Does corruption influence climate change in developing countries? Effects and transmission channels

Does corruption influence climate change in developing countries? 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Effects and transmission channels François-Cyrille EYEGHE-NTOUTOUME This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6528488/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Climate change is one of the most pressing issues of our time. While its effects are affecting our ecosystems, societies and economies, understanding the mechanisms by which certain institutional forces, such as corruption, exacerbate this crisis remains a key and under-explored issue. This study sheds new light on the issue by analysing the transmission channels through which corruption affects climate change in developing countries between 2000 and 2022. It highlights the central role of the participatory democracy index and energy poverty, in particular access to clean fuels and technologies. After several empirical approaches, the results of the structural equation mediation (SEM) suggest that corruption positively affects climate change through an indirect channel via these two dimensions. These results underline the urgency of a solid institutional framework and investment in clean energy to counter this dynamic in developing countries, providing a basis for better targeted climate policies. Environmental Policy Environmental Economics corruption climate change participatory democracy energy poverty mediation Figures Figure 1 1. Introduction Climate change is a reality. It is right in front of our eyes: droughts that last too long, floods that sweep away everything in their path, once fertile land that is turning into desert, carbon emission levels that continue to rise. And it is the developing countries that are suffering the most. But behind these disasters, another scourge, quieter but just as destructive, is making the situation even worse: levels of corruption. When funds earmarked for environmental projects disappear, when illegal operating permits are granted to polluting companies, when political decisions are motivated by private interests rather than the general interest, efforts to combat climate change collapse. Various factors can explain climate change, including environmental factors (Foley et al. 2005), economic factors (Stern, 2007 ; Cole, 2007 ), social factors (Harvey, 1982 ; Sen, 1999) and institutional factors (North, 1990; Acemoglu et al. 2012 ). Corruption covers many different aspects, and it is therefore difficult to give an exhaustive definition. The World Bank ( 1997 ) defines corruption as ‘the abuse of public power for personal gain’, while Transparency International ( 2020 ) sees it as ‘the abuse of power conferred for personal gain’. These are not the only definitions; Nye ( 1967 ) suggests that corruption is ‘behaviour that deviates from the official duties of a public office because of pecuniary advantage or private status (personal, close family, private clique); or that violates rules prohibiting the exercise of certain types of private influence’. According to the United Nations, climate change refers to long-term variations in temperature and weather conditions (UN, 2024). But since the 1800s, human activities have been the main driver of climate change, mainly due to the burning of fossil fuels such as coal, oil and gas. For the World Meteorological Organisation (2024), climate change is the term used to describe changes in the state of the climate that can be identified by changes in the mean and/or variability of its properties and that persist for an extended period, typically decades or more. The aim of this research is to analyse the effects and mechanisms of corruption on climate change in developing countries. There are two main reasons for choosing developing countries. The first reason is the manifestation of climate change, in particular extreme weather events, rising temperatures and changes in precipitation patterns pose significant threats to developing countries with fragile social, economic and political structures (Adom, 2024 ). Developing countries have contributed 95% of the increase in global emissions over the last decade and accounted for 75% (44 GT) of global emissions in 2023 (Climate Leadership Council, 2024). Projections show that this trend is set to continue. The second argument resides in the fact that corruption has serious consequences for the environment, particularly through the misappropriation of funds during the implementation of environmental programmes, the granting of permits and licences for the exploitation of natural resources, and the payment of bribes to public officials (Tacconi et Williams, 2020). Research in developing countries shows that corruption affects carbon dioxide emissions, which are a major contributor to pollution (Wang et al. 2018 , 2020 ). However, corruption can introduce a bias not only in the process of implementing environmental policies, but also in the process of applying and monitoring environmental laws (Lopez and Mitra, 2000; Abid et al. 2023 ). The article is divided into five main sections. The second presents some key trends, while the third reviews the main research on the subject. The fourth section explains the various stages of our empirical approach. The fifth section discusses the results obtained. The sixth section concludes with some policy recommendations. 2. Key trends The stylised facts examine the main trends relating to corruption controls, climate variables and the relationships between the phenomena in developing countries. Graph 1 shows the evolution of corruption control from 2000 to 2022 in developing countries. There is a general downward trend in the monitoring of corruption over the years. This could indicate that efforts to fight corruption have diminished or are ineffective over time. In addition, the negative values on the graph may seem surprising, but they show that the perceived level of corruption is above a certain tolerance threshold. More accurately, it shows the extent to which corruption is perceived as a problem within these countries. Looking at Graph 2 of annual temperatures in developing countries from 2000 to 2022, a clear trend emerges: temperatures are gradually rising. For example, in 2010, 2015, 2016, 2019, 2020, 2021 and 2022 temperatures are particularly high, exceeding 1°C and approaching 1.5°C. On the other hand, despite these upward variations, some periods show relatively stable temperatures between 2000 and 2009. This indicates that, even in a period of global warming, there are moments of respite. The graph 3 shows the challenges faced by developing countries in managing carbon dioxide emissions. On closer examination, it is clear that CO 2 emissions have grown steadily between 2000 and 2022. In 2000, they were much lower than in 2022, when they will reach their highest level. Graph 4 explores how nitrous oxide emissions are changing in developing countries over the period 2000-2022. Between 2000 and 2008, they grew slowly. But between 2008 and 2012, the increase is more rapid. After 2012, there is a slight slowdown, but they continue to rise. Graph 5 tells a worrying story: that of a world where corruption is on the rise, while the planet is suffering more and more. Since 2000, there has been a decline in the control of corruption, which means that, in the countries in our sample, laws are less respected, decisions are sometimes manipulated by private interests, and environmental policies are less effective. At the same time, emissions of carbon dioxide and nitrous oxide are increasing. These gases, which are responsible for global warming, are causing temperatures to rise, as shown by the yellow line in the graph. 3. Synthesis of previous work The relationship between growth and CO 2 emissions is widely discussed in the literature. However, in recent years much attention has been paid to the role of corruption in the relationship between growth and CO2 emissions, but little work has been done in this area of research. There are two schools of thought regarding the relationship between corruption and carbon dioxide emissions. The first approach defends the idea of a direct impact, showing that corruption reduces carbon dioxide emissions (Zhang et al. 2016 ; Arminen and Menegaki, 2019; Akhbari and Nejati, 2019 ; Balsalobre-Lorente et al, 2019 ; Devlina et al. 2022 ), on the one hand, and also increasing it (Masron and Subramaniam, 2018; Zhang and Chiu, 2020; Ren et al. 2021 ), on the other. As for the second approach, it attempts to show that corruption indirectly influences carbon emissions by acting on economic growth (Wang et al. 2018 ; Leitao, 2021); on energy consumption (Arminen and Menegaki, 2019; Balsalobre-Lorente et al. 2019 ). In addition, the theoretical literature highlights the income channel (Welsch, 2004 ; Cole, 2007 ; Biswas et al. 2012 ; Hassaballa, 2015 ) as well as the international trade channel (Ridzuan et al. 2019 ; Chen, 2023). Corruption also affects carbon emissions through institutions (Desai, 1998 ; Goel et al. 2013 ) and through information and communication technologies (Liu et al. 2021; Alam et al. 2022). 3.1 The direct effects of corruption on climate change Corruption can have a variety of impacts on the environment, influencing both positive and negative levels of CO 2 emissions. We will explore how corruption can potentially reduce and/or increase these impacts. 3.1.1 Corruption reduces carbon dioxide emissions Empirical research on the impact of corruption on carbon dioxide emissions indicates that corruption leads to a decrease in carbon emissions (Sekrafi et Sghaier 2018 ; Arminen et Menegaki 2019 ; Balsalobre-Lorente et al. 2019 ; Akhbari et Nejati 2019). In this context, Sekrafi and Sghaier (2018), study the impact of corruption on carbon dioxide emissions. Indeed, the authors formulated three equations using an ARDL model. The first equation assessed the correlation between corruption and economic growth, the second equation tested the role of corruption on carbon dioxide emissions, and finally, an equation for energy consumption as the dependent variable. Considering the empirical results and especially the carbon dioxide emissions equation, they find that per capita income negatively affects carbon dioxide emissions. Income per capita squared is positively associated with carbon dioxide emissions. However, corruption has a negative effect on carbon dioxide emissions. In another study, Akhbari and Nejati ( 2019 ) also examined this phenomenon over 61 developed and developing economies for the period 2003–2016 using panel data. Estimates with the fixed effects model show that the corruption result is ambiguous. When the authors consider only the corruption index variable, they observe a statistically significant negative effect on carbon emissions. Recent work by Zhang et al ( 2016 ) goes further by analysing the direct and indirect impact of corruption on carbon emissions in Asia-Pacific Economic Cooperation (APEC) countries for the period 1992–2012 using the quantile regression approach. They reach the same results as Akhbari and Nejati ( 2019 ). They find that the effect of corruption on carbon dioxide emissions is heterogeneous across APEC countries. Specifically, there is a significant negative effect in low-emission countries, but insignificant in high-emission countries. In addition, corruption may also have a positive indirect effect through its effect on GDP per capita. In addition, Devlina et al ( 2022 ) extend their scope of investigation to more than 200 countries, taking into account upper-middle, lower-middle and low-income countries over the period 2005 to 2016. The empirical evidence validates a negative and significant relationship between corruption and carbon emissions. 3.1.2 Corruption increases carbon emissions Another view is that corruption amplifies carbon dioxide emissions (Cole 2007 ; Sahlia et Rejeb 2015 ; Hassaballa 2015 ; Dincer et Fredriksson 2018 ; Masron et Subramaniam 2018 ; Ridzuan et al. 2019 ; Akhbari et Nejati 2019 ; Zhang et Chiu 2020). The empirical study by Ridzuan et al ( 2019 ) examines the correlation between corruption and carbon dioxide emissions in three ASEAN countries (Malaysia, Indonesia and the Philippines) for 1970–2017. The authors tested the effects of economic growth, energy consumption, foreign direct investment, trade openness, financial development, corruption index and urban population on carbon dioxide emissions. With regard to the econometric results using the distributed lag ARDL autoregressive model, the authors demonstrated that corruption positively affects CO 2 emissions in the long term. In addition, the foreign direct investment variable is negatively correlated with carbon dioxide emissions in Malaysia and the Philippines. Finally, the urban population variable positively affects carbon dioxide emissions in Indonesia and the Philippines. In addition, the empirical study by Lee et al. ( 2020 ) assessed corruption in Asian countries, namely Bangladesh, Brunei, China, India, Indonesia, Malaysia, Myanmar, Pakistan, Philippines, Singapore, Sri Lanka, Thailand and Vietnam, considering the period 1984 to 2017. The empirical results with pooled OLS, fixed effects and random effects are similar. The authors indicate that corruption has a positive impact on carbon dioxide emissions. The link between corruption and carbon dioxide emissions for the MENA region has been studied by Sahlia and Rejeb (2015) and Hassaballa ( 2015 ). The empirical work proves that corruption positively affects the environment. On the other hand, Sahlia and Rejeb (2015) find a positive effect between corruption and CO 2 emissions. The experience of developed and developing countries for the period 1999–2015 has been studied by Azami and Rezaei (2017). In this research, the researchers use panel data and quantile regression. The results indicate that corruption leads to an increase in carbon dioxide emissions in both developed and developing countries. The result is also evidence of the Kuznets environmental hypothesis. The question of the impact of corruption on the environment remains crucial, especially in contexts such as China where haze pollution is a major challenge. Zhao et al (2022) explored this dynamic using the generalized method of moments in 30 Chinese provinces over the period 2006–2018. The evaluations reveal that corruption positively affects haze pollution at the 1% significance level. In addition, Ren et al ( 2021 ) conduct a different study and find the same results within Chinese provinces through the autoregressive distributed lag modelling approach and panel quantile regressions, The results show that corruption increases per capita carbon emissions in the short run, reducing per capita carbon emissions in the long run. The empirical study by Sulemana and Kpienbaareh (2020) explores the association between corruption and air pollution in Africa and OECD countries. The authors use unbalanced panel data from 48 sub-Saharan African countries and 34 OECD countries for the period 1996–2014. The results show that a positive association between particulate matter was found in African and OECD countries and a negative association between corruption and carbon dioxide emissions for both samples. 3.2 The indirect effects of corruption on climate change Several theoretical and empirical studies highlight the fact that corruption affects climate change via energy consumption, economic growth, institutions and international trade. 3.2.1 Corruption, energy consumption and climate change As the fight against carbon emissions becomes ever more pressing, a crucial question emerges: how does corruption influence not only energy consumption but also indirectly the level of carbon emissions? Research by Arminen and Menegaki (2019) provides an answer to this question. Using dynamic panel data from 1985 to 2011, the authors formulated three equations: the first for economic growth, the second for energy consumption and the last for air pollution. The empirical results validate the bidirectional relationship between energy consumption and carbon dioxide emissions. According to the results, the corruption is not statistically significant for all the models. In addition, the work of Balsalobre-Lorente et al ( 2019 ) examines how energy innovations and corruption affect carbon emissions in a panel of 16 OECD countries. The empirical results show that when economic systems interact with corruption, the positive effects that energy innovations have on environmental quality are reduced. 3.2.2 Corruption, economic growth and climate change The policy debate on economic growth and CO 2 emissions is topical: corruption can affect this relationship by increasing pollution at given income levels and reducing per capita income (Lopez and Mitra, 2000; Welsch, 2004 ; Cole, 2007 ; Hassaballa, 2015 ). For example, Wang et al. ( 2018 ) apply a partial least squares regression model for a panel of BRICS countries from 1996 to 2015. Overall, the empirical results lead to the conclusion that the moderating role of corruption is crucial in the relationship between economic growth and carbon dioxide emissions and that controlling corruption reduces CO 2 emissions. In the US context, Dincer and Fredriksson (2018) assessed the relationship between corruption, trust and CO 2 emissions using panel data. The results confirmed that corruption and trust have a positive effect on CO 2 emissions. However, a negative relationship exists between the lagged variable of stringency, per capita income squared and CO 2 emissions. 3.2.3 Corruption, institutions and climate change Corruption also influences carbon dioxide emissions through the quality of institutions (Pellegrini and Gerlagh, 2006). This idea is shared by the work of Goel et al ( 2013 ). The results suggest that corrupt nations, particularly those with large underground economies, tend to contribute negatively to pollution levels. Furthermore, by including political instability as a variable in their study, Fredriksson and Svensson (2003) develop a theory of environmental policy formation, taking into account the degree of corruptibility and political turbulence. The results suggest that political instability has a negative effect on the rigour of environmental regulations if the level of corruption is low, but a positive effect when the level of corruption is high. Corruption reduces the stringency of environmental regulations, but its effect disappears as political instability increases. 3.2.4 Corruption, international trade and climate change Trade liberalisation, corruption and environmental policy-making were studied by Damania et al (2003). The contribution in this study is the use of a theoretical model that produces several testable predictions, including: (i) the effect of trade liberalisation on environmental policy stringency depends on the level of corruption; and (ii) corruption reduces environmental policy stringency. Using panel data from a mixture of developed and developing countries from 1982 to 1992, they find evidence to support these conjectures. In the context of the BRICS, Wang et al ( 2018 ) show that trade is negatively associated with carbon dioxide emissions. 3.3 Transmission channels This study identifies two main transmission channels through which corruption influences climate change in developing countries: the participatory democracy index [1] and energy poverty. 3.3.1 Corruption, participatory democracy and climate change A growing number of studies highlight the important role of participatory democracy in determining carbon emissions. Tsur ( 2025 ) shows that direct popular vote and civil society participation reduce greenhouse gas emissions from all sources. However, the increased emphasis on individual and political freedoms reduces the effectiveness of liberal democracy in reducing greenhouse gas emissions. Moreover, the benefits of democracy for climate change mitigation are limited in the presence of widespread corruption, which reduces the ability of democratic governments to meet climate targets and reduce CO₂ emissions. Povitrika (2018) reports that greater democracy is associated with lower CO₂ emissions only in low-corruption contexts. If corruption is high, democracies do not seem to fare better than authoritarian regimes. 3.3.2 Corruption, energy poverty and climate change The economic literature also considers energy poverty to be a determining factor in climate change. Pondie and Engwali (2024) demonstrate that access to electricity and clean energy for cooking has a positive and significant effect on deforestation and carbon emissions for a sample of 43 Sub-Saharan African countries. In addition, some studies have explored the influence of energy poverty on climate change, arguing that effective policy design can achieve a win-win situation between energy poverty reduction and climate change mitigation (Ürge-Vorsatz and Herrero, 2012; Churchill et al. 2022 ). 3.4 Literature gap The synthesis of the literature review shows that corruption has both positive and negative effects on climate change, but these effects remain relatively small. What is missing from existing research, however, is a full exploration of the role of participatory democracy and fuel poverty as mediating factors that could influence carbon dioxide emissions, especially in developing countries. This is where our study comes in to fill this gap by examining how corruption affects climate change through the transmission channels mentioned. To analyse all this, we use a statistical method, mediation by structural equations, to check whether these factors can indeed play a relevant mediating role. We will also take into account other indicators of climate change, such as average annual temperatures and nitrous oxide, in addition to carbon dioxide emissions. 4. Data description and empirical strategy 4.1 Data description Our study covers a panel of 70 developing countries over the period 2000–2022. Data on participatory democracy are taken from the V-DEM database; and data on corruption control and control variables from the World Development Indicator (WDI). Table 1 presents the descriptive statistics for the above variables. Table 1 Descriptive statistics. Obs. Mean Std. Dev Min Max Corruption Controlling corruption 1610 -0.562 0.602 -1.936 1.610 Political Corruption Index 1610 0.372 0.194 0.006 0.87 Executive Corruption Index 1610 0.692 0.179 0.144 0.965 Public Sector Corruption Index 1610 0.316 0.165 0.031 0.816 Climate change Carbon emissions (ln) 1610 7.068 0.939 5.011 10.058 Nitrous oxide (ln) 1610 6.720 0.729 4.714 8.660 Mean annual temperature 1610 0.951 0.431 -0.698 2.762 Control variables Foreign direct investment 1610 -3.709 15.525 -231.651 41.674 International trade (ln) 1610 1.791 0.199 1.236 2.343 Agriculture (ln) 1610 1.571 0.327 -0.348 1.932 Energy consumption (ln) 1610 3.564 0.582 2.164 4.593 Democracy 1610 0.479 0.203 0.072 0.914 Economic growth (ln) 1610 10.441 0.831 8.545 13.252 Square of economic growth (ln) 1610 109.723 17.705 73.025 175.626 Transmission channels Participatory democracy index 1610 0.301 0.148 0.011 0.778 Energy poverty Access to electricity (% of population) 1610 63.604 33.005 1.3 100 Rural access to electricity (% of rural population) 1610 50.910 38.361 0.6 100 Access to electricity.urban (% of urban population) 1610 81.825 22.310 0.9 100 Access to clean cooking fuels and technologies (% of population) 1610 43.904 36.764 0 100 Access to clean cooking fuels and technologies (% of urban population) 1587 58.743 36.967 0.1 100 Access to clean cooking fuels and technologies (% of rural population) 1610 30.497 35.792 0 100 Note : Obs. number of observations; Standard deviation: Standard deviation; Min: minimum value; Max: maximum value. Source : author 4.1.1 Dependent variable: climate change In several previous studies, carbon dioxide has been used as one of the main indicators of climate change, as it is widely recognised as one of the main causes of this phenomenon (Welsch, 2004 ; Cole, 2007 ; Leitao, 2021). In our research, we have chosen to examine three indicators to measure climate change: the logarithm of carbon dioxide emissions, which will be our main dependent variable, as well as the logarithm of nitrous oxide levels and mean annual temperatures. 4.1.2 Variable of interest: corruption Numerous measures are used to assess the level of corruption. One of these is the Corruption Perceptions Index (Tavares and Wacziarg, 2001; Bakare, 2011). However, it is generally accused of being biased due to its reliance on surveys that are subject to value judgement and perception. There is also another measure of corruption developed by the International Country Risk Guide that is used by some researchers (Wei, 1997; Cole, 2007 ; Zhou 2013). Although it is still based on perception, it is more comprehensive and depends on the aggregation of more than 30 indices reflecting the point of view of individuals, companies and experts. This indicator ranges from − 2.5 (low control of corruption) to + 2.5 (high control of corruption). In this study we use four indicators to measure corruption: control of corruption, political corruption index, public sector corruption index and executive corruption index. The last three indicators are taken from the V-DEM database. 4.1.3 Control variables Our control variables are chosen taking into account the existing literature on the effects of corruption on climate change (Shleifer and Vishny, 1993; Zhang et al. 2016 ; Yang et al. 2020). In our baseline model, we included seven control variables. The first two are economic growth and its square, according to the environmental Kuznets curve hypothesis (Grossman and Krueger, 1995), we expect a positive and negative sign. Furthermore, the results on the effects of foreign direct investment and international trade on carbon emissions are fairly mixed. On the other hand, by taking into account the ‘pollution haven’ hypothesis (Smarzynska and Wei, 2001; Cole, 2004; Copeland and Taylor, 2004), which suggests that corruption may encourage the installation of polluting industries in countries with weaker environmental regulations, we expect to see a positive relationship. In addition, we include energy consumption. Existing literature shows that energy consumption has a positive effect on carbon emissions. The work of Ahmed and Shimada ( 2019 ) in South Asia, Latin America, Africa and the Caribbean validates this perspective. This result is confirmed by Arnaut and Lidman (2021). We are hoping for a positive sign. Next, we include agriculture, which, according to Anderson and Marcouiller (2002), corruption in this sector often leads to a misallocation of resources, hindering innovation and the adoption of environmentally friendly technologies, from which we expect a positive sign. Several researchers (Torras and Boyce, 1998; Barrett and Graddy, 2000; Farzin and Bond, 2006; Galinato and Galinato, 2012; Abid, 2016) support the idea that democracy allows citizens to acquire information about the environment, to organise themselves and to show their preference for a quality environment, and therefore makes governments more sensitive to demands for environmental protection. A positive sign is expected. 4.2 Empirical strategy The aim of this study is to analyse the effects of corruption on climate change. We postulate that levels of corruption positively affect climate change in developing countries. To verify this hypothesis, we estimate the following Arp model: Where \(\:{LnE}_{it}\) _it represents the natural logarithm of the dependent variable E for entity i at a time t. Also \(\:\sum\:_{j=1}^{p}{\rho\:}_{j}{LnE}_{it-j}\) is the sum of the past effects of E (natural logarithm) over the period t - j, weighted by the coefficients ρ_j. In addition, cc is the control of corruption and the logarithms represent economic growth and its square justifying the environmental Kuznets curve; is the vector of control variables that include international trade, foreign direct investment, energy consumption, democracy and agriculture; and \(\:{\epsilon\:}_{it}\) and \(\:{\mu\:}_{it}\) are the error terms. In addition, \(\:{{\rm\:Y}}_{i}\) and \(\:{\tau\:}_{t}\:\) represent country- and year-specific effects. In addition, the GMM system estimator will be used. This estimator, proposed by Arellano and Bond (1998), exploits all the orthogonality conditions that exist between the endogenous lag variable and the error term. The contribution of this method lies both in the correct treatment of the problem associated with correlated individual effects and in the possibility of taking account of the potential endogeneity of the explanatory variables. The method also involves combining the first-difference equation with the level equation for each period. In the first difference equation, the predetermined variables are instrumented by their level values lagged by at least one period. In the level equation, the variables are instrumented by their first differences. Unlike the differential GMM, which can suffer from weak instruments and finite sampling bias. 5. Empirical results We discuss the results of the basic model, before carrying out a robustness analysis. 5.1 Results of the basic model Table 2 GMM estimates for systems Dependent variable: climate change Estimator (GMM in System) L.logarithm of carbon dioxide emissions 0.997*** (0.0164) Control of corruption -0.00290 (0.00388) Logarithm of economic growth (Y) -0.0154 (0.0514) Logarithm (Y 2 ) 0.00145 (0.00235) Foreign direct investment -0.000116* (6.04e-05) Logarithm of international trade 0.0950** (0.0387) Log energy consumption -0.0323** (0.0162) Logarithm agriculture 0.000117 (0.00651) Democracy 0.0229 (0.0145) Constant -0.0251 (0.234) Number of groups 70 Observation 1540 Number of instruments 67 AR(1) 0.000 AR(2) 0.419 Hansen 0.145 Standard errors in parenthese \(\:\mathbf{*}\mathbf{*}\mathbf{*}\mathbf{p}<0.01,\:\mathbf{*}\mathbf{*}\mathbf{p}<0.05,\:\mathbf{*}\mathbf{p}<0.1\) Source : author The results in Table 2 show that controlling corruption has no significant direct effect on carbon dioxide emissions in the countries in our sample. The results also confirm the validity of estimating the GMM in a two-stage system. The Arellano-Bond autocorrelation test proves a significant first-order correlation (AR(1)), which is expected in dynamic panel models, but the absence of second-order autocorrelation (AR(2)) confirms the validity of our instruments. In addition, the Hansen over-identification test (0.145) justifies the exogeneity of our instruments. Furthermore, among the explanatory variables, lagged carbon dioxide emissions exert a positive and significant effect (+ 0.997), underlining the persistence of carbon dioxide emission levels over time. The other significant variables are foreign direct investment, the logarithm of international trade and the logarithm of energy consumption. Foreign direct investment is statistically significant and negative on carbon dioxide emissions at the 1% level. This result rejects the ‘pollution haven’ hypothesis and validates the ‘pollution halo’ hypothesis. Zubair et al ( 2020 ) find that FDI inflows reduce Nigeria's carbon emissions and Pazienza, 2019 argues that FDI is a transfer of technological innovation that enables a cleaner production model. Also, international trade is significant and positive on carbon emissions at the 5% threshold. This result is in line with the work of Zhong et al ( 2021 ), who find that the development of international trade has led to a 5% increase in the inter-regional flow of carbon emissions on average, based on 39 large economies. Finally, energy consumption is significant and negative at the 5% threshold, justifying that it reduces carbon emissions. Haldar and Sethi (2023) show that renewable energy consumption significantly reduces carbon emissions in the long term. Furthermore, the results that economic growth (logpib) and its square (logpib²) are not significant. In other words, the environmental Kuznets curve within the countries in our sample does not exist. There are several reasons for this. Economic growth in these countries is often accompanied by heavy dependence on fossil fuels and unsustainable industrialisation, leading to a steady increase in polluting emissions. In addition, political instability can hamper the adoption of measures in favour of the environment, as economic priorities dominate. Also, the economic structure of these countries, often focused on polluting sectors such as extractive or intensive agriculture, limits the reduction of emissions. In addition, weak institutions and a lack of clean technologies prevent effective management of carbon emissions. Also, the absence of the Kuznets curve in developing countries may be due to an insufficient level of per capita income to reach the turning point. 5.2 Robustness check This subsection consists of a robustness check. First, we perform a sensitivity check by successively adding the control variables. Then, by integrating other measures of climate change as well as alternative measures of corruption. The last point uses alternative estimation methods. Table 3 Successive integration of control variables Dependent variable: climate change Estimator (GMM in System) (1) (2) (3) (4) (5) (6) (7) L.logco2 0.997*** 0.976*** 0.978*** 0.974*** 0.986*** 0.986*** 0.997*** (0.00158) (0.00772) (0.00787) (0.00894) (0.0130) (0.0143) (0.0164) cc -0.00287 -0.00245 -0.00242 -0.00321 -0.000997 -0.00118 -0.00290 (0.00243) (0.00263) (0.00263) (0.00303) (0.00333) (0.00348) (0.00388) logpib 0.0333 0.0633** 0.0550** 0.0278 0.0313 -0.0154 (0.0292) (0.0265) (0.0280) (0.0331) (0.0330) (0.0514) logpib 2 -0.000494 -0.00203 -0.00144 -0.000419 -0.000633 0.00145 (0.00142) (0.00132) (0.00143) (0.00158) (0.00155) (0.00235) ide -0.000200*** -0.000198*** -0.000148** -0.000152** -0.000116* (5.29e-05) (5.27e-05) (5.94e-05) (6.04e-05) (6.04e-05) logci 0.00995 0.0479** 0.0461** 0.0950** (0.0166) (0.0204) (0.0215) (0.0387) logce -0.0156* -0.0147 -0.0323** (0.00897) (0.0106) (0.0162) logagri 0.00242 0.000117 (0.00520) (0.00651) demo 0.0229 (0.0145) Constant 0.0392*** -0.111 -0.264* -0.234* -0.158 -0.178 -0.0251 (0.0114) (0.149) (0.136) (0.139) (0.155) (0.152) (0.234) Observation 1540 1540 1540 1540 1540 1540 1540 Number id 70 70 70 70 70 70 70 Number of instruments 67 67 67 67 67 67 67 AR(1) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 AR(2) 0.462 0.454 0.457 0.451 0.435 0.436 0.419 Hansen 0.294 0.251 0.243 0.219 0.191 0.178 0.145 Standard errors in parentheses : *** p < 0.01, ** p < 0.05, * p < 0.1 Source : author The results in Table 3 suggest that controlling corruption does not have a significant direct effect on carbon dioxide emissions. Also, the logarithm of economic growth is significant and positive in columns (3) and (4), justifying that an increase in economic growth increases carbon emissions in developing countries. These results are consistent with the findings of Yousefi-Sahzabi et al. ( 2011 ) and Bouznit and Maria del (2016) who confirm the existence of a strong positive correlation between economic growth and CO2 emissions in Iran and Algeria, respectively. In addition, foreign direct investment remains statistically negative on CO2 emissions. The logarithm of international trade also remains positive in columns (5, 6 and 7). Similarly, the logarithm of energy consumption is statistically significant and negative in columns (5) and (7). Table 4 Robustness by climate change indicators Estimator: GMM system LogCO 2 logN 2 O tempannuelles (1) (2) (3) L.logco2 0.997*** (0.0164) cc -0.00290 -0.000282 -0.00601 (0.00388) (0.00411) (0.0461) logpib -0.0154 0.149 1.025 (0.0514) (0.119) (0.796) logpib 2 0.00145 -0.00713 -0.0564 (0.00235) (0.00544) (0.0379) ide -0.000116* -0.000176 -0.000860 (6.04e-05) (0.000132) (0.000993) logci 0.0950** -0.107 -1.230*** (0.0387) (0.0748) (0.287) logce -0.0323** 0.0154 0.339*** (0.0162) (0.0124) (0.0942) logagri 0.000117 0.00434 0.0456 (0.00651) (0.00674) (0.0793) demo 0.0229 -0.0192 -0.321** (0.0145) (0.0246) (0.126) L.logN 2 O 0.987*** (0.0189) L.tempannuelles 0.599*** (0.0609) Constant -0.0251 -0.549 -3.039 (0.234) (0.456) (4.045) Observations 1,540 1,540 1,540 Number id 70 70 70 Number of instruments 67 67 67 AR(1) 0.000 0.073 0.000 AR(2) 0.419 0.120 0.621 Hansen 0.145 0.237 0.151 Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Source : author The results in Table 4 suggest that controlling corruption still has no significant direct effect on the various indicators measuring climate change. Individually, foreign direct investment is significant and negative in column (1). International trade is significant and positive in column (1), but negative in column (3). This result (column 3) is consistent with the work of Boyomo and Aurelien (2025) who prove that international trade, as a vector of wealth creation, directly reduces vulnerability to climate change in Sub-Saharan Africa. Moreover, energy consumption is significant and negative (Column 1), but positively influences average temperatures (Column 3). Alberini et al ( 2019 ) find evidence of a positive relationship between electricity consumption and temperatures. In addition, democracy is significant and negative on temperatures. The work of Persakis et al ( 2024 ) emphasises that democratic environments enhance the effectiveness of climate policies in reducing environmental degradation. To complete our robustness analysis, other indicators for measuring corruption are used. These are the political corruption index (incorrp), the public sector corruption index (incorrsp) and the executive corruption index (incorrexe). Table 5 Robustness by corruption measurement indicators Dependent variable: climate change Estimator (GMM in System) (1) (2) (3) (4) L.logco2 0.997*** 0.994*** 0.995*** 0.996*** (0.0164) (0.0159) (0.0159) (0.0166) cc -0.00290 (0.00388) logpib -0.0154 -0.00736 -0.00923 -0.0133 (0.0514) (0.0510) (0.0513) (0.0552) logpib 2 0.00145 0.00116 0.00121 0.00138 (0.00235) (0.00235) (0.00236) (0.00250) ide -0.000116* -0.000108* -0.000114* -0.000117* (6.04e-05) (6.36e-05) (6.66e-05) (6.09e-05) logci 0.0950** 0.0907** 0.0932** 0.0935** (0.0387) (0.0381) (0.0381) (0.0390) logce -0.0323** -0.0317** -0.0326** -0.0328** (0.0162) (0.0155) (0.0159) (0.0161) logagri 0.000117 -0.000509 -0.000279 -0.000597 (0.00651) (0.00620) (0.00626) (0.00643) demo 0.0229 0.00616 0.0174 0.0115 (0.0145) (0.0269) (0.0157) (0.0299) incorrp 0.0141 (0.0239) incorrsp 0.00291 (0.0201) incorrexe 0.00952 (0.0364) Constant -0.0251 -0.0501 -0.0477 -0.0309 (0.234) (0.233) (0.236) (0.248) Observations 1,540 1,540 1,540 1,540 Number id 70 70 70 70 Number of instruments 67 67 67 67 AR(1) 0.000 0.000 0.000 0.000 AR(2) 0.419 0.422 0.419 0.418 Hansen 0.145 0.144 0.142 0.143 Standard errors in parentheses : *** p < 0.01, ** p < 0.05, * p < 0.1 Source : author The results in Table 5 suggest that all indicators measuring corruption do not have a significant direct effect on CO2 emissions. Furthermore, FDI is still significant and negative for all columns and international trade is significant and positive. In addition, energy consumption remains significant and negative for all columns. 5.3 Mediation analysis This sub-section analyses the transmission channels through which corruption affects climate change in the countries in our sample. We have identified two main channels: the participatory democracy index and energy poverty. To assess the magnitude of these effects, we use the structural equation mediation (SEM) proposed by Mickinnon et al. (1995). The following figure illustrates this relationship. Model 1 : \(\:{\varvec{e}\varvec{n}\varvec{e}\varvec{r}\varvec{g}\varvec{y}\:\varvec{p}\varvec{o}\varvec{v}\varvec{e}\varvec{r}\varvec{t}\varvec{y}}_{\varvec{i}}=\:{\varvec{\alpha\:}}_{1}+{\varvec{b}}_{1}\varvec{c}\varvec{o}\varvec{r}\varvec{r}\varvec{u}\varvec{p}\varvec{t}\varvec{i}\varvec{o}\varvec{n}+{\varvec{c}}_{1}\varvec{c}\varvec{o}\varvec{n}\varvec{t}\varvec{r}\varvec{o}\varvec{l}+{\varvec{\mu\:}}_{\varvec{i}}\:\) Model 2 : \(\:{\varvec{C}\varvec{O}}_{2\varvec{i}}=\:{\varvec{\alpha\:}}_{2}+{\varvec{b}}_{2}\varvec{c}\varvec{o}\varvec{r}\varvec{r}\varvec{u}\varvec{p}\varvec{t}\varvec{i}\varvec{o}\varvec{n}+{\varvec{b}}_{3}\varvec{p}\varvec{a}\varvec{r}\varvec{t}\varvec{i}\varvec{c}\varvec{i}\varvec{p}\varvec{a}\varvec{t}\varvec{o}\varvec{r}\varvec{y}\:\varvec{d}\varvec{e}\varvec{m}\varvec{o}\varvec{c}\varvec{r}\varvec{a}\varvec{c}\varvec{y}\:\varvec{i}\varvec{n}\varvec{d}\varvec{e}\varvec{x}+{\varvec{c}}_{1}\varvec{c}\varvec{o}\varvec{n}\varvec{t}\varvec{r}\varvec{o}\varvec{l}+{\varvec{\gamma\:}}_{\varvec{i}}\) Indirect effect : \(\:{\varvec{b}}_{1}\) x \(\:{\varvec{b}}_{3}\) Direct effect : \(\:{\varvec{b}}_{2}\) Total effect: ( \(\:{\varvec{b}}_{1}\) x \(\:\:{\varvec{b}}_{3}\) ) + \(\:{\varvec{b}}_{2}\) The first regression is a relationship between corruption and the mediator variables (model 1) captured by the coefficient (b1). In addition, the indirect effect is captured by regressing corruption on climate change considering the effect of mediators (model 2). This influence is reflected by the weight of (b2). The indirect effect is the product of (b1) and (b3). It measures the impact of corruption on climate change through energy poverty and the participatory democracy index. Table 6 Transmission channels of the effect of corruption on climate change: energy poverty and participatory democracy index Access to clean cooking fuels and technologies (% of population) Participatory democracy index Access to clean cooking fuels and technologies (% of rural population) (A) Mediation test Coef Std_err T-stat Coef std_err T-stat Coef Std_err T-stat Sobel -0.018 0.004 0.000*** -0.023 0.004 0.000*** -0.024 0.004 0.000*** Aroian -0.018 0.004 0.000*** -0.023 0.004 0.000*** -0.024 0.005 0.000*** Goodman -0.018 0.004 0.000*** -0.023 0.004 0.000*** -0.024 0.004 0.000*** (B) Composition des effets Indirect effect (Sobel) -0.018 0.004 0.000*** -0.023 0.004 0.000*** -0.024 0.004 0.000*** Direct effect -0.063 0.013 0.000*** -0.058 0.013 0.000*** -0.057 0.013 0.000*** Total effect -0.081 0.012 0.000*** -0.081 0.012 0.000*** -0.081 0.012 0.000*** Proportion of total effect 22% 28.2% 29.3% Notes : *; **; *** significance at the 10%, 5% and 1% thresholds respectively Source: author The results of the mediation (Table 6 ) of the effects of corruption on climate change are visible through two transmission channels. Indeed, the mediation impact of the participatory democracy index has a Sobel statistic of -0.023, with a standard error of 0.004 and a p-value of 0.000, which validates the indirect effect of the participatory democracy index. In addition, for the energy poverty mediator measured by access to clean fuels and technologies for cooking (% of the population) presents a Sobel statistic of -0.018, a standard error of 0.004 and a p-value of 0.000, validating the indirect effect of the mediator. Also, access to clean cooking fuels and technologies (% of rural population) has a Sobel statistic of -0.024, with a standard error of 0.004 and a p-value of 0.000, confirming the indirect effect of the mediator. We also note that the results are the same for the other tests, in particular Aroian and Goodman. Furthermore, the results suggest that the mediating effect for access to clean cooking fuels and technologies (% of population) is 22%; for the participatory democracy index is 28.2% and finally for access to clean cooking fuels and technologies (% of rural population) is 29.3% of the total effect on the relationship between corruption and climate change in developing countries. More precisely, corruption increases climate change by reducing participatory democracy and access to clean fuels and technologies. Countries with high levels of corruption adopt more climate-related laws and policies. Despite this, corrupt countries have less stringent environmental policies, which shows that the policies adopted do not translate into emissions reductions. These countries may adopt more ambitious policies, but when corruption is widespread, the public authorities seem unable to properly implement and enforce the environmental laws and regulations adopted (Pavitkina, 2018; Pavitkina and Jagers, 2022). Corruption also increases climate change by negatively affecting access to clean fuels and technologies. Amoah et al (2022) show that corruption has a negative impact on the use of renewable energy in Africa. This is due to corrupt officials taking advantage of inefficient bureaucratic systems. These results highlight the importance of energy poverty and participatory democracy on the effects of corruption on climate change in developing countries. 6. Conclusion This study examined the effects of corruption on climate change in a sample of 70 developing countries over the period 2000–2022. We used the method of generalised moments in a system and structural equation mediation. The results suggest that corruption positively stimulates climate change. This relationship is mediated by active and strengthened participatory democracy (28.3%), by energy poverty through better access to clean fuels and technologies for the population in general (22%) and for the rural population in particular (29.3%). Based on these results, a number of economic policy suggestions can be made. Firstly, governments must put in place measures to monitor and combat corruption and put an end to impunity in order to limit its environmental impact. Secondly, it is crucial that developing countries consider investing in promising environmental projects, such as solar energy or hydroelectric dams, to further reduce dependence on polluting energies and alleviate fuel poverty. In terms of participatory democracy, it is essential to strengthen the place of women in the political sphere, guaranteeing them an active role and real decision-making power, particularly when it comes to taking a stand on climate change. Finally, it is vital to preserve civil liberties, by protecting the right to free and independent information. We must prevent the media from becoming instruments of indoctrination, especially when it comes to denouncing the problems associated with environmental degradation. Declarations Financing This study did not receive any external funding. CRediT authorship contribution statement The author conceived the entire study, collected and analysed the data and drafted the manuscript. No other person contributed to this article. Corresponding author : François-Cyrille EYEGHE-NTOUTOUME Declaration of competing interests The author declares that he has no financial, professional or personal conflicts of interest in connection with this research. 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The liberal model takes a "negative" view of political power insofar as it judges the quality of democracy by the limits placed on government. This is achieved by constitutionally protected civil liberties, strong rule of law, an independent judiciary, and effective checks and balances that, together, limit the exercise of executive power. To make this a measure of liberal democracy, the index also takes the level of electoral democracy into account. Graphs Graphs 1 to 5 are available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files Graph1.jpg Graph 1: Trends in the control of corruption in developing countries (2000-2022) Graph2.jpg Graph 2: Annual temperature trends in developing countries Graph3.jpg Graph 3: Carbon emissions in developing countries Graph4.jpg Graph 4: Nitrous oxide trends in developing countries Graph5.jpg Graph 5: Controlling corruption, Carbon dioxide emissions, Annual temperatures and Nitrous oxide Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6528488","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":447977445,"identity":"158e1392-1af3-4d14-a85c-69205d27ba3d","order_by":0,"name":"François-Cyrille EYEGHE-NTOUTOUME","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYJCCAw8KwDQbA0MFmxxEhJCWBAOYljN8xhARQtbAtTC2yCU2gEXwqOafkfsQaIuNnMHx9msPPjaYpc8POwwUYbCT023ArkXiRroBUEuascGZM+WGM3ek5W68nQYUYUg2NjuAXYuBRBrIL4cTN9zISZPmPXMsd+PsBJCWA4nb8Gv5X7/h/ps06b9t/9MNZ6d/IEYLEN1gPybN2MaWIC+dg98WiTPPQFqSDWeeyWGT7DnDZrhBOqcAJILTL/ztacwfPlTYyfMdP/5M4kcFm7z87PTNIBE5XFoYBBIgtMIBHkjsGIBVGuBQDrYGapZ8A/sDKAOP6lEwCkbBKBiRAABu/Wpv2dAq6AAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0005-5271-7630","institution":"Omar Bongo University","correspondingAuthor":true,"prefix":"","firstName":"François-Cyrille","middleName":"","lastName":"EYEGHE-NTOUTOUME","suffix":""}],"badges":[],"createdAt":"2025-04-25 11:25:31","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6528488/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6528488/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81515704,"identity":"63124ad5-a20c-4d70-b343-a8e4e720e8d1","added_by":"auto","created_at":"2025-04-28 07:07:21","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32604,"visible":true,"origin":"","legend":"\u003cp\u003eMediation analysis\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6528488/v1/cf7c55f3ace736c541dde0b8.jpg"},{"id":81516769,"identity":"b61bf454-b1a9-4873-930c-f3acf2e0a859","added_by":"auto","created_at":"2025-04-28 07:23:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1934004,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6528488/v1/fa875400-0ad6-4b0a-a24e-8ebd44868fda.pdf"},{"id":81515703,"identity":"5bf80b03-3158-401b-9e3c-c3c21ec5c002","added_by":"auto","created_at":"2025-04-28 07:07:21","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":125567,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraph 1\u003c/strong\u003e: Trends in the control of corruption in developing countries (2000-2022)\u003c/p\u003e","description":"","filename":"Graph1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6528488/v1/8219ac58ce76586b9bbcdeef.jpg"},{"id":81515709,"identity":"8b69d03d-34d7-4a42-8194-eba3b5712199","added_by":"auto","created_at":"2025-04-28 07:07:21","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":105288,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraph 2\u003c/strong\u003e: Annual temperature trends in developing countries\u003c/p\u003e","description":"","filename":"Graph2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6528488/v1/2c17a1c784e842b77aeb5318.jpg"},{"id":81515707,"identity":"c5b19ec3-c828-452c-a3f8-eab9e6c56c54","added_by":"auto","created_at":"2025-04-28 07:07:21","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":93637,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraph 3\u003c/strong\u003e: Carbon emissions in developing countries\u003c/p\u003e","description":"","filename":"Graph3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6528488/v1/6e2d0d344bf8014749d809d6.jpg"},{"id":81516382,"identity":"03acde5e-1b6a-4e9e-ba9d-cfb5d01a2422","added_by":"auto","created_at":"2025-04-28 07:15:21","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":111433,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraph 4\u003c/strong\u003e: Nitrous oxide trends in developing countries\u003c/p\u003e","description":"","filename":"Graph4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6528488/v1/f737c5c3b1b012a11f4b7cf6.jpg"},{"id":81515716,"identity":"c52b6a0b-c263-4771-9898-5176c46f809f","added_by":"auto","created_at":"2025-04-28 07:07:21","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":87391,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraph 5\u003c/strong\u003e: Controlling corruption, Carbon dioxide emissions, Annual temperatures and Nitrous oxide\u003c/p\u003e","description":"","filename":"Graph5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6528488/v1/c84125b4b6d4754ca09cdfa3.jpg"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDoes corruption influence climate change in developing countries? Effects and transmission channels\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eClimate change is a reality. It is right in front of our eyes: droughts that last too long, floods that sweep away everything in their path, once fertile land that is turning into desert, carbon emission levels that continue to rise. And it is the developing countries that are suffering the most. But behind these disasters, another scourge, quieter but just as destructive, is making the situation even worse: levels of corruption. When funds earmarked for environmental projects disappear, when illegal operating permits are granted to polluting companies, when political decisions are motivated by private interests rather than the general interest, efforts to combat climate change collapse. Various factors can explain climate change, including environmental factors (Foley et al. 2005), economic factors (Stern, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Cole, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), social factors (Harvey, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Sen, 1999) and institutional factors (North, 1990; Acemoglu et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCorruption covers many different aspects, and it is therefore difficult to give an exhaustive definition. The World Bank (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) defines corruption as \u0026lsquo;the abuse of public power for personal gain\u0026rsquo;, while Transparency International (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) sees it as \u0026lsquo;the abuse of power conferred for personal gain\u0026rsquo;. These are not the only definitions; Nye (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1967\u003c/span\u003e) suggests that corruption is \u0026lsquo;behaviour that deviates from the official duties of a public office because of pecuniary advantage or private status (personal, close family, private clique); or that violates rules prohibiting the exercise of certain types of private influence\u0026rsquo;. According to the United Nations, climate change refers to long-term variations in temperature and weather conditions (UN, 2024). But since the 1800s, human activities have been the main driver of climate change, mainly due to the burning of fossil fuels such as coal, oil and gas. For the World Meteorological Organisation (2024), climate change is the term used to describe changes in the state of the climate that can be identified by changes in the mean and/or variability of its properties and that persist for an extended period, typically decades or more.\u003c/p\u003e \u003cp\u003eThe aim of this research is to analyse the effects and mechanisms of corruption on climate change in developing countries.\u003c/p\u003e \u003cp\u003eThere are two main reasons for choosing developing countries. The first reason is the manifestation of climate change, in particular extreme weather events, rising temperatures and changes in precipitation patterns pose significant threats to developing countries with fragile social, economic and political structures (Adom, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Developing countries have contributed 95% of the increase in global emissions over the last decade and accounted for 75% (44 GT) of global emissions in 2023 (Climate Leadership Council, 2024). Projections show that this trend is set to continue. The second argument resides in the fact that corruption has serious consequences for the environment, particularly through the misappropriation of funds during the implementation of environmental programmes, the granting of permits and licences for the exploitation of natural resources, and the payment of bribes to public officials (Tacconi et Williams, 2020).\u003c/p\u003e \u003cp\u003eResearch in developing countries shows that corruption affects carbon dioxide emissions, which are a major contributor to pollution (Wang et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, corruption can introduce a bias not only in the process of implementing environmental policies, but also in the process of applying and monitoring environmental laws (Lopez and Mitra, 2000; Abid et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe article is divided into five main sections. The second presents some key trends, while the third reviews the main research on the subject. The fourth section explains the various stages of our empirical approach. The fifth section discusses the results obtained. The sixth section concludes with some policy recommendations.\u003c/p\u003e"},{"header":"2. Key trends","content":"\u003cp\u003eThe stylised facts examine the main trends relating to corruption controls, climate variables and the relationships between the phenomena in developing countries.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGraph 1\u003c/strong\u003e shows the evolution of corruption control from 2000 to 2022 in developing countries. There is a general downward trend in the monitoring of corruption over the years. This could indicate that efforts to fight corruption have diminished or are ineffective over time. In addition, the negative values on the graph may seem surprising, but they show that the perceived level of corruption is above a certain tolerance threshold. More accurately, it shows the extent to which corruption is perceived as a problem within these countries.\u003c/p\u003e\n\u003cp\u003eLooking at \u003cstrong\u003eGraph 2\u003c/strong\u003e of annual temperatures in developing countries from 2000 to 2022, a clear trend emerges: temperatures are gradually rising. For example, in 2010, 2015, 2016, 2019, 2020, 2021 and 2022 temperatures are particularly high, exceeding 1\u0026deg;C and approaching 1.5\u0026deg;C. On the other hand, despite these upward variations, some periods show relatively stable temperatures between 2000 and 2009. This indicates that, even in a period of global warming, there are moments of respite.\u003c/p\u003e\n\u003cp\u003eThe graph 3 shows the challenges faced by developing countries in managing carbon dioxide emissions. On closer examination, it is clear that CO\u003csub\u003e2\u003c/sub\u003e emissions have grown steadily between 2000 and 2022. In 2000, they were much lower than in 2022, when they will reach their highest level.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGraph 4\u003c/strong\u003e explores how nitrous oxide emissions are changing in developing countries over the period 2000-2022. Between 2000 and 2008, they grew slowly. But between 2008 and 2012, the increase is more rapid. After 2012, there is a slight slowdown, but they continue to rise.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGraph 5\u003c/strong\u003e tells a worrying story: that of a world where corruption is on the rise, while the planet is suffering more and more. Since 2000, there has been a decline in the control of corruption, which means that, in the countries in our sample, laws are less respected, decisions are sometimes manipulated by private interests, and environmental policies are less effective. At the same time, emissions of carbon dioxide and nitrous oxide are increasing. These gases, which are responsible for global warming, are causing temperatures to rise, as shown by the yellow line in the graph.\u003c/p\u003e"},{"header":"3. Synthesis of previous work","content":"\u003cp\u003eThe relationship between growth and CO\u003csub\u003e2\u003c/sub\u003e emissions is widely discussed in the literature. However, in recent years much attention has been paid to the role of corruption in the relationship between growth and CO2 emissions, but little work has been done in this area of research. There are two schools of thought regarding the relationship between corruption and carbon dioxide emissions. The first approach defends the idea of a direct impact, showing that corruption reduces carbon dioxide emissions (Zhang et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Arminen and Menegaki, 2019; Akhbari and Nejati, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Balsalobre-Lorente et al, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Devlina et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), on the one hand, and also increasing it (Masron and Subramaniam, 2018; Zhang and Chiu, 2020; Ren et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), on the other. As for the second approach, it attempts to show that corruption indirectly influences carbon emissions by acting on economic growth (Wang et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Leitao, 2021); on energy consumption (Arminen and Menegaki, 2019; Balsalobre-Lorente et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, the theoretical literature highlights the income channel (Welsch, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Cole, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Biswas et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hassaballa, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) as well as the international trade channel (Ridzuan et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chen, 2023). Corruption also affects carbon emissions through institutions (Desai, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Goel et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and through information and communication technologies (Liu et al. 2021; Alam et al. 2022).\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The direct effects of corruption on climate change\u003c/h2\u003e \u003cp\u003eCorruption can have a variety of impacts on the environment, influencing both positive and negative levels of CO\u003csub\u003e2\u003c/sub\u003e emissions. We will explore how corruption can potentially reduce and/or increase these impacts.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Corruption reduces carbon dioxide emissions\u003c/h2\u003e \u003cp\u003eEmpirical research on the impact of corruption on carbon dioxide emissions indicates that corruption leads to a decrease in carbon emissions (Sekrafi et Sghaier 2018 ; Arminen et Menegaki 2019 ; Balsalobre-Lorente et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e ; Akhbari et Nejati 2019). In this context, Sekrafi and Sghaier (2018), study the impact of corruption on carbon dioxide emissions. Indeed, the authors formulated three equations using an ARDL model. The first equation assessed the correlation between corruption and economic growth, the second equation tested the role of corruption on carbon dioxide emissions, and finally, an equation for energy consumption as the dependent variable. Considering the empirical results and especially the carbon dioxide emissions equation, they find that per capita income negatively affects carbon dioxide emissions. Income per capita squared is positively associated with carbon dioxide emissions. However, corruption has a negative effect on carbon dioxide emissions.\u003c/p\u003e \u003cp\u003eIn another study, Akhbari and Nejati (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) also examined this phenomenon over 61 developed and developing economies for the period 2003\u0026ndash;2016 using panel data. Estimates with the fixed effects model show that the corruption result is ambiguous. When the authors consider only the corruption index variable, they observe a statistically significant negative effect on carbon emissions. Recent work by Zhang et al (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) goes further by analysing the direct and indirect impact of corruption on carbon emissions in Asia-Pacific Economic Cooperation (APEC) countries for the period 1992\u0026ndash;2012 using the quantile regression approach. They reach the same results as Akhbari and Nejati (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). They find that the effect of corruption on carbon dioxide emissions is heterogeneous across APEC countries. Specifically, there is a significant negative effect in low-emission countries, but insignificant in high-emission countries. In addition, corruption may also have a positive indirect effect through its effect on GDP per capita.\u003c/p\u003e \u003cp\u003eIn addition, Devlina et al (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) extend their scope of investigation to more than 200 countries, taking into account upper-middle, lower-middle and low-income countries over the period 2005 to 2016. The empirical evidence validates a negative and significant relationship between corruption and carbon emissions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Corruption increases carbon emissions\u003c/h2\u003e \u003cp\u003eAnother view is that corruption amplifies carbon dioxide emissions (Cole \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e ; Sahlia et Rejeb 2015 ; Hassaballa \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e ; Dincer et Fredriksson 2018 ; Masron et Subramaniam 2018 ; Ridzuan et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e ; Akhbari et Nejati 2019 ; Zhang et Chiu 2020). The empirical study by Ridzuan et al (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) examines the correlation between corruption and carbon dioxide emissions in three ASEAN countries (Malaysia, Indonesia and the Philippines) for 1970\u0026ndash;2017. The authors tested the effects of economic growth, energy consumption, foreign direct investment, trade openness, financial development, corruption index and urban population on carbon dioxide emissions. With regard to the econometric results using the distributed lag ARDL autoregressive model, the authors demonstrated that corruption positively affects CO\u003csub\u003e2\u003c/sub\u003e emissions in the long term. In addition, the foreign direct investment variable is negatively correlated with carbon dioxide emissions in Malaysia and the Philippines. Finally, the urban population variable positively affects carbon dioxide emissions in Indonesia and the Philippines. In addition, the empirical study by Lee et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) assessed corruption in Asian countries, namely Bangladesh, Brunei, China, India, Indonesia, Malaysia, Myanmar, Pakistan, Philippines, Singapore, Sri Lanka, Thailand and Vietnam, considering the period 1984 to 2017. The empirical results with pooled OLS, fixed effects and random effects are similar. The authors indicate that corruption has a positive impact on carbon dioxide emissions.\u003c/p\u003e \u003cp\u003eThe link between corruption and carbon dioxide emissions for the MENA region has been studied by Sahlia and Rejeb (2015) and Hassaballa (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The empirical work proves that corruption positively affects the environment. On the other hand, Sahlia and Rejeb (2015) find a positive effect between corruption and CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e \u003cp\u003eThe experience of developed and developing countries for the period 1999\u0026ndash;2015 has been studied by Azami and Rezaei (2017). In this research, the researchers use panel data and quantile regression. The results indicate that corruption leads to an increase in carbon dioxide emissions in both developed and developing countries. The result is also evidence of the Kuznets environmental hypothesis.\u003c/p\u003e \u003cp\u003eThe question of the impact of corruption on the environment remains crucial, especially in contexts such as China where haze pollution is a major challenge. Zhao et al (2022) explored this dynamic using the generalized method of moments in 30 Chinese provinces over the period 2006\u0026ndash;2018. The evaluations reveal that corruption positively affects haze pollution at the 1% significance level. In addition, Ren et al (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) conduct a different study and find the same results within Chinese provinces through the autoregressive distributed lag modelling approach and panel quantile regressions, The results show that corruption increases per capita carbon emissions in the short run, reducing per capita carbon emissions in the long run.\u003c/p\u003e \u003cp\u003eThe empirical study by Sulemana and Kpienbaareh (2020) explores the association between corruption and air pollution in Africa and OECD countries. The authors use unbalanced panel data from 48 sub-Saharan African countries and 34 OECD countries for the period 1996\u0026ndash;2014. The results show that a positive association between particulate matter was found in African and OECD countries and a negative association between corruption and carbon dioxide emissions for both samples.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The indirect effects of corruption on climate change\u003c/h2\u003e \u003cp\u003eSeveral theoretical and empirical studies highlight the fact that corruption affects climate change via energy consumption, economic growth, institutions and international trade.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Corruption, energy consumption and climate change\u003c/h2\u003e \u003cp\u003eAs the fight against carbon emissions becomes ever more pressing, a crucial question emerges: how does corruption influence not only energy consumption but also indirectly the level of carbon emissions? Research by Arminen and Menegaki (2019) provides an answer to this question. Using dynamic panel data from 1985 to 2011, the authors formulated three equations: the first for economic growth, the second for energy consumption and the last for air pollution. The empirical results validate the bidirectional relationship between energy consumption and carbon dioxide emissions. According to the results, the corruption is not statistically significant for all the models. In addition, the work of Balsalobre-Lorente et al (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) examines how energy innovations and corruption affect carbon emissions in a panel of 16 OECD countries. The empirical results show that when economic systems interact with corruption, the positive effects that energy innovations have on environmental quality are reduced.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Corruption, economic growth and climate change\u003c/h2\u003e \u003cp\u003eThe policy debate on economic growth and CO\u003csub\u003e2\u003c/sub\u003e emissions is topical: corruption can affect this relationship by increasing pollution at given income levels and reducing per capita income (Lopez and Mitra, 2000; Welsch, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Cole, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Hassaballa, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). For example, Wang et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) apply a partial least squares regression model for a panel of BRICS countries from 1996 to 2015. Overall, the empirical results lead to the conclusion that the moderating role of corruption is crucial in the relationship between economic growth and carbon dioxide emissions and that controlling corruption reduces CO\u003csub\u003e2\u003c/sub\u003e emissions. In the US context, Dincer and Fredriksson (2018) assessed the relationship between corruption, trust and CO\u003csub\u003e2\u003c/sub\u003e emissions using panel data. The results confirmed that corruption and trust have a positive effect on CO\u003csub\u003e2\u003c/sub\u003e emissions. However, a negative relationship exists between the lagged variable of stringency, per capita income squared and CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Corruption, institutions and climate change\u003c/h2\u003e \u003cp\u003eCorruption also influences carbon dioxide emissions through the quality of institutions (Pellegrini and Gerlagh, 2006). This idea is shared by the work of Goel et al (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The results suggest that corrupt nations, particularly those with large underground economies, tend to contribute negatively to pollution levels.\u003c/p\u003e \u003cp\u003eFurthermore, by including political instability as a variable in their study, Fredriksson and Svensson (2003) develop a theory of environmental policy formation, taking into account the degree of corruptibility and political turbulence. The results suggest that political instability has a negative effect on the rigour of environmental regulations if the level of corruption is low, but a positive effect when the level of corruption is high. Corruption reduces the stringency of environmental regulations, but its effect disappears as political instability increases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4 Corruption, international trade and climate change\u003c/h2\u003e \u003cp\u003eTrade liberalisation, corruption and environmental policy-making were studied by Damania et al (2003). The contribution in this study is the use of a theoretical model that produces several testable predictions, including: (i) the effect of trade liberalisation on environmental policy stringency depends on the level of corruption; and (ii) corruption reduces environmental policy stringency. Using panel data from a mixture of developed and developing countries from 1982 to 1992, they find evidence to support these conjectures. In the context of the BRICS, Wang et al (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) show that trade is negatively associated with carbon dioxide emissions.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Transmission channels\u003c/h2\u003e \u003cp\u003eThis study identifies two main transmission channels through which corruption influences climate change in developing countries: the participatory democracy index \u003csup\u003e[1]\u003c/sup\u003e and energy poverty.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Corruption, participatory democracy and climate change\u003c/h2\u003e \u003cp\u003eA growing number of studies highlight the important role of participatory democracy in determining carbon emissions. Tsur (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) shows that direct popular vote and civil society participation reduce greenhouse gas emissions from all sources. However, the increased emphasis on individual and political freedoms reduces the effectiveness of liberal democracy in reducing greenhouse gas emissions.\u003c/p\u003e \u003cp\u003eMoreover, the benefits of democracy for climate change mitigation are limited in the presence of widespread corruption, which reduces the ability of democratic governments to meet climate targets and reduce CO₂ emissions. Povitrika (2018) reports that greater democracy is associated with lower CO₂ emissions only in low-corruption contexts. If corruption is high, democracies do not seem to fare better than authoritarian regimes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Corruption, energy poverty and climate change\u003c/h2\u003e \u003cp\u003eThe economic literature also considers energy poverty to be a determining factor in climate change. Pondie and Engwali (2024) demonstrate that access to electricity and clean energy for cooking has a positive and significant effect on deforestation and carbon emissions for a sample of 43 Sub-Saharan African countries. In addition, some studies have explored the influence of energy poverty on climate change, arguing that effective policy design can achieve a win-win situation between energy poverty reduction and climate change mitigation (\u0026Uuml;rge-Vorsatz and Herrero, 2012; Churchill et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Literature gap\u003c/h2\u003e \u003cp\u003eThe synthesis of the literature review shows that corruption has both positive and negative effects on climate change, but these effects remain relatively small. What is missing from existing research, however, is a full exploration of the role of participatory democracy and fuel poverty as mediating factors that could influence carbon dioxide emissions, especially in developing countries. This is where our study comes in to fill this gap by examining how corruption affects climate change through the transmission channels mentioned. To analyse all this, we use a statistical method, mediation by structural equations, to check whether these factors can indeed play a relevant mediating role. We will also take into account other indicators of climate change, such as average annual temperatures and nitrous oxide, in addition to carbon dioxide emissions.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Data description and empirical strategy","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Data description\u003c/h2\u003e \u003cp\u003eOur study covers a panel of 70 developing countries over the period 2000\u0026ndash;2022. Data on participatory democracy are taken from the V-DEM database; and data on corruption control and control variables from the World Development Indicator (WDI). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the descriptive statistics for the above variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Dev\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCorruption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControlling corruption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.610\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolitical Corruption Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExecutive Corruption Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic Sector Corruption Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClimate change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbon emissions (ln)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrous oxide (ln)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean annual temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eControl variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForeign direct investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-231.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.674\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternational trade (ln)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgriculture (ln)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.932\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy consumption (ln)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.593\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemocracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic growth (ln)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSquare of economic growth (ln)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e175.626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTransmission channels\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipatory democracy index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy poverty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to electricity (% of population)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural access to electricity (% of rural population)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to electricity.urban (% of urban population)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to clean cooking fuels and technologies (% of population)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to clean cooking fuels and technologies (% of urban population)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to clean cooking fuels and technologies (% of rural population)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNote\u003c/b\u003e: Obs. number of observations; Standard deviation: Standard deviation; Min: minimum value; Max: maximum value.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSource : author\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1 Dependent variable: climate change\u003c/h2\u003e \u003cp\u003eIn several previous studies, carbon dioxide has been used as one of the main indicators of climate change, as it is widely recognised as one of the main causes of this phenomenon (Welsch, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Cole, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Leitao, 2021). In our research, we have chosen to examine three indicators to measure climate change: the logarithm of carbon dioxide emissions, which will be our main dependent variable, as well as the logarithm of nitrous oxide levels and mean annual temperatures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2 Variable of interest: corruption\u003c/h2\u003e \u003cp\u003eNumerous measures are used to assess the level of corruption. One of these is the Corruption Perceptions Index (Tavares and Wacziarg, 2001; Bakare, 2011). However, it is generally accused of being biased due to its reliance on surveys that are subject to value judgement and perception. There is also another measure of corruption developed by the International Country Risk Guide that is used by some researchers (Wei, 1997; Cole, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Zhou 2013). Although it is still based on perception, it is more comprehensive and depends on the aggregation of more than 30 indices reflecting the point of view of individuals, companies and experts. This indicator ranges from \u0026minus;\u0026thinsp;2.5 (low control of corruption) to +\u0026thinsp;2.5 (high control of corruption).\u003c/p\u003e \u003cp\u003eIn this study we use four indicators to measure corruption: control of corruption, political corruption index, public sector corruption index and executive corruption index. The last three indicators are taken from the V-DEM database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.1.3 Control variables\u003c/h2\u003e \u003cp\u003eOur control variables are chosen taking into account the existing literature on the effects of corruption on climate change (Shleifer and Vishny, 1993; Zhang et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Yang et al. 2020). In our baseline model, we included seven control variables. The first two are economic growth and its square, according to the environmental Kuznets curve hypothesis (Grossman and Krueger, 1995), we expect a positive and negative sign.\u003c/p\u003e \u003cp\u003eFurthermore, the results on the effects of foreign direct investment and international trade on carbon emissions are fairly mixed. On the other hand, by taking into account the \u0026lsquo;pollution haven\u0026rsquo; hypothesis (Smarzynska and Wei, 2001; Cole, 2004; Copeland and Taylor, 2004), which suggests that corruption may encourage the installation of polluting industries in countries with weaker environmental regulations, we expect to see a positive relationship. In addition, we include energy consumption. Existing literature shows that energy consumption has a positive effect on carbon emissions. The work of Ahmed and Shimada (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) in South Asia, Latin America, Africa and the Caribbean validates this perspective. This result is confirmed by Arnaut and Lidman (2021). We are hoping for a positive sign. Next, we include agriculture, which, according to Anderson and Marcouiller (2002), corruption in this sector often leads to a misallocation of resources, hindering innovation and the adoption of environmentally friendly technologies, from which we expect a positive sign. Several researchers (Torras and Boyce, 1998; Barrett and Graddy, 2000; Farzin and Bond, 2006; Galinato and Galinato, 2012; Abid, 2016) support the idea that democracy allows citizens to acquire information about the environment, to organise themselves and to show their preference for a quality environment, and therefore makes governments more sensitive to demands for environmental protection. A positive sign is expected.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Empirical strategy\u003c/h2\u003e \u003cp\u003eThe aim of this study is to analyse the effects of corruption on climate change. We postulate that levels of corruption positively affect climate change in developing countries. To verify this hypothesis, we estimate the following Arp model:\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"676\" height=\"81\"\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{LnE}_{it}\\)\u003c/span\u003e\u003c/span\u003e_it represents the natural logarithm of the dependent variable E for entity i at a time t. Also \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{j=1}^{p}{\\rho\\:}_{j}{LnE}_{it-j}\\)\u003c/span\u003e\u003c/span\u003e is the sum of the past effects of E (natural logarithm) over the period t - j, weighted by the coefficients ρ_j. In addition, cc is the control of corruption and the logarithms represent economic growth and its square justifying the environmental Kuznets curve; is the vector of control variables that include international trade, foreign direct investment, energy consumption, democracy and agriculture; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{it}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{it}\\)\u003c/span\u003e\u003c/span\u003e are the error terms. In addition, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\rm\\:Y}}_{i}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\tau\\:}_{t}\\:\\)\u003c/span\u003e\u003c/span\u003erepresent country- and year-specific effects.\u003c/p\u003e \u003cp\u003eIn addition, the GMM system estimator will be used. This estimator, proposed by Arellano and Bond (1998), exploits all the orthogonality conditions that exist between the endogenous lag variable and the error term. The contribution of this method lies both in the correct treatment of the problem associated with correlated individual effects and in the possibility of taking account of the potential endogeneity of the explanatory variables. The method also involves combining the first-difference equation with the level equation for each period. In the first difference equation, the predetermined variables are instrumented by their level values lagged by at least one period. In the level equation, the variables are instrumented by their first differences. Unlike the differential GMM, which can suffer from weak instruments and finite sampling bias.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Empirical results","content":"\u003cp\u003eWe discuss the results of the basic model, before carrying out a robustness analysis.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Results of the basic model\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGMM estimates for systems\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDependent variable: climate change\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimator (GMM in System)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL.logarithm of carbon dioxide emissions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.997***\u003c/p\u003e \u003cp\u003e(0.0164)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl of corruption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00290\u003c/p\u003e \u003cp\u003e(0.00388)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLogarithm of economic growth (Y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0154\u003c/p\u003e \u003cp\u003e(0.0514)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLogarithm (Y\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00145\u003c/p\u003e \u003cp\u003e(0.00235)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForeign direct investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.000116*\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(6.04e-05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLogarithm of international trade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.0950**\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(0.0387)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog energy consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.0323**\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(0.0162)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLogarithm agriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000117\u003c/p\u003e \u003cp\u003e(0.00651)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemocracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0229\u003c/p\u003e \u003cp\u003e(0.0145)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0251\u003c/p\u003e \u003cp\u003e(0.234)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of instruments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHansen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eStandard errors in parenthese\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mathbf{*}\\mathbf{*}\\mathbf{*}\\mathbf{p}\u0026lt;0.01,\\:\\mathbf{*}\\mathbf{*}\\mathbf{p}\u0026lt;0.05,\\:\\mathbf{*}\\mathbf{p}\u0026lt;0.1\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eSource : author\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e show that controlling corruption has no significant direct effect on carbon dioxide emissions in the countries in our sample. The results also confirm the validity of estimating the GMM in a two-stage system. The Arellano-Bond autocorrelation test proves a significant first-order correlation (AR(1)), which is expected in dynamic panel models, but the absence of second-order autocorrelation (AR(2)) confirms the validity of our instruments. In addition, the Hansen over-identification test (0.145) justifies the exogeneity of our instruments.\u003c/p\u003e \u003cp\u003eFurthermore, among the explanatory variables, lagged carbon dioxide emissions exert a positive and significant effect (+\u0026thinsp;0.997), underlining the persistence of carbon dioxide emission levels over time. The other significant variables are foreign direct investment, the logarithm of international trade and the logarithm of energy consumption. Foreign direct investment is statistically significant and negative on carbon dioxide emissions at the 1% level. This result rejects the \u0026lsquo;pollution haven\u0026rsquo; hypothesis and validates the \u0026lsquo;pollution halo\u0026rsquo; hypothesis. Zubair et al (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) find that FDI inflows reduce Nigeria's carbon emissions and Pazienza, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e argues that FDI is a transfer of technological innovation that enables a cleaner production model. Also, international trade is significant and positive on carbon emissions at the 5% threshold. This result is in line with the work of Zhong et al (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who find that the development of international trade has led to a 5% increase in the inter-regional flow of carbon emissions on average, based on 39 large economies. Finally, energy consumption is significant and negative at the 5% threshold, justifying that it reduces carbon emissions. Haldar and Sethi (2023) show that renewable energy consumption significantly reduces carbon emissions in the long term.\u003c/p\u003e \u003cp\u003eFurthermore, the results that economic growth (logpib) and its square (logpib\u0026sup2;) are not significant. In other words, the environmental Kuznets curve within the countries in our sample does not exist. There are several reasons for this. Economic growth in these countries is often accompanied by heavy dependence on fossil fuels and unsustainable industrialisation, leading to a steady increase in polluting emissions. In addition, political instability can hamper the adoption of measures in favour of the environment, as economic priorities dominate. Also, the economic structure of these countries, often focused on polluting sectors such as extractive or intensive agriculture, limits the reduction of emissions. In addition, weak institutions and a lack of clean technologies prevent effective management of carbon emissions. Also, the absence of the Kuznets curve in developing countries may be due to an insufficient level of per capita income to reach the turning point.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Robustness check\u003c/h2\u003e \u003cp\u003eThis subsection consists of a robustness check. First, we perform a sensitivity check by successively adding the control variables. Then, by integrating other measures of climate change as well as alternative measures of corruption. The last point uses alternative estimation methods.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSuccessive integration of control variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eDependent variable: climate change\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eEstimator (GMM in System)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL.logco2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.997***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.976***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.978***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.974***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.986***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.986***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.997***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.00158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00772)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.00787)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.00894)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0130)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0143)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.0164)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.00321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.000997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.00118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.00290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.00243)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00263)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.00263)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.00303)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.00333)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.00348)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.00388)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elogpib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0633**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0550**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0292)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0265)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0280)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0331)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0330)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.0514)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elogpib\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.000494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.00144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.000419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.000633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.00132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.00143)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.00158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.00155)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.00235)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.000200***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.000198***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.000148**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-0.000152**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.000116*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(5.29e-05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(5.27e-05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5.94e-05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6.04e-05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(6.04e-05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elogci\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0479**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0461**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.0950**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0166)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0204)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0215)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.0387)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elogce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.0156*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.0323**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.00897)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.0162)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elogagri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.00520)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.00651)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edemo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.0145)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.0392***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.264*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.234*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0114)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.149)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.136)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.139)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.155)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.152)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.234)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber id\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of instruments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHansen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eStandard errors in parentheses : *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eSource : author\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e suggest that controlling corruption does not have a significant direct effect on carbon dioxide emissions. Also, the logarithm of economic growth is significant and positive in columns (3) and (4), justifying that an increase in economic growth increases carbon emissions in developing countries. These results are consistent with the findings of Yousefi-Sahzabi et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and Bouznit and Maria del (2016) who confirm the existence of a strong positive correlation between economic growth and CO2 emissions in Iran and Algeria, respectively. In addition, foreign direct investment remains statistically negative on CO2 emissions. The logarithm of international trade also remains positive in columns (5, 6 and 7). Similarly, the logarithm of energy consumption is statistically significant and negative in columns (5) and (7).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness by climate change indicators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eEstimator: GMM system\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLogCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003elogN\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003etempannuelles\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e(1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e(2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e(3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL.logco2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.997***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0164)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.000282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.00388)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00411)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0461)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elogpib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0514)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.119)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.796)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elogpib\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.00235)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00544)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0379)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.000116*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.000176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.000860\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(6.04e-05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000993)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elogci\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.0950**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-1.230***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0387)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0748)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.287)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elogce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.0323**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.339***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0162)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0942)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elogagri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0456\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.00651)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00674)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0793)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edemo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.321**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0246)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.126)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL.logN\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.987***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0189)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL.tempannuelles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.599***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0609)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.234)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.456)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.045)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber id\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of instruments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHansen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eStandard errors in parentheses\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSource : author\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e suggest that controlling corruption still has no significant direct effect on the various indicators measuring climate change. Individually, foreign direct investment is significant and negative in column (1). International trade is significant and positive in column (1), but negative in column (3). This result (column 3) is consistent with the work of Boyomo and Aurelien (2025) who prove that international trade, as a vector of wealth creation, directly reduces vulnerability to climate change in Sub-Saharan Africa. Moreover, energy consumption is significant and negative (Column 1), but positively influences average temperatures (Column 3). Alberini et al (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) find evidence of a positive relationship between electricity consumption and temperatures. In addition, democracy is significant and negative on temperatures. The work of Persakis et al (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) emphasises that democratic environments enhance the effectiveness of climate policies in reducing environmental degradation.\u003c/p\u003e \u003cp\u003eTo complete our robustness analysis, other indicators for measuring corruption are used. These are the political corruption index (incorrp), the public sector corruption index (incorrsp) and the executive corruption index (incorrexe).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness by corruption measurement indicators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eDependent variable: climate change\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eEstimator (GMM in System)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL.logco2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.997***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.994***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.995***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.996***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0164)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0159)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0159)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0166)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.00388)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elogpib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0514)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0510)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0513)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0552)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elogpib\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.00235)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00235)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.00236)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.00250)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.000116*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.000108*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.000114*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.000117*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(6.04e-05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(6.36e-05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(6.66e-05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(6.09e-05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elogci\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.0950**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0907**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0932**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0935**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0387)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0381)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0381)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0390)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elogce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.0323**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.0317**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.0326**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.0328**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0162)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0155)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0159)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0161)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elogagri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.000509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.000279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.000597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.00651)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00620)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.00626)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.00643)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edemo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0269)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0157)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0299)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eincorrp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0239)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eincorrsp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0201)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eincorrexe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00952\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0364)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0309\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.234)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.233)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.236)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.248)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber id\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of instruments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHansen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eStandard errors in parentheses : *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSource : author\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e suggest that all indicators measuring corruption do not have a significant direct effect on CO2 emissions. Furthermore, FDI is still significant and negative for all columns and international trade is significant and positive. In addition, energy consumption remains significant and negative for all columns.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Mediation analysis\u003c/h2\u003e \u003cp\u003eThis sub-section analyses the transmission channels through which corruption affects climate change in the countries in our sample. We have identified two main channels: the participatory democracy index and energy poverty. To assess the magnitude of these effects, we use the structural equation mediation (SEM) proposed by Mickinnon et al. (1995). The following figure illustrates this relationship.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eModel 1\u003c/b\u003e : \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{e}\\varvec{n}\\varvec{e}\\varvec{r}\\varvec{g}\\varvec{y}\\:\\varvec{p}\\varvec{o}\\varvec{v}\\varvec{e}\\varvec{r}\\varvec{t}\\varvec{y}}_{\\varvec{i}}=\\:{\\varvec{\\alpha\\:}}_{1}+{\\varvec{b}}_{1}\\varvec{c}\\varvec{o}\\varvec{r}\\varvec{r}\\varvec{u}\\varvec{p}\\varvec{t}\\varvec{i}\\varvec{o}\\varvec{n}+{\\varvec{c}}_{1}\\varvec{c}\\varvec{o}\\varvec{n}\\varvec{t}\\varvec{r}\\varvec{o}\\varvec{l}+{\\varvec{\\mu\\:}}_{\\varvec{i}}\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eModel 2\u003c/b\u003e : \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{C}\\varvec{O}}_{2\\varvec{i}}=\\:{\\varvec{\\alpha\\:}}_{2}+{\\varvec{b}}_{2}\\varvec{c}\\varvec{o}\\varvec{r}\\varvec{r}\\varvec{u}\\varvec{p}\\varvec{t}\\varvec{i}\\varvec{o}\\varvec{n}+{\\varvec{b}}_{3}\\varvec{p}\\varvec{a}\\varvec{r}\\varvec{t}\\varvec{i}\\varvec{c}\\varvec{i}\\varvec{p}\\varvec{a}\\varvec{t}\\varvec{o}\\varvec{r}\\varvec{y}\\:\\varvec{d}\\varvec{e}\\varvec{m}\\varvec{o}\\varvec{c}\\varvec{r}\\varvec{a}\\varvec{c}\\varvec{y}\\:\\varvec{i}\\varvec{n}\\varvec{d}\\varvec{e}\\varvec{x}+{\\varvec{c}}_{1}\\varvec{c}\\varvec{o}\\varvec{n}\\varvec{t}\\varvec{r}\\varvec{o}\\varvec{l}+{\\varvec{\\gamma\\:}}_{\\varvec{i}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eIndirect effect\u003c/b\u003e: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{b}}_{1}\\)\u003c/span\u003e\u003c/span\u003e \u003cb\u003ex\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{b}}_{3}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eDirect effect\u003c/b\u003e: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{b}}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eTotal effect: (\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{b}}_{1}\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003ex\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{\\varvec{b}}_{3}\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e) +\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{b}}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eThe first regression is a relationship between corruption and the mediator variables (model 1) captured by the coefficient (b1). In addition, the indirect effect is captured by regressing corruption on climate change considering the effect of mediators (model 2). This influence is reflected by the weight of (b2). The indirect effect is the product of (b1) and (b3). It measures the impact of corruption on climate change through energy poverty and the participatory democracy index.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTransmission channels of the effect of corruption on climate change: \u003cb\u003eenergy poverty\u003c/b\u003e and \u003cb\u003eparticipatory democracy index\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAccess to clean cooking fuels and technologies (% of population)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eParticipatory democracy index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eAccess to clean cooking fuels and technologies (% of rural population)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(A) Mediation test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd_err\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT-stat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCoef std_err T-stat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStd_err\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eT-stat\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSobel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.023 0.004 0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAroian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.023 0.004 0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoodman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.023 0.004 0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(B) Composition des effets\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect effect (Sobel)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.023 0.004 0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.058 0.013 0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.081 0.012 0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of total effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003e22%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e28.2%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e29.3%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNotes\u003c/b\u003e: *; **; *** significance at the 10%, 5% and 1% thresholds respectively\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eSource: author\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of the mediation (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) of the effects of corruption on climate change are visible through two transmission channels. Indeed, the mediation impact of the participatory democracy index has a Sobel statistic of -0.023, with a standard error of 0.004 and a p-value of 0.000, which validates the indirect effect of the participatory democracy index. In addition, for the energy poverty mediator measured by access to clean fuels and technologies for cooking (% of the population) presents a Sobel statistic of -0.018, a standard error of 0.004 and a p-value of 0.000, validating the indirect effect of the mediator. Also, access to clean cooking fuels and technologies (% of rural population) has a Sobel statistic of -0.024, with a standard error of 0.004 and a p-value of 0.000, confirming the indirect effect of the mediator. We also note that the results are the same for the other tests, in particular Aroian and Goodman.\u003c/p\u003e \u003cp\u003eFurthermore, the results suggest that the mediating effect for access to clean cooking fuels and technologies (% of population) is 22%; for the participatory democracy index is 28.2% and finally for access to clean cooking fuels and technologies (% of rural population) is 29.3% of the total effect on the relationship between corruption and climate change in developing countries. More precisely, corruption increases climate change by reducing participatory democracy and access to clean fuels and technologies. Countries with high levels of corruption adopt more climate-related laws and policies. Despite this, corrupt countries have less stringent environmental policies, which shows that the policies adopted do not translate into emissions reductions. These countries may adopt more ambitious policies, but when corruption is widespread, the public authorities seem unable to properly implement and enforce the environmental laws and regulations adopted (Pavitkina, 2018; Pavitkina and Jagers, 2022). Corruption also increases climate change by negatively affecting access to clean fuels and technologies. Amoah et al (2022) show that corruption has a negative impact on the use of renewable energy in Africa. This is due to corrupt officials taking advantage of inefficient bureaucratic systems. These results highlight the importance of energy poverty and participatory democracy on the effects of corruption on climate change in developing countries.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study examined the effects of corruption on climate change in a sample of 70 developing countries over the period 2000\u0026ndash;2022. We used the method of generalised moments in a system and structural equation mediation. The results suggest that corruption positively stimulates climate change. This relationship is mediated by active and strengthened participatory democracy (28.3%), by energy poverty through better access to clean fuels and technologies for the population in general (22%) and for the rural population in particular (29.3%). Based on these results, a number of economic policy suggestions can be made. Firstly, governments must put in place measures to monitor and combat corruption and put an end to impunity in order to limit its environmental impact. Secondly, it is crucial that developing countries consider investing in promising environmental projects, such as solar energy or hydroelectric dams, to further reduce dependence on polluting energies and alleviate fuel poverty. In terms of participatory democracy, it is essential to strengthen the place of women in the political sphere, guaranteeing them an active role and real decision-making power, particularly when it comes to taking a stand on climate change. Finally, it is vital to preserve civil liberties, by protecting the right to free and independent information. We must prevent the media from becoming instruments of indoctrination, especially when it comes to denouncing the problems associated with environmental degradation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFinancing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not receive any external funding.\u003c/p\u003e\n\u003ch2\u003eCRediT authorship contribution statement\u003c/h2\u003e\n\u003cp\u003eThe author conceived the entire study, collected and analysed the data and drafted the manuscript. No other person contributed to this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e: Fran\u0026ccedil;ois-Cyrille EYEGHE-NTOUTOUME\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that he has no financial, professional or personal conflicts of interest in connection with this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research uses publicly available secondary data, without human participants or harm. The work is original and complies with ethical standards.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbid, Lobna, Sana Kacem, et Haifa Saadaoui (2023). \u0026laquo; The Impacts of Economic Growth, Corruption, Energy Consumption and Trade Openness upon CO2 Emissions: West African Countries Case \u0026raquo;. \u003cem\u003eArab Gulf Journal of Scientific Research\u003c/em\u003e 42, n\u003csup\u003eo\u003c/sup\u003e 3, 464‑80. https://doi.org/10.1108/AGJSR-01-2023-0005.\u003c/li\u003e\n\u003cli\u003eAcemoglu, Daron, Philippe Aghion, Leonardo Bursztyn, et David Hemous (2012). \u0026laquo; The Environment and Directed Technical Change \u0026raquo;. \u003cem\u003eAmerican Economic Review\u003c/em\u003e 102, n\u003csup\u003eo\u003c/sup\u003e 1 131‑66. https://doi.org/10.1257/aer.102.1.131.\u003c/li\u003e\n\u003cli\u003eAdom, Philip Kofi (2024). \u0026laquo; The socioeconomic impact of climate change in developing countries over the next decades: A literature survey \u0026raquo;. \u003cem\u003eHeliyon\u003c/em\u003e 10, n\u003csup\u003eo\u003c/sup\u003e 15, e35134. https://doi.org/10.1016/j.heliyon.2024.e35134.\u003c/li\u003e\n\u003cli\u003eAhmed, Mun Mun, et Koji Shimada (2019). \u0026laquo; The Effect of Renewable Energy Consumption on Sustainable Economic Development: Evidence from Emerging and Developing Economies \u0026raquo;. \u003cem\u003eEnergies\u003c/em\u003e 12, n\u003csup\u003eo\u003c/sup\u003e 15, 2954. https://doi.org/10.3390/en12152954.\u003c/li\u003e\n\u003cli\u003eAkhbari, Reza, et Mehdi Nejati (2019). \u0026laquo; The effect of corruption on carbon emissions in developed and developing countries: empirical investigation of a claim \u0026raquo;. \u003cem\u003eHeliyon\u003c/em\u003e 5, n\u003csup\u003eo\u003c/sup\u003e 9, e02516. https://doi.org/10.1016/j.heliyon.2019.e02516.\u003c/li\u003e\n\u003cli\u003eAlberini, Anna, Giuseppe Prettico, Chang Shen, et Jacopo Torriti (2019). \u0026laquo; Hot weather and residential hourly electricity demand in Italy \u0026raquo;. \u003cem\u003eEnergy\u003c/em\u003e 177, 44‑56. https://doi.org/10.1016/j.energy.2019.04.051.\u003c/li\u003e\n\u003cli\u003eArminen, Heli, et Angeliki N. 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The liberal model takes a \"negative\" view of political power insofar as it judges the quality of democracy by the limits placed on government. This is achieved by constitutionally protected civil liberties, strong rule of law, an independent judiciary, and effective checks and balances that, together, limit the exercise of executive power. To make this a measure of liberal democracy, the index also takes the level of electoral democracy into account.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Graphs","content":"\u003cp\u003eGraphs 1 to 5 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"aucun","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"corruption, climate change, participatory democracy, energy poverty, mediation","lastPublishedDoi":"10.21203/rs.3.rs-6528488/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6528488/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change is one of the most pressing issues of our time. While its effects are affecting our ecosystems, societies and economies, understanding the mechanisms by which certain institutional forces, such as corruption, exacerbate this crisis remains a key and under-explored issue. This study sheds new light on the issue by analysing the transmission channels through which corruption affects climate change in developing countries between 2000 and 2022. It highlights the central role of the participatory democracy index and energy poverty, in particular access to clean fuels and technologies. After several empirical approaches, the results of the structural equation mediation (SEM) suggest that corruption positively affects climate change through an indirect channel via these two dimensions. These results underline the urgency of a solid institutional framework and investment in clean energy to counter this dynamic in developing countries, providing a basis for better targeted climate policies.\u003c/p\u003e","manuscriptTitle":"Does corruption influence climate change in developing countries? Effects and transmission channels","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-28 07:07:16","doi":"10.21203/rs.3.rs-6528488/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3a29db60-1f34-44ac-96e7-153e3456ba19","owner":[],"postedDate":"April 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47682492,"name":"Environmental Policy"},{"id":47682493,"name":"Environmental Economics"}],"tags":[],"updatedAt":"2025-04-28T07:07:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-28 07:07:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6528488","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6528488","identity":"rs-6528488","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}