Participatory and Intelligent Public Governance: What Impact on Carbon Dioxide Emissions in Developing Countries?

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Lauriane Maéva IBOUTSI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6769124/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 One of the objectives of governments today is to develop while limiting their impact on the environment. The involvement of citizens and the introduction of new technologies are all levers that can be used to help reduce carbon dioxide emissions. The aim of this study is to analyse the simultaneous effects of participatory and intelligent public governance on the level of carbon emissions in developing countries. We use a system GMM to determine the direct effects, then a structural equation mediation to show the indirect effects. We consider a sample of 83 developing countries from 2000 to 2023. The results of the system GMM revealed no direct effect between participatory and intelligent public governance and the level of carbon dioxide emissions. However, Foreign Direct Investment (FDI), population growth and energy consumption have a positive effect on the level of CO₂ emissions in developing countries. The mediation analysis shows the indirect effect that participatory and smart public governance has on carbon dioxide emissions through the vote buying and participation channel, the direct popular vote index and the information and communication technology index. We suggest that governments should encourage open democratic processes and rethink frameworks for the use of digital in governance, to prevent technological tools from becoming instruments of manipulation or disengagement. Environmental Economics Other Public Policy Participatory governance intelligent governance carbon dioxide emissions mediation developing countries Figures Figure 1 INTRODUCTION The urgent need to address climate change issues (UNEP[1] , 2023) and the desire to involve all stakeholders in the decision-making process to achieve sustainable development goals (Zhao et al., 2022 ; Wang et al., 2023 ) have put the central role of citizen participation in environmental protection back on the agenda. Climate change manifests itself mainly in developing countries in the form of drought, rising sea levels and rising temperatures, which are the consequences of the increase in carbon dioxide emissions in developed countries and which are having an impact on developing countries (Weikmans and Zaccai, 2017 ). Most of the increase in carbon dioxide emissions is caused by human activity through fossil fuel combustion for energy production and human consumption (Schmalensee et al., 1998 ). Participatory public governance means involving citizens (Bussu et al., 2022 ) in the decision-making process to achieve sustainable development. In today's governance model, the government authority no longer has sole decision-making power, as in an authoritarian democracy[2] or even delegated to a representative of the people (Casella et al., 2022 ; Culén, 2023 ). From now on, even new technologies will be useful for a better understanding and configuration of effective environmental policies (Pitkin, 2016 ). Participatory and intelligent public governance that not only takes account of citizen action but also integrates new technologies into the environmental decision-making process is a key issue for today's governments. Intelligent governance through smart cities contributes to reducing carbon dioxide emissions thanks to new technologies (Cavada et al., 2016 ). Intelligent governance through smart cities contributes to reducing the level of carbon dioxide emissions thanks to new technologies (Cavada et al., 2016 ). In addition to technology, smart governance also involves training citizens in new technologies so that they can be the main channels for transmitting environmental information on the ground (Capra, 2016 ; Tomor et al., 2019 ; Bull and Azennoud, 2016 ). The digital economy can also help to reduce carbon dioxide emissions through the dissemination of information on the Internet and the digitisation of control areas for real-time monitoring of the damage caused by climate change (Cai et al., 2021 ; Li et al., 2022 ). It can also influence carbon dioxide emissions in the transport sector, speeding them up at the low urbanisation stage and reducing emissions at the high urbanisation stage (Li et al., 2022 ). Similarly, participatory governance by involving citizens in the decision-making process can reduce emissions (Rousse, 2008 ). Authors emphasise the importance of citizen participation with strict, informal and corruption-free regulation as a tool for reducing emissions (Shi et al., 2023 ; Martens-Habbena and Sass, 2006 ; Zhang and Mora, 2023 ). The participation of environmental non-governmental organisations is also essential for influencing environmental policies (Zhang and Mora, 2023 ). In addition, heavy environmental regulations and low public awareness of environmental issues can act as a brake on citizen action in favour of sustainable development (Whitmarsh et al., 2012 ). It is interesting to ask what effects participatory and intelligent public governance has on the level of carbon dioxide emissions. To analyse the effects of participatory and intelligent public governance on the level of carbon dioxide emissions, we draw on the theory of multi-level governance (Hooghe and Marks, 2001 )[3] , the theory of common goods and natural resources (Ostrom, 1990 ; Agrawal and Gibson, 1999)[4] , the theory of social innovation (Mulgan, 2003 ; Moulaert et al., 2005 )[5] and the revisited Kuznets environmental curve (Grossman and Krueger, 1995; Stern et al., 1996; Jobert et al., 2012 ) .[6] So we can put forward the following hypothesis: participative and intelligent public governance reduces the level of carbon dioxide emissions. We choose developing countries for the following reasons: Firstly, participatory public governance involves all stakeholders in the decision-making process relating to environmental issues. Decentralisation of democracy (Bussu et al., 2022 ) is a necessary path to achieving carbon-neutral policies in developing countries. Secondly, the introduction of new technologies in smart cities (Cavada et al., 2016 ) for monitoring environmental trends is proving to be a necessity nowadays in developing countries to prevent natural disasters. Furthermore, citizen participation in developing countries is not yet sufficiently present in the decision-making process on environmental issues. For example, the scores for political pluralism and participation in Afghanistan are very low, as are those in India (0/4 in Afghanistan compared with 3/4 in India, according to Our World in data in 2024); Finally, the effects of global warming are already being felt in developing countries. Storms, floods and drought are among the main natural disasters reported in Africa between 1970 and 2019 (Statista, 2025). Our study adds new lines of discussion to the existing literature on the effects of participatory and, above all, intelligent public governance in developing countries, particularly in solving environmental problems. We are particularly interested in the indirect effects of participatory and smart public governance on carbon dioxide emissions with the introduction of new technologies in developing countries. Contrary to the existing literature, we use the participatory democracy index associated with three governance measures (control of corruption, government efficiency and regulatory quality) and the digital economy to measure the impact of participatory and smart public governance on emissions. The structural equation mediation method, which has yet to be used to analyse this link, will enable us to identify the transmission channels through which participatory and intelligent governance indirectly affects carbon dioxide emissions. In this article, the second part presents empirical observations and the correlation between our variables of interest. The third part deals with the literature review on the effects of participatory and intelligent public governance on the level of carbon dioxide emissions. Then in the fourth part we present the transmission channels, namely: vote buying and participation, the direct popular vote index and the information and communication technology (ICT) index. Then, in the fifth part, we describe the empirical strategy. Finally, in the last two parts, we present the results, conclude and propose appropriate economic policies. EMPIRICAL OBSERVATIONS Figure 1 shows that floods dominate with 60% of occurrences, followed by storms (17%) and drought (16%). Forest fires and extreme temperatures each account for (2%), while landslides are at (3%). This graph shows that floods are the main challenge in terms of natural disasters over this period in Africa, probably due to the continent's climatic and geographical conditions. Graph 2 shows an overall upward trend. At the start of the period, in 2000, emissions were around 1.1 units, with a slight increase until 2004. From 2004 onwards, there was more marked growth, reaching around 1.6 units in 2010, probably reflecting rapid industrialisation and increased economic activity. After stabilising slightly between 2010 and 2014, emissions rose significantly, peaking at almost 2 units around 2018–2019, suggesting an acceleration in energy needs and increased dependence on fossil fuels. Since 2020, emissions have shown fluctuations, with a slight drop in 2021 followed by a rise in 2022–2023, which could be linked to economic disruptions or initial mitigation efforts, although the trend remains upwards over the whole period. Graph 3 traces the evolution of the governance index in developing countries from 2000 to 2023. Starting from a level of around 2 in 2000, this index begins a marked fall to around 0.5 around 2003, perhaps reflecting initial challenges such as political or economic instability. There followed a period of fluctuation between 2003 and 2015, with modest rises, including a peak around 2012, suggesting sporadic efforts to strengthen institutions. However, after 2015, a downward trend begins again, with the index gradually falling to a low near 0 in 2023. This continued decline could reflect persistent difficulties, such as social tensions or weakened governance in the face of global crises. All in all, this rollercoaster ride is a reminder that the quest for solid governance remains a long-term challenge for developing countries. Graph 4 shows the correlation between participatory democracy and the digital economy (DPEN, in red) with CO₂ emissions (in blue) in developing countries from 2000 to 2023. We can see that the two curves climb overall over this period: DPEN progresses in a fairly linear fashion, rising from around 14 to 15 units, while CO₂ emissions increase more irregularly, fluctuating between 14 and 20 units, with peaks around 2010 and 2018. It gives the impression that the more DPEN grows, the more CO₂ emissions tend to rise, perhaps because of the energy needed to support this digital growth. But the variations in CO₂ also show that other factors, such as environmental policies or economic crises, can influence emissions, making the relationship less direct than it might seem. Literature review of the effects of participatory and intelligent public governance on carbon dioxide emissions in developing countries This review highlights the effects of smart cities and citizen participation in influencing the level of carbon dioxide emissions. For Tomor et al ( 2019 ) smart governance, is defined as a technological collaboration between citizens and local governments to advance sustainable development. 3 − 1 Smart cities and reducing carbon dioxide emissions Recent literature advocates the introduction of new technologies to achieve a reduction in carbon dioxide emissions. Cavada et al ( 2016 ) argue for this in a theoretical study of environmental issues. The authors note that 'smart cities', which are cities where technology is used for systems optimisation and leadership to successfully tackle climate issues (Walters, 2011 ), adopt technology-based solutions to enable efficient urban living and sustainable development. Indeed, cities that have adopted smart roadmaps have integrated the reduction of carbon dioxide emissions into their environmental sustainability agenda. Incorporating the participatory aspect of civil society, Bull and Azennoud ( 2016 ) discuss the role of smart cities in reducing the level of carbon dioxide emissions. Based on a case study of citizen engagement around a waste-to-energy infrastructure development. The results show the essential role of citizen involvement in the sustainable development process. Smart cities give citizens a greater opportunity to play a practical part in environmental decisions. 3 − 2 Direct effects of citizen participation and the digital economy on carbon dioxide emissions Recently, an environmental Kuznets curve type relationship was discovered between citizen participation and carbon dioxide emissions in the work of Zhang and Mora ( 2023 ). The findings reveal that public participation significantly reduces regional carbon emissions and regional carbon intensity (Zhang et al., 2021 ; Wang et al., 2022 ; He et al., 2022 ; Yang et al., 2021 ). Several studies have been carried out in China, in particular to analyse the link between the digital economy and the level of carbon dioxide emissions. Wang et al ( 2022 ) find a significant effect of the development of the digital economy on the reduction of carbon emission intensity. Also in China, Lee et al ( 2022 ) analyse how the digital economy can reduce carbon dioxide emissions, particularly in the transport sector. The digital economy accelerates carbon emissions in the transport sector at the low urbanisation stage, while it reduces carbon emissions at the high urbanisation stage. 3–3 Indirect effects of citizen participation and the digital economy on the level of carbon emissions Citizen participation plays a key role in the objective of reducing carbon dioxide emissions, even if a number of legal obstacles limit citizen action in the decision-making process on environmental issues. Chen et al ( 2022 ) also note that informal regulations, driven by public environmental concerns, can encourage companies to adopt green technologies and meet their environmental, social and governance commitments. This is achieved by leveraging public opinion, raising environmental awareness and promoting sustainable consumption. In the same country, Wu et al ( 2022 ) use data from network platforms (Sina Weibo and Baidu) from 2013 to 2018 to test whether internet public participation can help control environmental pollution emissions. The study found that public participation on the internet can significantly reduce industrial wastewater discharges. In addition, the government's mediating effect is significant on pollutant emissions. As a result, the informal environmental regulation represented by public participation is increasingly recognised by researchers around the world. Citizen-led mitigation and adaptation are key to advancing and accelerating climate policies, particularly in the context of urban development (Akerboom and Craig, 2022; Hao et al., 2023 ; Zhang and Mora, 2023 ; Zhang et al., 2022 ). Furthermore, Zhang et al. ( 2022 ) use the generalised method of moments to analyse the direct effect and an intermediate effect model is then applied to explore the indirect transmission mechanisms of the digital economy on CO₂ emissions. The results show that environmental governance, technological innovation and industrial structure upgrading are the three main channels through which the digital economy influences low-carbon development. The work of Wang et al ( 2024 ) differs from the existing literature by considering threshold variables such as natural resource rents and anti-corruption regulations to analyse the link between the digital economy[7] and carbon dioxide emissions. The results show that the digital economy as a whole increases carbon emissions. Current research does not combine participatory and intelligent public governance to assess their direct and indirect effects on carbon dioxide emissions. In addition, the majority of studies do not generally take into account other important variables for measuring participatory public governance. Existing studies are limited to using Kaufmann's indicators to measure governance overall. We aim to fill this gap in the literature by including other variables such as the participatory democracy index combined with three dimensions of governance and the digital economy to measure participatory and intelligent public governance. Analysis of transmission channels Analysing the effects of participatory and intelligent public governance on carbon dioxide emissions leads us to take other concepts into account. Indeed, we consider that vote buying and participation, the direct popular vote index and the ICT index[8] are equally important for understanding the level of carbon dioxide emissions in developing countries. Vote buying and turnout refers to the distribution of money or gifts to individuals in order to influence their decision to vote or not to vote (Pemstein et al. , 2024; V-Dem, 2024). Direct popular vote refers to an institutionalised process whereby the citizens of a region or country express their choice or opinion on specific issues by means of a ballot paper (V-Dem, 2024). Finally, the ICT index (internet access, ICT goods, ICT services) calculated from principal component analysis shows the effect that information and communication technologies have in determining the level of CO₂ emissions. 4 − 1 Vote buying channel and participation Several authors have shown the role of citizen participation in influencing carbon dioxide emissions (Rousse, 2008 ; Shen et al., 2023). Consequently, individuals play a crucial role in the transition to a low-carbon society (Nerini et al., 2021). Allowing citizens to participate in environmental governance brings self-satisfaction with political rights, enabling them to exercise the rights and responsibilities conferred on them by the Constitution (Chen et al., 2022 ). Buying votes could encourage citizens to participate or not in environmental projects. 4 − 2 Direct popular vote channel Voting is used by citizens to express their views and appoint a representative to defend their interests, particularly in the environmental field. Some studies have shown that voters can influence incumbent politicians to adopt pro-environmental behaviour in the run-up to general elections, notably by putting forward the reward-punishment hypothesis[9] (Stef et al., 2023; Dietz et al, 2009). 4-3 ICT (Information and Communication Technologies) index channel The ICT index is calculated using the principal component analysis method and is made up of Internet access, ICT goods and ICT services. In the context of the global development of the Internet and the widespread use of digital technologies, the digital economy is gradually becoming a key driver of low-carbon regional development (Zhang et al., 2022 ). The effects of ICTs on the environment can differ from one country to another. Indeed, ICTs improve environmental sustainability in countries with high ICT quality while degrading the environment in countries with moderate and low ICT quality (Appiah-Otoo et al., 2023). Empirical approach Our study considers a set of 83 developing countries spread across the following regions: Sub-Saharan Africa, South Asia, East Asia, Latin America and the Caribbean, and the MENA region. The study period runs from 2000 to 2023 (24 years), with a total of 1992 observations. The choice of this period is conditioned by the availability of variables for certain countries. The choice of these countries is linked to the availability of data and the challenge of sustainable development and adapting environmental policies to respond effectively to the damage caused by the deterioration in the quality of the environment. As our database is not cylindrical, the absence of data for certain years led us to proceed with smoothing by moving average. 5 − 1 Description of variables and presentation of the overall model Carbon dioxide (CO₂) represents the explained variable. Carbon dioxide emissions are generally used in work to demonstrate the impact of a variable on the environment (Zhang et al., 2023; Chen et al., 2022 ). Reliable data on this indicator is available in the World Bank's World Development Indicators (WDI) for selected countries, and is presented as carbon dioxide emissions (in metric tonnes per capita) from fossil fuel combustion and cement manufacture. The concept of governance is usually measured by the good governance indicators[10] defined by Kaufmann (2010). Contrary to the literature, we consider only three measures of good governance as variables of interest, namely: the control of corruption , which makes it possible to capture the opportunistic behaviour present among local elected representatives and citizens' representatives, the participation of social entities in environmental protection being closely linked to the characteristics of pollutants (Fu and Geng, 2019; Kaufmann, 2010; WDI[11] , 2024); the quality of regulation , which reflects the ability of governments to implement sound and credible public policies (Halkos and Tzeremes, 2013; Kaufmann, 2010; WDI, 2024); government effectiveness , which reflects perceptions of the quality of public services, the quality of the civil service and its degree of independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to these policies (Halkos and Tzeremes, 2013; Kaufmann, 2010; WDI, 2024). These measures will be grouped into a ' gov ' index, obtained using the Principal Component Analysis (PCA) method (Jolliffe, 2002 ; Larcher et al., 2012 ). To these we add the participatory democracy (PD) index , since citizen participation is mentioned by some authors as a way of reducing carbon dioxide emissions (Rousse, 2008 ; Chen et al., 2022 ; Zhang et al., 2023), and the digital economy (DE) through Internet access data (as a percentage of the population) (Wang et al., 2024 ; Zhang et al., 2022 ) taken from World Bank data for 2024. The digital economy is a determining variable of intelligent governance capable of impacting the level of carbon emissions. We consider the DP*EN product, which measures the interaction between participative and intelligent public governance. It reflects several important aspects of the interactions between governance, technology and environmental impacts. In the control variables, we retain GDP (Gross Domestic Product) growth , which is an important variable in our estimates since it is directly linked to carbon dioxide emissions, the data for which are taken from the United Nations (Kumar and Muhuri, 2020; Seri and de Juan Fernandez, 2021 ). And GDP squared is added to the model to test the validity of the Kuznets curve hypothesis (Grossman and Krueger, 1991, 1995). Also, population growth would be relevant in our study (WDI, 2024). Next, we have energy consumption , which allows us to explore whether and to what extent a government's policies or characteristics influence CO₂ emissions via their impact on energy consumption (WDI, 2024; Stern, 2007). Finally, FDI ( Foreign Direct Investment ) which can be an important driver of energy consumption and CO₂ emissions (Behera et al., 2017; WDI, 2024). Our estimation model AR (p)[12] thus takes the following form: \(\:{\text{C}\text{O}}_{2\text{i}\text{t}}\) = \(\:{\varphi\:}_{1}{CO}_{2it-1}+{\varphi\:}_{2}{CO}_{2it-2}+\dots\:\) + \(\:{\varphi\:}_{p}{CO}_{2it-p}+\:{\beta\:}_{1}{\text{l}\text{n}\text{I}\text{D}\text{E}}_{\text{i}\text{t}}\) + \(\:{\beta\:}_{2}{\text{P}\text{O}\text{P}}_{\text{i}\text{t}}+{\beta\:}_{3}ln{\text{P}\text{I}\text{B}}_{\text{i}\text{t}}+{\beta\:}_{4}{PIB}_{it}^{²}+{\beta\:}_{5}{\text{ln}ConsoEner}_{\text{i}\text{t}}\) + \(\:{\beta\:}_{6}{\text{g}\text{o}\text{u}\text{v}}_{\text{i}\text{t}}\) + \(\:{\beta\:}_{7}{\text{D}\text{P}\text{*}\text{E}\text{N}}_{\text{i}\text{t}}+{\epsilon\:}_{it}\) Where: \(\:{\text{C}\text{O}}_{2\text{i}\text{t}-\text{p}}\) emissions of retarded carbon dioxide of order p ; \(\:{\varphi\:}_{1}\dots\:\:{\varphi\:}_{p}\) the autoregressive coefficients ; lnIDE: direct investment abroad ; POP: Population growth ; lnGDP: economic growth ; \(\:{lnPIB}^{2}\:\) corresponds to economic growth squared (test of the Kuznets environmental curve hypothesis); lnConsoEner: Energy consumption ; DP*EN: reflecting the interaction between participatory democracy and the digital economy; The " gouv " index contains the following variables: CC: control of corruption ; QR: quality of regulation and EG: government efficiency ; 𝛆 error term which captures unobserved factors ; i : individual (country) ; t : year. The lagged terms capture the inertia of carbon dioxide emissions, which is typical of environmental phenomena. The institutional variables ( gov and DP*EN) allow us to analyse how participative and intelligent public governance influences carbon dioxide emissions. In the robustness analysis, we successively integrate control variables. 5 − 2 Estimation method The GMM (Generalized Method of Moments) estimator is the main method for estimating a dynamic AR(p) panel model. This method uses first differences to eliminate fixed effects and exploits the lagged values of the variables as instruments (Arellano and Bond, 1991). The GMM (generalized method of moments) system (Blundell and Bond, 1998 )[13] combines first difference and level equations to increase efficiency. A- Presentation and analysis of results In this section we analyse the basic results concerning the effect of participative and intelligent public governance on the level of carbon dioxide emissions. Table 1 shows the results of the descriptive statistics. Table 1 Descriptive statistics for variables Variables Comments Average Standard deviation Min Max CO₂ 1,992 1.67116 2.038804 0.021731 15.24536 lnIDE 1,992 11.27109 0.2394629 10.09924 11.64599 POP 1,992 1.858095 1.056401 -3.218371 9.992305 lnpib 1,992 -1.24e-14 0.8791697 -2.482416 2.848423 lnpib² 1,992 0.7725514 1.068255 6.79e-06 8.113516 lnConsoEner 1,992 3.57567 0.5975282 2.164314 4.762581 gouv 1,992 5.76e-10 1.537818 -3.60637 4.4176 DPEN 1,992 8.129038 10.6892 0.0000133 61.3467 Source : Author Table 1 highlights the marked diversity of developing countries over the period 2000–2023. CO₂ emissions are highly variable, reflecting major differences in levels of industrialisation and environmental policies between countries. Foreign direct investment (FDI) appears to be relatively stable between countries, while population growth (POP) remains highly contrasted, ranging from decline to rapid increase. Gross domestic product (lnGDP), centred for the analysis, reveals significant economic heterogeneity, and its square (lnGDP²) suggests a possible non-linear relationship with emissions, in line with the environmental Kuznets hypothesis. Energy consumption (lnConsoEner) also varies, reflecting different stages of industrial development. The governance index ( gouv ) shows significant variations, highlighting persistent institutional challenges. Finally, the interaction between participatory democracy and the digital economy ( DPEN ) reveals very uneven adoption of these modern levers of development. These disparities suggest the importance of a differentiated approach in analysing the effects between participatory and intelligent public governance and carbon dioxide emissions in developing countries. Table 2 Correlation matrix CO₂ lnIDE POP lnpib lnpib² lnConsoEner gouv DPEN CO₂ 1.0 lnIDE 0.1886 1.0 POP -0.3775 -0.146 1.0 lnpib 0.3993 -0.0807 -0.2622 1.0 lnpib² 0.2597 -0.222 -0.2337 0.2088 1.0 lnConsoEner 0.7678 0.2592 -0.5621 0.4596 0.192 1.0 gouv 0.3479 0.3054 -0.3058 -0.1087 0.0891 0.489 1.0 DPEN 0.3933 0.0841 -0.4036 0.3136 0.0673 0.5366 0.4487 1.0 Source : Author The correlation matrix in Table 2 shows that there is no correlation between the variables. We can move on to estimating the GMM in the system in Table 3 . Table 3 Estimation of GMM in system VARIABLES CO₂ Standard deviations L.CO₂ 0.874*** (0.0242) lnIDE 0.626* (0.365) POP 0.112* (0.0573) lnpib -0.271 (0.321) lnpib² 0.273 (0.266) lnConsoEner 0.717*** (0.266) gouv -0.0653 (0.0582) DPEN -0.00143 (0.00669) Constant -9.746** (4.206) Comments : 1909 Number of individuals 83 Number of instruments 45 AR (1) AR (2) 0.000 0.318 Hansen 0.135 *** p < 0.01, ** p < 0.05, * p < 0.1 Source : Author Table 3 shows the GMM-system estimation applied to a sample of 83 developing countries highlights several determinants of CO₂ emissions. The highly significant and positive character of the lagged variable of emissions (L.CO₂: 0.874, p < 0.01) confirms the structural persistence of pollution levels over time, as pointed out by El-Shahawi et al. (2010) and Arellano and Bover ( 1995 ). Foreign direct investment (lnIDE) shows a positive and significant effect at 10% (0.626), suggesting that FDI flows are still predominantly directed towards sectors that are not very environmentally friendly, reminiscent of the 'pollution haven' effect described by Cole et al. ( 2017 ). The population variable (POP), also significant at 10% (0.112), supports the idea that population growth fuels environmental pressure, in line with the STIRPAT approach developed by Dietz and Rosa (1994). Energy consumption (lnConsoEner) has a strong and highly significant positive effect (0.717, p < 0.01), reflecting the persistent dependence on fossil fuels in the developing countries in our study. Moreover, given that developing countries account for a large proportion of the world's population (with India the most populous country in the world in 2023 (INSEE, 2023)), the fact that energy consumption is significant is not surprising. Not least because of the growing demand for energy (lighting, cooking, etc.), rapid urbanisation, the construction of residential infrastructure and the increased exploitation of natural resources (forests, farmland, etc.) (Nejat et al., 2015 ; Muhammad et al., 2019). As for the two GDPs, the gov index and the interaction between participatory democracy and the digital economy, they have no significant effect on carbon dioxide emissions. This confirms that there are no direct effects between participatory and intelligent public governance and carbon dioxide emissions. B- Analysis of the mediation To strengthen our results, we carry out additional tests. We will carry out a robustness analysis by successive integration of the control variables. Then a mediation analysis to determine the indirect effects. Table 4 Robustness by successive integration of control variables Dependent variable : Carbon dioxide emissions Estimator (GMM in system) VARIABLES (1) (2) (3) (4) (5) (6) L.CO₂ 0.945*** 0.924*** 0.923*** 0.911*** 0.919*** 0.874*** (0.0127) (0.0216) (0.0205) (0.0148) (0.0126) (0.0242) lnIDE 0.905 0.799 1.024 0.586 0.626* (0.622) (0.626) (0.762) (0.693) (0.365) POP -0.0143 0.00334 0.0251 0.112* (0.0173) (0.0254) (0.0429) (0.0573) lnpib 0.128* -0.0773 -0.271 (0.0685) (0.253) (0.321) lnpib² 0.202 0.273 (0.235) (0.266) lnConsoEner 0.717*** (0.266) gouv 0.0716*** 0.0409 0.0346 0.0407 0.0850* -0.0653 (0.0259) (0.0506) (0.0560) (0.0617) (0.0466) (0.0582) DPEN -0.00151 0.000172 0.000369 -0.00224 -0.000634 -0.00143 (0.00222) (0.00363) (0.00377) (0.00373) (0.00544) (0.00669) Constant 0.110*** -10.06 -8.839 -11.37 -6.643 -9.746** (0.0233) (7.016) (7.060) (8.613) (7.756) (4.206) Comments 1,909 1,909 1,909 1,909 1,909 1,909 Number of ID 83 83 83 83 83 83 Number of instruments AR (1) AR (2) Hansen 45 0.000 0.336 0.149 45 0.000 0.335 0.197 45 0.000 0.337 0.195 45 0.000 0.335 0.114 45 0.000 0.334 0.077 45 0.000 0.318 0.135 Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Source : Author Table 4 shows a series of robustness regressions estimated by the system GMM method, applied to a sample of 83 developing countries, in order to assess the impact of participatory and intelligent public governance on carbon dioxide emissions. The governance index ( gov ) is significant (in 1 and 5) but this result lacks robustness since it disappears as soon as the model is enriched, while DPEN has no direct effect on CO₂ emissions. These results suggest that, in the context of developing countries, the quality of governance, both participatory and intelligent, does not appear to be a direct lever for reducing polluting emissions. This conclusion is in line with the work of Brett ( 2003 ) and Auld et al, ( 2014 ), who point out that citizen participation generally fails in contexts where local conditions make cooperative and collective action very difficult, and that policy innovations do not always have lasting consequences. B-1 Indirect effects of participatory and intelligent public governance on carbon dioxide emissions We have selected three channels through which participatory and intelligent public governance affects carbon dioxide emissions. These are: vote buying and participation, the direct popular vote index and the information and communication technology (ICT) index. To achieve this, we use a structural equation mediation analysis inspired by MacKinnon et al, 1995 , 2007 which consists of estimating two regression models as illustrated in Fig. 1 below. In the first relationship ( model 1 ), we have the indirect effect of participatory and intelligent public governance on the level of carbon dioxide emissions through vote buying and participation, the direct popular vote index and the information and communication technology index. The second relationship ( model 2 ) shows us the direct effect between participatory and intelligent public governance and carbon dioxide emissions. The coefficient associated with participative and intelligent public governance shows the scale of the direct effect. The indirect effect derives from the difference between the total effect and the direct effect, showing the influence of participative and intelligent public governance through these mediators. Table 5 shows the results of the mediation analysis. Table 5 Analysis of the indirect effects of participatory and intelligent public governance on carbon dioxide emissions Buying votes and participation The voting index popular direct The information and communication technologies index (A) Mediation test Coef AND T-stat Coef AND T-stat Coef AND T-stat Sobel 0.024 0.010 2.417*** 0.009 0.003 2.800 *** 0.013 0.004 3.566 *** Aroian 0.024 0.010 2.413 *** 0.009 0.003 2.766 *** 0.013 0.004 3.532 *** Goodman 0.024 0.010 2.420 *** 0.009 0.003 2.835 *** 0.013 0.004 3.601*** (B) Composition of effects Indirect effect (Sobel) 0.024 0.010 2.417 *** 0.009 0.003 2.800 *** 0.013 0.004 3.566 *** Direct effect 0.043 0.025 1.706* 0.050 0.023 2.145** 0.054 0.023 2.317** Total effect 0.067 0.023 2.889*** 0.059 0.023 2.533** 0.067 0.023 2.889*** Proportion of total effect ( gov ) 35.9% 15.7% 19.7% Sobel 0.013 0.002g 6.870 *** -0.001 0.000 -3.017*** − 0.001 0.000 -2.458** Arojan 0.013 0.002 6.862 *** -0.001 0.000 -2.983*** -0.001 0.000 -2.423** Goodman 0.013 0.002 6.877 *** -0.001 0.000 -3.052*** -0.001 0.000 -2.495** (B) Composition of effects Indirect effect (Sobel) 0.013 0.002 6.870 *** -0.001 0.000 -3.017*** -0.001 0.000 -2.458 ** Direct effect 0.031 0.004 7.052 *** -0.003 0.003 -1.045 -0.001 0.003 -0.246 Total effect 0.044 0.004 10.914*** -0.005 0.003 -1.485 -0.002 0.003 -0.565 Proportion of total effect ( DPEN ) 30% 29.9% 56.6% Notes : *; **; *** significance at the 10%, 5% and 1% thresholds respectively Source Author The results in Table 5 show us the indirect effects of participatory public governance (measured by the gouv governance index and the interaction between participatory democracy and the DPEN digital economy) on carbon dioxide emissions. For our developing countries as a whole, vote buying and participation has an effect of 35.9% through the gov index and 30% through DPEN ; the direct popular vote index has an effect of 15.7% through gov and 29.9% through DPEN ; and finally the information and communication technologies index (measured through internet access, ICT goods and services) has the highest effect, at 19.7% through gov and 56.6% through DPEN. We can see from these results that participatory and intelligent public governance has an indirect effect on the level of carbon dioxide emissions in developing countries through vote buying and participation, the direct popular vote index and the information and communication technology index. The indirect effect through vote buying and participation is rather positive while the results are mixed for the direct popular vote index and the ICT technology index which act positively through gov and negatively through DPEN . Overall, these results suggest that participatory and smart public governance acts positively on CO₂ emissions through the channel of vote buying and participation, and negatively through the channel of the direct popular vote index and the ICT technology index in developing countries. CONCLUSION The aim of this analysis was to show the effects of participatory and intelligent public governance on the level of carbon dioxide emissions in developing countries. Given the paucity of literature on the simultaneous analysis of the participatory aspect of governance and the integration of new technologies into inclusive decision-making processes. We thus mobilised a set of 83 developing countries over a period from 2000 to 2023. The GMM system analysis revealed no direct effect between participatory and intelligent public governance and carbon dioxide emissions. The direct effect results also show that Foreign Direct Investment (FDI), population and energy consumption have a positive effect on the level of CO₂ emissions in developing countries. The structural equation mediation analysis shows the indirect effect that participatory and smart public governance has on carbon dioxide emissions through vote buying and participation, the direct popular vote index and the information and communication technology index. The effect is positive through the channel of vote buying and participation, and negative through the channel of the direct popular vote index and the ICT technology index in developing countries. The results clearly underline that participatory public governance, intelligently articulated with digital technology, can play an important role in tackling carbon emissions in developing countries. We suggest that governments should encourage open, transparent and inclusive democratic processes, where every voice counts, particularly around climate issues. It would also be worth rethinking the frameworks for the use of digital technology in governance, to avoid technological tools becoming instruments of manipulation or disengagement. To achieve sustainable development objectives in developing countries, it is not enough to adopt climate policies: they need to be embedded in a participatory, transparent and intelligently digitised governance framework, while taking care to avoid the perverse effects associated with unregulated use of digital tools. References Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics , 68(1), 29-51. https://doi.org/10.1016/0304-4076(94)01642-D Agrawal, Arun, and Clark C Gibson. "Enchantment and Disenchantment: The Role of Community in Natural Resource Conservation. World Development 27, n o 4 (April 1999): 629-49. https://doi.org/10.1016/S0305-750X(98)00161-2. Akerboom, Sanne, and Robin Kundis Craig. "How Law Structures Public Participation in Environmental Decision Making: A Comparative Law Approach." Environmental Policy and Governance 32, n o 3 (2022): 232-46. https://doi.org/10.1002/eet.1986. Appiah-Otoo, Isaac, Alex O. Acheampong, Na Song, and Xudong Chen. "The Impact of Information and Communication Technology (ICT) on Carbon Dioxide Emissions: Evidence from Heterogeneous ICT Countries." Energy & Environment 34, n o 8 (1 December 2023): 3080-3102. https://doi.org/10.1177/0958305X221118877. Auld, Graeme, Alexandra Mallett, Bozica Burlica, Francis Nolan-Poupart, and Robert Slater. "Evaluating the effects of policy innovations: Lessons from a systematic review of policies promoting low-carbon technology". Global Environmental Change 29 (1 November 2014): 444-58. https://doi.org/10.1016/j.gloenvcha.2014.03.002. Behera, Smruti Ranjan, and Devi Prasad Dash. "The effect of urbanization, energy consumption, and foreign direct investment on the carbon dioxide emission in the SSEA (South and Southeast Asian) region." Renewable and Sustainable Energy Reviews 70 (April 1, 2017): 96-106. https://doi.org/10.1016/j.rser.2016.11.201. Blundell, R., & Bond, S. (1998). Initial Conditions and Moment Restrictions in Dynamic Panel Data Models. Journal of Econometrics , 87(1-2), 115-143. https://doi.org/10.1016/S0304-4076(98)00009-8 Brett, E.A. "Participation and accountability in development management". The Journal of Development Studies 40, n o 2 (1 December 2003): 1-29. https://doi.org/10.1080/00220380412331293747. Bull, Richard, and Marouane Azennoud. "Smart Citizens for Smart Cities: Participating in the Future". Proceedings of the Institution of Civil Engineers - Energy 169, n o 3 (August 2016): 93-101. https://doi.org/10.1680/jener.15.00030. Bussu, Sonia, Adrian Bua, Rikki Dean, and Graham Smith. "Introduction: Embedding participatory governance. Critical Policy Studies 16, n o 2 (May 2, 2022): 133-45. https://doi.org/10.1080/19460171.2022.2053179. Cai, Wenjia, Chi Zhang, Hoi Ping Suen, Siqi Ai, Yuqi Bai, Junzhe Bao, Bin Chen, et al. "The 2020 China Report of the Lancet Countdown on Health and Climate Change". The Lancet Public Health 6, n o 1 (1 January 2021): e64-81. https://doi.org/10.1016/S2468-2667(20)30256-5. Capra, Carlo Francesco. "The Smart City and Its Citizens: Governance and Citizen Participation in Amsterdam Smart City." International Journal of E-Planning Research (IJEPR) 5, n o 1 (January 1, 2016): 20-38. https://doi.org/10.4018/IJEPR.2016010102. Casella, Alessandra, Joseph Campbell, Lucas de Lara, Victoria Mooers, and Dilip Ravindran. "Liquid Democracy. Two Experiments on Delegation in Voting". SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, December 19, 2022. https://doi.org/10.2139/ssrn.4307183. Cavada, M. "The Governance of Smart Cities". In The Palgrave Encyclopedia of Urban and Regional Futures , edited by Robert C. Brears, 650-54. Cham: Springer International Publishing, 2022. https://doi.org/10.1007/978-3-030-87745-3_249. Cavada, Marianna, Dexter V.L. Hunt, and Chris D.F. Rogers. "Do smart cities realise their potential for lower carbon dioxide emissions?" Proceedings of the Institution of Civil Engineers - Engineering Sustainability 169, n o 6 (December 2016): 243-52. https://doi.org/10.1680/jensu.15.00032. Cavada, Marianna, and Christopher DF Rogers. "Serious gaming as a means of facilitating truly smart cities: a narrative review. Smart Cities at Play: Technology and Emerging Forms of Playfulness , 2023, 89-104. Cavada, Marianna, Miles R. Tight, and Christopher DF Rogers. "A smart city case study of Singapore-Is Singapore truly smart?" In Smart city emergence , 295-314. Elsevier, 2019. https://www.sciencedirect.com/science/article/pii/B9780128161692000146. Chen, Huangxin, Yuping Yang, Mengting Yang, and Hao Huang. "The impact of environmental regulation on China's industrial green development and its heterogeneity". Frontiers in Ecology and Evolution 10 (2022) : 967550. Chen, Shiyong, and Nan Liu. "Research on Citizen Participation in Government Ecological Environment Governance Based on the Research Perspective of "Dual Carbon Target"." Journal of Environmental and Public Health 2022, n o 1 (2022): 5062620. https://doi.org/10.1155/2022/5062620. Cole, Matthew A. "Trade, the pollution haven hypothesis and the environmental Kuznets curve: examining the linkages". Ecological Economics 48, n o 1 (1 January 2004): 71-81. https://doi.org/10.1016/j.ecolecon.2003.09.007. Cole, Matthew A., Robert J.R. Elliott, and Liyun Zhang. "Foreign Direct Investment and the Environment." Annual Review of Environment and Resources 42, n o 1 (October 17, 2017): 465-87. https://doi.org/10.1146/annurev-environ-102016-060916. Cole, Matthew A., Anthony J. Rayner, and John M. Bates. "The environmental Kuznets curve: an empirical analysis. Environment and development economics 2, n o 4 (1997): 401-16. Culén, Alma. "The role of participatory transition design in mitigating the erosion of participatory democracy". IASDR Lecture Series , 9 October 2023. https://dl.designresearchsociety.org/iasdr/iasdr2023/fullpapers/156. Dietz, Thomas, Paul C. Stern, and Amy Dan. "How Deliberation Affects Stated Willingness to Pay for Mitigation of Carbon Dioxide Emissions: An Experiment". Land Economics 85, n o 2 (1 May 2009): 329-47. https://doi.org/10.3368/le.85.2.329. El-Shahawi, M. S., A. Hamza, A. S. Bashammakh, and W. T. Al-Saggaf. "An overview on the accumulation, distribution, transformations, toxicity and analytical methods for the monitoring of persistent organic pollutants". Talanta 80, n o 5 (15 March 2010): 1587-97. https://doi.org/10.1016/j.talanta.2009.09.055. Grossman, Gene M., and Alan B. Krueger. Krueger, "Economic Growth and the Environment". The Quarterly Journal of Economics 110, n o 2 (1995): 353-77. https://doi.org/10.2307/2118443. Hao, Diandian, Ziyi Yan, Yanan Wang, and Bowen Wang. "Effect of Village Informal Institutions and Cadre-Mass Relationship for Farmers' Participation in Rural Residential Environment Governance in China." International Journal of Environmental Research and Public Health 20, n o 1 (January 2023): 3. https://doi.org/10.3390/ijerph20010003. He, Qiang, Xin Deng, Chuan Li, Zhongcheng Yan, Fangxia Kong, and Yanbin Qi. "The Green Paradox Puzzle: Fiscal Decentralization, Environmental Regulation, and Agricultural Carbon Intensity in China". Environmental Science and Pollution Research 29, n o 51 (1 November 2022): 78009-28. https://doi.org/10.1007/s11356-022-21149-2. Hooghe, Liesbet, and Gary Marks. Multi-Level Governance and European Integration . Rowman & Littlefield, 2001. Jobert, Thomas, Fatih Karanfil, and Anna Tykhonenko. "Environmental Kuznets Curve for carbon dioxide emissions: lack of robustness to heterogeneity?", 2012. https://shs.hal.science/halshs-00721675/. Jolliffe, I. T. Principal Component Analysis . Springer Science & Business Media, 2002. Jordan, Andrew, Rüdiger K.W. Wurzel, and Anthony R. Zito. "Still the century of 'new' environmental policy instruments? Exploring patterns of innovation and continuity". Environmental Politics 22, n o 1 (1 February 2013): 155-73. https://doi.org/10.1080/09644016.2013.755839. Kumar, Sandeep, and Pranab K. Muhuri. "A Novel GDP Prediction Technique based on Transfer Learning using CO₂ Emission Dataset". Applied Energy 253 (November 2019): 113476. https://doi.org/10.1016/j.apenergy.2019.113476. Kaufmann, Daniel, Kraay, Aart, & Mastruzzi, Massimo. (2010). The Worldwide Governance Indicators: Methodology and Analytical Issues . Hague Journal on the Rule of Law , 3(2), 220-246. Larcher, Anthony, Pierre-Michel Bousquet, Driss Matrouf, and Jean-François Bonastre. "Principal Component Analysis for i-vector extraction in speaker verification. [In French] ". In Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 1: JEP , 297-304, 2012. https://aclanthology.org/F12-1038.pdf. Lee, Chien-Chiang, Ying Yuan, and Huwei Wen. "Can Digital Economy Alleviate CO₂ Emissions in the Transport Sector? Evidence from Provincial Panel Data in China." Natural Resources Forum 46, n o 3 (2022): 289-310. https://doi.org/10.1111/1477-8947.12258. Li, Kai, Dan J. Kim, Karl R. Lang, Robert J. Kauffman, and Maurizio Naldi. "How should we understand the digital economy in Asia? Critical assessment and research agenda. Electronic Commerce Research and Applications 44 (November 1, 2020): 101004. https://doi.org/10.1016/j.elerap.2020.101004. Li, Xingwei, Jiachi Dai, Xiaowen Zhu, Jinrong He, Jingru Li, Xiang Liu, Yicheng Huang, and Qiong Shen, "What is the mechanism of government green development behavior considering multi-agent interaction? A meta-analysis". International Journal of Environmental Research and Public Health 19, n o 14 (2022): 8263. Liu, Hui, Lili Wang, and Yang Shen: "Can Digital Technology Reduce Carbon Emissions? Evidence from Chinese Cities". Frontiers in Ecology and Evolution 11 (August 28, 2023). https://doi.org/10.3389/fevo.2023.1205634. Ma, Qiang, Muhammad Tariq, Haider Mahmood, and Zeeshan Khan. "The nexus between digital economy and carbon dioxide emissions in China: The moderating role of investments in research and development". Technology in Society 68 (1 February 2022): 101910. https://doi.org/10.1016/j.techsoc.2022.101910. MacKinnon, D. P., Warsi, G., & Dwyer, J. H. (1995). A simulation study of mediated effect measures. Multivariate Behavioral Research , 30(1), 41-62. https://doi.org/10.1207/s15327906mbr3001_3 Martens-Habbena, Willm, and Henrik Sass. "Sensitive Determination of Microbial Growth by Nucleic Acid Staining in Aqueous Suspension." Applied and Environmental Microbiology 72, n o 1 (January 2006): 87-95. https://doi.org/10.1128/AEM.72.1.87-95.2006. Moulaert, Frank, Flavia Martinelli, Erik Swyngedouw, and Sara González. "Towards Alternative Model(s) of Local Innovation." Urban Studies 42, n o 11 (October 2005): 1969-90. https://doi.org/10.1080/00420980500279893. Muhammad, Bashir. "Energy consumption, CO₂ emissions and economic growth in developed, emerging and Middle East and North Africa countries". Energy 179 (July 15, 2019): 232-45. https://doi.org/10.1016/j.energy.2019.03.126. Mulgan, Geoffrey. "Joined up government in the United Kingdom: Past, present and future. Canberra Bulletin of Public Administration , n o 105 (2003): 25-29. Nath, Pradosh K., and Bhagirath Behera. "A Critical Review of Impact of and Adaptation to Climate Change in Developed and Developing Economies." Environment, Development and Sustainability 13, n o 1 (1 February 2011): 141-62. https://doi.org/10.1007/s10668-010-9253-9. Nejat, Payam, Fatemeh Jomehzadeh, Mohammad Mahdi Taheri, Mohammad Gohari, and Muhd Zaimi Abd. Majid. "A global overview of energy consumption, CO₂ emissions and policies in the residential sector (with an overview of the top ten CO₂ emitting countries)." Renewable and Sustainable Energy Reviews 43 (1 March 2015): 843-62. https://doi.org/10.1016/j.rser.2014.11.066. Ostrom, Elinor. Governing the Commons: The Evolution of Institutions for Collective Action . Cambridge University Press, 1990. Pitkin, Hanna Fenichel. " 27. The Concept of Representation". In Democracy: A Reader , edited by Ricardo Blaug and John Schwarzmantel, 155-58. Columbia University Press, 2016. https://doi.org/10.7312/blau17412-034. Rousse, Olivier. "Environmental and economic benefits from citizen participation in CO₂ emissions trading: an effective alternative to voluntary CO₂ offsetting." Energy Policy 36, n o 1 (1 January 2008): 388-97. https://doi.org/10.1016/j.enpol.2007.09.019. Schmalensee, Richard, Thomas M. Stoker, and Ruth A. Judson. "World Carbon Dioxide Emissions: 1950-2050. The Review of Economics and Statistics 80, n o 1 (1 February 1998): 15-27. https://doi.org/10.1162/003465398557294. Seri, C., and A. de Juan Fernandez. "The relationship between economic growth and environment. Testing the EKC hypothesis for Latin American countries". arXiv, 18 May 2021. https://doi.org/10.48550/arXiv.2105.11405. Shi, Kaifang, Guifen Liu, Yuanzheng Cui, and Yizhen Wu. "What urban spatial structure is more conducive to reducing carbon emissions? A conditional effect of population size". Applied Geography 151 (2023): 102855. Sobel, Michael E. "Asymptotic Confidence Intervals for Indirect Effects in Structural Equation Models". Sociological Methodology 13 (1982): 290-312. https://doi.org/10.2307/270723. Stef, Nicolae, and Sami Ben Jabeur. "Elections and Environmental Quality. Environmental and Resource Economics 84, n o 2 (1 February 2023): 593-625. https://doi.org/10.1007/s10640-022-00739-1. Stern, David I. "The environmental Kuznets curve: a review". The economics of nature and the nature of economics , 2001, 193. The Rise and Fall of the Environmental Kuznets Curve. World Development 32, n o 8 (2004): 1419-39. Stern, David I., Michael S. Common, and Edward B. Barbier. "Economic growth and environmental degradation: the environmental Kuznets curve and sustainable development. World development 24, n o 7 (1996): 1151-60. Tomor, Zsuzsanna, Albert Meijer, Ank Michels, and Stan Geertman. "Smart Governance For Sustainable Cities: Findings from a Systematic Literature Review." Journal of Urban Technology 26, n o 4 (October 2, 2019): 3-27. https://doi.org/10.1080/10630732.2019.1651178. Walters, David. "Smart cities, smart places, smart democracy: Form-based codes, electronic governance and the role of place in making smart cities". Intelligent Buildings International 3, n o 3 (1 July 2011): 198-218. https://doi.org/10.1080/17508975.2011.586670. Wang, Qiang, Jiayi Sun, Ugur Korkut Pata, Rongrong Li, and Mustafa Tevfik Kartal. "Digital economy and carbon dioxide emissions: examining the role of threshold variables". Geoscience Frontiers 15, n o 3 (2024): 101644. Wang, Xiaowen, Shuting Wang, and Yunsheng Zhang. "The impact of environmental regulation and carbon emissions on green technology innovation from the perspective of spatial interaction: Empirical evidence from urban agglomeration in China." Sustainability 14, n o 9 (2022): 5381. Wang, Xinyi, Gaoyuan Wang, Tianyi Chen, Zhiwei Zeng, and Chye Kiang Heng. "Low-carbon city and its future research trends: A bibliometric analysis and systematic review". Sustainable Cities and Society 90 (1 March 2023): 104381. https://doi.org/10.1016/j.scs.2022.104381. Weikmans, Romain, and Edwin Zaccai, "Why help developing countries? Why help developing countries? Conceptions of justice in aid for adaptation to climate change". Développement durable et territoires. Economics, Geography, Politics, Law, Sociology , n o Vol. 8, No. 1 (April 30, 2017). https://doi.org/10.4000/developpementdurable.11668. Whitmarsh, Lorraine, Irene Lorenzoni, and Saffron O'Neill. Engaging the public with climate change . Taylor & Francis, 2012. https://api.taylorfrancis.com/content/books/mono/download?identifierName=doi&identifierValue=10.4324/9781849775243&type=googlepdf. Wu, Wenqi, Wenwen Wang, and Ming Zhang. "Does internet public participation slow down environmental pollution?" Environmental Science & Policy 137 (November 1, 2022): 22-31. https://doi.org/10.1016/j.envsci.2022.08.006. Yang, Yuanhua, Xi Yang, and Dengli Tang. "Environmental Regulations, Chinese-Style Fiscal Decentralization, and Carbon Emissions: From the Perspective of Moderating Effect." Stochastic Environmental Research and Risk Assessment 35, n o 10 (1 October 2021): 1985-98. https://doi.org/10.1007/s00477-021-01999-x. Zhang, Jinning, Yanwei Lyu, Yutao Li, and Yong Geng. "Digital economy: An innovation driving factor for low-carbon development. Environmental Impact Assessment Review 96 (1 September 2022): 106821. https://doi.org/10.1016/j.eiar.2022.106821. Zhang, Jun, and Luca Mora. "Nothing but symbolic: Chinese new authoritarianism, smart government, and the challenge of multi-level governance." Government Information Quarterly 40, n o 4 (1 October 2023): 101880. https://doi.org/10.1016/j.giq.2023.101880. Zhang, Lu, Qiaoyu Wang, and Ming Zhang. "Environmental regulation and CO₂ emissions: based on strategic interaction of environmental governance." Ecological Complexity 45 (1 January 2021): 100893. https://doi.org/10.1016/j.ecocom.2020.100893. Zhang, Xin, Yongliang Yang, and Yi Li. "Does public participation reduce regional carbon emission?" Atmosphere 14, n o 1 (2023): 165. Zhao, Li, Ling Zhang, Jianxin Sun, and Pengfei He. "Can public participation constraints promote green technological innovation of Chinese enterprises? The moderating role of government environmental regulatory enforcement". Technological Forecasting and Social Change 174 (1 January 2022): 121198. https://doi.org/10.1016/j.techfore.2021.121198. Footnotes United Nations Environment Programme There are now several models of governance, including liquid democracy, which is a voting system presented as a happy medium between representative democracy and direct democracy: decisions are taken by referendum, but voters can delegate their vote as they wish (Casella et al., 2022 ; Jaume, 2021). Multi-level governance examines how different levels of governance interact to influence environmental policies and citizens' actions. Multi-level governance is relevant here because it highlights the interactions between governments, citizens and other stakeholders in the management of environmental policies (Jordan et al., 2013 ). This theory analyses the collective management of natural resources, focusing on citizen participation and governance mechanisms that can prevent the over-exploitation of resources and emissions (Ostrom, 1990 ). Social innovation refers to new practices, ideas and organisations that respond to unmet social needs. In the context of intelligent and participatory governance, social innovation could play a key role in reducing emissions by introducing innovative and collaborative solutions, notably through technology and citizen engagement (Mulgan, 2003 ). This revised Kuznets curve recognises that economic growth alone does not guarantee a reduction in emissions. It is highly dependent on institutions, public policies, technological innovations and citizen participation. This framework is therefore particularly relevant for analyses integrating governance, new technologies and sustainable development (Grossman and Krueger, 1995). Li et al ( 2020 ) define the digital economy as "a wide range of economic activities that use digitised information and knowledge as key production factors, modern information networks as an important business space, and information and communication technologies to drive productivity growth". Information and Communication Technology Index calculated using the PCA (Principal Component Analysis) method to take account of Internet access, ICT goods and services. This hypothesis implies that legislators can be held responsible by voters for increased pollution and/or environmental disasters. The good governance indicators are classified according to 6 key concepts: Control of corruption, effectiveness of government, quality of regulation, rule of law, stability and absence of violence and voice and accountability. World Development Indicator An AR(p) (Order Autoregressive) model is a statistical model used to model a time series in terms of its own past values. It is particularly useful for capturing the temporal dynamics of series dependent on their own history (Han and Phillips, 2013). It makes it possible to capture the complex temporal dynamics of emissions while studying the delayed and persistent effects of governance policies in the fight against climate change (Balsalobre-Lorente et al ., 2021). Small T and large N: GMM methods (Arellano and Bond, 1991; Blundell and Bond, 1998 ). Large T and large N: GLS (Generalised Least Squares Estimator) or Bayesian methods. Fixed effects bias: LSDVC (Least Squares Dummy Variable Corrected), which corrects for OLS bias in the presence of fixed effects for dynamic or GMM panels. Graphs Graphs are available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files G1.png Graph 1: Breakdown of types of natural disaster in developing countries between 1970 and 2019 (%) Source: Author based on data from Statista 2025 G2.png Graph 2: Carbon dioxide emissions (in metric tonnes per capita) between 2000 and 2023 in developing countries Source : Author g3.png Graph 3: Governance index in developing countries from 2000 to 2023 (estimated score) Source : Author G4.png Graph 4: Correlation between participatory democracy and the digital economy and carbon dioxide emissions in developing countries between 2000 and 2023 Source : Author Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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IBOUTSI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBACNgkwBSJ5GBg+gETYidFyAKqFcQZIhJmQNRAtDGAtzDwgmpAWPunmY9If/lhE88/IPfjY5tc2eT5mBsYPH3PwOEzmWJrEwTaJ3Bk38pKNc/tuG7YxMzBLztyGzy85ZhIHGyRyG27kmEnn9txmBGphY+YlpOXAH4nc+TdyzH9b9ty2J1ILm0TuBqAtzAw/bicS1iJzLNniLNAvG8+8S5bsbbid3MbM2IzXL/Kzmw/eqPhTlzvveO7BDz/+3Lad39588MNHPFoQQCCBgYGxDcRibCBGPRDwHwASf4hUPApGwSgYBSMKAACzZ1DLFEcw5wAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0007-8920-442X","institution":"Omar Bongo University","correspondingAuthor":true,"prefix":"","firstName":"Lauriane","middleName":"Maéva","lastName":"IBOUTSI","suffix":""}],"badges":[],"createdAt":"2025-05-28 14:38:03","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6769124/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6769124/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83593794,"identity":"e5d6f990-c2f5-4908-9989-7d3bc7a9fd87","added_by":"auto","created_at":"2025-05-29 07:12:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":22174,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of mediation\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/u\u003e\u003cstrong\u003e: \u003c/strong\u003eModified by the author from MacKinnon et \u003cem\u003eal \u003c/em\u003e(1995)\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6769124/v1/9dda22da70bd51fcdf274b13.png"},{"id":83595453,"identity":"b52d431d-b41c-4dd1-91d0-2737b372b862","added_by":"auto","created_at":"2025-05-29 07:44:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1373477,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6769124/v1/951e274c-1428-4b3f-95ae-54f5785104af.pdf"},{"id":83593885,"identity":"2b8db1ed-4ac1-4eaa-a3fa-1a8d4bd646b9","added_by":"auto","created_at":"2025-05-29 07:20:07","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":145102,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraph 1: \u003c/strong\u003eBreakdown of types of natural disaster in developing countries between 1970 and 2019 (%)\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e\u003cstrong\u003eSource: \u003c/strong\u003e\u003c/u\u003eAuthor based on data from Statista 2025\u003c/p\u003e","description":"","filename":"G1.png","url":"https://assets-eu.researchsquare.com/files/rs-6769124/v1/eaaa023df996f385dbb2d89e.png"},{"id":83593799,"identity":"cc48d6ae-5cac-4cb7-bffd-605a61c614f3","added_by":"auto","created_at":"2025-05-29 07:12:07","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":89011,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraph 2: \u003c/strong\u003eCarbon dioxide emissions (in metric tonnes per capita) between 2000 and 2023 in developing countries\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e\u003cstrong\u003eSource : \u003c/strong\u003e\u003c/u\u003eAuthor\u003c/p\u003e","description":"","filename":"G2.png","url":"https://assets-eu.researchsquare.com/files/rs-6769124/v1/2dd12519df73384c6f7b3f00.png"},{"id":83594570,"identity":"68d7add0-1da2-4f84-a31b-6b504644ccdf","added_by":"auto","created_at":"2025-05-29 07:28:07","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":105747,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraph 3: \u003c/strong\u003eGovernance index in developing countries from 2000 to 2023 (estimated score)\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e\u003cstrong\u003eSource : \u003c/strong\u003e\u003c/u\u003eAuthor\u003c/p\u003e","description":"","filename":"g3.png","url":"https://assets-eu.researchsquare.com/files/rs-6769124/v1/75b5735ad516f237e7be0560.png"},{"id":83594572,"identity":"1e687d9c-5630-4cce-92c6-8c2925ec7436","added_by":"auto","created_at":"2025-05-29 07:28:07","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":124087,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraph 4: \u003c/strong\u003eCorrelation between participatory democracy and the digital economy and carbon dioxide emissions in developing countries between 2000 and 2023\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e\u003cstrong\u003eSource : \u003c/strong\u003e\u003c/u\u003eAuthor\u003c/p\u003e","description":"","filename":"G4.png","url":"https://assets-eu.researchsquare.com/files/rs-6769124/v1/0cf0962899e6d3c42295bae2.png"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eParticipatory and Intelligent Public Governance: What Impact on Carbon Dioxide Emissions in Developing Countries?\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe urgent need to address climate change issues (UNEP[1]\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e, 2023) and the desire to involve all stakeholders in the decision-making process to achieve sustainable development goals (Zhao et al., 2022 ; Wang et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) have put the central role of citizen participation in environmental protection back on the agenda. Climate change manifests itself mainly in developing countries in the form of drought, rising sea levels and rising temperatures, which are the consequences of the increase in carbon dioxide emissions in developed countries and which are having an impact on developing countries (Weikmans and Zaccai, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Most of the increase in carbon dioxide emissions is caused by human activity through fossil fuel combustion for energy production and human consumption (Schmalensee et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eParticipatory public governance means involving citizens (Bussu et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in the decision-making process to achieve sustainable development. In today's governance model, the government authority no longer has sole decision-making power, as in an authoritarian democracy[2]\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e or even delegated to a representative of the people (Casella et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Cul\u0026eacute;n, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). From now on, even new technologies will be useful for a better understanding and configuration of effective environmental policies (Pitkin, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eParticipatory and intelligent public governance that not only takes account of citizen action but also integrates new technologies into the environmental decision-making process is a key issue for today's governments.\u003c/p\u003e \u003cp\u003eIntelligent governance through smart cities contributes to reducing carbon dioxide emissions thanks to new technologies (Cavada et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Intelligent governance through smart cities contributes to reducing the level of carbon dioxide emissions thanks to new technologies (Cavada et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In addition to technology, smart governance also involves training citizens in new technologies so that they can be the main channels for transmitting environmental information on the ground (Capra, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tomor et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bull and Azennoud, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe digital economy can also help to reduce carbon dioxide emissions through the dissemination of information on the Internet and the digitisation of control areas for real-time monitoring of the damage caused by climate change (Cai et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It can also influence carbon dioxide emissions in the transport sector, speeding them up at the low urbanisation stage and reducing emissions at the high urbanisation stage (Li et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilarly, participatory governance by involving citizens in the decision-making process can reduce emissions (Rousse, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Authors emphasise the importance of citizen participation with strict, informal and corruption-free regulation as a tool for reducing emissions (Shi et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Martens-Habbena and Sass, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Zhang and Mora, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The participation of environmental non-governmental organisations is also essential for influencing environmental policies (Zhang and Mora, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, heavy environmental regulations and low public awareness of environmental issues can act as a brake on citizen action in favour of sustainable development (Whitmarsh et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is interesting to ask what effects participatory and intelligent public governance has on the level of carbon dioxide emissions.\u003c/p\u003e \u003cp\u003eTo analyse the effects of participatory and intelligent public governance on the level of carbon dioxide emissions, we draw on the theory of multi-level governance (Hooghe and Marks, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2001\u003c/span\u003e)[3]\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e, the theory of common goods and natural resources (Ostrom, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Agrawal and Gibson, 1999)[4]\u003ca class=\"FNLink\" href=\"#Fn4\" id=\"#FNLinkFn4\"\u003e\u003c/a\u003e, the theory of social innovation (Mulgan, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Moulaert et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2005\u003c/span\u003e)[5]\u003ca class=\"FNLink\" href=\"#Fn5\" id=\"#FNLinkFn5\"\u003e\u003c/a\u003e and the revisited Kuznets environmental curve (Grossman and Krueger, 1995; Stern et al., 1996; Jobert et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) .[6]\u003ca class=\"FNLink\" href=\"#Fn6\" id=\"#FNLinkFn6\"\u003e\u003c/a\u003e\u003c/p\u003e \u003cp\u003eSo we can put forward the following hypothesis: participative and intelligent public governance reduces the level of carbon dioxide emissions.\u003c/p\u003e \u003cp\u003eWe choose developing countries for the following reasons:\u003c/p\u003e \u003cp\u003eFirstly, participatory public governance involves all stakeholders in the decision-making process relating to environmental issues. Decentralisation of democracy (Bussu et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) is a necessary path to achieving carbon-neutral policies in developing countries. Secondly, the introduction of new technologies in smart cities (Cavada et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) for monitoring environmental trends is proving to be a necessity nowadays in developing countries to prevent natural disasters.\u003c/p\u003e \u003cp\u003eFurthermore, citizen participation in developing countries is not yet sufficiently present in the decision-making process on environmental issues. For example, the scores for political pluralism and participation in Afghanistan are very low, as are those in India (0/4 in Afghanistan compared with 3/4 in India, according to Our World in data in 2024);\u003c/p\u003e \u003cp\u003eFinally, the effects of global warming are already being felt in developing countries. Storms, floods and drought are among the main natural disasters reported in Africa between 1970 and 2019 (Statista, 2025).\u003c/p\u003e \u003cp\u003eOur study adds new lines of discussion to the existing literature on the effects of participatory and, above all, intelligent public governance in developing countries, particularly in solving environmental problems.\u003c/p\u003e \u003cp\u003eWe are particularly interested in the indirect effects of participatory and smart public governance on carbon dioxide emissions with the introduction of new technologies in developing countries. Contrary to the existing literature, we use the participatory democracy index associated with three governance measures (control of corruption, government efficiency and regulatory quality) and the digital economy to measure the impact of participatory and smart public governance on emissions. The structural equation mediation method, which has yet to be used to analyse this link, will enable us to identify the transmission channels through which participatory and intelligent governance indirectly affects carbon dioxide emissions.\u003c/p\u003e \u003cp\u003eIn this article, the second part presents empirical observations and the correlation between our variables of interest. The third part deals with the literature review on the effects of participatory and intelligent public governance on the level of carbon dioxide emissions. Then in the fourth part we present the transmission channels, namely: vote buying and participation, the direct popular vote index and the information and communication technology (ICT) index. Then, in the fifth part, we describe the empirical strategy. Finally, in the last two parts, we present the results, conclude and propose appropriate economic policies.\u003c/p\u003e"},{"header":"EMPIRICAL OBSERVATIONS","content":"\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows that floods dominate with 60% of occurrences, followed by storms (17%) and drought (16%). Forest fires and extreme temperatures each account for (2%), while landslides are at (3%). This graph shows that floods are the main challenge in terms of natural disasters over this period in Africa, probably due to the continent's climatic and geographical conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGraph 2\u003c/strong\u003e shows an overall upward trend. At the start of the period, in 2000, emissions were around 1.1 units, with a slight increase until 2004. From 2004 onwards, there was more marked growth, reaching around 1.6 units in 2010, probably reflecting rapid industrialisation and increased economic activity. After stabilising slightly between 2010 and 2014, emissions rose significantly, peaking at almost 2 units around 2018–2019, suggesting an acceleration in energy needs and increased dependence on fossil fuels. Since 2020, emissions have shown fluctuations, with a slight drop in 2021 followed by a rise in 2022–2023, which could be linked to economic disruptions or initial mitigation efforts, although the trend remains upwards over the whole period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGraph 3\u003c/strong\u003e traces the evolution of the governance index in developing countries from 2000 to 2023. Starting from a level of around 2 in 2000, this index begins a marked fall to around 0.5 around 2003, perhaps reflecting initial challenges such as political or economic instability. There followed a period of fluctuation between 2003 and 2015, with modest rises, including a peak around 2012, suggesting sporadic efforts to strengthen institutions. However, after 2015, a downward trend begins again, with the index gradually falling to a low near 0 in 2023. This continued decline could reflect persistent difficulties, such as social tensions or weakened governance in the face of global crises. All in all, this rollercoaster ride is a reminder that the quest for solid governance remains a long-term challenge for developing countries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGraph 4\u003c/strong\u003e shows the correlation between participatory democracy and the digital economy (DPEN, in red) with CO₂ emissions (in blue) in developing countries from 2000 to 2023. We can see that the two curves climb overall over this period: DPEN progresses in a fairly linear fashion, rising from around 14 to 15 units, while CO₂ emissions increase more irregularly, fluctuating between 14 and 20 units, with peaks around 2010 and 2018. It gives the impression that the more DPEN grows, the more CO₂ emissions tend to rise, perhaps because of the energy needed to support this digital growth. But the variations in CO₂ also show that other factors, such as environmental policies or economic crises, can influence emissions, making the relationship less direct than it might seem.\u003c/p\u003e\n\n"},{"header":"Literature review of the effects of participatory and intelligent public governance on carbon dioxide emissions in developing countries","content":"\u003cp\u003eThis review highlights the effects of smart cities and citizen participation in influencing the level of carbon dioxide emissions. For Tomor et al (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) smart governance, is defined as a technological collaboration between citizens and local governments to advance sustainable development.\u003c/p\u003e\n\u003ch3\u003e3 − 1 Smart cities and reducing carbon dioxide emissions\u003c/h3\u003e\n\u003cp\u003eRecent literature advocates the introduction of new technologies to achieve a reduction in carbon dioxide emissions. Cavada et al (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) argue for this in a theoretical study of environmental issues. The authors note that 'smart cities', which are cities where technology is used for systems optimisation and leadership to successfully tackle climate issues (Walters, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), adopt technology-based solutions to enable efficient urban living and sustainable development. Indeed, cities that have adopted smart roadmaps have integrated the reduction of carbon dioxide emissions into their environmental sustainability agenda.\u003c/p\u003e \u003cp\u003eIncorporating the participatory aspect of civil society, Bull and Azennoud (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) discuss the role of smart cities in reducing the level of carbon dioxide emissions. Based on a case study of citizen engagement around a waste-to-energy infrastructure development. The results show the essential role of citizen involvement in the sustainable development process.\u003c/p\u003e \u003cp\u003eSmart cities give citizens a greater opportunity to play a practical part in environmental decisions.\u003c/p\u003e\n\u003ch3\u003e3 − 2 Direct effects of citizen participation and the digital economy on carbon dioxide emissions\u003c/h3\u003e\n\u003cp\u003eRecently, an environmental Kuznets curve type relationship was discovered between citizen participation and carbon dioxide emissions in the work of Zhang and Mora (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The findings reveal that public participation significantly reduces regional carbon emissions and regional carbon intensity (Zhang et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; He et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral studies have been carried out in China, in particular to analyse the link between the digital economy and the level of carbon dioxide emissions. Wang et al (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) find a significant effect of the development of the digital economy on the reduction of carbon emission intensity.\u003c/p\u003e \u003cp\u003eAlso in China, Lee et al (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) analyse how the digital economy can reduce carbon dioxide emissions, particularly in the transport sector. The digital economy accelerates carbon emissions in the transport sector at the low urbanisation stage, while it reduces carbon emissions at the high urbanisation stage.\u003c/p\u003e\n\u003ch3\u003e3–3 Indirect effects of citizen participation and the digital economy on the level of carbon emissions\u003c/h3\u003e\n\u003cp\u003eCitizen participation plays a key role in the objective of reducing carbon dioxide emissions, even if a number of legal obstacles limit citizen action in the decision-making process on environmental issues.\u003c/p\u003e \u003cp\u003eChen et al (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) also note that informal regulations, driven by public environmental concerns, can encourage companies to adopt green technologies and meet their environmental, social and governance commitments. This is achieved by leveraging public opinion, raising environmental awareness and promoting sustainable consumption.\u003c/p\u003e \u003cp\u003eIn the same country, Wu et al (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) use data from network platforms (Sina Weibo and Baidu) from 2013 to 2018 to test whether internet public participation can help control environmental pollution emissions. The study found that public participation on the internet can significantly reduce industrial wastewater discharges. In addition, the government's mediating effect is significant on pollutant emissions.\u003c/p\u003e \u003cp\u003eAs a result, the informal environmental regulation represented by public participation is increasingly recognised by researchers around the world. Citizen-led mitigation and adaptation are key to advancing and accelerating climate policies, particularly in the context of urban development (Akerboom and Craig, 2022; Hao et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang and Mora, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, Zhang et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) use the generalised method of moments to analyse the direct effect and an intermediate effect model is then applied to explore the indirect transmission mechanisms of the digital economy on CO₂ emissions. The results show that environmental governance, technological innovation and industrial structure upgrading are the three main channels through which the digital economy influences low-carbon development.\u003c/p\u003e \u003cp\u003eThe work of Wang et al (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) differs from the existing literature by considering threshold variables such as natural resource rents and anti-corruption regulations to analyse the link between the digital economy[7]\u003ca class=\"FNLink\" href=\"#Fn7\" id=\"#FNLinkFn7\"\u003e\u003c/a\u003e and carbon dioxide emissions. The results show that the digital economy as a whole increases carbon emissions.\u003c/p\u003e \u003cp\u003eCurrent research does not combine participatory and intelligent public governance to assess their direct and indirect effects on carbon dioxide emissions. In addition, the majority of studies do not generally take into account other important variables for measuring participatory public governance. Existing studies are limited to using Kaufmann's indicators to measure governance overall. We aim to fill this gap in the literature by including other variables such as the participatory democracy index combined with three dimensions of governance and the digital economy to measure participatory and intelligent public governance.\u003c/p\u003e"},{"header":"Analysis of transmission channels","content":"\u003cp\u003eAnalysing the effects of participatory and intelligent public governance on carbon dioxide emissions leads us to take other concepts into account. Indeed, we consider that vote buying and participation, the direct popular vote index and the ICT index[8]\u003ca class=\"FNLink\" href=\"#Fn8\" id=\"#FNLinkFn8\"\u003e\u003c/a\u003e are equally important for understanding the level of carbon dioxide emissions in developing countries. Vote buying and turnout refers to the distribution of money or gifts to individuals in order to influence their decision to vote or not to vote (Pemstein \u003cem\u003eet al.\u003c/em\u003e, 2024; V-Dem, 2024). Direct popular vote refers to an institutionalised process whereby the citizens of a region or country express their choice or opinion on specific issues by means of a ballot paper (V-Dem, 2024). Finally, the ICT index (internet access, ICT goods, ICT services) calculated from principal component analysis shows the effect that information and communication technologies have in determining the level of CO₂ emissions.\u003c/p\u003e\n\u003ch3\u003e4 − 1 Vote buying channel and participation\u003c/h3\u003e\n\u003cp\u003eSeveral authors have shown the role of citizen participation in influencing carbon dioxide emissions (Rousse, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Shen et al., 2023). Consequently, individuals play a crucial role in the transition to a low-carbon society (Nerini et al., 2021). Allowing citizens to participate in environmental governance brings self-satisfaction with political rights, enabling them to exercise the rights and responsibilities conferred on them by the Constitution (Chen et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Buying votes could encourage citizens to participate or not in environmental projects.\u003c/p\u003e\n\u003ch3\u003e4 − 2 Direct popular vote channel\u003c/h3\u003e\n\u003cp\u003eVoting is used by citizens to express their views and appoint a representative to defend their interests, particularly in the environmental field. Some studies have shown that voters can influence incumbent politicians to adopt pro-environmental behaviour in the run-up to general elections, notably by putting forward the reward-punishment hypothesis[9]\u003ca class=\"FNLink\" href=\"#Fn9\" id=\"#FNLinkFn9\"\u003e\u003c/a\u003e (Stef et al., 2023; Dietz et al, 2009).\u003c/p\u003e\n\u003ch3\u003e4-3 ICT (Information and Communication Technologies) index channel\u003c/h3\u003e\n \u003cp\u003eThe ICT index is calculated using the principal component analysis method and is made up of Internet access, ICT goods and ICT services. In the context of the global development of the Internet and the widespread use of digital technologies, the digital economy is gradually becoming a key driver of low-carbon regional development (Zhang et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The effects of ICTs on the environment can differ from one country to another. Indeed, ICTs improve environmental sustainability in countries with high ICT quality while degrading the environment in countries with moderate and low ICT quality (Appiah-Otoo et al., 2023).\u003c/p\u003e"},{"header":"Empirical approach","content":"\u003cp\u003eOur study considers a set of 83 developing countries spread across the following regions: Sub-Saharan Africa, South Asia, East Asia, Latin America and the Caribbean, and the MENA region. The study period runs from 2000 to 2023 (24 years), with a total of 1992 observations. The choice of this period is conditioned by the availability of variables for certain countries. The choice of these countries is linked to the availability of data and the challenge of sustainable development and adapting environmental policies to respond effectively to the damage caused by the deterioration in the quality of the environment. As our database is not cylindrical, the absence of data for certain years led us to proceed with smoothing by moving average.\u003c/p\u003e\n\u003ch3\u003e5 − 1 Description of variables and presentation of the overall model\u003c/h3\u003e\n\u003cp\u003eCarbon dioxide (CO₂) represents the explained variable. Carbon dioxide emissions are generally used in work to demonstrate the impact of a variable on the environment (Zhang et al., 2023; Chen et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Reliable data on this indicator is available in the World Bank's World Development Indicators (WDI) for selected countries, and is presented as carbon dioxide emissions (in metric tonnes per capita) from fossil fuel combustion and cement manufacture.\u003c/p\u003e \u003cp\u003eThe concept of governance is usually measured by the good governance indicators[10]\u003ca class=\"FNLink\" href=\"#Fn10\" id=\"#FNLinkFn10\"\u003e\u003c/a\u003e defined by Kaufmann (2010). Contrary to the literature, we consider only three measures of good governance as variables of interest, namely: \u003cem\u003ethe control of corruption\u003c/em\u003e, which makes it possible to capture the opportunistic behaviour present among local elected representatives and citizens' representatives, the participation of social entities in environmental protection being closely linked to the characteristics of pollutants (Fu and Geng, 2019; Kaufmann, 2010; WDI[11]\u003ca class=\"FNLink\" href=\"#Fn11\" id=\"#FNLinkFn11\"\u003e\u003c/a\u003e, 2024); \u003cem\u003ethe quality of regulation\u003c/em\u003e, which reflects the ability of governments to implement sound and credible public policies (Halkos and Tzeremes, 2013; Kaufmann, 2010; WDI, 2024); \u003cem\u003egovernment effectiveness\u003c/em\u003e, which reflects perceptions of the quality of public services, the quality of the civil service and its degree of independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to these policies (Halkos and Tzeremes, 2013; Kaufmann, 2010; WDI, 2024). These measures will be grouped into a '\u003cem\u003egov\u003c/em\u003e' index, obtained using the Principal Component Analysis (PCA) method (Jolliffe, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Larcher et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo these we add \u003cem\u003ethe participatory democracy\u003c/em\u003e (PD) \u003cem\u003eindex\u003c/em\u003e, since citizen participation is mentioned by some authors as a way of reducing carbon dioxide emissions (Rousse, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., 2023), and \u003cem\u003ethe digital economy\u003c/em\u003e (DE) through Internet access data (as a percentage of the population) (Wang et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) taken from World Bank data for 2024. The digital economy is a determining variable of intelligent governance capable of impacting the level of carbon emissions. We consider the \u003cem\u003eDP*EN\u003c/em\u003e product, which measures \u003cem\u003ethe interaction\u003c/em\u003e between participative and intelligent public governance. It reflects several important aspects of the interactions between governance, technology and environmental impacts.\u003c/p\u003e \u003cp\u003eIn the control variables, we retain \u003cem\u003eGDP\u003c/em\u003e (Gross Domestic Product) \u003cem\u003egrowth\u003c/em\u003e, which is an important variable in our estimates since it is directly linked to carbon dioxide emissions, the data for which are taken from the United Nations (Kumar and Muhuri, 2020; Seri and de Juan Fernandez, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). And \u003cem\u003eGDP squared\u003c/em\u003e is added to the model to test the validity of the Kuznets curve hypothesis (Grossman and Krueger, 1991, 1995). Also, \u003cem\u003epopulation growth\u003c/em\u003e would be relevant in our study (WDI, 2024). Next, we have \u003cem\u003eenergy consumption\u003c/em\u003e, which allows us to explore whether and to what extent a government's policies or characteristics influence CO₂ emissions via their impact on energy consumption (WDI, 2024; Stern, 2007). Finally, \u003cem\u003eFDI\u003c/em\u003e (\u003cem\u003eForeign Direct Investment\u003c/em\u003e) which can be an important driver of energy consumption and CO₂ emissions (Behera et al., 2017; WDI, 2024).\u003c/p\u003e \u003cp\u003eOur estimation model AR (p)[12]\u003ca class=\"FNLink\" href=\"#Fn12\" id=\"#FNLinkFn12\"\u003e\u003c/a\u003e thus takes the following form:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{C}\\text{O}}_{2\\text{i}\\text{t}}\\)\u003c/span\u003e \u003c/span\u003e \u003csub\u003e=\u003c/sub\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varphi\\:}_{1}{CO}_{2it-1}+{\\varphi\\:}_{2}{CO}_{2it-2}+\\dots\\:\\)\u003c/span\u003e\u003c/span\u003e+\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varphi\\:}_{p}{CO}_{2it-p}+\\:{\\beta\\:}_{1}{\\text{l}\\text{n}\\text{I}\\text{D}\\text{E}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e+ \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{2}{\\text{P}\\text{O}\\text{P}}_{\\text{i}\\text{t}}+{\\beta\\:}_{3}ln{\\text{P}\\text{I}\\text{B}}_{\\text{i}\\text{t}}+{\\beta\\:}_{4}{PIB}_{it}^{\u0026sup2;}+{\\beta\\:}_{5}{\\text{ln}ConsoEner}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{6}{\\text{g}\\text{o}\\text{u}\\text{v}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{7}{\\text{D}\\text{P}\\text{*}\\text{E}\\text{N}}_{\\text{i}\\text{t}}+{\\epsilon\\:}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{C}\\text{O}}_{2\\text{i}\\text{t}-\\text{p}}\\)\u003c/span\u003e \u003c/span\u003eemissions of retarded carbon dioxide of order p ;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\varphi\\:}_{1}\\dots\\:\\:{\\varphi\\:}_{p}\\)\u003c/span\u003e \u003c/span\u003e the autoregressive coefficients ;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003elnIDE: direct investment abroad ;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePOP: Population growth ;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003elnGDP: economic growth ;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{lnPIB}^{2}\\:\\)\u003c/span\u003e \u003c/span\u003ecorresponds to economic growth squared (test of the Kuznets environmental curve hypothesis);\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003elnConsoEner: Energy consumption ;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDP*EN: reflecting the interaction between participatory democracy and the digital economy;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe \"\u003cem\u003egouv\u003c/em\u003e\" index contains the following variables:\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCC: control of corruption ;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eQR: quality of regulation and\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEG: government efficiency ;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e\u0026#120518; error term which captures unobserved factors ;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003ei\u003c/em\u003e: individual (country) ;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003et\u003c/em\u003e: year.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe lagged terms capture the inertia of carbon dioxide emissions, which is typical of environmental phenomena. The institutional variables (\u003cem\u003egov\u003c/em\u003e and DP*EN) allow us to analyse how participative and intelligent public governance influences carbon dioxide emissions.\u003c/p\u003e \u003cp\u003eIn the robustness analysis, we successively integrate control variables.\u003c/p\u003e\n\u003ch3\u003e5 − 2 Estimation method\u003c/h3\u003e\n\u003cp\u003eThe GMM (Generalized Method of Moments) estimator is the main method for estimating a dynamic AR(p) panel model. This method uses first differences to eliminate fixed effects and exploits the lagged values of the variables as instruments (Arellano and Bond, 1991). The GMM (generalized method of moments) system (Blundell and Bond, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1998\u003c/span\u003e)[13]\u003ca class=\"FNLink\" href=\"#Fn13\" id=\"#FNLinkFn13\"\u003e\u003c/a\u003e combines first difference and level equations to increase efficiency.\u003c/p\u003e \u003cp\u003e \u003cb\u003eA- Presentation and analysis of results\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn this section we analyse the basic results concerning the effect of participative and intelligent public governance on the level of carbon dioxide emissions. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the results of the descriptive statistics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics for variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.67116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.038804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.021731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.24536\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnIDE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.27109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2394629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.09924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.64599\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.858095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.056401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.218371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.992305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnpib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.24e-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8791697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.482416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.848423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnpib\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7725514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.068255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.79e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.113516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnConsoEner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.57567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5975282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.164314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.762581\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egouv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.76e-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.537818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.60637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.4176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDPEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.129038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.6892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e61.3467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eSource\u003c/b\u003e : Author\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e highlights the marked diversity of developing countries over the period 2000\u0026ndash;2023. CO₂ emissions are highly variable, reflecting major differences in levels of industrialisation and environmental policies between countries. Foreign direct investment (FDI) appears to be relatively stable between countries, while population growth (POP) remains highly contrasted, ranging from decline to rapid increase. Gross domestic product (lnGDP), centred for the analysis, reveals significant economic heterogeneity, and its square (lnGDP\u0026sup2;) suggests a possible non-linear relationship with emissions, in line with the environmental Kuznets hypothesis. Energy consumption (lnConsoEner) also varies, reflecting different stages of industrial development. The governance index (\u003cem\u003egouv\u003c/em\u003e) shows significant variations, highlighting persistent institutional challenges. Finally, the interaction between participatory democracy and the digital economy (\u003cem\u003eDPEN\u003c/em\u003e) reveals very uneven adoption of these modern levers of development. These disparities suggest the importance of a differentiated approach in analysing the effects between participatory and intelligent public governance and carbon dioxide emissions in developing countries.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation matrix\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO₂\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003elnIDE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePOP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003elnpib\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003elnpib\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003elnConsoEner\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003egouv\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDPEN\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnIDE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.3775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnpib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.2622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnpib\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.2337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnConsoEner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.5621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egouv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.3058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.1087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDPEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.4036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.5366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.4487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003eSource\u003c/b\u003e : Author\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe correlation matrix in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that there is no correlation between the variables. We can move on to estimating the GMM in the system in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimation of GMM in system\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVARIABLES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO₂\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard deviations\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL.CO₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.874***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.0242)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnIDE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.626*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.365)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.112*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.0573)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnpib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.321)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnpib\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.266)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnConsoEner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.717***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.266)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egouv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.0582)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDPEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.00669)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-9.746**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(4.206)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComments :\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of individuals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of instruments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR (1)\u003c/p\u003e \u003cp\u003eAR (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHansen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eSource : Author\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the GMM-system estimation applied to a sample of 83 developing countries highlights several determinants of CO₂ emissions. The highly significant and positive character of the lagged variable of emissions (L.CO₂: 0.874, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) confirms the structural persistence of pollution levels over time, as pointed out by El-Shahawi et al. (2010) and Arellano and Bover (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Foreign direct investment (lnIDE) shows a positive and significant effect at 10% (0.626), suggesting that FDI flows are still predominantly directed towards sectors that are not very environmentally friendly, reminiscent of the 'pollution haven' effect described by Cole et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The population variable (POP), also significant at 10% (0.112), supports the idea that population growth fuels environmental pressure, in line with the STIRPAT approach developed by Dietz and Rosa (1994). Energy consumption (lnConsoEner) has a strong and highly significant positive effect (0.717, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), reflecting the persistent dependence on fossil fuels in the developing countries in our study. Moreover, given that developing countries account for a large proportion of the world's population (with India the most populous country in the world in 2023 (INSEE, 2023)), the fact that energy consumption is significant is not surprising. Not least because of the growing demand for energy (lighting, cooking, etc.), rapid urbanisation, the construction of residential infrastructure and the increased exploitation of natural resources (forests, farmland, etc.) (Nejat et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Muhammad et al., 2019). As for the two GDPs, the \u003cem\u003egov\u003c/em\u003e index and the interaction between participatory democracy and the digital economy, they have no significant effect on carbon dioxide emissions. This confirms that there are no direct effects between participatory and intelligent public governance and carbon dioxide emissions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eB- Analysis of the mediation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo strengthen our results, we carry out additional tests. We will carry out a robustness analysis by successive integration of the control variables. Then a mediation analysis to determine the indirect effects.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness by successive integration of control variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eDependent variable : Carbon dioxide emissions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eEstimator (GMM in system)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVARIABLES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL.CO₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.945***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.924***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.923***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.911***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.919***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.874***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0127)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0205)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0126)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0242)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnIDE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.626*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.622)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.626)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.762)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.693)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.365)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.112*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0254)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0429)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0573)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnpib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.128*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0685)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.253)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.321)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnpib\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.235)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.266)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnConsoEner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.717***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.266)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egouv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0716***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0850*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0259)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0506)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0560)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0617)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0466)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0582)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDPEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.00224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.000634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.00143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.00222)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00363)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.00377)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.00373)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.00544)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.00669)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.110***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-10.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-11.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-9.746**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0233)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(7.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(7.060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(8.613)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(7.756)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(4.206)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of ID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of instruments\u003c/p\u003e \u003cp\u003eAR (1)\u003c/p\u003e \u003cp\u003eAR (2)\u003c/p\u003e \u003cp\u003eHansen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003cp\u003e0.336\u003c/p\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003cp\u003e0.335\u003c/p\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003cp\u003e0.337\u003c/p\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003cp\u003e0.335\u003c/p\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003cp\u003e0.334\u003c/p\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003cp\u003e0.318\u003c/p\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eStandard errors in parentheses\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eSource : Author\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows a series of robustness regressions estimated by the system GMM method, applied to a sample of 83 developing countries, in order to assess the impact of participatory and intelligent public governance on carbon dioxide emissions. The governance index (\u003cem\u003egov\u003c/em\u003e) is significant (in 1 and 5) but this result lacks robustness since it disappears as soon as the model is enriched, while DPEN has no direct effect on CO₂ emissions. These results suggest that, in the context of developing countries, the quality of governance, both participatory and intelligent, does not appear to be a direct lever for reducing polluting emissions. This conclusion is in line with the work of Brett (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and Auld et al, (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), who point out that citizen participation generally fails in contexts where local conditions make cooperative and collective action very difficult, and that policy innovations do not always have lasting consequences.\u003c/p\u003e \u003cp\u003e \u003cb\u003eB-1 Indirect effects of participatory and intelligent public governance on carbon dioxide emissions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe have selected three channels through which participatory and intelligent public governance affects carbon dioxide emissions. These are: vote buying and participation, the direct popular vote index and the information and communication technology (ICT) index. To achieve this, we use a structural equation mediation analysis inspired by MacKinnon et al, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1995\u003c/span\u003e, 2007 which consists of estimating two regression models as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below.\u003c/p\u003e\u003cp\u003eIn the first relationship (\u003cstrong\u003emodel 1\u003c/strong\u003e), we have the indirect effect of participatory and intelligent public governance on the level of carbon dioxide emissions through vote buying and participation, the direct popular vote index and the information and communication technology index. The second relationship (\u003cstrong\u003emodel 2\u003c/strong\u003e) shows us the direct effect between participatory and intelligent public governance and carbon dioxide emissions. The coefficient associated with participative and intelligent public governance shows the scale of the direct effect. The indirect effect derives from the difference between the total effect and the direct effect, showing the influence of participative and intelligent public governance through these mediators. Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows the results of the mediation analysis.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnalysis of the indirect effects of participatory and intelligent public governance on carbon dioxide emissions\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eBuying votes and participation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eThe voting index\u003c/p\u003e\n \u003cp\u003epopular direct\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eThe information and communication technologies index\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(A) Mediation test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAND\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT-stat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAND\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT-stat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAND\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT-stat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSobel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.417***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.800 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.566 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAroian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.413 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.766 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.532 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGoodman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.420 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.835 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.601***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(B) Composition of effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndirect effect (Sobel)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.417 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.800 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.566 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.706*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.145**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.317**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.889***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.533**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.889***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of total effect (\u003cstrong\u003egov\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e35.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e19.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSobel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.870 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-3.017***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.458**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArojan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.862 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-2.983***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.423**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGoodman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.877 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-3.052***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.495**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(B) Composition of effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndirect effect (Sobel)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.870 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-3.017***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.458 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.052 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.914***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.565\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of total effect (\u003cstrong\u003eDPEN\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e30%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003e29.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e56.6%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eNotes : *; **; *** significance at the 10%, 5% and 1% thresholds respectively\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthor\u003c/p\u003e\n\u003cp\u003eThe results in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e show us the indirect effects of participatory public governance (measured by the \u003cem\u003egouv\u003c/em\u003e governance index and the interaction between participatory democracy and the \u003cem\u003eDPEN\u003c/em\u003e digital economy) on carbon dioxide emissions. For our developing countries as a whole, vote buying and participation has an effect of 35.9% through the \u003cem\u003egov\u003c/em\u003e index and 30% through \u003cem\u003eDPEN\u003c/em\u003e; the direct popular vote index has an effect of 15.7% through \u003cem\u003egov\u003c/em\u003e and 29.9% \u003cem\u003ethrough DPEN\u003c/em\u003e; and finally the information and communication technologies index (measured through internet access, ICT goods and services) has the highest effect, at 19.7% \u003cem\u003ethrough gov\u003c/em\u003e and 56.6% \u003cem\u003ethrough DPEN.\u003c/em\u003e We can see from these results that participatory and intelligent public governance has an indirect effect on the level of carbon dioxide emissions in developing countries through vote buying and participation, the direct popular vote index and the information and communication technology index. The indirect effect through vote buying and participation is rather positive while the results are mixed for the direct popular vote index and the ICT technology index which act positively through \u003cem\u003egov\u003c/em\u003e and negatively \u003cem\u003ethrough DPEN\u003c/em\u003e. Overall, these results suggest that participatory and smart public governance acts positively on CO₂ emissions through the channel of vote buying and participation, and negatively through the channel of the direct popular vote index and the ICT technology index in developing countries.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe aim of this analysis was to show the effects of participatory and intelligent public governance on the level of carbon dioxide emissions in developing countries. Given the paucity of literature on the simultaneous analysis of the participatory aspect of governance and the integration of new technologies into inclusive decision-making processes. We thus mobilised a set of 83 developing countries over a period from 2000 to 2023. The GMM system analysis revealed no direct effect between participatory and intelligent public governance and carbon dioxide emissions. The direct effect results also show that Foreign Direct Investment (FDI), population and energy consumption have a positive effect on the level of CO₂ emissions in developing countries. The structural equation mediation analysis shows the indirect effect that participatory and smart public governance has on carbon dioxide emissions through vote buying and participation, the direct popular vote index and the information and communication technology index. The effect is positive through the channel of vote buying and participation, and negative through the channel of the direct popular vote index and the ICT technology index in developing countries. The results clearly underline that participatory public governance, intelligently articulated with digital technology, can play an important role in tackling carbon emissions in developing countries. We suggest that governments should encourage open, transparent and inclusive democratic processes, where every voice counts, particularly around climate issues. It would also be worth rethinking the frameworks for the use of digital technology in governance, to avoid technological tools becoming instruments of manipulation or disengagement. To achieve sustainable development objectives in developing countries, it is not enough to adopt climate policies: they need to be embedded in a participatory, transparent and intelligently digitised governance framework, while taking care to avoid the perverse effects associated with unregulated use of digital tools.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArellano, M., \u0026amp; Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. \u003cem\u003eJournal of Econometrics\u003c/em\u003e, 68(1), 29-51. https://doi.org/10.1016/0304-4076(94)01642-D\u003c/li\u003e\n\u003cli\u003eAgrawal, Arun, and Clark C Gibson. \u0026quot;Enchantment and Disenchantment: The Role of Community in Natural Resource Conservation. \u003cem\u003eWorld Development \u003c/em\u003e27, n\u003csup\u003eo\u003c/sup\u003e4 (April 1999): 629-49. https://doi.org/10.1016/S0305-750X(98)00161-2.\u003c/li\u003e\n\u003cli\u003eAkerboom, Sanne, and Robin Kundis Craig. \u0026quot;How Law Structures Public Participation in Environmental Decision Making: A Comparative Law Approach.\u0026quot; \u003cem\u003eEnvironmental Policy and Governance \u003c/em\u003e32, n\u003csup\u003eo\u003c/sup\u003e3 (2022): 232-46. https://doi.org/10.1002/eet.1986.\u003c/li\u003e\n\u003cli\u003eAppiah-Otoo, Isaac, Alex O. Acheampong, Na Song, and Xudong Chen. \u0026quot;The Impact of Information and Communication Technology (ICT) on Carbon Dioxide Emissions: Evidence from Heterogeneous ICT Countries.\u0026quot; \u003cem\u003eEnergy \u0026amp; Environment \u003c/em\u003e34, n\u003csup\u003eo\u003c/sup\u003e8 (1 December 2023): 3080-3102. https://doi.org/10.1177/0958305X221118877.\u003c/li\u003e\n\u003cli\u003eAuld, Graeme, Alexandra Mallett, Bozica Burlica, Francis Nolan-Poupart, and Robert Slater. \u0026quot;Evaluating the effects of policy innovations: Lessons from a systematic review of policies promoting low-carbon technology\u0026quot;. \u003cem\u003eGlobal Environmental Change \u003c/em\u003e29 (1 November 2014): 444-58. https://doi.org/10.1016/j.gloenvcha.2014.03.002.\u003c/li\u003e\n\u003cli\u003eBehera, Smruti Ranjan, and Devi Prasad Dash. \u0026quot;The effect of urbanization, energy consumption, and foreign direct investment on the carbon dioxide emission in the SSEA (South and Southeast Asian) region.\u0026quot; \u003cem\u003eRenewable and Sustainable Energy Reviews \u003c/em\u003e70 (April 1, 2017): 96-106. https://doi.org/10.1016/j.rser.2016.11.201.\u003c/li\u003e\n\u003cli\u003eBlundell, R., \u0026amp; Bond, S. (1998). Initial Conditions and Moment Restrictions in Dynamic Panel Data Models. \u003cem\u003eJournal of Econometrics\u003c/em\u003e, 87(1-2), 115-143. https://doi.org/10.1016/S0304-4076(98)00009-8\u003c/li\u003e\n\u003cli\u003eBrett, E.A. \u0026quot;Participation and accountability in development management\u0026quot;. \u003cem\u003eThe Journal of Development Studies \u003c/em\u003e40, n\u003csup\u003eo\u003c/sup\u003e2 (1 December 2003): 1-29. https://doi.org/10.1080/00220380412331293747.\u003c/li\u003e\n\u003cli\u003eBull, Richard, and Marouane Azennoud. \u0026quot;Smart Citizens for Smart Cities: Participating in the Future\u0026quot;. \u003cem\u003eProceedings of the Institution of Civil Engineers - Energy \u003c/em\u003e169, n\u003csup\u003eo\u003c/sup\u003e3 (August 2016): 93-101. https://doi.org/10.1680/jener.15.00030.\u003c/li\u003e\n\u003cli\u003eBussu, Sonia, Adrian Bua, Rikki Dean, and Graham Smith. \u0026quot;Introduction: Embedding participatory governance. \u003cem\u003eCritical Policy Studies \u003c/em\u003e16, n\u003csup\u003eo\u003c/sup\u003e2 (May 2, 2022): 133-45. https://doi.org/10.1080/19460171.2022.2053179.\u003c/li\u003e\n\u003cli\u003eCai, Wenjia, Chi Zhang, Hoi Ping Suen, Siqi Ai, Yuqi Bai, Junzhe Bao, Bin Chen, et al. \u0026quot;The 2020 China Report of the Lancet Countdown on Health and Climate Change\u0026quot;. \u003cem\u003eThe Lancet Public Health \u003c/em\u003e6, n\u003csup\u003eo\u003c/sup\u003e1 (1 January 2021): e64-81. https://doi.org/10.1016/S2468-2667(20)30256-5.\u003c/li\u003e\n\u003cli\u003eCapra, Carlo Francesco. \u0026quot;The Smart City and Its Citizens: Governance and Citizen Participation in Amsterdam Smart City.\u0026quot; \u003cem\u003eInternational Journal of E-Planning Research (IJEPR) \u003c/em\u003e5, n\u003csup\u003eo\u003c/sup\u003e1 (January 1, 2016): 20-38. https://doi.org/10.4018/IJEPR.2016010102.\u003c/li\u003e\n\u003cli\u003eCasella, Alessandra, Joseph Campbell, Lucas de Lara, Victoria Mooers, and Dilip Ravindran. \u0026quot;Liquid Democracy. Two Experiments on Delegation in Voting\u0026quot;. SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, December 19, 2022. https://doi.org/10.2139/ssrn.4307183.\u003c/li\u003e\n\u003cli\u003eCavada, M. \u0026quot;The Governance of Smart Cities\u0026quot;. In \u003cem\u003eThe Palgrave Encyclopedia of Urban and Regional Futures\u003c/em\u003e, edited by Robert C. Brears, 650-54. Cham: Springer International Publishing, 2022. https://doi.org/10.1007/978-3-030-87745-3_249.\u003c/li\u003e\n\u003cli\u003eCavada, Marianna, Dexter V.L. Hunt, and Chris D.F. Rogers. \u0026quot;Do smart cities realise their potential for lower carbon dioxide emissions?\u0026quot; \u003cem\u003eProceedings of the Institution of Civil Engineers - Engineering Sustainability \u003c/em\u003e169, n\u003csup\u003eo\u003c/sup\u003e6 (December 2016): 243-52. https://doi.org/10.1680/jensu.15.00032.\u003c/li\u003e\n\u003cli\u003eCavada, Marianna, and Christopher DF Rogers. \u0026quot;Serious gaming as a means of facilitating truly smart cities: a narrative review. \u003cem\u003eSmart Cities at Play: Technology and Emerging Forms of Playfulness\u003c/em\u003e, 2023, 89-104.\u003c/li\u003e\n\u003cli\u003eCavada, Marianna, Miles R. Tight, and Christopher DF Rogers. \u0026quot;A smart city case study of Singapore-Is Singapore truly smart?\u0026quot; In \u003cem\u003eSmart city emergence\u003c/em\u003e, 295-314. Elsevier, 2019. https://www.sciencedirect.com/science/article/pii/B9780128161692000146.\u003c/li\u003e\n\u003cli\u003eChen, Huangxin, Yuping Yang, Mengting Yang, and Hao Huang. \u0026quot;The impact of environmental regulation on China\u0026apos;s industrial green development and its heterogeneity\u0026quot;. \u003cem\u003eFrontiers in Ecology and Evolution \u003c/em\u003e10 (2022) : 967550.\u003c/li\u003e\n\u003cli\u003eChen, Shiyong, and Nan Liu. \u0026quot;Research on Citizen Participation in Government Ecological Environment Governance Based on the Research Perspective of \u0026quot;Dual Carbon Target\u0026quot;.\u0026quot; \u003cem\u003eJournal of Environmental and Public Health \u003c/em\u003e2022, n\u003csup\u003eo\u003c/sup\u003e1 (2022): 5062620. https://doi.org/10.1155/2022/5062620.\u003c/li\u003e\n\u003cli\u003eCole, Matthew A. \u0026quot;Trade, the pollution haven hypothesis and the environmental Kuznets curve: examining the linkages\u0026quot;. \u003cem\u003eEcological Economics \u003c/em\u003e48, n\u003csup\u003eo\u003c/sup\u003e1 (1 January 2004): 71-81. https://doi.org/10.1016/j.ecolecon.2003.09.007.\u003c/li\u003e\n\u003cli\u003eCole, Matthew A., Robert J.R. Elliott, and Liyun Zhang. \u0026quot;Foreign Direct Investment and the Environment.\u0026quot; \u003cem\u003eAnnual Review of Environment and Resources \u003c/em\u003e42, n\u003csup\u003eo\u003c/sup\u003e1 (October 17, 2017): 465-87. https://doi.org/10.1146/annurev-environ-102016-060916.\u003c/li\u003e\n\u003cli\u003eCole, Matthew A., Anthony J. Rayner, and John M. Bates. \u0026quot;The environmental Kuznets curve: an empirical analysis. \u003cem\u003eEnvironment and development economics \u003c/em\u003e2, n\u003csup\u003eo\u003c/sup\u003e4 (1997): 401-16.\u003c/li\u003e\n\u003cli\u003eCul\u0026eacute;n, Alma. \u0026quot;The role of participatory transition design in mitigating the erosion of participatory democracy\u0026quot;. \u003cem\u003eIASDR Lecture Series\u003c/em\u003e, 9 October 2023. https://dl.designresearchsociety.org/iasdr/iasdr2023/fullpapers/156.\u003c/li\u003e\n\u003cli\u003eDietz, Thomas, Paul C. Stern, and Amy Dan. \u0026quot;How Deliberation Affects Stated Willingness to Pay for Mitigation of Carbon Dioxide Emissions: An Experiment\u0026quot;. \u003cem\u003eLand Economics \u003c/em\u003e85, n\u003csup\u003eo\u003c/sup\u003e2 (1 May 2009): 329-47. https://doi.org/10.3368/le.85.2.329.\u003c/li\u003e\n\u003cli\u003eEl-Shahawi, M. S., A. Hamza, A. S. Bashammakh, and W. T. Al-Saggaf. \u0026quot;An overview on the accumulation, distribution, transformations, toxicity and analytical methods for the monitoring of persistent organic pollutants\u0026quot;. \u003cem\u003eTalanta \u003c/em\u003e80, n\u003csup\u003eo\u003c/sup\u003e5 (15 March 2010): 1587-97. https://doi.org/10.1016/j.talanta.2009.09.055.\u003c/li\u003e\n\u003cli\u003eGrossman, Gene M., and Alan B. Krueger. Krueger, \u0026quot;Economic Growth and the Environment\u0026quot;. \u003cem\u003eThe Quarterly Journal of Economics \u003c/em\u003e110, n\u003csup\u003eo\u003c/sup\u003e2 (1995): 353-77. https://doi.org/10.2307/2118443.\u003c/li\u003e\n\u003cli\u003eHao, Diandian, Ziyi Yan, Yanan Wang, and Bowen Wang. \u0026quot;Effect of Village Informal Institutions and Cadre-Mass Relationship for Farmers\u0026apos; Participation in Rural Residential Environment Governance in China.\u0026quot; \u003cem\u003eInternational Journal of Environmental Research and Public Health \u003c/em\u003e20, n\u003csup\u003eo\u003c/sup\u003e1 (January 2023): 3. https://doi.org/10.3390/ijerph20010003.\u003c/li\u003e\n\u003cli\u003eHe, Qiang, Xin Deng, Chuan Li, Zhongcheng Yan, Fangxia Kong, and Yanbin Qi. \u0026quot;The Green Paradox Puzzle: Fiscal Decentralization, Environmental Regulation, and Agricultural Carbon Intensity in China\u0026quot;. \u003cem\u003eEnvironmental Science and Pollution Research \u003c/em\u003e29, n\u003csup\u003eo\u003c/sup\u003e51 (1 November 2022): 78009-28. https://doi.org/10.1007/s11356-022-21149-2.\u003c/li\u003e\n\u003cli\u003eHooghe, Liesbet, and Gary Marks. \u003cem\u003eMulti-Level Governance and European Integration\u003c/em\u003e. Rowman \u0026amp; Littlefield, 2001.\u003c/li\u003e\n\u003cli\u003eJobert, Thomas, Fatih Karanfil, and Anna Tykhonenko. \u0026quot;Environmental Kuznets Curve for carbon dioxide emissions: lack of robustness to heterogeneity?\u0026quot;, 2012. https://shs.hal.science/halshs-00721675/.\u003c/li\u003e\n\u003cli\u003eJolliffe, I. T. \u003cem\u003ePrincipal Component Analysis\u003c/em\u003e. Springer Science \u0026amp; Business Media, 2002.\u003c/li\u003e\n\u003cli\u003eJordan, Andrew, R\u0026uuml;diger K.W. Wurzel, and Anthony R. Zito. \u0026quot;Still the century of \u0026apos;new\u0026apos; environmental policy instruments? Exploring patterns of innovation and continuity\u0026quot;. \u003cem\u003eEnvironmental Politics \u003c/em\u003e22, n\u003csup\u003eo\u003c/sup\u003e1 (1 February 2013): 155-73. https://doi.org/10.1080/09644016.2013.755839.\u003c/li\u003e\n\u003cli\u003eKumar, Sandeep, and Pranab K. Muhuri. \u0026quot;A Novel GDP Prediction Technique based on Transfer Learning using CO₂ Emission Dataset\u0026quot;. \u003cem\u003eApplied Energy \u003c/em\u003e253 (November 2019): 113476. https://doi.org/10.1016/j.apenergy.2019.113476.\u003c/li\u003e\n\u003cli\u003eKaufmann, Daniel, Kraay, Aart, \u0026amp; Mastruzzi, Massimo. (2010). \u003cem\u003eThe Worldwide Governance Indicators: Methodology and Analytical Issues\u003c/em\u003e. \u003cem\u003eHague Journal on the Rule of Law\u003c/em\u003e, 3(2), 220-246.\u003c/li\u003e\n\u003cli\u003eLarcher, Anthony, Pierre-Michel Bousquet, Driss Matrouf, and Jean-Fran\u0026ccedil;ois Bonastre. \u0026quot;Principal Component Analysis for i-vector extraction in speaker verification. [In French] \u0026quot;. In \u003cem\u003eProceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 1: JEP\u003c/em\u003e, 297-304, 2012. https://aclanthology.org/F12-1038.pdf.\u003c/li\u003e\n\u003cli\u003eLee, Chien-Chiang, Ying Yuan, and Huwei Wen. \u0026quot;Can Digital Economy Alleviate CO₂ Emissions in the Transport Sector? Evidence from Provincial Panel Data in China.\u0026quot; \u003cem\u003eNatural Resources Forum \u003c/em\u003e46, n\u003csup\u003eo\u003c/sup\u003e3 (2022): 289-310. https://doi.org/10.1111/1477-8947.12258.\u003c/li\u003e\n\u003cli\u003eLi, Kai, Dan J. Kim, Karl R. Lang, Robert J. Kauffman, and Maurizio Naldi. \u0026quot;How should we understand the digital economy in Asia? Critical assessment and research agenda. \u003cem\u003eElectronic Commerce Research and Applications \u003c/em\u003e44 (November 1, 2020): 101004. https://doi.org/10.1016/j.elerap.2020.101004.\u003c/li\u003e\n\u003cli\u003eLi, Xingwei, Jiachi Dai, Xiaowen Zhu, Jinrong He, Jingru Li, Xiang Liu, Yicheng Huang, and Qiong Shen, \u0026quot;What is the mechanism of government green development behavior considering multi-agent interaction? A meta-analysis\u0026quot;. \u003cem\u003eInternational Journal of Environmental Research and Public Health \u003c/em\u003e19, n\u003csup\u003eo\u003c/sup\u003e14 (2022): 8263.\u003c/li\u003e\n\u003cli\u003eLiu, Hui, Lili Wang, and Yang Shen: \u0026quot;Can Digital Technology Reduce Carbon Emissions? Evidence from Chinese Cities\u0026quot;. \u003cem\u003eFrontiers in Ecology and Evolution \u003c/em\u003e11 (August 28, 2023). https://doi.org/10.3389/fevo.2023.1205634.\u003c/li\u003e\n\u003cli\u003eMa, Qiang, Muhammad Tariq, Haider Mahmood, and Zeeshan Khan. \u0026quot;The nexus between digital economy and carbon dioxide emissions in China: The moderating role of investments in research and development\u0026quot;. \u003cem\u003eTechnology in Society \u003c/em\u003e68 (1 February 2022): 101910. https://doi.org/10.1016/j.techsoc.2022.101910.\u003c/li\u003e\n\u003cli\u003eMacKinnon, D. P., Warsi, G., \u0026amp; Dwyer, J. H. (1995). A simulation study of mediated effect measures. \u003cem\u003eMultivariate Behavioral Research\u003c/em\u003e, 30(1), 41-62. https://doi.org/10.1207/s15327906mbr3001_3\u003c/li\u003e\n\u003cli\u003eMartens-Habbena, Willm, and Henrik Sass. \u0026quot;Sensitive Determination of Microbial Growth by Nucleic Acid Staining in Aqueous Suspension.\u0026quot; \u003cem\u003eApplied and Environmental Microbiology \u003c/em\u003e72, n\u003csup\u003eo\u003c/sup\u003e1 (January 2006): 87-95. https://doi.org/10.1128/AEM.72.1.87-95.2006.\u003c/li\u003e\n\u003cli\u003eMoulaert, Frank, Flavia Martinelli, Erik Swyngedouw, and Sara Gonz\u0026aacute;lez. \u0026quot;Towards Alternative Model(s) of Local Innovation.\u0026quot; \u003cem\u003eUrban Studies \u003c/em\u003e42, n\u003csup\u003eo\u003c/sup\u003e11 (October 2005): 1969-90. https://doi.org/10.1080/00420980500279893. \u003c/li\u003e\n\u003cli\u003eMuhammad, Bashir. \u0026quot;Energy consumption, CO₂ emissions and economic growth in developed, emerging and Middle East and North Africa countries\u0026quot;. \u003cem\u003eEnergy \u003c/em\u003e179 (July 15, 2019): 232-45. https://doi.org/10.1016/j.energy.2019.03.126.\u003c/li\u003e\n\u003cli\u003eMulgan, Geoffrey. \u0026quot;Joined up government in the United Kingdom: Past, present and future. \u003cem\u003eCanberra Bulletin of Public Administration\u003c/em\u003e, n\u003csup\u003eo\u003c/sup\u003e105 (2003): 25-29.\u003c/li\u003e\n\u003cli\u003eNath, Pradosh K., and Bhagirath Behera. \u0026quot;A Critical Review of Impact of and Adaptation to Climate Change in Developed and Developing Economies.\u0026quot; \u003cem\u003eEnvironment, Development and Sustainability \u003c/em\u003e13, n\u003csup\u003eo\u003c/sup\u003e1 (1 February 2011): 141-62. https://doi.org/10.1007/s10668-010-9253-9.\u003c/li\u003e\n\u003cli\u003eNejat, Payam, Fatemeh Jomehzadeh, Mohammad Mahdi Taheri, Mohammad Gohari, and Muhd Zaimi Abd. Majid. \u0026quot;A global overview of energy consumption, CO₂ emissions and policies in the residential sector (with an overview of the top ten CO₂ emitting countries).\u0026quot; \u003cem\u003eRenewable and Sustainable Energy Reviews \u003c/em\u003e43 (1 March 2015): 843-62. https://doi.org/10.1016/j.rser.2014.11.066.\u003c/li\u003e\n\u003cli\u003eOstrom, Elinor. \u003cem\u003eGoverning the Commons: The Evolution of Institutions for Collective Action\u003c/em\u003e. Cambridge University Press, 1990.\u003c/li\u003e\n\u003cli\u003ePitkin, Hanna Fenichel. \u0026quot; 27. The Concept of Representation\u0026quot;. In \u003cem\u003eDemocracy: A Reader\u003c/em\u003e, edited by Ricardo Blaug and John Schwarzmantel, 155-58. Columbia University Press, 2016. https://doi.org/10.7312/blau17412-034.\u003c/li\u003e\n\u003cli\u003eRousse, Olivier. \u0026quot;Environmental and economic benefits from citizen participation in CO₂ emissions trading: an effective alternative to voluntary CO₂ offsetting.\u0026quot; \u003cem\u003eEnergy Policy\u003c/em\u003e 36, n\u003csup\u003eo\u003c/sup\u003e1 (1 January 2008): 388-97. https://doi.org/10.1016/j.enpol.2007.09.019.\u003c/li\u003e\n\u003cli\u003eSchmalensee, Richard, Thomas M. Stoker, and Ruth A. Judson. \u0026quot;World Carbon Dioxide Emissions: 1950-2050. \u003cem\u003eThe Review of Economics and Statistics \u003c/em\u003e80, n\u003csup\u003eo\u003c/sup\u003e1 (1 February 1998): 15-27. https://doi.org/10.1162/003465398557294.\u003c/li\u003e\n\u003cli\u003eSeri, C., and A. de Juan Fernandez. \u0026quot;The relationship between economic growth and environment. Testing the EKC hypothesis for Latin American countries\u0026quot;. arXiv, 18 May 2021. https://doi.org/10.48550/arXiv.2105.11405.\u003c/li\u003e\n\u003cli\u003eShi, Kaifang, Guifen Liu, Yuanzheng Cui, and Yizhen Wu. \u0026quot;What urban spatial structure is more conducive to reducing carbon emissions? A conditional effect of population size\u0026quot;. \u003cem\u003eApplied Geography \u003c/em\u003e151 (2023): 102855.\u003c/li\u003e\n\u003cli\u003eSobel, Michael E. \u0026quot;Asymptotic Confidence Intervals for Indirect Effects in Structural Equation Models\u0026quot;. \u003cem\u003eSociological Methodology \u003c/em\u003e13 (1982): 290-312. https://doi.org/10.2307/270723.\u003c/li\u003e\n\u003cli\u003eStef, Nicolae, and Sami Ben Jabeur. \u0026quot;Elections and Environmental Quality. \u003cem\u003eEnvironmental and Resource Economics \u003c/em\u003e84, n\u003csup\u003eo\u003c/sup\u003e2 (1 February 2023): 593-625. https://doi.org/10.1007/s10640-022-00739-1.\u003c/li\u003e\n\u003cli\u003eStern, David I. \u0026quot;The environmental Kuznets curve: a review\u0026quot;. \u003cem\u003eThe economics of nature and the nature of economics\u003c/em\u003e, 2001, 193. The Rise and Fall of the Environmental Kuznets Curve. \u003cem\u003eWorld Development \u003c/em\u003e32, n\u003csup\u003eo\u003c/sup\u003e8 (2004): 1419-39.\u003c/li\u003e\n\u003cli\u003eStern, David I., Michael S. Common, and Edward B. Barbier. \u0026quot;Economic growth and environmental degradation: the environmental Kuznets curve and sustainable development. \u003cem\u003eWorld development \u003c/em\u003e24, n\u003csup\u003eo\u003c/sup\u003e7 (1996): 1151-60.\u003c/li\u003e\n\u003cli\u003eTomor, Zsuzsanna, Albert Meijer, Ank Michels, and Stan Geertman. \u0026quot;Smart Governance For Sustainable Cities: Findings from a Systematic Literature Review.\u0026quot; \u003cem\u003eJournal of Urban Technology \u003c/em\u003e26, n\u003csup\u003eo\u003c/sup\u003e4 (October 2, 2019): 3-27. https://doi.org/10.1080/10630732.2019.1651178.\u003c/li\u003e\n\u003cli\u003eWalters, David. \u0026quot;Smart cities, smart places, smart democracy: Form-based codes, electronic governance and the role of place in making smart cities\u0026quot;. \u003cem\u003eIntelligent Buildings International \u003c/em\u003e3, n\u003csup\u003eo\u003c/sup\u003e3 (1 July 2011): 198-218. https://doi.org/10.1080/17508975.2011.586670.\u003c/li\u003e\n\u003cli\u003eWang, Qiang, Jiayi Sun, Ugur Korkut Pata, Rongrong Li, and Mustafa Tevfik Kartal. \u0026quot;Digital economy and carbon dioxide emissions: examining the role of threshold variables\u0026quot;. \u003cem\u003eGeoscience Frontiers \u003c/em\u003e15, n\u003csup\u003eo\u003c/sup\u003e3 (2024): 101644.\u003c/li\u003e\n\u003cli\u003eWang, Xiaowen, Shuting Wang, and Yunsheng Zhang. \u0026quot;The impact of environmental regulation and carbon emissions on green technology innovation from the perspective of spatial interaction: Empirical evidence from urban agglomeration in China.\u0026quot; \u003cem\u003eSustainability \u003c/em\u003e14, n\u003csup\u003eo\u003c/sup\u003e9 (2022): 5381.\u003c/li\u003e\n\u003cli\u003eWang, Xinyi, Gaoyuan Wang, Tianyi Chen, Zhiwei Zeng, and Chye Kiang Heng. \u0026quot;Low-carbon city and its future research trends: A bibliometric analysis and systematic review\u0026quot;. \u003cem\u003eSustainable Cities and Society \u003c/em\u003e90 (1 March 2023): 104381. https://doi.org/10.1016/j.scs.2022.104381.\u003c/li\u003e\n\u003cli\u003eWeikmans, Romain, and Edwin Zaccai, \u0026quot;Why help developing countries? Why help developing countries? Conceptions of justice in aid for adaptation to climate change\u0026quot;. \u003cem\u003eD\u0026eacute;veloppement durable et territoires. Economics, Geography, Politics, Law, Sociology\u003c/em\u003e, n\u003csup\u003eo\u003c/sup\u003eVol. 8, No. 1 (April 30, 2017). https://doi.org/10.4000/developpementdurable.11668.\u003c/li\u003e\n\u003cli\u003eWhitmarsh, Lorraine, Irene Lorenzoni, and Saffron O\u0026apos;Neill. \u003cem\u003eEngaging the public with climate change\u003c/em\u003e. Taylor \u0026amp; Francis, 2012. https://api.taylorfrancis.com/content/books/mono/download?identifierName=doi\u0026amp;identifierValue=10.4324/9781849775243\u0026amp;type=googlepdf.\u003c/li\u003e\n\u003cli\u003eWu, Wenqi, Wenwen Wang, and Ming Zhang. \u0026quot;Does internet public participation slow down environmental pollution?\u0026quot; \u003cem\u003eEnvironmental Science \u0026amp; Policy \u003c/em\u003e137 (November 1, 2022): 22-31. https://doi.org/10.1016/j.envsci.2022.08.006.\u003c/li\u003e\n\u003cli\u003eYang, Yuanhua, Xi Yang, and Dengli Tang. \u0026quot;Environmental Regulations, Chinese-Style Fiscal Decentralization, and Carbon Emissions: From the Perspective of Moderating Effect.\u0026quot; \u003cem\u003eStochastic Environmental Research and Risk Assessment \u003c/em\u003e35, n\u003csup\u003eo\u003c/sup\u003e10 (1 October 2021): 1985-98. https://doi.org/10.1007/s00477-021-01999-x.\u003c/li\u003e\n\u003cli\u003eZhang, Jinning, Yanwei Lyu, Yutao Li, and Yong Geng. \u0026quot;Digital economy: An innovation driving factor for low-carbon development. \u003cem\u003eEnvironmental Impact Assessment Review \u003c/em\u003e96 (1 September 2022): 106821. https://doi.org/10.1016/j.eiar.2022.106821.\u003c/li\u003e\n\u003cli\u003eZhang, Jun, and Luca Mora. \u0026quot;Nothing but symbolic: Chinese new authoritarianism, smart government, and the challenge of multi-level governance.\u0026quot; \u003cem\u003eGovernment Information Quarterly \u003c/em\u003e40, n\u003csup\u003eo\u003c/sup\u003e4 (1 October 2023): 101880. https://doi.org/10.1016/j.giq.2023.101880.\u003c/li\u003e\n\u003cli\u003eZhang, Lu, Qiaoyu Wang, and Ming Zhang. \u0026quot;Environmental regulation and CO₂ emissions: based on strategic interaction of environmental governance.\u0026quot; \u003cem\u003eEcological Complexity \u003c/em\u003e45 (1 January 2021): 100893. https://doi.org/10.1016/j.ecocom.2020.100893.\u003c/li\u003e\n\u003cli\u003eZhang, Xin, Yongliang Yang, and Yi Li. \u0026quot;Does public participation reduce regional carbon emission?\u0026quot; \u003cem\u003eAtmosphere \u003c/em\u003e14, n\u003csup\u003eo\u003c/sup\u003e1 (2023): 165.\u003c/li\u003e\n\u003cli\u003eZhao, Li, Ling Zhang, Jianxin Sun, and Pengfei He. \u0026quot;Can public participation constraints promote green technological innovation of Chinese enterprises? The moderating role of government environmental regulatory enforcement\u0026quot;. \u003cem\u003eTechnological Forecasting and Social Change \u003c/em\u003e174 (1 January 2022): 121198. https://doi.org/10.1016/j.techfore.2021.121198.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e United Nations Environment Programme\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e There are now several models of governance, including liquid democracy, which is a voting system presented as a happy medium between representative democracy and direct democracy: decisions are taken by referendum, but voters can delegate their vote as they wish (Casella et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jaume, 2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Multi-level governance examines how different levels of governance interact to influence environmental policies and citizens' actions. Multi-level governance is relevant here because it highlights the interactions between governments, citizens and other stakeholders in the management of environmental policies (Jordan et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e This theory analyses the collective management of natural resources, focusing on citizen participation and governance mechanisms that can prevent the over-exploitation of resources and emissions (Ostrom, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1990\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Social innovation refers to new practices, ideas and organisations that respond to unmet social needs. In the context of intelligent and participatory governance, social innovation could play a key role in reducing emissions by introducing innovative and collaborative solutions, notably through technology and citizen engagement (Mulgan, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e This revised Kuznets curve recognises that economic growth alone does not guarantee a reduction in emissions. It is highly dependent on institutions, public policies, technological innovations and citizen participation. This framework is therefore particularly relevant for analyses integrating governance, new technologies and sustainable development (Grossman and Krueger, 1995).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Li et al (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) define the digital economy as \"a wide range of economic activities that use digitised information and knowledge as key production factors, modern information networks as an important business space, and information and communication technologies to drive productivity growth\".\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Information and Communication Technology Index calculated using the PCA (Principal Component Analysis) method to take account of Internet access, ICT goods and services.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e This hypothesis implies that legislators can be held responsible by voters for increased pollution and/or environmental disasters.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The good governance indicators are classified according to 6 key concepts: Control of corruption, effectiveness of government, quality of regulation, rule of law, stability and absence of violence and voice and accountability.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e World Development Indicator\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e An AR(p) (Order Autoregressive) model is a statistical model used to model a time series in terms of its own past values. It is particularly useful for capturing the temporal dynamics of series dependent on their own history (Han and Phillips, 2013). It makes it possible to capture the complex temporal dynamics of emissions while studying the delayed and persistent effects of governance policies in the fight against climate change (Balsalobre-Lorente et \u003cem\u003eal\u003c/em\u003e., 2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Small T and large N: GMM methods (Arellano and Bond, 1991; Blundell and Bond, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Large T and large N: GLS (Generalised Least Squares Estimator) or Bayesian methods. Fixed effects bias: LSDVC (Least Squares Dummy Variable Corrected), which corrects for OLS bias in the presence of fixed effects for dynamic or GMM panels.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Graphs","content":"\u003cp\u003eGraphs are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Omar Bongo University","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":"Participatory governance, intelligent governance, carbon dioxide emissions, mediation, developing countries","lastPublishedDoi":"10.21203/rs.3.rs-6769124/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6769124/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOne of the objectives of governments today is to develop while limiting their impact on the environment. The involvement of citizens and the introduction of new technologies are all levers that can be used to help reduce carbon dioxide emissions. The aim of this study is to analyse the simultaneous effects of participatory and intelligent public governance on the level of carbon emissions in developing countries. We use a system GMM to determine the direct effects, then a structural equation mediation to show the indirect effects. We consider a sample of 83 developing countries from 2000 to 2023. The results of the system GMM revealed no direct effect between participatory and intelligent public governance and the level of carbon dioxide emissions. However, Foreign Direct Investment (FDI), population growth and energy consumption have a positive effect on the level of CO₂ emissions in developing countries. The mediation analysis shows the indirect effect that participatory and smart public governance has on carbon dioxide emissions through the vote buying and participation channel, the direct popular vote index and the information and communication technology index. We suggest that governments should encourage open democratic processes and rethink frameworks for the use of digital in governance, to prevent technological tools from becoming instruments of manipulation or disengagement.\u003c/p\u003e","manuscriptTitle":"Participatory and Intelligent Public Governance: What Impact on Carbon Dioxide Emissions in Developing Countries?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-29 07:12:02","doi":"10.21203/rs.3.rs-6769124/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":"681b01b9-5629-4357-9a69-94d1e67999a9","owner":[],"postedDate":"May 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49210480,"name":"Environmental Economics"},{"id":49210481,"name":"Other Public Policy"}],"tags":[],"updatedAt":"2025-05-29T07:12:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-29 07:12:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6769124","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6769124","identity":"rs-6769124","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}