Municipal solid waste recycling effect on the environment quality in France: Evidence through the ARDL approach

doi:10.21203/rs.3.rs-3777246/v1

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Municipal solid waste recycling effect on the environment quality in France: Evidence through the ARDL approach

https://doi.org/10.21203/rs.3.rs-3777246/v1

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Waste is a serious problem threatening citizens and causing a serious danger to living beings and climate change. Part of the circular economy, recycling emerges as the magical solution to the problem of waste and an instrument that aims to achieve sustainable growth and environmental protection. In this context, our study examined the effect of different factors such as municipal solid waste to the environment quality in extended STIRPAT model.

Relying on the ARDL approach over the 1990–2018 period in France, our empirical analysis showed the existence of a short-term inverse and significant relationship between the quantity of municipal solid waste recycling per capita and CO₂ emissions. In other words, a 1% increase in municipal solid waste recycling helps reduce CO₂ emissions by 0.24. However, a long term significant positive relationship between the quantity of the recycled municipal solid waste per capita and CO₂ emissions has been proven. Our results also showed that population increase negatively influences the environment quality over the long term. Besides, energy efficiency and income per capita have a positive but non-significant long-term impact. This shows that energy consumption and economic growth degrade the environment. Over the short term, however, these two factors have a negative influence on energy efficiency and change sign for the income per capita.

Such results allow us to draw some policy implications where renewable energy could be an alternative to reduce CO₂ emissions resulting even from the recycling process. Politicians have to be aware of the advantageous resources of the green economy.

Environmental Economics

Municipal solid waste recycling

CO2 emissions

Environnemental performance

STIRPAT model

ARDL approach

France

Population growth, rapid urbanization and booming economy in most of developed and developing countries mean a more populated world resulting in increased human consumption. This fast-growing population consumption causes a swift increase of various domestic, commercial, chemical and industrial wastes. The different types of waste and the materials processing industry have harmful impacts on the environment i.e. human health, natural resources and ecosystems. This is explained by the remarkably huge quantities of waste, regardless of whether they are disposed of or incinerated, results in greenhouse gas emissions that seriously harm the natural equilibrium.

According to world Bank report (2019) “What a Waste 2.0”, the annual global amounts of municipal solid waste (MSW) are around 2.01 billion tons, among at least 33 percent are not managed in an environmentally safe way. When looking forward, global waste is expected to grow to 3.40 billion tons by 2050. In France, the volume of waste produced by all economic activities is 5.1 tons/inhabitant in 2018, while this increase recorded + 6.1% due in particular to the increase in waste from the service sector. In fact, the same report highlights that the quantity of municipal waste produced in France (536 kg/inhabitant - inhabitant) is higher than the EU average (488 kg/inhabitant).

Recycling, which came as part and partial of the circular economy, emerged as the magical solution to the problem of waste. It is seen as an instrument for achieving sustainable growth since it has become a source of energy that makes it possible to limit pollution¹ and the resources consumption due to the manufacture of new products. Thus, to reduce the harmful effect of waste on the quality of the environment, countries aim to create a formal industry that processes waste based on an innovative system, which helps create employment opportunities and enable economic growth.

A review of the existing literature reveals that most studies focus on the relationship between population, economic growth, urbanization and CO₂ emissions, on the one hand, and other factors such as energy consumption, intensity energy, technology, on the other. Such emissions have considerable negative effects on the environmental quality and are a major cause of climate change. The collection and recycling of solid waste is considered worldwide as a suitable process so as to guarantee the conservation of resources and the protection of the environment. Such a combination of the globally scarce resources suffering a massive consumption worldwide added to a rapid climate change has made sustainable development a challenge throughout the world. As a result, recent studies have opted for an integrated analysis to examine the dynamic relationship between waste recycling and CO₂ emissions.

Our objective in this research work was to find out whether the municipal solid waste recycling (MSW) is one among the several solutions that can be adopted to achieve sustainable development. This study, therefore, examined the effect of MSW recycling on environmental quality in France relying on a modified version of the STIRPAT model.

The remaining of this paper was structured as follows: a brief review of the literature on the link between several factors such as the MSW recycling and the quality of the environment was suggested in the first section. The second section introduced the econometric framework while the third section was devoted to discuss the empirical results. The last section drew the main conclusions and suggested some policy recommendations.

2.1. The IPAT model pioneers

Today, the protection of the environment has become a priority issue in the world. The existing literature reveals that most studies are interested in examining the key factors underlying the degradation of the quality of the environment using a very simple model called the "IPAT model" since the 1970s. The pioneers of this model are the Americans Paul Ehrlich and John Holdren (1971). This is why, the existing literature reveals that most studies have been interested in examining the key factors of the environmental quality degradation since the 1970s relying on the very simple "IPAT” model. This model takes into account three major descriptive impact factors of human populations, as well as their interactions and retroactions, to protect ecosystems and promote ecological transition not only in developed countries but also in developing countries.

The IPAT model can be used utilized to evaluate the influence of economic activity on the environment and the used amount of energy. The model puts into use a random effect regression to evaluate the environmental pressure brought on by demographic variables, wealth, and technological advancement. The model is can be expressed as follows: I = P.A.T where I is the human impact on the biosphere expressed as the product of components namely, P; that stands for population, A for affluence and T for technological aspects.

However, several recent works have conferred a stochastic content to the equation IPAT, while replacing T with a random term, allowing a statistical processing. To address this shortcoming the STIRPAT (Stochastic Impacts by Regression on Population (P), Affluence (A) and Technology (T)) model was suggested by Dietz and Rosa (1994).

It is most widely used by economists to assess the effect of population, wealth, technology and other factors on carbon dioxide (CO₂) amounts. It is another stochastic reformulation of the IPAT model. The STIRPAT model makes the application of statistical tools to social research easy. Noteworthy, the model was further developed by York, Rosa and Dietz (2003). Thus, when the T component is considered as a random term, other variables, assumed to be explanatory of environmental impact, can be incorporated into the IPAT equation.

The specificity of the STIRPAT model can be expressed as follows:

$$I=a{P}^{b}{A}^{c}{T}^{d}e$$

With I denotes the environmental impact, P expresses the population value, A is the economic activity variable, T is the technologies used while e is the random term, b is the ecological elasticity value of P, c is the ecological elasticity value of A and d denotes the ecological elasticity value of T.

Using the extended STIRPAT model, the equations give particular importance to the demographic and activity variables. However, as allowed by the STIRPAT model, other potential explanatory variables may be examined, based on their statistical significance and their relevance to explaining the endogenous variable, i.e. environmental quality.

2.2. Empirical background

Over the previous few decades, a good amount of the literature has examined several key factors in the environmental quality degradation relying on the extended STIRPAT model.

2.2.1. The relationship between population and CO₂ emissions

Previous theoretical and empirical contributions have extensively studied the relationship between population and CO₂ emissions and achieved various results.

The results reached in Shi’s study (2001) show that the increase in CO₂ emissions is caused by the rising energy consumption, emphasizing that there are no impacts from population or technology. Other studies, however, (Cole et al., 1997; Schmalensee et al., 1998) have shown that population and technology are among the key factors in CO₂ emissions. Cramer (1998, 2002) and Cramer and Cheney (2000) examined the impact of population on air pollution in California and proved that population is associated with several sources of CO₂ emissions.

In the context of the IPAT model, Dietz and Rosa (1997) and York, Rosa and Dietz (2003), focused on CO₂ emissions and energy use and examined the roles played by population, average resource and energy consumption per individual (affluence) and the used technologies. The authors found a positive linear relationship between population and CO₂ emissions.

Similarly, and relying on the context of a STIRPAT model, York et al., (2003) proved the existence of a positive impact of population on CO₂ emissions in their study of a panel of 146 developed and developing countries.

However, using the STIRPAT model over the period 1975–2000 in countries with different income levels, Ying et al., (2006) show that the 15–64 age group of the population has a negative impact on CO₂ emissions in high-income countries, but the effect is rather positive at other income levels.

Using an extended version of the STIRPAT model, Wang et al., (2013) examined the impact of several factors such as population, economic level, technology, urbanization, energy consumption structure and degree of foreign openness on environmental quality in their study. The achieved results show that population is the most important factor influencing CO₂ emissions in Guangdong, China.

2.2.2. The relationship between economic growth and CO₂ emissions

It should be recognized that Grossman and Krueger (1991) were pioneers in the study of the relationship between economic activity and the environment. Their study is based on the Kuznets environment curve, and described the relationship between a country's level of wealth (measured by GDP per capita) and its level of inequality.

The Kuznets environment curve can be referred to in the environmental field (Grossman and Krueger, 1991), where many health and environmental indicators deteriorate with economic growth. This has inspired a number of studies investigating whether economic growth is accompanied by environmental degradation. A review of the literature shows us that there are works that confirm the CKE hypothesis while others do not.

Among the studies that showed an inverse relationship between economic growth and environmental quality we can cite that of Ying et al., (2006). The authors used the STIRPAT model over the period 1975–2000 in countries with different income levels and their results showed a very high impact of GDP per capita on total CO₂ emissions in low-income countries.

On the other hand, Shoufu et al., (2009) analyzed the effect of GDP per capita alongside with population, urbanization level, industrialization degree and energy intensity on environmental quality in China over the period 1978 to 2006. Their results show that GDP per capita has a negative impact on environmental quality. In addition, it proved that population, urbanization level, industrialization degree and energy intensity have negative effects on the environment.

Other studies show an inverse relationship between economic growth and environmental quality. In fact, Shafik's study (1994) shows that economic growth is associated with environmental improvements where there are generalized local charges and substantial benefits. But if the costs of environmental degradation are borne by others (the poor or other countries), there is little incentive to change damaging behavior.

Using a STIRPAT model, Yeh and Liao (2017) show that the relationship between GDP per capita and CO₂ emission is inverse during the period 1990–2014 in Taiwan. This means that Taiwan has reached a stage of economic strength that allows it to reduce carbon emissions.

As for Bensbahou and Seyagh (2020), they examined whether economic growth affects environmental quality in Morocco for the period 1980–2018. The study used the ARDL method and considered CO₂ emissions (metric tons per capita) as an indicator to measure the environmental status. Their results showed the existence of an inverted U-shaped relationship between GDP per capita and CO₂ emissions. In other words, if CO₂ emissions rise, GDP goes up until the inflection point, where CO₂ emissions gradually fall, which reflects the importance of the environment.

2.2.3. The relationship between technology, energy consumption and CO₂ emissions

Most of the existing studies in the literature reveal their unprecedented interest in examining the key factors in the environmental quality degradation. This obviously proves that environmental protection has become a priority issue worldwide.

The study by Ghazali and Ghulam (2019) examined the influence of different factors on environmental degradation for ten newly industrialized countries (NICs), applying the STIRPAT model for the period 1991–2013. The authors used the "dynamic common correlated estimator" (DCCE) to analyze the effect of technology alongside population, GDP per capita, labor productivity, trade openness and energy intensity among others on CO₂ emissions. The results show that technology, population, GDP per capita and energy intensity are the major factors in the long-term degradation of environmental quality in the ten studied countries.

Similarly, a wealth of literature has explored the direction of causality between energy consumption and CO₂ emissions. Most of these studies have shown that energy consumption has a considerable impact on the environment and is a source of climate change. Kraft and Kraft (1978) were the pioneers to show that there is a relationship between energy consumption and growth, which is one of the driving factors of environmental degradation (Grossman and Krueger, 1991; Jumbe, 2004; Ying et al., 2006 among others).

Using the ARDL method, the results of the study by Kivyiro and Arminen (2014) confirmed the Kuznets Curve Hypothesis (KCH) that energy consumption has a negative impact on environmental quality in the Democratic Republic of Congo, Kenya, and Zimbabwe over the period 1971–2009 in the long term.

Esso and Keho (2016) performed a study on a sample of 12 Sub-Saharan countries covering the period 1971–2010. Relying on annual data collected from the countries under study, they showed that energy consumption and economic growth negatively influence environmental quality in Benin, Ivory Coast, Nigeria, Senegal, South Africa and Togo over the long run.

In a more recent study, Chontanawat (2020) used the co-integration and causality method to examine the dynamic relationship between energy consumption, economic growth and CO₂ emissions in ASEAN² countries over the period 1971–2015. Their findings show that increasing energy consumption, which has a positive influence on economic growth, degrades the quality of the environment (CO₂ emissions).

2.2.4. The relationship between waste recycling and CO₂ emissions

Most of the studies empirical results provide useful information in terms of policy implications. Policies that necessarily aim to improve renewable energy consumption, should reduce energy intensity and encourage waste recycling. This would help cut down CO₂ emissions, without significantly affecting economic growth. In fact, several studies have shown the significant contributions of waste recycling to environmental sustainability. Ayodele et al., (2018) showed that municipal solid waste recycling in some Nigerian cities contributed positively to the energy sector as well as many economic and environmental fields relying on a population model covering the 2017–2036 period. The environmental benefit of municipal solid waste recycling is determined based on the assessment of its greenhouse gas emission potentials. Similarly, Cudjoe et al., (2021) analyzed the energy (electricity) and environmental (GHG and air pollutant emissions) benefits of recycling steel, non-ferrous metals, plastic and paper waste from 2005 to 2017 in China. The results show that solid waste recycling saved 43.2% electricity and avoided a significant amount of GHG emissions and air pollutants. In addition, Razzaq et al., (2021) tested the effect of municipal solid waste (MSW) recycling on CO₂ emissions and economic growth in the United States using quarterly data during the period 1990–2017 and relying on the ARDL method. The results show that a 1% increase in MSW recycling leads to 0.317% (0.157%) increase in growth in the long term (short term) and reduces CO₂ emissions by 0.209% (0.087%) in the long term (short term). These results also mean that any MSW recycling-related policy generates significant changes in economic growth and carbon emissions.

In contrast, Bayar et al., (2021) examined the effects of municipal waste recycling and renewable energy on environmental sustainability indicated by CO₂ emissions in EU member states during the period 2004 to 2017 and using cointegration and panel causality analyses. The authors find that the results are mixed. In other words, the analyses show a negative impact of recycling and the use of renewable energies on CO₂ emissions, which varies from one country to another. In addition, Ramos-Meza et al., (2022) analyzed the effect of municipal solid waste recycling and other key variables on carbon emissions over the period 1975–2020 in China, using the ARDL "bounds testing approach" proposed by Pesaran et al., (2001). The results show a positive relationship between municipal solid waste recycling and CO₂ emissions. This contribution means that the recycling process is inefficient due to the considerable municipal solid waste production, which calls for a sustainable method of municipal solid waste disposal and recycling in the country.

From our literature review, we can deduce that most studies are interested in identifying the key factors influencing CO₂ emissions. These factors have considerable negative impacts on the environment and are a major cause of climate change. Accordingly, our study opted for an integrated analysis to examine the dynamic relationship between municipal solid waste recycling and CO₂ emissions using the extended STIRPAT model in France.

3.1. Model description

Our study followed the common features of the STIRPAT model to derive an empirical model as follows:

$${CO}_{2t}=f({POP}_{t}, {PI}_{t}, {RCY}_{t}, {EEF}_{t})$$

Therefore, Eq. 1 can be expressed as follows:

$${CO}_{2t}={\alpha }_{0 }+ {\alpha }_{1}{POP}_{t}+ {{\alpha }_{2}PI}_{t}+ {{\alpha }_{3}RCY}_{t}+ {{\alpha }_{4}EEF}_{t}+ {\epsilon }_{t}$$

where CO₂, POP, PI, RCY and EEF denote CO₂ emissions, population, per capita income, household waste recycling and energy efficiency, respectively. In addition, t expresses time. A detailed explanation of the different variables is given in the following section. Eq. (2) examines the influence of household waste recycling and energy efficiency on the environmental quality, considering the impact of other control variables introduced in the part of empirical background. The rapid population growth is driving carbon emissions up due to the increase in the generated solid waste. An extensive technology to recycle such waste seems, therefore, to be essential to alleviate environmental problems and reduce CO₂ emissions. The ultimate considered variable as depicted in the literature review was energy consumption and economic growth since they were shown to have a negative impact on environmental quality.

Thus, the coefficients signs of the variables in model (2) are expected to be as follows:

$${\alpha }_{1}>0 ;{\alpha }_{2}>0;{\alpha }_{4}>0 et {\alpha }_{3}<0$$

3.2. Data description

This study used annual data for France covering the period 1990–2018.

The CO₂ variable is CO₂ emissions in metric tons per capita. RCY expresses Quantity recycling of Municipal Waste generation per capita in Kg/capita. EEF describes the energy efficiency indicating the energy consumption to produce one unit of GDP in purchasing power parity. The POP variable is total population and finally, PI expresses income per capita which is derived through GDP divided by mid-year population. These variables are expressed in different units of measurement; so, it is useful to convert them into logarithms, which provides an interpretation in terms of elasticity (Shahbaz et al., 2020). All variables are taken from the World Development Indicators - World Bank (2022.05.26). Data are in constant 2015 U.S. dollars.

4.1. Unit root test

Before estimating model 2 using the co-integration method, testing the stationarity of the selected variables is required since stationarity is a necessary condition to avoid spurious relationships, and verify the hypothesis of variable co-integration, i.e. to ensure that the growth paths of the variables converge over the long run.

Unit root tests can be used to determine whether a chronicle is stationary or not, by identifying a deterministic or stochastic trend.

In fact, we refer to the Dickey-Fuller augmented test (1981) if the evolution of macroeconomic series shows no change over time, and fluctuations are stationary around a deterministic function. Furthermore, when the macroeconomic series present breakpoints, i.e. a change in level, or a change in slope, the fluctuations are not transitory, and then, we use the Perron test (1989).

4.2. ARDL-Bounds approach, Pesaran et al., (2001)

There are several valid methods to perform the co-integration test. The most commonly used ones are the Engle-Granger (1987) residual-based test, and Johansen’s (1991; 1995) and Johansen-Juselius’s (1990) maximum likelihood tests. These methods are applied when the variables are integrated of order 0 or integrated of order 1. To solve these problems, Pesaran et al., (1996), Pesaran and Shin (1995) and Pesaran et al., (2001) proposed a co-integration test procedure adapted to series integrated at different orders. This procedure is known as the "bounds test to cointegration". This test is applied against the background of a model on which it is based, the ARDL co-integrated specification, which takes the form of an error-correction model. The used ARDL specification to estimate our model is written as follows:

where ∆ denotes the first difference operator and p shows the optimal lag length. Next, the long-run co-integration is checked using Wald statistics, which are used to analyze the following null and alternative hypotheses:

$${H}_{0} : {\beta }_{1}= {\beta }_{2}= {\beta }_{3}= {\beta }_{4}=0$$

$${H}_{1} : {\beta }_{1}\ne {\beta }_{2}\ne {\beta }_{3}\ne {\beta }_{4}\ne 0$$

The null hypothesis indicates that there are no co-integration relationships between the variables, but the alternative hypothesis (${H}_{1}$) is verified. The regression analysis validated the model's convergence to equilibrium at a predetermined rate of model change. Thus, the equation is written as follows:

where ${ECT}_{t-1}$ expresses the error correction term and the model parameters adjustment.

This section of the study dealt with detailing the achieved results and discussing them.

5.1. Descriptive analysis

The results of the descriptive statistics analysis for the studied variables is given in Table 1. They include the mean, maximum, minimum, standard deviation (Std. Dev.), skewness and Kurtosis.

Table 1

Descriptive statistics
Variables	CO₂	POP	PI	RCY	EEF
Mean	5.675	61.027	33.752	145.807	101.343
Maximum	6.500	65.233	38.291	218.290	119.275
Minimum	4.502	56.961	28.523	55.072	84.025
Std.Dev	0.615	2.623	3.135	54.182	11.453
Skewness	-0.686	0.066	-0.504	-0.252	-0.071
Kurtosis	2.079	1.678	1.808	1.696	1.705
Source : Author’s calculation

The findings show that recycling has the highest mean, 145.807, while CO₂ emissions have the lowest one, 5.675 with a standard deviation of 0.615, indicating a low variability. However, the recycled amounts range between 55,072 Kg/capita and 218,290 Kg/capita with a standard deviation of 54.182 Kg/capita reflecting a high variability.

To analyze the data asymmetry and distribution, we considered the statistics skewness and kurtosis. Kurtosis indicated that the data have a highly platykurtic distribution. CO₂, PI, RCY and EEF have negative -skewed distributions, meaning that their left tails are longer, and the mass of the distribution is located on the right. POP has a positive-skewed distribution, and thus, its right tail is longer, and the mass of the distribution is located on the left.

5.2. Unit root tests

According to the shape of the curves of total population (POP), energy efficiency (EEF), income per capita (PI) and the quantity of household waste recycled per capita in logarithm (see appendix 1), it can be noted that their evolution over time shows a single, upward trend, and poses no breakage problems. This led us to test their stationarity using the Augmented Dickey-Fuller (ADF) test. The results of the ADF test are shown in Table 2. The results indicate that total population (POP), energy efficiency (EEF), income per capita (PI) and household waste recycled per capita and income per capita taken in logarithm are stationary after a first difference. This means that the four series taken separately are integrated of order one.

In the case of log-converted CO₂ emissions, we have noted from their shape that their evolution over time shows changes in slope and constant (see appendix 1): this led us to introduce two dummy variables: the first shows the change in constant while the second indicates the change in slope. Perron's (1998) test results are displayed in Table 2. They show that log CO₂ emission is stationary after an initial difference, which means that the series under study is integrated of order one.

Table 2

Unit root test
Traditional ADF Test
Variables	At level		At first difference		Decision
LPOP	0.68 (0.857)		-2.538 (0.0134)		I (1)
LRCY	-0.49 (0.977)		-5.16 (0.001)		I (1)
LEEF	-0.09 (0.940)		-8.09 (0.000)		I (1)
LPI	-1.157 (0.678)		-3.769 (0.008)		I (1)
PP Test with rupture
Variables	At level		At first difference		Decision
		Rupture year		Rupture year
LCO₂	-2.64 (0.815)	2010	-6.99 (0.01)	2006	I (1)
Source : Author’s calculation

It can be concluded that the selected series are non-stationary and integrated of order one. Therefore, the estimation method proposed by Pesaran et al., (1996) can be adopted in this study.

5.3. ARDL method estimation

Before estimating the suggested model using the ARDL-Bounds method proposed by Pesaran et al., (2001), it is necessary to determine the appropriate lag order in the regression. The achieved results are shown in Table 3. The various selection criteria show that three lags are the most suitable to apply. In other words, the optimal model is verified by an ARDL with 3 lags and no trend.

Table 3

Results of Lag order selection criteria
Number	Log L	LR	FPE	AIC	SC	HQ
0 1 2 3	285.0517 469.2670 525.3711 588.5419	NA 283.4082 64.73545 48.59295*	3.03e-16 1.52e-21 1.77e-22 1.96e-23*	-21.54244 -33.78977 -36.18239 -39.11861*	-21.30050 -32.33812 -33.52103 -35.24754*	-21.47277 -33.37175 -35.41602 -38.00388*
Source : Author’s calculation
*Lag order by selection criterion. LR: sequential modified LR test statistic (each test at 5% level). FPE: Final prediction error. AIC: Akaike information criterion. SC: Schwarz information criterion. HQ: Hannan-Quinn information criterion.

Thus, this study took three lags into account to achieve consistent results. The ARDL long and short -run estimates are shown in Table 4 and Table 5 respectively. They indicate that there is a significant positive long-run relationship between the amount of household waste recycled per capita and CO₂ emissions. In the short run, however, the relationship is negative and significant. The results also show that a 1% increase in municipal solid waste recycling helps reduce CO₂ emissions by 0.24 in the short run.

In the short run, our results confirm that solid waste collection and recycling are considered worldwide to be a necessary action for resource conservation and environmental protection. Similarly, our results endorse the findings of Ayodele et al., (2018) that proved the significant contributions of municipal solid waste recycling in selected Nigerian cities to the energy, economic and environmental sectors based on a population model covering the period 2017–2036. Similarly, Cudjoe et al., (2021) analyzed the energy benefits of recycling steel, non-ferrous metal, plastic and paper waste from 2005 to 2017 in China. In addition, Razzaq et al., (2021) tested the effect of municipal solid waste (MSW) recycling on CO₂ emissions in the USA using quarterly data during the period 1990–2017 and the ARDL method. Their findings show that a 1% increase in MSW recycling contributes to economic growth of 0.317% (0.157%) in the long run (short run) and reduces CO₂ emissions by 0.209% (0.087%) in the long run (short run). These results also mean that any policy intervention related to MSW recycling causes significant changes in carbon emission patterns. In fact, a 1% increase in the amount of municipal solid waste recycling increases CO₂ emissions by 0.72 in the long run. This contribution means that the recycling process is inefficient due to the considerable production of municipal solid waste, requiring a sustainable method to dispose of and recycle such waste throughout the country. Our findings in this context are in line with those reached by Ramos-Meza et al., (2022) who showed a positive relationship between MSW recycling and CO₂ emissions over the period 1975–2020 in the Chinese economy, based on the ARDL "bounds testing approach" proposed by Pesaran et al., 2001. Energy efficiency (EEF) and per capita income have a positive but non-significant impact in the long run. This shows that energy consumption and economic growth degrade the environmental quality. Such result could be explained by the fact that France relies heavily on non-renewable energy consumption, which is a basic source of CO₂ emissions. Our results conform with the findings of most empirical studies (Kraft and Kraft, 1978; Grossman and Krueger, 1991; Jumbe, 2004; Ying et al., 2006; Esso and Keho, 2016 and Chontanawat, 2020) where the authors have shown that energy consumption has considerable impact on environmental quality and might be a driver of climate change. But in the short run, these two factors negatively influence the environment after one and two lags for energy efficiency (EEF) and sign change for income per capita.

Similar to Ying et al., (2006), our results show that population growth has a negative influence on the long-run environmental quality. The authors examined an inverse relationship between the 15–64 age group population and the CO₂ emissions in high-income countries. Their results proved an inverse relationship between population and CO₂ emissions, which is in opposition with the findings of several other authors (Cole et al., 1997; Schmalensee et al., 1998; Dietz and Rosa, 1997; York, Rosa and Dietz, 2003; Wang et al., 2013) who showed a positive linear relationship between population and CO₂ emissions.

Table 4

The long-run estimated coefficients
Dependent variable lCO₂
Variables	Coefficients	Std. Error	t-Statistics
$\varvec{C}$	13.57	4.970	2.73
$\varvec{l}\varvec{R}\varvec{C}\varvec{Y}$	0.175	0.059	2.930
$\varvec{l}\varvec{E}\varvec{E}\varvec{F}$	0.478	0.343	1.392
$\varvec{l}\varvec{P}\varvec{I}$	0.515	0.476	1.082
$\varvec{l}\varvec{P}\varvec{O}\varvec{P}$	-4.090	0.710	-5.758
Source : Author’s calculation

Table 5

The short-run ARDL approach
Dependent variable lCO₂
Selected model : ARDL (3, 3, 3, 3, 3)
Variables	Coefficients	Std. Error	t-Statistics
${\varvec{\varDelta }\varvec{l}\varvec{C}\varvec{O}}_{2(\varvec{t}-1)}$	1.12	0.238	4.72
${\varvec{\varDelta }\varvec{l}\varvec{C}\varvec{O}}_{2(\varvec{t}-2)}$	0.57	0.164	3.50
$\varvec{\varDelta }\varvec{l}\varvec{R}\varvec{C}\varvec{Y}$	-0.24	0.096	-2.52
${\varvec{\varDelta }\varvec{l}\varvec{R}\varvec{C}\varvec{Y}}_{\varvec{t}-1}$	-0.34	0.124	-2.80
${\varvec{\varDelta }\varvec{l}\varvec{R}\varvec{C}\varvec{Y}}_{\varvec{t}-2}$	-0.56	0.118	-4.78
$\varvec{\varDelta }\varvec{l}\varvec{E}\varvec{E}\varvec{F}$	0.40	0.113	3.51
${\varvec{\varDelta }\varvec{l}\varvec{E}\varvec{E}\varvec{F}}_{\varvec{t}-1}$	-0.97	0.224	-4.35
${\varvec{\varDelta }\varvec{l}\varvec{E}\varvec{E}\varvec{F}}_{\varvec{t}-2}$	-0.49	0.175	-2.79
$\varvec{\varDelta }\varvec{l}\varvec{P}\varvec{I}$	1.23	0.220	5.60
${\varvec{\varDelta }\varvec{l}\varvec{P}\varvec{I}}_{\varvec{t}-2}$	-0.68	0.294	-2.33
${\varvec{\varDelta }\varvec{l}\varvec{P}\varvec{I}}_{\varvec{t}-2}$	0.82	0.268	3.05
$\varvec{\varDelta }\varvec{l}\varvec{P}\varvec{O}\varvec{P}$	-12.46	39.22	-0.31
${\varvec{\varDelta }\varvec{l}\varvec{P}\varvec{O}\varvec{P}}_{\varvec{t}-1}$	-12.11	72.19	-0.16
${\varvec{\varDelta }\varvec{l}\varvec{P}\varvec{O}\varvec{P}}_{\varvec{t}-2}$	79.43	40.106	1.98
${\varvec{E}\varvec{C}\varvec{M}}_{\varvec{t}-1}$	-2.44	0.310	-7.86
Source : Author’s calculation

The ARDL Bounds estimates displayed in Table 6 enable us to reject the null hypothesis of no co-integration. In other words, the variables are co-integrated and have long-run correlations allowing us to look for short-run relationships. The Fischer F bounds test statistics value (5.623) is significant at the 1% level and falls below the upper I(1) limit, allowing causal inferences and intertemporal correlations between the variables.

Table 6

ARDL Bounds test
Test statistics	Value	k (number of regressors
F-statistics	5.623	4
Critical Bounds Values
Significance	I(0) Bound	I(1) Bound
10%	2.2	3.09
5%	2.56	3.49
2.5%	2.88	4.87
1%	3.29	4.37
Source : Author’s calculation

The results of the model's various diagnostic tests are shown in Table 7. The Breusch-Godfrey (LM) serial autocorrelation test and the Harvey ARCH Heteroskedasticity test show that there are no autocorrelation or Heteroskedasticity problems in the given model. The Ramsey RESET test result shows that the suggested model does not involve any misspecification. Consequently, the ARDL bounds test yields unbiased and consistent estimates.

Table 7: Diagnostic tests Estimation

Source : Author’s calculation

This study examined the impact of household waste recycling and energy efficiency on environmental quality by considering the impact of other control variables introduced in the empirical study, namely the population expansion and economic growth on CO₂ emissions in France over the period 1990 - 2018. The ADF and PP tests were performed to validate the stationarity hypothesis. The results show that all the variables are stationary according at first difference. The variables are integrated of order 1 with a number of observations of 28, proving that the use of the ARDL method would be most suitable. The advantage of the ARDL method is that it shows infers short- and long-run results. The results proved a negative and significant relationship between the amount of recycled household waste per capita and CO₂ emissions in the short run. Simply put, a 1% increase in municipal solid waste recycling reduces CO₂ emissions by 0.24 over the short run. On the other hand, a significant positive long-run relationship between the amount of recycled household waste per capita and CO₂ emissions was also proven. This means that the recycling process is inefficient due to the considerable production of municipal solid waste, necessitating a sustainable method for disposing of and recycling such waste throughout the country.

In the long-run, our findings also point that population growth has a positive impact on environmental quality. Similarly, energy efficiency (EEF) and per capita income have a positive but non-significant impact over the long run. This implies that energy consumption and economic growth degrade the environmental quality. In the short run, however, these two factors have a negative impact after one and two lags for energy efficiency (EEF) and a change of sign for the income per capita.

We can suggest some policies that could be adopted and applied by the authorities where renewable energies should play an important role to reduce CO₂ emissions. Additionally, policymakers ought to persuade businessmen and entrepreneurs to seriously consider the recycling, reusing, and reprocessing of waste, which could be achieved through offering incentives and benefits in terms of grants or tax reduction.

Acknowledgements : We would like to thank Mr Abdelmajid Dammak for his help with the English and the proofreading of this paper.

Funding : This study has no funding.

Conflict of Interests : The author states that there is no conflict of interest.

Ethical Approval : Not Applicable.

Consent to Participate : Not Applicable.

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France set itself the objectives of “reducing greenhouse gas emissions by 40% between 1990 and 2030 and achieving carbon neutrality by 2050 by dividing greenhouse gas emissions by a factor greater than six between 1990 and 2050 Bilan environmental de la France - Édition 2020 “Émissions de gaz à effet de serre et empreinte carbone”
Indonesia, Malaysia, Philippines, Singapore and Thailand

The authors declare no competing interests.

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Municipal solid waste recycling effect on the environment quality in France: Evidence through the ARDL approach

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Version 1

Abstract

1. Introduction

2. Literature review

2.1. The IPAT model pioneers

2.2. Empirical background

2.2.1. The relationship between population and CO₂ emissions

2.2.2. The relationship between economic growth and CO₂ emissions

2.2.3. The relationship between technology, energy consumption and CO₂ emissions

2.2.4. The relationship between waste recycling and CO₂ emissions

3. Empirical analysis

3.1. Model description

3.2. Data description

4. Estimation method

4.1. Unit root test

4.2. ARDL-Bounds approach, Pesaran et al., (2001)

5. Results and discussion

5.1. Descriptive analysis

5.2. Unit root tests

5.3. ARDL method estimation

Conclusion

Declarations

References

Footnotes

Additional Declarations

Supplementary Files

Status:

Version 1

Municipal solid waste recycling effect on the environment quality in France: Evidence through the ARDL approach

Status:

Version 1

Abstract

1. Introduction

2. Literature review

2.1. The IPAT model pioneers

2.2. Empirical background

2.2.1. The relationship between population and CO2 emissions

2.2.2. The relationship between economic growth and CO2 emissions

2.2.3. The relationship between technology, energy consumption and CO2 emissions

2.2.4. The relationship between waste recycling and CO2 emissions

3. Empirical analysis

3.1. Model description

3.2. Data description

4. Estimation method

4.1. Unit root test

4.2. ARDL-Bounds approach, Pesaran et al., (2001)

5. Results and discussion

5.1. Descriptive analysis

5.2. Unit root tests

5.3. ARDL method estimation

Conclusion

Declarations

References

Footnotes

Additional Declarations

Supplementary Files

Status:

Version 1

2.2.1. The relationship between population and CO₂ emissions

2.2.2. The relationship between economic growth and CO₂ emissions

2.2.3. The relationship between technology, energy consumption and CO₂ emissions

2.2.4. The relationship between waste recycling and CO₂ emissions