Evaluation Review最新文献

The adoption of growth strategies based on foreign trade, especially in the previous century when liberal policies began to dominate, is one of the main reasons for the increase in output and indirectly for environmental concerns. On the other hand, there are complex claims about the environmental effects of liberal policies and thus of globalization. This study intends to analyze the effects of global collaborations involving 11 transition economies that have completed the transition process on the environmentally sustainable development of these nations. In this direction, the effects of financial and commercial globalization indices on carbon emissions are investigated. The distinctions of globalization are used to distinguish the consequences of the two types of globalization. In doing so, the de facto and de jure indicator distinctions of globalization are used to differentiate the consequences of two types of globalization. In addition, the effects of real GDP, energy efficiency, and use of renewable energy on environmental pollution are dissected. For the main purpose of the study, the CS-ARDL estimation technique that allows cross-sectional dependency among observed countries is used to separate the short and long-run influences of explanatory variables. In addition, CCE-MG estimator is used for robustness check. According to the empirical findings, the economic growth and increasing energy intensity increases carbon emissions, but the increase in renewable energy consumption improves environmental quality. Furthermore, trade globalization does not have a significant impact on the environment in the context of globalization. On the other hand, the increase in de facto and de jure financial globalization indices results in an increase in carbon emissions, but de jure financial globalization causes more environmental damage. The harmful impact of de jure financial globalization on environmental quality suggests that the decreasing investment restrictions and international investment agreements of transition countries have been implemented in a manner that facilitates the relocation of investments from pollution-intensive industries to these countries.

The impact of pro-environmental behavior on policymaking has been an exciting area of research. While the relationship between pro-environmental behavior and policymaking has been explored in numerous studies, there needs to be more synthesis on this topic. This is the first text-mining study of pro-environmental effects in which policymaking is a significant factor. In response, this study, for the first time, takes a novel approach by using text mining in R programming to analyze 30 publications from the Scopus database on pro-environmental behavior in policymaking, highlighting major research themes and prospective research areas for future investigation. Results from text mining yielded 10 topic models, which are presented with a synopsis of the published research and a list of the primary authors, as well as a posterior probability via latent Dirichlet allocation (LDA). Additionally, the study conducts a trend analysis of the top 10 journals with the highest impact factor, considering the influence of each journal's mean citation. The study offers an overview of the impacts of pro-environmental behavior in policymaking, showing the most relevant and frequently discussed themes, introduces the scientific visualization of papers published in the Scopus database, and proposes future study directions. These findings can help researchers and environmental specialists better understand how pro-environmental behavior can be fostered more effectively through policymaking.

Stroke is the leading cause of death and disability among people in China, and it leads to heavy burdens for patients, their families and society. An accurate prediction of the risk of stroke has important implications for early intervention and treatment. In light of recent advances in machine learning, the application of this technique in stroke prediction has achieved plentiful promising results. To detect the relationship between potential factors and the risk of stroke and examine which machine learning method significantly can enhance the prediction accuracy of stroke. We employed six machine learning methods including logistic regression, naive Bayes, decision tree, random forest, K-nearest neighbor and support vector machine, to model and predict the risk of stroke. Participants were 233 patients from Sichuan and Chongqing. Four indicators (accuracy, precision, recall and F1 metric) were examined to evaluate the predictive performance of the different models. The empirical results indicate that random forest yields the best accuracy, recall and F1 in predicting the risk of stroke, with an accuracy of .7548, precision of .7805, recall of .7619 and F1 of .7711. Additionally, the findings show that age, cerebral infarction, PM 8 (an anti-atrial fibrillation drug), and drinking are independent risk factors for stroke. Further studies should adopt a broader assortment of machine learning methods to analyze the risk of stroke, by which better accuracy can be expected. In particular, RF can successfully enhance the forecasting accuracy for stroke.

In 2003, Bloom, Hill, and Riccio (BHR) published an influential paper introducing novel methods for explaining the variation in local impacts observed in multi-site randomized control trials of socio-economic interventions in terms of site-level mediators. This paper seeks to improve upon this previous work by using student-level data to measure site-level mediators and confounders. Development of asymptotic behavior backed up with simulations and an empirical example. Students and training providers. Two simulations and an empirical application to data from an evaluation of the Health Professions Opportunity Grants (HPOG) Program. This empirical analysis involved roughly 6600 participants across 37 local sites. We examine bias and mean square error of estimates of mediation coefficients as well as the true coverage of nominal 95-percent confidence intervals on the mediation coefficients. Simulations suggest that the new methods generally improve the quality of inferences even when there is no confounding. Applying this methodology to the HPOG study shows that program-average FTE months of study by month six was a significant mediator of both career progress and long-term degree/credential receipt. Evaluators can robustify their BHR-style analyses by the use of the methods proposed here.

Program evaluations often investigate complex or multi-dimensional constructs, such as individual opinions or attitudes, by means of ratings. A different interpretation of the same question may affect cross-country comparability, leading to the Differential Item Functioning problem. Anchoring vignettes were introduced in the literature as a way to adjust self-evaluations from this interpersonal incomparability. In this paper, we first introduce a new nonparametric solution to analyse anchoring vignette data, recoding a variable based on a rating scale to a new corrected-variable that guarantees comparability in any cross-country analysis. Then, we exploit the flexibility of a mixture model introduced to account for uncertainty in the response process (the CUP model) to test if the proposed solution is effectively able to remove this reported heterogeneity. This solution is easy to construct and has important advantages compared with the original nonparametric solution adopted with anchoring vignette data. The novel indicator is applied to investigate self-reported depression in an old population. Data that will be analysed come from the second wave of the Survey of Health, Ageing and Retirement in Europe, collected in 2006/2007. Results highlight the need of correcting for reported heterogeneity comparing individual self-evaluations. Once interpersonal incomparability resulting from the different uses of response scales is removed from the self-assessments, some estimates are reversed in magnitude and signs with respect to the analysis of the collected data.

Economic corridors unlock new economic opportunities and tourism development in the region to achieve sustainable development goals. Green economic growth is conducive to environmental sustainability. Economic mega-projects of CPEC promote tourism that leads to communities' well-being and better quality of life. Modern infrastructure development contributes significantly to economic growth and tourism activities. This study's objectives emphasize exploring tourism and sustainable development pursuits under OBOR economic projects that open doors to improving residents' quality of life. The growing world is an eyewitness to a continuous rise in emissions and its severe consequences for humankind. It is necessary to show off the leading factors that result in tourism and economic activities causing environmental pollution rather than blame policymakers. Undoubtedly, many studies previously focused on demonstrating the influence of socio-economic factors that lead to better environmental quality. However, the empirical literature on tourism, social well-being, foreign direct investment, and the Environment in Belt and Road developed economies needed improvement. This research applied a series of advanced estimators that help demonstrate the study's probable results. This study explores the role of Social well-being (HDI), tourism development, FDI, renewable energy, information & communication technology (ICT), and urbanization on CO2 emissions in Belt and Road (BRI) developed economies.Estimated results exhibited the significant contribution of ICT and renewable energy to sustainability. Besides, FDI contributes to emissions reduction after its threshold level. Conversely, urbanization and tourism activities contribute to environmental pollution. The study outcomes stated inverted/EKC U-shaped hypotheses related to specified economies. Finally, the analysis based on the D-H panel causality test constructs exciting results.The present study concludes that economic corridor plays a vital role in tourism development, the community's well-being, and SDGs goals (sustainable development) impact on environmental safety. The findings suggest essential and applicable policies to attain the desired sustainability level. Findings contribute to the literature on tourism, well-being, and sustainability. Further studies can use insights using this methodology.

Technology innovation is the key driving force in achieving economic transformation and development. Financial development and the expansion of higher education can promote technological progress primarily by easing financing constraints and improving the level of human capital. This study examines the impact of financial development and higher education expansion on green technology innovation. It conducts an empirical analysis by constructing a linear panel model and a nonlinear threshold model. The present study sample is based on the urban panel data of China from 2003-2019. (1) Financial development can significantly promote the expansion of higher education. (2) The expansion of higher education can improve energy and environment-based technological progress. (3) Financial development can both directly and indirectly promote green technology evolution by expanding higher education. The joint financial development and higher education expansion can significantly empower green technology innovation. (4) In the process of promoting green technology innovation, financial development has a non-linear influence on it, with higher education as the threshold. The effect of financial development on green technology innovation varies according to the degree of higher education. Based on these findings, we put forward policy proposals for green technology innovation to promote economic transformation and development in China.