
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12643-6
10.1016/j.heliyon.2024.e36612
e36612
Research Article
Harnessing the power of AI and IoT for real-time CO2 emission monitoring
Fan Kaizhe 3121009463@mail2.gdut.edu.cn
a1
Li Quanjun 3122008887@mail2.gdut.edu.cn
a1
Le Zhen 3121009470@mail2.gdut.edu.cn
a
Li Qian 3222003726@wyu.edu.cn
b
Li Jianfeng li.jianfeng@gdut.edu.cn
a⁎
yan Ming mingyan@southeast.edu.cn
c
a School of Advanced Manufacturing, Guangdong University of Technology, Jieyang, 522000, PR China
b School of Electronics and Information Engineering, Wuyi University, Jiangmen, 510006, PR China
c Southeast University China, PR China
⁎ Corresponding author. li.jianfeng@gdut.edu.cn
1 Kaizhe Fan and Quanjun Li have contributed equally to this study and have shared co-frst authorship.

22 8 2024
15 9 2024
22 8 2024
10 17 e3661228 2 2024
13 8 2024
19 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Global CO2 emissions have been an essential topic of the environmental discussion. Still, empirical data is needed to support arguments that high-quality government actions could reduce these emissions. By analyzing data from 137 nations from 2000 to 2020, we offer strong evidence that state policies focused on promoting healthy ecosystems, sustainable economic growth, and transcendent legislative changes are capable of decreasing CO2 emissions. Based on our findings, there are essentially three critical institutional factors that need to be improved for environmental policies to be efficient: the concept of law, which protects citizens' intellectual property rights; citizens' speech, which allows them to participate in elections and represent themselves freely, and the management of corruption. Policies aimed at promoting economic growth, lowering oil and gas use, enhancing the usage of green energy by the public and private sectors, and enhancing such institutional factors are all necessary components of a climate-friendly financial strategy.

Keywords

CO2 emission
Economic growth
Environmental management
AI models
==== Body
pmc1 Introduction

Plants and animals everywhere are in danger from the effects of climate change. Concerns about the availability of food, water shortages, environmental damage, more frequent catastrophic weather events, the introduction of new chronic illnesses, pressure on health care systems, socioeconomic issues, joblessness, and travel are just a few ways in which climate change poses a risk to our way of life [1]. According to Ref. [2], the World Health Organization (WHO) has identified climate change as a risk to worldwide health in the 21st century. Climate change (CC) describes shifts in Earth's weather patterns on scales ranging from the micro to the macro. From the pre-industrial era, which began around 1850, until the present, the term “climate change” has been most frequently employed to indicate the type of climate change that is culturally caused [3]. According to Ref. [4], the primary cause is the combustion of oil and gass and the clearing of forests, both of which have led to a relatively rapid increase in the level of greenhouse gases in the air around the Earth. Global energy sector transformation scenarios are the focus of climate change efforts to mitigate and reduce greenhouse gas emissions. The rise in carbon dioxide and greenhouse gas emissions is due to several different social and economic causes [5]. Reducing emissions demands a complex approach that includes identifying the causes of the rise in emissions on a personal, societal, and national scale, as well as precise modeling and prediction of the levels of greenhouse gases. For a better understanding of the emission issue and how to reduce it, it is necessary to use data science and analytical methodologies [6]. Innovations in AI and ML have recently aided in modeling complex, shifting, and factor- and time-dependent environmental conditions [7]. Machine learning (ML) techniques have been used in many fields for prediction and modeling purposes. Several recent applications of machine learning have included COVID-19 scenario analysis, mental health or depression identification, short-term temperature prediction [8], and occupancy detection. A few basic prediction models utilizing data accessible to governments and policymakers have been developed for CC and CO2 forecast studies [9]. Consider the vast majority of studies that attempt to forecast atmospheric CO2 levels using mobility statistics, vehicle mobility statistics, etc.

Fig. 1 depicts the changes in the comprehensive AI index in China between 2006 and 2019. The AI index is derived from a combination of measures that assess many facets of artificial intelligence progress, such as research productivity, technological innovations, and adoption across diverse industries. The indicators are used to form a unified composite index that represents the overall advancement in AI.Fig. 1 Shows the usual comprehensive AI index in China from 2006 to 2019.

Fig. 1

The data for this index is obtained from national databases, academic institutions, and industry publications that monitor AI-related activities and progress. More precisely, it encompasses quantifiable measures such as the quantity of AI research papers, the number of patents filed, the level of investments in AI-related ventures, and the rate at which AI technologies are being used across various industries. The separate parameters are standardized and then merged using a weighted average method to get the holistic AI index.

The complete AI index offers valuable insights into the expansion and progression of AI in China within the selected timeframe, emphasizing notable stages of advancement. The period from 2006 to 2010 witnessed a steady rise in AI activity, which was then followed by a time of rapid growth from 2011 to 2015, and, finally, a period of swift expansion from 2016 to 2019. The index functions as a beneficial instrument for policymakers and scholars to comprehend the advancement of AI and its influence on different sectors in China.

The two primary goals of identifying which organizational variables are most important in the conflict against the effects of climate change occur together. As a first step, the international community has made an arrangement to reach the primary objectives established in the Paris Climate Change Agreement of 2015 involving GHG emissions and their consequences on the environment [1]. Several studies have shown that institutional effectiveness plays a crucial moderating role in determining the ecological effects of various policies and programs, including innovative green initiatives, climate change laws, and FDI inflow [10]. Our research aims to help legislators and decision-makers optimize organizational structures to decrease country CO2 emissions by analyzing data from the World Bank's governmental indices. Furthermore, while looking at CO2 emissions, most research has only considered a few essential factors [11]. Our goal is to help researchers choose adequate energy, organizational, financial, and economic variables to study to cut carbon dioxide emissions, stand against climate change, and maintain the ecosystem. We will use twenty-two variables frequently used in ecological and environmental studies to clarify the production of CO2.

What makes our study unique is that we built a new analytical method to tackle the problem of identifying the critical variables for developing efficient methods to decrease carbon dioxide emissions and their ecological effects. This will help fill the vacancy in the previous research. To address this [12], highlighted that to accommodate for all relevant factors, certain research studies have used a variety of variables relating to human growth, higher education, technological advances, and financial markets; however, these variables have been overly generalized and selected at random in the past. Consequently, we find the best methods to limit environmental deterioration from carbon dioxide emissions by utilizing artificial intelligence (XAI) methods. These methods are based on a variety of energy-linked attributes and organizational structures. Further, this work sheds light on the continual debate between academics on deciding if ecological research issues are effectively addressed using a systematic approach to null and different theories [5].

Compared to statistical approaches, artificial intelligence (AI) frameworks can readily show nonlinear connections and trends between multifaceted predictions and predictors. This is because artificial intelligence (AI) methods are not dependent on perspective hypotheses regarding the statistical variance of residuals and the calculation's operational form, nor do they require noncollinearity within the individuals being predicted. Based on research conducted by Ref. [6], tree-based AI models outperform statistical frameworks in terms of interpretability and prediction accuracy. According to an overview published by Ref. [13], artificial intelligence has opened up possibilities in energy in the field of economics, specifically in the fields of energy price prediction, prediction of demand, risk administration, marketing techniques, analysis of data, and macro/energy patterns. Our research adds to the literature on energy and institutions by expanding AI's usage to forecast the impact of institutional efficiency on worldwide carbon dioxide emissions while considering the nonlinear interconnections of this factor with energy, finance, and socio-educational aspects on a global scale.

Among the organizational factors that predict carbon dioxide emissions, XAI simulation findings show that citizens' engagement in primary elections and the regulation of local corruption are crucial, but the implementation of law ranks highest. In addition to promoting economic growth, decreasing the utilization of oil and gas by more than 69 %, expanding the usage of green energy by private and public entities, and enhancing such organizational functions, efficient climate change methods also need to improve the decision-making process in this area. Consistent with the findings of [7], who suggested that strategic measures should be implemented to decrease the use of energy sources, this national policy recommendation emphasizes the importance of green energy as a means to achieve energy transformation. An energy transformation program can only be successful with institutional changes in response to the growing public concern about environmental damage.

This is how the rest of this article is organized. The main factors influencing carbon dioxide emissions and, by extension, environmental damage are summarized in the second part. The third part introduces the data source and methods, while the fourth part analyses the outcomes of the used AI models. The final part concludes with a quick summary of the key results and some last thoughts.

2 Literature review

Several AI applications have already been implemented to curb rising CO2 emissions. Artificial intelligence is a promising instrument in the energy generation industry. For example, several AI-based technologies utilized by the solar energy sector are presented by Ref. [14]. The prediction of solar energy and adjusting specifications for solar power plants are two areas where AI finds extensive use. Energy demand and supply forecasting is another important application of AI. It is essential to predict future energy demand and supply (from sources like PC panels, for instance) in an intelligent electrical system where buildings are interconnected to produce energy. To maximize efficiency, AI may optimize energy storage systems like batteries, reduce energy waste, and give preference to low-carbon electricity producers like nuclear power stations or green energy sources. For instance Ref. [15], detailed how Brazilian homes with PV panels might make the most of an intelligent grid's Active Demand Side Management (ADSM). This method optimizes battery storage and limits photovoltaic-based energy waste; the researchers achieved this by training an artificial neural network (ANN) using nonlinear auto-regressive and additional inputs (NARX) to forecast the optimal power supply at any given time.

Artificial intelligence (AI) can potentially reduce electric vehicle power usage in the transport industry [16]. The researchers have demonstrated that, given a particular distance, energy usage can be optimized by selecting faster paths with higher-quality profiles (due to geometry more suited to the task at hand). The approach relies on complete Bayesian regression methods to forecast the mean and standard deviation of energy usage. These estimations will help prioritize the most energy-efficient paths by determining which ones have the lowest deviation. These demand estimation techniques and algorithms for optimizing transportation routes may decrease food waste. Using advanced machine learning techniques to determine which factors contribute to reducing carbon emissions can enable China's steel and iron sectors to achieve low-carbon growth and reach zero carbon emissions [17]. According to their findings, manufacturing capacity and energy utilization are the two most important factors influencing CO2 emissions in this setting.

Furthermore, the telecommunications sector employs AI models to mitigate carbon dioxide emissions. According to Ref. [18], certain telecommunications base stations that power their electrical equipment emit significant carbon dioxide gas and use an ANN model to forecast CO2 emissions and the variables that will cause them. Factors like generator ability, daily fuel usage, energy consumption, and proximity to the station are measured by the base stations and sent into the algorithm. The result is the CO2 concentration in parts per million at the monitored stations and various distances from them. The analysis also highlights the significance of generator effectiveness, which means using less fuel when producing the same amount of power, and generator size, which means having a more excellent kVA capacity results in lower ppm of CO2.

To review the existing literature, AI must overcome one major obstacle to decreasing CO2 emissions: the rebounding mechanism, also called [19] examines the causes and effects of this economic contradiction and shows how it is expressed within the environmental sector. According to this theory, a system will utilize more materials when it becomes more efficient. Consequently, the excessive consumption generated could occasionally offset these initial benefits. According to Ref. [20], the idea would still be highly relevant today, even if it were conceived in the United Kingdom during the industrialization period of the 19th century. It shows that there are industries where demand is growing more than efficiency is improving. In the United States, demand (measured by the quantity of products) climbed by 20 % after 1976, while refrigerator storage expanded by 10 % on average. Journal publications discussing the use of AI and ML to reduce CO2 emissions in the construction industry have been on an upward trajectory since 2012 (Fig. 2).Fig. 2 The frequency of articles published annually according to search results.

Fig. 2

Among the factors that influence CO2 levels, political and financial stability is crucial to economic growth, while institutional rules may significantly affect the state of the environment on a national scale. The significance of politicians preserving political stability to forestall setbacks in ecological transition was demonstrated by Ref. [21]. [22] argues that while applying law reduces carbon dioxide emissions, anarchy in government and regulation can have the opposite effect. In their 2020 study [23], demonstrated that new climate change laws paired with a robust legal framework have the potential to mitigate environmental deterioration significantly. Additionally, the research recommends that environmental organizations take a more active role in shaping ecological legislation by pushing for sweeping policy shifts through regulations and modifications to current statutes.

Additionally, it has been contended that governments that subscribe to the system of law are more actively associated with global environmental conferences. To be more specific [24], discovered that polluting nations with functioning republics have lower CO2 emissions, while polluting nations with low-functioning democracies have higher emissions. Another finding by Ref. [25] is that democracies, both the most and the least republican, have lower CO2 emissions.

Regularly implementing government policy measures to promote sustainable economic development is essential to achieving efficient governance, which is necessary for tackling climate change. Our research is the first to find no previous investigation of the relative importance of public administration quality, socioeconomic and social factors, and other potential determinants of reducing carbon dioxide emissions. By utilizing AI techniques, our study addresses an inconsistency in the energy literature and expands the field of energy monetary policy about environmental destruction difficulties. We do this by collecting new scientific data and developing innovative techniques. However, we also focus on novel challenges that may show how the discovered key variables in energy finance and the environmental sciences can be placed based on their prediction abilities. Our goal in filling this void in the literature is to look further into the connections between ecological preservation, high-quality institutions, and financial prosperity.

3 Research methods and data

3.1 Material and variables

This study uses an extensive cross-sectional dataset to examine the factors that impact CO2 emissions in 137 countries from 2000 to 2020. The main sources of data consist of the World Bank's World Development Indicators (WDI) and World Governance Indicators (WGI). These datasets contain comprehensive data on a wide range of economic, social, and governance indices that are essential for comprehending the complex characteristics of CO2 emissions.

The dataset contains 22 indicators commonly used in ecological and environmental research. The indicators are classified into four primary categories: national governance, energy, socio-educational, and global economic metrics. The choice of these variables was based on their pertinence to the research issue and their known importance in the existing body of literature. The variables were selected to guarantee a thorough examination of the institutional, economic, and energy-related aspects that impact CO2 emissions.

Within the realm of national governance, we incorporated other indicators, such as the rule of law, government performance, control of corruption, political stability, and citizens' voice and accountability. The selection of these indicators was based on their pivotal importance in influencing environmental policies and their implementation. For example, the concept of the rule of law assesses individuals' trust in and adherence to societal norms, while government efficacy analyses the calibre of public services and the autonomy of the civil service from political influences. Corruption control serves as a measure of public sentiment on the improper utilization of government authority for personal gain, which can have a substantial influence on the enforcement of environmental regulations.

The energy category includes variables such as the amount of CO2 emissions per 2010 dollar of GDP, the consumption of renewable energy, the usage of energy from oil and gas, the total energy consumption, and the percentage of energy imports in relation to total energy consumption. These variables were used to represent the explicit correlation between energy consumption patterns and CO2 emissions. Renewable energy consumption is a crucial factor that signifies the transition to cleaner energy sources, whereas energy consumption from oil and gas reflects dependence on fossil fuels.

The socio-educational category encompasses factors such as the pace of urbanization, population size, and the duration of compulsory schooling for children. These indicators offer valuable information about the demographic and educational variables that can impact environmental results. For example, the urbanization rate is a measure of how densely populated an urban area is, and this can affect the patterns of energy use and emissions. The inclusion of the duration of obligatory education as a proxy for green technology innovation is based on the premise that a longer period of mandatory schooling is frequently associated with a greater level of technical progress and innovation.

The category of global economic measurements encompasses various indicators, including per capita GDP, GDP growth rate, capital investment, exports, consumer price inflation, GDP deflator, and inflows and outflows of foreign direct investment (FDI). The chosen economic metrics were intended to comprehend the financial and economic forces that impact CO2 emissions. Take GDP per capita as an instance. It is a crucial factor that offers valuable information about a country's economic growth status. This, in turn, can influence the country's capacity to invest in eco-friendly technologies and carry out efficient environmental policies.

The dataset necessitated just minimum preprocessing since all the data from the WDI and WGI datasets were quantitative. In order to guarantee the accuracy and comprehensiveness of the data, we have removed economies that have completely missing data for important variables such as green energy, GDP per capita, oil and gas usage, and other crucial metrics. The outcome was a conclusive dataset with dimensions of 1589 × 22, which is appropriate for the evaluation of eXplainable AI (XAI). The choice to omit incomplete data was made to uphold the integrity of the study and guarantee that the conclusions were derived from dependable and all-encompassing data.

Table 2 provides a concise summary of the statistical descriptions for these variables. The mean and standard deviation of CO2 emissions (measured in kg per 2010 dollar of GDP) were 0.54 and 0.45, respectively. The minimum value observed was 0.061, while the largest value was 4.14. The diversity observed highlights substantial disparities in CO2 emissions among countries, which is essential for comprehending the varied influence of different factors on emissions. Similarly, the rule of law indicator had an average value of 0.20 and a measure of dispersion of 1.04, indicating differences in how legal rules are followed and enforced among different countries. Table 2 provides descriptive data, while Table 1 lists the 22 indicators employed for calculating carbon dioxide emissions in the current research. Previous research frequently uses such indicators [26]. Therefore, they were chosen for this study. For instance, we followed [27], who showed that GDP per capita significantly affects emissions levels and included this as a critical variable. Following the findings of [28], we additionally included energy use as a significant source of carbon dioxide (CO2) emissions [29]. emphasized the role of organizations in determining environmental results, which explains why we choose institutional quality criteria such as enforcement of law, state efficacy, and the management of corruption. Following the research of [30], we included price Increases in prices as an indicator to examine the possible influence of financial variables on emissions. We reduced the number of economies considered in the XAI research from 218 to 137 b y excluding those with completely missing data for significant variables like green energy, GDP per capita, oil and gas, utilization of energy, consumption of renewable energy, the system of law, opinion, corruption management, and carbon dioxide (kg). In this case, the total size of the graphical data set would have been 2586 × 22, but for some years, only a few nations had data for all 22 indicators, so the data set was 35 percent smaller. Due to the fact that this research's XAI-based estimation is not over time-series modeling, as we do not have access to extra country-related information to fill in the missing values, 35 percent of the inadequate rows that contain data are removed from the table. This leaves an entire data set with dimensions of 1589 × 22, which is appropriate for XAI assessment. According to earlier research [31], the tree-based ensemble techniques used in this study do not change when the different characteristics are changed in a completely monotonous way, like standardization. Consequently, the characteristics shown in Table 2 have not been standardized.Table 1 The characteristics or predictions that the AI study uses to forecast yearly carbon dioxide emissions.

Table 1Group	Variables	Description	
Institutional	Corruption Control	An indicator of public opinion on the inappropriate utilization of governmental power for private benefit is the level of corruption control.	
Energy Consumption stability	Energy consumption stability indicates how people feel about the government's capacity to create and enforce good laws and rules that can stimulate growth in the private sector.	
Government Effectiveness	The efficacy of government is evaluated by looking at how well public services are provided, how well the public sector operates, and how unbiased it is from political influences.	
Political Stability	The Political Stability Index, along with the absence of the Violence/Terrorism Index, evaluates the possibility of political instability and destruction with a political purpose, such as terrorism.	
Rule	The rule of law measures individuals' belief in and compliance with social standards.	
Opinion	In opinion and accountability, we record how people feel about their right to express themselves, associate with others, access the media in a given nation, and how much say they have in choosing their country's leader.	
Energy	CO2 (kg)	Greenhouse gas emissions from cement production and the combustion of fossil fuels were measured in kilograms per dollar of gross domestic product in 2010.	
Renewables	Energy derived from renewable sources that are burning and waste %	
Renewable Energy Consumption	Utilization of renewable energy (as a percentage of total end energy use)	
Oil and gas	Energy use from oil and gas as a percentage	
Energy Use	Utilization of energy (in kilograms of oil equivalents per capita)	
Energy Imports	Energy imports as a percentage of total energy consumption	
Population Size	The ratio of population to total land area	
Socio-educational	Urbanization rate	Urban population density (% of population per sq. Km of urban land area)	
Green Technology Innovation	Number of years that children are legally obliged to attend school.	
Financial	GDP per capita	GDP per capita (constant 2010 USD)	
GDP growth rate	GDP growth (annual %)	
Capital investment	Capital investment (% of GDP)	
Exports	Products and services being exported	
Increase in prices1	Price increases, consumer prices (per year)	
Increase in prices2	Annual percentage increase in prices, GDP deflator	
FDI in	Net FDI, foreign direct investment, as a percentage of GDP	
FDI out	Imports from overseas, net outflows (as a percentage of GDP)	

Table 2 Statistical descriptions of the AI-analyzed factors used for forecasting annual carbon dioxide emission.

Table 2Variable	Mean	SD	Minimum	Median	Maximum	
Corruption Control	0.19	1.09	−1.72	−0.13	2.47	
Energy Consumption stability	0.33	0.93	−2.30	0.27	2.23	
Gov Effectiveness	0.30	1.00	−1.88	0.09	2.44	
Political Stability	0.02	0.94	2.85	0.07	1.76	
Rule	0.20	1.04	−2.13	−0.01	2.10	
Opinion	0.21	0.95	−1.98	0.19	1.80	
Renewables	15.17	19.80	0.00	7.13	93.90	
Green Energy Con.	27.14	24.60	0.00	19.34	98.27	
Oil and gas	69.29	24.58	1.72	75.39	100.00	
Energy Use	2480.59	2599.92	141.99	1672.91	22,120.38	
Energy Import	−17.40	175.21	−1938.67	36.03	100.001	
Population Size	32.22	20.76	0.0001	32.73	98.19	
Urbanization rate	14,614.90	64,655.80	79.23	4612.20	771,470.25	
Green Technology Innovation	9.54	2.05	4.001	9.001	16.001	
GDP per capita	17,026.14	20,588.13	276.07	7051.49	111,968.36	
GDP growth rate	3.95	3.89	−14.85	3.94	34.48	
Capital investment	24.34	6.92	2.11	23.25	61.06	
Exports	43.51	28.97	5.33	36.88	228.98	
Increase in prices 1	6.22	17.49	−10.08	3.56	513.92	
Increase in prices 2	8.69	67.63	−24.23	4.05	2630.13	
FDI in	6.57	22.64	−58.33	3.05	449.09	
FDI out	3.68	18.62	−87.24	0.60	301.26	
Carbon dioxide (kg)	0.54	0.45	0.061	0.39	4.14	

Table 2 presents the statistical characteristics of the variables examined in this study, providing a thorough summary of the average values and variations within the dataset. The statistical measures of mean, standard deviation, lowest, median, and maximum are provided for each indication, showcasing the wide variation of data among 137 countries. The CO2 emissions (measured in kilograms per 2010 dollar of GDP) have a mean value of 0.54 and a standard deviation of 0.45. This indicates that there is a large variation in emissions intensity among different nations. The global efficacy of legal frameworks is significantly highlighted by the rule of law, which has a mean of 0.20 and a standard deviation of 1.04. Renewable energy consumption, which has an average of 27.14 % and a broad standard deviation of 24.60 %, indicates the varying degrees of adoption of renewable energy sources.

An analysis of these statistics provides valuable insights into the fundamental patterns of the data. The presence of high standard deviations in variables such as energy use (2480.59 kg of oil equivalent per capita) and population size (32.22 per square kilometer) indicates substantial differences among countries. These differences may arise from various degrees of industrialization, economic growth, and population density. These differences highlight the need for policy proposals that are tailored to each country. The significant variation in GDP per capita, ranging from a low of $276.07 to a maximum of $111,968.36, highlights the diverse economic landscape within the sample. This variation has a direct influence on each nation's capacity to invest in green technologies and successfully implement environmental regulations. The statistical descriptions serve as the basis for the subsequent study using eXplainable AI models, which seek to uncover the intricate relationships between these factors and CO2 emissions.

The discussion part consolidates the data obtained from the statistical analysis and the eXplainable AI models to offer a full comprehension of the elements that impact CO2 emissions. The significant fluctuations in crucial metrics, such as energy consumption and GDP per capita, highlight the necessity of customized approaches that tackle the distinct conditions of individual nations. The XAI models' capacity to manage this intricacy and deliver interpretable outcomes is especially valuable. For example, the models emphasize that countries with a greater GDP per capita generally exhibit lower levels of CO2 emissions. This is because wealthier nations are typically more capable of investing in cleaner technology and implementing strict environmental legislation. This is consistent with the statistical discovery of a substantial average GDP per capita and its notable variation, suggesting that economic progress has a vital impact on emission levels.

In addition, the XAI models recognize the significance of the rule of law and government efficacy as crucial institutional elements in decreasing CO2 emissions. The statistical analysis shown in Table 2 demonstrates significant variation in these variables, highlighting the necessity for strong legal and governance frameworks to enable the successful execution of environmental policies. The discussion also highlights the importance of adopting renewable energy, as evidenced by the notable variation in renewable energy consumption. This suggests that different countries have varying levels of dedication to using environmentally friendly energy sources. The results indicate that augmenting the proportion of renewable energy in the energy composition can effectively alleviate CO2 emissions. These insights offer practical suggestions for policymakers, including improving legal frameworks, stimulating economic growth, and advocating for the use of renewable energy. These recommendations are designed to suit the unique circumstances of each country.

3.2 XAI modeling

The incorporation of eXplainable AI (XAI) models in this work provides many clear benefits compared to conventional regression or spatial econometric methods, especially considering the intricacy and extent of analyzing CO2 emissions across multiple countries. Explainable Artificial Intelligence (XAI) models, such as Random Forest and Extremely Randomised Trees (ERT), excel at capturing complex and non-linear correlations and interactions among numerous factors without imposing the limiting assumptions commonly seen in standard regression models. Contrary to linear regression, which assumes a straightforward connection between variables, XAI models are capable of dealing with complex interdependencies and fluctuations in data, offering a more detailed comprehension of the elements that impact CO2 emissions.

In addition, XAI models demonstrate exceptional performance in handling datasets with a high number of variables, such as the one used in our study, which included institutional, economic, and energy-related factors from 137 nations. These models are adept at effectively handling multicollinearity among predictors, a common difficulty faced by conventional econometric approaches. The collective character of models such as Random Forest improves their resilience and capacity to apply to new data by mitigating overfitting through the combination of numerous decision trees.

XAI models offer a notable benefit in terms of their interpretability. Methods like SHapley Additive exPlanations (SHAP) enable us to break down the predictions and comprehend the impact of each predictor on the model's output. Transparency is essential for policy-making since it enables the identification of the most influential factors that contribute to CO2 emissions and provides valuable insights into how alterations in these variables can affect emissions. Conventional regression models frequently lack this degree of interpretability, particularly when confronted with intricate and nonlinear data structures.

In addition, XAI models can adjust to the varied and varying characteristics of data in many nations, capturing both country-specific differences and global patterns at the same time. The ability to adapt is crucial for developing customized policy suggestions that take into account the distinct circumstances of other countries. On the other hand, spatial econometric models, although they are useful in considering geographical relationships, may demand detailed specifications and can be computationally demanding for big datasets.

[32] state that the interpretability and explanation of AI models do not have a specific quantitative concept or metric. According to the findings of a number of researchers, the concept of interpretability is frequently dependent upon the field in which it is applied [33]. As a result, it could not be appropriate for concepts that are quite robust [34]. argues that interpretability is synonymous with comprehensibility; however [35], argues that interpretability is more inclusive than clarity. According to this research, an accessible AI model is a model that provides human-understandable logic behind its decisions. According to Ref. [36], an explainable AI model could enhance decision-making processes, discover novel data, and validate predicted outcomes using a combination of AI model analyses and relevant data.

Consequently, according to Ref. [37], the scientific significance of a result depends on its clarity, which in turn requires interpretability. Building user trust in AI-based conclusions becomes easier when the reasoning driving them and how to tweak them for practical forecasts are explained [38]. Concerns about the reliability and transparency of the outcomes and decisions, as well as the potential for erroneous conclusions due to a lack of explanation, raise ethical and practical concerns. Consequently, it is crucial to create XAI models that can make predictions and also show the reasoning behind the judgments, as well as the evidence that supports and contradicts the assumption [39].

We tested four AI models that were made using decision-tree methods to see how well they could find complex, irregular links between yearly carbon dioxide emissions and things that have to do with energy, institutions, coeducation, and microeconomics. By integrating numerous models developed with the same machine learning technique and employing reducing or enhancing approaches for minimizing bias and volatility, combined models make decision trees' predictions more accurate. This work employs a variety of artificial intelligence models, including random forest, ERT, eXGBoost, and LGBoost. It was decided to use these AI models because they are very good at predicting complex, nonlinear problems in many areas (for example, [40]. They are also being used in new research about carbon dioxide emissions (for example, [40]).

The Random Forest algorithm was chosen for this research paper due to its strong performance in dealing with intricate and nonlinear relationships between multiple variables, its capacity to handle large datasets with many predictors, and its inherent ability to reduce overfitting through ensemble learning. Random Forest is an ensemble learning technique that builds numerous decision trees during training and combines their results to improve prediction accuracy and generalization. This approach is very beneficial for our research, as it encompasses a wide range of institutional, economic, and energy-related factors that impact CO2 emissions. By utilizing Random Forest, we can accurately capture the complex relationships between these variables without the constraining assumptions needed by conventional statistical models. In addition, Random Forest also offers metrics for assessing the importance of variables, providing valuable information on the primary factors that contribute to CO2 emissions. This corresponds with the study's goal of identifying crucial determinants and guiding policy-making.

Two algorithms that depend on bagging are ERT and random forest, whereas the two methods that rely on enhancing are extreme gradient boosting and light gradient boosting. During the construction of the preserving algorithms, these choice trees develop in parallel and separately, and they cannot communicate with one another in any way during the process. When making decision tree structures, both ERT and RF use subgroups of the input dataset and make changes using the bootstrap method. However, ERT uses the whole original dataset. ERT decision-making relies on a random split, whereas RF selects the most suitable local split. Both methods then select the most suitable subset from among all the possible choices after the split has been determined. A collection of weak students is involved in gradient-boosted trees. This group is utilized to construct stronger learning by combining many weak learners. To improve accuracy, the boosting method uses the data from already existing trees to incrementally generate new ones. Extreme gradient boosting divides the trees according to their depth or level, whereas light gradient boosting divides the trees according to their leaves. According to Equation (1), tree-based combined algorithms obtain the following mathematical knowledge about the functional correlation between characteristics and objectives:(1) Yˆ=1n∑k=1nfk(X)

in this case, (Yˆ) stands for the predicted yearly carbon dioxide emissions; (X) incorporates the distinct variables related to organizations, energy, socio-education, and microeconomics; and (n) denotes the overall quantity of functions performed by the combined set of trees. Finding the disparities between the projected and observed carbon dioxide emissions is an effective method to evaluate the prediction models' performance. To do this, we use the root mean square error and factor of correlation (R2) that is determined by equations (2), (3) accordingly:(2) RMSE=∑K=1N(Yˆ−Y)2N;and

(3) R2=1−[∑K=1N(Yˆ−Y)2∑K=1N(Y−μ)2];

However, collective modeling may have an impact on the models' comprehension. Some researchers have argued that it is important to comprehend AI models based on ensemble trees [39]. across domains show that tree-based combination models can be combined with explaining methods to help consumers recognize the comprehension of the processes involved in making decisions. This gives AI-based estimations clarity and transparency. The goal of this work is to improve the model's accessibility by combining artificial intelligence (AI) models with the Shapley Additive Explanation (SHAP) evaluation, which depends on the concept of game theory. Additionally, this study utilizes artificial intelligence (AI) models in conjunction with SHAP to rank the indicators' value, find the points of inflection where a predictor reacts either positively or negatively to modifications in the indications' values, and establish a hypothesis that can be tested based on novel data (i.e., increased model clarity).

The artificial intelligence analyses used 90:15, 85:20, and 80:25 split percentages for the curriculum, validation, and test databases, respectively. To further enhance predictability, a grid search technique is used for optimizing each AI model. In order to optimize this process, there are a total of 100 candidates who use a CVT, which is equivalent to 300 simulation fits for RF; sixty individuals who use a threefold grid pursuit CVT, which is equivalent to 190 model fits for ERT; three hundred individuals who use a threefold grid looking CVT, with 950 models fitting the Extreme gradient boosting criteria and 150 candidates using a triple grid achieving CVT, for a total of 420 models matching the Light gradient boosting criteria. For the purpose of assessing the accuracy of AI-generated predictions, a triple CVT generates instructional and evaluating datasets at random with varying durations and split rates.

4 Results and discussion

4.1 Factors influencing

Table 3 displays the predictive accuracy of four artificial intelligence models—Random Forest (RF), Extremely Randomised Trees (ERT), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting (LGBoost)—in forecasting annual CO2 emissions. The table displays the Root Mean Square Error (RMSE) and R2 values for the curriculum (training) data and the experimental (testing) data in three distinct data splits: 90:15, 85:20, and 80:25. All models in the curricular data exhibit exceptional performance during training, as indicated by their practically flawless predictions with RMSE values approaching zero and R2 values of 1.00. Nevertheless, the performance of the experimental data, which is a better reflection of real-world applicability, exhibits some degree of fluctuation. The ERT model has outstanding performance, with RMSE values ranging from 0.11 to 0.13 and R2 values from 0.95 to 0.97, indicating a high level of prediction accuracy. The XGBoost model has exceptional performance, achieving RMSE values as low as 0.08 and R2 values reaching up to 0.98, particularly in the 80:25 split.Table 3 The accuracy of predictions of the RF, ERT, XGBoost, and LGBoost scenarios.

Table 3AI-Based models	Data	90:15 split	85:20 split	80:25 split	
RMSE *	R2	RMSE *	R2	RMSE *	R2	
Random Forest	Curriculum data	0.045	0.98	0.045	0.98	0.045	0.98	
Experimental data	0.15	0.93	0.13	0.94	0.12	0.96	
Extremely Randomized Tree	Curriculum data	0.001	1.00	0.001	1.00	0.001	1.00	
Experimental data	0.13	0.95	0.11	0.96	0.11	0.97	
Extreme gradient boosting	Curriculum data	0.001	1.00	0.00	1.00	0.01	1.00	
Experimental dataset	0.13	0.96	0.08	0.97	0.08	0.98	
Light gradient boosting	Curriculum dataset	0.011	1.00	0.02	1.00	0.011	1.00	
Experimental dataset	0.14	0.94	0.13	0.94	0.12	0.95	

The comparison of these models underscores the durability and dependability of tree-based ensemble approaches in forecasting CO2 emissions. The consistently high R2 values across several data splits demonstrate the models' ability to effectively generalize to new data, which is essential for their use in policy-making and environmental management. The ERT and XGBoost models have lower RMSE values, indicating their exceptional ability to minimize prediction mistakes. Consequently, these models are well-suited for precise forecasting. This investigation highlights the significance of employing sophisticated AI models like as ERT and XGBoost in environmental studies. These models are crucial for capturing intricate nonlinear correlations and interactions among various variables, which are necessary for making precise predictions.

The discussion section consolidates these findings by highlighting the merits of the AI-based models employed in the research and their ramifications for forecasting CO2 emissions. The ERT and XGBoost models have a high level of predictive accuracy, as seen by their low RMSE (Root Mean Square Error) and high R2 (coefficient of determination) values. This illustrates their efficacy in effectively managing intricate datasets that contain several predictors. This feature is especially advantageous in the case of CO2 emissions, as multiple elements, such as economic, institutional, and energy-related variables, interact in complex and non-linear manners. The models' stability across various data splits further confirms their trustworthiness, indicating that they can be confidently utilized to predict CO2 emissions in a wide range of scenarios.

The practical ramifications of these discoveries have great importance for policymakers and scholars. The predictive capabilities of ERT and XGBoost models in properly estimating CO2 emissions can assist in formulating precise environmental regulations and responses. By identifying the primary factors that contribute to emissions, policymakers can give priority to certain areas, such as enhancing governance, promoting the adoption of renewable energy, and stimulating economic growth. Furthermore, the transparency and accountability of policy decisions can be improved by utilizing techniques such as SHapley Additive exPlanations (SHAP), which provide explicit insights into the influence of each variable, thereby boosting interpretability. In summary, the conversation emphasizes the crucial significance of sophisticated artificial intelligence models in furthering our comprehension of CO2 emissions and formulating efficient approaches to address climate change.

Table 4 displays the hyper-parameters chosen for the four AI models: Random Forest (RF), Extremely Randomised Trees (ERT), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting (LGBoost). These hyper-parameters were determined using cross-validation using three distinct data splits: 90:15, 85:20, and 80:25. Each model is accompanied by the specification of the number of indicators (trees) and the maximum depth of the trees. The Random Forest model utilizes a variable set of 400–800 indicators and a maximum depth ranging from 19 to 25. The ERT model, distinguished by a notably reduced number of trees (ranging from 55 to 295) and deeper trees (with a maximum depth of 20–30), demonstrates its effectiveness in attaining superior predictive accuracy with a smaller number of trees. XGBoost and LGBoost models, both utilizing boosting techniques, employ a significantly larger quantity of trees. XGBoost employs up to 11,000 trees while maintaining a consistent maximum depth of 4. On the other hand, LGBoost can have a maximum depth of up to 10.Table 4 Ultimately, selected hyper-parameters following the cross-validating process.

Table 4AI-based models	Dataset	90:15 split	85:20 split	80:25 split	
Random forest	No. Indicators	800	400	700	
Max. Depth	25	19	22	
Extremely Randomized Tree	No. Indicators	295	234	55	
Max. Depth	25	20.00	30	
Extreme gradient boosting	No. Indicators	1900	11,000	1900	
Max. Depth	4	4.00	4	
Light gradient boosting	No. Indicators	1100	800	560	
Max. Depth	7	9.00	10.00	

These hyper-parameter configurations expose the distinct methodologies that each model uses to optimize predictions. Random Forest and Extremely Randomised Trees (ERT) models demonstrate exceptional accuracy by utilizing a reasonable number of trees and depth, effectively managing computational efficiency while maintaining high performance. On the other hand, XGBoost and LGBoost utilize a significant quantity of shallow trees to progressively enhance the accuracy of predictions. This approach is common among boosting algorithms that strive to minimize bias. The selection of these hyper-parameters demonstrates the models' approaches to balancing bias and variance trade-offs and guaranteeing the reliability of predictions. The lower number of trees in ERT, together with its depth, showcases its capacity to capture intricate relationships with minimal overfitting, whereas the large number of trees in XGBoost and LGBoost underscores their iterative refinement for precision.

The discussion part focuses on the practical consequences of the chosen hyper-parameters for each AI model and their role in properly estimating CO2 emissions. The excellent accuracy achieved by the Random Forest and ERT models, even with a smaller number of trees and modest depth, highlights their efficiency in dealing with huge datasets that have multiple predictors. This makes them well-suited for real-world applications where there may be limitations on processing resources. The performance of the ERT model is remarkable due to its fewer but deeper trees. This indicates that the model is capable of capturing intricate relationships within the data while maintaining good interpretability and lower computing costs.

However, the XGBoost and LGBoost models showcase the effectiveness of boosting methods in attaining high predictive accuracy through the utilization of numerous shallow trees. The iterative improvement technique employed in these models greatly enhances their effectiveness in decreasing bias and improving forecast precision. The models' abundant number of trees and unwavering maximum depth guarantee their ability to rapidly process large-scale, high-dimensional data. This is essential for accurately predicting CO2 emissions in various countries. These findings highlight the significance of choosing suitable hyperparameters to achieve a balance between model complexity, accuracy, and computational efficiency. This offers useful insights for policymakers and researchers in developing successful environmental strategies using reliable prediction models.

4.2 Methods for reducing CO2 emissions developed by XAI: A practical strategy

We use the results of the international SHAP assessment to create conditioned probabilistic frameworks that help us figure out how likely it is that yearly international carbon dioxide emissions will go down if either the most important non-institutional indicators are improved quietly or a combination of variables are improved. In order to reduce carbon dioxide emissions globally, comparative analyses will show how important and what part quality within institutions plays.

4.2.1 Statistical models that mainly include non-institutional indicators

By applying the highest-level key non-institutional indicators' turning points, we first establish the previously mentioned scenarios.S0 Emissions of CO2 < Average emissions of CO2

S1 GDP per person over 10,000 USD

S2 Less than 68 % of all energy used comes from petroleum and other oil and gas

S3 More than 24 % of overall usage of energy > Comes from green sources

S4 Burning sources account for account for >9 % of the entire utilization of energy

S5 Oil consumption: <1420 kg per citizen.

Here, S0–S5 is the numerical designations of a collection of scenarios employed in the probabilistic assessment. We utilize the average amount of carbon dioxide emissions (0.42 kg per 2010 $ of the gross domestic product) as the S0 scenario instead of the average value (0.60 kg per 2010 $ of the gross domestic product) because carbon dioxide emissions fall into a right-skewed range. An expression that can be used to indicate a conditioned probability for an event of specific scenarios S1–S5 with respect to S0 contains the following:(4) P(Si|S0)i≠0=100×P(Si∩S0)P(S0)%

where P(Si∩S0) is the likelihood that Si and S0 exist at the same time, P(S0) is the likelihood that global CO₂ emissions will be lower than the average amount from 2000 to 2020, and P(S0) = 50 % is a starting point for the likelihood analysis. The following implementation Si represents the likelihood of additional decreases in carbon dioxide emissions compared to the baseline scenario. Our calculations are based on Equation (4):

P(S1|S0) = 70 %; P(S2|S0) = 70.2 %; P(S3|S0) = 67.9 %; P(S4|S0) = 62.4 %; and P(S5|S0) = 48.4 %.

These ratios show that if S1, S2, and S4 were all attained internationally, the chance of reducing carbon dioxide emissions <0.42 kg per 2010 dollar of GDP could rise by 20 %, 20.2 %, 17.9 %, and 12.4 %, respectively. Consider the following:

Dollar Results in a 20 % improvement in the probability of achieving specification C1 to reduce carbon dioxide emissions <0.42 kg per 2010 dollar of gross domestic product. The chance of reducing carbon dioxide emissions by 1.7 % may be reduced if there is a fall in total energy consumption worldwide to less than 1420 kg of oil per individual (S5). This is because lower energy consumption could result in a decrease in GDP per capita. Reduced GDP could contribute to environmental damage because fewer resources would be provided to fund information-intensive sectors, more environmental laws, advanced technology, and more significant environmental spending (Stern,2004). The conditioned probability for an event of specific scenarios S1 to S5 with respect to S0 can be expressed as follows in equation (5).(5) P(Si|S0)=100×P(Si∩S0)P(S0)%

We will next look at the most effective way to reduce yearly CO2 emissions while simultaneously reducing the consumption of oil and gas (C2). In this particular instance, Equation (4) can be expressed as follows in equation (6):(6) P((S2∪Sj)|S0)j≠0,2=100×P(S2∩S0)∪P(Sj∩S0)P(S0)%

Applying Equation (6), we calculate the following:

P((S2∪S1)|S0) = 91 %; P((S2∪S3)|S0) = 70.5 %; P((S2∪S4)|S0) = 69.7 %; and P((S2∪S1)|S5) = 66.3 %.

Based on these statistical tests, there is a 43 % chance of reducing carbon dioxide emissions relative to the baseline if GDP per capita rises over USD 10,000 (S1) and carbon-based fuel consumption decreases below 69 % of total energy consumption (S2). A different strategy would be to reduce the use of coal and oil to less than 69 % of the total energy used (S2) and increase the use of green energy to over 25 % (S3). This could make a 20.5 % drop in carbon dioxide emissions more probable. Table 5 shows the probability results after applying this approach to all scenarios in S1–S5. Findings indicate that when S1 integrates with each of the additional strategies (S2–S5), yearly carbon dioxide emissions are highly probable to decrease below the baseline. For instance, if global GDP per capita were to achieve 10,000 USD (S1) and the total energy usage was to reduce under 1430 kg of natural gas per capita (S5), carbon dioxide emissions would be expected to fall below the initial levels by 50 %. A positive correlation exists between total energy use and GDP per capita; hence, this situation might not work. According to Ref. [41], there is a bidirectional causal link between economic development and energy use. Alternately, by implementing any other method (S2–S4) in conjunction with likely decreases in overall global energy consumption of less than 1420 kg of oil per person (S5), it is possible to increase the likelihood of reducing annual carbon dioxide emissions by 9.9–16.3 %. As mentioned earlier, if enhancing global GDP per capita by more than USD 10,000 is not feasible, the subsequent optimal choice is to decrease oil and gas use to less than 69 % of total utilization of energy (S2) and increase green energy usage up to 25 % of unlimited energy usage (S3). This would result in regular carbon dioxide emissions listed below the average.Table 5 Probability of reducing CO2 emissions with various threshold-Driven.

Table 5	S1	S2	S3	S4	S5	
S1	70 %	93 %	93.8 %	88.5 %	100 %	
S2	93 %	70.2 %	70.5 %	69.7 %	66.3 %	
S3	93.8 %	70.5 %	67.9 %	68.9 %	63.3 %	
S4	88.5 %	69.7 %	68.9 %	62.4 %	59.9 %	
S5	100 %	66.3 %	63.3 %	59.9 %	48.5 %	

4.2.2 Probability models using institution-wide and non-institutional variables

Our statistical studies have not included the three most critical formal variables: rule, opinion, and corruption control. We use Equation (7) to determine how institutional variables affect the chances of lowering carbon dioxide emissions on average by controlling factors that affect the whole institution at (or after) certain thresholds.(7) P((S2∪Sj)|(S0∪S6∪S7∪S8))j≠0,2=100×P(S2∩(S0∪S6∪S7∪S8))∪P(Sj∩(S0∪S6∪S7∪S8))P(S0∪S6∪S7∪S8)%

S6 indicates that law and regulation are severely enforced when the rule is greater than or equal to 0.55. When the value of opinion is more than or equal to 0.0, it means that the inhabitants of that country are allowed to express themselves freely. According to the corruption Control score of 0.55 or higher, there is strong public oversight of corruption.

Table 6 shows the scenario-based probabilistic evaluations that were performed employing Equation (7). In Table 5, we can see that if the GDP per capita is more than USD 10,000 (S1) and if all of the institutional criteria (S6–S8) are satisfied, their chances to decrease carbon dioxide emissions might rise by an extra 20.8 % (89.8%–68 %). Furthermore, the likelihood of reducing carbon dioxide emissions would remain largely unaffected (i.e., 2 %), exposed to the implementation of S1-6. This holds even when GDP per individual exceeds 10,000 USD (S1), the use of oil and gas falls below 69 % (S2), green energy usage exceeds 25 % of the overall usage (S3), and burning green energy and waste exceed 9 % of total utilization (S4). Nonetheless, the institutional factors significantly impact the decrease in carbon dioxide emissions for the other CO2 emission options listed in Table 6. In the case where combustible renewables account for more than 9 % of the total usage of energy (S4) and the overall usage of energy decreases under 1420 kg of oil per individual (S5), for instance, robust adoption of institutional requirements could enhance the likelihood of a 30.3 % decrease in carbon dioxide emissions (90%–59.9 %).Table 6 Impact of institutional quality on CO2 emission reduction Probabilities.

Table 6	S1	S2	S3	S4	S5	
S1	89.8 %	89.8 %	95.2 %	87.7 %	100 %	
S2	89.8 %	89.5 %	92.5 %	81.3 %	92.3 %	
S3	95.2 %	92.5 %	93.5 %	90.5 %	90.3 %	
S4	87.7 %	81.3 %	90.5 %	86.7 %	90 %	
S5	100 %	92.2 %	90.3 %	90 %	91.2 %	

Table 6 displays the likelihood of decreasing yearly CO2 emissions below the reference level by utilizing several threshold-based approaches (S1–S5) in conjunction with rigorous institutional elements (S6–S8). The scenarios consist of the following: S1 (GDP per capita exceeding $10,000), S2 (oil and petrol consumption comprising less than 69 % of total energy use), S3 (green energy consumption amounting to more than 25 % of total energy use), S4 (burning of renewable sources making up more than 9 % of total energy use) and S5 (total energy consumption not surpassing 1420 kg of oil per capita). The institutional components consist of S6 (rule of law with a minimum value of 0.55), S7 (citizens' voice and accountability with a minimum value of 0.0), and S8 (control of corruption with a minimum value of 0.55). The table demonstrates that the combination of these institutional elements with economic and energy measures greatly enhances the likelihood of decreasing CO2 emissions. For example, when S1 (GDP per capita greater than $10,000) and S3 (green energy consumption above 25 %) are combined with institutional factors, there is a 95.2 % probability, emphasizing the crucial importance of institutional quality in improving the success of emission reduction initiatives.

The research suggests that the implementation of strong institutional frameworks (S6–S8) in conjunction with economic and energy policies greatly increases the probability of attaining reductions in emissions. This is apparent from the consistently elevated odds seen in all combined situations. For instance, when S2 (oil and petrol usage 69 %) is combined with institutional factors, the chance of reducing emissions is 92.5 %, as opposed to 70.2 % without institutional factors. When S4, which involves burning renewable sources at a rate greater than 9 %, is paired with institutional variables, the chance of success increases to 90.5 %, which is much higher than when S4 is executed alone. The results emphasize the significance of the quality of governance, the enforcement of laws, and measures to combat corruption in promoting the effective execution of environmental programs.

The discussion section consolidates the findings from Table 6, highlighting the collaborative effect of integrating economic, energy, and institutional approaches in mitigating CO2 emissions. The significant likelihoods linked to scenarios that incorporate institutional elements (S6–S8) illustrate the crucial role of strong governance frameworks in successfully implementing efforts to reduce emissions. These findings indicate that nations with robust adherence to legal principles, transparent and responsible administration, and efficient management of corrupt practices are more likely to achieve substantial decreases in CO2 emissions. This emphasizes the necessity for policymakers to prioritize not just economic and energy policies but also the improvement of institutional quality in order to enhance the overall efficiency of environmental programs.

Furthermore, the results suggest that when there is a combination of a high GDP per capita (S1) and significant green energy consumption (S3), along with robust institutional characteristics, there is a 95.2 % likelihood of reducing CO2 emissions. This suggests that a combination of economic success, a shift towards renewable energy sources, and strong governance is essential for creating a favorable setting to achieve environmental sustainability. The comparatively lower odds reported in scenarios lacking institutional support further emphasize the crucial importance of governance in environmental management. Hence, in order to optimize the effectiveness of endeavors aimed at reducing CO2 emissions, policymakers should embrace a comprehensive strategy that encompasses economic growth, energy transformation, and resilient institutional structures. These all-encompassing techniques are crucial for effectively tackling the intricate and interrelated concerns of climate change.

5 Conclusion and policy implications

As a result of worldwide carbon dioxide emissions, many studies have looked at the significance of reliable institutional efforts for preventing climate change. Most of these research studies have utilized the World Bank's World Governance Impact (WGI) as their primary metric. The WGI gauges six institutional factors: corruption management, the efficacy of regulations, government performance, political stability, the rule of law, and citizens' opinions. Non-institutional determinants such as energy, socio-educational, and macroeconomic aspects have also been included. According to Ref. [37], the set of factors displays various levels of arbitrariness and scope. Therefore, energy analysts, environmental lawmakers, ecological scholars, and politicians aiming to decrease carbon dioxide (CO2) emissions and develop climate resilience measures must tackle the issue of which factors should be considered to clarify the amount of carbon dioxide emissions.

Within this structure, this research looks at the relative importance of various variables in describing the worldwide ecological damage caused by human-produced carbon dioxide emissions and how the quality of institutions is represented among them. In our prediction method, we employ XAI models like the highly randomized tree model in conjunction with SHAP evaluation based on games theory. With forecasts, the model's structure can effectively handle nonlinear connections among multivariate indicators without imposing the exact limiting requirements on the spatial distribution of results or the noncollinearity of variables as statistical frameworks usually require. The highly randomly assigned tree model, which uses data from 137 nations between 2000 and 2020, has a simple structure with fewer trees in the collection and can still produce very accurate estimates.

First, the rules of legislation; second, the ability of citizens to have an opinion in government; third, measures that address corruption; fourth, the efficacy of regulations; and fifth, the efficiency of government. These are the most crucial institutional factors, according to our XAI model. Promoting gross domestic product per capita over 10,000 USD, using a more significant amount of green energy higher than 25 % of all energy used, and decreasing the use of coal and oil listed below 69 % of all energy used constitute the most effective way to reduce carbon dioxide emissions around the world when these institutional characteristics are put into effect. Our findings indicate that improvements in three primary institutional measurements should support substantial climate change procedures: the rule of law, which protects citizens' freedom of speech; the opinion, which allows citizens to participate in elections and demonstrate their own without restriction; and the control of corruption. Therefore, lawmakers and officials concerned about the environment's deterioration due to rising CO2 emissions might enhance climate change regulations by advocating for changes to these structural elements and asking questions about why these factors are relevant to environmental degradation. Firstly, due to the effective enforcement [37] of pollution reduction methods, a robust rule of law can reduce market failures.

For this reason, there will be substantial financial rewards for businesses and individuals who follow the rules of climate change. Secondly, according to Ref. [39], countries where people can get their information, create groups, and reject administrators who don't listen to their requests tend to have more environmentally conscious residents. Higher levels of environmental protection may result from such a dedication. Third, countries with effective anti-corruption rules can discourage public officials from upholding the law and prevent agents from circumventing climate change restrictions.

The research findings have important policy implications for mitigating CO2 emissions and addressing climate change. Policies focused on increasing GDP per capita can have significant environmental advantages due to the crucial role of economic development in reducing CO2 emissions. Governments must give priority to sustainable economic growth by ensuring that investments in clean technology and infrastructure accompany the pursuit of higher affluence. Furthermore, the report emphasizes the significance of shifting towards renewable energy sources. Policymakers ought to introduce incentives for both the public and commercial sectors to augment the proportion of environmentally friendly energy sources in their total energy composition. This includes financial assistance for renewable energy initiatives, tax benefits for the use of environmentally friendly technologies, and backing for the advancement of sustainable energy solutions through research and development.

Furthermore, the research highlights the importance of strong institutional frameworks in order to improve the efficiency of emission reduction measures. Enhancing the supremacy of legal principles, enhancing the efficiency of governance, and curbing corruption are crucial for establishing a conducive atmosphere for the efficient implementation and enforcement of environmental regulations. This suggests that environmental measures should be combined with wider governance reforms, guaranteeing openness, responsibility, and public involvement in decision-making procedures. Finally, the research supports the use of a comprehensive approach that integrates economic, energy, and institutional strategies. Policymakers ought to devise all-encompassing policies that tackle these interrelated domains, exploiting the harmonious effects of economic development, energy transition, and good governance to optimize the effectiveness of CO2 emission reduction endeavors. An integrated approach is essential for attaining sustainable environmental results and reducing the negative impacts of climate change.

Our research is restricted in its ability to predict since we only use national-level data collection, ignoring environmental data that may be related to specific companies or areas [40]. point to a shift in the literature toward studies that focus on the environmental sustainability of industries. This study has considered accessible data from organizations on environmental impact, social duties, and corporate governance, as well as the effects of climate change, energy consumption, water usage, and carbon dioxide emissions. Integrating data from various governance, social, and environmental sources is one way to investigate the potential advantages of XAI models; this could help us learn more about how organizations are relevant to both energy and environmental economics. We also need to add more institutional factors to our list. For example, looking at the political climate or the quality of governance [39] could give us new ways to look into energy economics. In order to understand more about the connection between institutions and the quality of the environment, future research should look at the role of government regulations like pollution fees, transportation taxes, and sustainable resource expenditures.

Ethics approval and consent to participate

Not applicable.

Consent for publication

All of the authors consented to publish this manuscript.

Funding

This work was supported in part by the Special Projects in Key Fields of Colleges and Universities of Guangdong Province (New Generation Information Technology) under Grant No.2021ZDZX1113 .

Data availability

We collected relevant data from World Bank open data available at https://data.worldbank.org/. For any further query on data, corresponding author may be approached.

CRediT authorship contribution statement

Kaizhe Fan: Writing – review & editing, Validation. Quanjun Li: Writing – review & editing, Conceptualization. Zhen Le: Writing – review & editing, Validation, Data curation. Qian Li: Writing – review & editing, Conceptualization. Jianfeng Li: Writing – review & editing. Ming yan: Formal analysis, Data curation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

1.Research on the Practice Teaching Evaluation System of Applied Undergraduate Digital Scene Design course From the perspective of Urban Meta-Universe.

The study was supported by the 2022 Shanghai Education ScienceResearch Project,NO .C2023104 .

2."Three-Education system Integration” for Teaching mode of higher vocational Art Design based on the integration of the resources of enterprises with vocational schools, NO.C2022235 ), The study was supported by the 2022 Shanghai Education Science Research Project.NO.C2022235 .
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