Evaluating Ecological Footprints through Inbound
Tourism, Population Density, and Global Trade

Anser, Muhammad Khalid; Yousaf, Zahid; Nassani, Abdelmohsen A.; Abro, Muhammad Moinuddin Qazi; Zaman, Khalid; Kabbani, Ahmad

doi:10.15244/pjoes/122445

Cited by 19 publications

(12 citation statements)

References 21 publications

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“…The instrumental variables used here are the lagging second and third period values of lnhc and lnhc 2 . The result of column (3) shows that after considering the endogenous issue, lnef still rises first and then falls with the increase of lnhc at the 1% significance level. Finally, in view of the fact that some studies believe that there is a linear relationship between ecological footprint and human capital, 27,30 we observe whether the linear relationship exists by removing lnhc 2 .…”

Section: Results Of the Basic Modelmentioning

confidence: 90%

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Ecological footprint, human capital, and urbanization

Chen

2021

Energy & Environment

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The rapid development of the world economy has been accompanied by a significant increase in ecological footprint. Represented by population agglomeration, urbanization has exacerbated environmental pressure, but the improvements in the levels of human capital may offset this negative impact. This research thus uses cross-country data from 110 economies over the period 1990–2016 to explore the correlations among human capital, urbanization, and ecological footprint. The findings show that first, from a global perspective, human capital initially increases and then reduces ecological footprint. The analysis based on sub-samples shows that human capital of the high-income group decreases ecological footprint, whereas human capital of the low-income group and that of countries with large populations raise ecological footprint. Second, urbanization plays a linear moderating role in human capital’s impact on the ecological footprint. The higher the level of urbanization is, the higher is the turning point of human capital that is needed to improve environmental quality. Third, when the population size is larger or ecological footprint is greater, the turning point of human capital is higher under the same urbanization level. Therefore, countries should continue to enhance human capital to promote the upgrading of industrial structure, green technology innovation, and changes in energy-saving lifestyle. Moreover, it is beneficial for countries to pay close attention to the negative impact of population size or accelerated urbanization on environmental quality.

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Section: Results Of the Basic Modelmentioning

confidence: 90%

“…By calculating the first-order partial derivative of equation ( 2), we are able to know the marginal effect of lnhc on lnef. The results are shown in equation (3).…”

Section: Construction Of Modelsmentioning

confidence: 99%

Ecological footprint, human capital, and urbanization

Chen

2021

Energy & Environment

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“…After that, the H.A.R. model is described by (Anser et al 2020d), (Anser et al 2020c), (Anser et al 2020e), (Anser et al 2020a), and (Anser et al 2020b) as:…”

Section: Empirical Estimationmentioning

confidence: 99%

COVID-19 fear and volatility index movements: empirical insights from ASEAN stock markets

Sadiq

Hsu

Zhang

et al. 2021

Environ Sci Pollut Res

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This research aims to look into the effect of COVID-19 on emerging stock markets in seven of the Association of Southeast Asian Nations' (ASEAN-7) member countries from March 21, 2020 to April 31, 2020. This paper uses a ST-HAR-type Bayesian posterior model and it highlights the stock market of this ongoing crisis, such as, COVID-19 outbreak in all countries and related industries. The empirical results shown a clear evidence of a transition during COVID-19 crisis regime, also crisis intensity and timing differences. The most negatively impacted industries were health care and consumer services due to the Covid-19 drug-race and international travel restrictions. More so, study results estimated that only a small number of sectors are affected by COVID-19 fear including health care, consumer services, utilities, and technology, significance at the 1%, 5%, and 10%, that measure current volatility's reliance on weekly and monthly variables. Secondly, it is found that there is almost no chance that the COVID-19 pandemic would positively affect the stock market performance in all the countries, mainly Indonesia and Singapore were the countries most affected. Thirdly, results shown that Thailand's stock market output has dropped by 15%. Results shows that COVID-19 fear causes an eventual reason of public attention towards stock market volatility. The study presented comprehensive way forwards to stabilize movement of ASEAN equity market's volatility index and guided the policy implications to key stakeholders that can better help to mitigate drastic impacts of COVID-19 fear on the performance of equity markets.

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“…Various econometric techniques have been used in investigating the relationship between crimes and other economic and non-economic factors, including Arif et al (2017), Rahman and Prasetyo (2018), Hamid and Bitran (2021), Anser et al (2020), Schargrodsky and Freira (2021), Rosenfeld et al (2019), Eva (2021), and Peng et al (2021), among others. According to Hassan et al (2016), inflation is one of the determinants of crime and occurs when price levels increase continuously.…”

mentioning

confidence: 99%

“…As expected, the wages have a negative and significant effect on the crime rate in Indonesia. Another study conducted by Anser et al (2020) has examined the effect of poverty, income inequality, and social expenditure on the crime rate using a panel data approach. The panel data consists of 16 countries between 1990 and 2014.…”

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confidence: 99%

Factors Affecting Crime Rate in Malaysia Using Autoregressive Distributed Lag Modeling Approach

Zulkiflee¹,

Borhan²,

Hadrawi³

2022

JSSH

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An increase in the crime rate may jeopardize a country’s development and economic growth. Thus, understanding the relationship between crime and a few determinants is ,crucial in sustaining the economic growth in Malaysia. The four determinants used in this research are economic growth, population, education level, and inflation rate. The data covers the period from 1984 to 2019, and Autoregressive Distributed Lag (ARDL) modeling approaches were used in this research. The findings showed that only the population has a significant positive impact on crime rates for long-term and short-term relationships. Meanwhile, economic growth and education level have a significant long-term positive effect on the crime rate. On the other hand, the inflation rate did not significantly impact the crime rate in long-term and short-term relationships. Interestingly, it was found in the findings that the crime rate and population showed a bidirectional causal relationship indicating that the past population values are useful for a better prediction of the current crime rate and vice versa. Thus, the Malaysian government should encourage people to cooperate with the enforcement authorities to deter crime for future environmental safety effectively

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Evaluating Ecological Footprints through Inbound Tourism, Population Density, and Global Trade

Cited by 19 publications

References 21 publications

Ecological footprint, human capital, and urbanization

Ecological footprint, human capital, and urbanization

COVID-19 fear and volatility index movements: empirical insights from ASEAN stock markets

Factors Affecting Crime Rate in Malaysia Using Autoregressive Distributed Lag Modeling Approach

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