Network characterization of the Entangled Model for sustainability indicators. Analysis of the network properties for scenarios

Policy-makers require strategies to select a set of sustainability indicators that are useful for monitoring sustainability. For this reason, we have developed a model where sustainability indicators compete for the attention of society. This model has shown to have steady situations where a set of sustainability indicators are stable. To understand the role of the network configuration, in this paper we analyze the network properties of the Entangled Sustainability model. We have used the degree distribution, the clustering coefficient, and the interaction strength distribution as main measures. We also analyze the network properties for scenarios compared against randomly generated scenarios. We found that the stable situations show different characteristics from the unstable transitions present in the model. We also found that the complex emergent feature of sustainability shown in the model is an attribute of the scenarios, however, the randomly generated scenarios do not present the same network properties.

The result association was as show in the next table. These indicators are the core indicators that the Commission on Sustainable Development(CSD) has proposed in order to cover most of the issues that are relevant for sustainable development, these indicators provide critical information and are easily calculated. Even though all the CSD indicators were associated with one indicator from the model, the number of indicators in the model is higher than the CSD set. Not all vectors represent indicators of the CSD, but the vectors can be modified slightly so that most agents can be associated to specific indicators that will be built in the necessary case.
The association was used in the Scenarios section in order to exemplify the differences between scenarios and it was also used to propose the Paretian set for each scenario.

Appendix B
We represent specific scenarios through defining a specific J 0 matrix constructed by a questionnaire applied to stakeholders. The responses to this survey were used as indicated in Figure B Table A. Sustainability indicators association part 1.
10 Immunization against infectious childhood diseases 20 Gross intake into last year of primary education, by sex 21 Suicide rate 30 Total fertility rate 31 Prevalence of tobacco use 32 Number of intentional homicides per 100,000 population 33 Percent of population with access to primary health care facilities 102 Share of imports from developing countries and from LDCs 120 Number of internet users per 100 population 130 Contraceptive prevalence rate 131 Share of women in wage employment in the non-agricultural sector 133 Net enrollment rate in primary education 210 Domestic material consumption 212 Investment share in GDP 220 Assistance (ODA) given or received as a percentage of GNI 221 Ratio of local residents to tourists in major tourist regions 222 Human and economic loss due to natural disasters 230 Ratio of share in national income of highest to lowest quintile 231 Adult literacy rate, by sex 232 Gross savings 300 Current account deficit as percentage of GDP 310 Fixed telephone lines per 100 population 311 Dependency ratio 312 Debt to GNI ratio 313 Gross domestic product (GDP) per capita 320 Adjusted net savings The possible answers to the questionnaire were in the range of agreement from the values (2, 1, 0, −1, −2) corresponding to the Completely agree, Agree, No opinion, Disagree, Completely disagree. As there were four questions for the same J 0 matrix, then the four questions where averaged. Finally, each question with all the participants answers was averaged. Organic crops (crops that do not use synthetic products) have a positive effect on the economy[1] of the region you live in.
Current air quality has a positive effect on human well-being in the region you live in.
Current water quality has a positive effect on human well-being in the region you live in.
Current abundance of species has a positive effect on human well-being in the region you live in.    [1] With the word 'economy' in the questionnaire, we mean the state of the region regarding the production and consumption of non-environmental and environmental goods and services together with the supply of money. It refers to both private and public organizations.
[2] With the word 'institutions' in the questionnaire, we mean organizations or other formal social structures that govern a field of action in the region we are analysing. These organizations may be governmental agencies, NGOs, universities, sports clubs, families, etc.; but also this dimension includes social norms, principles, rules and decision-making procedures. A positive effect on institutions can be for example an increased credibility, efficiency, security or empowerment.
[3] With 'society' we mean the population living together in the region we are analyzing. A positive effect on the society can be for example an increased cohesion, harmony and/or order.
This questionnaire was used with the methodology described in Scenario creation section in order to design the J 0 matrix and determine the scenario to be simulated.

Appendix C
Next we present the results of the statistical tests comparing the different scenarios.
The test values on the three different tests consistently show lower values for the specific scenarios than for the random generated. For a 90% of confidence these values lead that the specific scenarios are statistically similar meanwhile the randomly generated scenarios are different.        Table L shows the Pearson correlation coefficient using three different simulations from the Trondheim scenario. The result shows that the simulations have a correlation. Table M shows the Pearson correlation coefficient using three different simulations from the Jalisco scenario. The result shows that the simulations have a correlation.  Table N shows the Pearson correlation coefficient using three different simulations from the random generated scenario number 2. The result shows that the simulations have no correlation.  Table O shows the Pearson correlation coefficient using three different simulations from the random generated scenario number 3. The result shows that the simulations have no correlation.
These results show differences between simulations from different specific scenarios (SS) and random generated scenarios (RGS). The SS simulations showed to be statistically similar between them and RGS simulations does not. The results were used in the Stability of simulations section.