Does corruption harm biodiversity in developing countries? Analysis of the effects of transmission channels

Does corruption harm biodiversity in developing countries? 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Analysis of the effects of transmission channels François-Cyrille EYEGHE-NTOUTOUME This is a preprint; it has not been peer reviewed by a journal.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-6735705/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 This paper contributes to the literature on the relationship between corruption and biodiversity in developing countries (DCs). The existing literature on this subject is sparse and not consensual, with empirical studies limited mainly to meta-analyses and case studies, without any real empirical analysis of this relationship. Furthermore, the transmission channels through which corruption influences biodiversity have not yet been clearly identified. This study fills this gap by examining the effects of transmission channels on a panel of 70 developing countries over the period 2000–2022. Using the GMM system method, the results indicate that corruption has no significant direct effect on biodiversity in developing countries. However, the structural equation mediation analysis reveals that corruption negatively affects biodiversity through the channels of total population, urbanisation rate, economic growth and energy consumption. For developing countries, protecting biodiversity requires an active fight against corruption, the implementation of sustainable growth, the strengthening of democratic institutions and the energy transition. These efforts must be accompanied by inclusive policies and the commitment of local communities to guarantee the effective and sustainable management of ecosystems. corruption biodiversity ecological footprint GMM system mediation Figures Figure 1 1- Introduction The decline of ecosystems is one of the most critical environmental challenges of the 21st century (Mosoh and al. 2024). Its degradation has reached alarming levels, particularly in developing countries, where the unsustainable exploitation of natural resources is often accentuated by economic vulnerabilities such as dependence on monocultures for export (Lui and al. 2018). Financial constraints also play a significant role, particularly through the underfunding of protected areas, which limits conservation efforts (Pimm and al. 2018). Population growth also intensifies degradation by stimulating urbanisation and agricultural expansion, reducing natural habitats (Jha and Bawa, 2006; Yang and al. 2020). Historically, colonial policies of massive extraction have weakened ecosystems, a legacy that persists in current development models (Mbaye, 2024 ). The climate factor, with its disruption of ecological cycles, increases the vulnerability of species (Bergholt and Lujala, 2012; Gray and Mueller, 2012; Schleuning and al. 2020). Finally, the institutional factor, in particular the quality of governance influenced by levels of corruption, further increases the degradation of ecosystems (Barrett and al. 2006; Smith and al. 2003). There are currently many definitions of biodiversity and most of them are vague, which probably reflects the uncertainty that reigns on the subject. The Convention on Biological Diversity (1992) defines biodiversity as:≪ the variability among living organisms from all sources including, inter alia, terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part; this includes diversity within species, between species and of ecosystems≫. Redford and Richter (2001) give another definition, stating that the term biodiversity remains ill-defined. According to them, biodiversity comprises three components: genetic, population/species and community/ecosystem. Each component has its own three attributes: composition, structure and function. It is also important to stress that corruption is a difficult concept to define precisely. Although it is widely recognised and denounced, attributing a single, universal definition to it remains complex. According to Klitgaard ( 1988 ), Lambsdorff and Graf (2007) and Rose-Ackerman and Palifka (2016), corruption means the abuse of an office for personal gain. Furthermore, it seems that the correlation between corruption and biodiversity health remains a topic of interest for economists (Barrett, 2003 ; Barrett and al. 2006). Nevertheless, few empirical studies have examined this issue in developing countries. From a theoretical point of view, corruption influences biodiversity both directly and indirectly. Directly, it weakens environmental institutions, compromising the application of nature protection policies (Shleifer and Vishny, 1993) by facilitating destructive practices such as the exploitation of natural resources (Bardhan, 1997 ). These effects also include deforestation (Koyuncu and Yilmaz, 2009; Abdul-Rahaman, 2016); poaching and illegal wildlife trade (Smith and Walpole, 2005; van Uhm and Moreto, 2018); illegal fishing (Sundstrom, 2012; Beseng, 2019 ; Zaelany, 2019 ); and land use (Bulte and al. 2007). Indirectly, corruption disrupts governance (Barrett and al. 2006; Li and al. 2023) and international trade (Barbier and al. 2005; Guo and al. 2023). Moreover, in the absence of a well-developed theory, empirical studies on this relationship are still in their infancy, particularly in developing countries. More specifically, we might mention the work of Smith and Walpole (2005). These authors used indicators of corruption and biodiversity at national level as part of a cross-national study, and found that there were significant and negative relationships between corruption and changes in elephant and rhino populations and in forest cover. The study of the relationship between corruption and biodiversity is necessary in developing countries for at least three reasons. First, these countries are home to much of the world's biodiversity, but often face socio-economic and environmental challenges (Laurance, 2004 ). Corruption can compound this vulnerability by undermining efforts to conserve and manage natural resources. Furthermore, corruption is often identified by its endemic nature, meaning that it is widespread and systemic. This point was made by Klitgaard and al (2000). The authors explain how corruption can become deeply rooted in the political and economic structures of these countries, compromising their socio-economic and environmental development. Finally, the populations in these countries are often closely dependent on ecosystems for their livelihoods, particularly through agriculture, fishing and traditional medicine. The loss of biodiversity due to corruption can therefore have devastating consequences for their well-being and livelihoods. It is therefore pertinent to ask: what is the influence of corruption on biodiversity in developing countries? The purpose of our study is to investigate the effects of corruption on biodiversity. To carry out this research, we draw on work relating to the Environmental Kuznets Curve (EKC) revisited for biodiversity. This approach is inspired by the Kuznets curve hypothesis in economics, but applied to environmental impact, in particular on biodiversity. More precisely, according to this hypothesis, at the start of a country's economic development, industrial and agricultural activities tend to entıner a degradation of biodiversity, through deforestation, pollution and the destruction of natural habitats. This results in an ascending phase of the curve, where the negative impact on biodiversity increases with economic development (Grossman and Krueger, 1995). Based on this theory, we will seek to test the following hypothesis: corruption compromises biodiversity in developing countries. The rest of our research is divided into sections. Section 2 reviews the existing literature on the relationship between corruption and biodiversity. Section 3 discusses the various stages of the empirical strategy. Section 4 discusses the results. Section 5 concludes with policy recommendations. 2- Literature review 2-1- Direct links between corruption and the illegal exploitation of natural resources The theoretical and empirical debate on the relationship between corruption and biodiversity is complex and multidimensional. Indeed, at a theoretical level, some perspectives suggest that corruption weakens the institutions responsible for conservation, thus compromising efforts to protect biodiversity (Brandon and Wells, 1992; Barrett and Arcese, 1995; Barrett and Graddy, 2000; Brandon, 2001; Aidt, 2009). On the contrary, other theories consider that corruption can sometimes be a way for local communities to circumvent excessively strict environmental regulations (Robbins, 2019). Empirically, studies differ on the exact nature of this relationship. Some research indicates a negative correlation between levels of corruption and ecosystem health (Laurance, 2004; Gren, 2017). However, others highlight situations where corruption can be used as a survival mechanism by marginalised communities, sometimes to the detriment of biodiversity (Zanetell and Knuth, 2002). Abdul-Rahaman (2016) examines the illegal exploitation of rosewood in northern Ghana using a case study based on a descriptive survey. The results of the study indicate that illegal exploitation of rosewood is a major environmental problem in the study area. In addition, empirical research using cross-national data to explore the causes of deforestation in relation to governance has been developing since the 1990s (Lopez and Galinato, 2005; Barrett and al. 2011). Previous case studies have shown that weak property rights are associated with the loss of forest cover. In this context, Smith and Walpole (2005) used two different dependent variables namely change in total forest cover and change in natural forest cover from 1990 to 1995 to estimate correlations between forests and governance. They examined the effect of governance scores per gross domestic product (GDP) per capita, the human development index (HDI) and population density on changes in total forest cover. They found that changes in total forest cover are positively correlated with GDP per capita and governance, but changes in natural forest cover are not correlated with governance. Sommer (2017) makes an interesting study on the quality of government to divide and compare countries according to measures of grand corruption and petty corruption over 87 countries from 2001 to 2014. Multiple regression models indicate that the effects of corruption have less overall impact on forests than population and economic growth. Corruption has been shown to be a key element in facilitating and sustaining illegal wildlife trade (Wyatt and Cao, 2015). The work of van Uhm and Moreto (2018) supports this by focusing on the role of corruption in facilitating illegal wildlife trade. Indeed, this research attempts to contribute to the literature by unravelling the existence, influence and intertwined nature of corruption in the illegal wildlife trade based on fieldwork conducted in China, Morocco, Russia and Uganda. Using Passas' concepts of symbiotic and antithetical relationships, the results support and extend the framework with the concept of legal exploitation, while highlighting the unique nature of corrupt practices influenced by different socio-political and cultural contexts. In a similar vein, Wyatt and al (2018) provide a literature-based investigation examining the role that specific acts of bribery play in the trafficking of ivory, reptile skins and live reptiles from, through or to Asia. It is proposed that not only do individual acts of corruption enable wildlife trafficking, but that corrupt structures in certain societies also contribute to trafficking and also enhance the resilience of trafficking. Corruption also facilitates illegal, unreported and unregulated (IUU) fishing operations, which are worth billions of dollars (FAO, 2022). These activities not only deplete fish stocks and threaten sustainability, but also directly threaten human health and well-being (Nunan and al., 2018; Zaelany, 2019). The work of Sundstrom (2012), examines the impact of corruption on compliance among small-scale South African fishermen. The results of scenario experiments conducted with 181 participants confirm that perceived corruption of the enforcement authority to comply with regulations. In this vein, Beseng (2019), lifts the veil on fisheries-related crime in Cameroon by highlighting how corruptible practices worsen the situation. The findings reveal a plethora of fishing-related elites, including corruption (i.e. bribery and abuse of power), document and identity fraud, illegal exploitation of fish mouths and endangered marine mammals. 2-2- Indirect links between corruption and biodiversity Corruption can play a decisive role in key sectors such as organic farming and renewable energies, which are crucial for biodiversity. Guo and al (2023) aim to examine the factors responsible for biodiversity loss as well as coping mechanisms to address this crisis in the context of 35 European economies covering the period 2009-2018. The results indicate that the use of renewable energies is exacerbating the biodiversity crisis, while organic farming is beneficial for the preservation of biodiversity in Europe. Corruption and the gender gap were also found to worsen biodiversity. Furthermore, Li and al (2023) develop the relationship between rents from the exploitation of natural resources and the governance of corruption. The results show that controlling corruption weakens the positive relationship between economic growth and the ecological footprint. Corruption exacerbates the negative impact of economic growth on the environment. In addition, Calabrese et al (2017) make an interesting contribution with different results. Specifically, they examine the influence of habitat and governance factors on elephant abundance in 13 Asian elephant range countries. The results of the estimates show that a relatively low level of corruption and effective governance are essential for maintaining Asian elephant populations. Still in the Asian context, Tan and al (2022) explain the reasons that could justify habitat modifications and therefore biodiversity loss in South and South-East Asian countries from 2013 to 2018. According to the negative binomial estimates, the results for habitat change measures are quantitatively similar for the impacts of agricultural land and arable land on biodiversity threats. Furthermore, in the context of international trade, Barbier and al (2005) analyse the relationship between corruption, trade and resource conversion. Recent evidence suggests that special interest groups significantly influence tropical deforestation through lobbying. They obtain testable predictions that are studied through a panel analysis of the cumulative expansion of agricultural land between 1960 and 1999 for low- and middle-income tropical countries. The results suggest that increased corruption and resource dependence directly favour land conversion, while improved terms of trade reduce conversion. This study makes two major contributions to the existing literature on the links between corruption and biodiversity in developing countries. First, while existing work is mainly based on meta-analyses and case studies, quantitative empirical research on this relationship remains scarce, particularly in the context of developing countries. In this context, this study fills this important gap by providing an empirical analysis, anchored in a contextualised perspective, which enriches the understanding of this issue. Furthermore, the majority of existing empirical studies establish a negative link between corruption and ecosystem degradation (Smith and Walpole, 2005), without exploring the mechanisms underlying this relationship. Using a structural equation mediation approach, our research identifies and quantifies the transmission channels through which corruption affects biodiversity. This approach highlights the relative influence of each mediator. 3- Methodological strategy We will present the empirical models and then the data collected. 3-1- Empirical models To empirically verify our working hypothesis, we use the model of Guo and al (2023), which analyses the role of organic farming, renewable energy and corruption in the biodiversity crisis in 35 European economies over the period 2009-2018. Biodiversity, the explained variable in our study, is generally assessed using several indicators, including the ecological footprint (Li and al., 2023; Asif and al. 2024), biocapacity (Foley and al., 2005; Yue and al. 2013) and land use (Smith and al. 2013). For this study, we use the ecological footprint, expressed in global hectares (gha) and presented as a logarithm, as the main indicator for measuring biodiversity. We also distinguish between explanatory variables of interest and control variables. Our variable of interest is corruption and several indicators are available, such as the control of corruption (Kaufmann and al. 2010), Transparency International's corruption perception index, as well as specific indices from V-DEM, such as those relating to political corruption, public sector corruption and executive sector corruption. Of these different options, we have chosen to give priority to the control of corruption indicator, because of its robustness and its wide use in the literature. In addition, the variable Z is a vector comprising several control variables that can influence biodiversity. These include the rate of urbanisation (Lutz, 2017; Jiang and O'Neill, 2017; Seto and al. 2012), total population (Crist, 2019; Cafaro and al. 2022), international trade (Stern, 2004), energy consumption (Foley and al. 2005), the level of democracy (Marquart-Pyatt , 2004; Li and Reuveny, 2006), foreign direct investment (Smarzynska and Wei, 2001; Mayer and al. 2014) and GDP and its square (Panayotou, 1993; Grossman and Krueger, 1995). Consequently, the AR(p) model for estimation purposes is written in the following dynamic form: 3-2- Estimation technique The econometric analysis highlights the descriptive statistics and correlation tests, as well as the estimation method. To analyse the effects of corruption on biodiversity in developing countries, a dynamic panel is estimated in the form of an AR(p) model based on the GMM system estimator developed by Blundell and Bond (1998). This estimation method has its own advantages. Three of them are mentioned here. Firstly, system GMM estimation allows not only lagged dependent variables but also any potentially endogenous explanatory variable to be instrumented by internal≪ ≫ instruments (i.e. lagged levels and lagged differences). Secondly, system GMM estimation proceeds by estimating the models in both levels and differences and thus allows the effects of time-invariant variables to be identified. Thirdly, this method deals with endogeneity bias by using internal instrumental variables based on past values. 3-3- Data presentation The data in this study comes from different sources. The data on the ecological footprint is taken from the Global Footprint Network. Data on per capita GDP, control of corruption, energy consumption (Kwh per capita), foreign direct investment (% of GDP) and international trade (% of GDP) are taken from the World Bank database. On the other hand, the level of democracy is taken from V-DEM; the urbanisation rate (% of urban population) and agriculture (% of GDP) are taken from the Our World in Data database. Table 1 : Statistical characteristics Variable Obs Average Standard deviation Min Max logef 1,610 7.3552 0.6977 5.7997 9.7223 cc 1,610 -0.5623 0.6025 -1.9367 1.6105 pop 1,610 1.8826 1.0444 -3.2184 9.9923 logpib 1,610 10.4419 0.8311 8.5455 13.2524 Logpib² 1,610 0.6903 0.9972 0.0000 7.8989 you 1,610 48.3907 20.8212 8.246 95.688 ide 1,610 -3.7096 15.5257 -231.6516 41.6749 logci 1,610 1.7913 0.1995 1.2362 2.3432 logce 1,610 3.5640 0.5830 2.1643 4.5936 logagri 1,610 1.5717 0.3275 -0.3480 1.9327 demo 1,610 0.4791 0.2031 0.0720 0.9140 Source : author The results of the descriptive statistics in Table 1 show that the average ecological footprint is 7.3552, which is relatively high in developing countries. Also, the standard deviation is 0.6970, justifying a moderate variation around the mean, showing that the ecological footprint is fairly homogeneous in some countries. In addition, the minimum and maximum values are 5.7997 to 9.7223, showing that the use of natural resources varies significantly between developing countries, with some countries exploiting natural resources much more intensively than others. Furthermore, the average control of corruption in the countries in our sample is :-0.5623. This negative value shows that, on average, developing countries are in a zone of low control of corruption. Similarly, the value of the standard deviation shows a fairly small variation around the mean, which implies that most DCs share similar levels of weakness in controlling corruption, although some countries may be exceptions. Also, the minimum and maximum values show that corruption is extremely problematic, while others manage to control it better. The total number of observations is 1,610. 4- Analysis of results We begin by evaluating the performance of the basic models, before turning to an analysis of their robustness. 4-1- Results of the basic model Table 2 : Correlation matrix logef cc pop logpib logpib² ide logci logce logagri demo you logef 1.0000 cc -0.0513 1.0000 pop -0.1418 -0.3297 1.0000 logpib 0.8678 0.0467 -0.3473 1.0000 logpib² 0.3446 0.0693 -0.2359 0.3246 1.0000 ide -0.4826 -0.0636 -0.2037 -0.4617 -0.6256 1.0000 logci -0.2109 0.2026 0.1460 -0.1433 -0.2167 0.1250 1.0000 logce 0.2687 0.3527 -0.5824 -0.5399 0.1603 -0.2233 0.3879 1.0000 logagri 0.0836 -0.0349 -0.0136 -0.0146 0.0218 -0.0401 -0.1101 -0.2373 1.0000 demo -0.0514 -0.4690 -0.2367 0.0438 -0.0527 -0.0637 0.0432 0.3590 -0.0924 1.0000 tu 0.0558 0.2827 -0.3366 0.3523 0.0734 -0.1125 0.2178 0.7540 -0.2515 0.4040 1.0000 Source: author Table 2 shows that there is a positive correlation between the logarithm of per capita GDP (logpib) and the logarithm of the ecological footprint (logef). In other words, the variables move in the same direction. Although the correlation matrix makes it possible to identify certain linear relationships between explanatory variables, it is not sufficient to accurately detect multicollinearity problems. Indeed, weak or moderate correlations can mask a more complex multicollinearity involving several variables at once. This is why it is necessary to calculate the Variance Inflation Factor (VIF), which provides a more rigorous assessment of the extent of linear redundancy between independent variables. Table 3 : Variance inflation factors (VIF) Variable VIF 1/VIF logce 5.62 0.177846 Urbanisation rate (tu) 2.64 0.379095 logpib 2.24 0.447113 ide 1.90 0.526989 logpib² 1.78 0.562982 logci 1.74 0.574598 pop 1.73 0.576674 demo 1.55 0.643785 cc 1.45 0.691474 logagri 1.13 0.882141 Mean VIF 2,18 Source: author The VIF results in Table 3 show that only the logce variable has a VIF greater than 5 (5.62), indicating moderate multicollinearity. On the other hand, all the other variables have FIVs well below 5. Also, the average VIF is 2.18, which is a good overall sign for the stability of the model. Table 4 : GMM estimates for systems (1) Variables Dependent variable: logef L.logef 0.654*** (0.127) cc -0.0235 (0.0368) Urbanisation rate -0.00380** (0.00168) pop 0.0152 (0.0158) logpib 0.106** (0.0472) logpib² -0.118 (0.0991) ide -0.00619 (0.00417) logci -0.369* (0.207) logce 0.220** (0.106) logagri 0.0429 (0.0494) demo -0.0807 (0.146) Constant 1.494** (0.752) Comments 1,540 Id numbers 70 Number of instruments 15 AR(1) 0.000 AR(2) 0.568 Hansen 0.518 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Source: author The results in Table 4 suggest that the lagged variable is significant at the 1% threshold, justifying a strong persistence of the ecological footprint. Indeed, a 1% increase in human pressure on the environment at time t-1 leads to a 12.7% increase at time t, suggesting that past ecological pressures influence current levels. Furthermore, the AR(1), AR(2) and Hansen tests support the robustness of the GMM model: the first-order autocorrelation is normal, the absence of second-order autocorrelation is reassuring, and the instruments are valid according to Hansen. In addition, the results suggest that controlling for corruption (cc) has no significant direct effect on the ecological footprint. The urbanisation rate is significant and negative at the 5% threshold: a 1% increase in the urbanisation rate leads to a 0.168% decrease in the ecological footprint. This result is contrary to the work of Keho (2023), who shows that urbanisation contributes to environmental degradation in Côte d'Ivoire by increasing the ecological footprint. Similarly, Al-Mulali and Ozturk (2015) find that urbanisation increases the level of ecological footprint for 14 MENA countries. Furthermore, the logarithm of GDP is significant and positive at the 5% threshold. A 1% increase in GDP is accompanied by a 4.72% increase in the ecological footprint. In other words, economic growth increases the pressure on natural resources, thereby increasing the ecological footprint. Danish and al (2019) reveal that economic growth increases the ecological footprint, thus contributing to environmental degradation. In addition, the logarithm of international trade is significant and negative at the 10% threshold. A 1% increase in international trade translates into a 20.7% reduction in the ecological footprint. This is because economic globalisation is accompanied by more rapid technological development and, as a result, less use of natural resources. Celikoz and al (2022) use an FGLS cointegration analysis to show that economic globalisation has a negative long-term impact on the ecological footprint. In addition, the logarithm of energy consumption has a positive sign and is significant at the 5% level. A 1% increase in energy consumption´ increases the ecological footprint by 10.6%. Liu and al (2022) point out that one of the most critical determinants of the ecological footprint is energy consumption, particularly the use of non-renewable energy resources, as this leads to a deterioration in the quality of the environment. 4-2- Sensitivity analysis We begin our robustness analysis with four tests, including the integration of other indicators for measuring biodiversity (biocapacity and land use). We then integrate other measures of corruption (the political corruption index, the public sector corruption index and the executive corruption index), by successively integrating the control variables. Finally, alternative estimation methods. Sensitivity through integration of other biodiversity measurement indicators Table 5 : Integration of other biodiversity measures (1) (2) (3) Estimator: GMM system VARIABLES L.logbioca 0.842 (0.574) cc -0.0318 -0.00137 -0.0235 (0.111) (0.0173) (0.0368) Urbanisation rate 0.00484 -0.000206 -0.00380** (0.00870) (0.000738) (0.00168) pop 0.0210 -0.000901 0.0152 (0.109) (0.00630) (0.0158) logpib -0.0252 0.00435 0.106** (0.151) (0.0257) (0.0472) logpib² -0.0148 -0.0389 -0.118 (0.207) (0.0294) (0.0991) ide -0.00237 -0.00126 -0.00619 (0.00481) (0.00136) (0.00417) logci -0.121 -0.0623 -0.369* (0.289) (0.0867) (0.207) logce 0.0269 0.0128 0.220** (0.416) (0.0248) (0.106) logagri 0.243 0.00590 0.0429 (0.952) (0.0271) (0.0494) demo -0.0813 -0.0144 -0.0807 (0.396) (0.0362) (0.146) L.loguterres 0.978*** (0.0788) L.logef 0.654*** (0.127) Constant 0.909 0.193 1.494** (2.676) (0.368) (0.752) Comments 1,540 1,540 1,540 Number of id 70 70 70 Number of instruments 13 15 15 AR(1) 0.094 0.032 0.000 AR(2) 0.918 0.352 0.568 Hansen 0.697 0.322 0.518 Source: author The results in Table 5 show that, despite the inclusion of other biodiversity indicators to test robustness, the control for corruption remains insignificant in all the models. This indicates that, in this analytical framework, the level of corruption control has no statistically detectable influence on the dependent variables. Sensitivity by integrating other indicators to measure corruption Table 6 : Integration of other measures of corruption (1) (2) (3) Variables Estimator: GMM system Dependent variable: logef L.logef 0.666*** 0.630*** 0.652*** (0.117) (0.119) (0.119) incorrsp -0.429 (0.273) Urbanisation rate -0.00368** -0.00452*** -0.00376** (0.00155) (0.00155) (0.00167) pop 0.00885 0.0246 0.0157 (0.0148) (0.0163) (0.0160) logpib 0.0948** 0.141** 0.113** (0.0446) (0.0567) (0.0503) logpib² -0.127 -0.137 -0.136 (0.100) (0.106) (0.111) ide -0.00639 -0.00563 -0.00664 (0.00419) (0.00400) (0.00467) logci -0.372* -0.362* -0.389* (0.206) (0.191) (0.219) logce 0.225** 0.217** 0.211** (0.0984) (0.0906) (0.104) logagri 0.0482 0.0709 0.0377 (0.0440) (0.0503) (0.0492) demo 0.178 0.780*** -0.145 (0.212) (0.286) (0.271) incorrect -1.023** (0.416) incorrp 0.0423 (0.283) Constant 1.537** 1.601** 1.547** (0.744) (0.708) (0.762) Comments 1,540 1,540 1,540 Number of id 70 70 70 Number of instruments 15 15 15 AR(1) 0.000 0.000 0.000 AR(2) 0.528 0.516 0.484 Hansen 0.551 0.761 0.529 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Source: author The results in Table 6 show that integrating other indicators to measure corruption cancels out the significant influence of corruption on biodiversity in developing countries. Sensitivity by integration of control variables Table 7: successive integration of control variables (1) (2) (3) (4) (5) (6) (7) (8) (9) Variables Estimator: GMM system Dependent variable: logef L.logef 0.794*** 0.629*** 0.662*** 0.818*** 0.835*** 0.838*** 0.606*** 0.651*** 0.682*** (0.103) (0.199) (0.142) (0.291) (0.251) (0.165) (0.199) (0.151) (0.195) cc 0.0365 -0.151 -0.124 0.354 0.335 0.306 0.0368 0.0311 0.143 (0.142) (0.137) (0.0800) (0.422) (0.439) (0.280) (0.332) (0.335) (0.379) ide -0.00729 -0.00607 -0.00508 0.000743 -0.00150 -0.00299 -0.00419 -0.00400 -0.00730 (0.00685) (0.00532) (0.00592) (0.0121) (0.00403) (0.00339) (0.00367) (0.00349) (0.00800) tu 0.00212 0.00111 -0.00428 -0.00427 -0.00362 -0.0106* -0.00939** -0.00832 (0.00229) (0.00133) (0.00478) (0.00474) (0.00318) (0.00567) (0.00421) (0.00573) pop -0.0279 0.0590 0.0548 0.0395 0.0853* 0.0802* 0.0591 (0.0221) (0.0892) (0.0818) (0.0516) (0.0508) (0.0421) (0.0588) logpib 0.177 0.157 0.162 0.216 0.187 0.189* (0.208) (0.210) (0.141) (0.133) (0.118) (0.0982) logpib² -0.0406 -0.108 -0.114 -0.112 -0.256 (0.153) (0.124) (0.119) (0.112) (0.299) logci -0.203 -0.504 -0.464* -0.615* (0.207) (0.331) (0.272) (0.368) logce 0.512 0.474 0.430 (0.395) (0.355) (0.353) logagri 0.0829 0.0698 (0.0942) (0.101) demo -0.316 (0.535) Constant 1.505** 2.525* 2.404** -0.206 -0.114 0.209 0.170 0.0190 0.502 (0.715) (1.330) (0.966) (1.182) (0.697) (0.626) (0.456) (0.423) (1.231) Obs 1,540 1,540 1,540 1,540 1,540 1,540 1,540 1,540 1,540 Nbre id 70 70 70 70 70 70 70 70 70 instruments 7 8 9 10 11 12 12 13 14 AR(1) 0.001 0.000 0.000 0.019 0.014 0.000 0.006 0.001 0.008 AR(2) 0.606 0.725 0.882 0.455 0.716 0.357 0.460 0.514 0.398 Hansen 0.469 0.527 0.582 0.762 0.774 0.642 0.560 0.466 0.645 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Source: author The results presented in Table 7 indicate that controlling corruption does not have a significant effect on biodiversity, regardless of the model considered. However, in light of the different estimates, it is possible to hypothesise the existence of transmission mechanisms through which corruption could affect biodiversity in developing countries. In order to examine this hypothesis, a mediation analysis based on a structural equation model (SEM) is implemented. Table 8 : Sensitivity to alternative estimation methods (1) (2) Dependent variable: logef VARIABLES MCO FIXED EFFECTS L.logef 0.997*** 0.750*** (0.00448) (0.0342) cc 0.000136 0.00463 (0.00216) (0.00526) pop 0.00348*** 0.00391*** (0.00124) (0.00124) logpib 0.00317 0.0333** (0.00386) (0.0134) logpib² 0.00103 -0.00314 (0.00127) (0.00337) ide -8.44e-05** -0.000126** (4.09e-05) (5.72e-05) logci 0.00946* 0.00933 (0.00528) (0.0111) logce -0.00126 0.0415*** (0.00345) (0.0114) logagri -0.00471 0.0686 (0.00374) (0.0528) demo 0.000833 0.0236* (0.00593) (0.0137) Urbanisation rate -0.000142* 0.000534 (7.73e-05) (0.000410) Constant -0.0105 1.188*** (0.0219) (0.164) Comments 1,540 1,540 R-squared 0.998 0.832 Prob > F 0.0000 0.0000 Number of id 70 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1; Source: author The results in Table 8 compare two estimation methods (OLS and Fixed Effects). Total population (pop), the logarithm of economic growth (logpib), the logarithm of energy consumption (logce), the logarithm of international trade (logci) and the level of democracy have significant and positive effects on biodiversity. On the other hand, foreign direct investment (ide) and the rate of urbanisation (tu) are significant and negative for biodiversity. In addition, control of corruption has no significant impact on the dependent variable. The OLS model explains 99.8% of the variance, while the fixed effects model explains 83.2%, which shows that the models fit the data well. 4-3- Analysis of mediation The purpose of this sub-section is to analyse the mediators through which corruption influences biodiversity in developing countries. To analyse the transmission channels we use structural equation mediation analysis (SEM). The following figure illustrates these dynamics. The approach is based on the estimation of two regression equations, as illustrated in Figure 1. First, the parameter (b 1 ) quantifies the impact of corruption on the mediator (model 1). Next, the indirect effect is assessed by regressing biodiversity on corruption while controlling the mediator (model 2). This influence is reflected by the weight of (b 2) . The indirect effect is also the product of (b 1 ) and (b 3 ) where b 3 measures the strength of the correlation between biodiversity and the mediators. Table 9 : Mediation outcome Total population Urbanisation rate (A) Mediation test Coef Std_err T-stat Coef std_err T-stat Sobel -0.034 0.005 0.000*** -0.070 0.008 0.000*** Aroian -0.034 0.005 0.000*** -0.070 0.008 0.000*** Goodman -0.034 0.005 0.000*** -0.070 0.008 0.000*** (B) Composition of effects Indirect effect (Sobel) -0.034 0.005 0.000*** -0.070 0.008 0.000*** Direct effect -0.020 0.014 0.142 -0.019 0.013 0.145 Total effect -0.054 0.014 0.000*** -0.089 0.014 0.000*** Proportion of total effect 63,1% 79,1 % log Economic growth log energy consumption (A) Mediation test Coef Std_err T-stat Coef std_err T-stat Sobel -0.090 0.023 0.000*** -0.089 0.011 0.000*** Aroian -0.090 0.023 0.000*** -0.089 0.011 0.000*** Goodman -0.090 0.023 0.000*** -0.089 0.011 0.000*** (B) Composition of effects Indirect effect (Sobel) -0.090 0.023 0.000*** -0.089 0.011 0.000*** Direct effect -0.017 0.014 0.205 -0.008 0.030 0.572 Total effect -0.107 0.027 0.000*** -0.097 0.031 0.000*** Proportion of total effect 84% 92,2% t statistics in parentheses **p<0.05,*p<0.1,***p<0.01 Source: author The results of the Sobel, Aroian and Goodman tests in Table 9 show that for all three mediators, the indirect effects are significant and negative, while the direct effect remains insignificant. This indicates that controlling corruption negatively affects biodiversity through each of the mediators. This justifies our hypothesis that corruption erodes biodiversity in developing countries . The proportions also show that the following mediators explain a significant part of the relationship between corruption and biodiversity: total population (63.1%), urbanisation rate (79.1%), logarithm of economic growth (84%) and logarithm of energy consumption (92.2%). The logarithm of energy consumption plays a central role, accounting for 92.2% of the total mediated effect, making it the most important transmission channel. These results indicate that corruption compromises biodiversity by promoting unsustainable development models, characterised by unregulated urbanisation, inefficient energy intensification and often extractive economic growth. Overall, these results underline the need to integrate anti-corruption governance strategies targeting key development sectors to strengthen biodiversity conservation. 5- Conclusion At the end of our work, we wanted to analyse the effects of corruption on biodiversity. The theoretical literature enabled us to identify two main approaches: the direct approach and the indirect approach. Using a panel of 70 developing countries from 2000 to 2022 and applying the GMM system, the empirical results show that controlling corruption has no significant direct effect on biodiversity. The results also show that there is no environmental Kuznets curve for biodiversity in these countries. In addition, a robustness analysis was used by integrating other measures of corruption (political corruption index, public sector corruption index and executive corruption index), biodiversity (biocapacity and land use) by progressively integrating control variables and by testing the sensitivity to alternative estimation methods. The results suggest that corruption is still not significant. On the other hand, by adopting the structural equation mediation (SEM), the results prove that controlling corruption has a significant and negative effect on biodiversity through the channels of population, economic growth, urbanisation rate and energy consumption. For developing countries, protecting biodiversity requires an active fight against corruption, the implementation of sustainable growth, the strengthening of democratic institutions and the energy transition. 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Agricultural Sciences 08, n o 05 (2017): 409‑25.https://doi.org/10.4236/as.2017.85031. Additional Declarations The authors declare potential competing interests as follows: The author declares that he has no conflicting interests that could influence the results of this research. Supplementary Files AdditionalfilesCORRUPTIONANDBIODIVERSITE.docx 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6735705","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":461152811,"identity":"bbf32480-5f41-4087-aa84-6428c76166b7","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":"","correspondingAuthor":true,"prefix":"","firstName":"François-Cyrille","middleName":"","lastName":"EYEGHE-NTOUTOUME","suffix":""}],"badges":[],"createdAt":"2025-05-23 21:43:52","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6735705/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6735705/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83596554,"identity":"959ea727-3357-48cc-8cc5-024af2fd06e2","added_by":"auto","created_at":"2025-05-29 08:04:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":43931,"visible":true,"origin":"","legend":"\u003cp\u003eMediation analysis\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6735705/v1/237bf4e9babcbb9a54eb207d.png"},{"id":83597412,"identity":"52c6e968-78b1-41f6-9c38-515d0c544ad9","added_by":"auto","created_at":"2025-05-29 08:13:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2173734,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6735705/v1/96b3624f-f4e6-4c2d-8912-a0b061b7de6f.pdf"},{"id":83596555,"identity":"35340313-545c-4ac9-9d05-9d58defa8346","added_by":"auto","created_at":"2025-05-29 08:04:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":42370,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"AdditionalfilesCORRUPTIONANDBIODIVERSITE.docx","url":"https://assets-eu.researchsquare.com/files/rs-6735705/v1/1ac64de6c24255394a5c94d6.docx"}],"financialInterests":"The authors declare potential competing interests as follows: The author declares that he has no conflicting interests that could influence the results of this research.","formattedTitle":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDoes corruption harm biodiversity in developing countries? Analysis of the effects of transmission channels\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","fulltext":[{"header":"1- Introduction","content":"\u003cp\u003eThe decline of ecosystems is one of the most critical environmental challenges of the 21st century (Mosoh and al. 2024). Its degradation has reached alarming levels, particularly in developing countries, where the unsustainable exploitation of natural resources is often accentuated by economic vulnerabilities such as dependence on monocultures for export (Lui and al. 2018). Financial constraints also play a significant role, particularly through the underfunding of protected areas, which limits conservation efforts (Pimm and al. 2018). Population growth also intensifies degradation by stimulating urbanisation and agricultural expansion, reducing natural habitats (Jha and Bawa, 2006; Yang and al. 2020). Historically, colonial policies of massive extraction have weakened ecosystems, a legacy that persists in current development models (Mbaye, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The climate factor, with its disruption of ecological cycles, increases the vulnerability of species (Bergholt and Lujala, 2012; Gray and Mueller, 2012; Schleuning and al. 2020). Finally, the institutional factor, in particular the quality of governance influenced by levels of corruption, further increases the degradation of ecosystems (Barrett and al. 2006; Smith and al. 2003).\u003c/p\u003e \u003cp\u003eThere are currently many definitions of biodiversity and most of them are vague, which probably reflects the uncertainty that reigns on the subject. The Convention on Biological Diversity (1992) defines biodiversity as:≪ the variability among living organisms from all sources including, inter alia, terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part; this includes diversity within species, between species and of ecosystems≫. Redford and Richter (2001) give another definition, stating that the term biodiversity remains ill-defined. According to them, biodiversity comprises three components: genetic, population/species and community/ecosystem. Each component has its own three attributes: composition, structure and function.\u003c/p\u003e \u003cp\u003eIt is also important to stress that corruption is a difficult concept to define precisely. Although it is widely recognised and denounced, attributing a single, universal definition to it remains complex. According to Klitgaard (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), Lambsdorff and Graf (2007) and Rose-Ackerman and Palifka (2016), corruption means the abuse of an office for personal gain.\u003c/p\u003e \u003cp\u003eFurthermore, it seems that the correlation between corruption and biodiversity health remains a topic of interest for economists (Barrett, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Barrett and al. 2006). Nevertheless, few empirical studies have examined this issue in developing countries. From a theoretical point of view, corruption influences biodiversity both directly and indirectly. Directly, it weakens environmental institutions, compromising the application of nature protection policies (Shleifer and Vishny, 1993) by facilitating destructive practices such as the exploitation of natural resources (Bardhan, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). These effects also include deforestation (Koyuncu and Yilmaz, 2009; Abdul-Rahaman, 2016); poaching and illegal wildlife trade (Smith and Walpole, 2005; van Uhm and Moreto, 2018); illegal fishing (Sundstrom, 2012; Beseng, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zaelany, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); and land use (Bulte and al. 2007). Indirectly, corruption disrupts governance (Barrett and al. 2006; Li and al. 2023) and international trade (Barbier and al. 2005; Guo and al. 2023). Moreover, in the absence of a well-developed theory, empirical studies on this relationship are still in their infancy, particularly in developing countries. More specifically, we might mention the work of Smith and Walpole (2005). These authors used indicators of corruption and biodiversity at national level as part of a cross-national study, and found that there were significant and negative relationships between corruption and changes in elephant and rhino populations and in forest cover.\u003c/p\u003e \u003cp\u003eThe study of the relationship between corruption and biodiversity is necessary in developing countries for at least three reasons. First, these countries are home to much of the world's biodiversity, but often face socio-economic and environmental challenges (Laurance, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Corruption can compound this vulnerability by undermining efforts to conserve and manage natural resources. Furthermore, corruption is often identified by its endemic nature, meaning that it is widespread and systemic. This point was made by Klitgaard and al (2000). The authors explain how corruption can become deeply rooted in the political and economic structures of these countries, compromising their socio-economic and environmental development. Finally, the populations in these countries are often closely dependent on ecosystems for their livelihoods, particularly through agriculture, fishing and traditional medicine. The loss of biodiversity due to corruption can therefore have devastating consequences for their well-being and livelihoods. It is therefore pertinent to ask: what is the influence of corruption on biodiversity in developing countries? The purpose of our study is to investigate the effects of corruption on biodiversity. To carry out this research, we draw on work relating to the Environmental Kuznets Curve (EKC) revisited for biodiversity. This approach is inspired by the Kuznets curve hypothesis in economics, but applied to environmental impact, in particular on biodiversity. More precisely, according to this hypothesis, at the start of a country's economic development, industrial and agricultural activities tend to entıner a degradation of biodiversity, through deforestation, pollution and the destruction of natural habitats. This results in an ascending phase of the curve, where the negative impact on biodiversity increases with economic development (Grossman and Krueger, 1995).\u003c/p\u003e \u003cp\u003eBased on this theory, we will seek to test the following hypothesis: \u003cb\u003ecorruption compromises biodiversity in developing countries.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe rest of our research is divided into sections. Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e2\u003c/span\u003e reviews the existing literature on the relationship between corruption and biodiversity. Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3\u003c/span\u003e discusses the various stages of the empirical strategy. Section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e4\u003c/span\u003e discusses the results. Section 5 concludes with policy recommendations.\u003c/p\u003e"},{"header":"2- Literature review","content":"\u003cp\u003e\u003cstrong\u003e2-1- Direct links between corruption and the illegal exploitation of natural resources \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe theoretical and empirical debate on the relationship between corruption and biodiversity is complex and multidimensional. Indeed, at a theoretical level, some perspectives suggest that corruption weakens the institutions responsible for conservation, thus compromising efforts to protect biodiversity (Brandon and Wells, 1992; Barrett and Arcese, 1995; Barrett and Graddy, 2000; Brandon, 2001; Aidt, 2009). On the contrary, other theories consider that corruption can sometimes be a way for local communities to circumvent excessively strict environmental regulations (Robbins, 2019). Empirically, studies differ on the exact nature of this relationship. Some research indicates a negative correlation between levels of corruption and ecosystem health (Laurance, 2004; Gren, 2017). However, others highlight situations where corruption can be used as a survival mechanism by marginalised communities, sometimes to the detriment of biodiversity (Zanetell and Knuth, 2002). \u0026nbsp;Abdul-Rahaman (2016) examines the illegal exploitation of rosewood in northern Ghana using a case study based on a descriptive survey. The results of the study indicate that illegal exploitation of rosewood is a major environmental problem in the study area. In addition, empirical research using cross-national data to explore the causes of deforestation in relation to governance has been developing since the 1990s (Lopez and Galinato, 2005; Barrett and al. 2011). Previous case studies have shown that weak property rights are associated with the loss of forest cover. In this context, Smith and Walpole (2005) used two different dependent variables namely change in total forest cover and change in natural forest cover from 1990 to 1995 to estimate correlations between forests and governance. They examined the effect of governance scores per gross domestic product (GDP) per capita, the human development index (HDI) and population density on changes in total forest cover. They found that changes in total forest cover are positively correlated with GDP per capita and governance, but changes in natural forest cover are not correlated with governance. Sommer (2017) makes an interesting study on the quality of government to divide and compare countries according to measures of grand corruption and petty corruption over 87 countries from 2001 to 2014. Multiple regression models indicate that the effects of corruption have less overall impact on forests than population and economic growth.\u003c/p\u003e\n\u003cp\u003eCorruption has been shown to be a key element in facilitating and sustaining illegal wildlife trade (Wyatt and Cao, 2015). The work of van Uhm and Moreto (2018) supports this by focusing on the role of corruption in facilitating illegal wildlife trade. Indeed, this research attempts to contribute to the literature by unravelling the existence, influence and intertwined nature of corruption in the illegal wildlife trade based on fieldwork conducted in China, Morocco, Russia and Uganda. Using Passas\u0026apos; concepts of symbiotic and antithetical relationships, the results support and extend the framework with the concept of legal exploitation, while highlighting the unique nature of corrupt practices influenced by different socio-political and cultural contexts. In a similar vein, Wyatt and al (2018) provide a literature-based investigation examining the role that specific acts of bribery play in the trafficking of ivory, reptile skins and live reptiles from, through or to Asia. It is proposed that not only do individual acts of corruption enable wildlife trafficking, but that corrupt structures in certain societies also contribute to trafficking and also enhance the resilience of trafficking.\u003c/p\u003e\n\u003cp\u003eCorruption also facilitates illegal, unreported and unregulated (IUU) fishing operations, which are worth billions of dollars (FAO, 2022). These activities not only deplete fish stocks and threaten sustainability, but also directly threaten human health and well-being (Nunan and al., 2018; Zaelany, 2019). The work of Sundstrom (2012), examines the impact of corruption on compliance among small-scale South African fishermen. The results of scenario experiments conducted with 181 participants confirm that perceived corruption of the enforcement authority to comply with regulations. In this vein, Beseng (2019), lifts the veil on fisheries-related crime in Cameroon by highlighting how corruptible practices worsen the situation. The findings \u0026nbsp;reveal a plethora of fishing-related elites, including corruption (i.e. bribery and abuse of power), document and identity fraud, illegal exploitation of fish mouths and endangered marine mammals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2-2- Indirect links between corruption and biodiversity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorruption can play a decisive role in key sectors such as organic farming and renewable energies, which are crucial for biodiversity. Guo and al (2023) aim to examine the factors responsible for biodiversity loss as well as coping mechanisms to address this crisis in the context of 35 European economies covering the period 2009-2018. The results indicate that the use of renewable energies is exacerbating the biodiversity crisis, while organic farming is beneficial for the preservation of biodiversity in Europe. Corruption and the gender gap were also found to worsen biodiversity. Furthermore, Li and al (2023) develop the relationship between rents from the exploitation of natural resources and the governance of corruption. The results show that controlling corruption weakens the positive relationship between economic growth and the ecological footprint. Corruption exacerbates the negative impact of economic growth on the environment. In addition, Calabrese et al (2017) make an interesting contribution with different results. Specifically, they examine the influence of habitat and governance factors on elephant abundance in 13 Asian elephant range countries. The results of the estimates show that a relatively low level of corruption and effective governance are essential for maintaining Asian elephant populations. Still in the Asian context, Tan and al (2022) explain the reasons that could justify habitat modifications and therefore biodiversity loss in South and South-East Asian countries from 2013 to 2018. According to the negative binomial estimates, the results for habitat change measures are quantitatively similar for the impacts of agricultural land and arable land on biodiversity threats.\u003c/p\u003e\n\u003cp\u003eFurthermore, in the context of international trade, Barbier and al (2005) analyse the relationship between corruption, trade and resource conversion. Recent evidence suggests that special interest groups significantly influence tropical deforestation through lobbying. They obtain testable predictions that are studied through a panel analysis of the cumulative expansion of agricultural land between 1960 and 1999 for low- and middle-income tropical countries. The results suggest that increased corruption and resource dependence directly favour land conversion, while improved terms of trade reduce conversion.\u003c/p\u003e\n\u003cp\u003eThis study makes two major contributions to the existing literature on the links between corruption and biodiversity in developing countries. First, while existing work is mainly based on meta-analyses and case studies, quantitative empirical research on this relationship remains scarce, particularly in the context of developing countries. In this context, this study fills this important gap by providing an empirical analysis, anchored in a contextualised perspective, which enriches the understanding of this issue. Furthermore, the majority of existing empirical studies establish a negative link between corruption and ecosystem degradation (Smith and Walpole, 2005), without exploring the mechanisms underlying this relationship. Using a structural equation mediation approach, our research identifies and quantifies the transmission channels through which corruption affects biodiversity. This approach highlights the relative influence of each mediator.\u003c/p\u003e"},{"header":"3- Methodological strategy","content":"\u003cp\u003eWe will present the empirical models and then the data collected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3-1-\u003c/strong\u003e\u0026nbsp; \u0026nbsp;\u003cstrong\u003e\u0026nbsp;Empirical models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo empirically verify our working hypothesis, we use the model of Guo and al (2023), which analyses the role of organic farming, renewable energy and corruption in the biodiversity crisis in 35 European economies over the period 2009-2018.\u003c/p\u003e\n\u003cp\u003eBiodiversity, the explained variable in our study, is generally assessed using several indicators, including the ecological footprint (Li and al., 2023; Asif and al. 2024), biocapacity (Foley and al., 2005; Yue and al. 2013) and land use (Smith and al. 2013). For this study, we use the ecological footprint, expressed in global hectares (gha) and presented as a logarithm, as the main indicator for measuring biodiversity. We also distinguish between explanatory variables of interest and control variables. Our variable of interest is corruption and several indicators are available, such as the control of corruption (Kaufmann and al. 2010), Transparency International\u0026apos;s corruption perception index, as well as specific indices from V-DEM, such as those relating to political corruption, public sector corruption and executive sector corruption. Of these different options, we have chosen to give priority to the control of corruption indicator, because of its robustness and its wide use in the literature.\u003c/p\u003e\n\u003cp\u003eIn addition, the variable Z is a vector comprising several control variables that can influence biodiversity. These include the rate of urbanisation (Lutz, 2017; Jiang and O\u0026apos;Neill, 2017; Seto and al. 2012), total population (Crist, 2019; Cafaro and al. 2022), international trade (Stern, 2004), energy consumption (Foley and al. 2005), the level of democracy (Marquart-Pyatt , 2004; Li and Reuveny, 2006), foreign direct investment (Smarzynska and Wei, 2001; Mayer and al. 2014) and GDP and its square (Panayotou, 1993; Grossman and Krueger, 1995).\u003c/p\u003e\n\u003cp\u003eConsequently, the AR(p) model for estimation purposes is written in the following dynamic form:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"746\" height=\"400\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3-2- Estimation technique\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe econometric analysis highlights the descriptive statistics and correlation tests, as well as the estimation method.\u003c/p\u003e\n\u003cp\u003eTo analyse the effects of corruption on biodiversity in developing countries, a dynamic panel is estimated in the form of an AR(p) model based on the GMM system estimator developed by Blundell and Bond (1998). This estimation method has its own advantages. Three of them are mentioned here. Firstly, system GMM estimation allows not only lagged dependent variables but also any potentially endogenous explanatory variable to be instrumented by internal≪\u0026nbsp;≫\u0026nbsp;instruments (i.e. lagged levels and lagged differences). Secondly, system GMM estimation proceeds by estimating the models in both levels and differences and thus allows the effects of time-invariant variables to be identified. Thirdly, this method deals with endogeneity bias by using internal instrumental variables based on past values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3-3- Data presentation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data in this study comes from different sources. The data on the ecological footprint is taken from the Global Footprint Network. Data on per capita GDP, control of corruption, energy consumption (Kwh per capita), foreign direct investment (% of GDP) and international trade (% of GDP) are taken from the World Bank database. On the other hand, the level of democracy is taken from V-DEM; the urbanisation rate (% of urban population) and agriculture (% of GDP) are taken from the Our World in Data database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e: Statistical characteristics\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003elogef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1,610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e7.3552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.6977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e5.7997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e9.7223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003ecc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1,610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.5623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.6025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-1.9367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.6105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003epop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1,610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.8826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.0444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-3.2184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e9.9923\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003elogpib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1,610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e10.4419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.8311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e8.5455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e13.2524\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLogpib\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1,610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.6903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.9972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e7.8989\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eyou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1,610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e48.3907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e20.8212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e8.246 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e95.688\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1,610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-3.7096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e15.5257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-231.6516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e41.6749\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003elogci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1,610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.7913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.1995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.2362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.3432\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003elogce\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1,610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e3.5640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.5830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2.1643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e4.5936\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003elogagri\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1,610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.5717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.3275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.3480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.9327\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003edemo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1,610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.4791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.2031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.9140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e: author\u003c/p\u003e\n\u003cp\u003eThe results of the descriptive statistics in \u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eshow that the average ecological footprint is 7.3552, which is relatively high in developing countries. Also, the standard deviation is 0.6970, justifying a moderate variation around the mean, showing that the ecological footprint is fairly homogeneous in some countries. In addition, the minimum and maximum values are 5.7997 to 9.7223, showing that the use of natural resources varies significantly between developing countries, with some countries exploiting natural resources much more intensively than others. Furthermore, the average control of corruption in the countries in our sample is :-0.5623. This negative value shows that, on average, developing countries are in a zone of low control of corruption. Similarly, the value of the standard deviation shows a fairly small variation around the mean, which implies that most DCs share similar levels of weakness in controlling corruption, although some countries may be exceptions. Also, the minimum and maximum values show that corruption is extremely problematic, while others manage to control it better. The total number of observations is 1,610.\u003c/p\u003e"},{"header":"4- Analysis of results","content":"\u003cp\u003eWe begin by evaluating the performance of the basic models, before turning to an analysis of their robustness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4-1- Results of the basic model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e: Correlation matrix\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecc\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003epop\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogpib\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogpib\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eide\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogci\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogce\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogagri\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edemo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eyou\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecc\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.0513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003epop\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.1418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.3297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogpib\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.8678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.3473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogpib\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.3446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.2359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.3246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eide\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.4826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.0636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.2037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.4617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.6256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogci\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.2109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.2026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.1460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.1433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.2167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.1250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogce\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.2687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.3527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.5824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.5399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.1603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.2233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.3879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogagri\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.0349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.0136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.0146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.0401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.1101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.2373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edemo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.0514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.4690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.2367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.0527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.0637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.3590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.0924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003etu\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.2827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.3366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.3523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.0734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.1125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.2178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.7540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.2515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.4040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: author\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eshows that there is a positive correlation between the logarithm of per capita GDP (logpib) and the logarithm of the ecological footprint \u0026nbsp;(logef). In other words, the variables move in the same direction. Although the correlation matrix makes it possible to identify certain linear relationships between explanatory variables, it is not sufficient to accurately detect multicollinearity problems. Indeed, weak or moderate correlations can mask a more complex multicollinearity involving several variables at once. This is why it is necessary to calculate the Variance Inflation Factor (VIF), which provides a more rigorous assessment of the extent of linear redundancy between independent variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e: Variance inflation factors (VIF)\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVIF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1/VIF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003elogce\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.62\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.177846\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eUrbanisation rate (tu)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.379095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003elogpib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.447113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.526989\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003elogpib\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.562982\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003elogci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.574598\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003epop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.576674\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003edemo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.643785\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003ecc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.691474\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003elogagri\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.882141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean VIF\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2,18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSource: author\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe VIF results in \u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eshow that only the logce variable has a VIF greater than 5 (5.62), indicating moderate multicollinearity. On the other hand, all the other variables have FIVs well below 5. Also, the average VIF is 2.18, which is a good overall sign for the stability of the model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e: GMM estimates for systems\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDependent variable: logef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL.logef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.654***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e(0.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecc\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e-0.0235\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e(0.0368)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrbanisation rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.00380**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e(0.00168)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003epop\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e0.0152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e(0.0158)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogpib\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.106**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e(0.0472)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogpib\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e-0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e(0.0991)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eide\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e-0.00619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e(0.00417)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogci\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.369*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e(0.207)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogce\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.220**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e(0.106)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogagri\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e0.0429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e(0.0494)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edemo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e-0.0807\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e(0.146)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.494**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e(0.752)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e1,540\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eId numbers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of instruments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAR(1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAR(2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHansen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eStandard errors in parentheses\u003c/p\u003e\n\u003cp\u003e*** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1\u003c/p\u003e\n\u003cp\u003eSource: author\u003c/p\u003e\n\u003cp\u003eThe results in \u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003esuggest that the lagged variable is significant at the 1% threshold, justifying a strong persistence of the ecological footprint. Indeed, a 1% increase in human pressure on the environment at time t-1 leads to a 12.7% increase at time t, suggesting that past ecological pressures influence current levels. Furthermore, the AR(1), AR(2) and Hansen tests support the robustness of the GMM model: the first-order autocorrelation is normal, the absence of second-order autocorrelation is reassuring, and the instruments are valid according to Hansen. In addition, the results suggest that controlling for corruption (cc) has no significant direct effect on the ecological footprint. \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe urbanisation rate is significant and negative at the 5% threshold: a 1% increase in the urbanisation rate leads to a 0.168% decrease in the ecological footprint. This result is contrary to the work of Keho (2023), who shows that urbanisation contributes to environmental degradation in C\u0026ocirc;te d\u0026apos;Ivoire by increasing the ecological footprint. Similarly, Al-Mulali and Ozturk (2015) find that urbanisation increases the level of ecological footprint for 14 MENA countries. Furthermore, the logarithm of GDP is significant and positive at the 5% threshold. A 1% increase in GDP is accompanied by a 4.72% increase in the ecological footprint. In other words, economic growth increases the pressure on natural resources, thereby increasing the ecological footprint. Danish and al (2019) reveal that economic growth increases the ecological footprint, thus contributing to environmental degradation. In addition, the logarithm of international trade is significant and negative at the 10% threshold. A 1% increase in international trade translates into a 20.7% reduction in the ecological footprint. This is because economic globalisation is accompanied by more rapid technological development and, as a result, less use of natural resources. Celikoz and al (2022) use an FGLS cointegration analysis to show that economic globalisation has a negative long-term impact on the ecological footprint. In addition, the logarithm of energy consumption has a positive sign and is significant at the 5% level. A 1% increase in energy consumption\u0026acute; increases the ecological footprint by 10.6%. \u0026nbsp;Liu and al (2022) point out that one of the most critical determinants of the ecological footprint is energy consumption, particularly the use of non-renewable energy resources, as this leads to a deterioration in the quality of the environment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4-2- Sensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe begin our robustness analysis with four tests, including the integration of other indicators for measuring biodiversity (biocapacity and land use). We then integrate other measures of corruption (the political corruption index, the public sector corruption index and the executive corruption index), by successively integrating the control variables. Finally, alternative estimation methods.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cem\u003eSensitivity through integration of other biodiversity measurement indicators\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e: Integration of other biodiversity measures\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"2\" style=\"width: 307px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimator: GMM system\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVARIABLES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL.logbioca\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.574)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecc\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.0318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.00137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.0235\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.111)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.0173)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.0368)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrbanisation rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.00484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.000206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.00380**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.00870)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.000738)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.00168)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003epop\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.000901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.0152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.00630)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.0158)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogpib\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.0252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.00435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.106**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.151)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.0257)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.0472)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogpib\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.0148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.0389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.207)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.0294)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.0991)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eide\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.00237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.00126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.00619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.00481)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.00136)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.00417)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogci\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.0623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.369*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.289)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.0867)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.207)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogce\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.0128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.220**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.416)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.0248)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.106)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogagri\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.00590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.0429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.952)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.0271)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.0494)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edemo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.0813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.0144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.0807\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.396)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.0362)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.146)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL.loguterres\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.978***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.0788)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL.logef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.654***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.494**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(2.676)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.368)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.752)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1,540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1,540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1,540\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of id\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of instruments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAR(1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAR(2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHansen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSource: author\u003c/p\u003e\n\u003cp\u003eThe results in \u003cstrong\u003eTable 5\u0026nbsp;\u003c/strong\u003eshow that, despite the inclusion of other biodiversity indicators to test robustness, the control for corruption remains insignificant in all the models. This indicates that, in this analytical framework, the level of corruption control has no statistically detectable influence on the dependent variables.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cem\u003eSensitivity by integrating other indicators to measure corruption\u003c/em\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e: Integration of other measures of corruption\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 325px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"2\" valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimator: GMM system\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDependent variable: logef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL.logef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.666***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.630***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.652***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.117)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(0.119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.119)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eincorrsp\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.273)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrbanisation rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.00368**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.00452***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.00376**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.00155)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(0.00155)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.00167)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003epop\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.00885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.0246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.0157\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.0148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(0.0163)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.0160)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogpib\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0948**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.141**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.113**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.0446)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(0.0567)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.0503)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogpib\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e-0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(0.106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.111)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eide\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.00639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e-0.00563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.00664\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.00419)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(0.00400)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.00467)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogci\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.372*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.362*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.389*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.206)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(0.191)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.219)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogce\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.225**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.217**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.211**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.0984)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(0.0906)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.104)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogagri\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.0482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.0709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.0377\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.0440)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(0.0503)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.0492)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edemo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.780***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.212)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(0.286)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.271)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eincorrect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.023**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(0.416)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eincorrp\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.0423\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.283)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.537**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.601**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.547**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.744)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(0.708)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e(0.762)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1,540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1,540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1,540\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of id\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of instruments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAR(1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAR(2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHansen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eStandard errors in parentheses\u003c/p\u003e\n\u003cp\u003e*** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1\u003c/p\u003e\n\u003cp\u003eSource: author\u003c/p\u003e\n\u003cp\u003eThe results in \u003cstrong\u003eTable 6\u0026nbsp;\u003c/strong\u003eshow that integrating other indicators to measure corruption cancels out the significant influence of corruption on biodiversity in developing countries.\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cem\u003eSensitivity by integration of control variables\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7: successive integration of control variables\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"7\" rowspan=\"2\" valign=\"top\" style=\"width: 539px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimator: GMM system\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDependent variable: logef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL.logef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.794***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.629***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.662***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.818***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.835***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.838***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.606***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.651***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.682***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e(0.199)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.291)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e(0.251)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.165)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.199)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.151)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.195)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecc\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e(0.137)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.0800)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.422)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e(0.439)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.280)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.332)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.335)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.379)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eide\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.00729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.00607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.00508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.000743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.00150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.00299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.00419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.00400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.00730\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.00685)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e(0.00532)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.00592)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.0121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n 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style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.00428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.00427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.00362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.0106*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.00939**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.00832\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n 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\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.0279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0853*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0802*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0591\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.0221)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.0892)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e(0.0818)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.0516)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.0508)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.0421)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.0588)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogpib\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.189*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.208)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e(0.210)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.141)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.133)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.118)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.0982)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogpib\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.0406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e(0.153)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.124)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.299)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogci\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.464*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.615*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.207)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.331)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.272)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.368)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogce\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.395)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.355)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.353)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogagri\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0698\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.0942)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.101)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edemo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.535)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.505**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.525*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.404**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.715)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e(1.330)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.966)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(1.182)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e(0.697)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.626)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.456)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.423)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(1.231)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1,540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1,540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1,540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1,540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1,540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1,540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1,540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1,540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1,540\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNbre id\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003einstruments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAR(1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAR(2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHansen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eStandard errors in parentheses\u003c/p\u003e\n\u003cp\u003e*** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1\u003c/p\u003e\n\u003cp\u003eSource: author\u003c/p\u003e\n\u003cp\u003eThe results presented in \u003cstrong\u003eTable 7\u0026nbsp;\u003c/strong\u003eindicate that controlling corruption does not have a significant effect on biodiversity, regardless of the model considered. However, in light of the different estimates, it is possible to hypothesise the existence of transmission mechanisms through which corruption could affect biodiversity in developing countries. In order to examine this hypothesis, a mediation analysis based on a structural equation model (SEM) is implemented.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8\u003c/strong\u003e: Sensitivity to alternative estimation methods\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 306px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDependent variable: logef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVARIABLES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMCO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFIXED EFFECTS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL.logef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.997***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.750***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e(0.00448)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e(0.0342)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecc\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.000136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.00463\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e(0.00216)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e(0.00526)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003epop\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.00348***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.00391***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e(0.00124)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e(0.00124)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogpib\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.00317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0333**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e(0.00386)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e(0.0134)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogpib\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.00103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e-0.00314\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e(0.00127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e(0.00337)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eide\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-8.44e-05**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.000126**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e(4.09e-05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e(5.72e-05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogci\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.00946*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.00933\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e(0.00528)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e(0.0111)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogce\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e-0.00126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0415***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e(0.00345)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e(0.0114)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogagri\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e-0.00471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.0686\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e(0.00374)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e(0.0528)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edemo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.000833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0236*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e(0.00593)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e(0.0137)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrbanisation rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.000142*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.000534\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e(7.73e-05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e(0.000410)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e-0.0105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.188***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e(0.0219)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e(0.164)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e1,540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e1,540\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR-squared\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProb \u0026gt; F\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of id\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eStandard errors in parentheses\u003c/p\u003e\n\u003cp\u003e*** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1; Source: author\u003c/p\u003e\n\u003cp\u003eThe results in \u003cstrong\u003eTable 8\u0026nbsp;\u003c/strong\u003ecompare two estimation methods (OLS and Fixed Effects). Total population (pop), the logarithm of economic growth (logpib), the logarithm of energy consumption (logce), the logarithm of international trade (logci) and the level of democracy have significant and positive effects on biodiversity. On the other hand, foreign direct investment (ide) and the rate of urbanisation (tu) are significant and negative for biodiversity. In addition, control of corruption has no significant impact on the dependent variable. The OLS model explains 99.8% of the variance, while the fixed effects model explains 83.2%, which shows that the models fit the data well.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4-3- Analysis of mediation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe purpose of this sub-section is to analyse the mediators through which corruption influences biodiversity in developing countries. To analyse the transmission channels we use structural equation mediation analysis (SEM). The following figure illustrates these dynamics.\u003c/p\u003e\n\u003cp\u003eThe approach is based on the estimation of two regression equations, as illustrated in Figure 1. First, the parameter (b\u003csub\u003e1\u003c/sub\u003e) quantifies the impact of corruption on the mediator (model 1). Next, the indirect effect is assessed by regressing biodiversity on corruption while controlling the mediator (model 2). This influence is reflected by the weight of (b\u003csub\u003e2)\u0026nbsp;\u003c/sub\u003e. The indirect effect is also the product of (b\u003csub\u003e1\u003c/sub\u003e) and (b\u003csub\u003e3\u003c/sub\u003e) where b\u003csub\u003e3\u003c/sub\u003emeasures the strength of the correlation between biodiversity and the mediators.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 9\u003c/strong\u003e: Mediation outcome\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 146px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 267px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal population\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 334px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Urbanisation rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(A) Mediation test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd_err\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT-stat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Coef std_err T-stat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 146px;\"\u003e\n \u003cp\u003eSobel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e-0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; -0.070 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.008 0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 146px;\"\u003e\n \u003cp\u003eAroian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e-0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; -0.070 0.008 0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 146px;\"\u003e\n \u003cp\u003eGoodman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e-0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; -0.070 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.008 0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(B) Composition of effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003eIndirect effect (Sobel)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;-0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; -0.070 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.008 0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003eDirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e-0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-0.019 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.013 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003eTotal effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e-0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; -0.089 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.014 0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003eProportion of total effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 267px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e63,1%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e79,1 %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 146px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 267px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elog Economic growth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 324px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elog energy consumption\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(A) Mediation test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd_err\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT-stat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Coef std_err T-stat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 146px;\"\u003e\n \u003cp\u003eSobel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e-0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-0.089\u0026nbsp;0.011 0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 146px;\"\u003e\n \u003cp\u003eAroian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e-0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; -0.089 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.011 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 146px;\"\u003e\n \u003cp\u003eGoodman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026nbsp;-0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-0.089\u0026nbsp;0.011 0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(B) Composition of effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003eIndirect effect (Sobel)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;-0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-0.089\u0026nbsp;0.011 0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003eDirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; -0.008 0.030 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003eTotal effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;-0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-0.097\u0026nbsp;0.031 0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003eProportion of total effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 267px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e84%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 324px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e92,2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003et statistics in parentheses **p\u0026lt;0.05,*p\u0026lt;0.1,***p\u0026lt;0.01\u003c/p\u003e\n\u003cp\u003eSource: author \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results of the Sobel, Aroian and Goodman tests \u003cstrong\u003ein Table 9\u0026nbsp;\u003c/strong\u003eshow that for all three mediators, the indirect effects are significant and negative, while the direct effect remains insignificant. This indicates that controlling corruption negatively affects biodiversity through each of the mediators. This justifies our hypothesis that \u003cem\u003ecorruption erodes biodiversity in developing countries\u003c/em\u003e. The proportions also show that the following mediators explain a significant part of the relationship between corruption and biodiversity: total population (63.1%), urbanisation rate (79.1%), logarithm of economic growth (84%) and logarithm of energy consumption (92.2%). The logarithm of energy consumption plays a central role, accounting for 92.2% of the total mediated effect, making it the most important transmission channel. These results indicate that corruption compromises biodiversity by promoting unsustainable development models, characterised by unregulated urbanisation, inefficient energy intensification and often extractive economic growth. Overall, these results underline the need to integrate anti-corruption governance strategies targeting key development sectors to strengthen biodiversity conservation.\u003c/p\u003e"},{"header":"5- Conclusion","content":"\u003cp\u003eAt the end of our work, we wanted to analyse the effects of corruption on biodiversity. The theoretical literature enabled us to identify two main approaches: the direct approach and the indirect approach. Using a panel of 70 developing countries from 2000 to 2022 and applying the GMM system, the empirical results show that controlling corruption has no significant direct effect on biodiversity. The results also show that there is no environmental Kuznets curve for biodiversity in these countries. In addition, a robustness analysis was used by integrating other measures of corruption (political corruption index, public sector corruption index and executive corruption index), biodiversity (biocapacity and land use) by progressively integrating control variables and by testing the sensitivity to alternative estimation methods. The results suggest that corruption is still not significant. On the other hand, by adopting the structural equation mediation (SEM), the results prove that controlling corruption has a significant and negative effect on biodiversity through the channels of population, economic growth, urbanisation rate and energy consumption. For developing countries, protecting biodiversity requires an active fight against corruption, the implementation of sustainable growth, the strengthening of democratic institutions and the energy transition. These efforts must be accompanied by inclusive policies and the commitment of local communities to ensure the effective and sustainable management of ecosystems.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eA. Calabrese ,JM Calabrese ,M. Songer ,M. Wegmann ,S. Hedges ,R. Rose \u0026amp;P. Leimgruber. \u0026laquo; Conservation status of Asian elephants: the influence of habitat and governance | Biodiversity and Conservation \u0026raquo;, 2017. https://link.springer.com/article/10.1007/s10531-017-1345-5.\u003c/li\u003e\n\u003cli\u003eAbdul-Rahman, A. \u0026laquo; Assessing The Environmental Factors Of Social Entrepreneurship In Ghana \u0026raquo;. University of Ghana, 2017. http://ugspace.ug.edu.gh/handle/123456789/23155.\u003c/li\u003e\n\u003cli\u003eAidt, Toke S. \u0026laquo; Corruption, institutions, and economic development \u0026raquo;. \u003cem\u003eOxford Review of Economic Policy\u003c/em\u003e 25, n\u003csup\u003eo\u003c/sup\u003e 2 (2009): 271‑91.\u003c/li\u003e\n\u003cli\u003eAl-Mulali, Usama, and Ilhan Ozturk. \u0026laquo; The effect of energy consumption, urbanization, trade openness, industrial output, and the political stability on the environmental degradation in the MENA (Middle East and North African) region \u0026raquo;. \u003cem\u003eEnergy\u003c/em\u003e 84 (2015): 382‑89. https://doi.org/10.1016/j.energy.2015.03.004.\u003c/li\u003e\n\u003cli\u003eAsif, Kiran, Samina Sabir, and Unbreen Qayyum. \u0026laquo; Corruption, Political Instability, and Environmental Degradation in South Asia: A Comparative Analysis of Carbon Footprint and Ecological Footprint \u0026raquo;. \u003cem\u003eJournal of the Knowledge Economy\u003c/em\u003e 15, n\u003csup\u003eo\u003c/sup\u003e 1 (2024): 4072‑96.\u003c/li\u003e\n\u003cli\u003eAuthors and Barbara A. 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Lavkulich. \u0026laquo; Integration of Agriculture and Wildlife Ecosystem Services: A Case Study of Westham Island, British Columbia, Canada \u0026raquo;. \u003cem\u003eAgricultural Sciences\u003c/em\u003e 08, n\u003csup\u003eo\u003c/sup\u003e 05 (2017): 409‑25.https://doi.org/10.4236/as.2017.85031.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"AUCUNE","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"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, biodiversity, ecological footprint, GMM system, mediation","lastPublishedDoi":"10.21203/rs.3.rs-6735705/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6735705/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper contributes to the literature on the relationship between corruption and biodiversity in developing countries (DCs). The existing literature on this subject is sparse and not consensual, with empirical studies limited mainly to meta-analyses and case studies, without any real empirical analysis of this relationship. Furthermore, the transmission channels through which corruption influences biodiversity have not yet been clearly identified. This study fills this gap by examining the effects of transmission channels on a panel of 70 developing countries over the period 2000\u0026ndash;2022. Using the GMM system method, the results indicate that corruption has no significant direct effect on biodiversity in developing countries. However, the structural equation mediation analysis reveals that corruption negatively affects biodiversity through the channels of total population, urbanisation rate, economic growth and energy consumption. For developing countries, protecting biodiversity requires an active fight against corruption, the implementation of sustainable growth, the strengthening of democratic institutions and the energy transition. These efforts must be accompanied by inclusive policies and the commitment of local communities to guarantee the effective and sustainable management of ecosystems.\u003c/p\u003e","manuscriptTitle":"Does corruption harm biodiversity in developing countries? Analysis of the effects of transmission channels","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-29 07:56:55","doi":"10.21203/rs.3.rs-6735705/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":"May 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-29T07:56:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-29 07:56:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6735705","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6735705","identity":"rs-6735705","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}