Improved CCM for variable causality detection in complex systems

Wang, Yalin; Hu, Fangxiang; Cao, Yue; Yuan, Xiaofeng; Yang, Chunhua

doi:10.1016/j.conengprac.2018.10.005

Cited by 24 publications

(9 citation statements)

References 31 publications

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“…• Significance test: In [15], the significance test is a Z-test with Fisher's z-transformation and a confidence level of 0.01. Reference [17] uses Monte Carlo simulations to determine a significance threshold.…”

Section: Discussionmentioning

confidence: 99%

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From oscillatory to non-oscillatory disturbances: A comparative review of root cause analysis methods

Lucke

Chioua

Thornhill

2022

Journal of Process Control

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Section: Discussionmentioning

confidence: 99%

“…Reference [16] refined the manifold construction and proposed an automatic method for parameter tuning. The authors of [17] proposed a method for choosing the embedding dimension.…”

Section: Convergent Cross Mappingmentioning

confidence: 99%

From oscillatory to non-oscillatory disturbances: A comparative review of root cause analysis methods

Lucke

Chioua

Thornhill

2022

Journal of Process Control

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“…2) for several periods of time after the event that causes a change in growth rate. If the data are available over long periods, using the time-series approach would facilitate the study and quantification of this lag as CCM allows to test for a lag in the response of two causally related variables during the course of the method implementation (Wang et al 2019).…”

Section: How Delayed Is the Response Of Biodiversity To A Cessation O...mentioning

confidence: 99%

Degrowth scenarios for biodiversity? Some methodological steps and a call for collaboration

Otero¹,

Rigal²,

Pereira³

et al. 2022

Preprint

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Economic growth contributes to biodiversity loss and does not necessarily contribute to wellbeing. Thus, when developing biodiversity scenarios, we should explore societal futures where economic growth is not a pre-condition. However, it is not clear how and by whom a degrowth scenario for biodiversity could be developed. This is so because there are different approaches to develop scenarios (some of them nascent) and because degrowth is only loosely connected to biodiversity questions. In this perspective paper we explain how the Nature Futures Framework (NFF) could be used to generate a degrowth scenario for biodiversity, Nature's Contributions to People (NCP) and Good Quality of Life (GQL) based on multiple societal values. We present key methodological avenues of such an endeavour, including: (i) generating degrowth visions for biodiversity, NCP and GQL; (ii) identifying the leverage points and characterizing the transition; (iii) identifying relevant social-ecological feedbacks and selecting indicators; and (iv) modelling biodiversity, NCP and GQL along a degrowth transition. We frame our proposal in current efforts to improve scenario development across the biodiversity and climate communities. We end with a call for collaboration between natural and social sciences, quantitative and qualitative approaches, and northern and southern perspectives. This collaboration could lead to a community of practice that tests and improves the scenario in national and international science-policy interfaces.

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“…By contrast, data-driven approaches can provide multiple ways to find causal relationships based on large amounts of historical data. Data-driven methods have been widely applied to identify the causal relationships between variables [17][18][19][20][21][22][23][24][25]. The Granger causality (GC) test proposed by Granger [26] is considered to be one of the earliest data-driven causality detection methods.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Influences of Driving Factors on Land Surface Temperature during 2003–2018 in China Using Convergent Cross Mapping Method

Shang

Duan

et al. 2022

Remote Sensing

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The relationship between land surface temperature (LST) and environmental factors is complex and nonlinear. To determine these relationships for China, this study analyzed the driving effects of air temperature, vegetation index, soil moisture, net surface radiation, precipitation, aerosols, evapotranspiration, and water vapor on LST based on remote-sensing and reanalysis data from 2003–2018, using a convergent cross-mapping method. During the study period, air temperature and net surface radiation were the dominant drivers of LST with a cross-mapping skill above 0.9. Vegetation index and evapotranspiration were the secondary drivers of LST with a cross-mapping skill that was higher than 0.5. Except for air temperature and net surface radiation, the direction and strength of the effects of the driving factors on LST were related to the climate type. The effects of air temperature and net radiation on LST diminished from north to south, indicating that LST was more sensitive to air temperature and net radiation in energy-limited regions. However, the effects of vegetation index and evapotranspiration on LST varied significantly across climate zones; that is, positive effects were mostly in non-monsoonal zones and negative effects were primarily in monsoonal zones. Our results quantified the driving role of environmental factors on LST and provided a comprehensive understanding of LST dynamics.

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Improved CCM for variable causality detection in complex systems

Cited by 24 publications

References 31 publications

From oscillatory to non-oscillatory disturbances: A comparative review of root cause analysis methods

From oscillatory to non-oscillatory disturbances: A comparative review of root cause analysis methods

Degrowth scenarios for biodiversity? Some methodological steps and a call for collaboration

Quantifying the Influences of Driving Factors on Land Surface Temperature during 2003–2018 in China Using Convergent Cross Mapping Method

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