as well as serve as role model to society by implementing a sustainability plan. This study established a spatial evaluation, estimation, and visualization of the CO2 emissions of King Abdullah University of Science and Technology (KAUST), Saudi Arabia. The data required for this study were collected from the overall coverage of the university campus buildings by transforming raster data from the satellite image to vector data in the form of polygons, and then multiplying the area by the number of floors of the individual building. ArcGIS 10.3® (ESRI, Redlands, CA, USA) software was used for this campus CO2 emissions evaluation and visualization. The overall estimate of the CO2 emissions for the university campus was 127.7-tons CO2 equivalent. The lowest emission was 0.02-tons CO2 equivalent while the maximum value was 20.9-tons of CO2 equivalent. By this ArcGIS-based evaluation, it is evident that geographically integrated model for campus estimation and visualization of CO2 emissions provides the information for decision makers to develop viable strategies for achieving a higher standard in overall campus sustainability and addressing the issue of climate change

A Lifecycle-Based Smart Sustainable City Strategic Framework for Realizing Smart and Sustainability Initiatives in Riyadh City

Smart cities rely on innovative technologies, guidelines, and mechanisms to advance city dwellers’ quality of life (QoL). Notwithstanding the global attention the smart city concept has received within the last decade, studies establishing pragmatic approaches for implementing smart sustainable city strategies in the Gulf region are rare. This study modelled a practical framework for implementing smart sustainable city strategies and megaprojects in one of the Gulf cities. A qualitative research methodology was used to assess smart city strategies from four cities to identify the optimum implementation strategies. This study design framework adopted a case study methodology, the identification of knowledge gaps, data collection and analysis, and interpretations of key findings. Best practices, paramount/relevant stakeholders, main issues/relevant considerations, 15 key performance indicators (KPI), and outputs/deliverables involved in diverse smart sustainable city strategies and project lifecycle phases were identified. The lifecycle phases adopted in the study were (i) conceptualization, (ii) planning/design, and (iii) installation/closure. A pragmatic understanding of how to effectively appraise, monitor, and implement smart sustainable city strategies and megaprojects is provided for policy/decision-makers and built environment experts in Saudi Arabia and globally. The proposed implementation strategic framework can perform the function of an appraisal tool for assessing each phase of the smart city project’s life cycle progress, informing preventions of delays or implementation challenges. This study’s contribution to research knowledge is the development of a model that reveals and illustrates the connections between different phases of smart sustainable city strategies and projects

The Development of a GIS-Based Model for Campus Environmental Sustainability Assessment

Sustainability indicators and assessments are vital in promoting campus sustainability. Despite the plethora of indicator frameworks, campus sustainability assessment in developing countries encounters many challenges including lack of, or restricted access to, data and difficulties in measuring indicators. There is also a limited application of Geographical Information Systems (GIS) in campus environmental sustainability assessment, although campus operations have spatial dimensions. This article proposes a GIS-based model for environmental sustainability assessment of campus operations and demonstrates its usefulness using King Fahd University of Petroleum and Minerals, Saudi Arabia. The model applies spatial analysis techniques, including inverse distance weighted (IDW) interpolation, to statistically assess the various campus operational activities by using land use data to estimate greenhouse gas emissions from energy use, water consumption, solid waste, and transportation. The integration of spatial dimension in the model facilitates the collection and measurement of spatially related indicators, helps identify hotspots of campus operations, and provides better visualization of the existing condition and future scenario of campus environmental sustainability status. This model can assist decision-makers to construct strategies for improving the overall environmental sustainability of university campuses. The paper concludes by highlighting how the model can address some challenges of campus sustainability assessment in developing countries

Novel Use of Social Media Big Data and Artificial Intelligence for Community Resilience Assessment (CRA) in University Towns

University towns face many challenges in the 21st century due to urbanization, increased student population, and higher educational institutions’ inability to house all their students on-campus. For university towns to be resilient and sustainable, the challenges facing them must be assessed and addressed. To carry out community resilience assessments, this study adopted a novel methodological framework to harness the power of artificial intelligence and social media big data (user-generated content on Twitter) to carry out remote studies in six university towns on six continents using Text Mining, Machine Learning, and Natural Language Processing. Cultural, social, physical, economic, and institutional and governance community challenges were identified and analyzed from the historical big data and validated using an online expert survey. This study gives a global overview of the challenges university towns experience due to studentification and shows that artificial intelligence can provide an easy, cheap, and more accurate way of conducting community resilience assessments in urban communities. The study also contributes to knowledge of research in the new normal by proving that longitudinal studies can be completed remotely