English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Sustainability is defined as “meeting the needs of the present generation without compromising the ability of future generations to meet their own needs.” (WCED ,1987). This is a concept that was developed to counteract the negative consequences of the culture of disposability and consumption based economy on the environment. Many policy makers have the perception that pursuing policies that embrace sustainability would compromise countries economic prosperity. Therefore, this dissertation introduces the new index of sustainable prosperity. This is a novel multiattribute measure that comprehensively assesses sustainable prosperity (SPI) for systems such as countries, hospitals, and products...etc. This index ensures that adapted policy contribute to progress toward sustainable development and equally important maintain the wealth of the system. Herein, we demonstrated its utility by applying it on the country case represented by the G-20 group. We investigated the drivers for culture of disposability and made recommendation on expanding the definition of disposable products to better define its contribution to GDP. Furthermore, the major sustainability indices that act as tools in the assessment of sustainability were investigated. The goal is to identify a viable index to comprehensively assess sustainability and the key attributes for a good measure. While valid, each index had its own disadvantages that limited their use. A list of attributes that should be considered to develop an index that successfully measures the progress of nations toward sustainable development was developed (Table 4.6). These attributes were used as a frame of reference for the SPI index and the selection of the domains and sub-domains that are mapped to systems investigated. The analytical methodology of SPI index is based on the use of Principle Components Analysis (PCA) combined with Data Envelopment Analysis (DEA) (PCA-DEA). This approach discriminates among systems i.e. G-20 countries investigated. It identifies the ones that are sustainable. It incorporates the use of inputs and outputs to calculate the efficiency score of each system investigated i.e. SPI. A total of 44 inputs (Chapters 6 and 7) were mapped to the domains and sub-domains of the framework. In addition, a total of 10 outputs were selected to capture the wealth of the nations, which is based on the maintenance of capital, or keeping capital stock least unchanged as proposed by Dasgupta (Dasgupta 2010 ). Initially, conventional DEA was implemented to calculate countries SPI. Since there was a large number of inputs and outputs utilized (a total of 54 variables) relative to the number of countries investigated (20 DMUs), this methodology fell short of discriminating among the G20 countries. Indeed, all countries were sustainable and acquired an SPI score of 1. Therefore, PCA was used to reduce the number of variables and transform the original inputs and outputs into principle components with minimal loss of information (capturing most of the original variance of the original data). PCA reduced the number of combined inputs and outputs to a total of 17 variables while maintaining around (80-85%) of the total cumulative variation. Interestingly, this PCA-DEA methodology provides a similar impact to that of weight restrictions addition. Still countries discrimination was not satisfactory using this methodology (Table 8.7). To overcome this shortcoming, we used various combinations of PCA inputs and outputs to improve discrimination among G-20 countries. The impact of different case scenarios of the number PCA inputs and outputs used and their combinations for the years of 19902012 was investigated (Table 8.9). Consistent with the literature reports, the use of one PCA from each dimension in the inputs and outputs provided the best differentiation among the G-20 group. This ensured that the SPI measure comprehensively assesses the progress toward attaining sustainable prosperity from the four dimensions, which is a key attribute of our novel index that many global indices lack. For robustness analysis, a Spearman Correlation Test (SCT) was performed to evaluate the relationships involved in the ranking of the G-20 countries using different PCA-DEA combinations. It was observed that there is high correlation between the countries ranking among different PCA-DEA combinations. Although the variability of the information has been reduced by integrating smaller number of PCA in the analysis, still we have effectively increased the discrimination among countries investigated. Collectively, the use of SCT was effective in demonstrating the validity of this methodology, consistent with literature reports. Developed countries among the G-20 group had higher average SPI scores compared to developing countries over the period investigated. However, between 2008 to 2012, developed countries realized a reduction in the overall average SPI scores from ≈0.8 to 0.6. This trend was not clearly observed for the developing countries. This is the time period that followed the 2008 financial meltdown, which threatened the total collapse of large financial institutions and was prevented by the bailout of banks by national governments. Interestingly, the GDP growth in 2009 was limited to the developing countries, while developed countries had poor GDP growth if any. A comparison between SPI results and key sustainability indices (GDP, EPI, and HDI) of the G-20 countries was determined. There is no link between GDP and SPI scores. The same trend was observed for HDI and EPI. The poor correlation observed between SPI and these indices is not unexpected since they assess only one aspect of sustainability considered, while SPI is a comprehensive sustainability index that integrates the three aspects of sustainability; environmental, economic, and social; in addition to the overall wealth as part of its assessment. In summary, there continues to be a quest to build an index that enables policy makers to assess progress toward achieving sustainable development. Despite, the scientific research that was conducted, there is no general consensus on a sustainability index that would replace GDP. This is partially attributed to the fact that sustainability is a complex system that incorporates many dimensions. This research started with identifying the attributes that should be considered when building a comprehensive index that assesses progress toward sustainable development. Unlike other indices, our proposed index has a key advantage, which include among others, its ability not only to comprehensively measure sustainable development, but also to ensure that adapted policies contribute to maintaining the wealth of the system. In addition to countries, this novel index can also be used to assess and compare other systems such as hospitals, products, and manufacturing facilities to name a few. This research is far more than an academic investigation; it is rather a response for unmet need to present sustainability in a form that makes it more appealing for policy makers to make the investment in maintaining wealth while demonstrating progress toward sustainability.

SUSTAINABILITY MODELING AND ASSESSMENT OF PRODUCT RECOVERY SYSTEMS - AN ENGINEERING APPROACH TO A SUSTAINABLE FUTURE