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This empirical analysis tries to examine determinants of private foreign direct investment (FDI) in Pakistan using the bounds test approach. Main determinants of FDI among them are the size of the market (Q) macroeconomic stability (r), political stability (PRS), real exchange rate (REX) and institutional quality (INQ).

This study used annual time-series data set starting from 1980 to 2016. This study has used time-series data with ARDL and error-correction model (ECM) and examined main determinants of FDI for Pakistan. The study used the Granger causality test (modified WALD test) to identify the causality among the variables.

Moreover, empirical findings indicate the long-run relationship between GDP, trade openness and institutional quality toward FDI. However, political instability, inflation and real exchange rate harm FDI in Pakistan. Furthermore, results of Granger causality indicate that the bidirectional causality running from FDI and Q toward FDI is significant, providing evidence of FDI-led growth hypotheses in Pakistan. This study determines the correlation between FDI and Q (GDP growth) related to the “feedback hypothesis” in the short and long run. This study also concludes that the short-run causal connection among FDI, REX, PRS, r and Q follows the “feedback hypothesis.” This describes that FDI, REX, PRS, r and Q variables are jointly determined and affected together.

This study also explores the causal association between FDI and its significant determinants, by using methods of Granger causality test and the approach of Toda-Yamamoto-DoladoLutkephol (TYDL) to examine the causal relationship and its directions among these variables.

This study examines the long-term and causal relationship among foreign direct investment (FDI) inflows, trade openness and economic growth from India.

This study has used annual time series data from the period 1985–2018 and applied the Johansen cointegration and vector autoregression (VAR) model.

The results of Johansen's cointegration confirm no long-term relationship among all the above three variables. Further, the results of VAR Granger causality indicate that FDI causes economic growth and economic growth causes FDI, which confirms the bi-directional causality. In contrast, this study found that there is no bi-directional causality between trade openness and economic growth.

Through this study, the government could take the decisions related to foreign investment after adopting more trade openness because the study results revealed that if India follows more trade openness, then how FDI will flow (upward and downward). With impulse analysis, researchers, government and policymakers take the decision-related FDI inflows for the forthcoming ten years after 2018.

This study has found the most exciting results from the impulse functions of FDI inflows, trade openness and economic growth, which showed the situation of these three variables as increase and decrease in the forthcoming ten years.

This paper aims to emphasize economic complexity, tourism, information and communication technology (ICT), renewable energy consumption and foreign direct investment (FDI) as the determinants of carbon emissions.

These economies rely on the tourism sector, and Asian countries rank among the top tourism economies worldwide in terms of tourism receipts. This study uses a series of empirical estimators, i.e. cross-sectional augmented auto-regression distributive lag and panel cointegration, to validate the main hypotheses.

The econometric results confirm an inverted U-shaped association between economic complexity and carbon emissions, validating the economic complexity index induced environment Kuznets curve hypothesis for the selected Asian economies.

Finally, the empirical results admit articulating some imperative policy suggestions to attain a sustainable environment on behalf of outcomes.

Furthermore, ICT and renewable energy consumption are environment-friendly indicators, while FDI and the international tourism industry increase environmental pressure in selected countries. In addition, this study also explores the interaction between renewable energy and ICT with FDI and their effects on carbon emissions. Interestingly, both interaction terms positively respond to the environmental correction process.

Because ICT with FDI may not reduce environmental pollution unless the energy used in FDI projects is greener. Moreover, in Asian economies, industrial and other sectors could increase environmental quality via the role of ICT in FDI.

修正亚洲前 8 大经济体的旅游环境库兹涅茨曲线假设：ict 和可再生能源消耗的作用

这些经济体依赖旅游业, 就旅游收入而言, 亚洲国家在全球旅游经济体中名列前茅。本研究使用一系列经验估计量, 即 CS-ARDL 和面板协整来验证我们的主要假设。

本文强调经济复杂性、旅游、信息和通信技术 (ICT)、可再生能源消费和外国直接投资 (FDI) 作为碳排放的决定因素

计量经济学结果证实了经济复杂性与碳排放之间的倒 U 型关联, 验证了 ECI 对选定亚洲经济体的环境库兹涅茨曲线 (EKC) 假设。

最后, 实证结果承认阐明了一些必要的政策建议, 以代表结果实现可持续环境。

此外, 信息通信技术和可再生能源消耗是环境友好型指标, 而外国直接投资和国际旅游业增加了选定国家的环境压力。此外, 本研究还探讨了可再生能源和 ICT 与外国直接投资之间的相互作用及其对碳排放的影响。有趣的是, 这两个交互项都对环境校正过程做出了积极响应。

ICT 与 FDI 可能不会减少环境污染, 除非 FDI 项目中的能源使用更环保。此外, 在亚洲经济体中, 工业和其他部门可以通过 ICT 在 FDI 中的作用提高环境质量。

环境库兹涅茨曲线; 外商直接投资;信息和通信技术; 可再生能源;旅游;亚洲主要旅游经济体

文章类型： 研究型论文

This research paper aims to analyze the stock exchanges of developed, emerging and developing countries to investigate the volatility in stock markets and to evaluate the rate of mean reversion.

The stock exchanges included in the research are NASDAQ, Tokyo stock exchange, Shanghai stock exchange, Bombay stock exchange, Karachi stock exchange and Jakarta stock exchange. Secondary daily data from Bloomberg are used to conduct the research for the period from January 2011 to December 2018. Generalized autoregressive conditional heteroskedasticity (GARCH) (1,1) model was applied to examine volatility and the half-life formula was used to calculate mean reversion in days.

The research concluded that all the stock exchanges included in the research satisfy the assumptions of mean reversion. Developing countries have the lowest volatility while emerging countries have the highest volatility which means that the rate of mean reversion is fastest in developing countries and slowest in emerging countries.

Future studies can determine the reasons for fastest rate of mean reversion in developing countries and slowest rate of mean reversion in emerging countries.

Developing countries show the lowest mean reversion in days while the emerging countries show the highest mean reversion in days indicating that developing countries take less time to revert to their mean position.

The majority of previous studies on univariate volatility models are mostly on applications of the models. Only a few researchers have taken the robustness of the models into account when applying them in emerging countries and not in developed, developing and emerging countries in one place. This makes the current study unique and more rigorous.

The purpose of this paper is to explore the current challenges and drivers for data mining in the AEC sector.

Following a comprehensive literature review, the data mining concept was investigated through a workshop with industry experts and academics.

The results showed that the key drivers for using data mining within the AEC sector is associated with the sustainability, process improvement, market intelligence, cost certainty and cost reduction, performance certainty and decision support systems agendas in the sector. As for the processes with the greatest potential for data mining application, design, construction, procurement, forensic analysis, sustainability and energy consumption and reuse of digital components were perceived as the main process areas. While the key challenges were perceived as being, data issues due to the fragmented nature of the construction process, the need for a cultural change, IT systems used in silos, skills requirements and having clearly defined business goals.

With the increasing abundance of data, business intelligence and analytics and its related concepts, data mining and Big Data have captured the attention of practitioners and academics for the last 20 years. On the other hand, and despite the growing amount of data in its business context, the AEC sector still lags behind in utilising those concepts in its end products and daily operations with limited research conducted to explore those issues at the sector level. This paper investigates the main opportunities and barriers for data mining in the AEC sector with a practical focus.

This study aims to focus on second grade fluid flow over a rotating disk in the presence of chemical reaction. Uniform magnetic field is also taken into account. Because of the smaller magnetic Reynolds number, induced magnetic field is negligible. Heat equation is constructed by considering heat source/sink.

Suitable variables are used to transform nonlinear partial differential equations to ordinary ones. Convergent series solutions are attained by applying homotopy analysis method.

Trends of different parameters on concentration, velocity and temperature are shown graphically. Skin friction coefficient and local Nusselt number are calculated and investigated under the effect of elaborated parameters. An elevation in the value of magnetic field parameter causes collapse in the velocity distributions. Velocity distribution in increasing function of viscoelastic parameter. Temperature and concentration profiles are decreasing functions of viscoelastic parameter. Concentration distribution reduces by increasing the chemical reaction parameter. There is more surface drag force for larger M, while opposite behavior is noted for β.

To the best of the authors’ knowledge, such consideration is yet to be published in the literature.

This study aims to investigate the literature related to the use of digital technologies for promoting circular economy (CE) in the construction industry.

A comprehensive approach was adopted, involving bibliometric analysis, text-mining analysis and content analysis to meet three objectives (1) to unveil the evolutionary progress of the field, (2) to identify the key research themes in the field and (3) to identify challenges hindering the implementation of digital technologies for CE.

A total of 365 publications was analysed. The results revealed eight key digital technologies categorised into two main clusters including “digitalisation and advanced technologies” and “sustainable construction technologies”. The former involved technologies, namely machine learning, artificial intelligence, deep learning, big data analytics and object detection and computer vision that were used for (1) forecasting construction and demolition (C&D) waste generation, (2) waste identification and classification and (3) computer vision for waste management. The latter included technologies such as Internet of Things (IoT), blockchain and building information modelling (BIM) that help optimise resource use, enhance transparency and sustainability practices in the industry. Overall, these technologies show great potential for improving waste management and enabling CE in construction.

This research employs a holistic approach to provide a status-quo understanding of the digital technologies that can be utilised to support the implementation of CE in construction. Further, this study underlines the key challenges associated with adopting digital technologies, whilst also offering opportunities for future improvement of the field.

The purpose of this study is to analyze the transportation of heat and mass in three-dimensional (3D) shear rate-dependent viscous fluid. Thermal enhancement plays a significant role in industrial and engineering applications. For this, the authors dispersed trihybrid nanoparticles into the fluid to enhance the working fluid’s thermal enhancement.

The finite element method is a numerical scheme and is powerful in achieving convergent and grid-independent solutions compared with other numerical techniques. This method was initially assigned to structural problems. However, it is equally successful for computational fluid dynamics problems.

Wall shear stress has shown an increasing behavior as the intensity of the magnetic field is increased. Simulations have predicted that Ohmic heat in the case of trihybrid nanofluid (MoS₂–Al₂O₃–Cu/C₂H₆O₂) has the greatest value in comparison with mono and hybrid nanofluids. The most significant influence of chemical reaction on the concentration in tri-nanofluid is noted. This observation is pointed out for both types of chemical reaction (destructive or generative) parameters.

Through a literature survey, the authors analyzed that no one has yet to work on a 3D magnetohydrodynamics Carreau–Yasuda trihybrid nanofluid over a stretched sheet for improving heat and mass transfer over hybrid nanofluids. Herein, molybdenum disulfide (MoS₂), aluminum oxide (Al₂O₃) and copper (Cu) nanoparticles are mixed in ethylene glycol (C₂H₆O₂) to study the thermal enhancement and mass transport of their corresponding resultant mono (Cu/C₂H₆O₂), hybrid (Al₂O₃–Cu/C₂H₆O₂) and trihybrid (MoS₂–Al₂O₃–Cu/C₂H₆O₂) nanofluids.

Due to an increase in energy demands, it has become vital to devise efficient energy policies. Literature has suggested multiple factors influencing the consumption of specific energy types. Among others, institutional quality (INQ) is another factor that can determine energy consumption. Given this, the current study aimed to investigate the impact of INQ on fossil fuel energy (FFE) and renewable energy consumption (REC).

The empirical analysis was conducted on 20 years (2000–2019) of data from South Asian economies, and regression among variables was established by employing the dynamic ordinary least square and fully modified ordinary least square models. The selection of both techniques is subject to the existence of cointegration identified by the Johansen cointegration test. Other pre-estimation techniques include cross-section dependence and unit root testing validating the estimation of coefficients in the long run.

The analysis mainly reveals the negative impact of INQ on FFE and the positive impact of INQ on REC. The authors further find the asymmetric impact of control variables including foreign direct investment inflow, economic growth, inflation rate, financial sector development and energy investment on the consumption of both types of energy.

Given the positive influence of INQ on REC, it is recommended to focus on improving the efficiency of institutions specifically those that are directly linked with energy-related policies. A better INQ can ensure environmental sustainability by enhancing the consumption of renewable energy. Therefore, it is advised to exert more efforts to improve the INQ.

In view of the positive influence of INQ on REC, it is recommended to focus on improving the efficiency of institutions specifically that are directly linked with energy-related policies. A better INQ can ensure environmental sustainability by enhancing the consumption of renewable energy. Therefore, it is advised to exert more efforts for improving the INQ.

This study offers robustness to the empirical findings of existing literature on the INQ-REC nexus and complements the underdeveloped literature on the INQ-FFE relationship.

Because the construction industry is one of the largest waste producers, understanding the primary reasons for waste production is essential. The goal of this study is to identify the major causes of waste production over the project life cycle in Iran's construction industry and to propose effective solutions based on modern technologies like BIM.

After identifying the primary causes of construction and demolition waste production through interviews and literature analysis, solutions based on building information modeling (BIM) were provided. Then, using questionnaires and exploratory factor analysis (EFA), the areas impacting waste reduction were found.

The findings suggest that “prefabrication” is the best approach for improving time and quality, while “detection and prediction of errors in the design and construction phases” is the most cost-effective technique for addressing cost and environmental issues.

Cost, time, quality and environmental concerns may all be influenced by effective waste management throughout the project life cycle. Furthermore, utilizing state-of-the-art technologies has far-reaching implications for reducing material waste, resulting in more environmental-friendly construction.