Assessing the effects of climate and human activity on vegetation change in Northern China

Fractional vegetation cover (FVC) has changed significantly under various disturbances over northern China in recent decades. This research examines the dynamics of FVC and how it is affected by climate and human activity during the period of 1990-2018 in northern China. The effects of climate change (i.e., temperature, precipitation, solar radiation, and soil moisture) and human activity (socioeconomic data and land use) on vegetation coverage change in northern China from 1990 to 2018 were quantified using the Sen + Mann-Kendall test, partial correlation analysis, and structural equation modelling (SEM) methods. The findings of this research indicate the following: (1) From 1990 to 2018, the overall trend in FVC in northern China was increased. The areas with obvious increases were mainly situated on the northern slope of Tianshan Mountains, Xinjiang, the Loess Plateau, the Northeast China Plain, and the Sanjiang Plain, while the areas with distinct degradation were located in the Inner Mongolia Plateau, the Changbai Mountain and the eastern part of north China. (2) In the past 29 years, the FVC in northern China has been mainly affected by precipitation and soil moisture. (3) Based on structural equation modelling, we discovered that certain variables impacted the main factors influencing the amount of FVC in northern China. Human activity has had a larger impact on FVC than climate change. Our findings can accelerate the comprehension of vegetation dynamics and their underlying mechanisms and provide a theoretical basis for regional ecological environmental protection.

Testing the moderating role of trade openness on the environmental Kuznets curve hypothesis: a novel approach

We explore the moderating role of trade openness (TO) by gauging its main and interaction effects on the economic growth and environmental quality nexus. In this direction, we implement a novel approach by using three different measures of pollution emissions (CO2-CH4-PM2.5) in the environmental Kuznets curve hypothesis and applying a structural equation modelling methodology to 115 countries, grouped into low-, middle- and high-income countries, spanning the period 1992-2018. The evidence suggests that energy consumption has a positive impact on CO2 emissions for all income panels whilst the moderating effect of TO appears to be a key degrading factor of environmental quality in low- and middle-income countries. In addition, TO's interaction with GDP growth is found to negatively affect environmental quality across all income groups. Given that global economies are on the verge of returning to pre-pandemic levels of industrial operations along with emissions in the wake of the failure of COP26 and that COVID-19 has reminded the world the urgency of developing sustainable approaches in fostering 'green economic growth' models; a host of policy measures are proposed in support of this whilst their likely implications are discussed with reference to different income level countries.

Validation of Internal structure of Self-Directed Learning Readiness Scale among Indian Medical Students using factor analysis and the Structural equation Modelling Approach

BACKGROUND

The Self-Directed Learning Readiness Scale (SDLRS) is a tool that helps in the assessment of the readiness of the students to pursue Self-Directed Learning (SDL). There are no documented studies on the validation of internal structure of the SDLRS among Indian medical students. Hence, the objective of this study is to validate the internal structure of SDLRS among Indian medical students using factor analysis and the Structural Equation Modelling (SEM) approach.

METHODS

We administered Fisher's 40-item SDLRS to 750 students after receiving the ethics clearance and the author's permission and taking written informed consent from all the study participants (response rate: 92%). The exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and Cronbach's alpha were performed using SPSS version 25 and the Lavaan package of R version 3.1.2.

RESULTS

The values of the comparative fit index (CFI), standardised root-mean-square residual (SRMR), and root mean square error of approximation (RMSEA) were ≥ 0.9, ≤ 0.08, and ≤ 0.08, respectively, for a model fit to be acceptable. EFA showed that except for Q2 (loading score: 0.210), Q12 (loading score: 0.384), Q13 (loading score: 0.362), and Q25 (loading score: -0.219), all the items loaded well. After the exclusion of the aforementioned items, the factor loading scores for the items in the self-management, desire for learning, and self-control factors ranged from 0.405 to 0.753 (Cronbach α: 0.775), 0.396 to 0.616 (Cronbach α: 0.730), and 0.427 to 0.556 (Cronbach α: 0.799), respectively. The updated model was used for CFA, which displayed a good model fit.

CONCLUSIONS

The resultant model consisting of 36 items is shown to have internal structure validity for Indian version of SDLRS, which can be used to assess medical students.

A practical method to control spatiotemporal confounding in environmental impact studies

Separating natural spatiotemporal variation from the impact of human activities has long been a challenge in environmental impact studies. To overcome this problem, a causal modelling method based on spatiotemporal data, integrated with existing statistical methods such as multivariate redundancy analysis, multiple regression and, ordination was used for inferring causal effects of wastewater on biotic ecosystems. The causal modelling techniques were structural equation modelling (SEM) and Bayesian Networks (BNs); SEM, with the help of statistical analysis, was used for building deterministic models while the composite hypothesis underlying the models was checked based on the principle of BNs. Both spatial and temporal variations were considered in the design of the study so that spatiotemporal confounding could be controlled by adjusting for ‘time’ and ‘distance’ in the models. This improved the external validity of the models, so they could be used for predicting the effect of interventions, e.g. manipulating the discharge loads. This could be possible where time-varying variables such as quantity of discharge effluent were included in the models. Models can be used for prediction the effect of an intervention in situations understood as causal. Thus, the causal structure of composite hypotheses of the study was tested using both local and global tests.

Development of the Personal Responsibility Scale for adolescents

Increasing interest by policy makers and educators in enhancing the non-cognitive factors that impact on the wellbeing of young people across the school years requires valid and reliable measurement scales. This paper reports the psychometric development of the Personal Responsibility Scale drawing on a preliminary 34-item measure developed using data from focus groups with secondary students in Australia. This scale was then administered to 513 students across Years 9-12. Exploratory factor analysis identified three factors, 'personal accountability', 'behavioural and emotional control', and 'cognitive control'. Structural equation modelling was used to examine the congeneric measurement models for these factors. The multidimensional nature of the personal responsibility construct was then examined through multilevel confirmatory factor analysis. These analyses supported a three-factor structure to propose a final 15-item scale. Additional validation requirements, future research possibilities, and implications of this study are addressed.