The environment is crucial for supporting life on Earth; nevertheless, the increasing global pollution and waste have emerged as a significant problem in recent years [1,2]. The majority of climate-altering and planet-warming substances emitted into the atmosphere are carbon dioxide (CO2) [3]. The United States (USA) distinguishes itself among these nations through its evolution as a dominant force in consumption as well as production [4]. In 2020, the USA emitted 5,416 metric tons (MT) of CO2, or almost 16% of global emissions [5]. However, in 2021, the United States reinforced its dedication to the Paris Agreement, adopting an ambitious Nationally Determined Contribution to reach a 50-52% drop in net greenhouse gas (GHG) emissions by 2030 [6,7]. The United States consumes the largest quantity of fossil fuel annually, totaling 913.3 million tons of oil, which is 50% greater than China's consumption, the second highest globally [8,9]. Alleviating the adverse effects of global climate change has emerged as a worldwide priority, with a crucial component of this effort being the reduction of CO2 emissions [10,11]. In accordance with the UN 2030 universal sustainable development objective, the advancement of renewable energy is prioritized above everything else. Consequently, the USA has considerable accountability for the climate crisis and global warming, being one of the foremost emitters of GHGs; thus, assessing its environmental sustainability is a critical issue. In this context, our research question is

It is clear from a thorough analysis of earlier research that not many studies have looked into the connection between GDP, energy use, FDI, CO2 emissions, and AI innovation. The bulk of publications concentrated on the effects of trade openness, urbanization, and green energy usage on environmental quality, even though several investigations looked at the ARDL model. The USA hasn't seen much indepth research on ecological degradation, a relatively young discipline. Nevertheless, the research drew upon a few previous studies to guide the selection of variables and methods. This section will address a select few of these questions. Numerous researches have been conducted to ascertain the correlation between environmental and economic activity. For example, Manu and Sulaiman [27] examined the impact of energy consumption and economic growth on Malaysia's CO2 emissions utilizing the OLS method for the years 1965 to 2015. Their research demonstrated that CO2 emissions diminish as revenue escalates. The favorable relationship between economic expansion and CO2 emission is found by Saudi et al. [28], and Zubair et al. [29]. Conversely, Sarkar et al. [30] analyzed time-series data from 1980 to 2016 to investigate the relationship among Malaysia's energy use, CO2 emissions, and economic expansion. The empirical evidence indicated that usage of energy and economic expansion substantially elevated CO2 emissions in Malaysia from 1980 to 2016. In order to examine the relationship between GDP and CO2 emissions, Chen et al. [31] used China's annual data from 1990 to 2020 and the QARDL technique. They discovered that China's GDP has a positive effect on CO2 emissions. Etokakpan et al. [32] analyzed the relationship between capital formation, globalization, CO2 emissions, and GDP in Malaysia using a dataset from 1980 to 2014 within a multivariate framework. The authors employed an innovative combined co-integration test to ascertain the magnitude of the long-run equilibrium relationship. The empirical findings indicated that GDP adversely affected environmental quality. Multiple researcher such as Wada et al. [33] within Brazil, Rjoub et al. [34] in Turkey and He at al. [35] within Mexico found the same conclusions. On the other hand, Muhammad et al. [36] employed FMOLS and two-stage least squares regression methods to analyze the correlation between GDP growth and CO2 emissions. The findings corroborated the U-shaped link indicated by the EKC theory in high-and upper-middle-income nations. The loss of biodiversity and global warming are complex concerns that require advanced and inventive solutions [37]. Environmental professionals anticipate several benefits from AI tools [38]. Policymakers can use AI innovation to develop scientifically supported plans and strategies for green ecosystems [39. Existing research indicates that investigations on carbon reduction related to AI are in their infancy. A pertinent study by Liu et al. [40] analyzes industrial robot data from 16 sectors in China from 2006 to 2016 to investigate the correlation between AI and energy intensity. The implementation of AI technology in the industrial sector diminishes energy intensity by enhancing industrial output while decreasing energy consumption and environmental degradation. Chen et al. [41] investigate the impact of AI on carbon emissions utilizing panel data from 270 Chinese cities spanning 2011 to 2017. Their empirical findings indicate that AI decreases carbon emissions by optimizing production processes, boosting communication facilities, and advancing green technological innovation. Negi [42] examines the investment trends in artificial intelligence originating from the three leading nations: China, India, and the United States. The paper delineates the measures the government has implemented to integrate artificial intelligence into its existing ecosystem. Green AI can enhance productivity and mitigate its adverse environmental impacts [43]. The primary contributor to climate change is the combustion of fossil fuels, which releases significant quantities of greenhouse gases into the environment [44]. But long-term cost reductions from using alternative or green energy sources will raise people's overall level of living [45]. Raihan et al. [46] examine the relationship between energy use and CO2 emissions in Malaysia from 1990 to 2019. They employed the ARDL and DOLS methodologies, which revealed a positive and significant energy use coefficient in relation to CO2 emissions, indicating a 0.91% rise in CO2 emissions for every 1% increase in energy usage. Adebayo and Kalmaz [47] employed ARDL, FMOLS, and DOLS estimators to reveal a substantial positive correlation between power consumption and CO2 emissions in Egypt, utilizing data from 1971 to 2014. Moreover, Adebayo et al. [48] identified a positive correlation between CO2 emissions and energy consumption by employing the ARDL model for MINT nations, covering the period from 1980 to 2018. Conversely, Raihan and Tuspekova [49] examined the correlation between green energy utilization and CO2 emissions in Peru from 1990 to 2018.
Using the DOLS and ARDL methodologies, they found a negative correlation and statistical significance in the utilization of clean energy, suggesting that a 1% increase in green power use results in a 0.52% reduction in CO2 emissions over the long term. Multiple researcher such as Abbasi and Adedoyin [50] in China, Balsalobre-Lorente et al. [51] across BRICS countries, Saqib [52] in MENA region corroborated the destructive effect of energy usage on environment quality. On the other hand, Younis et al. [53], Salari et al. [54] concluded that energy consumption boost the environment quality. In the end, energy-efficiency regulations can enhance energy conservation, while increased investment in energy production and the promotion of energy savings will diminish carbon emissions [55]. Foreign Direct Investment (FDI) can facilitate the transfer of cleaner technologies and sustainable practices, leading to enduring reductions in emissions [29,56]. Jafri et al. [57] investigate the asymmetric impact of FDI on CO2 emissions utilizing the NARDL methodology for China from 1981 to 2019. Their findings indicate that a positive shift in FDI has a comparatively greater impact on CO2 emissions. Moreover, several studies also found the same conclusion in different region [58,59,60,61]. On the other hand, Lin et al. [62] examine the impact of FDI on emission reduction in China from 2004 to 2015, employing geographic Durbin economic models with two-way fixed effects. The findings indicate that FDI facilitates a decrease in emissions nationwide. Furthermore, Eskeland and Harrison [63] contended that FDI typically accompanies energy-efficient technologies and may positively impact the natural world. Similar outcomes were also observed by Wang et al. [64] in China, Pata and Samour [65] within France, and Abbas et al. [66] in developing countries. Conversely, Haug and Ucal [67] shown that spikes in FDI had no statistically significant long-term effects on per capita CO2 emissions. Economic growth and industrialization-driven urbanization are contributing to the rising utilization of fuels that generate greenhouse gas emissions [68].The concluding section of the research examines previous studies on the empirical relationship between urbanization and CO2 emission. For instance, Mahmood et al. [69] examine the impact of urbanization on per capita CO2 emissions in Saudi Arabia, analyzing data from the years 1968 to 2014. The findings indicate that urbanization hinders the environment due to its elastic impact on emissions. Parshall et al. [70] examined the substantial impact of urbanization on the circular economy and environmental health in the USA. Raihan et al. [71] explore the impact of urbanization on the load capacity factor in Mexico from 1971 to 2018. This study utilizes the ARDL approach and demonstrates that urbanization decreases Mexico's LCF, thereby degrading the ecology. Sufyanullah et al. [72] examined the impact of urbanization on CO2 emissions in Pakistan. They employed the ARDL model and noted that CO2 emissions rise with increased urbanization. Additional studies [73,74,75,76,77] suggest that urbanization increases CO2 emissions in the atmosphere. In comparison, Xu et al. [78] evaluated the impact of urbanization on the ecosystem in Brazil from 1970 to 2017. Unexpectedly, the results of the ARDL methodology indicated that urbanization does not influence the surroundings in Brazil. Moreover, Haseeb et al. [79] revealed analogous findings utilizing FMOLS from 1995 to 2014, suggesting that URB had no significant impact on environmental quality in the BRICS nations. Nonetheless, as highlighted by Martinez et al. [80] urbanization may contribute to addressing climate change due to the heightened knowledge among these groups. Moreover, Diputra and Baek [81] determined that urbanization exerted no substantial impact on emissions in Indonesia. The current status of research indicates that studies on the USA are scarce. The empirical literature lacks an evaluation of the geographical implications of macroeconomic variables on CO2 emissions in the USA. The literature on the STIRPAT framework and ARDL methodology is deficient in the USA and similarly limited in other global locations. This study tries to fill in that gap by looking at modern variables using advanced methods from a U.S. point of view. The goal is to find the main and secondary effects of GDP, AI innovation, energy consumption, urbanization, and FDI on CO2 emissions in this area. By examining these procedures, the USA may determine whether leveraging technological innovation, financial integration, and commercial expansion can enhance its ecosystem quality and align it with global trends toward greater environmental sustainability. This study seeks to observe the impact of GDP, FDI, AI innovation, energy use, and urbanization on CO2 emissions in the USA. The study utilizes CO2 emissions as an indicator of ecological health, employing data from the World Development Indicators (WDI) database as the dependent variable. Statistics on AI innovation are sourced from Our World in Data, while data for GDP, energy consumption, foreign direct investment (FDI), and urbanization are derived from the revised WDI. These variables encompass annual data from 1995 to 2022. Table 1 presents a complete enumeration of each variable along with their respective details and a sign chosen for this research. The STIRPAT paradigm holds substantial importance for environmental research. This method is a versatile analytical tool that facilitates the understanding of intricate relationships between human societies and the environment, irrespective of the subject matter, including GHG emissions, air pollution, deforestation, or biodiversity loss [82]. By using STIRPAT, this study allows for an extensive evaluation of numerous variables that affect CO2 emissions in the context of USA. The IPAT model was proposed by Ehrlich and Holdren [83] and is phrased as follows:

Nonetheless, this model proved challenging to evaluate hypothetically [84]. To address these constraints, Dietz and Rosa [85] expanded the IPAT model, resulting in the STIRPAT model. This research can effectively assess the interplay between human activities and environmental outcomes by representing CO2 emissions as the 'I' or impact variable, urbanization as the 'P', and other variables like AI innovation, GDP, energy consumption, as the 'T' or technological variables. Equation (2) shows the functional form of the STIRPAT framework:

Based on a comprehensive review of relevant literature, the empirical model employed in this work yielded the subsequent approximations.
To assess the effects on the environment, this study uses CO2 emissions as a proxy indicator. This is the expression that may be used to derive Equation (4):

Here, GDP stands for gross domestic product; AI innovation was represented by AI, energy use through ENU, foreign direct investment via FDI and urbanization by URBA. Equation ( 5) makes use of the logarithmic transformation of variables to ensure that the information has a normal distribution. By employing the logarithmic structure, the info is standardized which makes it more consistent with the assumptions that underlie numerous statistical methods. This study utilized the ARDL approach for data analysis to explore the correlation between CO2 emissions and several independent variables in the USA. To ensure robustness, the Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegration Regression (CCR) methods were additionally employed. Initially, the author performed unit root tests (ADF, P-P, and DF-GLS) to verify stationarity. The properties of the time series data led to the employment of the ARDL limits test. Subsequently, both short-term and long-term ARDL estimates were derived, followed by the Pairwise Granger causality examination. Ultimately, multiple diagnostic assessments were conducted, allowing us to find the most precise and effective econometric model following a comprehensive evaluation procedure. Performing a unit root testing is needed to avert erroneous regression analysis. This test can determine the degree of integration [86]. The Dickey-Fuller Generalized Least Squares, [87] Phillips-Perron [88], and Augmented Dickey-Fuller [89] unit root tests were used to see if the data set was stationary. In comparison to the Dickey-Fuller (DF) method, the Augmented Dickey-Fuller (ADF) technique is more resilient and suitable for more complex procedures [90]. The DF-GLS test shows superior overall performance regarding small sample size and power, surpassing the conventional Dickey-Fuller test [91]. Before employing the ARDL bound test estimator for cointegration analysis, it is essential to conduct the ADF and PP unit root tests, as the estimator is applicable only when the variables are stationary at the level or first difference [92]. The ARDL technique, which includes the features of both distributed lag and autoregressive models, was proposed by Pesaran et al. [93]. It is a comprehensive dynamic regression model that offers various advantages over traditional cointegration methods. Firstly, the model allows for the integration of variables to multiple orders, including order one, order zero, and even fractional integration, with the exception of 1(2). Moreover, unlike previous cointegration approaches that necessitated prior identification of a series' integration characteristic, this approach does not mandate any preliminary validation [94]. Due to its efficiency, we can employ this methodology for data analysis in scenarios with limited and small sample sizes [95,96]. Equation ( 6) can be used to represent the ARDL bound test:

Where, m is the optimum lag length. Pesaran et al. [93] suggest using critical values for both upper and lower bounds to compare F-statistics. When the F-statistic surpasses the upper critical value, we reject the null hypothesis (H0), indicating a persistent relationship. If the F-statistic stays below the crucial value, we retain the null hypothesis (H0).The long-run coefficient estimate is derived from equation (7), which also validates the cointegration of the parameters. It uses an approximation of the Error Correction Term (ECT) to figure out short-term dynamic parameters based on long-term estimates [97]. The ECT is built into the ARDL structure. The equation ( 7) outlines the ARDL long-run equation presented below. To evaluate the precision of ARDL outcomes, we utilized the FMOLS, DOLS, and CCR techniques. The FMOLS approach is utilized to analyze a singular cointegrating relationship involving a combination of integrated orders of I(1) variables. The primary objective of this method is variable transformation. Phillips and Hansen [98] say that the FMOLS method fixes the problems with traditional cointegration methods that make it hard to draw conclusions. This makes the estimated t-statistics for long-term estimates more reliable. The DOLS technique may assist in integrating individual variables within a cointegrated framework when faced with a mixed order of integration. The dependent variable is calculated utilizing levels, leads, and lags as explanatory variables [99]. Nevertheless, as emphasized by Pesaran [100], the principal advantage of the DOLS prediction is its allowance for the varying order integration of distinct components within the cointegrated framework. Additionally, Park [101] proposed the CCR technique to examine cointegrating vectors within a model characterized by an integrated process of order I(1). The model's characteristics exhibit a significant similarity to FMOLS. The study employed the paired Granger-causality test, devised by Granger [102], to ascertain the existence of a causal relationship among the factors. We can use F tests to assess Granger causality between variables X and Y, and the OLS test for coefficient estimation. The symbols Xt and Yt denote the values of the variables at time t, illustrating the time series for this variable pair. A bivariate autoregressive model may exhibit the variables Xt and Yt. Here, the information criterion determines the "n" number of lags. The parameters used for the assessment were β1, β2, αi, Ωi, μi, and ∞i. The errors in Equation ( 7) must not exhibit serial correlation. This study employed various diagnostic techniques to verify the normality, serial correlation, and heteroscedasticity of the data. Three tests are needed in time series analysis to make sure that model assumptions are correct and that results are stable: the Lagrange Multiplier (LM) test, the Jarque-Bera test [103], and the Breusch-Pagan-Godfrey test [104]. The Jarque-Bera test assesses the normality of residuals, a crucial step since many econometric models require normally distributed errors for precise inference. The Lagrange multiplier test examines residuals for serial correlation to verify that errors do not correlate with time, thereby preventing biased and misleading estimates. The Breusch-Pagan-Godfrey test can yield inaccurate estimates and standard errors due to heteroscedasticity, or the non-constant variance of residuals. The model's stability was assessed by CUSUMSQ studies [105]. Moreover, all variables have relatively small standard deviations, indicating a tight clustering of data points around the mean with limited temporal variability.
Table 3 presents the results of all three stationarity tests (ADF, DF-GLS, and P-P) for log-transformed data in both level I(0) and firstdifference I(1) forms. In all three unit root assessments, it seems that only the urbanization variable is stationary at level I(0), whereas CO2, GDP, AI innovation, energy consumption, and FDI were nonstationary prior to examining their initial differences. This mixed sequence of integration prompts us to proceed with the assessment immediately, using the ARDL methodology. This research employed the ARDL bounds testing approach to ascertain the presence of co-integration among the variables. The F-statistic of 5.3421 is more than the critical value, indicating that the null hypothesis of no co-integration is rejected at the 1% significance level. Therefore, we can argue that the parameters of the model exhibit co-integrating relationships. This study cites urbanization, artificial intelligence innovation, foreign direct investment, gross domestic product, and energy consumption as the enduring driving forces. Furthermore, these factors necessitate the system's initial response to a typical stochastic disturbance. In summary, variations in these three parameters influence CO2 emissions in the United States. LGDP, LAI, LENU, LFDI, and LURBA on LCO2 in the USA. In terms of LGDP, a 1% boost in LGDP will increase the LCO2 by 0.028% in the long-term run and 0.012% in the short run. This suggests that economic expansion alone may notably contribute to environmental degradation in this setting, as GDP has a positive impact on the CO2 emission level. A few studies have concluded that a boost in the GDP has a negative impact on the environment. This includes Ahmed et al. [106] in Japan; Raihan and Tuspekova [107] within Kazakhstan; Orhan et al. [108] on India; Ali et al. [109] in Malaysia; Shang et al. [58] for ASEAN countries. However, Le and Ozturk [110], Zhan et al. [111], Tufail et al. [112] and He et al. [113] discovered the opposite outcome. They also concluded that economic pressures no longer adversely affect the natural world. Likewise, Awosusi et al. [114] demonstrated that there is no significant correlation between CO2 emissions and GDP in MINT economies. On the other hand, the coefficients for LAI indicate a positive correlation with LCO2, implying a 0.054% long-term fall and 0.076% short-term cut in LCO2 for every 1% rise in PAI. Thus, private investment in artificial intelligence in the United States significantly contributes to environmental sustainability. AI enhances energy efficiency and promotes environmental sustainability by decreasing carbon emissions. Multiple researchers like Nishant et al. [37], Chen et al. [31] and Zhao et al. [115] support this result, stating that AI technologies improve the environmental conditions in different regions. Moreover, Wang et al. [116] discovered that invention patents exhibited no significant correlation with emissions decrease. Conversely, LCO2 is negatively associated with LENU in both the long and short run, and this relationship is statistically significant. These findings suggest that energy consumption has an adverse impact on the USA ecosystem. Specifically, a 1% increase in LENU increases LCO2 by 0.617% in the long run and by 0.321% in the short run. The utilization of energy results in increased carbon emissions, as fossil fuels, the predominant energy source, emit substantial CO2 during combustion. This result is consistent with the research of Islam et al. [117] in Bangladesh, Kim [118] in OECD countries, Nurgazina et al. [119] in Malaysia, Akbota and Baek [120] within Kazakhstan, and Odugbesan and Adebayo [121] in Nigeria. On the other hand, the findings of Namahoro et al. [122], Bhat [123], and Sharif et al. [4] concluded that energy use can have a negative impact on the environment by increasing the pollution level. Similarly, there is an unfavorable correlation between LFDI and LCO2, with each 1% increase in FDI increases the CO2 emission by 0.018% in the long run and 0.031% in the short run. This result is significant at the 1% level. One possible reason is that FDI frequently results in elevated industrial activity, increased energy consumption, and intensified resource exploitation; hence, it amplifies environmental deterioration. Nie et al. [124], and Zhang et al. [125] corroborate this result. Conversely, Azam and Raza [126], Shah et al. [127], Pazienza [128] and Pradhan et al. [129] revealed that FDI can mitigate CO2 emissions and improve biodiversity quality. Additionally, the positive and statistically significant URBA coefficients indicate that both long-term and short-term increases in LURBA negatively affect environmental quality. A 1% increase in URBA raises LCO2 by 0.710% in the long run and by 0.641% in the short run. These findings suggest that the current urbanization structure in the United States is not conducive to reducing pollution. Several researchers have also observed a similar outcome, including Yuan et al. [130] in China, Ali et al. [131] in Pakistan, Anwar et al. [132] in Asian economies, Sikder et al. [133] in developing economies, and Raihan et al. [71]. However, studies conducted by Wang et al. [76], Acheampong [12], Zhu et al. [134], and Gasimli et al. [135] have demonstrated that urbanization enhances environmental sustainability by reducing carbon dioxide emissions. The DOLS, FMOLS, and CCR techniques are supplementary methodologies utilized to evaluate the validity and reliability of the ARDL results. Table 6 outlines the robustness findings. In the FMOLS model, the coefficients for LGDP are statistically significant at the 1% level and have positive values. A 1% increase in GDP causes the LCO2 to rise by 0.245%. Additionally, a 1% increment in LAI leads to a 0.034% drop in CO2 emission in the USA. Furthermore, a 1% boosts in LENU and LFDI and LURBA upsurges LCO2 by 0.074%, 0.053% and 0.231%, respectively. It indicates that GDP, energy consumption, FDI and urbanization are not good for better for the ecosystem in USA. These findings corroborate the ARDL short and long-run estimation results, with LGDP, LAI, LENU and LFDI significant at the 1% level, while LURBA are significant at the 5% level.
In the DOLS model, a 1% spike in LGDP, LENU, LFDI and LURBA results in an average rise of 0.316%, 0.023%, 0.039%, and 0.143% in LCO2, respectively. Similar to the ARDL findings, a 1% rise in LAI leads to a 0.010% reduction in LCO2, and the coefficient for LAI is significant at the 5% level. In the CCR model, a 1% increase in LGDP, LENU, LFDI and LURBA leading to an average rise of 0.217%, 0.354%, 0.049%, and 0.205% in LCO2, respectively. However, a 1% increase in LAI causes an average 0.037% decrease in LCO2, confirming the ARDL results except for the LAI case. In this case all the factors are significant at 1% level, while LURBA is significant at 5% thresholds. These robustness checks confirm that the ARDL model's findings are reliable, as evidenced by the statistically significant values across FMOLS, DOLS, and CCR computations. and LURBA all had a single-direction effect on LCO2. Consequently, under these conditions, we dismiss the null hypothesis, asserting the absence of causation. On the other hand, p-values higher than the usual significance level showed that there was no significant causal relationship from LCO2 to LGDP, LAI, LENU, LFDI and LURBA. The null hypothesis, which posits the absence of causality in these interactions, is not effectively disproved. Furthermore, we use the CUSUM and CUSUM-SQ statistics to seek structural stability in residuals over long and short periods. The CUSUM-SQ plot stays on the critical line, as indicated in the figure 1, indicating that the results are within the critical limits. This shows that the parameters are satisfactory and consistent at the 5% level of significance. This paper thoroughly examines the impact of AI innovation, economic growth, foreign direct investment, energy consumption, and urbanization on CO2 emissions in the USA from 1990 to 2022. Combining the ARDL framework with the STIRPAT structure, the authors found that while AI innovation reduces stress on the environment, economic growth, energy use, foreign direct investment, and urbanization make these problems worse. The results of the ADF, PP, and DF-GLS tests show that the variables have different levels of integration and no unit root problems. The ARDL boundaries test offers additional evidence of co-integration, signifying solid long-term interactions. The ARDL findings show a positive correlation between AI innovation and CO2 emissions in the USA, suggesting that AI technologies improve environmental health as long as appropriate environmental safeguards are in place. In contrast, the adverse correlations among GDP, ENU, FDI, URBA, and CO2 emissions indicate that these elements lead to detrimental environmental consequences. Significant technological advancements, including energy conservation, sustainable foreign direct investment, and urban planning, may stimulate new concepts and the implementation of environmentally friendly practices by enhancing competitiveness and facilitating access to advanced technologies. Robustness checks using FMOLS, DOLS, and CCR enhance the credibility of the ARDL results, thereby increasing their trustworthiness. Another thing is that Pairwise Granger causality tests show strong one-way links between LCO2 and LGDP, LAI, LENU, LFDI, and LURBA. These linkages highlight the significant impact of economic transformations, private investments in AI, and advancements in green energy utilization on ecological sustainability dynamics in the USA. The research suggests various policy measures to promote sustainable economic growth in the United States, including the use of foreign direct investment, technological advancements, the implementation of green energy, and the development of sustainable urban infrastructure. The findings emphasize the need for targeted policies to balance economic growth with environmental sustainability. Promoting AI-driven technologies and green energy initiatives can reduce CO2 emissions while fostering innovation. Policies should prioritize sustainable foreign direct investment (FDI) by incentivizing eco-friendly projects and encouraging energy-efficient practices. Urban planning reforms must focus on developing smart, sustainable cities to mitigate the environmental impacts of urbanization. Additionally, integrating AI in energy management and industrial processes can enhance efficiency and reduce environmental stress. These measures collectively support sustainable economic growth while safeguarding ecological health in the USA. A B