The nature and nurture of network evolution.

Although the origin of the fat-tail characteristic of the degree distribution in complex networks has been extensively researched, the underlying cause of the degree distribution characteristic across the complete range of degrees remains obscure. Here, we propose an evolution model that incorporates only two factors: the node's weight, reflecting its innate attractiveness (nature), and the node's degree, reflecting the external influences (nurture). The proposed model provides a good fit for degree distributions and degree ratio distributions of numerous real-world networks and reproduces their evolution processes. Our results indicate that the nurture factor plays a dominant role in the evolution of social networks. In contrast, the nature factor plays a dominant role in the evolution of non-social networks, suggesting that whether nodes are people determines the dominant factor influencing the evolution of real-world networks.

A multi-criteria decision-making approach to vulnerability assessment of rural flooding in Khyber Pakhtunkhwa Province, Pakistan.

Assessment of rural regions' vulnerability to flooding is gaining prominence on a global scale. However, researchers are greatly undermined in their efforts to make a comprehensive assessment owing to the multidimensional and non-linear link between different indicators and flood risk. Thus, a multi-criteria decision-making (MCDM) approach is proposed to assess the multifaceted vulnerability of rural flooding in Khyber Pakhtunkhwa Province, Pakistan. This research presents a hybrid model for flood vulnerability assessment by combining TOPSIS and the entropy weight method. Households' vulnerability to flooding in rural areas is assessed through four components (social, economic, physical, and institutional) and twenty indicators. All indicator weights are derived using the entropy weight method. The TOPSIS method is then used to rank the selected research areas based on their flood vulnerability levels. The ranking results reveal that flood vulnerability is highest in the Nowshehra District, followed by the Charsadda, Peshawar, and D.I. Khan Districts. The weighting results show that physical vulnerability is the most important component, while location of household's house from the river source (< 1 km) is the key indicator for assessing flood vulnerability. A sensitivity analysis is provided to study the impact of indicator's weights on the comprehensive ranking results. The sensitivity results revealed that out of twenty indicators, fourteen indicators had the lowest sensitivity, three indicators were reported with low sensitivity while the other three were considered highly sensitive for flood vulnerability assessment. Our research has the potential to offer policymakers specific guidelines for lowering flood risk in flood-prone areas.

Impact assessment of crude oil mix, electricity generation mix, and vehicle technology on road freight emission reduction in China.

To achieve net zero emissions, the global transportation sector needs to reduce emissions by 90% from 2020 to 2050, and road freight has a significant potential to reduce emissions. In this context, emission reduction paths should be explored for road freight over the fuel life cycle. Based on panel data from 2015 to 2020 in China, China's version of the GREET model was established to evaluate the impact of crude oil mix, electricity mix, and vehicle technology on China's reduction in road freight emissions. The results show that the import share of China's crude oil has increased from 2015 to 2020, resulting in an increase in the greenhouse gas (GHG) emission intensity of ICETs in the well-to-tank (WTT) stage by 7.3% in 2020 compared with 2015. Second, the share of China's coal-fired electricity in the electricity mix decreased from 2015 to 2020, reducing the GHG emission intensity of battery electric trucks (BETs), by approximately 6.5% in 2020 compared to 2015. Third, different vehicle classes and types of BETs and fuel cell electric trucks (FCETs) have different emission reduction effects, and their potentials for energy-saving and emission reduction at various stages of the fuel life cycle are different. In addition, in a comparative study of vehicle technology, the results show that (1) for medium-duty trucks (MDTs) and heavy-duty trucks (HDTs), FCETs have lower GHG emission intensity than BETs, and replacing diesel-ICETs can significantly reduce GHG emissions from road freight; (2) for light-duty trucks (LDTs), BETs and FCETs have the highest GHG emission reduction potential; thus, improving technologies such as electricity generation, hydrogen fuel production, hydrogen fuel storage, and transportation will help to improve the emission reduction capabilities of BETs and FCETs. Therefore, policymakers should develop emission standards for road freight based on vehicle class, type, and technology.

Synergy between climate risk perception, adaptation responses, and agricultural productivity: the case of rice farming communities in Pakistan.

The climate variability in Pakistan adversely affects rice crops and undermines the food security and livelihoods of millions of rural households whose survival depends directly on rice farming. This study examines farmers' risk perception, adaptation responses, and adaptation impact on rice productivity. We employed a multi-stage sampling method for selecting 480 farmers from the rice production zone of Punjab province, a region that produces more than 60% of the total rice in the country and faces significant production decline due to climate change. We used the risk matrix method to determine farmers' perception of climate change-induced risk and used the propensity score matching (PSM) technique to analyze the impact of adaptation measures on rice yield and crop returns. Results show that farmers had high perceptions and were concerned about biological and financial risks, followed by biophysical, atmospheric, and social risks. Farmers applied supplementary irrigation, changed rice cultivation dates, changed rice varieties, resized farms, and altered irrigation application times as adaptation measures to cope with changing climate effects. Probit regression analysis showed that the adaptation measures had been largely affected by farmers' socioeconomic attributes and risk perceptions. The PSM estimates showed that all adaptation measures had a positive impact on rice yield and crop return. Specifically, the cultivation of alternative rice varieties, farm resizing, and supplementary irrigation were the most effective strategies, followed by the adjustment in cultivation dates and irrigation time. Having implications beyond Pakistan, this study suggests improving farmers' access to irrigation water, credit, and farm advisory services to facilitate the extent of adaptation.

A New Consensus Model Based on Trust Interactive Weights for Intuitionistic Group Decision Making in Social Networks.

A promising feature for group decision making (GDM) lies in the study of the interaction between individuals. In conventional GDM research, experts are independent. This is reflected in the setting of preferences and weights. Nevertheless, each expert's role is played through communication, collaboration, and cooperation with other individuals. The interaction from others may affect the power of an expert as well as his/her opinion. Furthermore, it is noted that a link path with the highest degree of trust is the most efficient information transmission channel. Inspired by these findings, an optimal trust-induced consensus process is designed with the usage of intuitionistic fuzzy preference relation. The comprehensive weight of each expert is decomposed into two portions, namely: 1) the individual weights and 2) interactive weights. Three optimization models are constructed to achieve weight parameters under different decision situations, where the weight parameters are represented through a 2-order additive fuzzy measure and the Shapley value. To reflect the interaction, the Choquet integral is employed for aggregating opinions, and a novel distance measure is adopted for accomplishing a consensus index. An illustrative example and comparison are put in practice to show the effectiveness and improvements of the proposed method.

Social network analysis model for research on organizational structure of the pyramid scheme communication network.

In this article, we introduce a structural analysis model to analyze the characteristics of the communication network structure of pyramid scheme organizations. This model is a combination of SNA (Social Network Analysis) model, motifs analysis model, and exponential random graph model. It can analyze the network from three aspects: global structure analysis, microstructure analysis, and construction feature analysis. We use this model to analyze the characteristics of multiple aspects of a typical pyramid scheme organization's communication network, and the analysis results effectively expand the understanding of the characteristics of the pyramid scheme organization.•SNA model can be used to analyze the global structure of the pyramid scheme communication network.•Motifs analysis model can be used to analyze the microstructure characteristics of the pyramid scheme organization communication network.•Exponential Random Graph Model can be used to analyze the construction characteristics of the pyramid scheme communication network.

Research on Evaluation of Meteorological Disaster Governance Capabilities in Mainland China Based on Generalized λ-Shapley Choquet Integral.

According to the United Nations report, climate disasters have intensified in the past 20 years, and China has the largest number of disasters in the world. So the study of meteorological disaster governance capacities is critically important for China. We designed a meteorological disaster governance capacity evaluation system to calculate the evaluation values by using the generalized λ-Shapley Choquet integral, a method that considers the interaction between indicators. We used various official statistical yearbooks and internal data of China Meteorological Administration (CMA) and weight intervals set by meteorologists for each level of indicators to calculate the evaluation values of meteorological disaster governance capacity in mainland provinces, from 2014 to 2018. We compared them with other methods (entropy weight method, Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and Analytic Hierarchy Process (AHP)), and the results showed that the results calculated by the designed interaction method provided in this paper are more stable and differentiated. The results show that provincial meteorological disaster governance capacities in Mainland China are characterized by uneven development and a pro-slight polarization phenomenon. This leads to policy recommendations: Provinces should strengthen the construction of meteorological disaster information; provinces with outstanding capacity must strengthen the experience sharing with provinces with lower capacity.

Livelihood diversification in managing catastrophic risks: evidence from flood-disaster regions of Khyber Pakhtunkhwa Province of Pakistan.

Pakistan's agricultural productivity is considered to be low despite several agriculture promotion policies. Such policies concentrate primarily on on-farm development and overlook rich prospects for off-farm diversification. Livelihood diversification of small-scale farmers plays a major role in reducing hunger and mitigating the adverse impacts of climate change. Therefore, this paper seeks to analyze livelihood diversification in managing catastrophic risks among rural farm households of Khyber Pakhtunkhwa Province of Pakistan. We have interviewed a total of 600 farm households through a standardized questionnaire in two districts (Nowshera and Charsadda) of Khyber Pakhtunkhwa Province of Pakistan that were badly affected by the 2010 flood. For empirical analysis, a logistic regression model was chosen to analyze the important attributes that are correlated to livelihood diversification of the rural households in flood-susceptible areas of Pakistan. The survey findings indicate that 50% of the total sample respondents adopted off-farm livelihood diversification strategies, while 40.5% of farm households adopted on-farm livelihood diversification strategies in managing catastrophic risks. The logistic regression model results show that attributes including socioeconomic and demographic, institutional, and risk perception significantly influenced households' choices of livelihood diversification. Also, the findings indicated a wide range of livelihood diversification constrained including climatic risks and uncertainties (23%), inadequate natural resources (17%), limited level of skills and training (15%), lack of institutional support (12%), lack of credit facilities (11%), poor infrastructure including markets and roads (16%), and lack of labor availability (4%). The study urges the need for robust climate change adaptation policies, in particular, by aiming at training initiatives, improving access to services, and enhancing institutional assistance, and better infrastructure. The livelihood of small-scale farmers could only improve if the Government pays due consideration and adopts the right policy initiatives that promote the diversification of livelihoods as part of the creation of national jobs to save many lives and improve livelihoods.

Measuring education sector resilience in the face of flood disasters in Pakistan: an index-based approach.

School resilience is characterized as risk management techniques to build a safe environment for students. Recognizing the need of building disaster resilience for the education sector, this study is aimed at assessing flood disaster resilience of elementary schools in four extremely vulnerable districts of Khyber Pakhtunkhwa province of Pakistan. This paper established the assessment tool by incorporating climate resilience indices and 16 tasks of the Hyogo Framework for action designed for the education sector. It discusses four dimensions: physical conditions of elementary schools, human resources, institutional issues, and external relationships, each with three parameters and five variables. The data were obtained for 60 variables from 20 randomly selected elementary schools. Indicators of resilience were identified, and an index-based approach was used to get the composite values of the four dimensions of resilience. Correlations between the dimensions, components, and indicators were also checked in the current study. Results show that schools in Nowshera, followed by Charsadda, Peshawar, and Dera Ismail Khan, are the most resilient to flood disasters. For all 12 parameters under 4 dimensions, the relative resilience of study districts is the same. The findings further indicated that there is a strong correlation between the pairs of human resources and institutional issues as well as institutional issues and external relationships that can also enhance human resources and external relationships. Furthermore, institutional issues are also correlated with external relationships and human resources, which indicate that there is a triangular relationship among human resources, institutional issues, and external relationships. The findings would encourage policymakers and practitioners to develop an effective plan to improve the resilience of schools using the overall resilience situation. In short, education sector disaster resilience can be achieved by integrated planning and implementation approach. In this respect, disaster managers, public and private education sectors, school staff, students, and parents need to establish synergies to devise a comprehensive plan of action to enhance disaster education.

Influencing Factors of PM2.5 Pollution: Disaster Points of Meteorological Factors.

A chance constrained stochastic Data Envelopment Analysis (DEA) was developed for investigating the relations between PM2.5 pollution days and meteorological factors and human activities, incorporating with an empirical study for 13 cities in Jiangsu Province (China) to illustrate the model. This approach not only admits random input and output environment, but also allows the evaluation unit to exceed the front edge under the given probability constraint. Moreover, observing the change in outcome variables when a group of explanatory variables are deleted provides an additional strategic technique to measure the effect of the remaining explanatory variables. It is found that: (1) For 2013-2016, the influencing factors of PM2.5 pollution days included wind speed, no precipitation day, relative humidity, population density, construction area, transportation, coal consumption and green coverage rate. In 2016, the number of cities whose PM2.5 pollution days was affected by construction was decreased by three from 2015 but increased according to transportation and energy utilization. (2) The PM2.5 pollution days in southern and central Jiangsu Province were primarily affected by the combined effect of the meteorological factors and social progress, while the northern Jiangsu Province was largely impacted by the social progress. In 2013-2016, at different risk levels, 60% inland cities were of valid stochastic efficiency, while 33% coastal cities were of valid stochastic efficiency. (3) The chance constrained stochastic DEA, which incorporates the data distribution characteristics of meteorological factors and human activities, is valuable for exploring the essential features of data in investigating the influencing factors of PM2.5.

Haze Influencing Factors: A Data Envelopment Analysis Approach.

This paper investigates the meteorological factors and human activities that influence PM2.5 pollution by employing the data envelopment analysis (DEA) approach to a chance constrained stochastic optimization problem. This approach has the two advantages of admitting random input and output, and allowing the evaluation unit to exceed the front edge under the given probability constraint. Furthermore, by utilizing the meteorological observation data incorporated with the economic and social data for Jiangsu Province, the chance constrained stochastic DEA model was solved to explore the relationship between the meteorological elements and human activities and PM2.5 pollution. The results are summarized by the following: (1) Among all five primary indexes, social progress, energy use and transportation are the most significant for PM2.5 pollution. (2) Among our selected 14 secondary indexes, coal consumption, population density and civil car ownership account for a major portion of PM2.5 pollution. (3) Human activities are the main factor producing PM2.5 pollution. While some meteorological elements generate PM2.5 pollution, some act as influencing factors on the migration of PM2.5 pollution. These findings can provide a reference for the government to formulate appropriate policies to reduce PM2.5 emissions and for the communities to develop effective strategies to eliminate PM2.5 pollution.

Grey Language Hesitant Fuzzy Group Decision Making Method Based on Kernel and Grey Scale.

Based on grey language multi-attribute group decision making, a kernel and grey scale scoring function is put forward according to the definition of grey language and the meaning of the kernel and grey scale. The function introduces grey scale into the decision-making method to avoid information distortion. This method is applied to the grey language hesitant fuzzy group decision making, and the grey correlation degree is used to sort the schemes. The effectiveness and practicability of the decision-making method are further verified by the industry chain sustainable development ability evaluation example of a circular economy. Moreover, its simplicity and feasibility are verified by comparing it with the traditional grey language decision-making method and the grey language hesitant fuzzy weighted arithmetic averaging (GLHWAA) operator integration method after determining the index weight based on the grey correlation.

Priority of a Hesitant Fuzzy Linguistic Preference Relation with a Normal Distribution in Meteorological Disaster Risk Assessment.

As meteorological disaster systems are large complex systems, disaster reduction programs must be based on risk analysis. Consequently, judgment by an expert based on his or her experience (also known as qualitative evaluation) is an important link in meteorological disaster risk assessment. In some complex and non-procedural meteorological disaster risk assessments, a hesitant fuzzy linguistic preference relation (HFLPR) is often used to deal with a situation in which experts may be hesitant while providing preference information of a pairwise comparison of alternatives, that is, the degree of preference of one alternative over another. This study explores hesitation from the perspective of statistical distributions, and obtains an optimal ranking of an HFLPR based on chance-restricted programming, which provides a new approach for hesitant fuzzy optimisation of decision-making in meteorological disaster risk assessments.

On Consistency Test Method of Expert Opinion in Ecological Security Assessment.

To reflect the initiative design and initiative of human security management and safety warning, ecological safety assessment is of great value. In the comprehensive evaluation of regional ecological security with the participation of experts, the expert's individual judgment level, ability and the consistency of the expert's overall opinion will have a very important influence on the evaluation result. This paper studies the consistency measure and consensus measure based on the multiplicative and additive consistency property of fuzzy preference relation (FPR). We firstly propose the optimization methods to obtain the optimal multiplicative consistent and additively consistent FPRs of individual and group judgments, respectively. Then, we put forward a consistency measure by computing the distance between the original individual judgment and the optimal individual estimation, along with a consensus measure by computing the distance between the original collective judgment and the optimal collective estimation. In the end, we make a case study on ecological security for five cities. Result shows that the optimal FPRs are helpful in measuring the consistency degree of individual judgment and the consensus degree of collective judgment.

Selecting Cooking Methods to Decrease Persistent Organic Pollutant Concentrations in Food of Animal Origin Using a Consensus Decision-Making Model.

Persistent organic pollutants (POPs) pose serious threats to human health. Increasing attention has been paid to POPs to protect the environment and prevent disease. Humans are exposed to POPs through diet (the major route), inhaling air and dust and skin contact. POPs are very lipophilic and hydrophobic, meaning that they accumulate in fatty tissues in animals and can biomagnify. Humans can therefore be exposed to relatively high POP concentrations in food of animal origin. Cooking animal products can decrease the POP contents, and different cooking methods achieve different reduction rates. Here, a consensus decision-making model with interval preference relations is used to prioritize cooking methods for specific animal products in terms of reducing POP concentrations. Two consistency mathematical expressions (I-consistency and I I -consistency) are defined, then the ideal interval preference relations are determined for the cooking methods with respect to different social choice principles. The objective is to minimize disparities between individual judgments and the ideal consensus judgment. Consistency is used as a constraint to determine the rationality of the consistency definitions. A numerical example indicated that baking is the best cooking method for decreasing POP concentrations in grass carp. The I-consistency results were more acceptable than the I I -consistency results.