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Page 169
Climate Change 2016
October 27-29, 2016
Volume 7, Issue 9(Suppl) J Earth Sci Clim Change
ISSN: 2157-7617 JESCC, an open access journal
conferenceseries .com
October 24-26, 2016 Valencia, Spain
World Conference onClimate Change
Stars: Testing method for regime shifts detection
Luca Stirnimann1,2, Alessandra Conversi1,3 and Simone Marini3
1Plymouth University, UK
2Università Degli Studi di Genova, Italy
3CNR - ISMAR - La Spezia, Italy
Research focusing on regime shift in marine time series has increased in the last decade. Last year alone, there were 140
published papers and 5500 citations within the literature. One commonly used method to detect shifts in physical and
ecological time series is the sequential t-test analysis of regime shift (STARS). This method has a convenient Visual Basic
Application (for Excel) and therefore is widely used by marine ecologists. In this work, we analyse, using simulated data, the
limitations and accuracy of the STARS method for identifying threshold points in time series. We synthesized two groups
of time series generated with the program R, each one consisting of 1000 different random series containing known change
points and magnitude values. The two groups are as follows: 1) 1000 random time series without autocorrelation, and, 2) 1000
random time series with incorporated autocorrelation and seasonality. Then, all-time series are analysed using the STARS
method, utilizing a CRAN-package in R that replicates Rodionov’s program. The work is still in progress; however the first
results indicate that there are inaccuracies in STARS in determining the exact timing of change points. The aim of this work is
to provide researchers with useful indications on the limits this method for detecting regime shifts and to provide an R routine
accessible for all researchers.
The diffusion of information and behavior in social networks: Renewable energy technology adoption
in rural China
Marcella Veronesi
University of Verona, Italy
Adopting renewable energy technologies has been seen as a promising way to reduce CO2 emissions and address climate
change. This paper investigates how social networks may affect renewable energy technology adoption. We distinguish
two channels through which social networks may play a role: (i) the diffusion of information; and (ii) the diffusion of behavior.
Most empirical studies fail to quantitatively separate the diffusion of information and behavior in social networks. We conduct
a survey on biogas technology adoption in rural China to identify individuals’ egocentric information networks. In egocentric
social networks, the individual of interest is defined as “ego” and the people connected to the ego are defined as “alters”. We
find that both the diffusion of information and behavior drive farmers’ technology adoption. Farmers with larger egocentric
information networks and a larger fraction of known adopters are more likely to adopt the biogas technology. In addition, we
collect data on several attributes of alters to explore the composition of social networks. We find heterogeneous social network
effects across different types of alter. Alters who have close relationships with egos such as friends and relatives or that are
trusted by egos affect egos’ adoption through the diffusion of information, while less trusted alters such as government officials
affect egos’ adoption through their adoption behavior.
J Earth Sci Clim Change 2016, 7:9(Suppl)