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A Network-based Frequency Analysis of Inclusive Wealth to Track Sustainable Development in World Countries

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journal contribution
posted on 16.10.2018 by Nasir Ahmad, Sybil Derrible, Shunsuke Managi
Using human (HC), natural (NC), and produced (PC) capital from Inclusive Wealth as representatives of the triple bottom line of sustainability and utilizing elements of network science, we introduce a Network-based Frequency Analysis (NFA) method to track sustainable development in world countries from 1990 to 2014. The method compares every country with every other and links them when values are close. The country with the most links becomes the main trend, and the performance of every other country is assessed based on its 'orbital' distance from the main trend. Orbital speeds are then calculated to evaluate country-specific dynamic trends. Overall, we find an optimistic trend for HC only, indicating positive impacts of global initiatives aiming towards socio-economic development in developing countries like the Millennium Development Goals and 'Agenda 21'. However, we also find that the relative performance of most countries has not changed significantly in this period, regardless of their gradual development. Specifically, we measure a decrease in produced and natural capital for most countries, despite an increase in GDP, suggesting unsustainable development. Furthermore, we develop a technique to cluster countries and project the results to 2050, and we find a significant decrease in NC for nearly all countries, suggesting an alarming depletion of natural resources worldwide.

Funding

This research was supported, in part, by the National Science Foundation (NSF) Award CCF1331800, the NSF CAREER Award 1551731, the NSF CPS Award 1646395, and by the Department of Civil and Materials Engineering at the University of Illinois at Chicago.

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Citation

Ahmad, N., Derrible, S., & Managi, S. (2018). A network-based frequency analysis of Inclusive Wealth to track sustainable development in world countries. Journal of Environmental Management, 218, 348-354. doi:10.1016/j.jenvman.2018.04.070

Publisher

Elsevier

Language

en_US

issn

0301-4797

Issue date

15/07/2018

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