Measuring regenerative economics: 10 principles and measures undergirding systemic economic health

Fath, Brian D.; Fiscus, Daniel A.; Goerner, Sally J.; Berea, Anamaria; Ulanowicz, Robert E.

doi:10.1016/j.glt.2019.02.002

Cited by 43 publications

(30 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The process of the entire formula is based on the ecological network formula created by Ulanowicz etc. [40][41][42][43][44][45], and the derivation process is shown in S1 Text [46][47][48]. The concept of information entropy is used to scale the sustainable development capacity H of a single system:…”

Section: -1998mentioning

confidence: 99%

Complexity theory for the modern Chinese economy from an information entropy perspective: Modeling of economic efficiency and growth potential

et al. 2020

View full text Add to dashboard Cite

Complexity modelling of economic efficiency and growth potential is increasingly essential for countries and provinces. Evaluating the monetary flows, kinetic energy (efficiency) and potential capacity (resilience) provides crucial information for economic development. In the paper, the authors analyze growth opportunities for the Chinese economy from a system science point of view, using the perspective of information entropy, based on the input-output tables. Over the past four decades of reform and opening-up, China has made remarkable progress in its economic development. In 2007, China's GDP was at its fastest pace in history at 14.2% growth. However, after the financial crisis in 2008, the global economy experienced a downward trend and China's economic development also settled on a medium-low level of development. The traditional perspective is to rank regional development only based on GDP growth, whereas here, the authors advocate another evaluation method based on efficiency and potential growth. Unbalanced regional economic development has become problematic and has become a barrier for sustainability of China's economy. The results of the research indicate firstly that China's regional development in 2007 and 2012 has been unequal between the provinces. Secondly, the authors found that Shandong province had significantly higher indicators for efficiency and potential growth than others in the same circumstances. Authors observe that provinces tend to carry out industrial policies and adjust the structure of industry on a local level. This analysis demonstrates that the spatial imbalance of efficiency and potential of economic development under the perspective of provincial-level regions. From the perspective of industry, it indicates that the supply chain is too short, mainly focusing on the mining and processing of resources and minerals in the original upstream industry chain, while the downstream is not fully utilized. These represent some unique insights yielded through this type of analysis that authors advocate applying more broadly.PLOS ONE | https://doi.org/10.1371/journal.pone.0227206 January 28, 2020 1 / 24 OPEN ACCESSCitation: Yan J, Feng L, Denisov A, Steblyanskaya A, Oosterom J-P (2020) Complexity theory for the modern Chinese economy from an information entropy perspective: Modeling of economic efficiency and growth potential. PLoS ONE 15(1): e0227206. https://doi.org/10.

show abstract

Section: -1998mentioning

confidence: 99%

Complexity theory for the modern Chinese economy from an information entropy perspective: Modeling of economic efficiency and growth potential

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Regarding whole-system ecology, indexes arising from networks are becoming useful tools for the comparison of the status of different ecosystems (Wulff and Ulanowicz, 1989;Baird et al, 1991;Monaco and Ulanowicz, 1997;Christian et al, 2005), the evaluation of system health (Mageau et al, 1995;Pinto et al, 2009;Fath et al, 2019) and the identification of roles in systems (Zorach and Ulanowicz, 2003).…”

Section: A New Metaphysics?mentioning

confidence: 99%

Socio-Ecological Networks: A Lens That Focuses Beyond Physics

Ulanowicz

2021

Front. Ecol. Evol.

Self Cite

View full text Add to dashboard Cite

Contemporary systems ecology has long been occupied with mechanical explanations of behavior; however, the physical theory that undergirds such explanations has certain limits. It’s not that the physical force laws are ever violated, but with heterogeneous, irreversible relationships subject to aleatoric influences, physical laws can only constrain, but not determine, outcomes. Such complex systems are better treated in the framework of quantified networks of interrelations. The application of simple information theory to networks reveals that ecosystems cannot achieve maximal efficiency without growing vulnerable to novel disturbances. A degree of inefficient redundancies is always necessary to sustain ecological and social functioning; and, if they are to function reliably, such non-optimal features become requisite in the design of infrastructures like power grids, water distribution networks, traffic patterns, and supply chains. In particular, the postulate that economic market efficiency should never be compromised must be re-examined if society is to remain sustainable. Furthermore, the capability of networks to represent distributed causalities allows one to rationalize behaviors like endogenous selection, centripetal acquisition of resources, and the precedence of indirect mutualism over competition in living systems–all phenomena that challenge conventional evolutionary dogma.

show abstract

“…How resource and information flows are exchanged and overlap in these networks can inform policy analysts about the structural organisation and functional performance of urban metabolism (Zhang et al, 2016(Zhang et al, , 2018. In particular, we will encourage further case studies investigating the cycling, scaling, circulation, and trade-off between efficiency and redundancy, to give another picture of the circular and regenerative economy in cities (Fath et al, 2019).…”

Section: Box 71 Plural Rationality Computational Models and Analymentioning

confidence: 99%

Systems-based Approaches for Development Co-operation to Meet Diverse Needs and Aspirations in an Interdependent World

Sperling¹,

Lopes²,

Malhotra³

et al. 2020

New Approaches to Economic Challenges

View full text Add to dashboard Cite

In policymaking, we hear the word "system" all the time. The economic system. The education system. The financial system. The political system. The social system… However, we rarely hear the word system attached to the word "approach". But unless we adopt a systems approach, unless we employ systems thinking, we will fail to understand the world we are living in. Our world is made up of complex systems, systems of systems interacting with each other, and changing each other by that interaction and the links between them. The global economy now has a greater number of links than ever before. The global nature of supply chains; new ways of exchanging goods, services and ideas; increasing migration; and ever-greater digitalisation, all increase our global connectedness. Such interconnectedness, in turn, gives rise to complexity, and this can be good or bad. However, within mainstream economics, the understanding of why and when interconnectedness may increase stability or instability has remained fragmented. Complexity science helps us to understand the main features of the most important systems we have to deal with. Features such as emergence, when the overall effect of individuals' actions is qualitatively different from what each of the individuals is doing. Radical uncertainty is another important aspect. It describes surprises-outcomes or events that are unanticipated, that cannot be put into a probability distribution because they are outside our list of things that might occur. But despite radical uncertainty and the unanticipated, the future is born by anticipation. We take decisions and perform actions to influence the future, as individuals, societies or governments. And the imagined, probable, or expected outcomes influence our decisions and actions in the present. Even things that may never happen, or will only happen decades from now, can have an impact on what we do today. That is why we plan our day, buy insurance, and pay into pension funds. That is why we try to forecast everything from GDP to the weather to the results of elections or football matches. Of course, not all our decisions about the future are in tune with what economic rationality would like us to do. We promote evidence-based decision-making, but of course there is no evidence about the future. Moreover, experience shows that simply extrapolating from the past can be ridiculous, dangerous or at best misguided. A complexity approach helps us to avoid these errors. We are dealing with a world characterised by nonlinearities, tipping points, and asymmetrical relations where a small cause can have a big effect. In a systems approach, global issues need global solutions. Environmental problems do not respect borders. You need to import some goods and services to be able to export others. The digital revolution is making it hard to define what the "domestic" in "gross domestic product" is. Growing inequalities are creating discontent. If we are to tackle these issues, governments must change the ways in which they design and implement poli...

show abstract

Measuring regenerative economics: 10 principles and measures undergirding systemic economic health

Cited by 43 publications

References 28 publications

Complexity theory for the modern Chinese economy from an information entropy perspective: Modeling of economic efficiency and growth potential

Complexity theory for the modern Chinese economy from an information entropy perspective: Modeling of economic efficiency and growth potential

Socio-Ecological Networks: A Lens That Focuses Beyond Physics

Systems-based Approaches for Development Co-operation to Meet Diverse Needs and Aspirations in an Interdependent World

Contact Info

Product

Resources

About