Spatial and temporal heterogeneity of the impact of per capita income on household indirect carbon emissions in western China

With China entering the stage of high-quality development,the issue of carbon emission has become a hot research topic.This paper analyzes the different temporal and spatial effects of per capita income on household indirect carbon emissions in western China.Based on the data of Chinese Family Panel Studies(CFPS)in 2016 and 2018 in the western China,this paper uses Regression analysis and Bayesian correlation analysis to study the relationship between per capita income and household indirect carbon emissions.The results showed that the indirect carbon emissions generated by the expenditure on food,housing and household equipment in the household consumption structure in the western China were relatively high.In 2016-2018,the per capita income and per capita household consumption indirect carbon emissions in the western China showed an increasing trend.There was a positive correlation between per capita income and indirect carbon emissions of per capita household consumption,and its correlation was gradually enhanced in time dimension.In the spatial dimension,the household indirect carbon emissions in Yunnan,Qinghai,Guangxi Zhuang and Ningxia in the western China were greatly affected by per capita income,while the household indirect carbon emissions in Guizhou was least affected by per capita income.Finally,the paper puts forward some problems that we should consider in the process of facing the per capita income growth and climate change:the collection of carbon tax,the optimization of household consumption structure,the research and development of low-carbon products,and the differentiated carbon reduction.

Spatio-temporal Heterogeneity in the Performance of Rural Revitalization in Chifeng City of China:Based on a Range-wide EBM-GML Model

To solve the problems of rural revitalization performance research,a quantitative model of non-oriented range-wide EBM(Epsilon-Based Measure)-GML(Global-Malmquist)based on VRS(Variable Returns to Scale)conditions including non-desired outputs is constructed.A comprehensive spatio-temporal heterogeneity research index system of rural revitalization performance is also constructed.Taking the typical rural in Chifeng City as an example,the panel data from 2016-2020 are selected for empirical analysis,the conclusions and countermeasures are suggested as follows:1)In general,the rural revitalization performance of Chifeng City increases significantly during the five-year period,with significant spatio-temporal heterogeneity.The overall analysis shows that the overall performance value of rural revitalization in Chifeng City is 0.683 from 2016 to 2020.The highest performance value is 1 and the lowest performance value is 0.389.The performance growth rate increases year by year,with an average annual growth rate of 4.46%.2)From 2016 to 2020,the GML index of rural revitalization performance in Chifeng City is 1.174,showing an increasing trend.Based on the range of change of GML index,Chifeng City can be classified into three types:Continuous improvement,fluctuating improvement and fluctuating decline.3)Niujiayingzi,Guandongche,Zhaidamu,and Qiangangtai rural have the highest degree of technological progress.

Social effects of topic propagation on Weibo

Weibo,China’s largest microblogging platform,has become one of the key information-sharing platforms in modern society.This study examines topic propagation in relation to microblogging from the perspective of the“peer effect.”Using data of hot topics from Weibo,we analyze how the social effect and propagation pathway influence the topic propagation process.We propose a spatial and temporal heterogeneity diffusion model that includes endogenous and exogenous social effects and is based on but different from the Bass diffusion model.We find that most propagation pathways end after a single level of propagation.The endogenous social effect in microblogs primarily influences the inflow of topics.Such endogenous social effect,combined with the multiplier effect,motivates most users to share a microblog topic in a short period of time.The exogenous social effect primarily influences the outflow of topics,and therefore,the microblog topics of a small number of popular users’account for most of the share volume.Our results are robust to potential serial correlation,reflection problem,and potential self-selection due to user status.The findings reveal that group characteristics affect individuals’behaviors and choices in relation to the topic propagation process on microblogging platforms.The use of a spatial and temporal heterogeneity diffusion model and the robustness of the analysis process provide new information for scholars in this field.

The efect of landscape structure on the evolution of two alternative dispersal strategies

Background:Dispersal is an important event for most organisms at least once in their life cycle.The evolution of dispersal can be infuenced by local adaptation,landscape structure,and perceived temporal and spatial variation.The interaction between local adaptation,landscape heterogeneity,temporal variability and rules of dispersal may be more complex than previously assumed.Therefore,we sought to understand the infuence of emigration rules and landscape structure on emerging dispersal rates and traits.Here,we implemented an individual-based model(IBM)of trait evolution in scenarios characterized by diferent landscape structures and diferent degrees of spatial heterogeneity and global temporal variation.Individuals could evolve two traits coding for their environmental niche(position of niche optimum and niche width),and two traits determining nearest-neighbor dispersal:an individual emigrates with a probability defned by the frst trait(random emigration),but emigrates with certainty if the fertility expected in the patch of residence falls below a threshold specifed by the second trait(habitat-dependent emigration).Results:We note an interaction efect between dispersal strategy and spatial variance—lower emigration under habitat-dependent than under random emigration if spatial heterogeneity is low,but eventually a reversal of this ranking if heterogeneity becomes large.Landscapes with sharp transition of habitat attributes result in a high degree of spatial sorting,while fractal landscapes do not.Emigration rates are overall lowest,when spatial variation is highest.Conclusions:We conclude that emergent emigration rates are infuenced more by landscape structure and spatiotemporal heterogeneity than by the emigration strategy.With the ongoing land use change more research into this topic could help highlight the difculties species might face under the change from landscapes characterized by gradual transition zones to landscapes dominated by abrupt ecotones,the latter typical for agricultural and urban settings.

A time dimension of paper influence evaluation research:Improvement based on AMMAA algorithm

Aiming at the deficiency of h index and the lack of a comprehensive and effective evaluation index,this paper introduces an ammaa algorithm for paper evaluation and proposes an optimization algorithm integrating time dimension:t-ammaa algorithm,which reflects the influence evaluation of individual scholars through the evaluation of paper influence.We used Web of Science as the data source and focused on the paper published by the authors of Chinese library and info rmation science,to calculate the ammaa value and t-ammaa value of these papers,and then obtained the ammaa value and t-ammaa value of the scholar.The result ranking of the two algorithms and the scholar’s H-value ranking are normalized for empirical comparison and analysis.The results show that t-ammaa algorithm considering the cited times,the cited threshold limit,the co-authors’number,and the temporal heterogeneity of the cited papers,is a more reasonable measurement method for evaluating the influence of scholars.It can not only comprehensively evaluate the influence of single author and co-authored paper,but also eliminate the influence brought by time factor.