Sustainability, Complexity and Resilience: Insights from Complex Systems Approaches

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Complex Systems".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 1062

Special Issue Editor


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Guest Editor
Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield S10 2TN, UK
Interests: complex systems; transportation and mobility; dynamic network and graph theory; socio-technical systems; distributed control

Special Issue Information

Dear Colleagues,

The interactions between our engineering, social, urban and ecological systems is leading to their complexity to increase, with some forced adaptations happening at the interfaces of these diverse systems. As our scientific, social and technical understanding increases, the interactions become clearer. These adaptations change the nature and blur the boundary of the systems as we have known or designed them. Thus, the resilience of such systems translates as their ability to withstand the internal and exogenous pressures, some of which are possibly unknown at the design stage for human-made systems. In this scenario of balancing pressures, sustainability plays the role of ensuring such a balance is maintained, i.e., technical and policy innovations do not erode the natural and naturally emerging social systems to the advantage of those created by design. Thus, complexity, resilience and sustainability all have a stake in how our future will present itself, and it is in the understanding of this interplay that the key to ensuring such a future lies.

Dr. Giuliano Punzo
Guest Editor

Manuscript Submission Information

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Keywords

  • complex systems
  • socio-technical systems
  • emerging social systems
  • complexity
  • resilience
  • sustainability

Published Papers (1 paper)

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Research

23 pages, 5108 KiB  
Article
The Carbon Emission Reduction Effect and Spatio-Temporal Heterogeneity of the Science and Technology Finance Network: The Combined Perspective of Complex Network Analysis and Econometric Models
by Juan Liang, Rui Ding, Xinsong Ma, Lina Peng, Kexin Wang and Wenqian Xiao
Systems 2024, 12(4), 110; https://doi.org/10.3390/systems12040110 - 26 Mar 2024
Viewed by 697
Abstract
With the active promotion of the “carbon peaking and carbon neutrality” goals, science and technology finance (STF) is the important driving force of low-carbon development, and financial networks facilitate the aggregation and transformation of resources in space, so it is of great theoretical [...] Read more.
With the active promotion of the “carbon peaking and carbon neutrality” goals, science and technology finance (STF) is the important driving force of low-carbon development, and financial networks facilitate the aggregation and transformation of resources in space, so it is of great theoretical and practical significance to investigate the impact of science and technology finance networks (STFN) on carbon emissions (CE). Based on the 30 provinces of China from 2011 to 2019, this article used the STF development level in each province as the main indicator to construct the STFN. The complex network analysis and econometric models are combined, with the weighted degree values and betweenness centrality selected as typical network structure indicators incorporating into the econometric model to explore their impact on CE. Then, the Geographically and Temporally Weighted Regression (GTWR) model is applied to analyse the spatio-temporal heterogeneity of influencing factors. The results show the following: (1) From 2011 to 2019, the spatial structure of China’s STFN has changed significantly, and the status of the triangle structure consisting of Beijing–Tianjin–Hebei (BTH)–Yangtze River Delta (YRD)–Pearl River Delta (PRD) is gradually consolidated in the overall network, and the network structure tends to be stable. (2) The results of the benchmark regression show that the weighted degree value of the STFN has a significant inhibitory effect on CE, while betweenness centrality shows a certain positive effect on CE. (3) The weighted degree value has a more significant effect on CE reduction in the eastern region, while the betweenness centrality has a more significant effect on CE reduction in the central and western regions, but shows a significant promotion effect in the eastern region. (4) There is spatio-temporal heterogeneity in the effects of residents’ affluence, energy consumption, industrial structure, and environmental pollution on CE. Full article
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