The effects of smart governance on biodiversity: Which transmission channels exist in developing countries?

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Lauriane Maéva Iboustsi, EYEGHE-NTOUTOUME François-Cyrille This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8192996/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 study makes a novel contribution to the literature on the effects of smart governance on biodiversity in developing countries, a field that remains underexplored and marked by inconclusive results. In particular, the mechanisms through which smart governance impacts biodiversity are still insufficiently documented. This research addresses this gap by analysing a panel of 74 developing countries over the period 2000–2023. Using system GMM estimation, the results indicate that smart governance does not exert a statistically significant direct effect on biodiversity, as measured by the ecological footprint. The analysis further reveals an inverted U-shaped nonlinear relationship between economic growth and biodiversity, with a threshold estimated at USD 2,196 per capita, beyond which rising income is associated with improved environmental indicators. However, structural equation mediation analysis uncovers substantial indirect effects through several socio-economic channels. Three transmission mechanisms prove to be decisive: smart governance positively influences biodiversity through women’s political empowerment, while urbanisation rates and energy consumption transmit negative effects. Overall, the findings suggest that smart governance can contribute to better ecological outcomes when combined with social, institutional, and economic transformations that promote citizen participation, sustainable urban planning, and the energy transition. JEL Classification : Q01, Q56, Q57, D73, O13 Smart governance biodiversity developing countries mediation Environmental Kuznets Curve Figures Figure 1 Introduction The accelerated degradation of biodiversity today represents one of the major challenges for sustainable development, particularly in developing countries where human pressures on ecosystems are intense (Mosoh et al. 2024). Habitat loss, deforestation, uncontrolled urbanisation, illegal exploitation of natural resources, and pollution all exacerbate the ecological vulnerability of these countries (FAO 1 , 2022). In response to these dynamics, the modernisation of environmental governance tools appears to be an essential condition for preserving biodiversity and enhancing the effectiveness of public policies In this context, smart governance defined as the integration of digital technologies, open data, digital citizen participation, and advanced information systems into public action has attracted growing interest (Meijer et Bolívar, 2016; Gil-Garcia et al. 2019). Its potential lies in its ability to enhance transparency, strengthen environmental monitoring, facilitate institutional coordination, and make public decision-making faster and more accurate (Bolívar, 2018; OECD 2 , 2021). Furthermore, there are currently numerous definitions of biodiversity, most of which remain vague, reflecting the prevailing uncertainty 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.” Similarly, Redford and Richter (2001) offer another definition, noting that the term biodiversity remains poorly defined. According to them, biodiversity comprises three components: genetic, population/species, and community/ecosystem, with each component characterised by three attributes: composition, structure, and function. Thus, it is relevant to ask: what are the indirect effects of smart governance on biodiversity in developing countries? The use of digital technologies in environmental governance has grown considerably in recent years. Environmental data platforms, connected sensors and artificial intelligence tools make it possible to monitor the evolution of ecosystems in real time, rapidly detect pressures on biodiversity, and support the sustainable management of natural resources (Cowie et al., 2021; Williams et al., 2020). However, this development varies significantly across world regions, and developing countries often face limited institutional, financial and technological capacities to implement effective smart governance systems (UNEP-WCMC 3 , 2022). These disparities have a direct impact on biodiversity conservation. Some developing countries, such as Costa Rica, India, and Rwanda, have adopted advanced digital platforms for forest monitoring and ecosystem tracking, which has helped reduce deforestation, enhance environmental transparency, and strengthen the involvement of local communities (Bager et al. 2020; Kariuki & Birner, 2021). In contrast, other countries with a low level of digitalisation in their administrative practices continue to experience significant biodiversity loss, due to the absence of reliable monitoring tools or effective control mechanisms (FAO, 2022). Recent studies increasingly highlight the potential effects of smart governance on biodiversity protection. On the one hand, it promotes transparency and helps reduce corruption related to the use of natural resources (Transparency International, 2021). On the other hand, it supports environmental information systems through the sharing of geospatial and ecological data, enabling governments to make more informed and responsive decisions (Kogan & Lee, 2014; UNEP, 2022). Finally, it strengthens citizen participation, notably through digital platforms for reporting environmental offences or for community-based monitoring of wildlife and flora (Kariuki & Birner, 2021). Moreover, developing countries constitute a particularly relevant area of analysis for at least two reasons. First, these countries often face fragile institutions and a lack of resources, which makes them more vulnerable to the degradation of their ecosystems (Börner et al., 2015; Dasgupta, 2021). In addition, smart governance policies are sometimes poorly adapted to local contexts or remain largely theoretical, which limits their impact on biodiversity conservation (Myers et al., 2000). Despite these advances, there remains a limited number of empirical analyses assessing the extent to which smart governance actually influences biodiversity in developing countries. Existing studies often focus on smart cities, general digital governance, or climate policies, but rarely on the effects of smart governance on biodiversity, while identifying the transmission channels in developing countries (Meijer and Bolívar, 2016; Gil-Garcia et al. 2019). To fill this gap, this article examines the indirect effects of smart governance on biodiversity by identifying the transmission channels in developing countries. The purpose of this study is to analyse the effects of smart governance on biodiversity. To do so, we draw on the theory of social innovation (Mulgan, 2003; Moulaert et al. 2005), the revisited Environmental Kuznets Curve theory (Grossman and Krueger, 1995), and the theory of common goods and natural resources (Ostrom, 1990; Agrawal and Gibson, 1999). Thus, our central research hypothesis is as follows: smart governance indirectly affects biodiversity in developing countries. To test this hypothesis, we use a panel of 74 developing countries covering the period 2000 to 2023. The system Generalised Method of Moments (GMM) developed by Arellano and Bover (1995) and Blundell and Bond (1998) is employed to account for potential endogeneity between smart governance and ecological performance. Our results indicate that smart governance does not have a statistically significant direct effect on biodiversity preservation. However, structural equation modelling reveals an indirect link through the channels of women’s political empowerment, urbanisation rate, and energy consumption. This study makes three main contributions. First, it enriches the literature on smart governance by examining a relatively unexplored area: its influence on biodiversity. Second, it provides an empirical assessment covering a large sample of developing countries, allowing for the identification of robust trends. Third, it highlights the key channels through which smart governance affects ecosystem conservation in developing countries. The remainder of the paper is organised as follows: Section 2 presents the literature review; Section 3 details the methodology; Section 4 analyses the empirical results; and Section 5 concludes. Summary of the existing literature 2.1.1 A new governance in the digital era? Governments today are facing environmental challenges and are turning to digital technologies in order to achieve sustainable development. In this regard, Kloppenburg et al. (2022) distinguish three dimensions of governance (“seeing and knowing”, “participation and engagement”, and “interventions and actions”) to examine environmental governance in the digital age. For each dimension, the authors offer a critical perspective on the changes that digital technologies bring about in terms of governance. They reject the assumption that the use of digital technologies automatically leads to better outcomes or more democratic decision-making. Instead, attention should be paid to the broader political and normative context in which digital technologies are proposed, designed and used as tools for environmental governance. Similarly, Visseren-Hamakers et al. (2021) analyse transformative biodiversity governance from perspectives related to sustainable development. The authors argue that transformative governance (integrative, inclusive, adaptive, and pluralist) is necessary to enable the transformative change required to achieve global sustainability goals. Based on a literature review, the authors contend that governance becomes transformative only when these four governance approaches are implemented jointly, operationalised in a specific manner, and focused on addressing the indirect drivers underlying sustainability challenges. 2.1.2 Rethinking Governance for Improved Biodiversity Management It should be noted that biodiversity is a multidimensional concept that can be understood and measured in multiple ways. However, the next generation of digital biodiversity monitoring technologies, currently being funded and developed, fails to capture its multidimensional and relational aspects. Based on this observation, Westerlaken (2024) analyses digital twins 4 and the digital logics of biodiversity. Drawing on empirical data from interviews, webinars, etc., the study outlines four digital logics that shape the monitoring and understanding of biodiversity within recent technological developments. To better respond to the complex challenges of the global biodiversity crisis, it is crucial to develop digital technologies and practices capable of integrating a wider range of perspectives and understandings of relational and multidimensional approaches to biodiversity (Westerlaken, 2024). In a similar vein, Sheikh et al. (2023) analyse how to rethink smart urban governance 5 for multi-species justice 6 . Based on a contextualised approach centred on Brisbane, Australia, their research provides new knowledge (co)produced with stakeholders who identify four anthropocentric obstacles to smart urban governance: land ownership, green spaces, lobbying and donations, and the lack of environmental integration. Ruijer et al. (2023) also analyse smart governance by adopting an instrumental perspective and arguing that tools can help public-sector professionals address the challenges of smart governance. Based on a literature review, the authors show that few tools exist to assess the context of smart collaborative governance, facilitate collaborative structures, resolve technological issues, and measure the outcomes of smart city practices. Future design research should focus on developing the instruments needed to complete the smart governance toolbox. Geppert et al. (2024) further note that digital technologies for agricultural guidance and management have the overall potential to contribute to improving biodiversity in agricultural landscapes. Based on an online survey and an expert discussion, the authors conclude that digital and smart technologies nevertheless come with critical obstacles to their widespread acceptance and regular use by farmers. In addition, Tomor (2019) provides a systematic literature review on smart governance. The lack of empirical data on the positive effects of smart cities and smart governance motivated the study. The results show that empirical evidence of the claimed sustainability benefits is scarce. The article highlights the need for further empirical work and proposes a research agenda on the relationship between smart governance and sustainability outcomes. With the aim of showing how economics and digital tools can be mobilised for the monitoring, valorisation and governance of biodiversity, Soriano-Redondo et al. (2024) note that online digital data can be used to strengthen existing assessments of the status and trends of biodiversity, the pressures exerted on it, and the conservation solutions implemented, as well as to generate new insights into these aspects, nature’s contributions to people, and human–nature interactions. Furthermore, the digitalisation of agriculture is a significant development. Abbasi et al. (2022) show the transition from traditional agricultural methods to smart farming practices, also referred to as Agriculture 4.0. In particular, digital technologies such as autonomous robotic systems, the Internet of Things, and machine learning are widely explored. Along the same lines, Basso et al. (2020) note that the global food system must become more sustainable. Digital agriculture, which uses digital technologies, illustrates how this challenge can be addressed in order to balance the economic, environmental, and social dimensions of sustainable food production. Clapp et al. (2020) also analyse the rise of precision technologies in agriculture, notably digital agriculture and plant genome editing, and their implications for the political issues of environmental sustainability in the agri-food sector. Silvestro et al. (2022) show that biodiversity protection can be improved through artificial intelligence. Using the CAPTAIN methodology (Conservation Area Prioritization through Artificial Intelligence), the authors conclude that artificial intelligence is highly promising for enhancing the conservation and sustainable use of biological and ecosystem values in a rapidly changing world with limited resources. Pollock et al. (2025) find similar results, noting that AI also holds considerable untapped potential for reassessing major ecological issues. According to Silvestro et al. (2025), if AI can enhance biodiversity research across geological timescales, this improvement must be accessible to the entire scientific community. 2.2 Transmission Channels 2.2.1 The Energy Consumption Channel Smart governance can play a decisive role in the protection of natural resources. When it improves through more effective planning, greater transparency, and genuine participation of local communities it can help reduce energy consumption (Apergis and Payne, 2010; Sovacool et al., 2018). This reduction in consumption translates into a smaller ecological footprint, that is, a decrease in the pressure exerted by humans on ecosystems. Consequently, biodiversity directly benefits from this approach: natural habitats are less degraded, and ecosystems can function and regenerate more effectively (Wackernagel and Rees, 1996; Tilman et al., 2017). H1: Smart governance contributes to the preservation of biodiversity through the reduction of energy consumption. 2.2.2 The Women’s Political Empowerment Channel Women’s empowerment or political representation (share of women in parliament, elected mayors, local participation, etc.) can explain environmental outcomes (deforestation, forest cover, governance quality, conservation). Agarwal (2009) shows in her empirical study that the effective participation of women in local forest governance bodies is associated with better conservation outcomes (increased forest cover, improved local rules). Furthermore, Leisher et al. (2016) conclude that there is evidence (particularly in South Asia) that the presence of women in local management bodies improves resource governance and sometimes conservation outcomes. Similarly, Lau et al. (2020) discuss the mechanisms (power, participation, rights) through which women’s empowerment influences conservation. Asongu et al. (2022) show that women’s political empowerment reduces vulnerability to climate change and identify channels such as public spending on education and governance quality. Women’s political empowerment, as well as its components (civil liberties, participation in civil society, and engagement in political debates), reduces vulnerability to climate change. H2: Smart governance has a positive effect on biodiversity through women’s political empowerment. 2.2.3. The Urbanisation Rate Channel The impacts of urbanisation on native species remain poorly understood, but raising awareness among populations living in highly urbanised areas can greatly contribute to biodiversity conservation across all types of ecosystems (McKinney, 2002). According to Simkin et al. (2022), it is essential to understand how urbanisation and urban expansion affect species in order to implement informed urban planning that can limit biodiversity loss. Similarly, Liu et al. (2025) analyse these impacts in multiple cities around the world, highlighting the global scope of this phenomenon. Urbanisation influences biodiversity indirectly, through several synergistic mechanisms that affect both natural and anthropogenic environments (Feng et al. 2021). Simulations show that rapid changes in land use can lead to lasting alterations in species composition through the “extinction debt” mechanism, and that ecosystems often take more than ten years to fully recover (Jung et al., 2019). H3: Smart governance promotes the preservation of biodiversity due to its ability to slow the rate of urbanisation. Empirical Strategy We present the empirical model, followed by the estimation technique and the data collected. 3.1. Empirical Model In order to empirically test our working hypothesis, we draw on the model of Wang et al. (2024), who analyse the link between the digital economy and carbon dioxide emissions using natural resource rents and anti-corruption regulation as threshold variables. The authors employ a panel threshold model and a fixed-effects regression on a dataset of 97 countries for the period 2003 to 2019. Dependent Variable Biodiversity, the dependent variable in our study, is generally assessed through several indicators, including the ecological footprint (Li et al. 2023; Asif et al. 2024), biocapacity (Foley et al. 2005; Yue et al., 2013), and land use (Smith et al., 2017). For this study, we use the ecological footprint (lnef), expressed in global hectares (gha) and presented in logarithmic form, as the main proxy for biodiversity. Variable of Interest Smart governance (egouvernance), which constitutes our variable of interest, is measured through e-governance using the Online Service Development Index (OSDI). This index reflects the level of maturity of electronic administration in United Nations member states, taking into account not only the quality of online services provided by government websites but also access conditions, such as infrastructure and education levels. The aim is to capture a country’s actual capacity to mobilise information technologies to make its public services accessible and inclusive for the entire population. The OSDI combines three essential dimensions: the provision of online services, the development of telecommunications infrastructure, and the human skills available to support e-administration. The standardised scores of these three dimensions are aggregated to form an overall index ranging from 0 to 1, where values close to 1 reflect a high level of e-governance development, while values close to 0 indicate a limited capacity to deliver effective digital services. Control Variables In this study, six control variables are included to enrich the model: the level of economic development, measured by GDP (lnpib) and its square (lnpib²), allows us to test whether the relationship follows a pattern similar to the environmental Kuznets curve, where pressure on resources initially increases before declining at higher income levels (Grossman and Krueger, 1995; Panayotou, 1993). Energy consumption (lnConsoEner) is also a key factor, as high energy demand, particularly when based on fossil fuels, tends to increase pressure on ecosystems (Shahbaz et al. 2013; Nathaniel et al. 2021). Population growth (pop) is another determinant, as it is associated with increased land and biological resource use (Ehrlich and Holdren, 1971; Bongaarts, 1996). International trade (lnci), on the other hand, can either exacerbate the exploitation of natural resources or promote the adoption of cleaner technologies, depending on the trade structure (Antweiler, Copeland and Taylor, 2001). Finally, urbanisation (txcurba) plays an important role, as the expansion of urban areas alters land use and fragments natural habitats, which can affect biodiversity (Seto et al. 2012). Moreover, to account for potentially omitted factors, the model also includes three additional variables: the size of industry (lnIND), control of corruption (CC), and foreign direct investment (lnIDE). Consequently, the AR(p) model for estimation purposes is expressed in the following dynamic form: (1) In this specification, _it represents the ecological footprint in country i in year t ; encompasses all control variables; and refers to the autoregressive coefficients capturing the dependence of the ecological footprint on its past values. 3.2. Estimation Technique The econometric analysis includes descriptive statistics, correlation tests, and the estimation method. To analyse the effects of smart governance on biodiversity in developing countries, a dynamic panel is estimated in the form of an AR(p) model using the system GMM estimator developed by Blundell and Bond (1998). This estimation method offers several advantages, three of which are highlighted here. First, system GMM allows not only the lagged dependent variables but also any potentially endogenous explanatory variables to be instrumented using “internal” instruments (i.e., lagged levels and lagged differences). Second, system GMM estimates models in both levels and differences, thereby allowing the identification of the effects of time-invariant variables. Third, this method addresses endogeneity bias by using internal instrumental variables based on past values. 3.3. Data The study uses secondary data from various sources. The main sources consulted include the United Nations databases (egovernance), the World Bank Development Indicators (WDI), data from the Global Footprint Network (ecological footprint), and the V-Dem website. Table 1 : List of Countries in the Study Afghanistan, South Africa, Algeria, Angola, Argentina, Bangladesh, Benin, Bolivia, Botswana, Brazil, Burkina Faso, Burundi, Cambodia, Cameroon, Cape Verde, Chile, China, Colombia, Comoros, Costa Rica, Ivory Coast, Egypt, Ecuador, Ethiopia, Fiji, Gabon, Gambia, Ghana, Guatemala, Guinea-Bissau, Guinea, Haiti, Honduras, India, Indonesia, Jamaica, Kenya, Lesotho, Lebanon, Madagascar, Malaysia, Mali, Morocco, Mauritania, Mexico, Mongolia, Mozambique, Namibia, Nepal, Nicaragua, Niger, Uganda, Pakistan, Panama, Paraguay, Peru, Philippines, Democratic Republic of the Congo (DRC), Central African Republic, Republic of the Congo, Rwanda, Senegal, Sierra Leone, Singapore, Sri Lanka, Tanzania, Thailand, Togo, Tunisia, Uruguay, Venezuela, Vietnam, Zambia, Zimbabwe. Source : Authors Table 2 : Descriptive Statistics Variable Obs Mean Std. Dev. Min Max Sources lnef 1776 1.374 .679 -.2 3.722 Global footprint network egovernance 1776 .391 .173 0 .915 ONU lnci 1776 1.795 .216 1.314 2.641 WDI txcurba 1776 2.929 1.471 -4.17 10.171 WDI lnpib² 1776 .709 1.024 0 8.114 WDI lnpib 1776 .087 .838 -1.817 2.848 WDI pop 1776 1.88 1.03 -3.218 9.992 WDI lnConsoEner 1776 3.565 .592 2.164 4.594 WDI Variables additionnelles lnIDE 1776 11.259 .241 10.099 11.646 WDI lnIND 1776 -.598 .178 -1.621 -.142 WDI CC 1776 -.544 .613 -1.672 1.611 WDI Source : Authors Table 2 summarises the descriptive statistics for the 74 developing countries over the period 2000–2023, yielding 1,776 observations. The ecological footprint (lnef) has a mean of 1.374 with a standard deviation of 0.679, indicating that countries are fairly widely distributed around the mean: some countries exert much greater ecological pressure than others. In contrast, egouvernance has a mean of 0.391 with a standard deviation of 0.173, suggesting that the variation between countries is relatively small and that their governance performance remains fairly homogeneous. Analysis of Results 4.1. Results of the Baseline Model Table 3 : Correlation Matrix Variables (1) (2) (3) (4) (5) (6) (7) (8) (1) lnef 1.000 (2) egovernance 0.404 1.000 (3) lnci -0.173 0.271 1.000 (4) txcurba -0.102 -0.588 -0.119 1.000 (5) lnConsoEner 0.335 0.791 0.393 -0.603 1.000 (6) pop -0.171 -0.566 -0.131 0.799 -0.571 1.000 (7) lnpib 0.866 0.653 -0.086 -0.324 0.598 -0.350 1.000 (8) lnpib² 0.428 0.247 -0.217 -0.194 0.209 -0.288 0.410 1.000 Source : Authors Table 3 of correlations allows us to assess the existence of linear relationships between the ecological footprint, smart governance, and the other variables in the study. In other words, it shows whether two variables move in the same direction or, conversely, in opposite directions. Examination of the table above reveals that smart governance is not correlated with the other variables. In contrast, the ecological footprint shows a positive correlation with economic growth (lnpib). Furthermore, although the correlation matrix can identify certain linear relationships between explanatory variables, it is not sufficient to detect multicollinearity accurately. Indeed, weak or moderate correlations can mask more complex multicollinearity involving several variables simultaneously. This is why it is necessary to calculate the variance inflation factor (VIF), which provides a more rigorous assessment of the degree of linear redundancy among the independent variables. Table 4 : Variance Inflation Factor (VIF) Variables VIF 1/VIF lnConsoEner 3.825 .261 egovernance 3.445 .29 txcurba 3.264 .306 pop 3.026 .33 lnpib 2.53 .395 lnci 1.631 .613 lnpib² 1.322 .756 Mean VIF 2.72 . Source : Authors The VIF results presented in Table 4 indicate that all variables have values below 5, suggesting the absence of problematic multicollinearity. Furthermore, the mean VIF of 2.72 reinforces the notion that the model is generally stable and reliable. Table 5 : System GMM (1) Variables lnef L.lnef 0.811*** (0.0602) egovernance -0.168 (0.133) lnci -0.159** (0.072) txcurba 0.0688 (0.0576) lnConsoEner -0.00315 (0.0334) pop -0.127 (0.105) lnpib 0.170*** (0.0649) lnpib² -0.108* (0.0597) Constant 0.723*** (0.240) Observations 1,702 Number of ID 74 Number of Instruments 14 AR(1) 0.000 AR(2) 0.813 Hansen 0.559 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Source : Authors The results presented in Table 5 shows that the lagged variable is significant at the 1% level, indicating a strong persistence in the evolution of the ecological footprint. In other words, environmental pressures do not disappear from one period to the next: a 1% increase in period t–1 translates into an approximate 8.11% rise in the following period. This highlights that past behaviours leave a lasting imprint on the current situation. Furthermore, the AR(1), AR(2), and Hansen tests confirm the robustness of our model. The first-order autocorrelation is as expected, the absence of second-order autocorrelation reassures regarding the model’s dynamics, and the Hansen test indicates that the instruments used are appropriate. In addition, the results suggest that smart governance (egouvernance) does not exert a significant direct effect on biodiversity (ecological footprint). Furthermore, international trade shows a negative and significant effect at the 5% level, implying that a 1% increase in international trade is associated with a 15.9% decrease in the ecological footprint. Indeed, economic globalisation is accompanied by faster technological development and, consequently, a reduced use of natural resources. Celikoz et al. (2022), using an FGLS cointegration analysis, show that economic globalisation has a long-term negative impact on the ecological footprint. Similarly, Aşici and Acar (2015) demonstrate that international trade can have a negative effect on the ecological footprint if countries seeking foreign exchange decide to export goods whose production is relatively inefficient. Furthermore, the estimation results suggest that the coefficients of lnpib and lnpib² have a positive sign (0.170) and a negative sign (–0.108), respectively. This combination confirms the existence of an inverted U-shaped nonlinear relationship between economic growth and biodiversity. In other words, at low income levels, economic growth has a degrading effect on biodiversity, whereas beyond a certain threshold, increasing income contributes to the improvement of environmental quality. This dynamic is consistent with the Environmental Kuznets Curve (EKC) hypothesis (Grossman and Krueger, 1991; 1995). In accordance with the usual methodology of the Environmental Kuznets Curve (Stern, 2004; Song et al. 2008; Tachega et al. 2021), the turning point is obtained using the relation , with the coefficients γ₁ = 0.170 and γ₂ = –0.108 corresponding to lnpib and lnpib², respectively. This yields a per capita income of USD 2,196. This threshold is fully consistent with the most recent studies focusing on developing countries (Apergis and Ozturk, 2015; Kinda and Thiombiano, 2021). It suggests that, in developing countries, overall ecological pressure begins to stabilise or decline at the early stages of wealth accumulation, well before reaching the income levels typical of advanced economies. 4.2. Robustness Analysis The robustness analysis highlights a series of four tests, namely: alternative methods, the successive inclusion of control variables, the inclusion of additional variables, and regional analysis. Sensitivity to Alternative Methods To analyse the effects of smart governance on biodiversity, we re-specify the model considering several estimation methods. We start with Ordinary Least Squares (OLS), which provides an initial indication of the relationships between smart governance and biodiversity in developing countries. Although straightforward, this approach relies on strong assumptions, notably the absence of endogeneity and the homogeneity of countries in the panel, which can bias the results. To control for unobserved heterogeneity specific to each country, we employ the fixed effects estimator, which accounts for time-invariant characteristics unique to each unit. However, potential endogeneity may still affect the estimated coefficients. We then use the two-stage least squares (2SLS) estimator to obtain more reliable and unbiased estimates. Table 6: Robustness to Alternative Methods (1) (2) (3) Variables MCO Fixed Effects 2sls egovernance -0.505*** 0.0384 0.0246 (0.0820) (0.0586) (0.0224) lnci 0.179*** 0.0381 0.0501*** (0.0369) (0.0412) (0.0166) txcurba 0.0427*** 0.00115 0.00159 (0.00888) (0.00490) (0.00280) lnConsoEner -0.223*** 0.211*** 0.207*** (0.0285) (0.0614) (0.0212) pop -0.0173 0.00695 0.00746* (0.0132) (0.00801) (0.00398) lnpib 0.855*** 0.178*** 0.179*** (0.0148) (0.0294) (0.0109) lnpib² 0.0597*** -0.00962 0.00703 (0.00589) (0.0113) (0.00756) Constant 1.838*** 0.513** (0.0922) (0.214) Observations 1,776 1,776 1,776 R-squared 0.820 0.561 0.555 Number of ID 74 74 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Source : Authors The results presented in Table 6 confirm the robustness of the estimates when alternative methods are employed. Overall, they show that smart governance does not exert a significant effect on biodiversity. This conclusion remains unchanged despite variations in empirical approaches, which reinforces the reliability of the results obtained. Sensitivity to the Successive Inclusion of Control Variables Table 7: Robustness to the Successive Inclusion of Control Variables (1) (2) (3) (4) (5) Variables Estimator : System GMM L.lnef 0.938*** 0.887*** 0.793*** 0.805*** 0.809*** (0.113) (0.0928) (0.115) (0.0629) (0.0619) egovernance -0.211 -0.326 -0.488 -0.140 -0.0994 (0.311) (0.247) (0.351) (0.189) (0.138) pop -0.0184 -0.0197 -0.192 -0.0798 -0.0646 (0.0268) (0.0192) (0.222) (0.0917) (0.0853) lnpib 0.0635 0.114 0.189* 0.188** 0.182** (0.113) (0.0924) (0.115) (0.0764) (0.0737) lnpib² 0.00239 0.0220 -0.119** -0.128** (0.00253) (0.109) (0.0585) (0.0510) txcurba 0.110 0.0467 0.0387 (0.128) (0.0506) (0.0453) lnci -0.169** -0.177** (0.0766) (0.0770) lnConsoEner 0.00209 (0.0397) Constant 0.203 0.315 0.483* 0.711*** 0.698*** (0.318) (0.247) (0.262) (0.221) (0.246) Observations 1,702 1,702 1,702 1,702 1,702 Number of ID 74 74 74 74 74 Number of Instruments 6 7 10 10 11 AR(1) AR(2) Hansen 0.001 0.123 0.151 0.001 0.113 0.347 0.087 0.587 0.515 0.000 0.480 0.313 0.000 0.264 0.304 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Source : Authors The results presented in Table 7 indicate that smart governance does not exert a significant effect on biodiversity, regardless of the model considered. Sensitivity to the Inclusion of Additional Variables Table 8: Robustness to the Inclusion of Additional Variables (1) (2) (3) Estimator : System GMM Dependent Variable: lnef Variables L.lnef 0.797*** 0.808*** 0.817*** (0.0697) (0.0721) (0.0677) egovernance -0.0619 -0.0819 -0.0797 (0.106) (0.0996) (0.0967) lnpib 0.158** 0.173** 0.180** (0.0654) (0.0686) (0.0737) lnpib² -0.126** -0.135** -0.134** (0.0532) (0.0624) (0.0623) pop -0.0963 -0.0821 -0.0628 (0.0930) (0.0925) (0.0929) lnConsoEner 0.0345 0.0331 0.0330 (0.0377) (0.0418) (0.0479) lnci -0.160** -0.132 -0.122 (0.0753) (0.0841) (0.0865) txcurba 0.0606 0.0529 0.0435 (0.0502) (0.0502) (0.0508) lnIDE -0.159* -0.161* -0.146 (0.0841) (0.0946) (0.101) lnIND -0.0892 -0.118 (0.123) (0.122) CC -0.00601 (0.0308) Constant 2.340** 2.259** 2.034* (1.013) (1.109) (1.165) Observations 1,702 1,702 1,702 Number of ID 74 74 74 Number of Instruments 15 15 16 AR(1) 0.000 0.000 0.000 AR(2) 0.634 0.427 0.232 Hansen 0.397 0.399 0.346 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Source : Authors The results in Table 8 highlight that, despite the inclusion of additional variables, smart governance does not exhibit a significant effect on biodiversity. Sensitivity Across Different Regions We check the robustness of our results by conducting a robustness analysis on two separate geographic groups: the first group refers to Sub-Saharan African (SSA) countries, while the second concerns Latin American and Caribbean (LAC) countries. Table 9: Specification by Geographic Zone Estimator : system GMM (1) (2) VARIABLES ALC ASS L.lnef 0.841*** 0.552** (0.271) (0.252) egovernance 0.0445 -0.247 (0.483) (0.345) lnpib 0.103 0.121 (0.315) (0.147) lnpib² -0.0427 -0.224* (0.235) (0.128) pop 0.0659 -0.136 (0.330) (0.120) lnConsoEner -0.00621 0.0675 (0.149) (0.0675) lnci -0.137 -0.498* (0.263) (0.262) txcurba -0.0256 0.0592 (0.156) (0.0436) Constant 0.424 1.498* (0.924) (0.812) Observations 414 805 Number of ID 18 35 Number of Instruments 14 14 AR(1) 0.034 0.048 AR(2) 0.322 0.818 Hansen 0.114 0.559 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Source : Authors As shown in Table 9 , the analysis conducted across the two geographic areas reveals that the effect of smart governance on biodiversity remains statistically non-significant. 4.3. Mediation Analysis This subsection examines the mechanisms through which smart governance affects biodiversity in developing countries. To identify these transmission channels, we use a mediation analysis based on a structural equation model (SEM). The following figure presents the structure of this mechanism. The approach adopted is based on the estimation of two regression equations, as illustrated in Figure 1. First, the coefficient (b1) measures the effect of smart governance on the mediator (Model 1). Second, the indirect effect is obtained by regressing biodiversity on smart governance while controlling for the mediator (Model 2); this effect passes through the coefficient (b2). The indirect effect is then given by the product of (b1) and (b3), where b3 captures the relationship between biodiversity and the mediator. Table 10: Mediation Results Women’s political empowerment Urbanisation rate Energy consumption (A) Mediation test Est std_err T-stat Est Std_err T-stat Est Std-err T-stat Sobel 0.224 0.039 0.000*** -0.129 0.022 0.000*** -0.397 0.043 0.000*** Aroian 0.224 0.040 0.000*** -0.129 0.022 0.000*** -0.397 0.043 0.000*** Goodman 0.224 0.039 0.000*** -0.129 0.022 0.000*** -0.397 0.043 0.000*** (B) Composition of Effects Indirect effect (Sobel) 0.224 0.039 0.000*** -0.129 0.022 0.000*** -0.397 0.043 0.000*** Direct effect 1.130 0.129 0.000*** -0.057 0.073 0.000*** -0.514 0.073 0.000*** Total effect 1.354 0.132 0.000*** -0.081 0.071 0.000*** -0.911 0.062 0.000*** Proportion of total effect 16.6% 22.6% 43,6% Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Source : Authors Table 10 presents the results of the mediation analysis. Across all specifications, the estimates indicate that the mediators play a crucial role in the relationship between smart governance and biodiversity. However, the mediated effects differ according to the nature of the mediator: political empowerment of women exerts a positive indirect effect, whereas the urbanisation rate and energy consumption transmit a negative effect on biodiversity. The lower section of Table 10 reports the statistics from the Sobel-Goodman mediation tests. All three tests (Sobel, Aroian, and Goodman) confirm the existence of indirect effects significantly different from zero. Moreover, the p-values, all below the 5% threshold across specifications, allow us to reject the null hypothesis of no mediation. The results show that, over the period considered, political empowerment of women, the urbanisation rate, and energy consumption constitute significant transmission channels between smart governance and biodiversity. Specifically, 16.6% of the total effect passes through political empowerment of women, while 22.6% and 43.6% of the total effect are transmitted respectively via urbanisation and energy consumption, with a negative indirect impact. In other words, 16% of the overall effect of smart governance on biodiversity operates through a positive mechanism, namely political empowerment of women, whereas the other two mediators transmit a negative effect. Declarations Author Contributions and Statement : Lauriane Maéva IBOUTSI : Writing – original draft, formal analysis, conceptualization. François-Cyrille EYEGHE-NTOUTOUME : Writing – review & editing, methodology, data curation, conceptualization, formal analysis, translation. Declaration of Conflicts of Interest : The analyses, discussions, and conclusions presented in this study are entirely the responsibility of the authors. They do not in any way reflect the position of any international organisation, institution, or state. Furthermore, the authors certify that they have not received any funding that could have influenced the results of this research. Conclusion Understanding the influence of smart governance on biodiversity in developing countries is essential, as this topic remains relatively unexplored and existing results are often divergent. Despite the available literature, the precise role of smart governance in the preservation of ecosystems within developing countries remains largely unknown. This study aims to fill this gap. To this end, we used a panel of 74 developing countries and applied system GMM to empirically test several hypotheses. The primary hypothesis, central to this study, postulates that smart governance indirectly influences biodiversity through specific mechanisms. Other hypotheses explore potential transmission channels, considering that the effect of smart governance operates notably through political empowerment of women, the urbanisation rate, and energy consumption. The results show that smart governance does not have a significant direct effect on biodiversity. These conclusions remain valid even after using alternative methods, integrating control and additional variables, and conducting regional analyses. To better understand the underlying mechanisms, we conducted a mediation analysis using structural equations. The results reveal that smart governance influences biodiversity mainly through three levers: political empowerment of women, which accounts for 16.6% of the total effect, the urbanisation rate (22.6%), and energy consumption (43.6%). These findings highlight the importance of these channels for fully assessing the influence of smart governance on ecosystem preservation in developing countries. Based on these results, several public policy recommendations can be formulated to support biodiversity. 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Journal of Environmental Management 126 (septembre 2013): 13‑19. https://doi.org/10.1016/j.jenvman.2013.04.022 Footnotes Food and Agriculture Organization Organisation for Economic Co-operation and Development United Nations Environment Programme – World Conservation Monitoring Centre A “digital twin” is a network of different data sources, treated as a real-time digital copy of a physical entity. Smart urban governance refers to the use of digital technologies and data to inform urban governance processes (Sheikh et al., 2023 ). Multi-species justice refers to justice not only for humans but also for the interconnected relationships between humans and non-humans, which include animals, plants, microbes, rivers, forests, and natural ecosystems (Sheikh et al., 2023 ). Additional Declarations The authors declare no competing interests. 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06:39:24","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":197303,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8192996/v1/e2849087b82331aed4c91b36.html"},{"id":96966265,"identity":"614c1adc-5dfb-4d59-8676-391923dce1a6","added_by":"auto","created_at":"2025-11-28 06:39:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":30373,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMediation Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource: Authors, adapted from MacKinnon et al. (1995)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8192996/v1/4c2f6c97a915236d44ea4a39.png"},{"id":97144629,"identity":"3e74b3e4-58b8-4c6e-95cb-3f9f8c8320dd","added_by":"auto","created_at":"2025-12-01 10:11:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2022250,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8192996/v1/267b5c32-937b-4913-8f74-d1a00387a7da.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eThe effects of smart governance on biodiversity: Which transmission channels exist in developing countries?\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe accelerated degradation of biodiversity today represents one of the major challenges for sustainable development, particularly in developing countries where human pressures on ecosystems are intense (Mosoh et al. 2024). Habitat loss, deforestation, uncontrolled urbanisation, illegal exploitation of natural resources, and pollution all exacerbate the ecological vulnerability of these countries (FAO\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e1\u003c/sup\u003e, 2022). In response to these dynamics, the modernisation of environmental governance tools appears to be an essential condition for preserving biodiversity and enhancing the effectiveness of public policies\u003c/p\u003e\n\u003cp\u003eIn this context, smart governance defined as the integration of digital technologies, open data, digital citizen participation, and advanced information systems into public action has attracted growing interest (Meijer et \u0026nbsp;Bol\u0026iacute;var, 2016; Gil-Garcia et al. 2019). Its potential lies in its ability to enhance transparency, strengthen environmental monitoring, facilitate institutional coordination, and make public decision-making faster and more accurate (Bol\u0026iacute;var, 2018; OECD\u003ca href=\"#_ftn2\" name=\"_ftnref2\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e2\u003c/sup\u003e, 2021). Furthermore, there are currently numerous definitions of biodiversity, most of which remain vague, reflecting the prevailing uncertainty on the subject. The Convention on Biological Diversity (1992) defines biodiversity as \u0026ldquo;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.\u0026rdquo; Similarly, Redford and Richter (2001) offer another definition, noting that the term biodiversity remains poorly defined. According to them, biodiversity comprises three components: genetic, population/species, and community/ecosystem, with each component characterised by three attributes: composition, structure, and function.\u003c/p\u003e\n\u003cp\u003eThus, it is relevant to ask: \u003cstrong\u003e\u003cem\u003ewhat are the indirect effects of smart governance on biodiversity in developing countries?\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe use of digital technologies in environmental governance has grown considerably in recent years. Environmental data platforms, connected sensors and artificial intelligence tools make it possible to monitor the evolution of ecosystems in real time, rapidly detect pressures on biodiversity, and support the sustainable management of natural resources (Cowie et al., 2021; Williams et al., 2020). However, this development varies significantly across world regions, and developing countries often face limited institutional, financial and technological capacities to implement effective smart governance systems (UNEP-WCMC\u003ca href=\"#_ftn3\" name=\"_ftnref3\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e3\u003c/sup\u003e, 2022).\u003c/p\u003e\n\u003cp\u003eThese disparities have a direct impact on biodiversity conservation. Some developing countries, such as Costa Rica, India, and Rwanda, have adopted advanced digital platforms for forest monitoring and ecosystem tracking, which has helped reduce deforestation, enhance environmental transparency, and strengthen the involvement of local communities (Bager et al. 2020; Kariuki \u0026amp; Birner, 2021). In contrast, other countries with a low level of digitalisation in their administrative practices continue to experience significant biodiversity loss, due to the absence of reliable monitoring tools or effective control mechanisms (FAO, 2022).\u003c/p\u003e\n\u003cp\u003eRecent studies increasingly highlight the potential effects of smart governance on biodiversity protection. On the one hand, it promotes transparency and helps reduce corruption related to the use of natural resources (Transparency International, 2021). On the other hand, it supports environmental information systems through the sharing of geospatial and ecological data, enabling governments to make more informed and responsive decisions (Kogan \u0026amp; Lee, 2014; UNEP, 2022). Finally, it strengthens citizen participation, notably through digital platforms for reporting environmental offences or for community-based monitoring of wildlife and flora (Kariuki \u0026amp; Birner, 2021).\u003c/p\u003e\n\u003cp\u003eMoreover, developing countries constitute a particularly relevant area of analysis for at least two reasons. First, these countries often face fragile institutions and a lack of resources, which makes them more vulnerable to the degradation of their ecosystems (B\u0026ouml;rner et al., 2015; Dasgupta, 2021). In addition, smart governance policies are sometimes poorly adapted to local contexts or remain largely theoretical, which limits their impact on biodiversity conservation (Myers et al., 2000).\u003c/p\u003e\n\u003cp\u003eDespite these advances, there remains a limited number of empirical analyses assessing the extent to which smart governance actually influences biodiversity in developing countries. Existing studies often focus on smart cities, general digital governance, or climate policies, but rarely on the effects of smart governance on biodiversity, while identifying the transmission channels in developing countries (Meijer and Bol\u0026iacute;var, 2016; Gil-Garcia et al. 2019).\u003c/p\u003e\n\u003cp\u003eTo fill this gap, this article examines the indirect effects of smart governance on biodiversity by identifying the transmission channels in developing countries.\u003c/p\u003e\n\u003cp\u003eThe purpose of this study is to analyse the effects of smart governance on biodiversity. To do so, we draw on the theory of social innovation (Mulgan, 2003; Moulaert et al. 2005), the revisited Environmental Kuznets Curve theory (Grossman and Krueger, 1995), and the theory of common goods and natural resources (Ostrom, 1990; Agrawal and Gibson, 1999).\u003c/p\u003e\n\u003cp\u003eThus, our central research hypothesis is as follows: \u003cstrong\u003e\u003cem\u003esmart governance indirectly affects biodiversity in developing countries.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo test this hypothesis, we use a panel of 74 developing countries covering the period 2000 to 2023. The system Generalised Method of Moments (GMM) developed by Arellano and Bover (1995) and Blundell and Bond (1998) is employed to account for potential endogeneity between smart governance and ecological performance. Our results indicate that smart governance does not have a statistically significant direct effect on biodiversity preservation. However, structural equation modelling reveals an indirect link through the channels of women\u0026rsquo;s political empowerment, urbanisation rate, and energy consumption.\u003c/p\u003e\n\u003cp\u003eThis study makes three main contributions. First, it enriches the literature on smart governance by examining a relatively unexplored area: its influence on biodiversity. Second, it provides an empirical assessment covering a large sample of developing countries, allowing for the identification of robust trends. Third, it highlights the key channels through which smart governance affects ecosystem conservation in developing countries.\u003c/p\u003e\n\u003cp\u003eThe remainder of the paper is organised as follows: Section 2 presents the literature review; Section 3 details the methodology; Section 4 analyses the empirical results; and Section 5 concludes.\u003c/p\u003e"},{"header":"Summary of the existing literature","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.1.1 A new governance in the digital era?\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGovernments today are facing environmental challenges and are turning to digital technologies in order to achieve sustainable development. In this regard, Kloppenburg et al. (2022) distinguish three dimensions of governance (\u0026ldquo;seeing and knowing\u0026rdquo;, \u0026ldquo;participation and engagement\u0026rdquo;, and \u0026ldquo;interventions and actions\u0026rdquo;) to examine environmental governance in the digital age. For each dimension, the authors offer a critical perspective on the changes that digital technologies bring about in terms of governance. They reject the assumption that the use of digital technologies automatically leads to better outcomes or more democratic decision-making. Instead, attention should be paid to the broader political and normative context in which digital technologies are proposed, designed and used as tools for environmental governance.\u003c/p\u003e\n\u003cp\u003eSimilarly, Visseren-Hamakers et al. (2021) analyse transformative biodiversity governance from perspectives related to sustainable development. The authors argue that transformative governance (integrative, inclusive, adaptive, and pluralist) is necessary to enable the transformative change required to achieve global sustainability goals. Based on a literature review, the authors contend that governance becomes transformative only when these four governance approaches are implemented jointly, operationalised in a specific manner, and focused on addressing the indirect drivers underlying sustainability challenges.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.1.2 \u0026nbsp;Rethinking Governance for Improved Biodiversity Management\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt should be noted that biodiversity is a multidimensional concept that can be understood and measured in multiple ways. However, the next generation of digital biodiversity monitoring technologies, currently being funded and developed, fails to capture its multidimensional and relational aspects. Based on this observation, Westerlaken (2024) analyses digital twins\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e4\u003c/sup\u003e and the digital logics of biodiversity. Drawing on empirical data from interviews, webinars, etc., the study outlines four digital logics that shape the monitoring and understanding of biodiversity within recent technological developments. To better respond to the complex challenges of the global biodiversity crisis, it is crucial to develop digital technologies and practices capable of integrating a wider range of perspectives and understandings of relational and multidimensional approaches to biodiversity (Westerlaken, 2024).\u003c/p\u003e\n\u003cp\u003eIn a similar vein, Sheikh et al. (2023) analyse how to rethink smart urban governance\u003ca href=\"#_ftn2\" name=\"_ftnref2\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e5\u003c/sup\u003e for multi-species justice\u003ca href=\"#_ftn3\" name=\"_ftnref3\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e6\u003c/sup\u003e. Based on a contextualised approach centred on Brisbane, Australia, their research provides new knowledge (co)produced with stakeholders who identify four anthropocentric obstacles to smart urban governance: land ownership, green spaces, lobbying and donations, and the lack of environmental integration.\u003c/p\u003e\n\u003cp\u003eRuijer et al. (2023) also analyse smart governance by adopting an instrumental perspective and arguing that tools can help public-sector professionals address the challenges of smart governance. Based on a literature review, the authors show that few tools exist to assess the context of smart collaborative governance, facilitate collaborative structures, resolve technological issues, and measure the outcomes of smart city practices. Future design research should focus on developing the instruments needed to complete the smart governance toolbox.\u003c/p\u003e\n\u003cp\u003eGeppert et al. (2024) further note that digital technologies for agricultural guidance and management have the overall potential to contribute to improving biodiversity in agricultural landscapes. Based on an online survey and an expert discussion, the authors conclude that digital and smart technologies nevertheless come with critical obstacles to their widespread acceptance and regular use by farmers.\u003c/p\u003e\n\u003cp\u003eIn addition, Tomor (2019) provides a systematic literature review on smart governance. The lack of empirical data on the positive effects of smart cities and smart governance motivated the study. The results show that empirical evidence of the claimed sustainability benefits is scarce. The article highlights the need for further empirical work and proposes a research agenda on the relationship between smart governance and sustainability outcomes.\u003c/p\u003e\n\u003cp\u003eWith the aim of showing how economics and digital tools can be mobilised for the monitoring, valorisation and governance of biodiversity, Soriano-Redondo et al. (2024) note that online digital data can be used to strengthen existing assessments of the status and trends of biodiversity, the pressures exerted on it, and the conservation solutions implemented, as well as to generate new insights into these aspects, nature\u0026rsquo;s contributions to people, and human\u0026ndash;nature interactions.\u003c/p\u003e\n\u003cp\u003eFurthermore, the digitalisation of agriculture is a significant development. Abbasi et al. (2022) show the transition from traditional agricultural methods to smart farming practices, also referred to as Agriculture 4.0. In particular, digital technologies such as autonomous robotic systems, the Internet of Things, and machine learning are widely explored.\u003c/p\u003e\n\u003cp\u003eAlong the same lines, Basso et al. (2020) note that the global food system must become more sustainable. Digital agriculture, which uses digital technologies, illustrates how this challenge can be addressed in order to balance the economic, environmental, and social dimensions of sustainable food production. Clapp et al. (2020) also analyse the rise of precision technologies in agriculture, notably digital agriculture and plant genome editing, and their implications for the political issues of environmental sustainability in the agri-food sector.\u003c/p\u003e\n\u003cp\u003eSilvestro et al. (2022) show that biodiversity protection can be improved through artificial intelligence. Using the CAPTAIN methodology (Conservation Area Prioritization through Artificial Intelligence), the authors conclude that artificial intelligence is highly promising for enhancing the conservation and sustainable use of biological and ecosystem values in a rapidly changing world with limited resources. Pollock et al. (2025) find similar results, noting that AI also holds considerable untapped potential for reassessing major ecological issues. According to Silvestro et al. (2025), if AI can enhance biodiversity research across geological timescales, this improvement must be accessible to the entire scientific community.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.2 Transmission Channels\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.2.1 \u0026nbsp;The Energy Consumption Channel\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSmart governance can play a decisive role in the protection of natural resources. When it improves through more effective planning, greater transparency, and genuine participation of local communities it can help reduce energy consumption (Apergis and Payne, 2010; Sovacool et al., 2018). This reduction in consumption translates into a smaller ecological footprint, that is, a decrease in the pressure exerted by humans on ecosystems. Consequently, biodiversity directly benefits from this approach: natural habitats are less degraded, and ecosystems can function and regenerate more effectively (Wackernagel and Rees, 1996; Tilman et al., 2017).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH1: Smart governance contributes to the preservation of biodiversity through the reduction of energy consumption.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.2.2 \u0026nbsp;The Women\u0026rsquo;s Political Empowerment Channel\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWomen\u0026rsquo;s empowerment or political representation (share of women in parliament, elected mayors, local participation, etc.) can explain environmental outcomes (deforestation, forest cover, governance quality, conservation). Agarwal (2009) shows in her empirical study that the effective participation of women in local forest governance bodies is associated with better conservation outcomes (increased forest cover, improved local rules). Furthermore, Leisher et al. (2016) conclude that there is evidence (particularly in South Asia) that the presence of women in local management bodies improves resource governance and sometimes conservation outcomes. Similarly, Lau et al. (2020) discuss the mechanisms (power, participation, rights) through which women\u0026rsquo;s empowerment influences conservation. Asongu et al. (2022) show that women\u0026rsquo;s political empowerment reduces vulnerability to climate change and identify channels such as public spending on education and governance quality. Women\u0026rsquo;s political empowerment, as well as its components (civil liberties, participation in civil society, and engagement in political debates), reduces vulnerability to climate change.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH2: Smart governance has a positive effect on biodiversity through women\u0026rsquo;s political empowerment.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.2.3. The Urbanisation Rate Channel\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe impacts of urbanisation on native species remain poorly understood, but raising awareness among populations living in highly urbanised areas can greatly contribute to biodiversity conservation across all types of ecosystems (McKinney, 2002). According to Simkin et al. (2022), it is essential to understand how urbanisation and urban expansion affect species in order to implement informed urban planning that can limit biodiversity loss. Similarly, Liu et al. (2025) analyse these impacts in multiple cities around the world, highlighting the global scope of this phenomenon. Urbanisation influences biodiversity indirectly, through several synergistic mechanisms that affect both natural and anthropogenic environments (Feng et al. 2021). Simulations show that rapid changes in land use can lead to lasting alterations in species composition through the \u0026ldquo;extinction debt\u0026rdquo; mechanism, and that ecosystems often take more than ten years to fully recover (Jung et al., 2019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH3: Smart governance promotes the preservation of biodiversity due to its ability to slow the rate of urbanisation.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"Empirical Strategy","content":"\u003cp\u003eWe present the empirical model, followed by the estimation technique and the data collected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.1.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eEmpirical Model\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to empirically test our working hypothesis, we draw on the model of Wang et al. (2024), who analyse the link between the digital economy and carbon dioxide emissions using natural resource rents and anti-corruption regulation as threshold variables. The authors employ a panel threshold model and a fixed-effects regression on a dataset of 97 countries for the period 2003 to 2019.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eDependent Variable\u003c/em\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBiodiversity, the dependent variable in our study, is generally assessed through several indicators, including the ecological footprint (Li et al. 2023; Asif et al. 2024), biocapacity (Foley et al. 2005; Yue et al., 2013), and land use (Smith et al., 2017). For this study, we use the ecological footprint (lnef), expressed in global hectares (gha) and presented in logarithmic form, as the main proxy for biodiversity.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eVariable of Interest\u003c/em\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSmart governance (egouvernance), which constitutes our variable of interest, is measured through e-governance using the Online Service Development Index (OSDI). This index reflects the level of maturity of electronic administration in United Nations member states, taking into account not only the quality of online services provided by government websites but also access conditions, such as infrastructure and education levels. The aim is to capture a country\u0026rsquo;s actual capacity to mobilise information technologies to make its public services accessible and inclusive for the entire population. The OSDI combines three essential dimensions: the provision of online services, the development of telecommunications infrastructure, and the human skills available to support e-administration. The standardised scores of these three dimensions are aggregated to form an overall index ranging from 0 to 1, where values close to 1 reflect a high level of e-governance development, while values close to 0 indicate a limited capacity to deliver effective digital services.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eControl Variables\u003c/em\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn this study, six control variables are included to enrich the model: the level of economic development, measured by GDP (lnpib) and its square (lnpib\u0026sup2;), allows us to test whether the relationship follows a pattern similar to the environmental Kuznets curve, where pressure on resources initially increases before declining at higher income levels (Grossman and Krueger, 1995; Panayotou, 1993). Energy consumption (lnConsoEner) is also a key factor, as high energy demand, particularly when based on fossil fuels, tends to increase pressure on ecosystems (Shahbaz et al. 2013; Nathaniel et al. 2021). Population growth (pop) is another determinant, as it is associated with increased land and biological resource use (Ehrlich and Holdren, 1971; Bongaarts, 1996). International trade (lnci), on the other hand, can either exacerbate the exploitation of natural resources or promote the adoption of cleaner technologies, depending on the trade structure (Antweiler, Copeland and Taylor, 2001). Finally, urbanisation (txcurba) plays an important role, as the expansion of urban areas alters land use and fragments natural habitats, which can affect biodiversity (Seto et al. 2012). Moreover, to account for potentially omitted factors, the model also includes three additional variables: the size of industry (lnIND), control of corruption (CC), and foreign direct investment (lnIDE).\u003c/p\u003e\n\u003cp\u003eConsequently, the AR(p) model for estimation purposes is expressed in the following dynamic form:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"437\" height=\"25\" src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img176431135214.png\" alt=\"image\"\u003e\u0026nbsp;(1)\u003c/p\u003e\n\u003cp\u003eIn this specification,\u0026nbsp;\u003cimg width=\"53\" height=\"19\" src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1764311351.png\" alt=\"image\"\u003e_it represents the ecological footprint in country \u003cem\u003ei\u003c/em\u003e in year \u003cem\u003et\u003c/em\u003e; \u0026nbsp; encompasses\u0026nbsp;\u003cimg width=\"18\" height=\"19\" src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1764311352.png\" alt=\"image\"\u003e\u0026nbsp;all control variables;\u0026nbsp;\u003cimg width=\"14\" height=\"21\" src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img176431135297.png\" alt=\"image\"\u003e\u0026nbsp;and refers to the autoregressive coefficients capturing the dependence of the ecological footprint on its past values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.\u003c/strong\u003e \u003cstrong\u003e\u003cem\u003eEstimation Technique\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe econometric analysis includes descriptive statistics, correlation tests, and the estimation method.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo analyse the effects of smart governance on biodiversity in developing countries, a dynamic panel is estimated in the form of an AR(p) model using the system GMM estimator developed by Blundell and Bond (1998). This estimation method offers several advantages, three of which are highlighted here. First, system GMM allows not only the lagged dependent variables but also any potentially endogenous explanatory variables to be instrumented using \u0026ldquo;internal\u0026rdquo; instruments (i.e., lagged levels and lagged differences). Second, system GMM estimates models in both levels and differences, thereby allowing the identification of the effects of time-invariant variables. Third, this method addresses endogeneity bias by using internal instrumental variables based on past values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.3. Data\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study uses secondary data from various sources. The main sources consulted include the United Nations databases (egovernance), the World Bank Development Indicators (WDI), data from the Global Footprint Network (ecological footprint), and the V-Dem website.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 1\u003c/em\u003e\u003c/strong\u003e: List of Countries in the Study\u003c/p\u003e\n\u003ctable style=\"width: 100%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 100.0000%;\"\u003e\u003cem\u003eAfghanistan, South Africa, Algeria, Angola, Argentina, Bangladesh, Benin, Bolivia, Botswana, Brazil, Burkina Faso, Burundi, Cambodia, Cameroon, Cape Verde, Chile, China, Colombia, Comoros, Costa Rica, Ivory Coast, Egypt, Ecuador, Ethiopia, Fiji, Gabon, Gambia, Ghana, Guatemala, Guinea-Bissau, Guinea, Haiti, Honduras, India, Indonesia, Jamaica, Kenya, Lesotho, Lebanon, Madagascar, Malaysia, Mali, Morocco, Mauritania, Mexico, Mongolia, Mozambique, Namibia, Nepal, Nicaragua, Niger, Uganda, Pakistan, Panama, Paraguay, Peru, Philippines, Democratic Republic of the Congo (DRC), Central African Republic, Republic of the Congo, Rwanda, Senegal, Sierra Leone, Singapore, Sri Lanka, Tanzania, Thailand, Togo, Tunisia, Uruguay, Venezuela, Vietnam, Zambia, Zimbabwe.\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource\u0026nbsp;: Authors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e: Descriptive Statistics\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Dev.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSources\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;lnef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e3.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eGlobal footprint network\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;egovernance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eONU\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;lnci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eWDI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;txcurba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-4.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e10.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eWDI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;lnpib\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e8.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eWDI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;lnpib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-1.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eWDI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;pop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-3.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e9.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eWDI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;lnConsoEner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e4.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eWDI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables additionnelles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\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: 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: 113px;\"\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: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;lnIDE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e11.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e10.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e11.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eWDI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;lnIND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-1.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eWDI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-1.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eWDI\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\u0026nbsp;: Authors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e summarises the descriptive statistics for the 74 developing countries over the period 2000\u0026ndash;2023, yielding 1,776 observations. The ecological footprint (lnef) has a mean of 1.374 with a standard deviation of 0.679, indicating that countries are fairly widely distributed around the mean: some countries exert much greater ecological pressure than others. In contrast, egouvernance has a mean of 0.391 with a standard deviation of 0.173, suggesting that the variation between countries is relatively small and that their governance performance remains fairly homogeneous.\u003c/p\u003e"},{"header":"Analysis of Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.1. Results of the Baseline Model\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e: Correlation Matrix\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1) lnef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\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: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(2) egovernance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\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: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(3) lnci\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\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: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(4) txcurba\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\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: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(5) lnConsoEner\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\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: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(6) pop\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\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: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(7) lnpib\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.866\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\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: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(8) lnpib\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"top\" style=\"width: 593px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource\u0026nbsp;: Authors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e of correlations allows us to assess the existence of linear relationships between the ecological footprint, smart governance, and the other variables in the study. In other words, it shows whether two variables move in the same direction or, conversely, in opposite directions. Examination of the table above reveals that smart governance is not correlated with the other variables. In contrast, the ecological footprint shows a positive correlation with economic growth (lnpib). Furthermore, although the correlation matrix can identify certain linear relationships between explanatory variables, it is not sufficient to detect multicollinearity accurately. Indeed, weak or moderate correlations can mask more complex multicollinearity involving several variables simultaneously. This is why it is necessary to calculate the variance inflation factor (VIF), which provides a more rigorous assessment of the degree of linear redundancy among the independent variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e: Variance Inflation Factor (VIF)\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 style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVIF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\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 style=\"width: 123px;\"\u003e\n \u003cp\u003elnConsoEner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e3.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e.261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eegovernance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e3.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003etxcurba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e3.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e.306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003epop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e3.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003elnpib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e.395\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003elnci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e.613\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003elnpib\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e.756\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean VIF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.72\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\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 style=\"text-align: ;\"\u003eSource : Authors\u003c/p\u003e\n\u003cp\u003eThe VIF results presented in \u003cstrong\u003eTable 4\u003c/strong\u003e indicate that all variables have values below 5, suggesting the absence of problematic multicollinearity. Furthermore, the mean VIF of 2.72 reinforces the notion that the model is generally stable and reliable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e: System GMM\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: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\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: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elnef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eL.lnef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.811***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e(0.0602)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eegovernance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e-0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e(0.133)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003elnci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.159**\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\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e(0.072)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003etxcurba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.0688\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e(0.0576)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003elnConsoEner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e-0.00315\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e(0.0334)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003epop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e-0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e(0.105)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003elnpib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.170***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e(0.0649)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003elnpib\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.108*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e(0.0597)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.723***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e(0.240)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1,702\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eNumber of ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eNumber of Instruments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eAR(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eAR(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eHansen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.559\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 : Authors\u003c/p\u003e\n\u003cp\u003eThe results presented in \u003cstrong\u003eTable 5\u003c/strong\u003e shows that the lagged variable is significant at the 1% level, indicating a strong persistence in the evolution of the ecological footprint. In other words, environmental pressures do not disappear from one period to the next: a 1% increase in period t\u0026ndash;1 translates into an approximate 8.11% rise in the following period. This highlights that past behaviours leave a lasting imprint on the current situation. Furthermore, the AR(1), AR(2), and Hansen tests confirm the robustness of our model. The first-order autocorrelation is as expected, the absence of second-order autocorrelation reassures regarding the model\u0026rsquo;s dynamics, and the Hansen test indicates that the instruments used are appropriate. In addition, the results suggest that smart governance (egouvernance) does not exert a significant direct effect on biodiversity (ecological footprint).\u003c/p\u003e\n\u003cp\u003eFurthermore, international trade shows a negative and significant effect at the 5% level, implying that a 1% increase in international trade is associated with a 15.9% decrease in the ecological footprint. Indeed, economic globalisation is accompanied by faster technological development and, consequently, a reduced use of natural resources. Celikoz et al. (2022), using an FGLS cointegration analysis, show that economic globalisation has a long-term negative impact on the ecological footprint. Similarly, Aşici and Acar (2015) demonstrate that international trade can have a negative effect on the ecological footprint if countries seeking foreign exchange decide to export goods whose production is relatively inefficient.\u003c/p\u003e\n\u003cp\u003eFurthermore, the estimation results suggest that the coefficients of lnpib and lnpib\u0026sup2; have a positive sign (0.170) and a negative sign (\u0026ndash;0.108), respectively. This combination confirms the existence of an inverted U-shaped nonlinear relationship between economic growth and biodiversity. In other words, at low income levels, economic growth has a degrading effect on biodiversity, whereas beyond a certain threshold, increasing income contributes to the improvement of environmental quality. This dynamic is consistent with the Environmental Kuznets Curve (EKC) hypothesis (Grossman and Krueger, 1991; 1995).\u003c/p\u003e\n\u003cp\u003eIn accordance with the usual methodology of the Environmental Kuznets Curve (Stern, 2004; Song et al. 2008; Tachega et al. 2021), the turning point is obtained using the relation\u003cimg width=\"69\" height=\"20\" src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1764311446.png\" alt=\"image\"\u003e, with the coefficients \u0026gamma;₁ = 0.170 and \u0026gamma;₂ = \u0026ndash;0.108 corresponding to lnpib and lnpib\u0026sup2;, respectively. This yields a per capita income of USD 2,196. This threshold is fully consistent with the most recent studies focusing on developing countries (Apergis and Ozturk, 2015; Kinda and Thiombiano, 2021). It suggests that, in developing countries, overall ecological pressure begins to stabilise or decline at the early stages of wealth accumulation, well before reaching the income levels typical of advanced economies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.2. Robustness Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe robustness analysis highlights a series of four tests, namely: alternative methods, the successive inclusion of control variables, the inclusion of additional variables, and regional analysis.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eSensitivity to Alternative Methods\u003c/em\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo analyse the effects of smart governance on biodiversity, we re-specify the model considering several estimation methods. We start with Ordinary Least Squares (OLS), which provides an initial indication of the relationships between smart governance and biodiversity in developing countries. Although straightforward, this approach relies on strong assumptions, notably the absence of endogeneity and the homogeneity of countries in the panel, which can bias the results. To control for unobserved heterogeneity specific to each country, we employ the fixed effects estimator, which accounts for time-invariant characteristics unique to each unit. However, potential endogeneity may still affect the estimated coefficients. We then use the two-stage least squares (2SLS) estimator to obtain more reliable and unbiased estimates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6: Robustness to Alternative Methods\u003c/strong\u003e\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: 129px;\"\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: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\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: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eMCO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eFixed Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2sls\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\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: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eegovernance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.505***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.0820)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.0586)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.0224)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003elnci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.179***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0501***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.0369)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.0412)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.0166)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003etxcurba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0427***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.00115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.00159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.00888)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.00490)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.00280)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003elnConsoEner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.223***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.211***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.207***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.0285)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.0614)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.0212)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003epop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.0173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.00695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.00746*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.0132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.00801)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.00398)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003elnpib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.855***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.178***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.179***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\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: 96px;\"\u003e\n \u003cp\u003e(0.0294)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.0109)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003elnpib\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0597***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.00962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.00703\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.00589)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.0113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.00756)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.838***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.513**\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.0922)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e(0.214)\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\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: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1,776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1,776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1,776\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eNumber of ID\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: 96px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e74\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\u0026nbsp;: Authors\u003c/p\u003e\n\u003cp\u003eThe results presented in \u003cstrong\u003eTable 6\u003c/strong\u003e confirm the robustness of the estimates when alternative methods are employed. Overall, they show that smart governance does not exert a significant effect on biodiversity. This conclusion remains unchanged despite variations in empirical approaches, which reinforces the reliability of the results obtained.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eSensitivity to the Successive Inclusion of Control Variables\u003c/em\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7: Robustness to the Successive Inclusion of Control Variables\u003c/strong\u003e\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: 175px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 363px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimator :\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSystem GMM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eL.lnef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.938***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.887***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.793***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.805***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.809***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e(0.113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.0928)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e(0.115)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.0629)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.0619)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eegovernance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.0994\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e(0.311)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.247)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e(0.351)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.189)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.138)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003epop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.0184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.0197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.0798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.0646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e(0.0268)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.0192)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e(0.222)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.0917)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.0853)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003elnpib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.0635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.189*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.188**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.182**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e(0.113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.0924)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e(0.115)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.0764)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.0737)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003elnpib\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.00239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.119**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.128**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.00253)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e(0.109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.0585)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.0510)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003etxcurba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\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: 66px;\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0387\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\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: 66px;\"\u003e\n \u003cp\u003e(0.128)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.0506)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.0453)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003elnci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\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: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.169**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.177**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\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: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.0766)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.0770)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003elnConsoEner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\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: 66px;\"\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\u003e0.00209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\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: 66px;\"\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(0.0397)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.483*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.711***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.698***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e(0.318)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.247)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e(0.262)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.221)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(0.246)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1,702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1,702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1,702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1,702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1,702\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eNumber of ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eNumber of Instruments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e7 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e10\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: 76px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eAR(1)\u003c/p\u003e\n \u003cp\u003eAR(2)\u003c/p\u003e\n \u003cp\u003eHansen \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 0.123\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003cp\u003e0.587\u003c/p\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e0.480\u003c/p\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003cp\u003e0.304\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\u0026nbsp;: Authors\u003c/p\u003e\n\u003cp\u003eThe results presented in \u003cstrong\u003eTable 7\u003c/strong\u003e indicate that smart governance does not exert a significant effect on biodiversity, regardless of the model considered.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eSensitivity to the Inclusion of Additional Variables\u003c/em\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8: Robustness to the Inclusion of Additional Variables\u003c/strong\u003e\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: 198px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\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: 198px;\"\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: 293px;\"\u003e\n \u003cp\u003eEstimator\u0026nbsp;: System GMM\u003c/p\u003e\n \u003cp\u003eDependent Variable: lnef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\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: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\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: 198px;\"\u003e\n \u003cp\u003eL.lnef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.797***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.808***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.817***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.0697)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e(0.0721)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.0677)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eegovernance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-0.0619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.0819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.0797\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e(0.0996)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.0967)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003elnpib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.158**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.173**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.180**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.0654)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e(0.0686)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.0737)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003elnpib\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.126**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.135**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.134**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.0532)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e(0.0624)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.0623)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003epop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-0.0963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.0821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.0628\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.0930)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e(0.0925)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.0929)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003elnConsoEner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.0345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.0331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.0330\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.0377)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e(0.0418)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.0479)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003elnci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.160**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.0753)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e(0.0841)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.0865)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003etxcurba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.0606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.0529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.0435\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.0502)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e(0.0502)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.0508)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003elnIDE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.159*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.161*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\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: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(0.0841)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e(0.0946)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\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: 198px;\"\u003e\n \u003cp\u003elnIND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.0892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\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: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e(0.123)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.122)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.00601\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(0.0308)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.340**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.259**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.034*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e(1.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e(1.109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e(1.165)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1,702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1,702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1,702\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNumber of ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNumber of Instruments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e16 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eAR(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\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: 198px;\"\u003e\n \u003cp\u003eAR(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eHansen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.346\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\u0026nbsp;: Authors\u003c/p\u003e\n\u003cp\u003eThe results in \u003cstrong\u003eTable 8\u003c/strong\u003e highlight that, despite the inclusion of additional variables, smart governance does not exhibit a significant effect on biodiversity.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eSensitivity Across Different Regions\u003c/em\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe check the robustness of our results by conducting a robustness analysis on two separate geographic groups: the first group refers to Sub-Saharan African (SSA) countries, while the second concerns Latin American and Caribbean (LAC) countries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 9: Specification by Geographic Zone\u003c/strong\u003e\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: 312px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimator\u0026nbsp;: system GMM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eVARIABLES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eALC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eASS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\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: 312px;\"\u003e\n \u003cp\u003eL.lnef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.841***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.552**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e(0.271)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e(0.252)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eegovernance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.0445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e(0.483)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e(0.345)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003elnpib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e(0.315)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e(0.147)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003elnpib\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.0427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.224*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e(0.235)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e(0.128)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003epop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.0659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\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: 312px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e(0.330)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e(0.120)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003elnConsoEner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.00621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.0675\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e(0.149)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e(0.0675)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003elnci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.498*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e(0.263)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e(0.262)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003etxcurba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.0256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.0592\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e(0.156)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e(0.0436)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.498*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e(0.924)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e(0.812)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e805\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eNumber of ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eNumber of Instruments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e14 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e14 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eAR(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eAR(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eHansen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.559\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\u0026nbsp;: Authors\u003c/p\u003e\n\u003cp\u003eAs shown in \u003cstrong\u003eTable 9\u003c/strong\u003e, the analysis conducted across the two geographic areas reveals that the effect of smart governance on biodiversity remains statistically non-significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.3. Mediation Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis subsection examines the mechanisms through which smart governance affects biodiversity in developing countries. To identify these transmission channels, we use a mediation analysis based on a structural equation model (SEM). The following figure presents the structure of this mechanism.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1764311511.png\" width=\"747\" height=\"267\"\u003e\u003c/p\u003e\n\u003cp\u003eThe approach adopted is based on the estimation of two regression equations, as illustrated in Figure 1. First, the coefficient (b1) measures the effect of smart governance on the mediator (Model 1). Second, the indirect effect is obtained by regressing biodiversity on smart governance while controlling for the mediator (Model 2); this effect passes through the coefficient (b2). The indirect effect is then given by the product of (b1) and (b3), where b3 captures the relationship between biodiversity and the mediator.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 10: Mediation Results\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"745\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWomen\u0026rsquo;s political empowerment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrbanisation rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnergy consumption\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(A) Mediation test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEst\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; std_err \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; T-stat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEst\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd_err\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT-stat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEst \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Std-err \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; T-stat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\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 style=\"width: 134px;\"\u003e\n \u003cp\u003eSobel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.039 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.000*** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e-0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.022 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e-0.397 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.043 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eAroian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.040 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e-0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.022 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e-0.397 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.043 0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eGoodman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.039 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e-0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.022 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e-0.397 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.043 0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(B)\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eComposition of Effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eIndirect effect \u0026nbsp;(Sobel)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.039 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e-0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e-0.397 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.043 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eDirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e1.130 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 0.129 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e-0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e-0.514 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.073 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eTotal effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e1.354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.132 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e-0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e-0.911 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.062 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eProportion of total effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e16.6%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e22.6%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e43,6%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\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 : Authors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 10\u003c/strong\u003e presents the results of the mediation analysis. Across all specifications, the estimates indicate that the mediators play a crucial role in the relationship between smart governance and biodiversity. However, the mediated effects differ according to the nature of the mediator: political empowerment of women exerts a positive indirect effect, whereas the urbanisation rate and energy consumption transmit a negative effect on biodiversity.\u003c/p\u003e\n\u003cp\u003eThe lower section of Table 10 reports the statistics from the Sobel-Goodman mediation tests. All three tests (Sobel, Aroian, and Goodman) confirm the existence of indirect effects significantly different from zero. Moreover, the p-values, all below the 5% threshold across specifications, allow us to reject the null hypothesis of no mediation. The results show that, over the period considered, political empowerment of women, the urbanisation rate, and energy consumption constitute significant transmission channels between smart governance and biodiversity. Specifically, 16.6% of the total effect passes through political empowerment of women, while 22.6% and 43.6% of the total effect are transmitted respectively via urbanisation and energy consumption, with a negative indirect impact. In other words, 16% of the overall effect of smart governance on biodiversity operates through a positive mechanism, namely political empowerment of women, whereas the other two mediators transmit a negative effect.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions and Statement\u003c/strong\u003e :\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLauriane Ma\u0026eacute;va IBOUTSI\u003c/strong\u003e: Writing \u0026ndash; original draft, formal analysis, conceptualization.\u003cbr\u003e\u003cstrong\u003eFran\u0026ccedil;ois-Cyrille EYEGHE-NTOUTOUME\u003c/strong\u003e: Writing \u0026ndash; review \u0026amp; editing, methodology, data curation, conceptualization, formal analysis, translation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Conflicts of Interest\u003c/strong\u003e : The analyses, discussions, and conclusions presented in this study are entirely the responsibility of the authors. They do not in any way reflect the position of any international organisation, institution, or state. Furthermore, the authors certify that they have not received any funding that could have influenced the results of this research.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eUnderstanding the influence of smart governance on biodiversity in developing countries is essential, as this topic remains relatively unexplored and existing results are often divergent. Despite the available literature, the precise role of smart governance in the preservation of ecosystems within developing countries remains largely unknown. This study aims to fill this gap. To this end, we used a panel of 74 developing countries and applied system GMM to empirically test several hypotheses. The primary hypothesis, central to this study, postulates that smart governance indirectly influences biodiversity through specific mechanisms. Other hypotheses explore potential transmission channels, considering that the effect of smart governance operates notably through political empowerment of women, the urbanisation rate, and energy consumption. The results show that smart governance does not have a significant direct effect on biodiversity. These conclusions remain valid even after using alternative methods, integrating control and additional variables, and conducting regional analyses. To better understand the underlying mechanisms, we conducted a mediation analysis using structural equations. The results reveal that smart governance influences biodiversity mainly through three levers: political empowerment of women, which accounts for 16.6% of the total effect, the urbanisation rate (22.6%), and energy consumption (43.6%). These findings highlight the importance of these channels for fully assessing the influence of smart governance on ecosystem preservation in developing countries. Based on these results, several public policy recommendations can be formulated to support biodiversity. It is essential to strengthen the political empowerment of women by promoting their participation in local and national decision-making bodies, particularly in environmental policy. Sustainable urban planning should also be promoted, incorporating green spaces, ecological corridors, and environmentally friendly infrastructure to mitigate the negative effects of urbanisation on biodiversity. Finally, effective management of energy consumption should be encouraged through investment in renewable energy and improvements in energy efficiency, in order to reduce the ecological footprint while supporting economic development.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbasi R, Martinez P, Ahmad R. The digitization of agricultural industry \u0026ndash; a systematic literature review on agriculture 4.0. 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Environmental Management, 56, 1\u0026ndash;14.\u003c/li\u003e\n\u003cli\u003eYue, Dongxia, Jianjun Guo, et Cang Hui. \u0026laquo; Scale dependency of biocapacity and the fallacy of unsustainable development \u0026raquo;. \u003cem\u003eJournal of Environmental Management\u003c/em\u003e 126 (septembre 2013): 13‑19. https://doi.org/10.1016/j.jenvman.2013.04.022\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Food and Agriculture Organization\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Organisation for Economic Co-operation and Development\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e United Nations Environment Programme \u0026ndash; World Conservation Monitoring Centre\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e A \u0026ldquo;digital twin\u0026rdquo; is a network of different data sources, treated as a real-time digital copy of a physical entity.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Smart urban governance refers to the use of digital technologies and data to inform urban governance processes (Sheikh et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Multi-species justice refers to justice not only for humans but also for the interconnected relationships between humans and non-humans, which include animals, plants, microbes, rivers, forests, and natural ecosystems (Sheikh et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","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":"Smart governance, biodiversity, developing countries, mediation, Environmental Kuznets Curve","lastPublishedDoi":"10.21203/rs.3.rs-8192996/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8192996/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study makes a novel contribution to the literature on the effects of smart governance on biodiversity in developing countries, a field that remains underexplored and marked by inconclusive results. In particular, the mechanisms through which smart governance impacts biodiversity are still insufficiently documented. This research addresses this gap by analysing a panel of 74 developing countries over the period 2000–2023. Using system GMM estimation, the results indicate that smart governance does not exert a statistically significant direct effect on biodiversity, as measured by the ecological footprint. The analysis further reveals an inverted U-shaped nonlinear relationship between economic growth and biodiversity, with a threshold estimated at USD 2,196 per capita, beyond which rising income is associated with improved environmental indicators. However, structural equation mediation analysis uncovers substantial indirect effects through several socio-economic channels. Three transmission mechanisms prove to be decisive: smart governance positively influences biodiversity through women’s political empowerment, while urbanisation rates and energy consumption transmit negative effects. Overall, the findings suggest that smart governance can contribute to better ecological outcomes when combined with social, institutional, and economic transformations that promote citizen participation, sustainable urban planning, and the energy transition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classification\u003c/strong\u003e: Q01, Q56, Q57, D73, O13\u003c/p\u003e","manuscriptTitle":"The effects of smart governance on biodiversity: Which transmission channels exist in developing countries?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-28 06:39:02","doi":"10.21203/rs.3.rs-8192996/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":"November 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-28T06:39:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-28 06:39:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8192996","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8192996","identity":"rs-8192996","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}