Relationship between economic growth and mismanaged e-waste: Panel data evidence from 27 EU countries analyzed under the Kuznets curve hypothesis

Boubellouta, Bilal; Kusch-Brandt, Sigrid

doi:10.1016/j.wasman.2020.11.032

Cited by 41 publications

(21 citation statements)

References 86 publications

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“…Previous studies on the EKC hypothesis have used a range of indicators of environmental degradation, such as carbon dioxide, carbon monoxide, sulfur dioxide, and greenhouse gas (Asumadu and Vladimir 2019;Shahbaz and Sinha 2019;Mehmood and Tariq 2020;Ahmad et al 2021), but relatively few used solid waste (Waste Kuznets Curve) as an indicator. According to Boubellouta and Kusch-Brandt (2021b), there are 33 studies documented in literature, and 73% of these confirmed the WKC hypothesis. More recently, environmental abatement through recycling of solid waste was researched under the EKC hypothesis (Kasioumi and Stengos 2020).…”

Section: Environmental Kuznets Curve Hypothesis (Ekc)mentioning

confidence: 88%

“…Likewise, Boubellouta and Kusch-Brandt (2021a) found an inverted U-shaped relationship between GDP and e-waste generation for 174 countries for 2016. To capture the impact of economic growth on e-waste mismanagement, Boubellouta and Kusch-Brandt (2021b) used two indicators for environmental degradation, namely uncollected and non-recycled/non-reused e-waste, and looked at data from 27 European countries over the period 2008-2016, (4)…”

Section: Environmental Kuznets Curve Hypothesis (Ekc)mentioning

confidence: 99%

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Determinants of e-waste composition in the EU28 + 2 countries: a panel quantile regression evidence of the STIRPAT model

Boubellouta

Kusch-Brandt

2022

Int. J. Environ. Sci. Technol.

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Previous studies have examined the relationship between total e-waste generation and its determinants. However, e-waste categories have not received appropriate attention, and thus important information is missing for policymakers. This paper advances the state of knowledge by studying e-waste categories individually. Statistical data of e-waste in the EU28 + 2 countries over the period 2000–2015 is disaggregated into single categories, namely temperature exchange equipment, screens and monitors, lamps, large equipment, small equipment, and small IT and telecommunication equipment. To examine the main driving forces of e-waste in each category, the STIRPAT model (Stochastic Impacts by Regression on Population, Affluence, and Technology) and the environmental Kuznets curve (EKC) hypothesis are applied, using panel quantile regression as main method and pooled OLS to control robustness of findings. Results show that population, renewable energy consumption, trade openness, and urbanization are positively correlated with all e-waste categories. Renewable energy exploitation is a major e-waste driver of large and small equipment, screens and monitors, and small IT. Interestingly, an inverted U-shaped relationship between gross domestic product (GDP) per capita and the quantity of e-waste was found across all e-waste categories for most regressions when using the quantile regression method (28 out of 30 quantiles), and for all regressions when using the pooled OLS method. This confirms the EKC hypothesis and indicates that e-waste of all categories increases with GDP up to a certain level (turning point) but then decreases when GDP continues to grow.

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Section: Environmental Kuznets Curve Hypothesis (Ekc)mentioning

confidence: 88%

Section: Environmental Kuznets Curve Hypothesis (Ekc)mentioning

confidence: 99%

Determinants of e-waste composition in the EU28 + 2 countries: a panel quantile regression evidence of the STIRPAT model

Boubellouta

Kusch-Brandt

2022

Int. J. Environ. Sci. Technol.

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“…Moreover, heterogeneity in the cointegrated panel and long-run variance can be controlled by both FMOLS and DOLS estimators (Baloch et al, 2019). MM-estimation is classified as static specification, whereas FMOLS and DOLS are considered as dynamic specifications (Boubellouta and Kusch-Brandt, 2021). Pedroni (2001) proposes the FMOLS estimator and the panel FMOLS model as illustrated in Equation ( 7).…”

Section: Long-run Estimationmentioning

confidence: 99%

Evaluating the COVID-19 response policy's impact on carbon dioxide emissions in the top four CO₂ emission countries

Tan

Keh

Tang

et al. 2022

MEQ

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PurposeThe stringency policy and economic support policy in response to and to address the coronavirus disease 2019 have become a significant concern since the end of 2019. The motivation that led to this study is that, the selection of the stringency policy and the economic support policy appear to have brought about the opposite effects of the environmental costs of carbon dioxide emissions. The study's objective is to examine the contradictory impacts of these stringency and economic support policies on carbon dioxide emissions.Design/methodology/approachThis study applies panel data for the top four countries responsible for carbon dioxide emission, namely China, the United States of America, India and Russia. A fully modified ordinary least squares estimator and dynamic ordinary least squares estimator are employed to determine the long-run parameters.FindingsThe results indicate that the effect of reduced carbon dioxide emissions due to a one-unit increase in the stringency policy is greater than the effect of increased carbon dioxide emissions caused by a one-unit increase in the economic support policy. Hence, if the two policies are implemented simultaneously, a positive net effect on environmental costs will be gained.Research limitations/implicationsThe study investigates in a general scope, the impact these response policies have on the environment. Future researchers may enhance the research on environmental impact in different sectors due to the implementation of both policies to enrich the analytical perspective.Practical implicationsThe results have provided implications for policymakers to emphasize more on stringency-oriented policies while giving economic support to the low-income or unemployed households in order to reduce carbon dioxide emissions.Originality/valueDespite the foreseen effects of the stringency policy and economic support policy, there has hardly been any studies that have explored empirically the nexus between both policies with carbon dioxide emissions in one empirical model. Furthermore, the paper uses the high-frequency data in determining the contradictory impacts of stringency policy and economic support policy on CO2 emissions.

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“…In most of the regional panel analyses that examined the environment-growth-energy, their results indicate that energy consumption, particularly non-renewable energy consumption, contributes to CO2 emissions (Amin et al, 2020;Bajra et al, 2020;Boubellouta and Sigrid, 2021;Cheng et al, 2021;Nepal et al,2017;Pala, 2020;Pomfret, 2012;Taghizadeh-Hesary et al, 2020). For example, Nepal et al, (2017) examine the impacts of market-based economic reforms on per capita CO2 emissions in the European and Central Asian transition economies where environmental degradation was pervasive prior reforms.…”

Section: Literature Reviewmentioning

confidence: 99%

Renewable and Non-Renewable Energy Consumption, Carbon Dioxide Emissions, and Economic Growth: Empirical Evidence from Central Asian Countries

Turmunkh¹

2021

JEDS

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This study examines the relationships between non-renewable and renewable energy consumption, carbon dioxide emissions, economic growth, and population in Central Asian countries after the transition economics with the Panel Granger Causality, Panel Cointegration, and Panel non-stationarity tried to explain using the causality test, using 1992 to 2019 data from the World Development Indicators (WDI). The engagement of developing countries is an increasingly important part of addressing greenhouse gas (GHG) emission-driven climate change. As such, understanding the patterns of energy use, GHG emissions, and economic growth in developing countries is vital. Major Central Asian countries are important in this respect due to their size, rapid growth, and extensive energy reserves. It has experienced rapid growth in its economy, energy consumption, and GHG emissions in recent years. It performs tests to verify the existence of the longrun relationships among the variables and examines short and longrun causal relationships. It finds that increased fossil fuel use is the main cause of increased carbon dioxide (CO2) emissions.

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Relationship between economic growth and mismanaged e-waste: Panel data evidence from 27 EU countries analyzed under the Kuznets curve hypothesis

Cited by 41 publications

References 86 publications

Determinants of e-waste composition in the EU28 + 2 countries: a panel quantile regression evidence of the STIRPAT model

Determinants of e-waste composition in the EU28 + 2 countries: a panel quantile regression evidence of the STIRPAT model

Evaluating the COVID-19 response policy's impact on carbon dioxide emissions in the top four CO₂ emission countries

Renewable and Non-Renewable Energy Consumption, Carbon Dioxide Emissions, and Economic Growth: Empirical Evidence from Central Asian Countries

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Relationship between economic growth and mismanaged e-waste: Panel data evidence from 27 EU countries analyzed under the Kuznets curve hypothesis

Cited by 41 publications

References 86 publications

Determinants of e-waste composition in the EU28 + 2 countries: a panel quantile regression evidence of the STIRPAT model

Determinants of e-waste composition in the EU28 + 2 countries: a panel quantile regression evidence of the STIRPAT model

Evaluating the COVID-19 response policy's impact on carbon dioxide emissions in the top four CO2 emission countries

Renewable and Non-Renewable Energy Consumption, Carbon Dioxide Emissions, and Economic Growth: Empirical Evidence from Central Asian Countries

Contact Info

Product

Resources

About

Evaluating the COVID-19 response policy's impact on carbon dioxide emissions in the top four CO₂ emission countries