Early alert systems (EAS) are an important technological tool to help manage and improve student retention. Data spanning 16,091 students over 156 weeks was collected from a regionally based university in Australia to explore various microeconometric approaches that establish links between EAS and student retention outcomes. Controlling for numerous confounding variables, significant relationships between the EAS and student retention were identified. Capturing dynamic relationships between the explanatory variables and the hazard of discontinuing provides new insight into understanding student retention factors. We concluded that survival models are the best methods of understanding student retention when temporal data is available.

Man-made environmental degradation is a global challenge for both developed and developing countries. Over the last several decades, environmental degradation in the form of excessive emissions of carbon dioxide (CO₂), methane (CH₄), and fine particulate matter (PM_2.5) has exposed the world to destructive natural calamities and health threats. Against this backdrop, this study sets out to identify the impact of economic growth on environmental degradation in 115 countries for the 1990±2016 period.

Using the environmental Kuznets curve (EKC) as the conceptual framework, the results show that there is a long-run association between economic growth and environmental degradation. Specifically, we find the existence of the EKC for CO₂ and PM_2.5 emissions in the lower and upper middle income countries and for CH₄ emissions in the lower-middle, upper-middle, and high income countries. Moreover, we show economic growth and energy consumption to be the most important causes of CO₂ emissions. Finally, the impulse response function-based forecasts reveal that all three pollutants follow the EKC pattern in lower-middle, upper-middle, and high-income countries.

In terms of the agricultural and manufacturing sectors, we find that economic growth and energy consumption exert a long-run impact on CO₂, CH₄, and PM_2.5 emissions. Although further agricultural growth has no significant impact on CO₂ emissions, it shows an inverted U-shaped EKC for CH₄ emissions for the low, lower-middle, and high-income groups and for PM_2.5 emissions for all income groups. However, manufacturing growth has a U-shaped EKC for CO₂ emissions for all income groups, implying further emissions, while it shows an inverted U-shaped EKC for CH₄ emissions for all income groups and for PM_2.5 emissions for the low and lower middle-income groups.

We assess the moderation effects of several facilitator variables by testing a number of hypotheses. The moderation effect of financial development (FD), foreign direct investment (FDI), and the human development index (HDI) as the proxy of human capital formation has significant impacts on the EKC. FD exerts negative interaction effects for all three selected pollution emissions in all income groups, indicating that deeper financial reform may promote the installation of energy efficient and environmentally friendly technology. FDI has a long-run impact on reducing emissions for different panel income groups. For instance, after achieving a certain stage of GDP growth, the interaction effect of FDI reduces CO₂ emissions for the low and lower-middle income groups, suggesting that FDI may facilitate technological transfers from the developed to less-developed countries. Finally, the interaction effect of HDI on the growth±pollution nexus reduces all three types of emissions in the upper-middle and high-income groups, meaning that human capital formation helps to create awareness and use of green technologies.

The results suggest that efficient energy consumption, conservation of energy, and environmentally friendly technology can help to mitigate emissions without harming GDP growth. Overall, stringent environmental regulation by government, reduce reliance on non-renewable energy sources and encourage the use of renewable energy can help to mitigate pollution emissions. Moreover, sharing of knowledge and the transfer of green technologies between developed and developing countries, creating enough funds for environmental conservation can be an effective measure for attaining environmental sustainability in the production process.

Attracting foreign direct investment (FDI) remains a key developmental strategy that is prescribed by policy-makers in many developing economies. It rests on the premise that FDI accelerates economic growth in the host country through both tangible and intangible channels. In the extant literature, China’s dominance in the world’s vast FDI scene has captured many researchers’ attention. However, the majority of the existing studies have focused on either the national or provincial level, leaving the determinants of FDI and its effects on the host city largely under-explored. An in-depth understanding of the reasons behind the mixed experience of Chinese cities in hosting FDI provides invaluable input for local officials. With these points in mind, this thesis is partly designed to bridge the gap in our current understanding by examining FDI using city-level data from China, and, partly, to provide the Chinese policymakers with an insight into the interactions between FDI and the host city.

A comprehensive panel dataset of 287 prefectural cities in China for the 2001–2012 period forms the basis of this thesis. Findings from this thesis complement earlier results and offer important lessons for local development. From a methodological perspective, this thesis addresses aggregation bias by considering various subsamples based on geographical topology and city-specific characteristics. Furthermore, it controls potential endogeneity bias by applying panel instrumental variable (IV) or generalised method-of-moment (GMM) estimators.

This thesis is organised in a paper-based format and presented in three parts. Part I provides essential background information that links various facets of this thesis together. Part II presents the results of the empirical analyses in five distinct, but related, papers that are delineated by specific research questions and objectives. Part III discusses the main findings and their policy implications. This final part also lists the limitations of the current research and offers directions for future research.

The first two papers focus on the city-level determinants of FDI. Specifically, the first paper investigates the role of soft infrastructure as an attractor of FDI. After capturing the notion of soft infrastructure by the local banking sector (LBS), it finds a positive association between LBS development and FDI across Chinese cities. This finding suggests that local governments need to devote their limited resources to improving soft infrastructure, like access to financial services. Meanwhile, the second paper addresses the effect of capital-city status in attracting FDI. It finds that, all things being equal, capital cities manage to host more FDI compared to cities without capital-city status. This finding implies that Chinese policy-makers need to focus their attention on non-capital cities in order to achieve a balanced growth path within the province.

The remaining three papers apply GMM estimator to study the interactions between FDI and its host city in China. Paper three investigates whether FDI complements or substitutes for domestic investment (DI) in the Chinese cities. The analysis shows that a complementary FDI– DI nexus only exists in the eastern and central cities. This heterogeneous relationship is further supported by the results in paper four, which attributes the positive FDI–growth nexus in the eastern cities to soft infrastructure, such as local financial development and human capital, and in the inland cities to hard infrastructure, like transportation networks. Meanwhile, paper five finds a unidirectional causality running from FDI to growth in the eastern cities, but a bidirectional causality in the central cities. In general, these findings attest to the argument that the interactions between FDI and the host economy crucially depend on initial economic conditions and the local institutional environment. Without taking these city-specific characteristics into consideration, any attempt to attract and internalise the benefits associated with FDI is likely to be futile.

Overall, this thesis provides two major contributionsto the FDI literature. First, it demonstrates that soft infrastructure has played an increasingly important role in influencing the location choices of foreign investors in China. Second, it highlights the need to study the determinants and effects of FDI on a smaller geographical scale, particularly in a large country such as China.

The main objective of this study is to evaluate the relationship between Early Alert Systems (EAS) and student retention. Specifically, the study aims to: (i) examine the effects of demographic, institutional and learning environment variables on student retention, (ii) examine the effects of EAS on student retention, and (iii) assess the financial implications of the interaction between EAS and student retention. Selected microeconometric models were estimated using data for 16,124 undergraduate students extracted from a case study university. The data was captured over three years between 2011 and the beginning of 2014.

Key findings of this study show that demographic, institution, student performance and workload variables all exhibit statistically significant relationships with retention measures at the case study institution. Furthermore, the EAS had a positive effect on increasing students’ length of enrolment. Females are more likely to discontinue, but are also more likely to complete their course. Aboriginal and Torres Strait Islander (ATSI) students are more likely to be retained than non-ATSI students. Institutional factors such as the type of course, the school a student enrols in, or mode of enrolment all affect student’s retention rate. Variables capturing student performance and workload further affect retention. Periods of inactivity during students’ enrolment was one of the strongest factors affecting measures of student retention. The study also finds that demographic, institution, learning environment and EAS variables are subject to significant temporal effects. Using weekly observations, temporal effects were captured up to 156 weeks (3 years) of student enrolment, yielding a total of 1,119,170 observations. Using survival modelling, the study provides an unprecedented degree of accuracy in estimating the relationship between explanatory variables and the hazard of discontinuing over time.

Finally, the financial implications of the EAS was evaluated using treatment effects modelling. On average, students identified by the EAS for targeted support remained enrolled for an extra 14 weeks than students not identified by the EAS. The additional revenue in tuition fees caused by EAS identification is estimated to be $4,004 per student. It is concluded that early alert systems have significant financial benefits, initiating support services that positively impact on student outcomes.