Impact of State-Owned Capital Participation on ESG Performance of Private Enterprises

As global investors and stakeholders increasingly prioritize environmental,social,and governance(ESG)performance,corporate social responsibility and sustainability have become crucial factors in determining corporate success.In the context of China’s robust economy,the involvement of state-owned capital exerts a profound impact on the ESG performance of private enterprises.This paper,starting from the perspective of ESG,analyzes how state-owned capital participation influences the ESG performance of private enterprises.Additionally,it proposes recommendations for the involvement of state-owned enterprises in private enterprises,aiming to foster the sustainable development of private enterprises and enhance their social responsibility.

Spatial-temporal Analysis of Emotions in Society in News

Spatial-temporal analysis of emotions in society has become popular in many studies integrating geography with the humanities,and has shown its influence on social sensing and geo-computation for social sciences.Emotions in society are often volatile,irrational,and vulnerable to the social environment.A critical challenge is to analyze changes in long-term and large-scale emotions in society.In this paper,we propose exploiting this challenge by using spatial-temporal analysis.After extracting emotional,temporal,and spatial information,a spatial standardization approach based on adataset of administrative district changes addresses the problem of Chinese toponym changes.Finally,over 1.7 million news data from the People’s Daily from 1956 to 2014 were collected to explore the changes,spatial distribution,and driving factors of emotions in society using spatial-temporal analysis.The experimental results found that the spatial-temporal analysis of emotions in society in the news is consistent with the results of related sociological research.

Energy Efficient Social Routing Framework for Mobile Social Sensing Networks

Mobile social sensing network is one kind of emerging networks in which sensing tasks are performed by mobile users and sensing data are shared and collected by leveraging the intermittent inter-contacts among mobile users. Traditional ad hoc routing protocols are inapplicable or perform poorly for data collection or data sharing in such mobile social networks because nodes are seldom fully connected. In recent years, many routing protocols （especially social-based routing） are proposed to improve the delivery ratio in mobile social networks, but most of them do not consider the load of nodes thus may lead to unbalanced energy consumption among nodes. In this paper, we propose a simple Energy Efficient framework for Social-based Routing （EE-SR） in mobile social sensing networks to balance the load of nodes while maintaining the delivery ratio within an acceptable range by limiting the chances of forwarding in traditional social-based routing. Furthermore, we also propose an improved version of EE-SR to dynamically adjust the controlling parameter. Simulation results on real-life mobile traces demonstrate the efficiency of our proposed framework.

Big Earth data for disaster risk reduction

In an ever-changing world,where the frequency and intensity of natural and humanmade disasters are on the rise,disaster risk reduction has emerged as a crucial focal point of interdisciplinary research,governance,and public discourse.Disaster risk reduction,which aims to safeguard humans and protect environments from hazards and threats,is of high societal relevance and closely related to several of the United Nations Sustainable Development Goals(SDGs).The findings from research into disaster risk reduction contribute significantly to making cities and other settlements more inclusive,safe,resilient,and sustainable.

Identifying disaster-related tweets and their semantic,spatial and temporal context using deep learning,natural language processing and spatial analysis:a case study of Hurricane Irma

We introduce an analytical framework for analyzing tweets to(1)identify and categorize fine-grained details about a disaster such as affected individuals,damaged infrastructure and disrupted services;(2)distinguish impact areas and time periods,and relative prominence of each category of disaster-related information across space and time.We first identify disaster-related tweets by generating a human-labeled training dataset and experimenting a series of deep learning and machine learning methods for a binary classification of disasterrelatedness.We employ LSTM(Long Short-Term Memory)networks for the classification task because LSTM networks outperform other methods by considering the whole text structure using long-term semantic word and feature dependencies.Second,we employ an unsupervised multi-label classification of tweets using Latent Dirichlet Allocation(LDA),and identify latent categories of tweets such as affected individuals and disrupted services.Third,we employ spatiallyadaptive kernel smoothing and density-based spatial clustering to identify the relative prominence and impact areas for each information category,respectively.Using Hurricane Irma as a case study,we analyze over 500 million keyword-based and geo-located collection of tweets before,during and after the disaster.Our results highlight potential areas with high density of affected individuals and infrastructure damage throughout the temporal progression of the disaster.

An IEEE value loop of human-technology collaboration in geospatial information science

Geosensing and social sensing as two digitalization mainstreams in big data era are increasingly converging toward an integrated system for the creation of semantically enriched digital Earth.Along with the rapid developments of AI technologies,this convergence has inevitably brought about a number of transformations.On the one hand,value-adding chains from raw data to products and services are becoming value-adding loops composed of four successive stages–Informing,Enabling,Engaging and Empowering(IEEE).Each stage is a dynamic loop for itself.On the other hand,the“human versus technology”relationship is upgraded toward a game-changing“human and technology”collaboration.The information loop is essentially shaped by the omnipresent reciprocity between humans and technologies as equal partners,co-learners and co-creators of new values.The paper gives an analytical review on the mutually changing roles and responsibilities of humans and technologies in the individual stages of the IEEE loop,with the aim to promote a holistic understanding of the state of the art of geospatial information science.Meanwhile,the author elicits a number of challenges facing the interwoven human-technology collaboration.The transformation to a growth mind-set may take time to realize and consolidate.Research works on large-scale semantic data integration are just in the beginning.User experiences of geovisual analytic approaches are far from being systematically studied.Finally,the ethical concerns for the handling of semantically enriched digital Earth cover not only the sensitive issues related to privacy violation,copyright infringement,abuse,etc.but also the questions of how to make technologies as controllable and understandable as possible for humans and how to keep the technological ethos within its constructive sphere of societal influence.