Suchergebnisse

In recent decades, China's cement production has been the highest in the world, but the extensive development model, which has been formed for a long time, has brought serious damage to the natural environment. In order to promote the transformation of the production mode of China's cement industry, this paper adopts the nonparametric frontier method to analyze the environmental efficiency of China's cement manufacturing industry using the input–output and pollutant emission data of China's cement manufacturing industry from 2004 to 2016. The results show that the overall environmental efficiency of China's cement industry is low, and there is still much room for improvement. Moreover, there are serious imbalances from very low to very high between different regions. Further investigation found that during the study period, strict environmental supervision brought an average compliance cost of CNY 23.41 billion to China's cement manufacturing industry, but the overall environmental efficiency increased by 23.9 percentage points. Based on these findings, we believe that the focus of environmental supervision of China's cement manufacturing industry at this stage is to reduce pollution emissions, and force cement enterprises to carry out technological innovation through mandatory emission reduction measures. When formulating policies, the Chinese government needs to explore the best way for environmental supervision between minimizing compliance costs and maximizing efficiency, so as to promote the sustainable development of China's cement manufacturing industry.

The theme of this thesis is about Australian macroeconomic issues. It features three self-contained chapters, each dealing with a different aspect of the Australian economy.Chapter 1 examines the effects of a compulsory superannuation scheme on the Australian economy. I set up a general equilibrium, overlapping generations life-cycle model to study the effects of superannuation on housing purchases, general wealth accumulation, government age pension liabilities, and the broad economy. The quantitative model suggests that superannuation generates a significantly higher level, and a more equal distribution, of wealth. Superannuation also delays housing purchases, though it increases the aggregate level of the housing stock. In addition, it reduces age pension payments and the tax rate. Furthermore, if the domestic capital supply can alter the interest rate, then superannuation increases productivity and housing wealth. However, the interest rate change diminishes non-housing wealth and raises age pension liabilities.Chapter 2 is about the mining boom and Australia's "two-speed economy", in which there is a divergence in output between mining-related industries and the rest of the economy. I estimate a sectoral Structural Vector Autoregression (SVAR) model for Australia to investigate the aggregate and industry-level effects of a boom in commodity prices and the contributions made by the macroeconomic stabilisation tools of monetary policy and a flexible exchange rate. Using counterfactual policy exercises, I find evidence of the macroeconomic stabilisation policies contributing to the formation of a two-speed economy. Furthermore, using a forecast error variance decomposition I show that commodity price shocks have significant long-run ramifications for the Australian economy.Chapter 3 tests whether exchange rate movements can be explained by economic fundamentals associated with the Taylor rule, namely the price level, the output gap, and inflation. Using an Unobserved Components model based on the asset pricing approach of Engel and West (2005), the evidence suggests that these fundamentals only play a small role for the bilateral exchange rates (against the US dollar) of Australia, Canada, and the United Kingdom.

We analyzed the effects of leadership substitute factors on job satisfaction and performance of knowledge workers, based on existing studies on substitutes for leadership. The data were collected from 130 knowledge workers in China via survey questionnaires based on a modified version
of the 41-item scale revised by Podsakoff and MacKenzie (1994), a performance scale based on the dimensions of task performance and contextual performance as classified by Motowildo, Borman, and Schmit (1997), and also used in research conducted by Sun and Jiao (2002), and the Minnesota Satisfaction
Questionnaire (Weiss, Dawis, England, & Lofquist, 1967). The findings showed that 7 of the 13 variables of the model of substitutes for leadership proposed by Kerr and Jermier (1978) had positive effects on the job satisfaction and performance of the knowledge workers who took part in
this study.

Uncertainty in fertility intentions is a major obstacle to understanding contemporary trends in fertility decision-making and its outcomes. Quantifying this uncertainty by structural factors such as income, ethnicity, and housing conditions is recognized as insufficient. A recently proposed framework on subjective narratives has opened up a new way to gauge factors behind fertility decision-making and uncertainty. Through surveys, such narratives can be elicited with open-ended questions (OEQs). However, analyzing answers to OEQs typically involves extensive human coding, imposing constraints on sample size. Natural Language Processing (NLP) techniques assist researchers in grasping aspects of the underlying reasoning behind responses with much less human effort. In this study, using automatic neural topic modeling methods, we identify and interpret topics and themes underlying the narratives on fertility intention uncertainty of women in the Netherlands. We used Contextualized Topic Models (CTMs), a neural topic model using pre-trained representations of Dutch language, to conduct our analyses. Our results show that nine topics dominate the narratives about fertility planning, with age and health-related issues as the most prominent ones. In addition, we found that uncertainty in fertility intentions is not homogeneous, as women who feel uncertain due to real-life constraints and those who have no fertility plans at all put their stress on vastly different narratives.

Abstract
Networks offer a powerful approach to modeling complex systems by representing the underlying set of pairwise interactions. Link prediction is the task that predicts links of a network that are not directly visible, with profound applications in biological, social, and other complex systems. Despite intensive utilization of the topological feature in this task, it is unclear to what extent a feature can be leveraged to infer missing links. Here, we aim to unveil the capability of a topological feature in link prediction by identifying its prediction performance upper bound. We introduce a theoretical framework that is compatible with different indexes to gauge the feature, different prediction approaches to utilize the feature, and different metrics to quantify the prediction performance. The maximum capability of a topological feature follows a simple yet theoretically validated expression, which only depends on the extent to which the feature is held in missing and nonexistent links. Because a family of indexes based on the same feature shares the same upper bound, the potential of all others can be estimated from one single index. Furthermore, a feature's capability is lifted in the supervised prediction, which can be mathematically quantified, allowing us to estimate the benefit of applying machine learning algorithms. The universality of the pattern uncovered is empirically verified by 550 structurally diverse networks. The findings have applications in feature and method selection, and shed light on network characteristics that make a topological feature effective in link prediction.

We tested whether self-training in mindfulness-based cognitive therapy could help improve the mental health of adolescents with mild depression. We conducted a wait-list controlled trial during which 14 adolescents in a self-training group completed the 8-week mindfulness-based cognitive
therapy in daily life program, and 18 adolescents in a control group did not do any mindfulness self-training until the adolescents in the self-training group had completed the 8-week program. The mixed-measures 2 (testing stage) × 2 (group type) analyses of variance showed significant
interaction effects on depression, mindfulness, affect balance, and meaning in life. Specifically, there was no significant difference between the pretest and posttest scores for depression, mindfulness, affect balance, and meaning in life among adolescents in the control group. For adolescents
in the self-training group, however, the posttest scores for depression were significantly lower than the scores in the pretest, and the posttest scores for mindfulness, affect balance, and meaning in life were significantly higher. Our findings show that adolescents with mild depression can
improve their mental health by completing a program of self-training in mindfulness-based cognitive therapy.