Heterogeneous effects of industrialization on the environment: Evidence from panel quantile regression

Opoku, Eric Evans Osei; Aluko, Olufemi Adewale

doi:10.1016/j.strueco.2021.08.015

Cited by 124 publications

(54 citation statements)

References 61 publications

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“…Another relevant standard deviation is distinguished in the situation of industry variable (value is 7.879628% of GDP). A high weight of the industrial sector leads to high environmental degradation, according to the studies of Rai and Rawat (2022), Opoku and Aluko (2021), Patnaick (2018), Sunny et al (2012). Countries like Saudi Arabia (with 66.76% of GDP in 2008), Indonesia (with 48.06% of GDP in 2006) and China (47.56% of GDP) have a significant industry compared with their gross domestic product.…”

Section: Resultsmentioning

confidence: 99%

The impact of governance quality and educational level on environmental performance

Dincă

Bărbuță

Negri

et al. 2022

Front. Environ. Sci.

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Environmental degradation and its impact on sustainable development have sparked the interest of national and international policymakers, specialists, and academia. This paper aims to demonstrate the empirical nexus between environmental performance, measured by carbon dioxide emissions, and education levels together with institutional quality in a society. To achieve this goal, the regression model includes the main variables that reflect the quality of governance (government effectiveness, regulatory quality, control of corruption, and rule of law), together with education dimension, gross domestic product, renewable energy consumption, fossil fuel energy consumption, and industry. The data were collected for the 1995–2020 period, for a set of 43 countries, consisting of all European Union (EU) members and The Group of Twenty (G20) states. The research uses three estimations methods, respectively Pooled ordinary least squares (Pooled OLS), Fixed effects model (FEM) and Random effects model (REM), together with a two-step dynamic GMM model, to address the endogeneity issue as well. The main results show that all the independent variables reflecting institutional quality from a technical point of view, included in the model when considering the PCSE estimation, have a direct and positive link to CO2 emissions’ level, with control of corruption variable being the only one to influence in a positive manner CO2 emissions at a significant level. Education level, together with economic growth, fossil fuel energy consumption and industry, had a negative significant impact as well upon environmental performance, an increase of one unit in these variables contributing to increased carbon dioxide levels in the EU and G20 sample when considering both the panel corrected model as well as the GMM scenario. Renewable energy is the only independent variable to manifest a significant positive and direct link with environmental performance, drawing attention to the need of adapting the primary sources of energy, in line with international organizations’ sustainable development policy recommendations. Also, there is a need to improve citizens’ perceptions of public services and institutions by building confidence in government’s ability to formulate and implement regulations.

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Section: Resultsmentioning

confidence: 99%

The impact of governance quality and educational level on environmental performance

Dincă

Bărbuță

Negri

et al. 2022

Front. Environ. Sci.

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“…Conversely, areas with high industrial carbon emission intensity and weak emission reduction capacity may be more negatively affected by the transfer of highcarbon industries. In other words, the influence of the interaction of factor mobility and industrial transfer on industrial carbon emission intensity may be heterogeneous at different industrial carbon emission levels, and the panel quantile regression is one of the effective methods to verify the above point (Opoku and Aluko, 2021). The results reported at different quantiles of industrial carbon emission intensity are shown in Table 5.…”

Section: Endogeneity Discussion and Robustness Testmentioning

confidence: 97%

Factor Mobility, Industrial Transfer and Industrial Carbon Emission: A Spatial Matching Perspective

Zheng

Yang

2022

Front. Environ. Sci.

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This paper proposes a dynamic industrial transfer index for the first time to characterize the spatial matchability of factor mobility and industrial transfer, and then explores the correlation between factor mobility, industrial transfer and industrial carbon emission intensity by using panel data from 30 provinces in China during 2004–2019. The results show that China’s ability to reduce carbon emissions is improving, and the intensity of industrial carbon emission has dropped by 44.84%. In addition, there is an obvious spatial mismatch between factor mobility and industrial transfer. Specifically, production factors generally flow from high-carbon emission intensity areas to low-carbon emission intensity areas, while the direction of industrial transfer is generally opposite to that of factor mobility. And the empirical analysis finds that the interaction between factor mobility and industrial transfer will affect the carbon reduction. If the direction of capital mobility is the same as that of capital-intensive industries, it is unfavorable to the carbon emission reduction. While the carbon reduction effect will be enhanced if the directions of labor and technology mobility are consistent with those of labor-intensive and technology-intensive industrial transfer, respectively. Finally, based on the findings, some recommendations are provided for governments to formulate policies.

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“…Models with non-additive disturbances, which are functions of both unobserved and observed factors, are included in the framework. Finally, the generalized quantile regression model is estimated using adaptive Markov Chain Monte Carlo (MCMC) sampling and numerical optimization (see Opuku and Aluko 2021 ; Powell 2020 ).…”

Section: Data Sources and Methodologymentioning

confidence: 99%

“…To achieve our objective, the generalized quantile regression method is used to solve endogeneity (i.e. omitted variable bias, simultaneity bias) of variables using an instrumental variables approach (see Opuku and Aluko 2021 ; Powell 2020 ). Furthermore, we apply the generalized panel quantile regression since the 48 countries selected in SSA differ substantially in terms of natural resource depletion, renewable energy use, and environmental degradation.…”

Section: Introductionmentioning

confidence: 99%

Exploring the nexus between natural resource depletion, renewable energy use, and environmental degradation in sub-Saharan Africa

Byaro

Nkonoki

Mafwolo

2022

Environ Sci Pollut Res

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This study explores the nexus between natural resource depletion, renewable energy use, and environmental degradation in 48 sub-Saharan African (SSA) countries from the period 2000 to 2020 using generalized panel quantile regression. The findings show that, at 90 th quantiles the magnitude of natural resource depletion is positive and stronger associated with environmental degradation in SSA. This is probably attributed by countries with higher natural resource depletion such as Congo Republic (37.10%), Equatorial Guinea (27.60%), Angola (21.14%), Gabon (12.84%), Chad (12.19%), Burundi (8.92%), Uganda (6.16%), and Congo Democratic (5.24%). Furthermore, at lower quantiles (30 th and 10 th ), natural resource depletion negatively affects environmental degradation in SSA. This might be attributed by countries with negligible natural resource depletion like Carbo Verde (0.16%), Central African Republic (0.04%), Comoros (1.17%), Eswatini (0.01%), Gambia (0.92%), Guinea-Bissau (0.33%), and Madagascar (0.07%). Moreover, the findings show that renewable energy use reduces environmental degradation and is statistically significant at almost all quantiles. Finally, the findings reveal that industrialization, trade, and economic growth all contribute to environmental degradation (i.e. carbon emissions) in SSA. The policy implication is to adopt measures that reduce poverty, which is linked to natural resource depletion, and scale up renewable energy use technologies for SSA. Policymakers should develop strategies to reduce carbon dioxide emissions and enable better use of natural resources by enforcing environmental laws. Concurrently, we propose natural resource management to be multi-sectoral and integrated into institutional structures by allocating funds to the natural resources sector for intervention programs in SSA countries.

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Heterogeneous effects of industrialization on the environment: Evidence from panel quantile regression

Cited by 124 publications

References 61 publications

The impact of governance quality and educational level on environmental performance

The impact of governance quality and educational level on environmental performance

Factor Mobility, Industrial Transfer and Industrial Carbon Emission: A Spatial Matching Perspective

Exploring the nexus between natural resource depletion, renewable energy use, and environmental degradation in sub-Saharan Africa

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