hypothesis on environmental pollution

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• Published: 21 February 2024

Rethinking the environmental Kuznets curve hypothesis across 214 countries: the impacts of 12 economic, institutional, technological, resource, and social factors

• Qiang Wang   ORCID: orcid.org/0000-0002-8751-8093 1 , 2 ,
• Yuanfan Li 1 &
• Rongrong Li 1 , 2  

Humanities and Social Sciences Communications volume  11 , Article number:  292 ( 2024 ) Cite this article

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• Development studies
• Environmental studies

Research over the past three decades has provided rich empirical evidence for the inverted U-shaped EKC theory, but current problems facing advancing climate mitigation actions require us to re-examine the shape of global EKC rigorously. This paper examined the N-shaped EKC in a panel of 214 countries with 12 traditional and emerging variables, including institutions and risks, information and communication technology (ICT), artificial intelligence(AI), resource and energy use, and selected social factors. The two-dimensional Tapio decoupling model based on N-shaped EKC to group homogeneous countries is developed to explore the inter-group heterogeneous carbon emission effects of each variable. Global research results show that the linear and cubic terms of GDP per capita are significantly positive, while the quadratic term is significantly negative, regardless of whether additional variables are added. This means the robust existence of an N-shaped EKC. Geopolitical risk, ICT, and food security are confirmed to positively impact per capita carbon emissions, while the impact of composite risk, institutional quality, digital economy, energy transition, and population aging are significantly negative. The impact of AI, natural resource rents, trade openness, and income inequality are insignificant. The inflection points of the N-shaped EKC considering all additional variables are 45.08 and 73.44 thousand US dollars, respectively. Combining the turning points and the calculated decoupling coefficients, all countries are categorized into six groups based on the two-dimensional decoupling model. The subsequent group regression results show heterogeneity in the direction and magnitude of the carbon emission impacts of most variables. Finally, differentiated carbon emission reduction strategies for countries in six two-dimensional decoupling stages are proposed.

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Introduction

The suitability and stability of the Earth’s climate are crucial for human survival (Carlson et al., 2022 ; Cavicchioli et al., 2019 ) and development (Brown et al., 2023 ; Ray et al., 2015 ; Diaz and Moore, 2017 ). However, as of January 2024, global surface temperatures have accelerated by ~1.5 °C since the pre-industrial period (Berkeley, 2024 ). According to the IPCC’s 7th climate assessment report (AR 7), the global warming is “unequivocally” caused primarily by human activities and anthropogenic greenhouse gas emissions (IPCC, 2023 ). Upon the societal acknowledgment of the causality, the question of how to transform the existing development paradigm to conform with climate mitigation principles emerges as a central focus for the governmental (Duan et al., 2021 ; Dai et al., 2023 ) and scholarly attention (Fawzy et al., 2020 ; Hoegh-Guldberg et al., 2019 ; Fankhauser et al., 2022 ).

The environmental Kuznets curve (EKC) is among the most prevalent economic-environmental simulation models utilized for policy analysis (Zou et al., 2022 ; Koondhar et al., 2021 ; Mao et al., 2022 ). The EKC hypothesis linked economic development to environmental quality in such a paradigm: per capita income and per capita carbon dioxide emissions increase together until a certain inflection point in income is reached, after which the growth of pollutants levels off and then overturns (Grossman and Krueger, 1995 ). Such inverted U-shaped nonlinear patterns have been empirically confirmed in countless number of early studies and explanations are mainly given by inefficient and dirty production techniques in the pre-industrial stages and the strengthened environmental regulations and advanced clean technologies in the developed stages (Balsalobre-Lorente et al., 2018 ). Over the past three decades, such paradigms of “grow first and clean later” have often served as a benchmark for other climate models and even as references for national plans (Koondhar et al., 2021 ; Kaika and Zervas, 2013a ).

However, many of the news and events we hear and observe recently suggest a weakening relationship between advanced economies and improved environment, which seems to contradict the existing explanation. First, some developed countries including the United States are known to be ecologically deficit (Usman et al., 2020 ; Lark et al., 2020 ). Moreover, in November 2020, as the world’s largest economy and second-largest carbon emitter, the U.S. vowed to withdraw from the Paris Agreement to restart its traditional and highly polluting sectors (Schiermeier, 2020 ; Roelfsema et al., 2020 ). Finally, many developed countries, including Germany, the Netherlands, and Austria, are once again turning to coal for electricity and heating as they struggle with a European energy crisis triggered by the Russia-Ukraine conflict beginning in 2022 (Tollefson, 2022 ; Guan et al., 2023 ).

These events highlight the need to revisit the global EKC, specifically discussing the N-shaped curve. On the one hand, the new paradigm of the N-shaped curve is more suitable for the current actual situation than the inverted U-shaped EKC. This is because the carbon emission trajectory may still be bent upward at a high level of economic development if the technological effects brought about by green innovation cannot keep up with the growing scale effects (Lorente and Álvarez-Herranz, 2016 ). On the other hand, once the “development first, clean later” commitment of the previous inverted U-shaped EKC theory is disillusioned, then developing countries can no longer sit back and be excluded from the climate agenda and emission reduction plans (Koondhar et al., 2021 ; Kaika and Zervas, 2013a ). Hence, an accurate understanding of the current shape of global EKC is critical for different categories of countries to identify their unique drivers of carbon emissions and adopt prompt action.

The current situation, on the other side, also demonstrates the complexity of the determining rules of carbon emissions. Although some additional variables including trade openness, institutional quality, and energy consumption have been widely incorporated into the EKC framework (Leal and Marques, 2022 ), recent research has established some new links between external factors and carbon emissions. For example, geopolitical risks and national political, economic, and financial risks are proven to have the potential to change countries’ economic development and energy use patterns (Anser et al., 2021 ; Hassan et al., 2022 ; Qiang Wang et al., 2023a ). Digital technology and artificial intelligence, as key to the third and fourth industrial revolutions, have been found to have carbon-reducing effects in some studies (Jinning Zhang et al., 2022a ; Ding et al., 2023 ). The transition from traditional fossil fuels to new energy sources has been empirically confirmed to effectively mitigate air pollution and accelerate carbon neutrality (Feng Dong et al., 2022b ; Naeem et al., 2023 ). Finally, some social issues, including population aging (Fan et al., 2021 ) and food security (Naseem et al., 2020 ), are also included in the environmental analysis. As (Stern, 2017 ) note, it is necessary to reveal the effects of other variables other than economic growth within the EKC framework. Banking on these current issues of global environment and carbon emissions, we ask the following questions: What is the current shape of global EKC when new additional factors are taken into account? Is there heterogeneity in the impact of various factors on carbon emissions in different types of countries?

This article aims to answer the above questions by empirically examining the current shape of the global EKC and fully considering the impact of multiple carbon emission drivers and the heterogeneity in the panel. Specifically, this paper contributes to the literature in the following three aspects. First, this paper studies N-shaped EKC in 214 countries and incorporates 12 traditional or novel institutional, technological, resource, and social factors as additional variables in the EKC equation. This work provides preliminary but crucial information for mitigation actions by elucidating the relationship between global economic development and carbon emissions and identifying potential drivers of carbon emissions. Second, to handle the heterogeneity of countries, we develop a two-dimensional Tapio decoupling model based on N-type EKC to evaluate the economic development stage and decoupling situation of each country. We believe that starting a classified and detailed discussion can help to more systematically reveal the different situations or problems in economic development and carbon emission reduction in countries around the world. Finally, based on the grouping results, we analyze the inter-group heterogeneity of the carbon emission effects of economic and additional variables, providing a baseline reference for countries at different stages to formulate targeted and effective carbon emission reduction policies.

The remainder of this article is arranged as follows. The section “Literature review and theoretical background” reviews studies related to EKC and additional variables and develops key hypotheses. The section “Method and data” briefly introduces the methodology, model and data. In the section “Empirical results”, we discuss the global and group research results. The section “Concluding remarks” is left for conclusion.

Literature review and theoretical background

Literature review, the origin, evolution, and relevant criticism of ekc.

The nonlinear relationship between per capita income and income inequality proposed by (Kuznets, 1955 ) was re-interpreted as the environmental Kuznets curve (EKC) in the environmental economics field in the 1990s. The EKC concept, originally used to describe the inverted U-shaped relationship between economic growth and environmental degradation, was proposed by (Grossman and Krueger, 1991 ) in their landmark study of the environmental impacts of the North American Free Trade Agreement. Early EKC researchers analyzed the relationship between economic growth and various environmental degradation indicators without any other explanatory variables and obtained much evidence for an inverted U-shaped of first positive and then negative linkages between the two (Grossman and Krueger, 1991 ; Beckerman, 1992 ; Grossman and Krueger, 1995 ; Heerink et al., 2001 ).

Subsequent research mainly made two key improvements to the classic EKC empirical research framework. On the one hand, N-shaped EKCs have been theoretically proposed and they were empirically confirmed in global, cross-country, and single-country studies in the latest empirical studies. For example, Allard et al. ( 2018 ) tested the relationship between CO2 emissions and GDP per capita in four income groups (low, upper-middle, lower-middle, and high-income groups) containing a total of 74 countries and found N-shaped EKC for all income groups except upper-middle-income countries. Using ecological footprint as a proxy variable for environmental pollution, Numan et al. ( 2022 ) obtained very similar conclusions among the country groups of four income levels including 85 countries. In addition, N-shaped curves were also found in the case of nine top nuclear-producing countries (Jahanger et al., 2023 ), OPEC countries (Ullah et al., 2023 ), E-10 countries (Fakher et al., 2023 ), and 14 emerging countries (Rashdan et al., 2021 ). N-shaped linkages between growth and pollution were also individually detected in India (Hossain et al., 2023 ), China (Zhengxia et al., 2023 ), Algeria (Shehzad et al., 2022 ), and other countries.

Another major improvement is to add additional variables to the right-hand side of the empirical EKC equations. By stripping out political, social, or technological factors and discussing their impact on carbon emissions separately, scholars have narrowed and clarified the concept of economic development in the traditional EKC equation (Leal and Marques, 2022 ). These studies provide more accurate and richer insights than would be possible without the inclusion of control variables. In this regard, first established by (Cole, 2004 ), trade openness has so far become a key additional variable in the EKC framework. The inclusion of this variable is often relevant for testing the Pollution Haven Hypothesis (PHH). Because under the trade-opening paradigm, polluting industries shifting from developed countries to developing countries will directly affect the level of economic development and carbon emission levels as well as the relationship between the two (Qiang Wang et al., 2023b ). Energy consumption is another well-established additional variable (Qiang Wang et al., 2024a ). In the pioneering literature (Ang, 2008 ), energy consumption was included in the EKC framework and the results confirmed the long-term and positive Granger causality between economic growth, energy use, and pollution in Malaysia. In terms of national institutions, Apergis and Ozturk ( 2015 ) incorporated the four institutional quality variables of political stability, government efficiency, regulatory quality, and corruption control into the research equation and confirmed the EKC hypothesis in a panel of 14 Asian countries. They emphasized that national institutions provide a mechanism within which to share knowledge and diffuse technologies on energy efficiency and emissions control. Lantz and Feng ( 2006 ) and Higón et al. ( 2017 ) discuss the role of technological progress and ICT in the EKC framework. Both results indicate that traditional EKC does not exist and is replaced by an inverted U-shaped techonology-carbon emissions nexus.

Finally, this article reviews some theoretical and empirical criticisms of EKC. They are related to the research ideas of this article and the development directions of future research. The first issue that has been widely criticized relates to the alleged heterogeneity present in the EKC panel study. To provide a broader assessment and reference, a considerable number of EKC studies choose to use cross-sectional or panel data including a group of countries (Kaika and Zervas, 2013b ). Although these studies often argue that the selection of country groups is based on the close links between them (such as regional links, trade links, international agreements, etc.), however, the EKC curves obtained from country groups often prove to be unsuitable for each of them (Leal and Marques, 2022 ). A second thorny criticism questions the practical relevance of the EKC conceptual framework and development model (Gill et al., 2018 ). Even if each country’s EKC curve were accurately assessed, it would only depict a baseline development trajectory for the future. However, the more important issue at present is how to reduce environmental pollution and repair environmental damage according to the EKC insight rather than just “grow now and clean later”(Gill et al., 2018 ). The third issue is proposed in (Stern, 2017 ). After reviewing the 25 years of evolution of EKC research, he worried that the EKC model ignores the impact of other effects except growth and underscored that “they are the opposite force of the scale effect.” In addition, other econometrics issues, including the selection of pollution variables, the sensitivity of results to measurement methods, and the rationality of inflection points, were also pointed out (Leal and Marques, 2022 ).

Research gaps and research design

Through an in-depth discussion of the literature on EKC, we are convinced that our research is based on solid foundations. However, it is necessary to point out the research gap to realize the contribution of this article and promote the development of related literature. First, to the best of the authors’ knowledge, there is still a lack of global panel testing of the existence of N-shaped EKC, especially for a total of 214 countries. Second, existing studies often subjectively select one perspective or a few variables to incorporate into the EKC equation, rather than using a comprehensive variable set to cover various aspects that potentially affect carbon emissions. Third, based on the criticism of the heterogeneity of EKC, scholars often study the shape of EKC in different income groups defined by the World Bank to handle heterogeneity. However, this solution still suffered from two main limitations. On the one hand, it is not effective enough to handle heterogeneity by grouping countries only from the economic perspective, ignoring the different environmental situations faced by countries at the same stage of development. On the other hand, with the second criticism of EKC, previous studies still focus on determining the shape of EKC after grouping, while neglecting the question of how each group of countries can reduce carbon emissions or repair environmental problems, which may lead to a lack of feasible policy implications and action reference.

To address the first issue, we initiated an empirical examination of the existence of N-shaped EKCs in a complete global panel of 214 countries. For the second problem, we selected 12 additional variables to be included in the EKC equation. These variables cover four perspectives: institutions and risks, digital technology, resource and energy utilization, and other social factors, and cover some new variables that have rarely been included in EKC in the past. Regarding the last one, we develop a two-dimensional decoupling model that can fully consider the heterogeneity of countries in terms of economic development and carbon emission decoupling status to achieve country grouping. In the group study, we focused on the heterogeneous impact of various factors rather than the shape of EKC, to propose more targeted and feasible solutions for each group of countries to reduce carbon emissions.

Theoretical background and hypothesis

Based on the research design, we review in this section the theoretical background regarding EKC shape and additional variables to be included in the study. Accordingly, we will propose specific research hypotheses for further empirical research.

Theory about the shape of EKC

The traditional EKC theory believes that there is an inverted-U shape between economic growth and environmental pollution (Grossman and Krueger, 1995 ; 1991 ) and implicitly assumes structural changes in the process of economic growth (Dinda, 2004 ). In other words, as economic development moves from a lower stage to a higher stage, the production sector also changes from agricultural production to industrial production, and finally to the third sector. In addition, under the scheme of EKC, in each of the above processes, scale effects, composition effects, and technical effects will successively dominate. Within this framework, scholars use the relative sizes of technical effects and scale effects to explain changes in the relationship between economic development and environmental pollution (Dinda, 2004 ; Koondhar et al., 2021 ; Andreoni and Levinson, 2001 ): In the first stage of U-shaped EKC, that is, the pre-industrial stage, the development of primary, low-efficiency industries will produce scale effects and pollution. The reason for the emergence of the second stage of EKC is explained as the replacement of dirtier technologies with cleaner technologies in the production process. Therefore technological effects brought about by innovation exceed the scale effects and achieve environmental improvements as in the developed stage (Andreoni and Levinson, 2001 ).

However, scholars supporting N-shaped EKC have proposed a different theory. They believed that even at a higher stage of economic development if innovation cannot keep up with the speed of economic growth and the increase in technological effects is slower than the scale effect, environmental pollution may reappear (Lorente and Álvarez-Herranz, 2016 ). In other words, the shape of the EKC may extend from an inverted U-shape to an N-shape. This N-shaped model assumes that economic growth leads to environmental degradation that reverses with economic progress after reaching a positive peak and that pollution levels approach a negative peak before starting to rise again with further economic progress. Compared with the inverted U-shaped EKC, the most critical gap lies in the rebound of environmental pollution in the third stage. Scholars refer to this pattern as the technological obsolescence effect, representing the re-emergence of scale effects and overcoming the compositional and technological effects that preceded the second turning point (Lorente and Álvarez-Herranz, 2016 ; Balsalobre-Lorente et al., 2018 ). The emergence of this phenomenon is believed to be related to neglected environmental regulations and slow technological innovation. Many studies have shown how technological innovation combined with environmental regulation can weaken technological obsolescence and delay new rising trends in pollution (Álvarez-Herránz et al., 2017 ; Lorente and Álvarez-Herranz, 2016 ). These theoretical analyses indicate that N-shaped EKC may appear when the development of environmental protection awareness, low-carbon energy utilization, and clean production technology is slow. Combined with the large number of real-life cases from developed countries discussed in the introduction, this phenomenon does exist and we proposed the hypothesis below:

H1: The current shape of the global EKC is N-shaped.

H2: The N-shaped EKC remains robust with additional variables.

The relationship between additional variables and carbon emissions

The purpose of this section is to establish the relationship between the involved 12 additional variables and carbon emissions based on previous literature. These factors cover four perspectives: institutions and risks, digital technology, resource and energy utilization, and other social factors.

Sound institutions and a stable national environment are fundamental to a country’s development (Asiedu, 2006 ). They are considered closely linked to carbon emissions through policy, economic, and technical pathways (Karim et al., 2022 ). Institutional quality is a relatively traditional research variable, which represents the effectiveness and stability of a country’s policy formulation, laws, regulations, and governance structures. In a study aimed at validating the EKC in 47 emerging markets and developing economies (Le and Ozturk, 2020 ), institutional quality was found to boost carbon emissions. However, using annual data from 30 sub-Saharan African countries (Karim et al., 2022 ) confirmed that corruption control, regulatory quality, and rule of law in six WGI indices can significantly reduce carbon dioxide emissions. Epidemics and conflicts have recently led to the accumulation of global and domestic risks. National risks and geopolitical risks have attracted the attention of some scholars. However, there is no consensus on the impact of various risks on carbon emissions. Anser et al. ( 2021 ) and Hassan et al. ( 2022 ), respectively, found that geopolitical risk and political risk promoted carbon emissions, while Jun Zhao et al. ( 2021a ) found that financial risks reduce carbon emissions.

Technical effects are the key force in pollution reduction in EKC theory. Among them, digital and communication technologies are considered to have triggered the third industrial revolution. Therefore, the relationship between digital technology and carbon emissions has received much research attention. ICT is a representative digital technology variable, generally represented by the availability of communication equipment or digital functions (Charfeddine and Umlai, 2023 ). Scholars have conducted numerous studies on the ICT-carbon emissions nexus (Qiang Wang et al., 2024b ). As is well summarized by Charfeddine and Umlai ( 2023 ), the research findings cover irrelevant, positive relationships, negative relationships, U-shaped relationships, and inverted U-shaped relationships due to the differences in scope, methods, and proxies in the research. A relatively novel approach to measuring digital technology comes from a so-called digital economy perspective, which pays more attention to the integration among smart technology and the industries than ICT. This stream of research generally constructs multi-index indices to measure the digital economy from dimensions such as ICT infrastructure, economic effects and social benefits. Unlike the fierce controversy in ICT research, most research results support the reduction effect of digital technology on carbon emissions (Feng Dong et al., 2022a ; Yi et al., 2022 ), though opposing views exist (Lu Zhang et al., 2022b ). The latest digital technology variable is related to AI. As the energy consumption of training large language models such as Chatgpt has recently attracted the attention of the public and scholars, the energy and environmental impact of AI has been increasingly studied. In recent empirical studies, most scholars used the industrial robot stock to proxy AI (Zhong et al., 2023 ), and a few scholars constructed a composite index for AI development in specific countries like China (Ding et al., 2023 ). Most research results support the reduction effect of AI or industrial robots on carbon emissions (Zhong et al., 2023 ; Yaya Li et al., 2022 ; Mingfang Dong et al., 2023 ), although (Luan et al., 2022 ) got the opposite result and (Liu et al., 2024 ) hold a nonlinear view.

Human society’s use of natural resources and energy is directly related to anthropogenic carbon emissions. Natural resource rents represent the degree of human utilization of traditional resources. However, the impact of natural resource rents on carbon emissions is subject to intense debate. Empirically Rongrong Li et al. ( 2023 ) found that the exploration of natural resources directly triggers economic growth and subsequently stimulates an increase in ecological footprint. In contrast, combined with clean energy and environmental protection technology, some scholars (Xiaoman et al., 2021 ; Rongrong Li et al., 2024 ) believe that natural resource extraction can also improve the local environment. On the other hand, energy transition represents the result of human beings taking the initiative to eliminate traditional fossil fuels and shift to clean energy. Empirical results support that this systemic transformation of the energy system can effectively reduce carbon emissions in most countries (Dogan and Seker, 2016 ; Inglesi-Lotz and Dogan, 2018 ) though the heterogeneity among country in different income groups is often significant (Nguyen and Kakinaka, 2019 ).

Social issues have recently been increasingly linked to climate change. The first variable to be included in the study is trade openness, which is used to test the existence of the pollution paradise hypothesis in the literature (Qiang Wang et al., 2023b ). The opposite view to PHH is called the pollution halo hypothesis, which holds that developed countries spread advanced clean technologies to host countries in the process of foreign investment (Bashir, 2022 ; Qiang Wang et al., 2023b ). However (Bashir, 2022 ) summarized that no single view currently dominates, and that studies supporting positive and negative correlations between trade openness and carbon emissions are both growing. Income inequality is initially closely related to EKC theory as a key variable. However, the impact of income inequality on carbon emissions is not clear. Scholars explain this as the dual impact of income inequality: severe income inequality weakens the high-carbon consumption capacity of the large number of middle- and low-income groups, while triggering rich people to adopt a high-pollution lifestyle (Rojas-Vallejos and Lastuka, 2020 ). The carbon impact of an aging population is also considered twofold. Although an aging society reduces the production capacity and demand for high-carbon industrial products, it also increases the demand for medical and other services (Zhou et al., 2023 ). The latter is an energy- and pollution-intensive industry. After the pandemic, food safety has become a top priority for many underdeveloped countries. However, judging from a recent review (Cheng et al., 2023 ), empirical studies linking food security and carbon emissions are lacking. The only literature seems to focus on Pakistan, where (Akbar et al., 2019 ) found a negative causal relationship between cereal yields and carbon emissions, while (Naseem et al., 2020 ) revealed that an increase in the food security index may bring additional carbon emissions. Based on the above discussion, we found that the above variables may each have an impact on carbon emissions through various channels. Furthermore, there are controversies in findings in the literature for different research subjects, suggesting that there may be country heterogeneity in the relationship between these variables and carbon emissions. Therefore we propose the following hypothesis:

H3: When included in the EKC model, all of the 12 additional variables have significant impacts on carbon emissions.

H4: The relationship between the 12 additional variables and carbon emissions is heterogeneous among different groups of countries.

Method and data

Methodology, preliminary test methods, multicollinearity test method: variance inflation factor (vif).

Multicollinearity results from one independent variable being accurately linearly predicted by other independent variables, which reduces the stability of parameter estimation for the affected variables. This article aims to analyze the shape of the global EKC curve under the influence of 12 additional variables. Even though we have made the best efforts to select determinants from different lenses to approach the carbon emission curve, the original data used in the calculation of different proxy variables may partially overlap. Therefore, we examine the degree of multicollinearity in the model to ensure balance between comprehensiveness and non-replication of variable combinations.

The variance inflation factor (VIF) tests the severity of the multicollinearity problem by measuring how much multicollinearity increases the variance of the estimated coefficients. For example, in the following multiple regression:

the variance of the estimated value of β 1 is:

where \({{\rm{R}}}_{1}^{2}\) is the determining coefficient of the regression of x1 on x 2 -x 6 . The VIF of x 1 is defined as:

Therefore, VIF 1 is a multiplier of the variance of \({\hat{{\rm{\beta }}}}_{1}\) . A larger VIF means a larger \({\rm{Var}}({\hat{{\rm{\beta }}}}_{{\rm{i}}})\) , and lower accuracy of the estimation of β i . According to a rule of thumb, if the combination of explanatory variables has a VIF greater than 10, it means that serious multicollinearity problems exist and must be solved.

Panel unit root test methods

The basic idea of the unit root test (URT) is to test whether there is an obvious upward or downward trend in variables by proposing the null hypothesis that a unit root exists in the sample sequence. If the null hypothesis is rejected, it means that there is no obvious trend in the sequence and the variable is considered stable. The most commonly used panel URT methods include IPS, LLC, Fisher tests (including Fisher-ADF test and Fisher-PP test), etc (Im et al., 2023 ). Because this paper uses unbalanced data, we choose the Fisher test which applies to our case. Taking fisher-ADF as an example, in the simple autoregressive process of variable y it :

where ρ is the auto-correlation coefficient, and the Fisher-ADF statistic is:

where d i represents the p -value of the ADF test for the i-th group of the cross-section. The significant Fisher-ADF statistic represents the rejection of the null hypothesis that ρ  = 1 in Eq. ( 4 ), which means that the series of variable y it does not have a unit root and is stationary.

Cointegration test methods

Most regression models assume that the disturbance term is a normal random variable with zero mean and constant variance. Once the disturbance term is not stationary, the estimation results will become biased and incredible, which is so-called pseudo-regression. To prevent pseudo-regression problems, the cointegration test is often applied to test whether the station of residual sequence. This paper focuses on the Kao test (Kao, 1999 ), which applies to the unbalanced panels in our case. This method is based on the premise that both the explanatory and dependent variables are first-order stationary. Taking the simple regression form without a trend term as an example, in the following regression equation:

x it and y it should first satisfy the first-order stationary condition:

where i represents the individual and t represents time. u it and v it are random disturbance terms with zero mean and constant variance. Kao ( 1999 ) proposed to use DF or ADF statistics to test the stationarity of ε it . Specifically, the residual autoregressive equation applicable to the DF test is:

The residual autoregressive equation applicable to the ADF test is:

where p represents the selected optimal lag length. v it and v itp are random disturbance terms. The cointegration relationship between x it and y it is tested by the null hypothesis ρ  = 1 and the alternative hypothesis ρ  < 1.

Empirical framework of inverted U-shaped and N-shaped EKC

In the pioneer literature (Grossman and Krueger, 1991 ; 1995 ), the traditional EKC hypothesis was first proposed. This hypothesis holds that there is an inverted U-shaped relationship between environmental pollution levels and per capita GDP. Empirically, scholars use the following econometric models to describe and test the shape of EKC (Dinda, 2004 ):

where EP it represents the environmental pollution level of country i in year t, and pgdp it represents the per capita income level. α 0 theoretically represents other factors influencing environmental pollution levels, while are often simply treated as constants for the convenience of analysis. ε it is the random disturbance term. Researchers judge the shape of the EKC by the sign of α 1 and α 2 : a positive α 1 and a negative α 2 represent a U-shaped EKC, and a negative α 1 and a positive α 2 will yield an inverted U-shape one. If α 1 it is not significant or is 0, it means there is a linear relationship between per capita GDP and environmental pollution.

In the subsequent literature, scholars also considered a more complex N-shaped EKC, which can be expressed by the following equation (Lorente and Álvarez-Herranz, 2016 ):

It can be seen that the cubic term of pgdp is included in this equation. The new equations expand the possible shapes of the EKC. When α1 is nonzero, the curve is cubic; otherwise, it will be reduced to a quadratic curve. Specifically, researchers can follow Table 1 to determine the EKC shape.

Based on the determined shape, the inflection points of the curve can be computed. When GDP per capita passes through these points, the direction of change in environmental pollution will overturn, such as from negative to positive or from positive to negative. Among them, N and inverted N shapes belong to cubic curves, yielding two inflection points, respectively

U and inverted U shapes are quadratic curves with an only inflection point given by:

Two-dimensional Tapio decoupling model based on N-shaped EKC

The EKC hypothesis, whether it is an inverted U-shape or an N-shape, describes a one-to-one relationship between economic development and environmental pollution. However, researchers currently cannot find an EKC that is optimal for all countries (Kaika and Zervas, 2013b ). Therefore, in addition to EKC, other methods are also needed to determine the relationship between economic development and environmental pollution to verify and supplement EKC.

Understanding EKC from the perspective of Tapio decoupling coefficient

The Tapio decoupling coefficient exploits the relative elasticity between economic development and environmental pollution to illustrate the extent to which they change simultaneously (Tapio, 2005 ; Kaifeng Wang et al., 2021 ). The coefficient is computed by:

where E, EP, and Y represent the Tapio decoupling elasticity coefficient, environmental pollution level, and economic development level of country i in period t, respectively. ∆ indicates the change between periods t and t−1.

Different decoupling elasticities correspond to different relationships between economic development and environmental pollution. The larger the elasticity coefficient, the closer the connection between economic development and environmental pollution. Tapio ( 2005 ) divides the decoupling level into eight states according to the direction of change of ΔY and the size of the decoupling elasticity (as shown in Table 2 ). Economic growth (ΔY > 0) and economic recession (ΔY < 0) each correspond to four states, with decoupling elasticities of 0, 0.8, and 1.2 as the critical values.

EKC describes the nonlinear relationship between environmental pollution (EP) and economic development (Y) measured by GDP per capita. EKC can also be explained from the perspective of Tapio decoupling states (Kaifeng Wang et al., 2021 ). Specifically, before the inflection point in the inverted U-shaped EKC shown in Fig. 1 , as Y increases, the elasticity gradually diminishes from more than 1.2 to less than 0.8. It goes through the stages of expansion negative decoupling, expansion connection, and relative decoupling in sequence. However, when Y exceeds the inflection point y1, the elasticity will remain negative and maintain absolute decoupling. There is an additional inflection point y2 in N-shaped EKC as shown in the right graph of Fig. 1 . The situation before y2 is consistent with the inverted U-shaped EKC; however, after y2, economic growth “reconnects” with pollution after absolute decoupling. It can be seen that the decoupling coefficient changes to positive again and gradually increases and finally surpasses 1.2. Therefore, the N-shaped EKC ultimately includes seven decoupling stages, and these stages show a symmetrical change pattern. Certainly, as the economy recesses along the EKC curve, it may go through four other stages of decoupling (States 5–8), which, however, are beyond the scope of the EKC theory.

Tapio decoupling states corresponding to EKCs.

Two-dimensional decoupling model

The effectiveness of the change patterns described in the decoupling states discussed above depends on the effectiveness of EKC. However, EKC fails to function sometimes, especially when considering the possibility that some countries may be ahead or lagging in their development process (Kaifeng Wang et al., 2021 ). For example, although some countries have lower per capita GDP, economic development and environmental pollution can maintain an absolute decoupling for a long time. On the other hand, countries belonging to different development stages also probably have the same Tapio decoupling elasticity, see N-shaped EKC. Therefore, the relationship between economic development and environmental pollution of a country cannot be accurately judged solely by the absolute level of economic development or the state of decoupling.

A two-dimensional (2D) decoupling model is used to address the above issues (Kaifeng Wang et al., 2021 ). Based on the inflection point of EKC and the Tapio decoupling elasticity coefficient, the model constructs multiple quadrants in the two-dimensional plane coordinate system of Y-E (economic development-decoupling elasticity) to accommodate different economic development-environmental pollution relationships. Based on the shape of the EKC, the two-dimensional decoupling model can contain one inflection point (U-shape, inverted U-shape) or two inflection points (N-shape, inverted N-shape). Taking the 2D decoupling model based on N-shaped EKC as an example, two vertical lines y = y1 and y = y2 divide the horizontal axis into three areas. Different regions represent different stages of N-shaped EKC. In each area, according to E = 0 and E = 0.8, the vertical axis is further divided into three areas, corresponding to absolute decoupling, relative decoupling, and non-decoupling (including Expansive coupling and Expanding negative decoupling). Therefore, the entire plane is divided into nine areas, corresponding to nine 2D decoupling states (see Fig. 2 ).

Two-dimensional decoupling model based on N-shaped EKC.

This model relaxes the strict assumption in the EKC model that Y and EP are directly corresponding, and also overcomes the problem of Tapio decoupling that only considers the relative change relationship between the two. Through this division, we can clearly and accurately classify countries with similar economic and environmental development conditions into the same category to conduct systematic and targeted research. Considering that many countries lack data for individual years, to reduce statistical errors and random errors, we use the ten-year average as one period to classify the economic development and carbon emission levels of each country. Specifically, we divide countries into different EKC stages according to the average GDP per capita from 2010 to 2020. We also calculate the decoupling status using the average GDP per capita and carbon emissions per capita from 2000 to 2010 and 2010 to 2020 as data in period t-1 and period t, respectively.

Empirical model settings

To test H1 and investigate the shape of the global EKC curve, we first establish the following regression model based on the classic EKC empirical model corresponding to Eq. 10 :

Among them, PCE and PGDP are dependent variables and independent variables, respectively, representing per capita carbon emissions and per capita GDP.

We further add control variables to the above model to test H2 and H3:

where X represents the vector of control variables, including geopolitical risk, composite risk, institutional quality, ICT, digital economy, AI development, natural resource rents, energy transition, trade openness, population aging, income inequality, and food security. Based on the estimation results of this equation, we can determine the shape of the EKC based on the results of β1, β2, and β3 and discuss the influence of additional variables based on the results of γ .

Finally, to examine H4 and explore the determinants of carbon emissions in country groups at different two-dimensional decoupling stages, we first divided the full sample of 214 countries into several groups according to the two-dfimensional decoupling model, and carried out the following regressions in each group:

with X it consistent with the equation.

Variable definition and data description

According to the requirements of the methods and models discussed previously, this article uses per capita carbon emissions as the explained variable, per capita GDP as the explanatory variable, and 12 variables covering institutional risks, digital technology, resource utilization, and social issues as control variables. Based on data availability, the research scope of this article is determined to be 214 countries from 1960 to 2020. The definitions, measurement units, and data sources of all variables are summarized in Table S1 . It can be seen that the explanatory variables and the explained variables are all derived from the WDI database developed by the World Bank (Worldbank, 2023a ).

Control variables include 12 variables. Institutional risk includes three variables, among which geopolitical risk comes from the calculation work of (Caldara and Iacoviello, 2022 ). This database is updated every day and can comprehensively and effectively reflect the risk level of geopolitical threats and actions of various countries. Composite risk is derived from the ICGR published by PRS Group since 1980 (PRS, 2023 ), which takes into account domestic political, economic, and financial risks. Institutional quality is calculated by the average of six indices of the World Governance Index(WGI) released by the World Bank (Worldbank, 2023b ), which reflects the integrity and effectiveness of each country’s institutions.

Technology variables include ICT, digital economy, and artificial intelligence (AI). Following (Shufang Zhao et al., 2022 ), ICT is represented by mobile cellular subscriptions. The digital economy is calculated by the entropy weight method and represents the development level of a country’s digital technology and digital industry. The calculation process uses 12 secondary indicators from the International Telecommunication Union (ITU, 2023 ) and WDI, covering four aspects: infrastructure, social support, social effects, and economic effects, as shown in Table S2 . AI is represented by the number of industrial robots operational stocks which comes from the World Robotics Report released by the International Federation of Robotics (IFR, 2023 ).

Variables related to resource use include total natural resource rents and the level of energy transition represented by the share of renewable energy consumption. Finally, among the social variables, population aging is expressed as the proportion of the population over 65 years old. Food safety is derived from the food production index from WDI database. Trade openness is measured by a country’s trade volume as a share of its GDP. The GINI coefficient represents the level of income inequality. These variables are all from WDI. The descriptive statistics of the variables are shown in Table S3 .

Empirical results

Preliminary test, handling the multicollinearity problem.

First, we show the correlation coefficient matrix and VIF (see Fig. S1 ). The highest correlation coefficient reached 0.87, exceeding the safety line of 0.85, representing the need to analyze VIF to rule out the multicollinearity problem. The results show that the VIF of all variables is less than 10, which means that the multicollinearity problem between variables is not serious. Considering that the VIF of institutional quality is closer to 10 compared to other variables, we choose to add it in the last step of the stepwise regression to avoid potential interference.

Handling the pseudo-regression problem

To avoid pseudo-regression, we need to test the stationarity of the disturbance term, that is, test the cointegration relationship between variables. The cointegration test requires that each variable sequence is first-order integrated, and a unit root test needs to be performed on each variable first. Therefore, fisher-adf and fisher-pp tests are used to test the data stationarity. Both results of all variables in the two test methods are stationary in first order (see Table S4 ). Finally, the Kao test is used to test the cointegration (see Table S4 ). The results of Kao ADF statistics reject the null hypothesis that there is no cointegration relationship between panel variables at the 1% level. This indicates that the long-term stable relationship between variables exists and the pseudo-regression problem does not hold.

Selecting the estimation model

Fixed effects models (FE), two-way fixed effects models, and dynamic (system-/difference-) GMM models are among the most popular models in previous panel studies. The fixed effects model is one of the most commonly used models in research. It eliminates endogeneity problems caused by unobservable factors related to individuals through within-group estimators. Both two-way fixed effects and dynamic GMM improve FE by adding explanatory variables to the model: the former adds a vector of time dummy variables, while the latter includes the lagged term of the explained variable. However, this study has selected additional variables from various perspectives to accurately track the changes in the carbon emissions curve and open the black box of the determinants of carbon emissions, making the addition of more variables to the model redundant and potentially harmful (such as exacerbating multicollinearity problems). Therefore, we finally choose to adopt the basic FE models in our empirical process. Other models are used as robustness check methods.

Empirical results of global panel

Confirm n-shaped ekc without additional variables.

To test H1, we perform estimation and robustness testing on the regression equation without additional variables Eq. ( 16 ). The results are shown in Table 3 . First, the coefficient of the cubic term of per capita GDP (PGDP3) in Model 1 is significantly greater than 0 at the 1% level, indicating a cubic EKC. In addition, the coefficient of the quadratic term is significantly negative and the coefficient of the linear term is significantly positive. It shows that as PGDP gradually increases, per capita carbon emissions conform to the N-shaped trend. Including the above regression coefficients into Eq. ( 13 ), it can be calculated that the EKC inflection points without considering control variables are 34.44 and 77.54 (thousand US dollars/person), respectively. We can think of these as two thresholds. Initially, the increase in per capita GDP leads to an increase in per capita carbon emissions and reaches a peak when per capita GDP reaches 34.44. However, when PGDP gradually increases and exceeds this value, per capita carbon emissions will reverse and change from rising to falling. Finally, when per capita GDP further increased and reached 77.54, the trend of per capita carbon emissions changed upward again.

We also utilize four methods to check the robustness of N-shaped EKC, as shown in Model R1-R4 of Table 3 . First, change the environmental pollution indicator from per capita carbon emissions to total carbon emissions. Second, the fixed effects estimation model is replaced by an individual-time-two-way fixed effects model. Third, a random effects model will be used. Fourth, use the system GMM. First, the Sargan test p -value and AR(2) in Model R4 are greater than 0.1, indicating that the dynamic model estimation results are valid. All four models show that the coefficients of PGDP and its cubic term are significantly greater than 0, while the coeffecients of the quadratic term is significantly less than 0. In addition, the results of y1 and y2 show that there are two inflection points in the EKC corresponding to the four models, and they all fall within the sample interval. In conclusion, the global N-shaped EKC exists when control variables are not considered and H1 is confirmed. Farooq et al. ( 2022 ) took 185 countries around the world as a sample, confirmed the positive linear term and negative quadratic term of PGDP, and obtained the result of the inverted U-shaped EKC. However, our results show that the positive cubic term also exists robustly, so this paper resets the shape of the global EKC to an N-shape. From another perspective, this result generalizes the N-shaped EKC confirmed in Numan et al. ( 2022 ) in 85 countries to 191 countries.

Determine the EKC shape and inflection points under additional variables

Table 4 shows the results of adding control variables for testing H2 and H3. To reduce the impact of differences in sample sizes of different variables and alleviate the problem of multicollinearity, we adopt stepwise regressions Footnote 1 to display the results of adding covariates. Among them, Model 1 only uses PGDP and its quadratic and cubic terms as explanatory variables, and the R square is 0.122. Model 2-Model 6 gradually added 12 control variables, and the R square increased accordingly to 0.718 in Model 6. This shows that the addition of explanatory variables can accurately explain changes in carbon emissions. As control variables are continuously added to Models 2–6, the cubic term of PGDP is always significantly positive at the 1% statistical level. In addition, the sign and statistical level of PGDP and its quadratic term are also completely consistent with Model 1. This means that after adding control variables, the shape of the EKC is still a standard N-shape and H2 is confirmed.

After calculation, the two inflection points of the N-shaped EKC in Model 6 are 45.08 and 73.44 (thousand dollars per person), respectively. This result is different from the EKC inflection point in Model 1. We juxtapose the cubic equation curves corresponding to the estimated coefficients in Fig. 3 to discuss the influence of the control variables on the shape of the EKC. Figure 3 shows that after taking control variables into account, not only did the economic growth space between the two inflection points shrink from 43.10 thousand US dollars to 28.35 thousand US dollars, but also the reduction of per capita carbon emissions between the inflection points is compressed from about 2.70 metric tons to about 1.05 metric tons. This suggests that the EKC appears to be bent upwards when trying to account for other explanatory variables. As a result, the only declining part of the N-shaped carbon emissions curve has shrunk in both “duration” and “descending space,” revealing a more severe global climate mitigation situation.

Note: This graph is for comparison of curve shapes only and the position of the curve along the vertical axis does not represent the actual intercept.

Discuss the role of additional variables on carbon emissions

Institutional, technology, resources, and social factors not only affect the shape of EKC but also have an important impact on carbon emissions. This leads to the discussion of H3. Model 2 adds four control variables based on Model 1. Taking the 5% significance level as the standard, the impact coefficients of ICT and food security are positive, and the impact coefficients of digital economy and population aging are negative. We examine the robustness of the effects of these variables in conjunction with other results of the stepwise regressions. Comparing the estimation results of Model 2 with the subsequent four models (Model 3–6), it shows that the above results can be confirmed by at least two models. Specifically, the results of ICT are significantly positive in both Model 3 and Model 4; the positive coefficient of food security exists in Models 3–5; the negative coefficients of digital economy and population aging hold in all models. This shows that the estimation results of ICT, food security, digital economy, and population aging in Model 2 are reliable as decisions.

The estimators of the three factors added in Model 3 are all significant. Among them, the impact coefficient of natural resource rent is positive, and the impact of trade openness and energy transition is negative. However, results from subsequent models support the effects of energy transition rather than that of natural resource rents and trade openness. All models show that the impact coefficient of energy transition is significantly negative, while the effects of natural resource rents and trade openness are insignificant in two of the three subsequent models. Model 4 adds comprehensive risk. Combined with the estimation results of Model 4–6, the impact of comprehensive risk on carbon emissions is determined to be negative, which indicates that the higher the overall country risk level, the higher the corresponding carbon emissions. By comparing the results of Model 5 and Model 6, we determined that the effects of artificial intelligence and income inequality are not significant. Finally, the results of Model 6 show that geopolitical risks intensify carbon emissions, while improvements in institutional quality can reduce carbon emissions. These results show that H3 is partially confirmed.

We summarize the impact of each variable on carbon emissions in Table 5 to make further discussion. From the perspective of institutional risk factors, the rise in geopolitical risks and national comprehensive risks has significantly promoted carbon emissions. This is consistent with some recent research results on geopolitical risks (Anser et al., 2021 ) and political risks (Hassan et al., 2022 ), but is inconsistent with the research results on financial risks (Jun Zhao et al., 2021a ). Risks may cause panic and short-sightedness among governments and investors, which are often linked to irresponsible production models and misuse of fossil fuels (Vakulchuk et al., 2020 ; Zuoxiang Zhao et al., 2023 ). The reducing effect of institutional quality on global carbon emissions is consistent with the regulatory effect hypothesis and consistent with previous research for 3 Asian countries (Salman et al., 2019 ) and 30 Sub-Saharan African countries (Karim et al., 2022 ). Therefore, a stable international and domestic development environment and effective systems play an irreplaceable role in reducing carbon emissions and achieving global climate mitigation.

The results of technological factors are sobering. The digital economy can reduce carbon emissions, while the single ICT industry promotes carbon emissions. The two variables are conceptually similar, but why could they have completely opposite impacts on global carbon emissions? Despite a lack of discussion and explanation of this phenomenon in the literature, a recent study (Jinning Zhang et al., 2022a ) offers a good perspective. They discussed the impact of different dimensions of the digital economy on low-carbon development and found that industrial digitalization has the most significant impact on low-carbon development, followed by digital industrialization, with the carrier playing the smallest role. In other words, it is the integration of digital technology and the economy, that promotes emission reduction, not just the development of ICT technology and equipment (Jinning Zhang et al., 2022a ). The impact of AI is not significant, possibly because the effects of AI in different countries cancel each other out. Luan et al. ( 2022 ) found that industrial robots promoted air pollution in 74 countries while (Zhong et al., 2023 ) claimed that AI reduced carbon emissions in 66 countries. In summary, inconsistent with the views of technological rationalists, our results suggest that smart technological developments alone may not necessarily improve the environment. Alternatively, the rational use of technology to benefit the economy and society may be an efficient approach to achieving carbon emission reductions.

The results on resource utilization emphasize the urgency of clean energy development and deployment. On the one hand, the results confirm the significant reduction effect of the energy transition on carbon emissions. This shows that replacing existing energy infrastructure with one that relies on renewable energy can indeed significantly reduce carbon emissions per capita, which does not exceed the research conclusions of other scholars (Dogan and Seker, 2016 ; Inglesi-Lotz and Dogan, 2018 ). On the other hand, the impact of natural resource rents is uncertain, consistent with the controversy in the literature. The results also highlight the potential impact of social factors on carbon emissions. Population aging is a negative contributor to carbon emissions. This corresponds with (Zhou et al., 2023 ) while contradicting the findings of (Balsalobre-Lorente et al., 2021 ) and (Fan et al., 2021 ). Food security is a positive contributor to carbon emissions, supported by (Naseem et al., 2020 ). It shows that more attention needs to be paid to deforestation, land use, and high-carbon food consumption issues while ensuring food safety in global agricultural development. The effects of trade openness and income inequality are not significant.

Group 214 countries through two-dimensional decoupling model

Before testing H4, we first classify countries according to the development stage and emission reduction status to solve the heterogeneity problem that exists when testing the EKC hypothesis using the traditional panel method. Therefore, this paper first uses a two-dimensional decoupling model to achieve an accurate classification of countries based on their economic development levels and the carbon emissions decoupling states. The calculation and grouping process can be broken down into three steps: (1) Group countries according to the average per capita GDP in the past decade and the EKC inflection point. (2) Group countries according to Tapio decoupling elasticity between the past two decades and the critical values (0 and 0.8). (3) Match the results of the above two steps with the nine two-dimensional decoupling stages shown in Fig. 2 , and obtain the final grouping based on the two-dimensional decoupling model. The results are shown in Fig. 4 .

a Classification results based on EKC turning points. b Classfication results based on Tapio decoupling coefficients. c Final classification results of two-dimensional decoupling state.

According to Fig. 4a , the 214 sample countries are divided into three groups based on the N-shaped EKC inflection points estimated in the baseline regression results. The first phase of EKC is still the main theme of global development. More than 90% of countries are in this stage, with their average per capita GDP between 2010 and 2020 being less than US$45,080. There are 11 countries in the second stage. Their ten-year average per capita GDP exceeds 45,080 but is less than 73,438. These countries have relatively high levels of economic development, mainly including the United States, Australia, and some European countries. According to the EKC hypothesis, they have the best hope of achieving economic growth while reducing carbon emissions. Only 9 countries have a ten-year average per capita GDP higher than 73,439 and are in the third stage, almost all of which are located in Europe. These countries have reached extremely high levels of economic development, but they may be directly responsible for the second upslope of N-type EKC and the rebound in pollution levels.

Figure 4b shows that the number of countries in different decoupling states is relatively even and that there are regional aggregation characteristics. The number of countries with non-decoupling, weak decoupling, and strong decoupling is 57, 47, and 55, respectively, and 34 countries are suffering an economic recession. The results of non-decoupling countries are concerning. The results show that these countries with decoupling elasticity still higher than 0.8 in the past 20 years are mainly located in sub-Saharan Africa, Latin America, and the Middle East. These countries are characterized by economic development that relies heavily on the extraction or utilization of natural resources (Do, 2021 ). How to help them reduce carbon emissions is an important challenge for global climate mitigation. It can be seen that most of the weak decoupling countries are located in non-Middle East Asia. Many of these countries, such as India and China, have achieved a degree of industrialization and urbanization, thereby gradually decoupling their economies from carbon emissions. Strong decoupling countries are mainly distributed in North America, Europe, and Oceania, and have a high degree of overlap with the countries in the second and third stages of the EKC. Finally, some African countries, including Libya and the Central African Republic, are facing economic recession. Stabilizing the economy is the top priority for these countries.

Finally, we classify countries based on the two-dimensional decoupling model. The number of countries in each stage is shown in Fig. 4c . First, the results show that all countries in the sample whose GDP per capita is higher than the first inflection point of the EKC have achieved strong decoupling. In other words, among the six two-dimensional decoupling stages corresponding to the second and third stages of EKC (Stage 4–9), 9 countries are in Stage 6, and 3 countries are in Stage 7 Footnote 2 with no country in other stages. This means that from the perspective of the past 20 years, those countries with relatively advanced economic development have achieved a relatively ideal decoupling of economic development and carbon emissions. Among the three two-dimensional decoupling states (Stage 1–3) belonging to the first stage of EKC, there are 43, 47, and 57 countries in strong decoupling, weak decoupling, and non-decoupling, respectively, which is also not entirely consistent with the EKC theory. In the EKC theory, the economic development of countries whose per capita GDP is lower than the first turning point will be accompanied by environmental deterioration, but two-dimensional decoupling analysis shows that the relationship between economic development and environmental pollution in countries at this stage also has different conditions. In summary, we successfully classified countries of the same type through the two-dimensional decoupling model, so that we can conduct specific research on the determinants of carbon emissions in each country group.

Heterogeneous effects of all variables on carbon emissions

Based on the six divided country groups, we perform panel unit root tests and cointegration tests for each sample. The results show that at the 10% significance level, in each sample, the variables are first-order stationary and cointegrated (see Table S6 ). To discuss the heterogeneous impact of economic, institutional, technological, resource, and social factors on carbon emissions and examine H4, the regression results of six country groups are shown in Table 6 .

First, there is obvious heterogeneity in the impact of per capita GDP on carbon emissions. In the first stage of the EKC, according to the estimation results of Model 7 and Model 9, economic development in both non-decoupling and weak-decoupling countries is accompanied by much carbon emissions. In non-decoupling and weakly decoupling countries, for every US$1 increase in per capita GDP, per capita carbon emissions increase by an average of 0.2658 metric tons and 0.3806 metric tons, respectively. It may be puzzling at first that the impact coefficient of economic development on carbon emissions is more pronounced in weak decoupling countries than in non-decoupling countries. The reason can be that the economic development models of these countries, including most Asian developing countries, have been optimized to a certain extent in the past two decades, but their average environmental costs throughout the development process are still high (Rongrong Li et al., 2021 ). These countries need to make full use of the results of past economic development and strive to transform from weak decoupling to strong decoupling (Hanif et al., 2019 ). Model 9 shows that in the sample of strong decoupling in the first stage of EKC, the impact of per capita GDP on per capita carbon emissions is not significant. This is consistent with the decoupling theory, suggesting that they are not developing directly at a high ecological cost. Model 10 shows that in countries whose economic development has entered the second stage of EKC, for every US$1 increase in GDP per capita, carbon emissions per capita increase by 0.0855 metric tons. Considering the relatively small size of the coefficient, it can be considered that N-shaped EKC is supported, that is, economic development accompanied by less environmental pollution in the second phase of EKC. However, Model 11 shows that when economic development enters the third stage of EKC, economic growth and carbon emissions are reconnected. Compared to the second phase of EKC, carbon emissions per capita for every $1 increase in GDP per capita will double from 0.0855 metric tons to 0.1721 metric tons. This result confirmed the technical obsolescence effect (Jahanger et al., 2023 ). Finally, the carbon emission costs of economic development in countries that have faced economic recession in the past 20 years are also extremely high. These countries urgently need to stabilize their economies at controllable environmental costs.

From the perspective of institutional risk, the impact of geopolitical risk is not significant in most samples, indicating that geopolitical risk has not yet significantly affected carbon emissions in most countries around the world. However, in the non-decoupling sample in the first phase of the EKC, geopolitical risk significantly reduces carbon emissions. This may be related to conflicts and crises caused by the rich natural resources of these countries (Do, 2021 ). The negative relationship is supported by (Weijun Zhao et al., 2021b ) who asserted that geopolitical risks may inhibit investment, trade, and energy consumption to reduce carbon emissions. Each unit increase in the composite risk index can reduce per capita carbon emissions by 0.0123 metric tons and 0.0067 metric tons, respectively, in countries in Stage 2 and Stage 1 of two-dimensional decoupling, which is consistent with the full sample research result. The results for institutional quality are rich in heterogeneity. Its effect of reducing carbon emissions is only reflected in Stage 3 countries, and these non-decoupling countries need to strengthen the construction of the national institution. However, in Stage 1, Stage 6, and Stage 10, institutional quality increases carbon emissions. As (Le and Ozturk, 2020 ) pointed out, cumbersome regulations and bureaucracy may in turn delay the implementation of environmental protection actions.

It is interesting to analyze the impact of digital technology variables. The conclusion that DE, rather than ICT, can significantly reduce carbon emissions and achieve low-carbon development is true in Stage 1, Stage 6, and Stage 10, indicating that most countries should focus on the digitalization of industry and economy. However, this does not always hold, because in Stage 3 it is exactly the opposite: ICT reduces carbon emissions, while the digital economy promotes carbon emissions. Although these countries are in a weak decoupling state, the impact coefficient of PGDP on carbon emissions is the highest among all samples. The combination of a rough economic development model and digital technology may lead to excessive carbon emissions (Feng Dong et al., 2022a ). Therefore, these countries need to focus on the carbon footprint of the digital economic development model in the process of developing ICT. AI reduces emissions in Stage 3 countries while increasing emissions in Stage 2 countries, which is not significant in other groups, indicating that AI needs to be used in the economy and society with more caution.

In terms of resource use, the energy transition significantly reduced carbon emissions in all six samples according to Table 6 . However, this effect was most pronounced in countries in EKC stage 3, followed by EKC stage 2. Every 1% increase in the proportion of renewable energy will lead to a decrease in per capita carbon emissions of 0.4661 metric tons in the former and 0.1096 metric tons in the latter. The emission reduction effects of countries in the first stage of EKC are relatively weak, diminishing in countries with strong decoupling, weak decoupling, and non-decoupling. The distribution pattern of the influence coefficient shows that increased levels of economic development and decoupling lead to better energy transition outcomes. This result is supported by (Nguyen and Kakinaka, 2019 ), who conclude that in the low-income group, renewable energy development inhibits economic development and promotes carbon emissions; while in the high-income group, the opposite holds. On the contrary, natural resource rent promotes carbon emissions in Stage 2 and Stage 6 countries and has no significant impact on other countries.

Finally, among the four social factors, the impact of population aging is relatively consistent. It mainly affects Stage 2 and Stage 1 countries and brings about reductions in carbon emissions. The result of OPEN does not support the existence of the PHH because there is no obvious evidence that countries with better environmental regulations have reduced carbon emissions through openness. Honestly, trade openness brings more carbon emissions in most samples (Stage 1, 7, 10), and reduces carbon emissions only in Stage 2 samples. This means that frequent exchanges of goods and services may increase the logistics and transportation burdens of many countries (Rongrong Li et al., 2021 ). The effects of food security and income inequality are also heterogeneous and may have opposite effects in different countries. Specifically, food security reduces carbon emissions in Stage 1 countries but increases carbon emissions in Stage 2 and Stage 10 countries. Income inequality is a contributor to carbon emissions for Stage 1 and Stage 6 countries, but the opposite is true for Stage 2 countries. The above results highlight between-group heterogeneity in the direction and magnitude of carbon emissions effects for all variables, which establishes H4.

Differential carbon reduction strategies of countries in different two-dimensional decoupling stages

A preliminary discussion of the results of Table 6 shows that not only does the relationship between per capita GDP and carbon emissions exhibit nonlinear and heterogeneous rules, but the impact of the additional variables on carbon emissions also significantly varies in different types of countries. Therefore, countries in different two-dimensional decoupling states can adopt differentiated institutional, technological, resource, and social development strategies to achieve carbon emission reductions. We summarize the positive and negative drivers of carbon emissions in Fig. 5 according to the two-dimensional decoupling stage.

Note: The light red box on the left means positive drivers and the light blue box on the right means negative drivers. The red, dark blue, orange, and light blue ovals mean institutional risks, digital technology, natural resource utilization, and social factors, respectively.

Stage 1 countries belong to the strong decoupling state of the first stage of the EKC. This is the only group of countries where the impact of economic growth on carbon emissions is not significant, meaning that the past development models of these countries did not come at the expense of environmental quality. However, the results in Fig. 5 indicate that in addition to economic development, many other factors significantly affect environmental quality and therefore require special attention. First, in terms of institutions, institutional quality promotes carbon emissions, while the comprehensive country risk index reduces carbon emissions. The policy implication is that these countries should improve the construction and implementation of environmental regulations while striving to stabilize the domestic economic, political, and financial environment (Karim et al., 2022 ). In terms of digital technology, ICT promotes carbon emissions, while digital technology reduces carbon emissions. It shows that these countries should pay attention to the carbon footprint issue of the ICT industry and encourage the penetration of ICT into other economic sectors to form a broader and deeper digital economic industry. In terms of resource utilization, the emission reduction effect of the energy transition is significant. Considering that the magnitude of the effect is not ideal, countries currently in Stage 1 should prioritize economic development and gradually deploy the production and utilization of clean energy in conjunction with economic development. In terms of social management, income inequality, and trade openness bring more carbon emissions, while population aging and food security reduce carbon emissions. It seems that, on the one hand, these countries should introduce fiscal and agricultural production policies to promote fair distribution of wealth and improve food production security (Akbar et al., 2019 ). On the other hand, they need to introduce more green products and technologies in the process of international trade or investment promotion (Qiang Wang et al., 2023b ).

For Stage 2 countries, they are in a state of relative decoupling, between non-decoupling and complete decoupling. It shows that these countries have certain methods to reduce pollution in development, but there is still a lot of room for emission reduction. These countries need to comprehensively understand the positive and negative factors affecting carbon emissions to achieve a transformation from relative decoupling to absolute decoupling. In particular, the results of this study indicate that AI, natural resource rents, and food security are contributing to per capita carbon emissions. Therefore, strengthening environmental regulations and introducing normative and environmentally friendly technologies in artificial intelligence technology, natural resource extraction, and food production processes may be the key to achieving strong decoupling. Disincentives to carbon emissions suggest that maintaining national stability, energy transition and trade openness are also important. Countries in Stage 3 are non-decoupling. Their economic development is accompanied by high carbon emissions, so the most important thing is to solve the problem of rough development. In addition, the control variables still provide emission reduction options from institutional, technical, and natural resource perspectives. For them, the positive driver of carbon emissions is primarily the digital economy. In comparison, the development of ICT and AI technologies cannot curb carbon emissions. This further emphasizes the need to decouple economic development and carbon emissions. In addition, rising levels of geopolitical risks, institutional quality, and energy transition can also reduce carbon emissions.

Stage 6 countries have a relatively high level of development and have entered the second stage of EKC and achieved strong decoupling. But they can also pursue better environmental outcomes. Based on the negative drivers of carbon emissions, these countries should focus on building environmental institutions, limiting the use of traditional natural resources, and optimizing income structures. In line with the positive drivers of carbon emissions, further development of the digital economy and energy transition has an important role in improving environmental quality.

For Stage 7 countries, their economic development level is higher than that of Stage 6, but their economic development and carbon emissions have been reconnected. Due to the limited sample, the results of most factors are not significant. However, our results still point to two emission reduction ideas. On the one hand, the effects of energy transformation in these countries are far better than in other countries, and clean energy should be further developed and deployed in these countries; on the other hand, trade openness is positively related to carbon emissions, and they should more strictly scrutinize imported goods or foreign investment. While Stage 10 countries are facing economic recession, economic development has a high coefficient of impact on carbon emissions. These countries not only need to stabilize their economy but are also supposed to find ways to reduce environmental costs. The results indicate that they need to establish more robust environmental regulations and address carbon emissions issues in ICT, food production, and trade openness. In addition, the digital economy and energy transition can be seen as further developments in green industries.

Concluding remarks

Early research provided a solid theoretical foundation and empirical evidence for the inverted U-shaped EKC. However, with the increasing urgency of mitigating climate change, we have observed that many developed countries are struggling with environmental degradation, high energy consumption, and carbon emissions. This forces us to start considering N-shaped global EKC. In addition, the impact of risks, digital technology, energy transition, and various social factors on social activities, economic development, and energy use are also proven to be significant contributors to carbon emissions. Against this background, we included as many countries as possible in the study, re-examined the shape of the EKC in the global panel, and incorporated 12 traditional and novel institutional, technological, resource, and social factors as additional variables into the EKC equation. In addition, we developed a two-dimensional Tapio decoupling model based on the inflection point in N-shaped EKC to achieve group discussion of sample countries. Finally, based on grouping, we discussed the heterogeneous impact of all variables and the differential emission reduction solutions of each group between groups and obtained a series of conclusions.

First, in the global panel, N-shaped EKC exists robustly regardless of whether additional variables are taken into account. In other words, the cubic and linear terms of GDP per capita are significantly positive, while the quadratic term is significantly negative. Among them, when additional variables are not considered, the N-shape has passed four robustness tests. When considering additional variables, this N-shaped EKC also holds at each step of the stepwise regression. Thus H1 and H2 are completely confirmed. After determining the existence of the N-shape, we finally calculated the inflection points of EKC including all control variables, which were 45.08 and 73.44 (thousand dollars/person), respectively. By comparing the final N-shaped EKC curve with the N-shaped curve without adding control variables, we found that both the “duration” and “dropping space” of the only declining part in the curve have shrunk, which makes global climate mitigation even more severe. Through mutual verification of each step of the stepwise regression, we finally determined the direction of influence of all additional variables. Among them, geopolitical risks, ICT, and food security were confirmed to have a positive impact on per capita carbon emissions. However, comprehensive risks, institutional quality, digital economy, energy transition, and population aging were found to have a robust negative impact. Artificial intelligence, natural resource rents, trade openness, and income inequality have insignificant effects on carbon emissions. These results partially confirmed H3.

In the group study, we first used the per capita GDP of 45.08 and 73.44 (thousand US dollars per person) obtained in the EKC as the basis for classification, and initially divided all countries into three EKC stages. As a result, 194 countries belong to the first stage, 11 countries belong to the second stage, and 9 countries belong to the third stage. Secondly, we calculated the Tapio decoupling elasticity coefficient between the past two decades for all countries and further divided the countries into three states using 0 and 0.8 as thresholds. The results show that there are 57, 47, 55, and 34 countries in non-decoupling, weak decoupling, strong decoupling, and recession, respectively. Based on this, we established two-dimensional decoupling coordinates and combined the three states of each of the above two dimensions with each other to obtain nine two-dimensional decoupling states. We divide the sample into five groups by matching the non-recession sample results to the nine decoupling conditions. Among them, there are 43, 47, 57, 9, and 3 countries in Stages 1, 2, 3, 6, and 7, respectively. Adding the declining countries (Stage 10) we get 6 panels. We also conduct multiple linear regressions in these panels and discuss the heterogeneous effects of economic development and 12 additional variables on carbon emissions. The results completely validate H4 and show that the effects of most variables vary according to country conditions. The most robust variable is the energy transition, which shows a significant carbon reduction effect in all groupings. However, the magnitude of the impact of energy transition is also heterogeneous. In countries with higher levels of economic development and decoupling, the effects of energy transition are stronger. Finally, we discuss the differential emission reduction plans of countries in different two-dimensional decoupling stages based on the direction of influence of variables.

The above findings have valuable policy implications. First of all, global results show that the N-shaped EKC is more severe than the inverted U-shaped EKC. The empirical validation of technological obsolescence underscores the imperative for developed nations worldwide to bolster their focus on internal environmental regulations and elevate levels of innovation in clean technologies. Furthermore, the inclusion of additional variables in our analysis underscores the pivotal role played by a stable international and domestic developmental milieu alongside effective institutional frameworks in curbing carbon emissions and advancing global climate mitigation efforts. The uncritical pursuit of purely digital or smart technological advancements does not inherently translate into environmental amelioration. Conversely, a judicious harnessing of technology for the betterment of both the economy and society presents a promising avenue for achieving reductions in carbon emissions. Moreover, our findings on resource utilization underscore the pressing need for accelerated development and adoption of clean energy sources. Addressing social determinants, particular attention is warranted towards optimizing land usage and curtailing the prevalence of high-carbon footprint foods within agricultural production. Finally, specific emission reduction ideas for each country have also been discussed in depth in the section “Differential carbon reduction strategies of countries in different two-dimensional decoupling stages”.

We need to point out the limitations of this article to provide ideas for improvement. First, due to non-uniform data sources, our empirical process uses an unbalanced panel. In some steps, the effective sample does not cover all 214 countries. This may ignore information from certain countries. Second, we include some important or novel additional variables to comprehensively discuss the determinants of carbon emissions other than growth. However, we cannot consider all factors. Second, we developed Tapio two-dimensional decoupling based on N-shape to divide countries into 9 stages to fully consider the heterogeneity between countries. However, this classification also fails to fully handle heterogeneity. Our overall study is not a substitute for single-country and sub-national case studies. Therefore, subsequent scholars have conducted more in-depth research from the perspective of improving the unity of data sources, providing more novel and critical additional variables, and conducting more refined case studies.

Data availability

The datasets publicly available should be through https://doi.org/10.7910/DVN/0I1EYG .

When determining the order in which variables are added, we consider the data characteristics of the variables, particularly the data volume, so that as many countries as possible are included in each step.

The number of countries belonging to the second and third stages of EKC in two-dimensional decoupling in Fig. 4c is smaller than that in Fig. 4a . This is because some countries only have per capita GDP data but do not have enough carbon emission data in the past two decades.

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This work is supported by the National Natural Science Foundation of China (Grant Nos. 72104246, 71874203).

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Wang, Q., Li, Y. & Li, R. Rethinking the environmental Kuznets curve hypothesis across 214 countries: the impacts of 12 economic, institutional, technological, resource, and social factors. Humanit Soc Sci Commun 11 , 292 (2024). https://doi.org/10.1057/s41599-024-02736-9

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Revisiting the environmental Kuznets curve and pollution haven hypotheses: MIKTA sample

Affiliations.

• 1 Department of Economics, Aksaray University, Adana Yolu, Kampus, 68100, Aksaray, Turkey. [email protected].
• 2 Department of Economics, Aksaray University, Adana Yolu, Kampus, 68100, Aksaray, Turkey.
• PMID: 28639013
• DOI: 10.1007/s11356-017-9462-y

This study aims to examine the validity of the environmental Kuznets curve (EKC) and pollution haven hypotheses in Mexico, Indonesia, South Korea, Turkey, and Australia (MIKTA) countries from 1982 to 2011 by using a panel vector auto regressive (PVAR) model. Empirical findings imply that the EKC hypothesis is rejected by the MIKTA sample. However, PVAR estimations reveal Granger causality from income level, foreign direct investment (FDI) inward, and energy consumption to CO 2 emissions. Orthogonalized impulse-response functions are derived from PVAR estimations. According to the analysis results, the response of CO 2 emissions to a shock on FDI is positive. These results assert that FDI has a detrimental effect on environmental quality in MIKTA countries which means the pollution haven hypothesis is confirmed by the MIKTA sample. Therefore, MIKTA countries should revise their current economic growth plans to provide sustainable development and also re-organize their legal infrastructure to induce usage of renewable energy sources.

Keywords: Carbon dioxide emissions; Economic growth; Environmental Kuznets curve; Foreign direct investment; MIKTA; Panel VAR; Pollution haven hypothesis.

• Conservation of Natural Resources / economics*
• Environmental Pollution / economics*
• Investments / economics*
• Models, Econometric*
• Republic of Korea

Reducing environmental pollution: what affects the waste sorting of Chinese urban residents? The theory of planned behavior with community convenience

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• Dehua Zhang 1 ,
• Jiawen Li 1 &
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The increasing amount of household waste has caused harmful environmental pollution and land occupation problems. Implementing separate waste collection is considered an effective waste management method. This study used structural equation modelling (SEM) to investigate the garbage separation behaviour of 657 residents in Beijing. This study investigates the intention of residents to categorise behaviour from a psychological perspective, and previous studies on individual behaviour have overlooked the influence of their environment. Combining external factors of community convenience with the theory of planned behaviour (TPB) enriches research on environmental behaviour. The research results indicate that residents’ attitudes, subjective norms, and perceived behavioral control have a positive and substantial impact on their willingness to participate in garbage classification behaviour. Community convenience as an exogenous variable plays an important role in directly and indirectly influencing personal garbage classification tendencies. The new model constructed has stronger explanatory power than the original model. The study also provides some practical suggestions, such as strengthening the dissemination of classification knowledge, improving residents’ environmental awareness, increasing investment in classification facilities by the government and community, reducing the perceived difficulty of classification behaviour by residents, and creating a convenient atmosphere for garbage recycling and utilisation.

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This work was supported by Heilongjiang Province Philosophy and Social Science Fund Project (21JYE394); Heilongjiang Province Philosophy and Social Science Fund Project (21JYD272); “Youth Research and Innovation Talents” Cultivation Plan of Harbin University of Commerce (2023-KYYWF-1044); Graduate Innovation Project of Harbin University of Commerce (YJSCX2023-770HSD); Graduate Innovation Project of Harbin University of Commerce (YJSCX2022-761HSD); National Social Science Foundation Major Project of China (23&ZD069).

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Zhang, D., Li, J. & Lou, S. Reducing environmental pollution: what affects the waste sorting of Chinese urban residents? The theory of planned behavior with community convenience. J Mater Cycles Waste Manag (2024). https://doi.org/10.1007/s10163-024-01943-5

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New hypothesis implicates environmental chemicals in Parkinson’s cause

Imma Perfetto

Imma Perfetto is a science journalist at Cosmos. She has a Bachelor of Science with Honours in Science Communication from the University of Adelaide.

A new theory has been proposed for the cause of Parkinson’s disease, which affects millions of people worldwide and its growth sees no signs of slowing down.

Parkinson’s disease (PD) is a progressive neurodegenerative disease estimated to effect more than 8.5 million individuals in 2019 though the exact causes remain uncertain.

Symptoms of PD include motor symptoms – such as tremor, involuntary movement, imbalance, and rigidity – and non-motor symptoms such as cognitive impairment, mental health and sleep disorders, and dementia.

Recent theories around the causes suggest the disease is the result of processes that start in either the brain’s olfactory system (brain-first) or the body’s intestinal tract (body-first).

Now a new idea – which has yet to be tested – proposes environmental exposure to toxicants may cause either body-first or brain-first PD.

The article outlining the theory has been printed as an ‘hypothesis paper’ in the Journal of Parkinson’s Disease, to coincide with World Parkinson Day (April 11). The hypotheses in the paper are based on a review of the available scientific evidence and have not been tested yet.

“In both the brain-first and body-first scenarios the pathology arises in structures in the body closely connected to the outside world,” says Ray Dorsey, a professor of neurology at the University of Rochester Medical Center in the US and co-author of the paper.

“Here we propose that Parkinson’s is a systemic disease and that its initial roots likely begin in the nose and in the gut and are tied to environmental factors increasingly recognised as major contributors, if not causes, of the disease.

“This further reinforces the idea that Parkinson’s , the world’s fastest growing brain disease, may be fuelled by toxicants and is therefore largely preventable.”  

Robotic exosuit improves walking for person with Parkinson’s disease

Inside the brains of people with PD, a misfolded protein called alpha-synuclein accumulates in clumps called Lewy bodies. These cause progressive dysfunction and death of many types of nerve cells, including ones in the dopamine-producing regions of the brain that control motor function. 

Dorsey and collaborators reviewed the evidence and suggests the industrial chemicals trichloroethylene (TCE) and perchloroethylene (PCE), the herbicide paraquat, and air pollution, could be common causes for the formation of toxic alpha-synuclein. 

They propose the toxicants’ different routes of entry into the body result in different forms of the disease.

In the brain-first model, they propose chemicals are inhaled through the nose and enter the brain via olfactory nerves. From the brain’s olfactory bulb the alpha-synuclein spreads to other areas, principally on one side of the brain.

In the body-first model they propose chemicals are ingested and pass through the lining of the gastrointestinal tract where they may enter the brain via the vagus nerve. In this sub-type of PD alpha-synuclein can spread to both sides of the brain and spinal cord.

“These environmental toxicants are widespread and not everyone has Parkinson’s disease,” says Dorsey.

“The timing, dose, and duration of exposure and interactions with genetic and other environmental factors are probably key to determining who ultimately develops Parkinson’s.

“In most instances, these exposures likely occurred years or decades before symptoms develop.” 

They outline several testable hypotheses to be explored in future research, including investigating whether theprevalence of Lewy body disorders will vary based on the exposure to environmental toxicants in a given region, and whether animal models will demonstrate a body-first pathological spread for ingested toxicants and a brain-first for inhaled ones.

In addition to the effects of ongoing exposures, the authors say the proposed link also leaves many questions unanswered – such as the role of the skin and the influence of the microbiome.

“Despite these limitations,” they write, “the interaction of exogenous factors with the nose and the gut may explain many of the mysteries of Parkinson’s disease and open the door toward the ultimate goal – prevention.”

Originally published by Cosmos as New hypothesis implicates environmental chemicals in Parkinson’s cause

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An overview of the environmental pollution and health effects associated with waste landfilling and open dumping

Ayesha siddiqua.

2 Department of Environmental and Biological Sciences, College of Arts and Science, Qatar University, P.O. Box 2713, Doha, Qatar

John N. Hahladakis

1 Waste Management (FEWS) Program, Center for Sustainable Development, College of Arts and Science, Qatar University, P.O. Box 2713, Doha, Qatar

Wadha Ahmed K A Al-Attiya

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Landfilling is one of the most common waste management methods employed in all countries alike, irrespective of their developmental status. The most commonly used types of landfills are (a) municipal solid waste landfill, (b) industrial waste landfill, and (c) hazardous waste landfill. There is, also, an emerging landfill type called “green waste landfill” that is, occasionally, being used. Most landfills, including those discussed in this review article, are controlled and engineered establishments, wherein the waste ought to abide with certain regulations regarding their quality and quantity. However, illegal and uncontrolled “landfills” (mostly known as open dumpsites) are, unfortunately, prevalent in many developing countries. Due to the widespread use of landfilling, even as of today, it is imperative to examine any environmental- and/or health-related issues that have emerged. The present study seeks to determine the environmental pollution and health effects associated with waste landfilling by adopting a desk review design. It is revealed that landfilling is associated with various environmental pollution problems, namely, (a) underground water pollution due to the leaching of organic, inorganic, and various other substances of concern (SoC) contained in the waste, (b) air pollution due to suspension of particles, (c) odor pollution from the deposition of municipal solid waste (MSW), and (d) even marine pollution from any potential run-offs. Furthermore, health impacts may occur through the pollution of the underground water and the emissions of gases, leading to carcinogenic and non-carcinogenic effects of the exposed population living in their vicinity.

Graphical abstract

Introduction

Environmental pollution has inherently been associated with health issues including the spread of diseases, i.e., typhoid and cholera, some of which are largely seen as waterborne diseases (Zhao et al. 2015 ). There are also non-communicable diseases (NCDs) that are brought about due to environmental pollution, such as cancer and asthma, or several defects evident at birth among infants (Reinhart and Townsend 2018 ). The significant adverse effects of environmental pollution on health-related outcomes have largely been evidenced in low-income countries, where an estimated 90% of the deaths are, in fact, caused by that type of pollution. The two most established forms of pollution in low-income countries are those of air and water. This is contrary to the economies that are rapidly developing, where the toxicity of chemicals and pesticides constitutes the main forms of environmental pollution (Xu et al. 2018 ).

Several human activities that include, among others, technological applications to change the ecosystems may, also, result in environmental pollution (Nadal et al. 2016 ). Other forms of pollution may be energy oriented, e.g., light, heat, sound, or several other chemical substances of concern (SoC). The pollutants can either be foreign energies/substances or contaminants that occur naturally (Gworek et al. 2016 ).

The urbanization and industrialization growth around the world has resulted into introduction of several SoC into the air, hence bringing about the respective type of pollution. It is through the earth’s atmosphere that life on our planet is fully supported (Duan et al. 2015 ).

Yang et al. ( 2018 ) identified five classes of pollutants: particulates, sulfur oxides, nitrogen oxides (NOx), hydrocarbons, and carbon monoxide (CO). In their study, they reported that in cities and centers, like Karachi and Islamabad, the leading air pollutants included carbon emissions and lead (Pb) (Yang et al. 2018 ). On the other hand, several types of water pollution exist, resulting in waterborne diseases (Joshi et al. 2016 ). Some of these waterborne diseases include typhoid, amoebiasis, and ascariasis. Various elements, depending on the concentration they occur, are considered toxic to humans. Therefore, if such an element is released in the air, water, or land, it can result into health complications/issues.

The different types of pollutants can be classified into inorganic, organic, or biological. Organic pollutants include the domestic, agricultural, and industrial waste that adversely harm the life and health of animals and human beings living on the earth. Inorganic pollutants mostly include the potentially toxic elements (PTEs), like mercury (Hg), lead (Pb), and cadmium (Cd). Most of these SoC get accumulated within supply chains, thereby largely harming the earth living organisms (Majolagbe et al. 2017 ). There are, also, biological pollutants that are anthropogenic derived. The key types of biological pollutants within the environment include viruses, bacteria, and/or several forms of pathogens (Marfe and Di Stefano 2016 ).

PTEs are regarded as one of the most important environmental pollutants, mainly due to their non-degradability, high persistence, and toxicity (Hahladakis et al. 2013 , 2016 ). In their simplest form, PTEs occur naturally, and they have high atomic weight and density as compared to the one that water has. Of all the pollutants, greater attention has been given to PTEs (Mazza et al. 2015 ). Usually, these PTEs are present in trace levels in the naturally produced water, but the key challenge is that some of these PTEs are equally toxic even at low concentration levels. Some of these metals like zinc (Zn), cobalt (Co), Hg, Cd, and Pb and the metalloid arsenic (As) have high toxicity even when present in traces. When the body metabolizes these PTEs, they become toxic, being accumulated on soft tissues. There are various avenues through which these PTEs can gain access to human bodies, for instance, through absorption via the skin, food, and air, as well as water (Damigos et al. 2016 ).

There are various adverse environmental effects related with the PTEs. The majority of the PTEs are non-biodegradable and thus cannot go through degradation either chemically or microbially. Hence, their long-term influence is released via the ground and through the soil. At the same time, the PTEs can slowly find their way through drinking water which enters the human body. Reportedly, the contamination of water by PTEs has significant influence on all forms of animals (Annamalai 2015 ).

Toxic chemicals have emerged as a critical source of pollution all over the world. Their situation as environmental pollutants has largely been demonstrated and underpinned among low-income countries, where poor or inappropriate environmental controls take place. Common examples of toxic chemicals being major pollutants include any exposure to PTEs, e.g., Pb and Hg. Of the entire population across the planet, children are the most affected people when it comes to environmental pollution since any particle getting through their system may potentially results in long-term disabilities, as well as premature deaths (Kumar et al. 2017 ).

In an effort to prevent the aforementioned forms of environmental pollution, most countries have devised ways of preventing or minimizing any occurring impacts through proper disposal and/or burying of waste. Two ways are the most commonly applied: open dumping and/or landfilling. A dump is considered as an opening on the ground that is used for burying trash (Gavrilescu et al. 2015 ). On the other hand, a landfill is seen as a structure properly designed and built into or on the top of the ground. It is through a landfill that the necessary isolation of waste from the surrounding occurs. A controlled landfill ensures that waste is buried in an engineered manner, isolated from the ground water, while mostly maintaining the waste in a dry form (Indelicato et al. 2017b ).

The rationale for the increased use of landfills is the environmental protection and prevention of pollutants entering the soil and, in turn, the underground water. This is obtained via a two way procedure: (a) application of a clay liner to ensure waste does not leave the landfill (sanitary landfills) and (b) application of synthetic liners, including plastic, to ensure that the landfilled waste is separated from the land (municipal landfill) (Mmereki et al. 2016 ). Although landfilling is structured with the aim of reducing waste, it may affect the three types of media previously identified and usually polluted (land, air, and water). After the waste is disposed in landfills, they are compacted to fill the entire area before being buried (Joshi et al. 2017 ). The rationale for this is to ensure that it will not come into contact with the environment. It, also, ensures that the waste is kept as dry as possible, limiting its contact with air so that it does not easily rot. It has been estimated that about 55% of the waste generated in the USA in 2008 was landfilled (US EPA 2008 ). Due to its widespread use, it is important to examine environmental pollution and health issues related with the landfills that have emerged across the world presently (Domingo et al. 2015 ).

Methodology

The present study will adopt a desk review methodology. Przydatek and Kanownik ( 2019 ) define desk study as the collection of information from available sources, and it is one of the low-cost techniques, compared to field work (Przydatek and Kanownik 2019 ). During desk review, the study scans the available body of literature, carries out an analysis of the secondary data in place, and establishes a reference list at the end of the information/data collected. This helps in ensuring that the produced document is well organized and presented in a manner that is easily accessible.

Various scientific databases have been searched for this purpose, such as ResearchGate, ScienceDirect, eNature, JSTOR, LiveScience, Google Scholar, and Scopus. Different terms have been used in the search field areas, like “Water landfilling” AND “Health impacts” OR “Uncontrolled filling” AND “environment” “Health impacts” OR “Opened dump sites” AND “Health” OR “Landfills” OR “Pollution” OR “Dumpsite” “Environmental issues” OR “Health issues” OR “Waste management.” The produced results were narrowed down to include the last 10 years of publication from 2010 to 2020 to have an updated and critical review. The selected articles included both research and review articles. Upon this selection, the final results were then scanned for relevance to the review by previewing the abstracts and the titles. The relevant articles were then downloaded and reviewed thoroughly.

In the present review article, the delivered information will be organized under the following themes and sections: the third section, “Waste landfilling”; the fourth section, “Waste landfilling and environmental pollution”; and the fifth section, “Waste landfilling and human health risks.”

Waste landfilling

A landfill is an engineered pit, particularly designed for receiving compacted solid waste and equipped with specific covering, so that the waste can be disposed of. There is a lining at the bottom of the landfill so to ensure that the waste does not pollute underground water (see Fig. ​ Fig.1). 1 ). The design of landfills is such that they accept concentrated wastes in compacted layers so as to lower the volume.

Typical layout of a waste landfill. (Redrawn from source: available at http://ocw.jhsph.edu )

The bottom of a landfill is protected to ensure that underground water is not contaminated. In essence, the deposited waste should be covered by soil at the end of each day. This will ensure that animals and flies are not able to dig up the waste. It also prevents undesired odors to get in the air and pollute the environment. In advanced — engineered — landfills, the bottom comprises of liner systems on the sides; there is also a leachate system and an underground monitoring system, as well as a gas extraction system. The gas extracted from landfills is used for energy production. There are, also, landfills possessing anaerobic or aerobic bioreactors: these help in accelerating the process of decomposition of organic waste within the landfill. The overall system provides, also, a conducive environment for microorganisms to decompose the existing waste.

The construction of landfills nearby residential areas is usually associated with effects like the accumulation of CH 4 gases and contamination of underground water, as well as destruction of properties. This is particularly evident when landfills are not well engineered and/or maintained in a decent operational state; in such cases, there might be some leakages within the underground water, adversely affecting the life of the adjacent residents. In such a situation, people might need to consider relocating. In rural areas, most of the landfills are closed and small in size that rarely affect the quality of living; however, there might influence the value of the nearby properties.

Types of waste landfills

The most commonly used types of landfills are (a) municipal solid waste landfills, (b) industrial waste landfills, and (c) hazardous waste landfills. There is, also, an emerging landfill type called “green waste landfill” that is, occasionally, being used. All the aforementioned types should, above all, be sanitary. So, before analyzing each independent type separately, it is considered necessary to elaborate and describe the “sanitary” term and present the main characteristics of a sanitary landfill.

Sanitary landfills

A sanitary landfill is simply a pit whose bottom is protected with a lining so that waste and other forms of trash are buried in layers, thus making it more solid/stable. It is at the sanitary landfills that waste is isolated from the environment in such a way that it is rendered safe. The waste is only considered to be safe after it has undergone complete biological, chemical, and physical degradation. The degree waste isolation within the sanitary landfills differs on the basis of the classification of the economies. For instance, in high-income economies, the degree of isolation is deemed to be very high (Ziraba et al. 2016 ).

The key role in the sanitary landfill is to ensure that all waste is placed in as safe as possible manner. It, also, facilitates safe decomposition of waste with the layers playing an important role in speeding up the process. The CH 4 gas produced by the decomposition of the landfilled waste is harnessed and used to generate energy. Furthermore, the existing clay layer within the sanitary landfills ensures waste isolation from the environment (Rahmat et al. 2017 ). In addition, various designs and engineering methods are implemented since this is considered an important step in ensuring that there is no environmental contamination from the solid waste disposed in the sanitary landfills. In the event that the land used for the purpose of landfilling is filled up, impervious clay is used for sealing it and rendering it safe, so that the area can be further used for other activities (Qasim and Chiang 2017 ).

As earlier indicated, sanitary landfills largely operate by ensuring that waste is layered in large holes. There are various levels of layering that facilitate the entire process of waste decomposition, besides trapping the released toxic gases. The structure of these layers is such that the bottom part carries the smallest volume of waste, whereas the top part should bear the largest one. This is important to ensure that the surrounding land area does not collapse.

There are four specific layers within the sanitary landfills that play an important role in the entire process of the waste decomposition. The first layer is the one found at the bottom, which acts as the foundation of the sanitary landfill. This layer is made of dense and compact clay so that there is no waste seepage and thus no environmental (underground) pollution. It is on the basis of this reason that the clay used within the sanitary landfills is regarded as impervious (Rajaeifar et al. 2015 ).

The second layer is the drainage system. This layer protects the landfill from any decomposing that any waste oriented liquids could cause. Since this liquid is regarded as highly toxic, any seepage past the liner layer should be prevented. The role of the drainage system is to drain away the toxic liquids so that it does not get close to the liner system. At the same time, rainfall as well as snow may also create liquids that need to be drained out by this layer. Most of these liquids may contain contaminants that could result into corrosion of the liner system and/or contaminate the soil. In order to reduce these risks, the upper part of the landfills has perforated pipes on the greater part of the liner system. These pipes help to collect the liquids that may access the bottom of the landfills via leaching, hence the name leachates. This leachate is then directed to treatment plants via a plumbing system where it is treated for being reused (Adamcová et al. 2017 ).

The gas collection system constitutes the third layer of the sanitary landfills. Just as the way the liquids are produced within the landfills, gases are, also, naturally produced. One of these gases is CH 4 . CH 4 is toxic, as well as volatile; thereby, its release to the atmosphere could significantly contribute to the global warming effect. To prevent this from happening, extraction pipes are used to ensure the CH 4 gas is trapped and then transported to the plants for treatment and/or for generation of electricity.

Finally, the fourth layer is used to store the waste. This is the top and largest layer, used to store the waste collected by various companies. To minimize the space needed, the waste is compacted on a daily basis. At the end of this compaction process, a layer of compacted soil is applied on the surface of the sanitary landfill, so as to reduce any odors and the growth of microorganisms that are harmful, e.g., flies and pests.

Generally, sanitary landfills are designed to extend as deep as hundreds of feet, and it can take up to several years before being fully filled, after the compaction process. In the event that they are filled up, a capping is applied. In that case, a clay or plastic layer that is synthetic is introduced in the same manner as at the bottom. This is done to ensure that CH 4 gas does not escape to the atmosphere and to prevent undesirable odors. At the same time, the top layers are firmly reinforced with an approximately 2–3 feet soil layer, and then plants are planted. In turn, this land may be reclaimed and used for other reasons.

However, despite all these safety processes and measures, there is a large possibility of underground contamination due to the high toxicity of the water oriented from the buried waste. The potential pathways of these toxic wastes may include the water, as well as cultivated soil for the production of edible plants. To minimize the risk, any filled or repurposed for gardening sanitary landfills are regularly monitored for decades. Their soil is, also, regularly tested to identify any irregularities. In the event any plants are dying, it could be an indication of CH 4 release from the land. Only when the land has been tested and proven to be safe it can be used for other purposes. However, any heavy-duty activities, i.e., construction works, are not permitted in any case.

Municipal waste landfills

Municipal waste (also known as trash or garbage) is composed of all solid or semi-solid state waste and mostly includes domestic or household waste. The municipal landfills are one of the preferred methods for dealing with the largely increasing solid waste challenge. Municipal waste landfills are specifically designed so as to receive the household waste and other non-hazardous waste (Krčmar et al. 2018 ). As of 2009, there are approximately 1,908 municipal landfills in the USA, and these are managed by the states within the area of establishment (US EPA 2009 ).

Industrial waste landfills

An industrial waste landfill is where industrial waste is disposed of. While any type of solid industrial waste can be brought to these landfills, they are most often used for construction and demolition (C&D) waste disposal, which is why they are commonly known as C&D landfills. Waste could include concrete, gypsum, asphalt, bricks, and other building components (US EPA 2011 ).

Hazardous waste landfills

For obvious reasons, these types of landfills are the most closely regulated and structured landfills. They are specifically designed to hold hazardous wastes in a way that virtually eliminates the chance of it being leached and/or released into the environment. Some of the design requirements for hazardous waste landfills include double liners, double leachate collection and removal systems, leak detection systems, dispersal controls, construction quality assurance, etc. In addition to these design specifications, hazardous waste landfills undergo inspection multiple times a year to ensure that the facility is according to the latest high standards (Hazardous Waste Experts 2019 ; US EPA 2022 ).

Green waste landfills

While these landfills are not officially sanctioned landfills by the EPA, many municipalities are starting to adopt them for placing organic materials so as to get naturally decomposed. These composting sites are on the rise because most standard landfills and transfer stations are not accepting organic waste like fruits and vegetables.

Common types of green waste will include mulch, weeds, leaves, tree branches, flowers, biodegradable food waste, grass trimmings, etc.

The EPA has estimated that green waste landfills are making a bit of a difference with more than 24,000 tons of yard trimmings sent to these landfills in 2017 (US EPA 2017 ). The purpose of green waste landfills is to save space in other MSW landfills by keeping a material out that is meant to naturally decompose on its own.

Theoretical underpinning

Various theories have been developed to explain the waste management and environmental conservation achieved through the establishment of landfills. These theories include the theory of environmentally responsible behavior (ERB), the reasoned/responsible action theory, the theory of planned behavior, the environmental citizenship, the model of human interaction with the environment and the value–belief–norm theory of environmentalism. The ERB theory was originally formulated by Hines, Hungerford, and Tomera in 1986 (Hines et al. 1986 ). The theory argues that having an intention to act is a key factor that influences responsible behavior for taking care of the environment. Moreover, it debates that the intention of acting, the locus of control, the attitudes, the sense of responsibility at the personal level, and knowledge are key tenets influencing the overall ERB (Akintunde 2017 ; Hines et al. 1986 ).

The various interactions between the tenets of ERB are summarized in Fig. ​ Fig.2. 2 . According to this theory, the internal control center has an influence on the intention of people to act.

Schematic representation of the “Theory of Environmentally Responsible Behavior” (ERB). (Redrawn from source: Akintunde ( 2017 )

In the management of waste, no single factor exists that brings about a change in current behavior. For instance, despite the existence of stiff regulations forbidding people from damping waste materials, some people still damp waste or other materials in large cities. As indicated in Fig. ​ Fig.2, 2 , knowledge on its own is not adequate enough to lead to responsible actions and behaviors towards the environment.

The reasoned/responsible action theory was initially introduced by Martin Fishbein in 1967 and advanced and extended by Fishbein and Icek Ajzen (Akintunde 2017 ; Fishbein 1967 ). The theory argues that the various human behaviors are influenced and shaped by rational thoughts. According to this theory, there is a link between intentions to act and the final behavior of an individual as predicted by the attitudes. They are the subjective beliefs and norms that shape these attitudes. The theory of reasoned action is used to account for the time when individuals are guided by good intentions, but ensuring that these intentions are translated in good actions is affected by inadequate confidence Fig. ​ Fig.3 3 .

Theory of reasoned/responsible action. (Redrawn from source: Akintunde ( 2017 ))

Waste landfilling and environmental pollution

Landfills have been regarded as the leading avenues that contribute towards emission of greenhouse gases (GHG) across the globe. This is because a large portion of gases, including carbon dioxide (CO 2 ) and carbon IV oxide are released by the landfills to the air. It is the degradation process that results into all these gases polluting the environment (Papargyropoulou et al. 2015 ). In addition, the operations carried out at the landfills have been associated with contamination of the underground water sources through the produced landfill leachate. This occurs, particularly, when the liners within the landfills are not as adequate as required. There are, also, odors coming from the landfills that pollute the air, especially of those living in nearby areas. Other pollutants associated with landfills include dust, liter, and rodents (Ilankoon et al. 2018 ).

According to Hossain et al. ( 2014 ), landfill pollution is traditionally classified in several aspects. Maybe the most common categories are those that deal with the receiving air (emissions), water (effluents), and soil (dumps and disposals). A slightly more advanced breakdown would differentiate between inland and marine waters, surface and groundwater, and troposphere and stratosphere, and perhaps, considering the satellites and other types of debris, we should probably add outer space, as well. Most of the debate and regulation of pollution is based around these classifications, but focus is increasingly moving to inter-media impacts, such as the acidification of lakes and streams induced by air pollution or the disposal of sludge and other residuals from air and water pollution control measures on soil or in the ocean.

There are several factors that shape and determine the emission of landfill by-products: the quantity, as well as quality of deposited waste, the number of years a landfill has been operating for, and the climatic factors that surround it. There are some complicated microbiological and chemical reactions occurring within landfills that create gases to the air and hence air pollution. Some of the gases being released from landfills include sulfur dioxide (SO 2 ) and as well as nitrogen dioxide (NO 2 ), and these gases have an adverse effect on the environment. Inhaling any of these gases could result into throat and nose irritations that could potentially create asthma. Some of the landfill gases expose people that live around the area of such establishments with respiratory infections (Cucchiella et al. 2017 ).

The rainfall on landfill sites results in dissolution of inorganic and organic elements of the landfilled waste. In turn, this releases toxic chemicals that leak to the underground water systems. Such type of water shall have high metal content, and it will be toxic if consumed by humans. In the event that these chemicals get towards the lake or river systems may pose adverse influence on aquatic life (Zhang et al. 2016 ). Waste landfills have, also, been associated with air pollution across the world. For instance, it is projected that about two-third of the landfills are made of organic materials that are biodegradable. The decomposition of these materials results into release of CH 4 gas (Babayemi et al. 2016 ). This CH 4 gas helps in trapping heat in the atmosphere since it is regarded as a GHG. The effect of waste landfilling on underground water pollution is illustrated in Fig. ​ Fig.4 4 .

Route of underground water pollution-oriented landfills due to leaching. (Redrawn from source: SPREP ( 2010 ))

The development of waste landfilling affects, also, the biodiversity. For instance, developing the landfills implies that some 30–300 animal species are lost in every hectare. At the same time, there are some changes among the local species, where some of the birds and mammals are replaced with species feeding of refuse like crows and rats.

Njoku et al. ( 2019 ) performed a study in South Africa attempting to establish the link between landfills and environmental pollution. The formulated hypothesis was that the decomposed materials on landfills impact the environment of the surrounding area. It was shown from the results that about 78% of the people who live around these landfills are affected by air pollution. The people living close to landfills report, also, higher health issues including irritation of their eyes and flu. In this study, it was recommended to proper cover the landfill at the end of each day and place agents to dilute the odors (Njoku et al. 2019 ).

Vaverková et al. ( 2018 ) examined, also, landfills and their influence on the environment. In this study, it was shown that the investigated landfill had no direct and/or significant influence on the quality of water (Vaverková et al. 2018 ).

Danthurebandara et al. ( 2013 ) investigated the environmental impact of landfills and concluded that landfills do, actually, play a key role (Danthurebandara et al. 2013 ). However, it is from these landfills that approximately 20% of the global CH 4 quantity is obtained. Besides CH 4 , there are gases released from these landfills that have high level of toxicity. It is possible that leachate can find its way through the underground water mainly via the flaws found on the liners. Constructing landfills may have an adverse influence in the life of fauna and flora.

Paul et al. ( 2019 ) reported in his study that municipal solid waste (MSW) treatment in Bangladesh had a large impact on the environment. More specifically, they reported that MSW leachate caused water pollution affecting, in turn, aquatic species. They, also, reported that open dumping caused soil pollution in Islamabad, affecting soil quality and thereby crop growth, production, and agriculture. Open dumping of solid waste in Nepal led to the spread of infectious diseases. They also reported that as landfills age, the process of mineralization of waste occurs which increases the leaching properties of the waste in the landfill (Paul et al. 2019 ).

Aljaradin and Persson ( 2012 ) studied the influence of landfills on the environment in Jordan. It was shown that the most widely used method for waste management is landfilling (Aljaradin and Persson 2012 ). However, it was reported that most of the landfills are associated with higher levels of pollution, with periodic leachate and the gas release to the underground water, creating an alarming environmental situation.

Mouhoun-Chouaki et al. ( 2019 ) conducted a study on landfills and their influence on the environment. Their specific focus was on establishing the influence of disposal of solid waste on the quality of soil within Nigerian landfills (Mouhoun-Chouaki et al. 2019 ).

Conte et al. ( 2018 ) examined the influence of landfills on air pollution with reference to Italy. It was found that landfills result to air, land, and water pollution to a large degree (Conte et al. 2018 ).

Adamcová et al. ( 2017 ) conducted a study on the environmental assessment of the effects of a municipal landfill on the content and distribution of PTEs in Tanacetum vulgare. Much attention was drawn to the effect of landfills on water sources, underpinning the need of taking mitigating actions since most of the population in the area depends on the water on a daily basis. It was, furthermore, reported that in terms of environmental contamination, social inclusion, and economic sustainability, landfill mismanagement is a worldwide problem that needs integrated assessment and holistic approaches/methods for its solution. Attention should be paid in developing and developed countries, where unsustainable solid waste management is prevalent. Differences should be identified between the development of large towns and rural regions where management problems differ, particularly with regard to the quantity of waste produced and the equipment available for landfill management (Adamcová et al. 2017 ).

Wijesekara et al. ( 2014 ) investigated the fate and transport of pollutants through a MSW landfill leachate in Sri Lanka. Due to the fast pace of natural resource exploitation, technological growth, and industrial expansion, the most striking reason for the landfill and thus worldwide environmental crisis is the deteriorating relationship between man and environment. The pace of change in the environment and its resulting degradation induced by human operations has been so rapid and common. Man’s effect on the environment through his financial operations is diverse and extremely complicated, as the natural situation and process transformation or alteration leads to a sequence of modifications in the biotic and abiotic components of the environment. Landfill mismanagement causes severe toxic metal pollution in water, soil, and crops, whereas open burning causes atmospheric pollutant emissions like CO 2 . Toxic metal-oriented environmental pollution is considered one of the most harmful types of contamination, particularly to human health. Finally, the authors of that study concluded that mismanagement of landfill is a serious danger to the environment as it inhibits sustainable development growth (Wijesekara et al. 2014 ).

Huda et al. ( 2017 ) investigated the treatment of raw landfill leachate via electrocoagulation and with the use of iron-based electrodes; all the parameters involved in the process were studied and optimized. Man’s environmental effects can either be direct and intentional or indirect and unintentional. Direct or deliberate effects of human activity are pre-planned and premeditated because man is conscious of the effects, both positive and negative, of any program initiated to alter or modify the natural environment for the economic development of the region involved. Within a brief period of time, the impacts of anthropogenic modifications in the setting are noticeable and reversible. On the other side, the indirect environmental effects of human operations are not premeditated and pre-planned, and these effects arise from those human operations aimed at accelerating the pace of economic growth, particularly industrial development. After a long time, when they become cumulative, the indirect effects are encountered (Huda et al. 2017 ). These indirect impacts of human economic activity can alter the general natural environment structure, and the chain impacts sometimes degrade the environment to such a degree that it becomes suicidal to humans.

Kalčíková et al. ( 2015 ) investigated the application of multiple toxicity tests in monitoring the landfill leachate treatment efficiency. Landfilling is still the prevalent option globally. It has been the main disposal technique of MSW in the latest decades as it is the easiest and most economical practice in many nations, especially in developing ones. Unfortunately, by hosting various stray animals and proliferating insect vectors of a lot of illnesses, these open landfills lead to severe health hazards. By producing both leachate and biogas, they also pose nuisance and significant environmental effects. The leachate conveys a significant pollution load that mainly consists of toxic metals, organic matter, and a significant community of pathogenic organisms: it causes organic, bacteriological, and toxic metal pollution of soil, surface water, and groundwater by leaching and ground infiltration.

Talalaj and Biedka ( 2016 ) conducted a study on the quality assessment of groundwater near landfill sites using the landfill water pollution index (LWPI). Due to the increase in human population and industrial and technological revolutions, waste management has become increasingly challenging and complicated, while processes that regulate the destiny of waste in the soil are complicated and some even poorly known. Sanitary landfill is the most popular and convenient technique of MSW disposal. Sanitary landfills provide better odor-free esthetic control. Often, however, unknown content industrial waste is mixed with domestic waste. Infiltration of groundwater and water supply contamination are prevalent. Unless properly managed, leaching and migration of SoC from waste sites or landfills and the release of various pollutants from sediments (under certain circumstances) pose a high threat to groundwater resources. Protection of groundwater has become a major environmental problem that needs to be addressed. Open dumps are the oldest and most popular way to dispose solid waste, and while thousands have been closed in the latest years, many are still being used (ISWA 2016 ). Some of the MSW disposal techniques that are frequently used include composting, sanitary landfilling, pyrolysis, recycling, and reuse (Talalaj and Biedka 2016 ).

Jayawardhana et al. ( 2016 ) investigated on MSW biochar for preventing pollution from landfill leachate. The immediate input of (primarily human) waste materials into the environment is usually connected with conventional or classic pollutants. Rapid urbanization and fast population growth have resulted in sewage issues as treatment facilities have failed to keep pace with the need. Untreated sewage from municipal wastewater systems and septic tanks in untreated fields contribute important amounts of nutrients, suspended solids, dissolved solids, petroleum, metals/metalloids (As, Hg, Cr, Pb, Fe, and Mn), and biodegradable organic carbon to the water ecosystem. Conventional pollutants can cause a multitude of issues with regard to water pollution. Excess suspended solids block the sun’s energy and thus influence the process of transformation of carbon dioxide–oxygen, which is essential for maintaining the biological food chain. In addition, elevated levels of suspended solids silt up waterways and channels of navigation, necessitating frequent dredging. For drinking and crop irrigation, excess dissolved solids render the water undesirable (Jayawardhana et al. 2016 ).

Another study conducted on an unlined MSW landfill in the Varanasi district of Uttar Pradesh in India showed that rainfall can have a major impact on the migration of leachate such as Fe, nitrate (NO 3 − ,) total dissolved solids (TDS), phosphate (PO 4 − ), and ions responsible for the electrical conductivity. Post monsoon, the groundwater quality, at several sampled stations, dropped either below the acceptable limit or the extent of groundwater pollution increased (Mishra et al. 2019 ).

The impact of landfill on the surrounding environment can be diverse depending on the different processes or methods that have been employed to it. In the work conducted by Yadav and Samadder ( 2018 ), different scenarios of MSW landfilling were studied, such as collection and transportation (S 1 ); recycling, open burning, open dumping, and unsanitary landfilling without energy recovery (S 2 ); composting and landfilling (S 3 ); recycling, composting and landfilling (S 3 ); and recycling, composting, and landfilling of inert waste without energy recovery (S 4 ). It was found that each of the scenarios showed different degrees of environmental impact. For example, S 1 had the highest contribution to ecotoxicity in the marine ecosystem; S 2 contributed largely to global warming, acidification, eutrophication, and human toxicity; S 3 had high impact on the depletion of abiotic resources such as fossil fuels and also responsible for aquatic and terrestrial ecotoxicity among others (Yadav and Samadder 2018 ). This demonstrates how a variety of processes can interplay in the landfill system to create a number of impacts, even with human interventions.

Although improper waste disposal results in the emissions of unwanted environmental pollutants such as GHG, a study conducted by Araújo et al. ( 2018 ) confirmed that simple sanitary landfills generated the highest amount of CO 2 , followed by sanitary landfill with CH 4 collection, municipal incineration, and finally reutilization of woody waste (Araújo et al. 2018 ). This sheds some hope that proper intervention, such as reutilization and controlled release of pollutants, can be a potential method to reduce the emissions from landfilling.

Kazour et al. ( 2019 ) focused on the sources of microplastic pollution in the marine ecosystem. The study concluded that landfills close to the coastal waters were important sources of microplastic pollution in the ocean. Microplastics (MPs) were found in the leachate of active and closed landfills, suggesting that the location of the landfill also plays significant role in its characteristics of releasing plastics. The study found that inner lagoons with low water movement accumulated large amounts of MPs than the outer lagoon, which suggests that these MPs will be available as a contaminant in the marine environment (Kazour et al. 2019 ).

Another study conducted by He et al. ( 2019 ) reported that landfills that accumulate plastics do not act as final sinks for plastics but rather as a new source of MPs. They suggested that these MPs undergo breakdown due to exposure to the UV light and the prevalent conditions in the landfill (He et al. 2019 ). This study underpinned the impact of the landfill on coastal environments which are considered fragile ecosystems harboring large diversities.

Meanwhile, a study conducted by Brand and Spencer ( 2019 ) investigated the ecological impact of historical landfills located in the coastal zones. They reported that changing climate and proximity to coast can increase the changes of waste release into the waters due to erosion, storms, or even the collapse of the landfill due to age and infiltration of water. Historic landfills are unregulated as they predate modern environmental regulations and are no longer maintained or managed by previous operators. Thus, unmanaged landfills have detrimental impact especially because such landfills can have a wide mixture of waste. The authors of this study speculated that any metal release (derived from the wastes) to the adjacent Thames estuary, should they erode completely, will, i.e., increase the copper (Cu) levels 6.4 times. This will have long-term ecological impacts on the flora and fauna in the immediate vicinity and throughout the marine ecosystem. As of now, most metals exceed interim sediment quality guidelines (ISQG) levels (Brand and Spencer 2019 ). This study highlights the importance of maintaining the landfills of today’s society and their maintenance. Future considerations must also be made to existing landfills so that they may be managed well into the future without threatening the societal ecological balance.

Adamcová et al. ( 2017 ) pointed in two ominous directions: (a) towards big and increasing release of certain chemicals, primarily from burning fossil fuels, which are now considerably modifying natural systems on a worldwide scale, and (b) towards constant rises in the use and release of countless biocide goods and poisonous substances into the atmosphere. These raise a more severe issue presenting tremendous problems to the societies, both developed and developing. They concluded that several large-scale social and technological transitions are required to tackle the severe pollution problems in the coming decades (Adamcová et al. 2017 ).

Guerrero-Rodriguez et al. ( 2014 ) suggested that today’s pollution from landfill is integrally linked to financial manufacturing, contemporary technology, lifestyles, sizes of populations of humans and animals, and a host of other variables. Except for wide macro-transitions with various social benefits, it is unlikely to yield. These transitions include moving away from fossil fuels and waste-intensive techniques, bringing to bear our most advanced science, changing prices and other financial incentives, perceiving emissions as either trans boundary or global, and moving towards world population that is very stable (Guerrero-Rodriguez et al. 2014 ).

According to Majolagbe et al. ( 2017 ), land is frequently used as a waste treatment recipient, accepting spills of waste. Land pollution is the degradation of the earth’s land surface by bad farming methods, mineral exploitation, industrial waste dumping, and indiscriminate urban waste disposal. For a lot of municipal and some industrial waste, recycling of materials is practical to some extent, where a tiny, but increasing percentage of solid waste, is being recycled. However, when waste is mixed, recovery becomes hard and costly.

The former statement has been analyzed, along with new proposed methods in order to sort ferrous and nonferrous metals, plastics, paper, glass, etc., and many communities are implementing recycling programs that require separation of commingled waste. Developing better handling techniques, inventing new products for recycled materials, and finding new markets for them still remain crucial problems for the recycling sector (Hahladakis and Aljabri 2019 ; Hahladakis and Iacovidou 2018 , 2019 ; Hahladakis et al. 2018 ; Majolagbe et al. 2017 ).

Waste landfilling and human health risks

Love Canal is one of the most widely acknowledged landfill which is located in New York. During the periods of the 1930s to the 1940s, a huge volume of toxic materials was deposited. This was followed by establishing residential houses and learning institutions around this landfill in the 1950s. As of the mid-1970s, a number of chemicals were detected to have been leaked to the nearby streams and sewers. This has resulted into various studies being carried out to explore how this affected the human health. Most of the studies carried out have revealed that landfilling has, indeed, been associated with health issues, as a result of emissions of SoC to the air.

In Italy, studies have been carried out to reveal any effects associated with living closer to areas where there is landfilling. It was revealed that hydrogen sulfide (H 2 S) was associated with lung cancer and other respiratory health issues. The most affected part of the population was the children.

Vrijheid ( 2000 ) reported on the health issues that are related with people living closer to landfilling. The trigger point for this study was the fact that some specific form of cancer and defects at birth as well as low birth weight have been linked with individuals that live closer to landfilling areas. It was shown that living closer to landfilling areas is associated with respiratory diseases like asthma. This is largely attributed to the emissions of the gases to the air that affect the health outcomes of individuals (Vrijheid 2000 ).

Limoli et al. ( 2019 ) reported that illegal landfilling has adverse health effects on people living near the landfills and that it is more harmful to children, as their immune systems are still developing and because they spend most of the time outside their homes. They noted that health impacts can range from acute intoxication to carcinogenicity, endocrine-related toxicity, genotoxicity, and mutagenicity, depending on the contaminants. Upon contact with water, some contaminants dissolve and leach into the soil and contaminate the underwater table. Such pollutants that dissolve into the liquid phase include ammonium nitrogen that can cause eutrophication, chlorides that can alter the reproductive rates of marine animals and plants, organic matter that contributes to the deterioration of the water quality, persistent organic pollutants (POPs) that can cause bioaccumulation, and biomagnification in the food chain and sulfates that may increase nutrient levels in the water body, leading to eutrophication, in addition to fostering the production of methylmercury by some bacteria which is toxic. As part of the gaseous emissions, NOx triggers photochemical smog and contributes to acid rain and phytotoxic, particulate organic matter reduces photosynthetic rate and aids in photochemical smog formation, sulfur oxides cause acid rains, and volatile organic compounds (VOCs) cause the formation of harmful ground-level ozone. Besides these, many types of hazardous wastes can also be added such as PTEs that lower water quality; radionuclides and pathogenic waste are severely harmful for the living organisms (Limoli et al. 2019 ).

Mattiello et al. ( 2013 ) sought to determine how disposing solid waste in landfills affects health outcomes. The study systematically reviewed the available information on the subject under consideration. It was shown that the health issues linked with landfills include respiratory diseases and possible hospitalization especially among children (Mattiello et al. 2013 ). Maheshwari et al. ( 2015 ) focused on landfill waste and its influence on health outcomes. The review of information showed that landfills are associated with air, water, and land pollution problems around the world. These forms of pollution have adverse influence on people especially children who have weak immunity systems. Pollution of the environment through dumping of waste is associated with health issues on a long-term basis. The gases that are emitted from the landfills result into environmental pollution, and they are also associated with a number of issues related with cancer (Maheshwari et al. 2015 ).

Xu et al. ( 2018 ) conducted a study to find out the correlation of air pollutants associated with land filling on the respiratory health of children living in the proximity of a particular landfill in china. They reported that CH 4 , H 2 S, CO 2 , NH 4 , and other air pollutants were released with anaerobic decomposition of waste in the MSW landfills. While the concentration of these pollutants have been published to be lower than regulatory limits, any exposure to land fill gases (LFG) such as those of H 2 S and NH 4 , even at lower concentrations, had a negative impact on the respiratory system and the general immunity of children living near the landfill. Children living closer to the landfills showed lower levels of lysozyme associated with exposure to CH 4 and H 2 S and lower SIgA levels associated with H 2 S and NH 3 . These two factors are measured as they are among the first line of defense in the human body, and their lower levels in children reduced their immunity. They, also, established that as the distance from landfill increases, the effects are reduced (Xu et al. 2018 ). This experiment yet again establishes the health impact landfills have on young children as a manifestation of a pathology and as an impact on their immune system and its development.

Triassi et al. ( 2015 ) conducted a study on the environmental pollution from illegal waste disposal and health effects. Improper landfill management and shipments of illegal waste can have adverse environmental and public health effects. Different handling and disposal operations may result in negative effects arising in land, water, and air pollution. Insufficiently disposed or untreated waste can trigger severe health issues for communities surrounding the disposal zone. Waste leakages can contaminate soils and streams of water and cause air pollution by, i.e., emissions of PTEs and POPs, thereby creating eventually health risks. Other nuisances created by uncontrolled or mismanaged landfills that can negatively impact individuals include local-level effects such as deterioration of the landscape, local water, air pollution, and littering. Therefore, proper and environmentally sound management of landfill is essential for health purposes (Triassi et al. 2015 ).

A study conducted in Serbia revealed similar findings of high concentration of PTEs, such as Cu and Pb in groundwater and Hg in soil due to the leaching from uncontrolled local MSW landfills. Hg was reported to have high ecological risk for that region (Krčmar et al. 2018 ).

Melnyk et al. ( 2014 ) conducted a study on chemical pollution and toxicity of water samples from stream receiving leachate from a controlled MSW landfill. A relevant factor concerning health effects of landfill management is how much and which population is involved in such risks. Unlike in the case of urban air pollution, exposure to pollution from landfill mismanagement facilities does not affect all the inhabitants of an urban area but only a small proportion of the population residing nearby the landfill. Living in the vicinity of a landfill can pose a health danger to citizens as they may be subjected to pollutants through various routes: inhalation of SoC emitted by the site and contact with water or polluted soil, either directly or through the consumption of products or contaminated water. The greatest issues are illegal, uncontrolled landfills that receive waste at source without any choice (Melnyk et al. 2014 ).

Palmiotto et al. ( 2014 ) conducted a study on the influence of a MSW landfill in the surrounding environment. Landfill has been regarded as the oldest form of waste treatment and the most prevalent technique of structured waste disposal and has remained so in many parts of the globe. A modern landfill is an engineered establishment, specially built and equipped with protected cells. Despite the reality that growing quantities of waste are being reused, recycled, or energetically valued, landfills still play a significant role in the waste management infrastructure of many countries. The degradation of waste in the landfill results in the production of leachate and gases. These emissions pose potential threats to human health and environmental quality. Landfilling has environmental impacts, primarily because of the long-term manufacturing of CH 4 and leachate (Palmiotto et al. 2014 ).

A research by Abd El-Salam and Abu-Zuid ( 2015 ) on the effect of waste leachate on soil quality in Egypt proposed the need to adjust variables to enhance anaerobic biodegradation leading to leachate stability in relation to ongoing groundwater surveillance and leachate therapy procedures. Landfill construction and management have ecological impacts that can lead to modifications in the landscape, habitat loss, and wildlife displacement. Socio-economic effects of landfills include hazards to public health arising from leachate contamination of the ground or groundwater, the spread of litter into the wider setting, and insufficient recycling operations on site. Nuisances like flies, odors, smoke, and noise are often cited among the reasons why people do not want to live near landfills. However, depending on the real distance from the landfill, landfills are likely to have an adverse impact on housing values (Abd El-Salam and Abu-Zuid 2015 ).

Furthermore, Rezapour et al. ( 2018 ) found that uncontrolled leak of leachate from landfills drastically increased the concentration of various PTEs in the soil which interacted with the crops grown there. They reported that a number of metals were found in moderate quantities, except Cd which was above limits and posed moderate intensity non-carcinogenic risk to the people consuming the wheat. This study however reported that the cancer risk to the local resident was low. This study illustrates the extent of landfilling-generated pollution. The PTEs could interact with the soil system and enter the food chain, thus causing harmful effects to the human population (Rezapour et al. 2018 ).

Giusti ( 2009 ) stated that the ways of exposure that result in health effects associated with waste landfilling are inhalation, consumption, and the food chain. He, also, noted that the health risks associated with individuals directly involved in the waste management system is much higher due to their proximity to the hazard and that the cases of adverse effects are higher among workers than the residents near the landfill. Moreover, he underpinned the fact that the waste management industry has the highest occupational accidents than other professions. For populations living in close proximity to landfills, the risk of birth defects and cancer increased (Giusti 2009 ).

A study conducted in the island of Mauritius, dealt with the impact of non-hazardous solid waste coming from the only landfill of the island. It was found that vomiting and nausea were consistent symptoms among the population. A large difference in the body mass index of men as compared to their control group was, also, noticed, a pattern that was not observed among women or children, thereby indicating that the effects of pollution can vary on the gender of the individual. Interestingly, it was also found that many other symptoms of health issues were reported; however, they were attributed to either the confounding factors or to a “pan symptom” effect, personal bias. Although this exclusion may be due to the nature of this study being dependent on patient’s information, it provides new dimension to think about personal bias or the placebo effects especially when counteracting seemingly non-threatening diseases associated with landfills, unless proved otherwise by medicinal science (Goorah et al. 2009 ).

Other studies conducted by various researchers showed that there was an increased risk of malformation of babies among women who lived close to hazardous landfill sites in Washington state and the risk increased among those living in urban areas compared to rural areas (Kuehn et al. 2007 ).

In the research of Damstra ( 2002 ), it was stated that exposure to endocrine-disrupting compounds (EDCs) can put women at risk for breast cancer among other factors, although there are no studies that show a direct increase in the levels of breast cancer with exposure to EDC. However, Damstra claimed that the time of exposure of these chemicals in these women’s lifespan determines the risk. He also reported that studies have shown that exposure to polychlorinated biphenyls (PCBs) in newborn and young children has resulted in neurobehavioral changes, such as immaturity in motor functions, abnormal reflexes, and low psychomotor scores, and these changes may continue into their childhood. He, also, reported that studies suggest that when mothers exposed to low levels of PCBs give birth, the babies have subtle neurobehavioral alterations (Damstra 2002 ).

Martí ( 2014 ) performed a human health risk assessment of a landfill based on volatile organic compounds emission, emission, and soil gas concentration measurements. Direct dumping of untreated waste in rivers, seas, and lakes can cause severe health hazards to accumulate toxic substances in the food chain through the plants and animals that feed on it. Human health may be affected by exposure to hazardous waste, with kids being more susceptible to these pollutants. Indeed, immediate exposure can lead to illnesses through chemical exposure, as chemical waste release into the atmosphere leads to chemical poisoning (Martí 2014 ).

Agricultural and industrial waste can also pose severe health hazards. Other than this, the co-disposal of municipal, industrial, and hazardous waste can expose individuals to chemical and radioactive risks. Uncollected solid waste can also obstruct the runoff of storm water, leading to the formation of stagnant water bodies that become the disease’s breeding ground. Waste dumped near a source of water also causes water body or groundwater source contamination (Krčmar et al. 2018 ).

Sharifi et al. ( 2016 ) performed a risk assessment on sediment and stream water polluted by toxic metals released by a MSW composting plant. Solid waste disposed of in landfills is generally subjected to complicated biochemical and physical procedures resulting in both leachate and gaseous emissions being produced. When leachate leaves the landfill and reaches water resources, it can lead to pollution of surface water and groundwater. Gas and leachate generation, mainly due to microbial decomposition, climatic circumstances, refuse features, and landfilling activities are unavoidable implications of the practice of solid waste disposal in landfills. In both current and new installations, the migration of gas and leachate away from landfill limits and their release into the atmosphere pose severe environmental concerns. These issues result to fires and explosions, vegetation harm, unpleasant odors, landfill settlement, groundwater pollution, air pollution, and worldwide warming in addition to potential health risks (Sharifi et al. 2016 )

Liu et al. ( 2016 ) conducted a study on health risk impact analysis of fugitive aromatic compound emissions from the working face of a MSW landfill in China. Over the past three decades, worldwide concern has been growing with regard to the effects of landfill mismanagement on public health. Human exposure to pollution from landfill is thought to be more intense in human life now more than ever. Pollution from landfills can, also, be caused by human activity and natural forces. The significance of environmental factors to the health and well-being of human populations is increasingly apparent. Landfill is a global issue, and it has a huge ability to impact human population health.

Landfill, in the densely settled urban-industrial centers of the more developed countries, reaches its most severe proportions. More than 80% of polluted water was used for irrigation in poor nations around the globe, with only 70–80% of food and living safety in urban and semi-urban-industrial regions (Assou et al. 2014 ).

Kret et al. ( 2018 ) conducted a study on respiratory health survey of a subsurface smoldering landfill. The water we drink is vital to our well-being and a healthy life, but unfortunately polluted water and air are prevalent worldwide. Landfill is tangled with unsustainable anthropogenic activity, leading to significant public health issues. Some of the illnesses connected with landfill pollution are infectious diseases such as cancer, birth defects, and asthma. Environmental health issues are not just a conglomerate of worries about radiological health, treatment of water and wastewater, control of air pollution, disposal of solid waste, and occupational health, but also a danger to future generation (Kret et al. 2018 ).

By looking at its definition, pollution is considered to be very harmful, too much of which occurs at the incorrect location. However, some erstwhile pollutants are useful in suitable amounts. Aquatic life requires phosphates and other plant nutrients; however, too much of these nutrients and the outcomes of eutrophication are harmful. CO 2 in the atmosphere helps to maintain the earth warm enough to be habitable, but the accumulation of vast amounts of surplus CO 2 , generated by the use of fossil fuel and other sources, is now threatening to change the climate of the planet. Other pollutants, such as dioxin and PCBs, are so toxic that even the smallest quantities pose health risks, such as cancer and impairment of reproduction. Pollutant releases to the environment are most frequently the casual by-product of some helpful activity, such as electricity generation or cow rearing. This sort of pollution is a form of waste disposal. It happens when the financial expenses of eliminating pollution are greater than the financial advantages, at least the polluter benefits (Zhang et al. 2016 ).

Although nutrients such as nitrogen and phosphorus are vital to the aquatic habitat, they may trigger over fertilization and accelerate the lakes’ natural aging (eutrophication) cycle. In turn, this acceleration generates an overgrowth of aquatic vegetation, huge overall shifts, and a general change in the biological community from low productivity with many varied species to elevated productivity with big numbers of a few less desirable species (Koda et al. 2017 ). Bacterial action oxidizes organic carbon that is biodegradable and consumes dissolved oxygen in water which may cause a threat to the aquatic life. In extreme cases where the loading of organic carbon is high, oxygen consumption may result in an oxygen depression that is adequate to cause fish killing and severely interrupt the development of related organisms that require oxygen to survive. A result of this pollution is water hyacinth and other floating aquatic vegetation.

It was deemed appropriate and necessary to tabulate the rest of the articles reviewed in an effort to include as much information as possible on the environmental and health effects associated with landfilling. Table ​ Table1 1 summarizes and depicts a consolidated view of these articles reviewed, together with any associated environmental and/or health impact of the various types of landfills reported therein.

Environmental and health impacts of landfilling

Conclusions

This study aimed at assessing the environmental pollution and health effects associated with waste landfilling. A desk review design was adopted, and information was gathered from the already available sources. The literature review was centered along three themes: waste landfilling, waste landfilling and environmental pollution, and waste landfilling and health issues.

From the reviewed information, it was established that landfills play an important role as far as disposal of solid waste is concerned. It was shown that majority of the countries have adopted landfilling as waste management systems. The literature indicates that some landfills have lining at the bottom to prevent leakage of the waste into the underground water. The present review revealed, also, that landfills are meant to create conducive environment that enhances microorganisms’ activities and thus decomposition of the waste.

Despite the role played by landfills in the waste management sector, the reviewed literature showed that they are linked with environmental pollution. Landfills were seen to have an influence on biodiversity and the flora and fauna, as well as the aquatic life. Literature indicates that landfills are associated with environmental pollutants including mice and other rodents. The gases released from landfills result into air pollution of the area surrounding the establishment, in addition to the release of bio-contaminants. Landfills are, also, associated with pollution of the underground water, especially when the lining at the bottom is not sufficient to prevent leakage of the waste and a large body of literature supports this.

This article investigated, also, the health issues associated with landfilling. It was concluded that through landfills, there are possible chances of emission of gases into the air like CO 2 , H 2 S, CH 4 , and NO x . These gases have been associated with respiratory health challenges and some specific types of cancer, e.g., lung cancer. Carcinogenic risks were found to vary between studies but were mostly attributed to the varying characteristics of the landfill. A variety of literature suggests, also, that the environmental pollution caused by landfills creates greater risks to children living in the vicinity of the landfills. Teratogenic effects of certain elements found in the contaminated groundwater were, also, observed. Unarguably, humans produce a large amount of waste, and landfills provide the easiest and relatively efficient way of tackling these waste. However, landfilling has larger deleterious effects that seem to overweigh the benefits it provides. Better technological involvement in waste segregation and appropriate waste management techniques, stronger enforcement of regulations surrounding landfills, and setting up a larger concrete minimum distance for settlements are some of the necessary measures to be seriously considered and taken in the near future.

Acknowledgements

The authors would like to acknowledge that Open Access funding was provided by the Qatar National Library.

Nomenclature

Author contribution.

J. H.: conceptualization, investigation, writing—original draft, and writing—review and editing

A.S.: investigation and writing—original draft editing

W.A.: investigation and writing—original draft

Open Access funding provided by the Qatar National Library.

Data availability

Declarations.

We wish to confirm that there are no known conflicts of interest associated with the publication of the present work and there has been no financial support for this work that could have influenced its outcome.

We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us.

We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property.

We understand that the corresponding author is the sole contact for the editorial process (including the editorial manager and direct communications with the office). He/she is responsible for communicating with the other authors about progress, submissions of revisions, and final approval of proofs. We confirm that we have provided a current, correct email address which is accessible by the corresponding author and which has been configured to accept email from: [email protected] and/or [email protected].

• Landfilling is still the predominant waste management option in many countries.

• Open dumping entails numerous environmental and, more importantly, health risks.

• Even a controlled landfill may pose environmental and human health implications.

• As per the waste hierarchy, landfilling should be the final waste management option.

• Open burning/dumping should be eliminated, and open dumpsites should close.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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No, study doesn't show electric cars pollute 1,850 times as much as gas cars | Fact check

The claim: Study shows EVs pollute 1,850 times more than fuel-powered vehicles

A March 14 Instagram video ( direct link , archive link ) shows a person talking about the purported results of a 2022 study conducted by the U.K.-based private research group Emissions Analytics.

"During a 1,000 mile journey, electric vehicles release 1,850 times more pollutants into the surrounding environment than gas-powered vehicles," the person says.

The video, which was posted on social media by Lifesite News, showed the headline and text from a since-deleted article on the Lifesite News website.

The video garnered more than 100 likes in three weeks.

More from the Fact-Check Team: How we pick and research claims | Email newsletter | Facebook page

Our rating: False

The post misrepresents the study, which did not compare gas vehicles to electric vehicles. Instead, it compared particulate pollution emissions from gas car tailpipes to particulate pollution emissions from tires. Electric vehicles were not included in the study, according to the Emissions Analytics CEO.

Study did not analyze pollution from EVs

Particulate pollution − tiny bits of material − shed from car tires cause water pollution and wildlife deaths.

The 2022 study , which was not peer-reviewed, compared the amount of particulate pollution shed from gas car tires to the amount emitted by gas car tailpipes. It concluded that tires shed 1,850 times as much particulate pollution as the tailpipes of new gas-powered vehicles under the conditions analyzed in the study.

Electric vehicles weren't included in the study at all, Nick Molden , the CEO of Emissions Analytics told USA TODAY. However, Molden said that, without intervention, the EV transition stands to worsen tire pollution because EVs allow drivers to apply more torque to tires and EV batteries increase the weight of the vehicles − both factors that contribute to tire wear.

Reducing EV size, weight and acceleration ability as well as the use of special wear-resistant tires are potential options to help mitigate this issue, according to reporting by The Atlantic.

Fact check : CO2 emissions from gas cars outweigh electric, even with battery manufacturing

USA TODAY reached out to the Instagram user who shared the post for comment but did not immediately receive a response.

AFP also debunked the post.

Our fact-check sources:

• Nick Molden , April 1, Interview with USA TODAY
• Environmental Protection Agency, accessed April 2, Particulate Matter (PM) Basics
• Los Angeles Times, Oct. 2, 2019, The biggest likely source of microplastics in California coastal waters? Our car tires
• University of Washington, Dec. 3, 2020, Tire-related chemical is largely responsible for adult coho salmon deaths in urban streams
• Bloomberg, Jan. 6, 2023, Extreme Acceleration Is the New Traffic Safety Frontier
• Axios, April 28, 2023, EVs are much heavier than gas vehicles, and that's posing safety problems
• Atlantic, July 19, 2023, EVs Are Sending Toxic Tire Particles Into the Water, Soil, and Air
• Emissions Analytics, accessed April 3, Gaining traction, losing tread Pollution from tire wear now 1,850 times worse than exhaust emissions

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