Does constitutional protection of the environment matter for environmental quality?

doi:10.21203/rs.3.rs-4437105/v1

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Does constitutional protection of the environment matter for environmental quality?

https://doi.org/10.21203/rs.3.rs-4437105/v1

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This paper extends the literature on the role of institutions on environmental quality, by assessing the impact of constitutional environmental protection (CEP) on environmental quality. Using a panel dataset covering 119 countries over the period 1990 to 2020, and after employing Entropy Balancing Methodology (EBM), we find that, adoption of CEP significantly increases environmental quality. We demonstrate that this finding is extremely robust to different alternative estimation methods such as propensity score matching (PSM), the Inverse Probability Treatment Weighting (IPTW), the Augmented Inverse Probability Treatment Weighting (AIPTW) and the dynamic panel analysis. Moreover, we show that the effect of constitutional environmental protection varies systematically depending on the region, types of legal origin and the level of pollution. Finally, we provide evidence that state experience in CEP, improves consideration of environmental concerns and consequently increases environmental quality.

Constitutional Environmental Protection

Environmental Quality

Entropy Balancing

Panel Data

Increasing carbon dioxide emissions from fossil fuels and other heat-trapping gases, such as methane, are known to be the cause of global warming, and the last ten years have been the warmest on record (Smoke & Cook, 2022) (also see Fig. A 1 that provide the annual average of temperature in appendix). Globally, total GHG emissions increased by 1.5% per year between 2009 and 2018, reaching 2.0% in 2018. Numerous initiatives have been taken to combat weather disruption, most notably the 2015 Paris Agreement. Following on from the 1997 Kyoto Protocol, the signatory countries of the Paris Agreement committed to keeping global temperature change below 2.0°C compared with pre-industrial levels, while continuing efforts to limit it to 1.5°C and to adapt more aggressively to the impacts of climate change. However, reductions in greenhouse gas emissions remain largely out of reach under the initial national commitments of the Paris Agreement, as the current rate of emissions, according to UNEP (2020), is likely to lead to a warming of 3.2°C by the end of the century.

The serious global challenge posed by the environment calls for joint mobilization at both national and international level. The degradation of environmental quality increases human vulnerability, creating opportunities for political oppression and other forms of human rights violations that disproportionately threaten the lives, health and dignity of future generations (May & Daly, 2021). In the literature, several factors have been identified as determinants of the weak of environmental quality including, high level of fossil energy consumption, increasing of deforestation, lack of technology adaption, limited legal institutions quality and state capacity and the lack of pro-environmental behaviors. Research based on states' environmental commitment emphasizes that the adoption of international climate agreements is an instrument enabling states to commit to environmental policy actions and objectives (Perrin & Bernauer, 2010; Von Stein, 2008; Young, 1994). Those focusing on climate change and environmental governance highlight the importance of institutional change for the consideration of environmental issues (Baloch & Wang, 2019; Castiglione et al., 2012; Hussain & Dogan, 2021; Paavola & Adger, 2005; Salman et al., 2019).

In the search for institutional mechanisms for decarbonizing the economy, the idea of taking into account and inserting environmentally-oriented provisions into constitutions has gained ground over the years. It has become an integral part of the institutional organization of environmental protection in many countries around the world (see Fig. A 2 for a world map of constitutional environmental protection adoption in appendix). Since its introduction in 1947 in Venezuela and its accelerating during the 1990s with a total of 35 countries that integrated environmental protection in their constitutions during this decade. The adoption of constitutional rules to protect environment has emerged as effective way to promote green environment and is considered as having immediate consequences than international treaties (Imhof et al., 2016).

Constitutional environmental protection (CEP) has received considerable attention in the existing literature in recent decades. Early studies, essentially legal, present a constitutional provision relating to the environment as a directive principle, the application of which can lead to the establishment of an environmental protection system. It plays a key role, providing a stable framework for environmental democracy by increasing short-term political incentives for longer-term environmental protection (Davies & Hickey, 2021). It also helps build resilience to the security, economic and political challenges posed by climate change (Lindvall, 2021). In addition, other studies believes that constitutional environmental provision establishes the guiding principles for clarifying the allocation of institutional responsibilities in the definition and enforcement of laws, articulating the scope as well as the content and defining the legal standards they require, from the judiciary to the political organs of the state. They also establish that the constitution of the right to the environment leads to the development of mechanisms to ensure that the state complies with the constitutional obligation to promote social values including climate change mitigation and environmental protection (Hellner & Epstein, 2023; Weis, 2018). The underlying idea is that a constitutional right to the environment enables rights-holders to hold political decision-makers to account if their rights are violated. Consequently, constitutional environmental rights are meta-rights that can induce legislation and regulation, and/or give rights holders the ability to bring legal action (Boyd, 2011).

However, legislative power could also establish these constraints with political decision-makers. But rights granted by statutory law only constrain policymakers as defined by law, and these rights can be modified or removed by majorities, even transitional ones (Jeffords & Minkler, 2016). By contrast, constitutional rights that are legally enforceable are often broader and protected even against majorities by the judiciary and constitutional courts (Osiatynski, 2007). As constitutions are difficult to change, they represent what is most permanently important for a country. Researchers such as Buchanan (2003), (North and Weingast (1989) and Persson and Tabellini (2003) initiated the argument that constitutions are important because they establish rules that constrain political decision-makers. Politicians are not simply passive executors of their constituents' interests; on the contrary, like everyone else, they have their own utility functions. So, even if politicians really did prefer to devote time and resources to environmental policymaking over the course of a mandate, constitutional environmental rules would constrain them should their preferences diverge over time.

The question of whether constitutional environmental protection (CEP) promotes environmental quality or contributes to mitigating environmental degradation has been little debated in the economic literature. The first economic reflections are essentially based on countries determinants of constitutional environmental protection. In this literature, constitutional environmental protection (CEP) is shaped by political institutions favoring redistribution (Congleton, 1992; McGuire & Olson, 1996; Neumayer, 2002; Spilker, 2013) and by a society whose intertemporal preferences are future-oriented (Gupta & McIver, 2016; Imhof et al., 2016). Jeffords and Minkler (2016) analyze the role of the constitution on the achievement of environmental performance and conclude that amending the constitution to insert environmental provisions is important to mitigate the degradation of environmental quality. These authors also point out that, the more recent the constitution, the more likely it is to contain an environmental provision, while at the same time strengthening the institutional framework for combating climate change. In a descriptive approach, Boyd (2011) finds that constitutional environmental rights have had a positive effect on the filing and adjudication of environmental lawsuits in 78 of the 92 countries in his sample.

However, there is a paucity of empirical studies on potential effect of constitutional environmental protection on quality of environment. This paper fills this gap in the literature, by assessing the impact of constitutional environmental protection on quality of environment. It does this by relying on entropy balancing, an impact analysis method developed by Hainmueller (2012). Using a sample of 119 countries of both developed and developing countries over the period 1990–2020. We show that constitutional environmental protection adoption decreases carbon dioxide emissions in CEP countries relative to non-CEP countries. This result is robust to several robustness tests. Including a using of alternative estimation methods such as propensity score matching (PSM), the Inverse Probability Treatment Weighting (IPTW), the Augmented Inverse Probability Treatment Weighting (AIPTW), panel fixed effect and the system-GMM. The findings on the heterogeneity test performs on the dynamic panel suggest that, i) constitutional environmental protection negatively affect carbon dioxide emissions only in Latin America, MENA and sub-Saharan Africa countries but not in Europe and Asia Pacific; ii) the magnitude of the effect of constitutional environmental protection (CEP) is more important in countries with French civil law system as legal origin than countries with English common law system; iii) for countries that start with high level of CO2 emissions, constitutional environmental protection (CEP) is associated with better environmental quality. Going further in analysis, we provide additional evidence that state experience in CEP increase environmental quality by reducing carbon dioxide emissions.

The rest of the paper is organized as follows. Section 2 presents the methodology. Section 3 presents the data and descriptive statistics. The results are presented in Section 4, followed by a robustness analysis in Section 5. Section 6 concludes.

This paper aims to analyze the effect of constitutional environmental protection adoption on environmental performance. The biggest challenge is to establish the link between CEP adoption and the quality of environment. CEP adoption is not a random event, it may depend on a country’s pro-environmental attitudes and behaviors, a speed of country’s environmental degradation, economy performance and political and legal institutions. These factors, which can also influence the quality of environment make the CEP adoption endogenous through the selection bias problem. To circumvent this problem, we use Entropy Balancing Method (EBM), which is an impact assessment method developed by Hainmueller (2012). This method is widely use in the literature (Apeti, 2023; Apeti & Edoh, 2023; Balima & Sy, 2021; Balima, 2020; Neuenkirch & Neumeier, 2016).

Entropy Balancing allow us to identify the impact of constitutional environmental protection comparing CEP and non-CEP countries. that are similar in observable characteristics, while controlling for time and country-fixed effects. This method offers some advantages compared to concurrent impact analysis methods such as propensity score matching (PSM) or difference-in-differences (Hainmueller, 2012). First, it achieves for a high degree of balance between the treatment and control groups by creating a synthetic group as close as possible to the treatment group. Second, unlike other impact analysis methods such as PSM, it does not require an empirical model for constitutional environmental protection adoption, thus limiting specification and multicollinearity problems. Third, unlike classical matching methods, Entropy Balancing uses a more flexible reweighting approach by keeping the weights closer to the base weights to avoid information loss. Unlike conventional matching, which is based on the assumption of conditional independence, the fourth advantage is that Entropy Balancing allows us to exploit the panel aspect of our data and control for time and country-fixed effects in the second stage of our regression.

The approach used in this study is based on the principle that constitutional environmental protection adoption is the treatment variable, and carbon dioxide emissions represent the outcome variable. The units of observation are country-year observations. The observations with constitutional environment protection represent the treatment group, and those without CEP represent the control group. The measure of interest we wish to estimate is the well-known average treatment effect on the treated, ATT, defined as follows:

$ATT=E\left[{CO2}_{\left(1\right)}|T=1\right]-E\left[{CO2}_{\left(0\right)}\right|T=1]$ (Eq. 1)

where ${CO2}_{(.)}$ is the outcome variable measuring carbon dioxide emissions. T indicates whether the observation unit is subject to CEP adoption (T = 1) or not (T = 0). Consequently, $E\left[{CO2}_{\left(1\right)}\right|T=1]$ is the expected outcome for CEP countries (treatment group) and $E\left[{CO2}_{\left(0\right)}\right|T=1]$ is the counterfactual outcome for the countries that have adopted CEP, i.e the level of carbon dioxide emissions in CEP countries if they have not adopted CEP.

The issue is that $E\left[{CO2}_{\left(0\right)}\right|T=1]$ is not observable due to the non-random nature of CEP adoption. If it were observable, the ATT could easily be identified by comparing Carbon dioxide emissions in CEP countries with that of non-CEP countries. Hence, the identification of ATT requires a good proxy for that $E\left[{CO2}_{\left(0\right)}\right|T=1]$. To do so, we match CEP units with non-CEP units (after purging for some specific factors) that are as close as possible in terms of observable characteristics that meet two criteria, namely correlation with CEP adoption and CO₂ emissions. Under the condition that the non-CEP units are relatively close to the CEP units, any difference in CO₂ is attributable to CEP adoption. Based on these different elements, we can rewrite Eq. (1) as follows:

$ATT=E\left[{CO2}_{\left(1\right)}|T=1, X=x\right]-E\left[{CO2}_{\left(0\right)}\right|T=0, X=x]$ (Eq. 2)

where X = x is the vector of observable covariates that can affect both countries’ decision to adopt CEP and their level of carbon dioxide emissions; $E\left[{CO2}_{\left(1\right)}|T=1, X=x\right]$ represents C02 emissions for CEP countries, and $E\left[{CO2}_{\left(0\right)}\right|T=0, X=x]$ is the expected CO2 emissions for non-CEP countries (synthetic control units).

Estimating ATT by entropy balancing requires two steps. The first step is to compute the weights of the control groups (untreated groups). These weights may satisfy pre-specified balanced constraints involving the sample moments of observable characteristics (X). Following Neuenkirch and Neumeier, (2016) and Apeti and Edoh (2023), we choose equilibrium constraints that impose equal covariate means between the treatment and control groups. By doing so, we want to ensure that the control group, on average, has non-treatment units that are as similar as possible to the treated units. The second stage uses the weights from the first stage in a regression analysis where carbon dioxide emission is the dependent variable, and the CEP dummy is the main explanatory variable, in order to estimate in a second stage, the average treatment effect of CEP (ATT) on CO₂. In the second stage, we also control for entropy balancing covariates as well as for time and country-specific effects, as in a randomization experiment, to increase the efficiency of the estimations.

Although entropy balancing is our baseline method, we also use other impact analysis methods such as Propensity Score Matching (PSM), Inverse Probability Treatment Weighting (IPTW), and Augmented Inverse Probability Treatment Weighted (AIPTW) to test the robustness of our results. Moreover, like any impact analysis method, entropy balancing would fail to control endogeneity bias resulting from unobserved time-varying factors that affect both environmental quality and constitutional environmental protection adoption or omitted variables. Accordingly, we use additional methods such as panel fixed-effects regression and the system-GMM.

3.1 Data

To assess the effect of constitutional environmental protection, we draw a large panel data covering 119 countries over the period 1990–2020. The time horizon is mainly determined by the fact that, the CEP adoption accelerated during the 1990s. A total of 35 countries integrated environmental protection in their constitutions during this decade (see Fig. A 3 that provides the evolution of countries with constitutional environmental protection norms in the world in appendix). This upward trend follows the adoption of the United Nation Framework Convention on Climate Change in Rio De Janeiro in 1992, also the Kyoto protocol signed in 1997 in Japan.

Given our objective and following the literature, we use carbon dioxide (CO₂ per capital measured in kt) emissions as a proxy of environmental quality. Nonetheless, various gases emissions determine the quality of environment. Yet, CO₂ is the most widely used measure of environmental quality as it has a considerable portion of greenhouse gas emissions.

Information on our treatment variable, CEP adoption, is drawn from Elkins et al. (2013) and Imhof et al. (2016) dataset. Elkins et al. (2013) attempt to code all written constitution since 1789 on annual basis using 669 different variables. Their dataset contains a set of variables capturing the existence and design of CEP. Imhof et al. (2016) update this dataset and cross-validate the variable using information from Boyd, (2011) and other sources. We define a dummy variable taking 1 from the year of the CEP adoption and zero otherwise, reflecting whether or not the constitution of a country prescribes the protection environment or provides a right to people to enjoy environment. The Table A1 in appendix lists our final coding for all countries.

Regarding the control variables, we select the control group of units with no CEP, that is, on average, as similar as possible to the treatment group of CEP units in terms of relevant pretreatment characteristics. Following the literature on the determinants of CEP adoption and environmental performance, we select the following control variables: i) Gross National Income per capital (GNIpc); ii) energy consumption; iii) annual population growth, iv) financial development, v) population density; vi) rule of law.

3.2 Descriptive statistic

Prior in a form of econometric analysis, we begin by present some descriptive statistic obtained before and after the weighting use to estimate the treatment impact of CEP adoption. The evidence in Table 1 suggest that countries that have adopted the CEP (column 1) differ from countries that have not (column 2). The pre-weighting descriptive statistics reveals structural and cyclical characteristics differences between CEP and non-CEP countries. Indeed, countries that CEP exist are characterized by (i) lower population density although the difference is not statistically significant, (ii) lower GNI per capital, (iii) lower financial development, (iv) larger annual population growth, (v) lower energy consumption, and (vi) lower legal institutions quality, compared to other countries. These differences across CEP and non-CEP countries clearly demonstrate the importance of selecting an appropriate control group when computing the treatment effect of CEP to avoid incorrectly estimated treatment effect.

Table 1

descriptive statistic before weighting
	[1]	[2]	[3] = [2]-[1]	[4]	[5]
Variables	CEP	No CEP	Difference	t-test	p-value
Population density t-1	4,147	4,481	0,334	0.775	0.438
Financial development t-1	3,435	3,984	0,549	12.018	0.000
GNI t-1	9,254	9,744	0,49	9.143	0.000
Population growth t-1	1,578	1,467	-0,111	-1.701	0.089
Energy use t-1	7,092	7,368	0,276	10.067	0.000
Rule of law t-1	0,019	0,538	0,519	13.152	0.000
Notes: This table presents the pre-weighting sample means of the matching covariates for country-year observations where the protection of environment has been integrated in the constitution (the treatment group) in column [1] and country-year observations where CEP do not exist (the potential control group) in column [2]. Column [3] reports the differences in means between treated and control group, and the corresponding t test statistics and p values in columns [4]-[5] .

However, after having created the balanced sample using the covariate moments, the results in Table 2 clearly show no significant differences between the two groups. It appears that most of the covariates are perfectly balanced between the two groups and no significant difference remains after weighting, suggesting that controlling for these covariates in the second step may improve the quality of the matching. Consequently, the lack of differences between the groups strongly demonstrates the effectiveness of the entropy balancing method in building a desired balance.

Table 2

descriptive statistic after weighting
	[1]	[2]	[3] = [2]-[1]	[4]	[5]
Variables	CEP	No CEP	Difference	t-test	p-value
Population density t-1	4,147	4,147	0	1.308	0.905
Financial development t-1	3,435	3,435	0	12.018	1
GNI t-1	9,254	9,254	0	9.143	1
Population growth t-1	1,578	1,578	0	-1.701	0.911
Energy consumption t-1	7,092	7,092	0	10.067	1
Rule of law t-1	0,019	0,02	0,001	13.152	1
Notes: This table presents the sample means matching covariates after weighting across the treated CEP adoption group in column [1] and the synthetic control group obtained from entropy balancing in column [2]. Columns [3; 4 and 5] show the differences in means, the t test statistics and the associated p values respectively.

Using the synthetic control group computed in Table 2 The main finding is reported in Table 3. Column [1]-[4] show the regression’s result without the matching covariates in the second step of the entropy balancing. Column [1] excludes country and time-fixed effects. Columns [2]-[3] include country and time fixed effects, respectively, while column [4] includes these two effects jointly. Column [5]-[8] bring the covariates into the regression. Columns [6]-[7] control for country and year fixed-effects, respectively. Finally, column [8] gathers the covariates as well as year and regional fixed-effects into the regression. Including the matching covariates in the second stage of entropy balancing increases the quality of the matching while controlling for country and year fixed-effects eliminates any country or year-specific effects. The results are strong and robust. Irrespective of the specification, the estimated effect of CEP on CO2 emissions is negative and statistically significant. The CEP adoption significantly decreases CO2 emissions in CEP countries compared to non-CEP countries. The estimated impact ranges from − 0.260 (column [2]) to -0.946 (columns [3]). The average estimated impact is about − 0.48. meaning that CEP adoption decreases carbon dioxide emissions by -0.48 percentage point in CEP countries compared to non-CEP countries. These results imply that the adoption of constitutional environmental protection, promotes green environment by strengthening the legal and institutional framework for the protection of the right to a healthy environment, and enabling the pro-environmental attitudes and behaviors.

Table 3

CEP and environmental quality: Entropy Balancing estimates
	Outcome variable: CO2 emissions
VARIABLES	(1)	(2)	(3)	(4)		(5)	(6)	(7)	(8)
CEP	-0.658	-0.260*	-0.946**		-0.377**	-0.580***	-0.356***	-0.526***	-0.296**
	(0.462)	(0.137)	(0.446)		(0.151)	(0.172)	(0.119)	(0.172)	(0.121)
Bootstrap in the 1st step	500	500	500		500	500	500	500	500
Covariates in the 2nd step	No	No	No		No	Yes	Yes	Yes	Yes
Year fixed effects in the 2nd step	No	No	Yes		Yes	No	No	Yes	Yes
Country fixed effects in the 2nd step	No	Yes	No		Yes	No	Yes	No	Yes
Adjusted R²	0.002	0.979	0.038		0.980	0.814	0.986	0.815	0.988
Observations	1,071	1,071	1,071		1,071	1,071	1,071	1,071	1,071

Unreported constant included. Robust standard errors in brackets. ***p < 0.01, **p < 0.05, *p < 0.1.

Our main empirical findings in Table 3 shows that the presence of CEP lowers the carbon dioxide emissions in CEP countries, compared to non-CEP countries. In the following, we test robustness of these results.

5.1 Alternative matching methods

The negative impact of CEP adoption on CO2 emissions is also confirmed using three different methods of matching: propensity score matching developed by Rosenbaum and Rubin (1983), the Inverse Probability Treatment Weighting (IPTW) and the Inverse Probability Treatment Weighted Augmented. These approaches consist of comparing country-year observations where CEP have been adopted with counterfactual country-year observations without CEP that have similar likelihood of having CEP. Under these approaches, the probability of adopting CEP is estimated in a first step for each country-year observations based on a vector of observable variables. The treatment effect of CEP adoption is then computed in a second step using the propensity scores of CEP adoption estimated and different varieties of matching algorithms. Following Balima et al. (2017) and Avom et al. (2023), we implement the PSM using these matching algorithms: the kernel matching, the radius matching (with a radius of 0.045, 0.090 and 0.18), the N-nearest neighbor (with N = 1, 2, 3), and the local linear matching1. However, the IPTW and AIPTW differs from the PSM as its use all the data available without discarding any observations in the treatment and controls groups in contrast to PSM (Obsuth et al., 2023). Furthermore, AIPTW estimator combines both the properties of the regression-based estimator and the inverse probability weighted (IPW) estimator and is therefore a “doubly robust” method in that it requires only either the propensity or outcome model to be correctly specified but not both (Kurz, 2022).

The PSM, IPTW and the AIPTW’s results are reported in Table 4. The findings confirm the negative impact of CEP adoption on CO2 emissions.

The PSM results through standard analysis tools (Caliendo & Kopeinig, 2008; Li, 2013) show that the quality of matching is quite satisfactory. For example, Fig. 1 below shows the distribution of propensity score estimates for the two groups and the common support region. It can be clearly observed in Fig. 1 that the common support assumption is satisfied, and all treated and untreated observations are within the region of common support.

Source: author’s construction

Columns [1]-[4] in Table 4 presents the results of the propensity score estimates. This is the estimate of the impact of the CEP on environment quality. Overall, the results show a positive and significant impact of CEP on CO2 emissions at the 5% level, regardless of the algorithm used. Thus, the adoption of CEP decreases carbon dioxide emission by approximately − 0.35 to -1 percentage point depending on the algorithm used. The impact of CEP on environment quality remains negative and significant at 1% when we use IPTW and AIPTW matching methods. However, the average magnitude (-1,06) of the estimates in different cases is somewhat higher than the − 0.48-percentage point estimate of the entropy balancing.

5.2 Dynamic panel approaches

In this subsection, we move to a dynamic panel regression method, in applying first the fixed effect methods and then the system generalized method of moments (SGMM) to account for the dynamic of the environment quality and to also deal with endogeneity problems in the model. We perform OLS fixed effect methods of estimation because, OLS is generally used as analytical framework to give the general trend of the results. However, FE-OLS method is generally affected by some limitations, in particular the problems of endogeneity.

Endogeneity may arise in this study from omitted bias, measurement errors or reverse causality. The reduction of greenhouse gases is influenced by pro-environmental behavior. Maintaining this behavior over time requires the adoption of an appropriate institutional framework, such as the amendment of the constitution to include provisions relating to environmental protection. with persistently high CO2 emissions and the dynamic evolution of CEP adoption in the world points out the possible existence of the reverse causality effects between CEP and environmental quality. Thus, to address this issue, we apply the two-step SGMM (Arellano & Bover, 1995; Blundell & Bond, 1998), which allows the introduction of more instruments by adding a second equation that should improve estimation efficiency. The consistency of the GMM estimator depends on two elements: the validity of the hypothesis that the error term does not have serial correlation (AR2) and the validity of the instruments (Hansen’s test). Too many instruments can seriously weaken and bias Hansen’s test of over-identification of restrictions, and therefore the rule of thumb is that the number of instruments must be less than the number of countries (Roodman, 2009). In order to filter out business cycle influences on results, we take 5-year averages for each variable over the period.

In Table 5, column [1] provides an empirical estimate using FE-OLS. the estimated result show that CEP negatively and significantly affect CO₂ emissions at 1% threshold. In column [2] we also estimate causal effect by applying the Driscoll-Kraay estimator, which estimates fixed effects (Within) regression model with Driscoll-Kraay standard errors (Hoechle, 2007). The Driscoll and Kraay (1998) standard errors are robust to general forms of heteroscedasticity and autocorrelation. The estimation results in column [2] shows that the coefficient of CEP remains negative and statistically significant at 1% level. Meaning that Constitutional Environmental Protection increases environmental quality.

we estimate again the impact of CEP on environmental quality using System-GMM method. As it can be seen in column [3], the regression satisfies the specification tests (AR1, AR2 and Hansen test). The number of instruments used is lower than the number of countries in the sample. Indeed, in order to limit the proliferation of instruments in the implementation of the GMM estimator, (Roodman, 2009) recommends specifying the model so that the number of instruments does not exceed the number of countries.

Subsequently, autocorrelation test allows us to deduce the presence of autocorrelation of the residuals to order 1 and the absence of serial autocorrelation to order 2 (Arellano and Bond, 1991). Finally, the regressions pass the Hansen’s test and confirm the validity of the instruments. The finding in column [3] is qualitatively identical to those previously obtained. It confirms the negative and statistically significant relationship between CEP and carbon dioxide emissions. In other world, CEP induces the reduction of CO₂ emissions, as a result, the environmental quality is improved.

Table 4

CEP and environmental quality: PSM, IPTW and AIPTW matching
	Propensity score matching
	Kernel matching	Radius matching			Nearest-Neighbor matching			Local Linear matching
	BW = 0.06	R = 0.045	R = 0.090	R = 0.18	N = 1	N = 2	N = 3	BW = 0.06	RA	IPTW	Augmented IPTW
ATT	-0.4786***	-0.4620**	-0.5443***	-0.9963***	-0.9913***	-0.9590***	-0.8951***	-0.3514**	-0.9103***	-3.0024***	-2.0603***
	(0.158)	(0.197)	(0.159)	(0.231)	(0.245)	(0.249)	(0.253)	(0.166)	(0.217)	(0.248)	(0.328)
Observations	2,389	2,389	2,389	2,389	2,389	2,389	2,389	2,389	1,071	1,071	1,071

bootstrapped standard errors based on 500 replications reported in brackets. ***p < 0.01, **p < 0.05, *p < 0.1

Table 5

Dynamic panel estimate
	(1)	(2)	(3)
VARIABLES	OLS-FE	Driscoll/Kraay-FE	SGMM
L.CO2pc			0.777***
			(0.144)
CEP	-0.4401***	-0.4401***	-0.092**
	(0.142)	(0.135)	(0.046)
GNIpc	9.2363***	9.2363**	2.094***
	(1.816)	(4.074)	(0.682)
GNIpc²	-0.4725***	-0.4725*	-0.118***
	(0.102)	(0.235)	(0.038)
Energy consumption	4.7598***	4.7598***	0.369***
	(0.279)	(0.445)	(0.136)
Financial development	-0.0824	-0.0824	-0.027
	(0.114)	(0.093)	(0.048)
Population density	-5.0751***	-5.0751***	-0.080*
	(0.344)	(1.313)	(0.040)
Rule of Law	-0.4153***	-0.4153**	0.062
	(0.157)	(0.163)	(0.095)
Ananual Population growth	0.0859**	0.0859	0.001
	(0.036)	(0.153)	(0.010)
Constant	-51.5114***	-51.5114***	-10.981***
	(8.063)	(14.917)	(3.631)
Observations	1,010	1,010	1,069
Number of countries	81	81	81
number of instruments			50
R-squared	0.3786
Time fixed effects			Yes
Countries fixed effects			Yes
AR(1)			0.000120
AR(2)			0.246
Hansen test			0.945
Note: Robust standard errors are reported in brackets, *, , *; represent the statistical significance at the level of 1%, 5% and 10%, respectively. The regression in column 3 are based on the two-steps system GMM

5.2.1 Heterogeneity of the results

Our previous finding reveals that adoption of CEP increases environmental quality by reducing CO2 emissions. In the following, we provide evidence of the sensitivity of the above finding based on the world regions, the types of legal origin and the level of pollution.

World region and Types of legal origin

the reaction of the quality of environment following the CEP adoption might depend on where countries are located. The result in columns [1]-[5] in Table 6 show that the coefficient associated with CEP are negative and significant in Latin America, Middle East and North Africa (MENA) and Sub-Saharan Africa (ASS) countries. However, the magnitude of the effect is greater in Latin American and in MENA countries than Sub-Saharan countries. These coefficients remind negative but not significant in European and Asian countries. We also grouped countries into French civil law and English common law legal origin. The results reported in columns [6]-[7] in Table 6 show that the effect of CEP on CO2 emissions is still negative and significant at 5% level across all types of legal origin. However, the magnitude of the effect is greater in countries with French civil law than those with English common law.

Level of pollution

We also addressed the concern of irregular data distribution of our sample regarding the pollution level. We adopt a novel non-parametric estimation method, which could deal with the issue of abnormality in the data. Therefore, we use the fixed effect method of moments quantile regression (MMQR) approach for analyze the heterogenous relationship between the adoption of CEP and environmental quality across the sample. In the quantile, five quantiles of 0.15, 0.25, 0.50, 0.75 and 0.95 were chosen to estimate the coefficient of the dependent variables. The results are displayed in Table 7. The CEP has a significant negative coefficient across all the distribution (columns [1]-[4]) with the exception of 95th quantile where the coefficient is negative but not significant (column [5]). It suggests that the effect of constitutional environmental protection (CEP) is greatest in countries with the highest level of CO2 emissions. In the other words, for countries that start with a high level of CO2 emissions, amending the constitution to include environmental protection is associated better environmental quality.

5.2.2 Alternative measure of CEP

In this sub-section, we draw a new proxy of CEP base on CEP experience of the state. In order to make this variable continuous, we calculated the lifetime of the constitutional protection of environment from the year of insertion of the first constitutional provision relating to the environment to the last year of our study period.

$${CEP experience}_{it}=\stackrel{-}{{T}_{i}}-{x}_{it}$$

Where $\stackrel{-}{{T}_{i}}$ is the year of adoption of the first constitutional provision for environment in country i and ${x}_{it}$ the different years t following the adoption of the CEP in country i.

In baseline result, CEP is a category variable and it is negatively correlated to carbon dioxide emissions. A potential issue with estimate in baseline specifications is that constitutional amendment regarding environmental protection may be the driving force to obtain a green environment. However, once a country adopted the right to the environment in its constitution, this is not automatically accompanied by an improvement in environmental quality. It may take some time for the new constitution amendment to leads to an institutional adjustment aimed at implementing or strengthening environmental laws and governance.

The results reported in Table 8 show a negative and statistically significant relationship between CEP experience and CO₂ emissions depending on whether the model is estimated by OLS-FE (column [1]) or SGMM (column [3]). Suggesting that, countries that have amended their constitutions early on to incorporate environmental protection provisions can benefit from a significant reduction of carbon dioxide emissions, helping to improve green environment.

[1] The nearest neighbor matches a CEP country observation with the N nearest neighbor non-CEP country observations using the estimated probability. The radius matching compares CEP and non-CEP observations using a threshold metric of distance. The kernel matching uses an inversed weight to match CEP and non-CEP units, while the local linear approach follows the kernel matching but does include a linear term in the weighting function. For a discussion between these varieties of PSM, see Caliendo and Kopeinig (2008)

This study analyzes whether adopting constitutional environmental protection improve environmental quality. Using Entropy Balancing to control endogeneity on a sample of 119 countries over 1990–2020. The paper connects the effect of constitution on environmental policy-making or decision-making with the broader literature that studies the relationship between institutions and environmental quality.

Our study conclude that constitutional environmental protection increases environmental quality by leading to increase the likelihood of reducing carbon dioxide emissions by about 48% in CEP countries compared to non-CEP countries. It shows that constitutionalized the environment is important to establish pro-environmental behaviors. This finding is robust to alternative specifications, including alternative matching methods and dynamic panel approaches. In addition, we provide evidence that: i) the impact of CEP adoption is more important in Latin America and MENA than others regions in the world. ii) the impact is greatest in French civil law legal origin; iii) countries with high level of pollution record a greatest impact of the adoption of CEP on environmental quality than countries with lower pollution levels. The results also indicate that state experience in CEP leads to economy decarbonization.

From policy perspective, we suggest, following Jeffords & Minkler (2016) that, we should not only pay attention to the incentives confronting polluters and resource users, but also to the incentives and constraints confronting those policymakers who initiate, monitor, and enforce environmental policies. Thus, the more countries commit to considering the right to healthy environment as a fundamental right, the greater the likelihood that the world will be able to significantly reverse the curve of pollution and global warming.

Table 6

Dynamic panel estimate, with heterogeneity
	(1)	(2)	(3)	(4)	(5)	(6)	(7)
VARIABLES	Latin America	MENA	ASS	Europe	Asia Pacific	Common law	Civil law
L.CO2pc	0.872***	0.830***	0.837***	0.652***	0.867***	0.883***	0.883***
	(0.193)	(0.215)	(0.182)	(0.192)	(0.140)	(0.186)	(0.209)
CEP	-0.112**	-0.111**	-0.094*	0.036	-0.060	-0.085**	-0.111**
	(0.047)	(0.051)	(0.049)	(0.082)	(0.050)	(0.035)	(0.055)
GNI	2.111*	2.120**	1.952***	1.844**	1.611***	2.043**	2.021**
	(1.071)	(1.037)	(0.736)	(0.872)	(0.575)	(0.915)	(0.817)
GNI2pc	-0.122**	-0.121**	-0.113***	-0.093**	-0.091***	-0.119**	-0.117***
	(0.057)	(0.057)	(0.040)	(0.045)	(0.031)	(0.050)	(0.044)
Energy consumption	0.282	0.319	0.332*	0.353**	0.293**	0.293	0.271
	(0.195)	(0.211)	(0.183)	(0.172)	(0.115)	(0.193)	(0.214)
Financial development	-0.043	-0.029	-0.023	-0.030	-0.036	-0.029	-0.042
	(0.062)	(0.066)	(0.054)	(0.047)	(0.044)	(0.066)	(0.069)
Rule of Law	0.115	0.097	0.070	-0.011	0.012	0.080	0.101
	(0.139)	(0.143)	(0.114)	(0.097)	(0.092)	(0.129)	(0.132)
Population Density	-0.089*	-0.079*	-0.087**	0.112*	-0.091***	-0.078**	-0.078**
	(0.048)	(0.042)	(0.035)	(0.057)	(0.031)	(0.038)	(0.038)
Annual Population growth	-0.005	0.005	0.006	0.016*	0.002	0.004	0.003
	(0.014)	(0.012)	(0.013)	(0.009)	(0.010)	(0.013)	(0.012)
South and Latin America	-0.087
	(0.203)
North Africa and Middle East		-0.060
		(0.087)
Sub-Saharan Africa			-0.046
			(0.102)
Europe and Nord America				-0.130
				(0.150)
Asia and Pacific					0.105
					(0.083)
Common law						0.027
						(0.105)
Civil law							-0.025
							(0.082)
Constant	-10.039*	-10.576*	-9.879**	-11.582**	-8.401**	-9.977**	-9.712**
	(5.460)	(5.470)	(4.062)	(5.267)	(3.316)	(4.609)	(4.600)
Observations	1,069	1,069	1,069	1,069	1,069	1,063	1,063
Number of Countries	81	81	81	81	81	81	81
Number of instruments	55	56	56	46	51	56	56
Time fixed effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Countries fixed effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes
AR(1)	0.000455	0.00122	0.000400	0.00461	6.31e-05	0.000342	0.000652
AR(2)	0.195	0.226	0.224	0.261	0.237	0.221	0.216
Hansen test	0.636	0.635	0.559	0.980	0.971	0.593	0.715
Note: Robust standard errors are reported in brackets, *, , *; represent the statistical significance at the level of 1%, 5% and 10%, respectively.

Table 7

Quantile regression
	(1)	(2)	(3)	(4)	(5)
VARIABLES	q15	q25	q50	q75	q95
CEP	-0.221***	-0.169***	-0.113***	-0.067***	0.026
	(0.036)	(0.037)	(0.025)	(0.022)	(0.069)
GNI	3.808***	3.701***	3.381***	3.436***	1.304***
	(0.248)	(0.188)	(0.219)	(0.305)	(0.415)
GNI2pc	-0.163***	-0.161***	-0.153***	-0.157***	-0.053***
	(0.014)	(0.011)	(0.012)	(0.019)	(0.019)
Energy consumption	0.691***	0.688***	0.773***	0.725***	0.810***
	(0.046)	(0.046)	(0.028)	(0.044)	(0.102)
Financial development	-0.003	0.085***	0.109***	0.054*	0.053
	(0.044)	(0.033)	(0.026)	(0.028)	(0.035)
Rule of Law	-0.161***	-0.214***	-0.172***	-0.129***	-0.184***
	(0.038)	(0.035)	(0.023)	(0.030)	(0.039)
Population Density	0.052***	0.028***	0.008	0.008	-0.022**
	(0.012)	(0.007)	(0.007)	(0.007)	(0.011)
Annual Population growth	0.018*	0.028***	0.034***	0.032***	-0.006
	(0.010)	(0.010)	(0.010)	(0.010)	(0.014)
Constant	-25.707***	-24.979***	-23.091***	-22.638***	-12.017***
	(1.163)	(0.869)	(1.010)	(1.420)	(1.905)
Observations	1,071	1,071	1,071	1,071	1,071
Time fixed effects	Yes	Yes	Yes	Yes	Yes
Countries fixed effects	Yes	Yes	Yes	Yes	Yes
Bootstrap replications	100	100	100	100	100
Note: standard errors are reported in brackets, *, , *; represent the statistical significance at the level of 1%, 5% and 10%, respectively.

Table 8

Dynamic panel estimate, alternative measure of CEP
	(1)	(2)	(3)
VARIABLES	OLS-FE	Driscoll/Kraay-FE	SGMM
L.C02pc			0.589***
			(0.202)
CEP Experience	-0.1729*	-0.1729	-0.007**
	(0.095)	(0.172)	(0.003)
GNIpc	6.2692***	6.2692	2.642***
	(2.024)	(5.704)	(0.946)
GNIpc²	-0.2938**	-0.2938	-0.156***
	(0.114)	(0.330)	(0.051)
Energy consumption	4.2864***	4.2864***	0.725***
	(0.288)	(0.577)	(0.217)
Annual Population growth	0.1813***	0.1813	-0.003
	(0.036)	(0.161)	(0.014)
Financial development	0.0361	0.0361	-0.020
	(0.130)	(0.117)	(0.077)
Population Density	-4.1021***	-4.1021**	-0.081
	(0.456)	(1.880)	(0.069)
Rule of Law	-0.5926***	-0.5926***	0.137
	(0.186)	(0.191)	(0.109)
Constant	-41.1722***	-41.1722*	-15.036***
	(8.973)	(20.959)	(5.461)
Observations	884	884	1,069
Number of Countries	81	81	81
Number of instruments			50
R-squared	0.3583
Time fixed effects			Yes
Countries fixed effects			Yes
AR(1)			0.0116
AR(2)			0.357
Hansen test			0.835
Note: Robust standard errors are reported in brackets, *, , *; represent the statistical significance at the level of 1%, 5% and 10%, respectively. The regression in column 3 are based on the two-steps system GMM

Funding statement

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Competing interest’s statement

The authors have no relevant financial or non-financial interests to disclose.

Authors contribution

All authors contributed to the study conception and design. Investigation, Data collection, data curation and analysis were performed by Charly Tsala Ondobo, Hermann Ndoya and Donald Okere. Methodology and the first draft of the manuscript was written by Charly Tsala Ondobo and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Data availability

The data used in this study are openly available in the WDI database and Quality of Government Environmental Indicators Dataset. Moreover, all data that support the findings of this study are available from the corresponding author upon reasonable request.

Ethics approval statement

This study did not require ethic approval.

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Does constitutional protection of the environment matter for environmental quality?

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Abstract

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1 Introduction

2 Methodology

3 Data and descriptive statistics

3.1 Data

3.2 Descriptive statistic

4 Baseline results

5 Robustness check

5.1 Alternative matching methods

5.2 Dynamic panel approaches

5.2.1 Heterogeneity of the results

5.2.2 Alternative measure of CEP

6 Conclusion

Declarations

References

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